FRONTIERS EBOOK COPYRIGHT STATEMENT

The copyright in the text of individual articles in this ebook is the property of their respective authors or their respective institutions or funders. The copyright in graphics and images within each article may be subject to copyright of other parties. In both cases this is subject to a license granted to Frontiers.

The compilation of articles constituting this ebook is the property of Frontiers.

Each article within this ebook, and the ebook itself, are published under the most recent version of the Creative Commons CC-BY licence. The version current at the date of publication of this ebook is CC-BY 4.0. If the CC-BY licence is updated, the licence granted by Frontiers is automatically updated to the new version.

When exercising any right under the CC-BY licence, Frontiers must be attributed as the original publisher of the article or ebook, as applicable.

Authors have the responsibility of ensuring that any graphics or other materials which are the property of others may be included in the CC-BY licence, but this should be checked before relying on the CC-BY licence to reproduce those materials. Any copyright notices relating to those materials must be complied with.

Copyright and source acknowledgement notices may not be removed and must be displayed in any copy, derivative work or partial copy which includes the elements in question.

All copyright, and all rights therein, are protected by national and international copyright laws. The above represents a summary only. For further information please read Frontiers’ Conditions for Website Use and Copyright Statement, and the applicable CC-BY licence.

ISSN 1664-8714
ISBN 978-2-8325-3906-4
DOI 10.3389/978-2-8325-3906-4

About Frontiers

Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals.

Frontiers journal series

The Frontiers journal series is a multi-tier and interdisciplinary set of open-access, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too.

Dedication to quality

Each Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world’s best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public - and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation.

What are Frontiers Research Topics?

Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers editorial office: frontiersin.org/about/contact

Molecular biomarkers for the diagnosis, prognosis, and risk prediction of cancer

Topic editors

Hossain Shekhar – University of Dhaka, Bangladesh

Xin Wang – The Chinese University of Hong Kong, China

Ismail Hosen – University of Dhaka, Bangladesh

Florence Le Calvez-Kelm – International Agency For Research On Cancer (IARC), France

Tianhui Chen – Zhejiang Cancer Hospital, China

William C. Cho – QEH, Hong Kong, SAR China

Citation

Shekhar, H., Wang, X., Hosen, I., Calvez-Kelm, F. L., Chen, T., Cho, W. C., eds. (2023). Molecular biomarkers for the diagnosis, prognosis, and risk prediction of cancer. Lausanne: Frontiers Media SA. doi: 10.3389/978-2-8325-3906-4

Table of Contents

Systematic Analysis of the Clinical Significance of Hyaluronan-Mediated Motility Receptor in Colorectal Cancer

Yan-ping Tang, Yi-xin Yin, Ming-zhi Xie, Xin-qiang Liang, Ji-lin Li, Ke-zhi Li and Bang-li Hu

Clinical Associations of Preoperative and Postoperative Serum CEA and Lung Cancer Outcome

Zonglin Jiao, Shoubo Cao, Jianhua Li, Nan Hu, Yinghui Gong, Linduo Wang and Shi Jin

Evaluation of the Diagnostic Potential of Candidate Hypermethylated Genes in Epithelial Ovarian Cancer in North Indian Population

Alka Singh, Sameer Gupta and Manisha Sachan

A Pan-Cancer Landscape of HOX-Related lncRNAs and Their Association With Prognosis and Tumor Microenvironment

Wei Shao, Qian Ding, Yugang Guo, Juan Xing, Zheng Huo, Zhan Wang, Qian Xu and Yue Guo

Omics- and Pharmacogenomic Evidence for the Prognostic, Regulatory, and Immune-Related Roles of PBK in a Pan-Cancer Cohort

Yi Liu, Juan Xiang, Gang Peng and Chenfu Shen

MXD3 as an Immunological and Prognostic Factor From Pancancer Analysis

Xiaoyu Zhang, Xiaoqin He, Yue Li, Yangtao Xu, Wenliang Chen, Xin Liu, Xinyao Hu, Lin Xiong and Ximing Xu

Dynamically Accumulating Homologous Recombination Deficiency Score Served as an Important Prognosis Factor in High-Grade Serous Ovarian Cancer

Rongjia Su, Yuan Liu, Xiaomei Wu, Jiangdong Xiang and Xiaowei Xi

Integrative Analysis of Immune-Related Genes in the Tumor Microenvironment of Renal Clear Cell Carcinoma and Renal Papillary Cell Carcinoma

Bin Zheng, Fang Xie, Fajuan Cheng, Jianwei Wang, Zhongshun Yao, Wei He and Zhihong Niu

Bioinformatic Analyses and Experimental Verification Reveal that High FSTL3 Expression Promotes EMT via Fibronectin-1/α5β1 Interaction in Colorectal Cancer

Yuanjie Liu, Jiepin Li, Shuhong Zeng, Ying Zhang, Yonghua Zhang, Zhichao Jin, Shenlin Liu and Xi Zou

SPARC Overexpression Promotes Liver Cancer Cell Proliferation and Tumor Growth

Zhao-wei Gao, Chong Liu, Lan Yang, Ting He, Xia-nan Wu, Hui-zhong Zhang and Ke Dong

A Novel Diagnostic Biomarker, PZP, for Detecting Colorectal Cancer in Type 2 Diabetes Mellitus Patients Identified by Serum-Based Mass Spectrometry

Jiayue Yang, Weigang Fang, Wenjun Wu, Zhen Tian, Rong Gao, Lu Yu, Dayang Chen, Xiaohua Weng, Shengwei Zhu and Cheng Yang

Development and Validation of Ten-RNA Binding Protein Signature Predicts Overall Survival in Osteosarcoma

Jian Zhang, Xinxin Miao, Tianlong Wu, Jingyu Jia and Xigao Cheng

SEMA6B Overexpression Predicts Poor Prognosis and Correlates With the Tumor Immunosuppressive Microenvironment in Colorectal Cancer

Tiegang Li, Zheng Yan, Weiqi Wang, Rixin Zhang, Wenqiang Gan, Silin Lv, Zifan Zeng, Yufang Hou and Min Yang

Identification and Validation of CYBB, CD86, and C3AR1 as the Key Genes Related to Macrophage Infiltration of Gastric Cancer

Haiyan Chen, Qi Sun, Cangang Zhang, Junjun She, Shuai Cao, Meng Cao, Nana Zhang, Ayarick Vivian Adiila, Jinjin Zhong, Chengyun Yao, Yili Wang, Hongping Xia and Linhua Lan

The Involvement of TRIB3 and FABP1 and Their Potential Functions in the Dynamic Process of Gastric Cancer

Songyi Liu, Chuxuan Ni, Yizhi Li, Honghao Yin, Chengzhong Xing, Yuan Yuan and Yuehua Gong

Comprehensive Analysis of the Potential Immune-Related Biomarker Transporter Associated With Antigen Processing 1 That Inhibits Metastasis and Invasion of Ovarian Cancer Cells

Xiaoxue Li, Shiyu Zeng, Yiling Ding, Yanting Nie and Mengyuan Yang

An Integrated Fibrosis Signature for Predicting Survival and Immunotherapy Efficacy of Patients With Hepatocellular Carcinoma

Long Liu, Zaoqu Liu, Lingfang Meng, Lifeng Li, Jie Gao, Shizhe Yu, Bowen Hu, Han Yang, Wenzhi Guo and Shuijun Zhang

Identification and Validation of a Prognostic Prediction Model of m6A Regulator-Related LncRNAs in Hepatocellular Carcinoma

Chen Jin, Rui Li, Tuo Deng, Jialiang Li, Yan Yang, Haoqi Li, Kaiyu Chen, Huihua Xiong, Gang Chen and Yi Wang

An Unfolded Protein Response Related Signature Could Robustly Predict Survival Outcomes and Closely Correlate With Response to Immunotherapy and Chemotherapy in Bladder Cancer

Facai Zhang, Dechao Feng, Xiaoming Wang, Yiwei Gu, Zhiyong Shen, Yubo Yang, Jiahao Wang, Quliang Zhong, Dengxiong Li, Huan Hu and Ping Han

BTBD10 is a Prognostic Biomarker Correlated With Immune Infiltration in Hepatocellular Carcinoma

Jianhui Li, Xiaojuan Tian, Ye Nie, Ying He, Wenlong Wu, Xinjun Lei, Tianchen Zhang, Yanfang Wang, Zhenzhen Mao, Hong Zhang, Xuan Zhang and Wenjie Song

Hypermethylated PCDHGB7 as a Biomarker for Early Detection of Endometrial Cancer in Endometrial Brush Samples and Cervical Scrapings

Jiangjing Yuan, Zhanrui Mao, Qi Lu, Peng Xu, Chengyang Wang, Xiaona Xu, Zhaowei Zhou, Tongsheng Zhang, Wenqiang Yu, Shihua Dong, Yudong Wang and Weiwei Cheng

Development and Verification of a Hypoxic Gene Signature for Predicting Prognosis, Immune Microenvironment, and Chemosensitivity for Osteosarcoma

Fengfeng Wu, Juntao Xu, Mingchao Jin, Xuesheng Jiang, Jianyou Li, Xiongfeng Li, Zhuo Chen, Jiangbo Nie, Zhipeng Meng and Guorong Wang

Prognostic Value of Oral Epstein–Barr Virus DNA Load in Locoregionally Advanced Nasopharyngeal Carcinoma

Yong-Qiao He, Ting Zhou, Da-Wei Yang, Yi-Jing Jia, Lei-Lei Yuan, Wen-Li Zhang, Tong-Min Wang, Ying Liao, Wen-Qiong Xue, Jiang-Bo Zhang, Xiao-Hui Zheng, Xi-Zhao Li, Pei-Fen Zhang, Shao-Dan Zhang, Ye-Zhu Hu, Fang Wang, William C. Cho, Jun Ma, Ying Sun and Wei-Hua Jia

Bioinformatic Analyses of the Ferroptosis-Related lncRNAs Signature for Ovarian Cancer

Jianfeng Zheng, Jialu Guo, Yahui Wang, Yingling Zheng, Ke Zhang and Jinyi Tong

Identification of Five Ferroptosis-Related LncRNAs as Novel Prognosis and Diagnosis Signatures for Renal Cancer

Xiangjun Shu, Zaiqi Zhang, Zhi-Yong Yao and Xiao-Liang Xing

Development of Personalized Signature Based on the Immune Landscape to Predict the Prognosis of Osteosarcoma and the Response to Immunotherapy and Targeted Therapy

Xiaofei Feng, Zhenrui Zhao, Yuhao Zhao, Zhengdong Song, Yao Ma and Wenji Wang

Integrative Genomic Analyses of 1,145 Patient Samples Reveal New Biomarkers in Esophageal Squamous Cell Carcinoma

Binbin Zou, Dinghe Guo, Pengzhou Kong, Yanqiang Wang, Xiaolong Cheng and Yongping Cui

Over-Expression and Prognostic Significance of FATP5, as a New Biomarker, in Colorectal Carcinoma

Qi-Shun Geng, Mei-Jia Yang, Li-Feng Li, Zhi-Bo Shen, Long-Hao Wang, Yuan-Yuan Zheng, Wen-Hua Xue and Jie Zhao

Down-Regulated miR-130a/b Attenuates Rhabdomyosarcoma Proliferation via PPARG

Yi Pan, Junyang Li, Susu Lou, Wanbiao Chen, Yihang Lin, Nan Shen and Youjin Li

FOXM1/lncRNA TYMSOS/miR-214-3p–Mediated High Expression of NCAPG Correlates With Poor Prognosis and Cell Proliferation in Non–Small Cell Lung Carcinoma

Yixiao Yuan, Xiulin Jiang, Lin Tang, Juan Wang, Dahang Zhang, William C. Cho and Lincan Duan

Decreased Expression of circ_0000160 in Breast Cancer With Axillary Lymph Node Metastasis

Ya-Wen Wang, Xu Chen, Yaru Tian, Long Liu and Peng Su

Clinical Prognostic Value of the PLOD Gene Family in Lung Adenocarcinoma

Yiming Meng, Jing Sun, Guirong Zhang, Tao Yu and Haozhe Piao

Pan-Cancer Analysis and Validation Reveals that D-Dimer-Related Genes are Prognostic and Downregulate CD8⁺ T Cells via TGF-Beta Signaling in Gastric Cancer

Yiming Guan, Bing Xu, Yi Sui, Zhezhou Chen, Yu Luan, Yan Jiang, Lijuan Wei, Wenjing Long, Sansan Zhao, Lei Han, Dakang Xu, Lin Lin and Qi Guan

Comprehensive Analyses of the Immunological and Prognostic Roles of an IQGAP3AR/let-7c-5p/IQGAP3 Axis in Different Types of Human Cancer

Yixiao Yuan, Xiulin Jiang, Lin Tang, Hong Yang, Juan Wang, Dahang Zhang and Lincan Duan

Determining the Prognostic Value of Spliceosome-Related Genes in Hepatocellular Carcinoma Patients

Jun Liu, Liming Gu, Dangui Zhang and Wenli Li

Identification of Down-Regulated ADH1C is Associated With Poor Prognosis in Colorectal Cancer Using Bioinformatics Analysis

Ming Li, Ziming Liu, Jia Song, Tian Wang, Hongjie Wang, Yanan Wang and Jiguang Guo

The N6-Methylandenosine-Related Gene BIRC5 as a Prognostic Biomarker Correlated With Cell Migration and Immune Cell Infiltrates in Low Grade Glioma

Xiulin Jiang, Yulin Shi, Xi Chen, Haitao Xu, Xiaobin Huang, Lihua Li and Jun Pu

Integrated Analysis of Transcriptomic and Genomic Data Reveals Blood Biomarkers With Diagnostic and Prognostic Potential in Non-small Cell Lung Cancer

Ibrahim H. Kaya, Olfat Al-Harazi, Mustafa T. Kaya and Dilek Colak

Conflicting Roles of ZFP36L1 in Regulating the Progression of Muscle Invasive Bladder Cancer

Simin Yuan, Yujia Zhai, Tao Tao, Xiaolong Zhang, Ghassan Bashir, Guangzhi Li, Gang Wang and Song Wu

Siglec-9, a Putative Immune Checkpoint Marker for Cancer Progression Across Multiple Cancer Types

Yuliang Wu, Wei Huang, Yutong Xie, Chunyan Wang, Ning Luo, Yingying Chen, Liefu Wang, Zhongping Cheng, Zhengliang Gao and Shupeng Liu

LncRNA FAM13A-AS1 Promotes Renal Carcinoma Tumorigenesis Through Sponging miR-141-3p to Upregulate NEK6 Expression

Xin Jun Wang, Si Li, Jiang Fang, Zhi Jian Yan and Guang Cheng Luo

Comprehensive Analysis of mTORC1 Signaling Pathway–Related Genes in the Prognosis of HNSCC and the Response to Chemotherapy and Immunotherapy

Zhao Ding, Hailong Shen, Ke Xu, Yu Wu, Shuhao Wang, Fangzheng Yi, Daming Wang and Yehai Liu

The Prognostic Model and Drug Sensitivity of LKB1-Mutant Lung Adenocarcinoma Based on Immune Landscape

Guanghui Wang, Haotian Zheng, Xiaogang Zhao, Yadong Wang, Yukai Zeng and Jiajun Du

Rare c-KIT c.1926delA and c.1936T>G Mutations in Exon 13 Define Imatinib Resistance in Gastrointestinal Stromal Tumors and Melanoma Patients: Case Reports and Cell Experiments

Chi Yan, Chengzhi Zhao, Ke Yang, Hongyan Zhou, Limin Jing, Weixing Zhao, Wenguang Dou, Qingxin Xia, Jie Ma, Bing Wei and Yongjun Guo

ORIGINAL RESEARCH
published: 26 October 2021
doi: 10.3389/fmolb.2021.733271

Systematic Analysis of the Clinical Significance of Hyaluronan-Mediated Motility Receptor in Colorectal Cancer

Yan-ping Tang¹†, Yi-xin Yin¹†, Ming-zhi Xie², Xin-qiang Liang¹, Ji-lin Li¹, Ke-zhi Li¹* and Bang-li Hu¹*

¹Department of Research, Guangxi Medical University Cancer Hospital, Nanning, China

²Department of Chemotherapy, Guangxi Medical University Cancer Hospital, Nanning, China

Edited by:
Hossain Shekhar, University of Dhaka, Bangladesh

Reviewed by:
Dragana Nikitovic, University of Crete, Greece
Prihantono Prihantono, Hasanuddin University, Indonesia

* Correspondence: Ke-zhi Li, likezhi@gxmu.edu.cn; Bang-li Hu, hubangli@gxmu.edu.cn

†These authors have contributed equally to this work

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 30 June 2021
Accepted: 23 September 2021
Published: 26 October 2021

Citation: Tang Y-p, Yin Y-x, Xie M-z, Liang X-q, Li J-l, Li K-z and Hu B-l (2021) Systematic Analysis of the Clinical Significance of Hyaluronan-Mediated Motility Receptor in Colorectal Cancer. Front. Mol. Biosci. 8:733271. doi: 10.3389/fmolb.2021.733271

Background: The role of hyaluronan-mediated motility receptor (HMMR) in colorectal cancer (CRC) remains unclear. The present study aimed to explore the association of HMMR with the development and prognosis of CRC using sequence datasets, clinical tissues, blood samples, and cell lines.

Methods: CRC datasets were downloaded from TCGA and GEO databases. Forty CRC tissue samples, 120 CRC blood samples, and 100 healthy controls were collected. Four CRC cell lines (HCT116, HT-29, LoVo, and SW480) and one normal human colon mucosal epithelial cell line (NCM460) were cultured. RT-qPCR was used to determine the expression of HMMR in the tissues and cell lines. ELISA was used to measure HMMR levels in the blood samples.

Results: The expression of HMMR was significantly increased in CRC tissues than in corresponding adjacent tissues based on TCGA and GEO datasets, and clinical CRC tissues. No associations were found between the expression of HMMR and the TNM stage or other clinical parameters. The expression of HMMR varied in different CRC cell lines. The blood levels of HMMR tended to be higher in patients with CRC than in healthy controls. TCGA and GEO datasets showed inconsistent results regarding the association of HMMR expression with the survival of patients with CRC.

Conclusion: The expression of HMMR is increased in CRC tissues but not in the blood. The expression of HMMR is independent of CRC development and has no prognostic significance in patients with CRC.

Keywords: colorectal cancer, hyaluronan-mediated motility receptor, tissues, blood, cell lines

INTRODUCTION

Colorectal cancer (CRC) is one of the most common malignancies worldwide, according to the GLOBOCAN 2020 reports (Sung et al., 2021). Despite advances in surgical techniques, and adjuvant and neoadjuvant therapies, the prognosis of patients with CRC at a later stage remains poor. The pathogenesis of CRC is a multifactorial process, and molecular changes are one of the crucial factors that lead to the development of CRC. Several molecules are associated with the development and progression of CRC, and some of them can serve as indicators of CRC diagnosis, treatment monitoring, and prognosis (Di Nicolantonio et al., 2021). However, other molecules still need to be explored.

The hyaluronan-mediated motility receptor (HMMR, also known as RHAMM), a hyaluronan receptor, is localized in the cytoplasm, nucleus, and cell surface and regulates cell motility and cell cycle (Evanko and Wight, 1999; Jiang et al., 2013; Carvalho et al., 2021). Evidence suggests that the HMMR is involved in the occurrence and development of tumors that are dependent on hyaluronan-mediated signaling (Tolg et al., 2003; He et al., 2020). Previous studies indicate that the HMMR is overexpressed in and is a potential prognostic factor in various cancer types, including breast cancer (Liu et al., 2016), head and neck squamous cell carcinoma (Lu et al., 2021), hepatocellular carcinoma (Lu et al., 2020), and other cancers.

Several studies have reported the role of HMMR in CRC. Zlobec et al. (Zlobec et al., 2008c) reported that high expression of HMMR was significantly associated with the five-year survival rate in patients with CRC. Sun et al. (Sun et al., 2020) analyzed data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and found that the HMMR was significantly associated with overall survival in patients with CRC. The HMMR was also found to involve in the metastasis and treatment response in CRC (Lugli et al., 2006; Mele et al., 2017; Burren et al., 2021). These results indicate that the HMMR is a predictor of CRC in patients. However, due to limited data, the robustness of the results is undermined. Thus, the role of HMMR in CRC needs to be further elucidated. In the present study, we explored the role of HMMR in CRC by comprehensively analyzing its expression in CRC sequencing databases, tissue and serum samples, and cell lines; this study may provide more reliable results to validate the role of HMMR in CRC.

MATERIALS AND METHODS

HMMR Data in CRC From TCGA and GEO Databases

TCGA data of HMMR in colon adenocarcinoma (COAD) and rectal adenocarcinoma (READ) were downloaded from the UCSC Xena database (http://xena.ucsc.edu/) that includes 440 colon cancer and 158 rectal cancer samples. The HMMR values and phenotypes of patients were extracted together. Twelve CRC microarray datasets were downloaded from the GEO database with accession numbers GSE21510 (num. 148), GSE21815 (num. 141), GSE31279 (num. 110), GSE44076 (num. 246), GSE32323 (num. 44), GSE113513 (num. 28), GSE164191 (num. 121), GSE47756 (num. 93), GSE17537 (num. 55), GSE12945 (num. 62), GSE17536 (num. 177), and GSE14333 (num. 290). Of these, GSE164191 and GSE47756 provided data from the blood of patients with CRC, and the remaining samples were from CRC tissues.

Clinical CRC Sample Collection

We collected 40 CRC tissues and corresponding adjacent normal tissues from the BioBank of the Guangxi Medical University Cancer Hospital between January 2017 and December 2019. Fresh tissue samples were frozen immediately after surgery and stored in liquid nitrogen until further use. All tissues were histologically confirmed to be CRC. In addition, the blood samples of 120 CRC patients and 100 healthy controls were collected. The patients with CRC did not receive chemotherapy or other treatments prior to surgery, or suffer from diseases, such as immune diseases and severe major organ dysfunction. This study was approved by the Ethics Committee of the Guangxi Medical University Cancer Hospital (2021-KY-043), and written informed consent was obtained from all participants.

Cell Lines and Culture

Four CRC cell lines, HCT116, LoVo, SW480, and HT-29, and one human colon mucosal epithelial cell line, NCM460, were obtained from the Chinese Academy of Sciences (Shanghai, China). HCT116, LoVo, SW480, and HT-29 cells were cultured in DMEM with GlutaMAX (Gibco) supplemented with 10% FBS and 1% streptomycin/penicillin. NCM460 cells were cultured in phenol red–free RPMI 1640 (Gibco) supplemented with 10% FBS and 1% streptomycin/penicillin. The identity of the cell lines was confirmed by STR profiling.

RNA Isolation From Colon Cancer Tissues and Cells

Total RNA from CRC tissues and cell lines was isolated using TRIzol reagent (Invitrogen; Thermo Fisher Scientific, Inc., Waltham, MA, United States), according to the manufacturer’s instructions. The RNA concentration was tested using NanoDrop ND-1000, and the quality was assessed using electrophoresis with 1.5% denaturing agarose gels.

RT-qPCR Procedure

First-strand cDNA was synthesized by reverse transcription from 1 µg RNA using the PrimeScript RT reagent Kit with gDNA Eraser (Takara, Dalian, China). qPCR was performed using the standard protocol from the SYBR® Premix Ex Taq kit (Takara, Dalian, China), according to the manufacturer’s instructions. GAPDH was used as an internal control. The following primers were used: HMMR: forward, 5′-GCA GAA CCA ACT CAA GCA ACA G-3′, and reverse, 5′-TCT TCA TAG AGG AGA CGC CAC T-3'; GAPDH: forward, 5′-GCA CCG TCA AGG CTG AGA AC-3′, and reverse, 5′-TGG TGA AGA CGC CAG TGG A-3'. The expression of HMMR was quantified using the 2−ΔΔCT method.

Enzyme-Linked Immunosorbent Assay (ELISA) for HMMR

A 5 ml fasting peripheral blood sample was collected from each subject. Serum samples were obtained by centrifuging at 1,500 ×g for 10 min at 4°C and then stored at −80°C for further use. Serum samples from healthy controls were collected in the morning. The ELISA kit (Signalway Antibody, California, United States, No. EK4982) was used to measure serum HMMR levels. The experiments were performed according to the manufacturer’s instructions.

Statistical Analysis

Data are presented as mean ± standard deviation. The χ² test was used to compare differences in categorical variables, and Student’s t-test was used to compare differences between two groups for continuous variables. The one-way ANOVA test was employed to compare the continuous variables for over three groups. Kaplan–Meier survival curves were applied to depict the survival of patients, and the log-rank test was used to compare the survival rates. Statistical analyses were performed using R (version 3.4.1). A p-value < 0.05 was considered to be statistically significant.

RESULTS

Expression of HMMR in Cancers in TCGA and GEO Datasets

We first analyzed the expression of HMMR mRNA in gastrointestinal cancers in TCGA, including colon cancer (COAD), rectal cancer (READ), liver cancer (LIHC), gastric cancer (STAD), and esophageal cancer (ESCA) datasets, and found that HMMR mRNA was significantly overexpressed in gastrointestinal cancer tissues than in normal tissues (Figure 1A, p < 0.05). Furthermore, we analyzed the expression of HMMR mRNA in GSE21815, GSE31279, GSE44076, GSE32323, and GSE113513 datasets and found that the expression of HMMR was significantly increased in the CRC tissues than in normal tissues (Figures 1B–F, p < 0.05).

FIGURE 1 | (A) Expression of HMMR in colorectal cancer (CRC) based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. (B-F) Comparison of HMMR in CRC and normal tissues in five GEO datasets.

Validation of HMMR in Clinical Colon Cancer Tissues

Next, we checked the expression of HMMR in 40 colon cancer tissues and corresponding adjacent colon tissues by RT-qPCR. The analysis suggested that HMMR mRNA was significantly overexpressed in colon cancer tissues than in adjacent colon tissues (Figure 2A, p < 0.05), which verified the results from the online datasets.

FIGURE 2 | Expression of HMMR mRNA in (A) clinical tissues and (B) CRC cell lines and colon mucosal epithelial cell line. *p < 0.05.

Expression of HMMR in Colon Cancer Cells

We checked the expression of HMMR mRNA in HCT116, HT-29, LoVo, SW480, and NCM460 using the RT-qPCR assay. The expression of HMMR mRNA was significantly increased in HT-29 cells, but decreased in HCT116, LoVo, and SW480 cells, especially in the HCT116 cells, than in NCM460 cells (Figure 2B).

Clinical Significance of HMMR in CRC

We analyzed the clinical significance of HMMR in CRC using TCGA dataset, GEO dataset (GSE21815), and clinical datasets. The median HMMR level in CRC tissues was calculated and used as the cutoff value. As listed in Table 1, the expression of HMMR in CRC tissues was not associated with the patients’ age, sex, TNM stage, clinical stage, tumor location, and KRAS mutation status (p > 0.05), indicating that the expression of HMMR in CRC tissues was independent of these clinical parameters.

TABLE 1 | Results of clinical significance of HMMR in CRC using three datasets.

HMMR Levels in Blood Samples

Furthermore, to explore the expression of HMMR in peripheral blood, two GEO datasets (GSE164191 and GSE47756) analyzing the expression of HMMR in peripheral blood were included in the analysis, and the blood samples of 120 patients with CRC and 100 healthy controls were collected and tested by ELISA. As shown in Figures 3A, B, HMMR levels in blood samples were not significantly different between patients with CRC and healthy controls in the GSE164191 and GSE47756 datasets; but HMMR levels, analyzed by ELISA, were significantly elevated in blood samples from patients with CRC than in those from healthy controls (Figure 3C, p < 0.01).

FIGURE 3 | Comparison of blood levels of HMMR in CRC and healthy controls: (A) results of GSE164191; (B) results of GSE47756, including patients with metastatic and non-metastatic cancer; (C) results of clinical blood samples.

Next, we examined the association between HMMR levels and clinical parameters in CRC patients, as listed in Table 2, using median HMMR levels as the cutoff value. No significant differences in patients’ age, sex, TNM stage, clinical stage, tumor location, and the four tumor biomarkers (CEA, CA125, CA153, and CA199) (p < 0.05) were observed between samples with high and low HMMR levels; this suggested that HMMR levels in the peripheral blood of patients with CRC were independent of the clinical parameters and tumor biomarkers.

TABLE 2 | Association of blood levels of HMMR with the clinical parameters.

Survival Analysis of HMMR in Patients With CRC

To determine the prognostic value of HMMR in patients with CRC, we analyzed the overall survival (OS) and disease-free survival (DFS) using data from TCGA and four GEO datasets (GSE17537, GSE12945, GSE17536, and GSE14333). As shown in Figures 4A–D, low expression of HMMR indicated poor survival rate in TCGA; but there was no significant association between HMMR expression and OS in patients with CRC in three GEO datasets. Furthermore, analyses of two GEO datasets (GSE17536 and GSE14333) indicated a significant association between HMMR expression and DFS in patients with CRC, although the other two datasets (GSE17537 and GSE12945) were inconsistent with these results (Figures 4E–H). Taken together, these results fail to confirm the association between HMMR expression and survival in patients with CRC.

FIGURE 4 | Association of the expression of HMMR with survival in patients with CRC. (A) OS results in TCGA-COAD and -READ datasets; (B) OS results in the GSE17537 dataset; (C) OS results in the GSE12945 dataset; (D) OS results in the GSE17536 dataset; (E) DFS results in the GSE17537 dataset; (F) DFS results in the GSE12945 dataset; (G) DFS results in the GSE17536 dataset; (H) DFS results in the GSE14333 dataset. OS: overall survival; DFS: disease-free survival.

DISCUSSION

In the present study, we comprehensively analyzed the expression of HMMR in multiple types of CRC sample tissues, blood, and cell lines and in sequence datasets. The results indicated that the expression of HMMR mRNA was significantly increased in CRC tissues than in corresponding control tissues. However, the blood levels of HMMR were not significantly different between patients with CRC and healthy controls. Moreover, the clinical analysis failed to reveal the association of HMMR expression in tissues and peripheral blood with the TNM stage and clinical stage, suggesting that HMMR expression was independent of the progression of CRC. Furthermore, although the HMMR mRNA level was elevated in HT-29 cells than in normal colon mucosal epithelial cells, there was considerably decreased expression in the other three CRC cell lines, highlighting the variable expression of HMMR mRNA in CRC. Finally, analyses of datasets from TCGA and GEO failed to confirm the prognostic value of HMMR in OS and DFS in patients with CRC, suggesting that the predictive role of HMMR in CRC prognosis remains to be verified.

The molecular structure and biological function of HMMR have been documented in previous studies. Evidence suggests that the HMMR is a hydrophilic protein that lacks hydrophobic signal peptides or potential hydrophobic transmembrane domains (Choi et al., 2019); it also lacks the structural properties required for canonical extracellular export (Kouvidi et al., 2014). The expression of HMMR is critical for orientation of the mitotic spindle in human mitotic cells and is regulated by proliferation-associated transcription factors or signaling pathway members, such as FOXM1, E2F4, MYC (Lachmann et al., 2018), and Hippo (Wang et al., 2014). A study also revealed that the expression of HMMR is transcriptionally downregulated by the tumor suppressor p53 (Sohr and Engeland, 2008). The presence of HMMR-positive CRC cells is significantly associated with poor survival outcomes, independent of the TNM stage and adjuvant therapy (Koelzer et al., 2015). These findings suggest that the HMMR plays a critical role in the pathogenesis of CRC.

The expression of HMMR was reported to be upregulated in several cancer types (Shigeishi et al., 2014; Singleton, 2014; Buttermore et al., 2017), but it was also downregulated in other cancer types than in normal controls based on the results from the LAML and TGCT projects. In the present study, we found that the HMMR was significantly upregulated in gastrointestinal tract cancers, and results from TCGA and GEO datasets and clinical samples further validated the elevated expression of HMMR in CRC tissues. To determine the role of HMMR in the development of CRC, we analyzed the association between the expression of HMMR and clinical parameters. However, the results from the two large cohorts failed to reveal such associations. Additionally, the experimental analyses also confirmed only small associations, which indicated that the expression of HMMR in CRC tissues was independent of the tumor stage. Moreover, the expression of HMMR varied significantly between different CRC cell lines. These results demonstrated that the expression of HMMR was different in the tissues and cell lines.

As compared to the tissue samples, the role of HMMR levels in the blood of patients is more interesting to clinicians. Therefore, we examined HMMR levels in the blood of patients with CRC. However, the expression of HMMR in the blood was not significantly different between patients with CRC and healthy controls. The ELISA indicated significant differences in the expression of HMMR between patients with CRC and healthy controls; but considering the relatively small sample size, these results still need validation. Additionally, similar to the results from tissues, HMMR levels in the blood were not significantly associated with the clinical parameters, including the TNM stage.

A previous study suggested that high expression of HMMR was associated with poor survival in patients with CRC (Zlobec et al., 2008a). Another study showed similar results using immunohistochemistry (Zlobec et al., 2008b). However, in the present study, the OS and DFS in four GEO datasets were inconsistent, and those from TCGA were even opposite to the results of a previous study. Moreover, the association between HMMR and DFS was not significant in the three datasets. These discrepancies may be due to differences in treatment after diagnosis of CRC or the different detection methods of HMMR. Therefore, the prognostic value of HMMR in CRC remains to be validated in a larger cohort using a consistent detection method.

Previous studies have reported the expression of HMMR in CRC (Karamitopoulou et al., 2011; Mele et al., 2017); however, due to the limited number or types of samples, the clinical and prognostic significance of HMMR in CRC remains to be elucidated. The present study, as opposed to previous studies, included many types of samples with larger sample sizes and, thus, achieved more reliable results. However, some limitations of this study should be noted. First, several confounders, such as gene mutation and microsatellite instability status, may have affected the gene expression and prognosis of patients; but we could not analyze the effect of these factors on the expression of HMMR due to the limited data. Second, the number of clinical samples was relatively small, and the results still need to be verified in a larger cohort. Third, the mechanism of action of HMMR in CRC pathogenesis needs to be explored in vivo and in vitro.

CONCLUSION

By comprehensively analyzing the sequencing and microarray data, clinical samples, blood samples, and cell lines, the present study demonstrated that the expression of HMMR was increased in CRC tissues but not in the blood. Moreover, the expression of HMMR was independent of CRC development and had no prognostic significance in patients with CRC.

DATA AVAILABILITY STATEMENT

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

ETHICS STATEMENT

The studies involving human participants were reviewed and approved by the Ethics Committee of Guangxi Medical University Cancer Hospital. The patients/participants provided their written informed consent to participate in this study.

AUTHOR CONTRIBUTIONS

TY and HB conceptualized and designed the study. YY and XM collected and assembled the data. YX, XM, and LK performed the experiment. HB, TY, LK, LX, LJ, and XM were involved in data analysis and interpretation. All authors wrote, reviewed, read, and approved the manuscript in its current state.

FUNDING

This study was partially supported by research funding from the National Natural Science Foundation (No. 81860417), Natural Science Foundation of Guangxi (No. 2018JJA140136), and Basic Competence Promotion Project for Young and Middle-aged Teachers in Guangxi (No. 2020KY0348).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

REFERENCES

Burren, S., Reche, K., Blank, A., Galvàn, J. A., Dawson, H., Berger, M. D., et al. (2021). RHAMM in Liver Metastases of Stage IV Colorectal Cancer with Mismatch-Repair Proficient Status Correlates with Tumor Budding, Cytotoxic T-Cells and PD-1/pd-L1. Pathol. - Res. Pract. 223, 153486. doi:10.1016/j.prp.2021.153486

Buttermore, S. T., Hoffman, M. S., Kumar, A., Champeaux, A., Nicosia, S. V., and Kruk, P. A. (2017). Increased RHAMM Expression Relates to Ovarian Cancer Progression. J. Ovarian Res. 10 (1), 66. doi:10.1186/s13048-017-0360-1

Carvalho, A. M., Soares da Costa, D., Paulo, P. M. R., Reis, R. L., and Pashkuleva, I. (2021). Co-localization and Crosstalk between CD44 and RHAMM Depend on Hyaluronan Presentation. Acta Biomater. 119, 114–124. doi:10.1016/j.actbio.2020.10.024

Choi, S., Wang, D., Chen, X., Tang, L. H., Verma, A., Chen, Z., et al. (2019). Function and Clinical Relevance of RHAMM Isoforms in Pancreatic Tumor Progression. Mol. Cancer 18 (1), 92. doi:10.1186/s12943-019-1018-y

Di Nicolantonio, F., Vitiello, P. P., Marsoni, S., Siena, S., Tabernero, J., Trusolino, L., et al. (2021). Precision Oncology in Metastatic Colorectal Cancer - from Biology to Medicine. Nat. Rev. Clin. Oncol. 18, 506–525. doi:10.1038/s41571-021-00495-z

Evanko, S. P., and Wight, T. N. (1999). Intracellular Localization of Hyaluronan in Proliferating Cells. J. Histochem. Cytochem. 47 (10), 1331–1341. doi:10.1177/002215549904701013

He, Z., Mei, L., Connell, M., and Maxwell, C. A. (2020). Hyaluronan Mediated Motility Receptor (HMMR) Encodes an Evolutionarily Conserved Homeostasis, Mitosis, and Meiosis Regulator rather Than a Hyaluronan Receptor. Cells 9 (4), 819. doi:10.3390/cells9040819

Jiang, J., Mohan, P., and Maxwell, C. A. (2013). The Cytoskeletal Protein RHAMM and ERK1/2 Activity Maintain the Pluripotency of Murine Embryonic Stem Cells. PLoS One 8 (9), e73548. doi:10.1371/journal.pone.0073548

Karamitopoulou, E., Zlobec, I., Panayiotides, I., Patsouris, E. S., Peros, G., Rallis, G., et al. (2011). Systematic Analysis of Proteins from Different Signaling Pathways in the Tumor center and the Invasive Front of Colorectal Cancer. Hum. Pathol. 42 (12), 1888–1896. doi:10.1016/j.humpath.2010.06.020

Koelzer, V. H., Huber, B., Mele, V., Iezzi, G., Trippel, M., Karamitopoulou, E., et al. (2015). Expression of the Hyaluronan-Mediated Motility Receptor RHAMM in Tumor Budding Cells Identifies Aggressive Colorectal Cancers. Hum. Pathol. 46 (11), 1573–1581. doi:10.1016/j.humpath.2015.07.010

Kouvidi, K., Nikitovic, D., Berdiaki, A., and Tzanakakis, G. N. (2014). Hyaluronan/RHAMM Interactions in Mesenchymal Tumor Pathogenesis. Adv. Cancer Res. 123, 319–349. doi:10.1016/B978-0-12-800092-2.00012-5

Lachmann, A., Torre, D., Keenan, A. B., Jagodnik, K. M., Lee, H. J., Wang, L., et al. (2018). Massive Mining of Publicly Available RNA-Seq Data from Human and Mouse. Nat. Commun. 9 (1), 1366. doi:10.1038/s41467-018-03751-6

Liu, W., Ma, J., Cheng, Y., Zhang, H., Luo, W., and Zhang, H. (2016). HMMR Antisense RNA 1, a Novel Long Noncoding RNA, Regulates the Progression of Basal-like Breast Cancer Cells. Breast Cancer ( London: Dove Med Press), 8, 223–229. doi:10.2147/BCTT.S119997

Lu, D., Bai, X., Zou, Q., Gan, Z., and Lv, Y. (2020). Identification of the Association between HMMR Expression and Progression of Hepatocellular Carcinoma via Construction of a Co-expression N-etwork. Oncol. Lett. 20 (3), 2645–2654. doi:10.3892/ol.2020.11844

Lu, T., Zheng, Y., Gong, X., Lv, Q., Chen, J., Tu, Z., et al. (2021). High Expression of Hyaluronan-Mediated Motility Receptor Predicts Adverse Outcomes: A Potential Therapeutic Target for Head and Neck Squamous Cell Carcinoma. Front. Oncol. 11, 608842. doi:10.3389/fonc.2021.608842

Lugli, A., Zlobec, I., Günthert, U., Minoo, P., Baker, K., Tornillo, L., et al. (2006). Overexpression of the Receptor for Hyaluronic Acid Mediated Motility Is an Independent Adverse Prognostic Factor in Colorectal Cancer. Mod. Pathol. 19 (10), 1302–1309. doi:10.1038/modpathol.3800648

Mele, V., Sokol, L., Kölzer, V. H., Pfaff, D., Muraro, M. G., Keller, I., et al. (2017). The Hyaluronan-Mediated Motility Receptor RHAMM Promotes Growth, Invasiveness and Dissemination of Colorectal Cancer. Oncotarget 8 (41), 70617–70629. doi:10.18632/oncotarget.19904

Shigeishi, H., Higashikawa, K., and Takechi, M. (2014). Role of Receptor for Hyaluronan-Mediated Motility (RHAMM) in Human Head and Neck Cancers. J. Cancer Res. Clin. Oncol. 140 (10), 1629–1640. doi:10.1007/s00432-014-1653-z

Singleton, P. A. (2014). Hyaluronan Regulation of Endothelial Barrier Function in Cancer. Adv. Cancer Res. 123, 191–209. doi:10.1016/B978-0-12-800092-2.00007-1

Sohr, S., and Engeland, K. (2008). RHAMM Is Differentially Expressed in the Cell Cycle and Downregulated by the Tumor Suppressor P53. Cell Cycle 7 (21), 3448–3460. doi:10.4161/cc.7.21.7014

Sun, Z., Liu, C., and Cheng, S. Y. (2021). Identification of Four Novel Prognosis Biomarkers and Potential Therapeutic Drugs for Human Colorectal Cancer by Bioinformatics Analysis. J. Biomed. Res. 35 (1), 21–35. doi:10.7555/JBR.34.20200021

Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., et al. (2021). Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA A. Cancer J. Clin. 71 (3), 209–249. doi:10.3322/caac.21660

Tolg, C., Poon, R., Fodde, R., Turley, E. A., and Alman, B. A. (2003). Genetic Deletion of Receptor for Hyaluronan-Mediated Motility (Rhamm) Attenuates the Formation of Aggressive Fibromatosis (Desmoid Tumor). Oncogene 22 (44), 6873–6882. doi:10.1038/sj.onc.1206811

Wang, Z., Wu, Y., Wang, H., Zhang, Y., Mei, L., Fang, X., et al. (2014). Interplay of Mevalonate and Hippo Pathways Regulates RHAMM Transcription via YAP to Modulate Breast Cancer Cell Motility. Proc. Natl. Acad. Sci. USA 111 (1), E89–E98. doi:10.1073/pnas.1319190110

Zlobec, I., Baker, K., Terracciano, L. M., and Lugli, A. (2008b). RHAMM, P21 Combined Phenotype Identifies Microsatellite Instability-High Colorectal Cancers with a Highly Adverse Prognosis. Clin. Cancer Res. 14 (12), 3798–3806. doi:10.1158/1078-0432.CCR-07-5103

Zlobec, I., Baker, K., Terracciano, L., Peter, S., Degen, L., Beglinger, C., et al. (2008a). Two-marker Protein Profile Predicts Poor Prognosis in Patients with Early Rectal Cancer. Br. J. Cancer 99 (10), 1712–1717. doi:10.1038/sj.bjc.6604729

Zlobec, I., Terracciano, L., Tornillo, L., Gunthert, U., Vuong, T., Jass, J. R., et al. (2008c). Role of RHAMM within the Hierarchy of Well-Established Prognostic Factors in Colorectal Cancer. Gut 57 (10), 1413–1419. doi:10.1136/gut.2007.141192

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Tang, Yin, Xie, Liang, Li, Li and Hu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 27 October 2021
doi: 10.3389/fmolb.2021.686313

Clinical Associations of Preoperative and Postoperative Serum CEA and Lung Cancer Outcome

Zonglin Jiao¹†, Shoubo Cao²†, Jianhua Li³, Nan Hu⁴, Yinghui Gong¹, Linduo Wang¹ and Shi Jin⁵*

¹Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, China

²Department of Medical and Radiation Oncology, Linyi People’s Hospital, Linyi, China

³Department of Neurosurgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital and Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China

⁴Department of Oncology, Heilongjiang Agricultural Reclamation Bureau General Hospital, Harbin, China

⁵Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital and Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China

Edited by:
Hossain Shekhar, University of Dhaka, Bangladesh

Reviewed by:
Pedro José Carlos Rondot Radío, University of Buenos Aires, Argentina
Yichao Zheng, Tsinghua University, China

* Correspondence: Shi Jin, catherine-jinshi@163.com

†These authors have contributed equally to this work and share first authorship

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 26 March 2021
Accepted: 12 October 2021
Published: 27 October 2021

Citation: Jiao Z, Cao S, Li J, Hu N, Gong Y, Wang L and Jin S (2021) Clinical Associations of Preoperative and Postoperative Serum CEA and Lung Cancer Outcome. Front. Mol. Biosci. 8:686313. doi: 10.3389/fmolb.2021.686313

Background: Serum carcinoembryonic antigen (CEA), a classic tumour marker, is widely used in lung cancer in clinical practice. Nevertheless, few studies have elucidated the influence of dynamic changes in CEA in the perioperative phases, as a prognostic indicator, on lung cancer prognosis.

Methods: This retrospective cohort analysis included consecutive patients with stage I-III lung cancer who underwent curative resection between December 2010 and December 2014. The patients were grouped into three cohorts: group A included patients with normal preoperative CEA, group B included patients with elevated preoperative CEA but normal postoperative CEA, and group C included patients with elevated preoperative and postoperative CEA. Five-year overall survival (OS) was estimated by Kaplan-Meier analysis (log-rank test). Multivariate analyses were performed with Cox proportional hazard regression.

Results: A total of 1662 patients with stage I-III lung cancer were enrolled in our study. Patients with normal preoperative CEA had 15.9 and 20.1% better 3- and 5-year OS rates than the cohort with elevated preoperative CEA (p < 0.001). Furthermore, group C had 36.0 and 26.6% lower 5-year OS rates (n = 74, 32.4%) than group A (n = 1188, 68.4%) and group B (n = 139, 59.0%) (p < 0.001). Group B had poorer OS than group A (p = 0.016). For patients with different pathological TNM stages, subgroup analyses showed that group C had the shortest OS in stages I and II (p < 0.05), and patients with a post-preoperative CEA increment had poorer OS than those without an increment (p = 0.029). Multivariate analyses suggested that group C (HR = 2.0, 95% CI, 1.5–2.7, p < 0.001) rather than the group with normalized postoperative CEA (HR = 1.2, 95% CI, 0.9–1.5, p = 0.270) was an independent prognostic factor. In subgroup analysis of adenocarcinoma (ADC), survival analyses suggested that group C predicted a worse prognosis. Multivariate analysis of ADC indicated that group C was an independent adverse prognostic factor (HR = 1.9, 95% CI, 1.4–2.7, p < 0.001).

Conclusions: Combined elevated preoperative and postoperative CEA is an independent adverse prognostic factor for stage I-III lung adenocarcinoma. Additionally, routine perioperative detection of serum CEA can yield valuable prognostic information for patients after lung cancer surgery.

Keywords: lung cancer, carcinoembryonic antigen, prognosis, stage I, stage II, stage III

INTRODUCTION

Cancer Statistics, 2021 indicated that in both sexes, lung cancer is a commonly diagnosed cancer (11.4% of total cancer cases) and the leading cause of cancer mortality (18.0% of total cancer deaths) (Sung et al., 2021). There is no doubt that lung cancer will place a tremendous burden on society in the coming decades.

Serum tumour markers are widely used in the diagnosis and prognostic monitoring of lung cancer, and carcinoembryonic antigen (CEA) is one of the most sensitive markers (Moertel et al., 1993; Plebani et al., 1995; Molina et al., 2009). CEA is a glycoprotein associated with cell adhesion and is usually produced during foetal development but ceases to be secreted before birth. Specifically, CEA is a glycosylphosphatidylinositol (GPI) cell surface-anchored glycoprotein that serves as a ligand for L-selectin and E-selectin but is usually not present in healthy adult blood (Thomas et al., 2008; Konstantopoulos and Thomas, 2009). CEA has been widely acknowledged and recommended as a reliable tumour marker in colorectal cancer. However, it also plays vital roles in lung cancer diagnosis, progression, recurrence, metastasis, and various treatment effects (Wang et al., 2014; Chen et al., 2018; Konishi et al., 2018). Some studies have reported that a high preoperative serum CEA level is an independent prognostic factor and that a high postoperative level of serum CEA always indicates a poor prognosis in lung cancer (Sawabata et al., 2002; Sawabata et al., 2004a; Okada et al., 2004). For non-small lung cancer (NSCLC), in patients with a high preoperative serum CEA level, their postoperative serum CEA level has better prognostic value than their post/preoperative serum CEA ratio (Tomita et al., 2015). However, some studies have reported controversial findings on whether serum CEA can serve as a prognostic and predictive marker in lung cancer. The authors concluded that CEA is of little use as a diagnostic marker for small cell lung cancer (SCLC) and NSCLC (Ford et al., 1981; Schneider et al., 2000; Hatzakis et al., 2002).

The value of CEA in lung cancer prognosis related to dynamic changes in preoperative and postoperative serum CEA levels has not been demonstrated systematically. In our analysis, we attempted to observe whether perioperative changes in CEA could provide more prognostic information. More specifically, we sought to explore whether patients with an elevated preoperative CEA level that normalizes after curative resection have a similar risk of death as those with a CEA level that is elevated throughout the perioperative period.

METHODS

Study Design and Data Collection

This study was a retrospective clinical study and was approved by the Ethics Committee of Harbin Medical University Cancer Hospital. All the consecutive patients were from the Harbin Medical University Cancer Hospital and pathologically diagnosed with stage I to III lung cancer between December 2010 and December 2014. The exclusion criteria were as follows: 1) treatment for malignancy within 5 years; 2) no available preoperative CEA value; 3) preoperative chemotherapy or radiotherapy; and 4) a lack of complete follow-up information.

Data on the patients’ clinical characteristics, including demographics, pathological reports, and perioperative clinical outcomes, were acquired from the departmental database and electronic patient records. Pathological stage was defined based on the seventh edition of the AJCC lung cancer criteria. We defined preoperative CEA as the CEA value recorded closest to the surgery time, and postoperative CEA as the CEA value recorded within 12 weeks after surgery but before postoperative adjuvant therapy. The normal reference CEA value for the assay was 0.0–5.0 ng/ml (Roche Cobas e 602 ECL analyser). Patients were grouped as follows: 1) (Group A) normal preoperative CEA group, patients with a normal preoperative CEA level (≤5 ng/ml); 2) (Group B) normalized postoperative CEA group, patients with an elevated preoperative CEA level (>5 ng/ml) but a normal postoperative CEA level; and 3) (Group C) elevated postoperative CEA group, patients with elevated preoperative and postoperative CEA levels. We also used a CEA cut-off of 10.0 ng/ml for repeat analyses (Konishi et al., 2018). According to authoritative guidelines, all patients were followed up by history, physical examination, chest CT and PET/CT or MRI. Survival statuses were effectively updated by telephone, email, or medical history. Overall survival (OS) was defined as the time from surgery to death or last follow-up. Patients alive at the last follow-up date were censored.

Statistical Analysis

All statistical data in this study were analysed with IBM SPSS 21.0 statistical software and GraphPad Prism 8.0. Three- and 5-year overall survival (OS) was estimated by the Kaplan-Meier method with the log-rank test for univariate analysis. The various indicators of prognosis with p-values of less than 0.05 in the univariate analysis were examined in multivariate analyses. Hazard ratios (HRs), 95% confidence intervals (CIs), and the effects of prognostic factors were estimated by Cox regression in the multivariate Cox regression. All p-values were two-sided, and p-values < 0.05 were considered significant.

RESULTS

A total of 2477 consecutive patients with stage I-III lung cancer who underwent curative resection were included in our study. According to the exclusion criteria, patients who had prior cancer treatment within 5 years (n = 128), were missing a preoperative CEA value (n = 432), had received preoperative chemotherapy or radiotherapy (n = 75), or had incomplete follow-up information (n = 180) were excluded. Among the remaining 1662 patients, 1188 (71.5%) had a normal preoperative CEA level, and 474 (28.5%) had an elevated CEA level. A total of 261 of the 474 patients with an elevated preoperative CEA level had no available postoperative CEA data within 12 weeks; 139 of the remaining patients had a normalized postoperative CEA value, and 74 had an elevated postoperative CEA value (Figure 1).

FIGURE 1 | Study design.

Descriptive statistics of patient and tumour characteristics for 1401 patients are shown in Table 1. There were 859 males (61.3%) and 542 females (38.7%) in our study. The median age (IQR) of all these patients was 58 (52–64) years (mean age, 57.9 ± 8.6 years). More than half of the patients (56.6%, n = 796) had a smoking history. Most patients (83.6%, n = 1171) did not have a family history of cancer. The main types of pathology included adenocarcinoma (ADC; 61.7%, n = 864), squamous cell carcinoma (SCC; 34.3%, n = 480) and others (4.1%, n = 57). For T stage, 648 patients (46.3%) had T1 disease, and 561 (40.0%) had T2 disease, while there were only 96 in stage T3 or T4. A total of 479 patients had lymph node metastasis, including 182 patients (13%) with N1 disease, 294 (21%) with N2 disease and 3 (0.2%) with N3 disease. The median (IQR) preoperative CEA level was 2.7 (1.8–4.1) ng/ml. In 608 patients with postoperative CEA data, the median postoperative CEA level was 2.1 (1.3–3.5) ng/ml, and the median (IQR) days from surgery to CEA testing was 38 (32.0–54.8) days (Table 2). The median (IQR) follow-up time was 76 (67–87) months. According to our follow-up data, a total of 537 patients died. The 5-year OS rate for all these patients was 65.6%.

TABLE 1 | Patient and tumor characteristics.

TABLE 2 | Patient with postoperative CEA characteristics.

Kaplan-Meier survival curves were used to assess the effect of different CEA groups. The 3- and 5-year OS rates for the 1188 patients with a normal preoperative CEA level were 77.9 and 68.4%, which were much higher than the corresponding OS rates of 62 and 48.3% for the 474 patients with an elevated preoperative CEA level (p < 0.001) (Figure 2A). The 3- and 5-year OS rates for the 74 patients whose CEA value remained elevated after surgery at 12 weeks were 50 and 32.4% compared with 77.1 and 67.4% for the 1327 patients with either a normalized postoperative CEA level (n = 139) or a normal preoperative CEA level (n = 1188) (p < 0.001) (Figure 2B). The 3- and 5-year OS rates were 69.8 and 59% for the 139 patients in group B, which were significantly different from those in group A and group C (group A vs. group B, p = 0.016; group A vs. group C, p < 0.001; group B vs. group C, p < 0.001) (overall log-rank p < 0.001) (Figure 2C). Moreover, using a CEA cut-off of 10.0 ng/ml for repeat analyses produced similar results. Group A had the best prognosis, while group C had the worst (group A vs. group B, p < 0.001; group A vs. group C, p < 0.001; group B vs. group C, p = 0.048) (overall log-rank p < 0.001) (Figure 2D).

FIGURE 2 | Overall survival (OS) curves according to preoperative and postoperative CEA level. (A) K-M curve for OS in patients with normal vs. elevated preoperative CEA. (B) K-M curve for OS in patients with normal preoperative or normalized postoperative CEA vs. elevated postoperative CEA. (C) K-M curve for OS in patients with different subgroups. (D) K-M curve for OS in patients with different subgroups using a CEA cut-off of 10 ng/ml. aGroup A vs. Group B, p = 0.016; Group A vs. Group C, p < 0.001; Group B vs. Group C, p < 0.001. bGroup A vs. Group B, p < 0.001; Group A vs. Group C, p < 0.001; Group B vs. Group C, p = 0.048.

Subgroup analyses were used to assess the effect of CEA levels on specific stages. In stage I, the OS rate was significantly different among the three cohorts (p < 0.001); in particular, the 3-year and 5-year OS rates of group C were observably lower than those of the other two groups (group A vs. group B, p = 0.039; group A vs. group C, p < 0.001; group B vs. group C, p = 0.018) (Figure 3A). In patients with stage II disease, OS was significantly lower in group C than in group A or group B (group A vs. group C, p = 0.004; group B vs. group C, p = 0.014) (overall log-rank p < 0.001) (Figure 3B). In stage III, the three cohorts did not differ significantly (p = 0.184) (Figure 3C).

FIGURE 3 | Overall survival curves of patients with different subgroups, grouped by TNM disease stage. aGroup A vs. Group C, p < 0.001; Group A vs. Group B, p = 0.039; Group B vs. Group C, p = 0.018. bGroup A vs. Group C, p = 0.004; Group B vs. Group C, p = 0.014.

Subgroup analysis was conducted according to the pathological type of lung cancer. The 3- and 5-year OS rates of the ADC patients in group B were 74.5% and 60.8, respectively, and there were statistically significant differences among the three groups (group A vs. group B, p = 0.014; group A vs. group C, p < 0.001; group B vs. group C, p < 0.001) (overall log-rank p < 0.001) (Figure 4A). However, there were no significant differences in OS between SCC and other subtypes of lung cancer (Figures 4B,C). Furthermore, using a CEA cut-off of 10.0 ng/ml for repeat analyses in ADC produced similar results (group A vs. group B, p < 0.001; group A vs. group C, p < 0.001; group B vs. group C, p = 0.029) (overall log-rank p < 0.001) (Figure 4D). Similarly, there were no statistically significant differences in OS between SCC and other pathological subtypes using a CEA cut-off of 10.0 ng/ml. The above analysis results suggested that the OS differences among the three groups (groups A, B, and C) defined by our inclusion data might be mainly reflected in lung adenocarcinoma.

FIGURE 4 | Overall survival curves of patients with different subgroups, grouped by pathological types. (A–C) K-M curves for OS in ADC, SCC, and Others. (D) K-M curve for OS in ADC using a CEA cut-off of 10 ng/ml. aGroup A vs. Group C, p < 0.001; Group A vs. Group B, p = 0.014; Group B vs. Group C, p < 0.001. bGroup A vs. Group B, p < 0.001; Group A vs. Group C, p < 0.001; Group B vs. Group C, p = 0.029.

We also created subgroups to test the effect of post-preoperative CEA on prognosis. The post-preoperative CEA increment meant that the postoperative CEA level was higher than the preoperative CEA level. The 3- and 5-year OS rates for the 121 patients with a post-preoperative CEA increment were 69.4 and 51.2% lower than the rates of 72.3 and 61.6% for the 487 patients without a post-preoperative CEA increment (p = 0.029) (Figure 5).

FIGURE 5 | Overall survival curve by post-preoperative CEA increment.

The results for univariate and multivariate analyses revealing the clinical factors associated with OS are shown in Table 3. Using K-M curves in the univariate analysis, our results suggested that sex, smoking history, TNM (tumour-node-metastasis) stage, and different groups of changes in CEA levels were associated with OS. However, age, family cancer history, and pathology had no significance for OS. Furthermore, the multivariate analyses indicated that an elevated postoperative CEA level was an independent prognostic factor for OS (HR = 2.0, 95% CI, 1.5–2.7, p < 0.001) rather than a normalized postoperative CEA level (HR = 1.2, 95% CI%, 0.9–1.5, p = 0.270). Additionally, shorter OS was associated with male sex and a higher TNM stage.

TABLE 3 | Univariate and multivariate analyses of overall survival.

In the subgroup analysis, we found that the differences in OS among the three groups might be mainly reflected in ADC. Therefore, univariate and multivariate analyses were conducted in patients with ADC. Our results indicated that sex, TNM stage, and different groups of changes in CEA levels were associated with OS. Moreover, the multivariate analyses suggested that an elevated postoperative CEA level was an independent prognostic factor for OS (HR = 1.9, 95% CI, 1.4–2.7, p < 0.001) rather than a normalized postoperative CEA level (HR = 1.1, 95% CI%, 0.8–1.5, p = 0.676). Additionally, shorter OS was closely associated with male sex and a higher TNM stage (Table 4).

TABLE 4 | Univariate and multivariate analyses of overall survival in ADC.

DISCUSSION

In our study, the data showed that changes in the perioperative CEA level provided an informative prognostic reference for patients with stage I-III lung cancer who underwent curative resection. Patients with a normal preoperative CEA level had 15.9 and 20.1% higher 3- and 5-year OS rates than those with an elevated preoperative CEA level, and this result was consistent with the literature (Icard et al., 1994; Foa et al., 1999; Suzuki et al., 1999; Okada et al., 2003; Matsuoka et al., 2007). Following surgery, more than 65% of patients had a normalized CEA level, and the outcomes of these patients were worse than those of patients with a normal preoperative CEA level but better than those of patients with an elevated postoperative CEA level. Repeat analyses using a CEA cut-off of 10.0 ng/ml obtained similar results. Furthermore, those patients with an elevated CEA level following surgery had an absolute 27.1 and 35% lower 3- and 5-year OS rates than those with either a normalized postoperative CEA level or a normal preoperative CEA level. Subgroup analyses clearly showed a significant difference in survival among the three groups in stage I, with similar trends in stages II, and revealed poor survival in patients with a post-preoperative CEA increment. However, perioperative CEA is not able to stratify patients with stage III disease, likely due to the high tumor load of stage III patients and the early recurrence and metastasis. Moreover, in our data, fewer patients were included in group B and group C, which may overestimate the survival rate of these two groups. Univariate analysis suggested that sex, smoking history, TNM stage, and different groups of changes in CEA levels were related to OS. In addition, multivariable analyses demonstrated that a persistently elevated CEA level following surgery was an independent prognostic factor for lung cancer, consistent with previous research (Wang et al., 2010; Kozu et al., 2013; Duan et al., 2015). We found that lung adenocarcinoma accounted for the majority (61.7%) of all pathological subtypes we enrolled. We conducted survival analysis according to different pathological subtypes by the K-M method and found that only lung adenocarcinoma had results similar to those reported above. Furthermore, univariate and multivariate analyses found that an elevated postoperative CEA level was an independent prognostic factor in lung adenocarcinoma. Using a CEA cut-off of 10 ng/ml, consistent trend results were obtained. This indicated that the positive results obtained in all patients enrolled in our study were probably due to the large proportion of lung adenocarcinoma patients.

The findings are similar to those of other studies (Sawabata et al., 2002; Okada et al., 2004); evaluating perioperative serum CEA levels in patients with lung cancer following surgery has significant prognostic value. In most patients, preoperative CEA levels returned to normal after surgery. These patients’ survival status was significantly better than that of patients whose postoperative CEA level remained elevated, indicating that a normalized postoperative CEA level is an essential and favourable prognostic indicator for patients with a higher preoperative CEA level than normal. We concluded that patients with persistent CEA elevation have the worst prognosis even after apparent surgical success and require more careful follow-up. Another study noted that patients with low levels of postoperative CEA (<2.5 ng/ml) had an extremely favourable prognosis compared with those with normal or elevated postoperative CEA levels among patients with pathologic stage Ia NSCLC (Sawabata et al., 2004b). However, some studies have reached the controversial conclusion that CEA is not useful as a diagnostic marker in lung cancer (Hanagiri et al., 2011; Takahashi et al., 2011). Therefore, these data must be interpreted with care. CEA may carry prognostic information, but it might not be an adequate prognostic indicator for guiding clinical decisions. The conflicting results may indicate that serum CEA alone is not sufficiently sensitive for monitoring patient outcomes; however, the combination of CT findings and pre/postoperative serum CEA levels provides reliable prognostic information for DFS and OS in lung cancer patients (Takamochi et al., 2004; Higashi et al., 2009; Yamazaki et al., 2015). In addition, the combined detection of serum tumour markers before lung cancer surgery has significant prognostic value. For example, combined detection of CEA and CYFRA21-1, tumour marker indexes, may be a promising approach for assessing patient prognosis (Muley et al., 2008; Tomita et al., 2010).

As a tumour marker with a prognostic role in lung cancer, CEA is convenient to detect during preoperative or postoperative periods and has been widely used in clinical practice. However, guidelines published by the American Thoracic Society and European Respiratory Society stated that CEA routine testing was not recommended for staging or disease prognosis (Jett et al., 1997). Furthermore, the NCCN guidelines for the management of NSCLC issued in 2021 did not recommend CEA as a pretreatment evaluation indicator (NCNN, 2020). Nevertheless, Ozeki et al. emphasized the importance of follow-up by CEA monitoring for patients after lung cancer surgery (Ozeki et al., 2014). In clinical practice, should we refer to perioperative serum CEA levels or dynamic changes in CEA levels to provide treatment or follow-up recommendations for patients with lung cancer? We believe that serum tumour markers play an important role in lung cancer, but these markers have not received enough attention in clinical practice. Therefore, our study may provide meaningful insights for clinicians. Of course, there were some limitations to our study. First, this was a retrospective study subject to the inherent limitations and biases of observational retrospective research. For example, the number of patients with a normalized postoperative CEA level or an elevated postoperative CEA level was smaller than that of patients with a normal preoperative CEA level. Second, the limitations of this observational study were typical for cohorts of patients treated at a single academic institution, including potential selection biases, generalizability, and attrition (only half of the patients with an elevated CEA level at baseline had postoperative CEA information). In comparison, prospective studies will provide more convincing evidence of the significance of CEA in lung cancer and these data need to be further verified by multiple centres. In addtion, in our study, some factors were missing or not recorded, such as comorbidities and economic status, so the suggestive effect of perioperative detection of serum CEA on the risk of death may be overestimated, and its real effect needs to be further studied. Again, we studied only one tumour marker, and the combination of multiple indicators (other tumor biomarkers or imaging methods) might provide more clinical significance. Finally, the patients in our research were all Chinese.

In conclusion, our results demonstrate the effect of dynamic changes in serum preoperative and postoperative CEA levels in a large cohort of patients undergoing resection for lung cancer. They indicate that the postoperative CEA level may inform the frequency of surveillance. Therefore, this study will provide valuable information for lung cancer patients as a clinical reference for follow-up treatment.

CONCLUSION

Patients with a normalized postoperative CEA level or an elevated postoperative CEA level had worse OS than those with a normal preoperative level. Unlike a normalized postoperative CEA level, an elevated postoperative CEA level is an independent adverse prognostic factor in lung cancer patients undergoing surgery, especially in lung adenocarcinoma. It is emphasized that CEA monitoring during the perioperative period will provide more valuable prognostic information for patients with lung adenocarcinoma.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Materials, further inquiries can be directed to the corresponding author.

ETHICS STATEMENT

The studies involving human participants were reviewed and approved by Ethics Committee of Harbin Medical University Cancer Hospital. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

AUTHOR CONTRIBUTIONS

SJ and ZJ designed the study concept and took responsibility for the integrity of the data. ZJ analyzed all data and drafted the manuscript. SC designed the study and revised the manuscript. JL revised the manuscript and supervised the study. NH, YG, and LW collected clinical and follow-up data from the Harbin Medical University Cancer Hospital.

FUNDING

The work was supported by Shenzhen Science and Technology Program (Grant No. RCJC20200714114436049) and Cancer Hospital, Chinese Academy of Medical Sciences, Shenzhen Center/ShenZhen Cancer Hospital Research Project (No. SZ2020ZD006).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

ACKNOWLEDGMENTS

We thank the ethics committee and the electronic medical record department sincerely.

REFERENCES

Chen, Z.-q., Huang, L.-s., and Zhu, B. (2018). Assessment of Seven Clinical Tumor Markers in Diagnosis of Non-small-cell Lung Cancer. Dis. Markers 2018, 1–7. doi:10.1155/2018/9845123

Cui, Y., Duan, X., Li, H., Shi, G., Wu, B., Liu, M., et al. (2015). High Preoperative and Postoperative Levels of Carcinoembryonic Antigen and CYFRA 21-1 Indicate Poor Prognosis in Patients with Pathological Stage I Nonsmall Cell Lung Cancer. Indian J. Cancer 52 (Suppl. 3), 158–E163. doi:10.4103/0019-509X.186564

Foa, P., Fornier, M., Miceli, R., Seregni, E., Santambrogio, L., Nosotti, M., et al. (1999). Tumour Markers CEA, NSE, SCC, TPA and CYFRA 21.1 in Resectable Non-small Cell Lung Cancer. Anticancer Res. 19 (4C), 3613–3618.

Ford, C. H., Stokes, H. J., and Newman, C. E. (1981). Carcinoembryonic Antigen and Prognosis after Radical Surgery for Lung Cancer: Immunocytochemical Localization and Serum Levels. Br. J. Cancer 44, 145–153. doi:10.1038/bjc.1981.164

Hanagiri, T., Sugaya, M., Takenaka, M., Oka, S., Baba, T., Shigematsu, Y., et al. (2011). Preoperative CYFRA 21-1 and CEA as Prognostic Factors in Patients with Stage I Non-small Cell Lung Cancer. Lung Cancer 74 (1), 112–117. doi:10.1016/j.lungcan.2011.02.001

Hatzakis, K. D., Froudarakis, M. E., Bouros, D., Tzanakis, N., Karkavitsas, N., and Siafakas, N. M. (2002). Prognostic Value of Serum Tumor Markers in Patients with Lung Cancer. Respiration 69, 25–29. doi:10.1159/000049366

Higashi, K., Sakuma, T., Ito, K., Niho, S., Ueda, Y., Kobayashi, T., et al. (2009). Combined Evaluation of Preoperative FDG Uptake on PET, Ground-Glass Opacity Area on CT, and Serum CEA Level: Identification of Both Low and High Risk of Recurrence in Patients with Resected T1 Lung Adenocarcinoma. Eur. J. Nucl. Med. Mol. Imaging 36 (3), 373–381. doi:10.1007/s00259-008-0961-4

Icard, P., Regnard, J.-F., Essomba, A., Panebianco, V., Magdeleinat, P., and Levasseur, P. (1994). Preoperative Carcinoembryonic Antigen Level as a Prognostic Indicator in Resected Primary Lung Cancer. Ann. Thorac. Surg. 58 (3), 811–814. doi:10.1016/0003-4975(94)90755-2

Jett, J., Feins, R., Kvale, P., Mccloud, T., Miller, Y. W., Perry, M., et al. (1997). Pretreatment Evaluation of Non-small-cell Lung Cancer. The American Thoracic Society and the European Respiratory Society. Am. J. Respir. Crit. Care Med. 156 (1), 320–332. doi:10.1164/ajrccm.156.1.ats156.1

Konishi, T., Shimada, Y., Hsu, M., Tufts, L., Jimenez-Rodriguez, R., Cercek, A., et al. (2018). Association of Preoperative and Postoperative Serum Carcinoembryonic Antigen and Colon Cancer Outcome. JAMA Oncol. 4 (3), 309. doi:10.1001/jamaoncol.2017.4420

Konstantopoulos, K., and Thomas, S. N. (2009). Cancer Cells in Transit: The Vascular Interactions of Tumor Cells. Annu. Rev. Biomed. Eng. 11, 177–202. doi:10.1146/annurev-bioeng-061008-124949

Kozu, Y., Maniwa, T., Takahashi, S., Isaka, M., Ohde, Y., and Nakajima, T. (2013). Prognostic Significance of Postoperative Serum Carcinoembryonic Antigen Levels in Patients with Completely Resected Pathological-Stage I Non-small Cell Lung Cancer. J. Cardiothorac. Surg. 8, 106. doi:10.1186/1749-8090-8-106

Matsuoka, K., Sumitomo, S., Nakashima, N., Nakajima, D., and Misaki, N. (2007). Prognostic Value of Carcinoembryonic Antigen and CYFRA21-1 in Patients with Pathological Stage I Non-small Cell Lung Cancer. Eur. J. Cardio-Thoracic Surg. 32 (3), 435–439. doi:10.1016/j.ejcts.2007.05.014

Moertel, C. G., Fleming, T. R., Macdonald, J. S., Haller, D. G., Laurie, J. A., and Tangen, C. (1993). An Evaluation of the Carcinoembryonic Antigen (CEA) Test for Monitoring Patients with Resected Colon Cancer. JAMA 270 (8), 943–947. doi:10.1001/jama.270.8.943

Molina, R., Augé, J. M., Bosch, X., Escudero, J. E. M., Viñolas, N., Marrades, R. O. N., et al. (2009). Usefulness of Serum Tumor Markers, Including Progastrin-Releasing Peptide, in Patients with Lung Cancer: Correlation with Histology. Tumor Biol. 30, 121–129. doi:10.1159/000224628

Muley, T., Fetz, T.-H., Dienemann, H., Hoffmann, H., Herth, F. J. F., Meister, M., et al. (2008). Tumor Volume and Tumor Marker Index Based on CYFRA 21-1 and CEA Are strong Prognostic Factors in Operated Early Stage NSCLC. Lung Cancer 60 (3), 408–415. doi:10.1016/j.lungcan.2007.10.026

NCCN. 2020). Clinical Practice Guideline in Oncology, Non-small-cell Lung Cancer. Version 6.2020. Pennsylvania: NCCN.

Okada, M., Sakamoto, T., Nishio, W., Uchino, K., and Tsubota, N. (2003). Characteristics and Prognosis of Patients After Resection of Nonsmall Cell Lung Carcinoma Measuring 2 Cm or Less in Greatest Dimension. Cancer 98 (3), 535–541. doi:10.1002/cncr.11530

Okada, M., Nishio, W., Sakamoto, T., Uchino, K., Yuki, T., Nakagawa, A., et al. (2004). Prognostic Significance of Perioperative Serum Carcinoembryonic Antigen in Non-small Cell Lung Cancer: Analysis of 1,000 Consecutive Resections for Clinical Stage I Disease. Ann. Thorac. Surg. 78 (1), 216–221. doi:10.1016/j.athoracsur.2004.02.009

Ozeki, N., Fukui, T., Taniguchi, T., Usami, N., Kawaguchi, K., Ito, S., et al. (2014). Significance of the Serum Carcinoembryonic Antigen Level during the Follow-Up of Patients with Completely Resected Non-small-cell Lung Cancer. Eur. J. Cardio-Thoracic Surg. 45 (4), 687–692. doi:10.1093/ejcts/ezt424

Plebani, M., Basso, D., Navaglia, F., De Paoli, M., Tommasini, A., and Cipriani, A. (1995). Clinical Evaluation of Seven Tumour Markers in Lung Cancer Diagnosis: Can Any Combination Improve the Results?Br. J. Cancer 72, 170–173. doi:10.1038/bjc.1995.296

Sawabata, N., Ohta, M., Takeda, S.-i., Hirano, H., Okumura, Y., Asada, H., et al. (2002). Serum Carcinoembryonic Antigen Level in Surgically Resected Clinical Stage I Patients with Non-small Cell Lung Cancer. Ann. Thorac. Surg. 74, 174–179. doi:10.1016/s0003-4975(02)03662-7

Sawabata, N., Maeda, H., Yokota, S., Takeda, S.-i., Koma, M., Tokunaga, T., et al. (2004a). Postoperative Serum Carcinoembryonic Antigen Levels in Patients with Pathologic Stage IA Nonsmall Cell Lung Carcinoma. Cancer 101, 803–809. doi:10.1002/cncr.20421

Sawabata, N., Maeda, H., Yokota, S., Takeda, S.-I., Koma, M., Tokunaga, T., et al. (2004b). Postoperative Serum Carcinoembryonic Antigen Levels in Patients with Pathologic Stage IA Nonsmall Cell Lung Carcinoma. Cancer 101 (4), 803–809. doi:10.1002/cncr.20421

Schneider, J., Velcovsky, H. G., Morr, H., Katz, N., Neu, K., and Eigenbrodt, E. (2000). Comparison of the Tumor Markers Tumor M2-PK, CEA, CYFRA 21-1, NSE and SCC in the Diagnosis of Lung Cancer. Anticancer Res. 20, 5053–5058.

Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., et al. (2021). Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 71, 209–249. doi:10.3322/caac.21660

Suzuki, K., Nagai, K., Yoshida, J., Moriyama, E., Nishimura, M., Takahashi, K., et al. (1999). Prognostic Factors in Clinical Stage I Non-small Cell Lung Cancer. Ann. Thorac. Surg. 67 (4), 927–932. doi:10.1016/s0003-4975(99)00140-x

Takahashi, N., Suzuki, K., Takamochi, K., and Oh, S. (2011). Prognosis of Surgically Resected Lung Cancer with Extremely High Preoperative Serum Carcinoembryonic Antigen Level. Gen. Thorac. Cardiovasc. Surg. 59 (10), 699–704. doi:10.1007/s11748-011-0797-x

Takamochi, K., Yoshida, J., Nishimura, M., Yokose, T., Sasaki, S., Nishiwaki, Y., et al. (2004). Prognosis and Histologic Features of Small Pulmonary Adenocarcinoma Based on Serum Carcinoembryonic Antigen Level and Computed Tomographic Findings. Eur. J. Cardio-Thoracic Surg. 25 (5), 877–883. doi:10.1016/j.ejcts.2004.01.049

Thomas, S. N., Zhu, F., Schnaar, R. L., Alves, C. S., and Konstantopoulos, K. (2008). Carcinoembryonic Antigen and CD44 Variant Isoforms Cooperate to Mediate Colon Carcinoma Cell Adhesion to E- and L-Selectin in Shear Flow. J. Biol. Chem. 283 (23), 15647–15655. doi:10.1074/jbc.M800543200

Tomita, M., Shimizu, T., Ayabe, T., Yonei, A., and Onitsuka, T. (2010). Prognostic Significance of Tumour Marker index Based on Preoperative CEA and CYFRA 21-1 in Non-small Cell Lung Cancer. Anticancer Res. 30 (7), 3099–3102.

Tomita, M., Ayabe, T., Chosa, E., and Nakamura, K. (2015). Postoperative Serum CEA Level Is a More Significant Prognostic Factor Than Post/Preoperative Serum CEA Ratio in Non-small Cell Cancer Patients. Asian Pac. J. Cancer Prev. 16 (17), 7809–7812. doi:10.7314/APJCP.2015.16.17.7809

Wang, C.-Y., Huang, M.-S., Huang, M.-H., Lee, H.-C., and Hsu, H.-S. (2010). Persistently High Serum Carcinoembryonic Antigen Levels After Surgery Indicate Poor Prognosis in Patients with Stage I Non-small-cell Lung Cancer. J. Surg. Res. 163 (2), e45–e50. doi:10.1016/j.jss.2010.04.039

Wang, X.-B., Li, J., and Han, Y. (2014). Prognostic Significance of Preoperative Serum Carcinoembryonic Antigen in Non-small Cell Lung Cancer: A Meta-Analysis. Tumor Biol. 35 (10), 10105–10110. doi:10.1007/s13277-014-2301-6

Yamazaki, M., Ishikawa, H., Kunii, R., Tasaki, A., Sato, S., Ikeda, Y., et al. (2015). A Combination of Preoperative CT Findings and Postoperative Serum CEA Levels Improves Recurrence Prediction for Stage I Lung Adenocarcinoma. Eur. J. Radiol. 84 (1), 178–184. doi:10.1016/j.ejrad.2014.10.009

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Jiao, Cao, Li, Hu, Gong, Wang and Jin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 28 October 2021
doi: 10.3389/fmolb.2021.719056

Evaluation of the Diagnostic Potential of Candidate Hypermethylated Genes in Epithelial Ovarian Cancer in North Indian Population

Alka Singh¹, Sameer Gupta² and Manisha Sachan¹*

¹Department of Biotechnology, Motilal Nehru National Institute of Technology, Allahabad, India

²Department of Surgical Oncology, King George Medical University, Lucknow, India

Edited by:
Xin Wang, City University of Hong Kong, Hong Kong SAR, China

Reviewed by:
Zhongxu Zhu, City University of Hong Kong, Hong Kong SAR, China
Varun Sasidharan Nair, Helmholtz Association of German Research Centers (HZ), Germany

* Correspondence: Manisha Sachan, manishas@mnnit.ac.in

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 01 June 2021
Accepted: 12 October 2021
Published: 28 October 2021

Citation: Singh A, Gupta S and Sachan M (2021) Evaluation of the Diagnostic Potential of Candidate Hypermethylated Genes in Epithelial Ovarian Cancer in North Indian Population. Front. Mol. Biosci. 8:719056. doi: 10.3389/fmolb.2021.719056

Most ovarian cancers, despite improvement in management of cancer, are still diagnosed at an advanced stage. Early detection plays an essential role in reducing ovarian cancer mortality and, therefore, is critically needed. Liquid biopsies-based approaches hold significant promise for cancer detection. The present study investigates a panel of epigenetic biomarkers for the detection of epithelial ovarian cancer. A qPCR assay has been developed based on the assessment of DNA methylation markers in circulating cell-free DNA as a minimally invasive tool. Herein, the promoter methylation of seven ovarian cancer-specific genes (RASSF1A, DAPK1, SOX1, HOXA9, HIC1, SPARC, and SFRP1) was analyzed quantitatively in 120 tissue samples by MethyLight assay. The best-performing genes were further evaluated for their methylation status in 70 matched serum cell-free DNA of cancerous and non-cancerous samples. Additionally, DNA methylation patterns of these best-performing genes were validated by clonal bisulfite sequencing. The ROC (Receiver-operator characteristic) curves were constructed to evaluate the diagnostic performances of both individual and combined gene panels. The seven candidate genes displayed a methylation frequency of 61.0–88.0% in tissue samples. The promoter methylation frequencies for all the seven candidate genes were significantly higher in cancer samples than in normal matched controls. In tissue samples, the multiplex MethyLight assay for HOXA9, HIC1, and SOX1 were the best performing gene panels in terms of sensitivity and specificity. The three best-performing genes exhibited individual frequencies of 53.0–71.0% in serum CFDNA, and the multiplex assay for these genes were identified to discriminate serum from cancer patients and healthy individuals (area under the curve: HOXA9+HIC1 = 0.95, HIC1+SOX1 = 0.93 and HOXA9+SOX1 = 0.85). The results of MethyLight showed high concordance with clonal bisulfite sequencing results. Individual genes and combined panel exhibited better discriminatory efficiencies to identify ovarian cancer at various stages of disease when analyzed in tissue and serum cell-free DNA. We report a qPCR-based non-invasive epigenetic biomarker assay with high sensitivity and specificity for OC screening. Our findings also reveal the potential utility of methylation-based detection of circulating cell-free tumor DNA in the clinical management of ovarian cancer.

Keywords: cell free DNA, early diagnosis, DNA methylation, epigenetic biomarker, epithelial ovarian cancer, liquid biopsy

INTRODUCTION

Ovarian cancer (OC) is the fifth most incident and deadly gynecological malignancy affecting women worldwide, mainly under the age of 55–74. It encompasses heterogeneity at the molecular, clinical, and histopathological level and exhibits the worst prognosis and highest mortality figures for cancer death in women. Approximately 313,959 new cases of ovarian cancer and over 207,252 deaths were estimated to occur worldwide in 2020 (Torre et al., 2018; Sung et al., 2021). While in India, the incidence rate for OC is 5.7 per 100,000 women, affecting 5.4% of all female cancer patients. Overall, this lethal disease accounts for 2.5% of all female associated malignancies but 5% of cancer death in women due to lack of signs and symptoms at the early stage of the disease, lower survival rates mainly driven by advanced stage diagnosis, high recurrence rate, and limited success with existing early diagnosis methods (Siegel et al., 2015).

Epithelial cancers are the most predominant malignant neoplasm accounting for 90% of all OC cases. High-grade serous ovarian carcinoma, characterized by rapid growth with no identified precursor lesions, is the most aggressive tumor and constitutes the most predominant subtype of ovarian cancer, accounting for more than 80% of ovarian cancer-related fatality. Furthermore, no significant improvement to the 5-years survival figures has been observed over the last decade which still hovers at ∼ 35% for advanced stage EOC patients (FIGO stage III/IV). However, diagnosis of disease at preclinical stage (FIGO stage I/II) is highly curable and might substantially improve the survival rates up to 93% (Bowtell et al., 2015; Menon, 2007; Siegel et al., 2011). Early detection is certainly essential to improve the survival rate and effective treatment for better patient outcomes. Therefore it is crucial to identify the most effective and reliable molecular biomarkers involved in ovarian cancer tumorigenesis at the genetic and epigenetic level, which could further aid in the early detection of OC.

Although cancer antigen-125 (CA125) is the most utilized serum-based biochemical marker; its utility as an EOC screening marker is significantly impeded because of high false-positive rate, the lower sensitivity of detection for early stages of EOC, poor specificity as its elevation may also be associated with various benign conditions and several malignancies including lung and colon cancer (Jacobs and Bast, 1989; Woolas et al., 1993). However, the clinical significance of CA125 majorly falls in evaluating and predicting OC recurrence (Rosenthal et al., 2006). To date, only two biochemical markers (CA125 and HE4) have attained approval from the FDA as a marker for monitoring the progression of disease and recurrence. However, these biomarkers are not sufficiently suitable for screening of disease. Furthermore, current conventional screening methods for OC detection exhibit high false-positive rate and lower sensitivity and specificity. Therefore, these methods have failed to overcome the non-specific manifestation of the OC and reduce population mortality morbidity.

Aberrant CpG island hypermethylation in the gene promoter is the most extensively studied epigenetic alternation which is closely associated with transcriptional repression and is involved in carcinogenesis. Furthermore, aberrant methylation patterns represent the earliest carcinogenic event during malignant transformation and constitute the most frequent epigenetic event in cancer progression. Importantly, these altered methylation patterns are sufficiently chemically and biologically stable and can be easily quantified with minimal invasiveness in liquid biopsies (serum/plasma, peritoneal fluids, urine, etc.), constituting a valuable tool to interrogate the presence of tumor DNA and to indicate the presence of the disease prevalence at an earlier stage (Board et al., 2008; Brock et al., 2008; Lofton-Day et al., 2008; Vlassov et al., 2010).

Accumulated evidence from previous studies has identified promoter hypermethylation-induced transcriptional repression of several tumor suppressor genes in ovarian cancer, indicating their potential as a clinically significant biomarker for EOC diagnosis (Balch et al., 2010; Gloss and Samimi, 2014; Nguyen et al., 2014; Singh et al., 2019). Despite several studies on methylation-based diagnostic biomarkers for OC, to date, not even a single epigenetic marker qualifies to preciously and accurately detect the disease in either tumor tissue or minimally invasive body fluid. Additionally, quite a few prior literature indicates the assessment of concurrent methylation of genes promoter in panel towards constructing an assay for EOC diagnosis (de Caceres et al., 2004; Melnikov et al., 2009; Su et al., 2009; Liggett et al., 2011; Zhang et al., 2013). Q-PCR-based multiplex MethyLight assay has been used to quantitate methylation of epigenetic markers in different cancers (Friedrich, 2004; Kang et al., 2008; He et al., 2010; Begum et al., 2011) but has not been reported to be used for developing a biomarker-based assay for early-stage detection of OC. Moreover, detection of differential methylation signatures in serum/plasma with high sensitivity is critically needed for an early EOC diagnosis. Analysis of a panel of potential epigenetic-based biomarkers in liquid biopsies facilitating non-invasive detection of ovarian cancer may add value to the clinical implementation as a sensitive test aiding significantly in clinical decision making (Singh et al., 2019).

Seven tumor suppressor genes (RASSF1A, DAPK1, SOX1, HOXA9, HIC1, SPARC, and SFRP1) were chosen after the extensive screening to construct the multiplex MethyLight assay. We have recently reported HOXA9 and HIC1 as potential markers for non-invasive detection of EOC (Singh et al., 2020). Accumulated evidence from various studies has highlighted extensive frequent hypermethylation-induced downregulation of selected genes under the present study in EOC (Yoon et al., 2001; de Caceres et al., 2004; Takada et al., 2004; Choi et al., 2006; Collins et al., 2006; Melnikov et al., 2009; Socha et al., 2009; Su et al., 2009).

The present study reports the exploration of the performance (the sensitivity and specificity) of epigenetic-based biomarkers, which have been identified to be frequently silenced due to promoter hypermethylation and could potentially serve as a biomarker for non-invasive detection of OC. Herein, we also assessed the feasibility of analyzing hypermethylation trails in cell-free DNA to develop a non-invasive methylation-based assay using a multiplex qPCR assay for early EOC detection. Additionally, site-specific methylation patterns of the best performing genes in tissue samples were validated by clonal bisulfite sequencing to map the consistently hypermethylated CpGs at the promoter region. Subsequently, Receiver operator characteristic (ROC) curves were constructed to assess the diagnostic performances of the selected genes and genes panels to evaluate their clinical utility in predicting EOC at an early stage.

MATERIAL AND METHODS

Screening Candidate Genes

A large number of hypermethylated targets have been identified in EOC with reported methylation frequencies which varied widely across several independent studies. Seven tumor suppressor genes (RASSF1A, DAPK1, SOX1, HOXA9, HIC1, SPARC, and SPRF1) were chosen as DNA methylation markers, to construct the multiplex MethyLight assay towards noninvasive detection of EOC at the preclinical stage. The selected candidate genes have been previously reported to be aberrantly hypermethylated in epithelial ovarian cancer particularly serous subtype through gene specific methylation assay as well as genome-wide methylation mapping. Therefore, these genes may qualify as potential candidates to be used in diagnostic biomarker panel.

Patients and Clinical Samples

Clinical samples comprising of fresh/Formalin-fixed, paraffin-embedded (FFPE) tumor samples and matched peripheral blood specimens were collected randomly upon availability from 120 subjects who underwent surgical resection at the Department of Surgical Oncology, King’s George Medical University, Lucknow, and Motilal Nehru Medical College, Prayagraj since July 2014 till Dec 2019. The sample set included histologically verified primary tumor, and blood samples from ovarian cancer patients (n = 85; five benign, six mucinous, and 74 serous EOC) with no prior history of chemotherapy and radiation ranging from FIGO stage I to IV (early stage: I/II (n = 20); advanced stage: III/IV (n = 65)), and pathologically confirmed healthy control samples (n = 35). The median patient age at diagnosis was 50 years (range, 18–70 years). The patient’s clinicopathological characteristics data such as CA125 level, age, menopausal status, tumor size, FIGO stage and histology were retrieved from pathological reports and the patient’s record and are listed in Supplementary File 1: Table S3A,B). The present study was approved by the Institutional ethical committee of Motilal Nehru National Institute of Technology Allahabad (Ethics Committee reference number: IEC/16-17/025), and informed consent was obtained for all the participating patients and control subjects whose samples were analyzed in this study.

All the primary tissue samples were freshly obtained, snap frozen on liquid nitrogen, and further stored at −80°C. 5 ml peripheral blood was collected from participating subjects into BD Vacutainer® separating gel procogulating tubes. Serum was separated by double centrifugation (3500 g for 1 min at 4°C). Serum aliquots were immediately frozen at −80°C.

DNA Extraction

Fresh-frozen tissue samples (around 10–30 mg) were processed to extract genomic DNA using the standard phenol/chloroform procedure as described previously (Singh et al., 2020). Genomic DNA extraction from FFPE tumor samples was performed with QIAamp® DNA FFPE Tissue kit (Qiagen, Germany) following the protocol described in the user manual.

Cell-free DNA was extracted from 1 ml of serum using MagMax™ Cell-Free DNA isolation kit (Thermo Fisher Scientific, United States) as described previously (Singh et al., 2020), and finally dissolved in 20 µL of LoTE (Life Technologies™, United States). Further, quantification was performed using Qubit® 2.0 Fluorometer (Invitrogen, Life Technologies™, United States) using Qubit® dsDNA HS (High Sensitivity) Assay Kits (Invitrogen, Life Technologies™, United States) and Agilent 2,100 Bioanalyzer (Agilent Technologies, Germany) using Agilent High Sensitivity DNA Kit (Agilent Technologies, Germany). Around 50 ng serum CFDNA was extracted per ml of serum.

Bisulfite Modification

2 µg of tissue DNA and entire extracted serum CFDNA was used as input material for bisulfite treatment which was performed using the Premium Bisulfite kit (Diagenode, Belgium) as described previously (Singh et al., 2020). Following the bisulfite conversion, all DNA samples were stored at −20°C.

MethyLight Assay

RASSF1A, DAPK1, SOX1, SPARC, and SFRP1 promoter methylation were analyzed by MethyLight assay in tissue and serum CFDNA samples. HOXA9 and HIC1 have previously been reported (Singh et al., 2020). Two sets of primers and probes designed particularly for bisulfite-treated DNA were used: a methylated set for the candidate genes under study and a reference set for the COL2A1 gene to normalize for the input DNA; primer and probe sequences were purchased from Eurogentec, Belgium. The sequences are listed in (Supplementary File 1: Table S1A). The primer and probe for COL2A1 were designed as previously published studies (Friedrich, 2004; Ogino et al., 2006; Kang et al., 2008; Shih et al., 2013; Liu et al., 2014).

MethyLight PCR was carried out in duplicates in a reaction volume of 10 µL including 0.5 µM each of forward and reverse PCR primers; 0.2 µM probe; 1 µL of bisulfite-modified DNA, and 1x Takyon Rox Probe MasterMix dTTP Blue (Eurogentec, Belgium). Fragments were amplified at 95°C for 4 min, then 50 cycles of 95°C for 15 s followed by annealing/extension at 60°C for 35 s using the ABI StepOne Plus system (Thermo Fisher Scientific, United States). The median value was used for data analysis. Multiplex reactions were carried out with the same primers and probes and under similar amplification conditions as singleplex MethyLight assay.

Bisulfite converted fully methylated M. SssI-treated human genomic DNA served as a positive reference for the qMSP reaction, and standard curves were generated from serial dilutions (1:5 series) of the positive control for quantification, as described previously (Singh et al., 2020). The qMSP output was determined as percent of methylated reference (PMR) values in accordance with previously published reports (Eads et al., 2000; He et al., 2010; Reinert et al., 2012; Andresen et al., 2015; Olkhov-Mitsel et al., 2015). Briefly, the median Gene: COL2A1 ratio of the sample was divided by the median Gene: COL2A1 ratio of the fully methylated positive control and multiplied by 100.

For DNA methylation analysis, no standard cutoff value has so far been defined to categorize the methylated and unmethylated loci. In order to ensure high specificity, the highest PMR value across all normal control samples and all genes was used to fix the threshold (cutoff value = 4) for scoring positive methylation, irrespective of the gene in question as described previously (Singh et al., 2020). The samples with PMR values below the scoring thresholds were designated as “unmethylated” (methylation negative) and those with PMR values higher than the scoring threshold were designated as “Methylated” (methylation positive).

Clonal Bisulfite Sequencing

The methylation status of CGs in the promoter region of the best performing genes (HOXA9, SOX1, and HIC1) in representative tissue samples (OC, n = 5 (serous histology) and normal, n = 3) was validated by clonal bisulfite sequencing. The bisulfite primers targeting the respective gene promoter region, in particular flanking the section assessed by the MethyLight assay were used for PCR as described previously (Singh et al., 2020). The sequences of clonal bisulfite sequencing primers and PCR conditions are listed in (Supplementary File 1: Table S1B). The PCR product was purified with QIAquick gel extraction kit (Qiagen, United States) and cloned into T-vector using an InsTAclone™ PCR cloning kit (Thermo Fisher Scientific, United States), and sequenced. Blue-white screening was performed to pick for positive transformed clones, and for each PCR product, 10-12 independent randomly chosen clones were sequenced using 3730XL Genetic Analyzer (Thermo Fisher Scientific, United States).

Statistics Analysis

For statistical analysis, GraphPad Prism V5.0 (GraphPad Software, San Diego, CA) and R version 3.4.4 software were used. pheatmap package in R was used to construct the Heatmap. Microsoft Excel 2007 was used for PMR value calculation. Mann-Whitney U test and Student t-test were used to evaluate the potential association between promoter hypermethylation and patient’s clinicopathological characteristics features. Pearson’s χ² or Fisher’s extract test were used for comparison of categorical variables. Relative gene expression was calculated by 2−ΔΔCT (Livak method). Percent methylation reference values were used as input to generate Receiver operator characteristic (ROC) curves to assess the accuracy of methylation levels in detecting OC. Logistic regression model was used to construct ROC curves to discriminate cancers from healthy controls. Pairwise comparison of ROC curves to access the accuracy of prediction of marker panels was performed by DeLong’s test. Both sensitivity and specificity were considered significant parameters to define the optimal cutoff value (highest Youden Index) and accuracy. Clonal bisulfite sequencing results were analyzed in Chromas (Technelysium Pty Ltd., Australia). p-values of <0.05 were defined as statistically significant.

RESULTS

MethyLight Assay Development and Standardization in Tissue

The promoter methylation levels of selected candidate genes (RASSF1A, DAPK1, SOX1, HOXA9, HIC1, SPARC, and SFRP1) were quantitatively evaluated by MethyLight in tumor DNA samples of patients with EOC. To examine the linearity and accuracy of the qMSP assay, standard curves for COL2A1, DAPK1, SOX1, RASSF1A, HOXA9, SFRP1, SPARC, and HIC1 were generated with the fully-methylated positive control (Supplementary File 1: Figure S1). Correlation coefficients (R²) for the test of a linear association were 0.9952, 0.9963, 0.9628, 0.9341, 0.9790 and 0.9699 for COL2A1, DAPK1, SOX1, RASSF1A, SFRP1, and SPARC respectively. The standard curve for HOXA9 and HIC1 gene (R² = 0.9672 and 0.9657) has been previously reported (Singh et al., 2020). These findings indicate good reproducibility and high sensitivity of detection in clinical samples. A representation of methylation detection in tumor samples by qMSP for all the candidate genes in singleplex and multiplex assays is presented in (Supplementary File 1: Figure S2A).

A significantly strong correlation between the normalized ratio of the singleplex and multiplex assay was observed for all the selected genes under investigation in the present study, signifying equivalent performance in facilitating methylation detection in these clinical samples (Supplementary File 1: Figure S3).

Methylation Status of Candidate Genes in Primary Tumor

To evaluate cancer-specificity, the promoter methylation levels of RASSF1A, DAPK1, SOX1, HOXA9, HIC1, SPARC, and SFRP1 were quantitatively analyzed by MethyLight in singleplex and multiplex assays. The distribution of PMR values for each gene under investigation is presented in (Figure 1).

FIGURE 1 | Distribution of PMR value of candidate genes in EOC and corresponding noncancerous normal tissue samples (Ai–v) and serum samples (Bi) respectively. Distribution of PMR value of HOXA9 and HIC1 gene in EOC and corresponding noncancerous normal tissue and serum samples has been published (Singh et al., 2020). PMR: percentage of methylated reference.

The median promoter methylation level in tumor DNA of OC patients and normal control subjects was 19.64 (95% CI, 5.17-60.53) and 2.14 (95% CI, 1.46-3.81) respectively for SOX1; 15.10 (95% CI, 3.60-58.28) and 2.97 (95% CI, 2.10-4.20) respectively for RASSF1A; 27.19 (95% CI, 3.13-55.90) and 2.24 (95% CI, 1.48-4.67) respectively for DAPK1; 10.09 (95% CI, 3.93-40.62) and 3.17 (95% CI, 1.78-4.19) respectively for SFRP1 and 10.43 (95% CI, 2.36-44.66) and 3.12 (95% CI, 1.40-5.59) respectively for SPARC (Table 1). The methylation level of HOXA9 and HIC1 gene in tumor samples of patients with epithelial ovarian carcinoma and healthy control group has been previously described (Singh et al., 2020).

TABLE 1 | PMR value of candidate genes in tissue and serum samples of patients with epithelial ovarian carcinoma and healthy control group. PMR: percentage of methylated reference. p-value: Mann-Whitney U two-tailed tests. aThe PMR value of HOXA9 and HIC1 gene in tissue and serum samples of patients with epithelial ovarian carcinoma and healthy control group has been previously described (Singh et al., 2020).

DNA methylation of HOXA9, HIC1, DAPK1, SOX1, RASSF1A, SFRP1 and SPARC was observed in 82.3% (70/85), 80.0% (68/85), 69.4% (59/85), 78.8% (67/85), 71.8% (61/85), 74.1% (63/85) and 61.2% (52/85) of the tissue samples of patients with EOC. Nevertheless, these seven markers were found to be methylated in only 14.3% (5/35), 22.9% (8/35), 28.6% (10/35), 20.0% (7/35), 28.6% (10/35), 28.6% (10/35) and 31.4% (11/35) of healthy control samples.

The methylation level in the methylated EOC tumor samples ranged from 4 to 97% for HOXA9; 7–89% for HIC1; 4–97% for SOX1; 4–95% for DAPK1; 5–97% for SFRP1; 6–99% for SPARC and 4–98% for RASSF1A gene, respectively. However, most of the unmethylated healthy samples reflected methylation levels in the range of 0–3% (Figure 2).

FIGURE 2 | Heat map showing distribution of PMR values for the candidate biomarker genes SOX1, DAPK1, SFRP1, SPARC and RASSF1A. Representations of Methylation level of candidate genes in tissue DNA (85 EOC/35 Control) and in serum cell free DNA (45 EOC/25 Control). The range of PMR levels which corresponds to different colors in the figure is shown. White squares (N/A) indicate that data were not available for those samples. PMR value data for HOXA9 and HIC1 in tissue and serum samples has been published elsewhere (Singh et al., 2020).

For all the seven candidate genes, a significantly higher promoter methylation frequency was observed in tumor samples of patients with OC in comparison to the normal control samples (Pearson chi-square: HOXA9, p < 0.00001; HIC1, p < 0.00001; DAPK1, p = 0.00004; SOX1, p=<0.00001; RASSF1A, p = 0.00001; SFRP1, p < 0.00001 and SPARC, p = 0.00302 respectively) (Table 2).

TABLE 2 | Performance assessment of promoter methylation as biomarker for detection of EOC in tissue and serum samples. Table illustrates the Sensitivity, Specificity, AUC, Accuracy, Optimal Cutoff and Methylation Index of singleplex and multiplex MethyLight assays for the detection of control and epithelial ovarian cancer patients. AUC: Area under the curve; PPV: Positive predictive value; NPV: Negative prediction value; MI: Methylation Index; p-value: Mann-Whitney U test.

Identification of Best-Performing Gene/Gene Panel in Tissue Samples

In the singleplex assay, high methylation (>70% of EOC samples) was evident for HOXA9, HIC1, SOX1, SFRP1, and RASSF1A (in decreasing order of methylation frequency). DAPK1 and SPARC revealed the presence of methylation with a sensitivity of 69.41 and 61.18% and a specificity of 71.43 and 68.57%, respectively (AUC = 0.81 and 0.73, respectively). The highest sensitivity and specificity was shown by HOXA9 (82.35 and 85.71%, respectively; AUC = 0.92), while SPARC presented the lowest sensitivity and specificity. The methylation status of biomarkers: HIC1, SOX1, SFRP1 and RASSF1A in a series of EOC tumors versus non-malignant control samples resulted in a sensitivity of 80.0, 78.82, 74.12 and 71.76%, respectively and a specificity of 77.14, 80.0, 71.43 and 71.43%, respectively (AUC = 0.85, 0.88, 0.80 and 0.78, respectively) (Table 2).

The cutoff value of methylation level of the seven candidate gene (RASSF1A, DAPK1, SOX1, HOXA9, HIC1, SPARC, and SFRP1), calculated using Youden index formula was 0.42, 0.49, 0.38, 0.77, 0.68, 0.47 and 0.66, respectively. The PPV, NPV and accuracy for these genes were, for RASSF1A, 85.92, 51.02 and 73.30%; for DAPK1, 85.51, 49.02 and 75.8%; for SOX1, 90.54, 60.87 and 80.8%; for HOXA9, 99.33, 66.67 and 86.6%; for HIC1, 89.5, 61.4 and 82.5%; for SPARC, 82.54, 42.11 and 73.30%; and for SFRP1, 86.30, 53.19 and 75.0%, respectively (Table 2 and Supplementary File 1: Figure S4). The diagnostic implication of methylation-based epigenetic markers-HOXA9 and HIC1 in singleplex and multiplex MethyLight assay has been previously published (Singh et al., 2020).

The two-gene combination marker panel was evaluated through multiplex MethyLight assay to narrow down the best performing genes for their further validation of diagnostic significance in serum samples of EOC patients. The use of marker panel highlighted the demonstrable differences in the discriminatory power of both the genes compared to the utility of a single gene by itself.

In the multiplex assay, when either or both gene promoter exhibited positive methylation, the highly sensitive and specific discrimination of EOC patients from normal controls was observed for HOXA9 + HIC1 (88.24% sensitivity, 88.57% specificity; AUC = 0.92 (previously published (Singh et al., 2020)), HOXA9 + SOX1 (85.88% sensitivity, 88.57% specificity; AUC = 0.92) and HIC1 +SOX1 (83.53% sensitivity, 88.57% specificity; AUC = 0.89), respectively. Simultaneous methylation of HIC1 + SFRP1 and DAPK1 + SOX1 exhibited sensitivity of 78.82 and 72.94%, respectively with specificity of 77.14% (AUC = 0.87 for both gene panels) for cancer detection. The combined methylated HOXA9 + RASSF1A achieved 76.47% sensitivity and 80.0% specificity with AUC of 0.87 (Table 2 and Figure 3).

FIGURE 3 | Receiver operator characteristic (ROC) curve differentiating EOC patients from healthy control in tissue samples. The area under the ROC curve (AUC) suggests the accuracy of the biomarkers in distinguishing ovarian carcinoma from normal healthy control sample, is depicted for the biomarkers panels (Bi) HIC1+SOX1, (ii) HOXA9+SOX1, (iii) DAPK1+SOX1, (iv) HIC1+SFRP1 and (v) HOXA9+RASSF1A, respectively. [Combined two DNA methylation marker panel] which is based on the sum of the two PMR values. AUC: area under the curve. The ROC curve for biomarker panel HOXA9+HIC1 in tissue samples has been previously published (Singh et al., 2020).

Methylated HOXA9, SOX1, and HIC1 exhibited the highest sensitivity, specificity, AUC, and accuracy for cancer detection in both singleplex and multiplex MethyLight assay, thereby asserting that their promoter hypermethylation was highly cancer-specific. HOXA9, HIC1, and SOX1 were the best performing gene panels in tissue samples and were screened to validate their diagnostic relevance in serum samples from EOC patients.

We further cross-validated the performance of the multiplex two-gene marker panel in discriminating cancer vs normal by data-partitioning (training data set and validation data set) randomly using decision tree analysis. The threshold value to discriminate cancer and normal was initially determined in training data set and then the analysis model was further validated in validation data set to calculate the accuracy, misclassification error and Goodness of fit. There was no significant difference between the two data sets with respect to any of the clinicopathological characteristics among the cases. The performance of all two-gene marker panels was found significant and the gene panels differentiated cancer vs normal with an higher accuracy in tissue samples (Supplementary File 1: Table S2). The performance of two-gene marker panel HOXA9 and HIC1 in tissue samples by data partitioning into training data set and test data set randomly using decision tree analysis has been previously published (Singh et al., 2020).

Methylation Status in Various Stages of EOC

All the genes under study were found highly positive for methylation at early stages (Stage I/II), thereby reflecting their potential to identify the early stage of the disease and serve as an early detection marker. Methylation of HOXA9 was found positive in 80.0% (16/20) patients with stage I/II cancer, 81.5% (44/54) of stage III, and 90.9% (10/11) of stage IV ovarian cancer patients. Methylated HIC1 and SOX1 exhibited a similar trend to identify ovarian cancer at various stages of disease (70.0% (14/20) of stage I/II, 85.2% (46/54) and 83.3% (45/54) of stage III, respectively, and 72.7% (8/11) of stage IV ovarian cancers).

Higher sensitivity in identifying the early stages of ovarian cancer was exhibited by each of the two-gene combination marker panels. The combined panel of HOXA9 + HIC1 showed the best discriminatory efficiencies at all stages of ovarian cancer (80.0% (16/20) of stage I/II, 90.7% (49/54) of stage III, and 90.9% (10/11) of stage IV of ovarian cancer). Simultaneous methylation of SOX1+HOXA9 exhibited positivity for methylation in 75.0% (15/20) of stage I/II, 85.2% (46/54) of stage III, and 81.8% (9/11) of stage IV of ovarian cancer. Methylation frequency of other genes and combined two-gene panels to detect cancer at different stages of epithelial ovarian cancer has been described in (Table 3).

TABLE 3 | Methylation frequency of candidate gene and two-gene marker panels in different stages of epithelial ovarian carcinoma.

No significant differences in the promoter methylation levels of all candidate genes/marker panels were apparent across the various pathological stage and histological subtypes. Nevertheless, a significant correlation was observed between the promoter hypermethylation of all candidate genes and marker panels and the age of the patient and their menopausal status, as evident from (Table 4).

TABLE 4 | Association of promoter hypermethylation status of candidate genes with patient’s clinico-pathological features in EOC tissue samples. p-value: Pearson’s chi-square and Fisher’s exact tests. Abbreviation: Pos: biomarker panel positive for methylation; Neg: biomarker panel negative for methylation; NS: not significant. A positive biomarker panel is defined as when either or both gene promoter show methylation.

Methylation Mapping of the Best-Performing Gene in Tissue Samples by Clonal Bisulfite Sequencing

Methylation pattern was analyzed in ovarian cancer tissue samples through clonal sodium bisulfite DNA sequencing in the promoter region harboring a total of 19, 14, and 26 CpG sites for HOXA9, HIC1, and SOX1 gene, respectively. The clonal bisulfite sequencing results for HOXA9 and HIC1 have been published previously, which strongly confirms the promoter hypermethylation status of these genes as assessed by qMSP in EOC (Singh et al., 2020).

Bisulfite sequencing confirms the promoter hypermethylation status of SOX1 in EOC, as assessed by MethyLight. Highly dense methylation was observed in the tumor samples, particularly in the region assessed by qMSP. However, no hypermethylation was observed in the normal samples. As expected, malignant samples with higher PMR values reflected substantially dense methylated CpG sites. In contrast, the normal samples with lower PMR values displayed few methylated CpG sites. Additionally, 10-fold higher methylation was observed in tumor tissue compared to their normal counterpart (Figure 4).

FIGURE 4 | Clonal Bisulfite sequencing of SOX1 in normal and EOC tissues. (A) Promoter region of SOX1 comprising 26 CpG sites. The distribution of CpG sites (vertical bar) and position of the hybridized PCR primers (F: forward primer, R: reverse primer, p: probe) for MethyLight assay and for clonal Bisulfite sequencing (indicated with red color arrow) are shown. (B) Nucleotide sequence of the promoter region of SOX1 for MethyLight and bisulfite sequencing. (C) Clonal bisulfite sequencing results for normal and malignant tissues samples of EOC. For each selected tissue sample, 12 randomly chosen clones were sequenced and the methylation status all 26CpG sites is indicated by the circles: closed (black) and open circles represent methylated and unmethylated CpG sites, respectively. Closed (red) represents methylated CpGs in the region analyzed by MethyLight assay.

MethyLight Assay Development and Standardization in Serum

To validate cancer specificity, performance, and utility as an efficient non-invasive biomarker, the promoter methylation levels of best-performing genes (HOXA9, HIC, and SOX1) were evaluated in matched serum CFDNA samples of patients with EOC. A representation of methylation detection by qMSP for these genes in singleplex and multiplex assays is presented in (Supplementary File 1: Figure S2B). A strong correlation between the normalized ratio of the singleplex and multiplex assay was observed for these markers, indicating equivalent performance in facilitating methylation detection (Figure 5).

FIGURE 5 | Correlation between the ΔCq values obtained with singleplex and multiplex assays for (Bi,ii) SOX1 (multiplexed with HIC1 and HOXA9), (iii) HIC1 (multiplexed with SOX1) and (iv) HOXA9 (multiplexed with SOX1) genes, respectively in serum samples. The ΔCq values between singleplex and multiplex MethyLight assay differ due to differences in set thresholds. The normalized ratios of singleplex and multiplex MethyLight assay for HOXA9 (multiplexed with HIC1) and HIC1 (multiplexed with HOXA9) in serum samples has been previously published (Singh et al., 2020).

Methylation Status of Best Performing Genes in Matched Serum Cell-Free DNA

The distribution of PMR values for SOX1 is demonstrated in (Figure 1). The median promoter methylation level of SOX1 in CFDNA of OC patients was 4.60 (95% CI, 2.13-28.04), whereas that of healthy control was 1.73 (95% CI, 0.61-2.72), respectively (Table 1). Findings for the distribution of methylation levels of HOXA9 and HIC1 in serum CFDNA of OC has been previously described (Singh et al., 2020). The methylation level of HOXA9, SOX1, and HIC1 in serum CFDNA of OC ranged from 4 to 87.4%, 4–80.6%, and 4–58.7%, respectively. However, most of the unmethylated healthy samples reflected methylation levels in the range of 0–3% (Figure 2).

DNA methylation of HOXA9, HIC1, and SOX1 was observed in 62.2% (28/45), 71.1% (32/45), and 53.3% (24/45) of the serum samples of patients with EOC. On the contrary, no promoter hypermethylation was found in serum CFDNA of control subjects for HOXA9 and HIC1. Nevertheless, SOX1 was found to be methylated in only 4.0% (1/25) of healthy control serum samples.

For all the three candidate genes, a significantly higher methylation frequency was found in serum samples of patients with OC, while in comparison to the normal controls samples (Pearson chi-square: HOXA9, p < 0.00001; HIC1, p < 0.00001 and SOX1, p = 0.00003, respectively).

Methylated HOXA9, HIC1, and SOX1 occurred in 62.2, 71.1, and 53.3% of OC serum samples when their promoter methylation was evaluated in the singleplex assay (AUC = 0.81, 0.88, and 0.77, respectively). The cutoff value of methylation level of all three candidate genes (HOXA9, HIC1, and SOX1), calculated using the Youden index formula, was 0.70, 0.71, and 0.62, respectively. The PPV, NPV and accuracy for these genes were, for HOXA9, 100, 59.52 and 77.10%; for HIC1, 100, 65.75 and 82.8% and for SOX1, 96.0, 53.33 and 72.8%, respectively.

In multiplex MethyLight assay, the combined methylated HIC1 + SOX1 achieved 80.0% sensitivity and 96.0% specificity with an AUC of 0.93. Simultaneous methylation of SOX1 + HOXA9 exhibited a sensitivity of 66.67% with a specificity of 96.0% (AUC = 0.85) for cancer detection in serum cell-free DNA. However, the combined sensitivity for our previously reported marker panel (HOXA9 + HIC1) was 88.9% with 100% specificity, achieving an AUC of 0.95 for differentiating serum samples of patients with EOC from normal control samples, thereby reflecting as the best discriminatory marker panel for non-invasive detection of EOC cancer using serum cell-free DNA (Table 2 and Figure 6).

FIGURE 6 | Receiver operator characteristic (ROC) curve differentiating EOC patients from healthy control in serum samples. The area under the ROC curve (AUC) suggests the accuracy of the biomarkers in distinguishing ovarian carcinoma from normal healthy control sample, is depicted for the biomarkers (Ci) SOX1, (ii) HIC1+SOX1 and (iii) HOXA9+SOX1 respectively. [Combined two DNA methylation marker panel] which is based on the sum of the two PMR values. AUC: area under the curve. The ROC curve for biomarker panel HOXA9+HIC1 in serum samples has been previously published (Singh et al., 2020).

We further cross-validated the performance of the best performing gene panel in discriminating cancer vs normal by data partitioning (training data set and validation data set) randomly using decision tree analysis. The performance of all the two-gene marker panels (HOXA9 + SOX1) and (HIC1 + SOX1) was found significant and the gene panels differentiated cancer vs normal with a higher accuracy in serum samples (Supplementary File 1: Table S2). The performance of two-gene marker panel HOXA9 + HIC1 in serum samples by data partitioning into training data set and test data set randomly using decision tree analysis has been previously published (Singh et al., 2020).

Methylation Status of HOXA9, HIC1, and SOX1 in Various Stages of EOC

HOXA9, HIC1, and SOX1 were found highly positive for methylation at early stages (Stage I/II), thereby reflecting their potential to identify the early stage of the disease and serve as an early detection marker. Methylated HOXA9, HIC1, and SOX1 exhibited a similar trend to identify ovarian cancer at various stages of disease in 70.0% (7/10) patients with stage I/II cancer, 56.7% (17/30), 70.0% (21/30), and 43.3% (13/30) of stage III, respectively and 80.0% (4/5) of stage IV ovarian cancer patients.

The combined panel of HOXA9 + HIC1 and HIC1 + SOX1 exhibited the best discriminatory efficiencies at all stages of ovarian cancer (100.0% (10/10) of stage I/II, 80.0% (24/30) and 70.0% (21/30) of stage III, respectively and 80.0% (4/5) and 100.0% (5/5) of stage IV of ovarian cancer, respectively). Similarly, the two-gene panel of SOX1 + HOXA9 was found positive for methylation in 80.0% (8/10) of stage I/II, 56.7% (17/30) of stage III, and 80.0% (4/5) of stage IV ovarian cancers (Table 3).

DNA methylation status of the investigated gene promoters in OC in serum samples was independent of the tumor pathological stage and histology. No significant differences in methylation levels for these clinical features were observed. However, a significant positive association was found between the patient’s age and menopausal status with the HOXA9, HIC1, and SOX1 methylation levels in both singleplex and multiplex assay (Table 5).

TABLE 5 | Association of promoter hypermethylation status of candidate genes with patient’s clinico-pathological features in EOC serum samples. p-value: Pearson’s chi-square and Fisher’s exact tests. Abbreviation: Pos: biomarker panel positive for methylation; Neg: biomarker panel negative for methylation; NS: not significant. A positive biomarker panel is defined as when either or both gene promoter show methylation.

DISCUSSION

Seven tumor suppressor genes (SOX1, SFRP1, HOXA9, RASSF1A, DAPK1, HIC1, and SPARC) were selected for the present study which has been previously reported to be extensively hypermethylated in EOC particularly serous histotype through various independent gene-specific methylation assay as well as genome-wide methylation mapping. These candidate genes are associated with various cellular functions and signaling pathways such as apoptosis (DAPK1); microtubule instability and cell cycle and mitotic arrest (RASSF1A); suppression of Wnt/β catenin signaling pathway (SFRP1 and SOX1); cell differentiation (HOXA9); cell adhesion, tumor invasion and angiogenesis (SPARC); and transcriptional repression and invasion (HIC1) (Gloss and Samimi, 2014; Grimm et al., 2019; Singh et al., 2019). Additionally, the promoter region of specified gene selected for the present study was analyzed in UCSC genome browser and the hypermethylated region in gene promoter was selected for further analysis through MethyLight and to validate the clinical significance of analyzed DMRs in the promoter region of selected genes for the diagnosis of ovarian cancer, clonal bisulfite sequencing was performed to map the consistently methylated CpGs in the gene promoter which could be responsible for downregulation of the selected genes during cancer progression.

Varying frequency of promoter hypermethylation has been reported for these seven candidate genes among various independent studies in EOC. Promoter hypermethylation of HIC1 was identified with a frequency of 17–35% in EOC tumors and was correlated with the invasive nature of the disease (Strathdee et al., 2001; Rathi et al., 2002; Teodoridis et al., 2005; Tam et al., 2007). Hypermethylation of HOXA9 was marked preferentially in early-stage epithelial ovarian cancer tumors (methylation frequency 51%) and in endometrioid subtype, strongly underlining its association with increased risk of EOC (Wu et al., 2007; Widschwendter et al., 2009). RASSF1A was detected to be methylated in EOC tumors with a frequency of 15–50% and was associated with the presence of malignancy (Yoon et al., 2001; Makarla et al., 2005; Choi et al., 2006; Bol et al., 2010). Similarly, Socha et al. reported hypermethylation of SPARC in 68% of EOC tumors with high methylation exhibited by high-grade serous EOC (Socha et al., 2009). DAPK1 was reported to be methylated in EOC tumors with a frequency of 50–67%, and its silencing induced by promoter methylation was correlated with metastatic disease (Collins et al., 2006; Häfner et al., 2011). Methylation of SFRP1 was identified with a frequency of 5–35% in epithelial ovarian cancer and was correlated with survival (Takada et al., 2004; Teodoridis et al., 2005; Su et al., 2009). Su et al. reported SOX1 methylation in 58.7% of EOC tumors and was correlated with overall survival (Su et al., 2009). The majority of these studies were analyzed by the MSP-PCR technique in tissue samples. However, no single report has examined the simultaneous methylation of the selected tumor suppressor genes/genes panel by MethyLight assay in tissue or any body fluids.

Considering previously published reports on gene promoter hypermethylation in EOC, we aimed to define the most specific gene panel for EOC detection in tissue and cell-free DNA samples. Herein, we report the exploration of potential genes for their significant diagnostic relevance as a minimally invasive methylation-based biomarker and their utility to construct a multiplex MethyLight assay to access the feasibility of concurrent analysis of the promoter methylation status of candidate gene/gene panels in tissue and serum cell-free DNA. Numerous reports based on quantitative methylation analysis of gene promoter using MethyLight assay have confirmed its high sensitivity for detecting methylated alleles and its potential for clinical applications (He et al., 2010; Lind et al., 2011; Warren et al., 2011; Andresen et al., 2015). The feasibility of analyzing multiple loci simultaneously in a single assay along with the minute utility of template DNA makes the multiplex MethyLight assay an excellent fit for analysis of clinical samples with minimal DNA amount, such as when working with liquid biopsies. We estimated the sensitivity, specificity, and reliability of detection of the multiplex MethyLight assay, along with interassay variability. The proposed assay exhibited high sensitivity of detection and good reproducibility, as indicated by the linearity and accuracy of the MethyLight assay. A strong correlation was observed between the normalized ratio of a singleplex and multiplex assay for all the candidate genes under study in tissue and serum samples, reflecting equal performance and higher sensitivity of the assay.

We evaluated the diagnostic performance of the promoter hypermethylation of individual genes and gene panels in tissue samples for early detection of EOC. Our findings have confirmed the aberrant promoter hypermethylation status of these tumor suppressor genes when analyzed in singleplex MethyLight assay in tissue samples from ovarian cancer patients, which is in accordance with the previously published studies. In addition, our findings showed that the methylation levels of candidate genes (HIC1, HOXA9, SOX1, DAPK1, RASSF1A, SFRP1, and SPARC) were significantly elevated in patients with ovarian cancer and was highly cancer-specific, which is in concord with the previously published reports. Moreover, higher sensitivity in identifying early stages of ovarian cancer was exhibited by some of the two-gene combination marker panels, thereby reflecting their potential to serve as early detection marker. Our result also suggests that the methylation of these seven candidate genes might be a common epigenetic alternation associated with the development and progression of epithelial ovarian cancer and could also be involved in the early stages of ovarian cancer tumorigenesis.

The performance of these genes excluding SPARC (which disclosed the lowest sensitivity and specificity) was further analyzed through multiplex MethyLight assay to select the best performing gene cassette for further evaluation of their promoter methylation in CFDNA. The possible two gene combinations (HOXA9+HIC1, SOX1+HIC1, HIC1+SFRP1, RASSF1A + HOXA9, DAPK1+SOX1, and HOXA9+SOX1) were able to provide a high sensitivity varying in a range from 73.0–88.0% and specificity in a range from 77–89%. Considering sensitivity, specificity, and accuracy to identify disease, the best performing gene panels in EOC tissue samples were a two-gene combination of HOXA9+HIC1, HOXA9+SOX1, HIC1+SOX1. Our previously identified two gene-marker panel of HOXA9+HIC1 attained a sensitivity of 88.2% and specificity of 88.57% [AUC = 0.92] when either or both genes showed promoter methylation in tissues samples (Singh et al., 2020). The 2-gene combination (HOXA9+SOX1 or SOX1+HIC1) exhibited similar specificity and almost equivalent sensitivity for EOC detection compared to other gene methylation marker panels evaluated in the present study. Owing to this, to investigate the diagnostic relevance as a non-invasive biomarker for EOC, these genes were screened for further validation of their methylation status in serum DNA. To the best of our knowledge, this study reports for the first time the concurrent promoter hypermethylation of these gene panels in tissue samples of EOC patients through multiplex qPCR assay. We also performed qRT-PCR for some of the tumor samples (18 malignant and 10 controls) to evaluate the expression profiling of candidate genes used in the present study. Here, we found that HIC1, DAPK1, HOXA9 and SOX1 were downregulated while RASSF1A was found marginally upregulated (Supplementary File 1: Figure S6).

Relatively few studies evaluating concurrent methylation of multiple genes have been reported in EOC. For instance, Melnikov et al. using a microarray-based technique identified -10 potentially informative genes in tissue samples, which demonstrated a maximum sensitivity and specificity of 69 and 70%, respectively, in various gene combinations, indicating the presence of cancer. However, the clinical relevance of this panel towards early-stage detection of EOC is less understood, as all advanced-stage tumors (either stage IIIA or higher) were analyzed in the study (Melnikov et al., 2009). Similarly, another study highlighted that promoter methylation of at least one of the six genes in the panel (BRCA1, p16ink4a, APC, p14arf, RASSF1A, and DAPK) could efficiently differentiate patients with ovarian cancer from healthy control with 99% (70/71) sensitivity and 100% specificity, utilizing multiplex methylation-specific PCR (MS-PCR) (de Caceres et al., 2004). However, the major limitation to conclude the clinical significance of the reported finding was the sample size and the sensitivity of the technique used for the study. Su et al. demonstrated that hypermethylation of any one of SOX1, PAX1, and SFRP1 genes exhibited 73.08% sensitivity with a specificity of 75% in discriminating malignant ovarian cancer samples from healthy controls. The diagnostic relevance of this marker panel as a potential test for OC detection cannot be underlined as neither the test score are probably high enough (though higher than CA125 alone), nor any specification with respect to tumor stage within the studied group was furnished (Su et al., 2009).

Considering non-invasive detection of ovarian cancer, relatively very few studies have highlighted analyses of the methylation status of potential genes, which might serve as a biomarker in serum/plasma samples. For instance, DAPK promoter hypermethylation in peripheral blood achieved 54% (14/16) sensitivity and 100% ((10/10) specificity using methylation-specific PCR (MS-PCR) (Collins et al., 2006). Similarly, in another study by Melnikov et al., five potentially informative genes were identified in plasma samples using a Microarray-based multiplex assay which achieved a maximum sensitivity and specificity of 61 and 85%, respectively to indicate the presence of OC (Melnikov et al., 2009). Promoter hypermethylation of RASSF1A and BRCA in serum CFDNA could signify the presence of ovarian cancer with a sensitivity and specificity of 82 and 100%, respectively, using MS-PCR (de Caceres et al., 2004). Another study by Zhang et al. showed that the hypermethylation of at least one of seven gene panel, which includes RUNX3, APC, CDH1, TFPI2, RASSF1A, SFRP5, and OPCML, in serum cell-free DNA could be detected with sensitivity and specificity of 85.3 and 90.5% respectively, in stage I EOC using a novel multiplex MS-PCR assay. The detection figures achieved using this assay were noticeably higher than CA125 alone, which reflected a sensitivity and specificity of 56.1 and 64.15%, respectively (Zhang et al., 2013). The majority of the previously reported studies have utilized MS-PCR to analyze gene promoter hypermethylation which facilitates a “yes” or “no” answer to reflect the presence of malignancy.

We evaluated and validated the promoter methylation status of the best performing genes (HIC1, HOXA9, and SOX1) in serum CFDNA of 70 matched EOC samples. The promoter hypermethylation status of HOXA9 and HIC1 in serum CFDNA has been previously published (Singh et al., 2020). Our finding confirms promoter hypermethylation of these genes in cell-free DNA, which was detected more frequently in serum of cancer patients than healthy controls. In accordance with other published studies, a lower methylation level was observed in circulating serum cell-free DNA compared to the tissue tumor samples. The considerably minute amount of CFDNA, its loss during extraction and sodium bisulfite conversion step, and the high background of normal DNA could be the plausible rationale for this. Individual genes (HOXA9, HIC1, and SOX1) and all possible 2-gene combination marker panels were highly positive for methylation at early stages (Stage I/II), thereby reflecting their potential to identify the early stage of the disease and could serve as an early detection marker.

The possible two gene combinations (HOXA9+HIC1, SOX1+HIC1, HOXA9+SOX1) provided a high sensitivity varying in a range from 67.0–89.0% and specificity in a range from 96–100% in serum CFDNA. Considering sensitivity, specificity, and accuracy to identify disease, the best discriminatory gene panel in EOC serum samples was the two-gene combination of HOXA9+HIC1, which has been previously published (Singh et al., 2020). The other two-gene cassette comprising of SOX1+HIC1 exhibited combined sensitivity of 80.0% with a specificity of 96% in serum CFDNA [AUC = 0.93] in differentiating malignant EOC samples and cancer-free healthy control serum samples. The two-gene combination of SOX1 and HOXA9 also attained similar specificity [AUC = 0.85]. These results highlight the enhanced diagnostic potential of methylated markers in serum CFDNA from epithelial ovarian cancer patients. To the best of our knowledge, this study reports for the first time the concurrent promoter hypermethylation of these gene panels in serum samples of EOC patients through multiplex qPCR assay. Furthermore, we assessed the performance of the candidate marker panel along with clinical predictor CA125 to evaluate the accuracy of prediction of ovarian cancer. Tissue biopsies recruited for the present study were histologically verified and confirmed for their malignant status. Elevated CA125 was not significant in discriminating 16 malignant cases. However, the candidate marker panels performed better in discriminating these samples as malignant (HIC + SOX [n = 7/16]; HOXA9+HIC1 [n = 12/16]; SOX + HOXA9 [n = 6/16]). In addition, the gene panels combined with CA125 exhibited improved diagnostic accuracy in discriminating cancer from normal samples as discussed in (Supplementary File 1: Figure S5 and Table S4).

Furthermore, the quantitative evaluation of gene promoter methylation through MethyLight assay was confirmed and validated by clonal bisulfite sequencing of selected tumor tissue and normal DNA samples for HOXA9, HIC1 (Singh et al., 2020), and SOX1 gene. The methylation density in the tumor sample was substantially higher, especially in the region assessed by MethyLight assay in contrast to their normal counterpart, where no hypermethylation was observed. The results obtained from the MethyLight assay exhibited high concordance with the clonal bisulfite sequencing data, thereby strongly supporting the highly sensitive detection of ovarian cancer through multiplex MethyLight assay.

As shown in Table 1, the median PMR values of tumor tissues and serum exhibited significant differences when compared with tissues and serum obtained from healthy subjects. As listed in Tables 4, 5, no significant association could be observed between gene/gene panel promoter methylation level and CA125 level, histology, or FIGO stage. However, a significant positive correlation between candidate gene promoter methylation and age and menopausal status of patients was observed.

Our data confirm the high diagnostic performance of the novel epigenetic marker panels constituting of SOX1, HOXA9, and HIC1 gene in various combinations, in matched tissue and serum samples from EOC patients. Overall, our study strongly highlights the diagnostic relevance of serum DNA methylation markers in predicting the presence of EOC. However, further validation of our finding in a larger cohort still needs to be investigated. Two significant limitations of the present study are the limited sample size and the lack of long-term follow-up, especially of the asymptomatic controls, which tested positive and could probably develop ovarian cancer. Moreover, we could not establish any association between the concurrent hypermethylation of combined gene panels with disease prognosis or presence of residual disease or resistance to drug or recurrence or overall survival rate, etc., due to lack of relevant follow-up patient information and ongoing treatment. Moreover, higher sensitivity in identifying early stages of ovarian cancer was exhibited by HOXA9, HIC1, and SOX1, which further confirms their utility as a potential biomarker for early-stage detection of EOC. Furthermore, the study has provided a “Proof of concept” that highlights the feasibility of multiplex assay and the potential of epigenetic signatures to develop an effective non-invasive biomarker panel for ovarian cancer diagnostics.

The previously published report of multiplexing two-gene (HOXA9 and HIC1) aims at the development of an assay based on DNA methylation signatures as biomarkers for EOC detection at an early stage. Moreover, the present study primarily highlights the exploration of potential genes for their significant diagnostic relevance (sensitivity and specificity) as a minimally invasive epigenetic-based biomarker, and further implicates that multiplexing of more than two genes could be of great significance towards the development of a more sensitive and specific assay in future. Considering the enhanced performance of this multiplex MethyLight assay, the study may further be taken forward for evaluating the clinical utility of SOX1, HOXA9, and HIC1 genes as DNA methylation-based biomarkers, in a larger pool of serum samples, particularly of early-stage (I/II) in a multi-centric approach. Further, evaluation and validation of the diagnostic performance of triple gene combination marker panel (HOXA9+HIC1+SOX1) alone or along with some novel biomarkers identified from integrated methylome analysis might hold great promise to facilitate the basis for an early-stage diagnostic assay for EOC screening. This method is highly specific, sensitive, and reproducible; moreover, it also facilitates rapid and accurate detection of biologically significant information taking into the utility of minute amounts of modified DNA in patient blood samples. This multiplex MethyLight assay holds the potential to serve as an efficient and reliable tool in clinical ovarian cancer blood-based testing in the future.

CONCLUSION

The present study would lead to the utilization of specific methylation signatures through multiplex MethyLight assay towards developing an effective non-invasive tool for early detection of ovarian cancer using serum/plasma as a sample. The findings further underline highly sensitive and specific minimally invasive multiplex-methylation-based test (based on concurrent methylation analysis), which might improve patient compliance, increase tumor diagnosis at an earlier stage, and would be valuable in significantly reducing ovarian cancer-associated morbidity and mortality. Nevertheless, the potential utility of these methylation-based biomarkers requires further validation in a larger cohort of EOC suspects to develop a minimally-invasive methylation-based test for accurate detection of clinically significant EOC.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

ETHICS STATEMENT

The studies involving human participants were reviewed and approved by the institutional ethical committee of Motilal Nehru National Institute of Technology Allahabad, Prayagraj, India approved the present study (Reference number: IEC/16-17/025). The patients/participants provided their written informed consent to participate in this study.

AUTHOR CONTRIBUTIONS

AS and MS contributed to the concept and design. AS collected and processed the study samples, optimized and performed all the experiments, contributed to experimental design, analyzed the data, interpreted the results, and drafted the manuscript. SG provided clinical ovarian cancer samples for the study. MS supervised the study, participated in data interpretation and manuscript preparation, and revision. All authors discussed the results, critically reviewed the manuscript, read and approved the final manuscript.

FUNDING

This work was supported by grants from the Indian Council of Medical Research ICMR: (Sanction Order No. 5/13/58/2015/NCD-III), New Delhi, and the Technical Education Quality Improvement Programme TEQIP-II: (137/TEQIP-II/Innovative Research/2014) to MS. AS is grateful to the Council of Scientific and Industrial Research (CSIR), New Delhi for senior research fellowship.

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

ACKNOWLEDGMENTS

The authors would like to extend their appreciation to all our patients and control groups for participation in the study. In addition, the authors are also grateful to Kachnar Verma, Department of pathology, Motilal Nehru Medical College, Prayagraj, India for her assistance in interpreting patient clinicopathological reports.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2021.719056/full#supplementary-material

ABBREVIATION

HIC1, hypermethylated in cancer 1; HOXA9, homeobox gene A9; DAPK1, death associated protein kinase 1; RASSF1A, ras association domain family 1 isoform A; SFRP1, secreted frizzled-related protein 1; SOX1, SRY-Box Transcription Factor 1; SPARC, secreted protein, acidic and rich in cysteine; CFDNA, cell free DNA; EOC, epithelial ovarian cancer; OC, ovarian cancer; ROC, receiver operator characteristics curve; AUC, area under the curve; PMR, percentage of methylated reference; PPV, positive predictive value; NPV, negative predictive value; Pos, biomarker panel positive for methylation; Neg, biomarker panel negative for methylation; NS, not significant; R², correlation coefficient; MS-PCR, methylation-specific PCR; CA125, cancer antigen-125; FFPE, formalin-fixed, paraffin-embedded.

REFERENCES

Andresen, K., Boberg, K. M., Vedeld, H. M., Honne, H., Jebsen, P., Hektoen, M., et al. (2015). Four DNA Methylation Biomarkers in Biliary Brush Samples Accurately Identify the Presence of Cholangiocarcinoma. Hepatology. 61, 1651–1659. doi:10.1002/hep.27707

Balch, C., Matei, D. E., Huang, T. H.-M., and Nephew, K. P. (2010). Role of Epigenomics in Ovarian and Endometrial Cancers. Epigenomics. 2, 419–447. doi:10.2217/epi.10.19

Begum, S., Brait, M., Dasgupta, S., Ostrow, K. L., Zahurak, M., Carvalho, A. L., et al. (2011). An Epigenetic Marker Panel for Detection of Lung Cancer Using Cell-Free Serum DNA. Clin. Cancer Res. 17, 4494–4503. doi:10.1158/1078-0432.CCR-10-3436

Board, R. E., Knight, L., Greystoke, A., Blackhall, F. H., Hughes, A., Dive, C., et al. (2008). DNA Methylation in Circulating Tumour DNA as a Biomarker for Cancer. Biomark Insights. 2, 307–319. doi:10.1177/117727190700200003

Bol, G. M., Suijkerbuijk, K. P. M., Bart, J., Vooijs, M., van der Wall, E., and van Diest, P. J. (2010). Methylation Profiles of Hereditary and Sporadic Ovarian Cancer. Histopathology. 57, 363–370. doi:10.1111/j.1365-2559.2010.03642.x

Bowtell, D. D., Böhm, S., Ahmed, A. A., Aspuria, P.-J., Bast, R. C., Beral, V., et al. (2015). Rethinking Ovarian Cancer II: Reducing Mortality From High-Grade Serous Ovarian Cancer. Nat. Rev. Cancer. 15, 668–679. doi:10.1038/nrc4019

Brock, M. V., Hooker, C. M., Ota-Machida, E., Han, Y., Guo, M., Ames, S., et al. (2008). DNA Methylation Markers and Early Recurrence in Stage I Lung Cancer. N. Engl. J. Med. 358, 1118–1128. doi:10.1056/NEJMoa0706550

Choi, Y.-L., Kang, S. Y., Choi, J. S., Shin, Y. K., Kim, S. H., Lee, S.-J., et al. (2006). Aberrant Hypermethylation of RASSF1A Promoter in Ovarian Borderline Tumors and Carcinomas. Virchows Arch. 448, 331–336. doi:10.1007/s00428-005-0091-3

Collins, Y., Dicioccio, R., Keitz, B., Lele, S., and Odunsi, K. (2006). Methylation of Death-Associated Protein Kinase in Ovarian Carcinomas. Int. J. Gynecol. Cancer. 16, 195–199. doi:10.1111/j.1525-1438.2006.00506.x

de Caceres, I. I., Battagli, C., Esteller, M., Herman, J. G., Dulaimi, E., Edelson, M. I., et al. (2004). Tumor Cell-specific BRCA1 and RASSF1A Hypermethylation in Serum, Plasma, and Peritoneal Fluid From Ovarian Cancer Patients. Cancer Res. 64, 6476–6481. doi:10.1158/0008-5472.CAN-04-1529

Eads, C. A., Danenberg, K. D., Kawakami, K., Saltz, L. B., Blake, C., Shibata, D., et al. (2000). MethyLight: a High-Throughput Assay to Measure DNA Methylation. Nucleic Acids Res. 28, E32. doi:10.1093/nar/28.8.e32

Friedrich, M. G., Weisenberger, D. J., Cheng, J. C., Chandrasoma, S., Siegmund, K. D., Gonzalgo, M. L., et al. (2004). Detection of Methylated Apoptosis-Associated Genes in Urine Sediments of Bladder Cancer Patients. Clin. Cancer Res. 10, 7457–7465. doi:10.1158/1078-0432.CCR-04-0930

Gloss, B. S., and Samimi, G. (2014). Epigenetic Biomarkers in Epithelial Ovarian Cancer. Cancer Lett. 342, 257–263. doi:10.1016/j.canlet.2011.12.036

Grimm, D., Bauer, J., Wise, P., Krüger, M., Simonsen, U., Wehland, M., et al. (2020). The Role of SOX Family Members in Solid Tumours and Metastasis. Semin. Cancer Biol. 67, 122–153. doi:10.1016/j.semcancer.2019.03.004

Häfner, N., Diebolder, H., Jansen, L., Hoppe, I., Dürst, M., and Runnebaum, I. B. (2011). Hypermethylated DAPK in Serum DNA of Women With Uterine Leiomyoma Is a Biomarker Not Restricted to Cancer. Gynecol. Oncol. 121, 224–229. doi:10.1016/j.ygyno.2010.11.018

He, Q., Chen, H.-Y., Bai, E.-Q., Luo, Y.-X., Fu, R.-J., He, Y.-S., et al. (2010). Development of a Multiplex MethyLight Assay for the Detection of Multigene Methylation in Human Colorectal Cancer. Cancer Genet. Cytogenet. 202, 1–10. doi:10.1016/j.cancergencyto.2010.05.018

Jacobs, I., and Bast, R. C. (1989). The CA 125 Tumour-Associated Antigen: a Review of the Literature. Hum. Reprod. 4, 1–12. doi:10.1093/oxfordjournals.humrep.a136832

Kang, G. H., Lee, S., Cho, N.-Y., Gandamihardja, T., Long, T. I., Weisenberger, D. J., et al. (2008). DNA Methylation Profiles of Gastric Carcinoma Characterized by Quantitative DNA Methylation Analysis. Lab. Invest. 88, 161–170. doi:10.1038/labinvest.3700707

Liggett, T. E., Melnikov, A., Yi, Q., Replogle, C., Hu, W., Rotmensch, J., et al. (2011). Distinctive DNA Methylation Patterns of Cell-Free Plasma DNA in Women With Malignant Ovarian Tumors. Gynecol. Oncol. 120, 113–120. doi:10.1016/j.ygyno.2010.09.019

Lind, G. E., Danielsen, S. A., Ahlquist, T., Merok, M. A., Andresen, K., Skotheim, R. I., et al. (2011). Identification of an Epigenetic Biomarker Panel With High Sensitivity and Specificity for Colorectal Cancer and Adenomas. Mol. Cancer. 10, 85. doi:10.1186/1476-4598-10-85

Liu, Z., Zhang, J., Gao, Y., Pei, L., Zhou, J., Gu, L., et al. (2014). Large-Scale Characterization of DNA Methylation Changes in Human Gastric Carcinomas With and Without Metastasis. Clin. Cancer Res. 20, 4598–4612. doi:10.1158/1078-0432.CCR-13-3380

Lofton-Day, C., Model, F., Devos, T., Tetzner, R., Distler, J., Schuster, M., et al. (2008). DNA Methylation Biomarkers for Blood-Based Colorectal Cancer Screening. Clin. Chem. 54, 414–423. doi:10.1373/clinchem.2007.095992

Makarla, P. B., Saboorian, M. H., Ashfaq, R., Toyooka, K. O., Toyooka, S., Minna, J. D., et al. (2005). Promoter Hypermethylation Profile of Ovarian Epithelial Neoplasms. Clin. Cancer Res. 11, 5365–5369. doi:10.1158/1078-0432.ccr-04-2455

Melnikov, A., Scholtens, D., Godwin, A., and Levenson, V. (2009). Differential Methylation Profile of Ovarian Cancer in Tissues and Plasma. J. Mol. Diagn. 11, 60–65. doi:10.2353/jmoldx.2009.080072

Menon, U. (2007). Ovarian Cancer: Challenges of Early Detection. Nat. Rev. Clin. Oncol. 4, 498–499. doi:10.1038/ncponc0906

Nguyen, H. T., Tian, G., and Murph, M. M. (2014). Molecular Epigenetics in the Management of Ovarian Cancer: Are We Investigating a Rational Clinical Promise?Front. Oncol. 4, 71. doi:10.3389/fonc.2014.00071

Ogino, S., Kawasaki, T., Brahmandam, M., Cantor, M., Kirkner, G. J., Spiegelman, D., et al. (2006). Precision and Performance Characteristics of Bisulfite Conversion and Real-Time PCR (MethyLight) for Quantitative DNA Methylation Analysis. J. Mol. Diagn. 8, 209–217. doi:10.2353/jmoldx.2006.050135

Olkhov-Mitsel, E., Zdravic, D., Kron, K., van der Kwast, T., Fleshner, N., Bapat, B., et al. (2015). Novel Multiplex MethyLight Protocol for Detection of DNA Methylation in Patient Tissues and Bodily Fluids. Sci. Rep. 4, 4432. doi:10.1038/srep04432

Rathi, A., Virmani, A. K., Schorge, J. O., Elias, K. J., Maruyama, R., Minna, J. D., et al. (2002). Methylation Profiles of Sporadic Ovarian Tumors and Nonmalignant Ovaries From High-Risk Women. Clin. Cancer Res. 8, 3324–3331.

Reinert, T., Borre, M., Christiansen, A., Hermann, G. G., Ørntoft, T. F., and Dyrskjøt, L. (2012). Diagnosis of Bladder Cancer Recurrence Based on Urinary Levels of EOMES, HOXA9, POU4F2, TWIST1, VIM, and ZNF154 Hypermethylation. PLoS ONE. 7, e46297. doi:10.1371/journal.pone.0046297

Rosenthal, A. N., Menon, U., and Jacobs, I. J. (2006). Screening for Ovarian Cancer. Clin. Obstet. Gynecol. 49, 433–447. doi:10.1097/00003081-200609000-00004

Shih, Y.-L., Hsieh, C.-B., Yan, M.-D., Tsao, C.-M., Hsieh, T.-Y., Liu, C.-H., et al. (2013). Frequent Concomitant Epigenetic Silencing of SOX1 and Secreted Frizzled-Related Proteins (SFRP S) in Human Hepatocellular Carcinoma. J. Gastroenterol. Hepatol. 28, 551–559. doi:10.1111/jgh.12078

Siegel, R. L., Miller, K. D., and Jemal, A. (2015). Cancer Statistics, 2015. CA: A Cancer J. Clinicians. 65, 5–29. doi:10.3322/caac.21254

Siegel, R., Ward, E., Brawley, O., and Jemal, A. (2011). Cancer Statistics, 2011. CA: A Cancer J. Clinicians. 61, 212–236. doi:10.3322/caac.20121

Singh, A., Gupta, S., Badarukhiya, J. A., and Sachan, M. (2020). Detection of Aberrant Methylation of HOXA9 and HIC1 through Multiplex MethyLight Assay in Serum DNA for the Early Detection of Epithelial Ovarian Cancer. Int. J. Cancer. 147, 1740–1752. doi:10.1002/ijc.32984

Singh, A., Gupta, S., and Sachan, M. (2019). Epigenetic Biomarkers in the Management of Ovarian Cancer: Current Prospectives. Front. Cell Dev. Biol. 7. doi:10.3389/fcell.2019.00182

Socha, M. J., Said, N., Dai, Y., Kwong, J., Ramalingam, P., Trieu, V., et al. (2009). Aberrant Promoter Methylation of SPARC in Ovarian Cancer. Neoplasia. 11, 126–IN1. doi:10.1593/neo.81146

Strathdee, G., Appleton, K., Illand, M., Millan, D. W. M., Sargent, J., Paul, J., et al. (2001). Primary Ovarian Carcinomas Display Multiple Methylator Phenotypes Involving Known Tumor Suppressor Genes. Am. J. Pathol. 158, 1121–1127. doi:10.1016/S0002-9440(10)64059-X

Su, H.-Y., Lai, H.-C., Lin, Y.-W., Chou, Y.-C., Liu, C.-Y., and Yu, M.-H. (2009). An Epigenetic Marker Panel for Screening and Prognostic Prediction of Ovarian Cancer. Int. J. Cancer. 124, 387–393. doi:10.1002/ijc.23957

Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., et al. (2021). Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA A. Cancer J. Clin. 71, 209–249. doi:10.3322/caac.21660

Takada, T., Yagi, Y., Maekita, T., Imura, M., Nakagawa, S., Tsao, S.-W., et al. (2004). Methylation-associated Silencing of the Wnt Antagonist SFRP1 Gene in Human Ovarian Cancers. Cancer Sci. 95, 741–744. doi:10.1111/j.1349-7006.2004.tb03255.x

Tam, K. F., Liu, V. W. S., Liu, S. S., Tsang, P. C. K., Cheung, A. N. Y., Yip, A. M. W., et al. (2007). Methylation Profile in Benign, Borderline and Malignant Ovarian Tumors. J. Cancer Res. Clin. Oncol. 133, 331–341. doi:10.1007/s00432-006-0178-5

Teodoridis, J. M., Hall, J., Marsh, S., Kannall, H. D., Smyth, C., Curto, J., et al. (2005). CpG Island Methylation of DNA Damage Response Genes in Advanced Ovarian Cancer. Cancer Res. 65, 8961–8967. doi:10.1158/0008-5472.CAN-05-1187

Torre, L. A., Trabert, B., DeSantis, C. E., Miller, K. D., Samimi, G., Runowicz, C. D., et al. (2018). Ovarian Cancer Statistics, 2018. CA: A Cancer J. Clinicians. 68, 284–296. doi:10.3322/caac.21456

Vlassov, V., Laktionov, P., and Rykova, E. (2010). Circulating Nucleic Acids as a Potential Source for Cancer Biomarkers. Curr. Mol. Med. 10, 142–165. doi:10.2174/156652410790963295

Warren, J. D., Xiong, W., Bunker, A. M., Vaughn, C. P., Furtado, L. V., Roberts, W. L., et al. (2011). Septin 9 Methylated DNA is a Sensitive and Specific Blood Test for Colorectal Cancer. BMC Med. 9. doi:10.1186/1741-7015-9-133

Widschwendter, M., Apostolidou, S., Jones, A. A., Fourkala, E. O., Arora, R., Pearce, C. L., et al. (2009). HOXA Methylation in Normal Endometrium From Premenopausal Women Is Associated With the Presence of Ovarian Cancer: a Proof of Principle Study. Int. J. Cancer. 125, 2214–2218. doi:10.1002/ijc.24599

Woolas, R. P., Xu, F.-J., Jacabs, I. J., Yu, Y.-H., Daly, L., Brechuck, A., et al. (1993). Elevation of Multiple Serum Markers in Patients With Stage I Ovarian Cancer. JNCI J. Natl. Cancer Inst. 85, 1748–1751. doi:10.1093/jnci/85.21.1748

Wu, Q., Lothe, R. A., Ahlquist, T., Silins, I., Tropé, C. G., Micci, F., et al. (2007). DNA Methylation Profiling of Ovarian Carcinomas and Their In Vitro Models Identifies HOXA9, HOXB5, SCGB3A1, and CRABP1 as Novel Targets. Mol. Cancer. 6, 45. doi:10.1186/1476-4598-6-45

Yoon, J.-H., Dammann, R., and Pfeifer, G. P. (2001). Hypermethylation of the CpG Island of theRASSF1A Gene in Ovarian and Renal Cell Carcinomas. Int. J. Cancer. 94, 212–217. doi:10.1002/ijc.1466

Zhang, Q., Hu, G., Yang, Q., Dong, R., Xie, X., Ma, D., et al. (2013). A Multiplex Methylation-Specific PCR Assay for the Detection of Early-Stage Ovarian Cancer Using Cell-Free Serum DNA. Gynecol. Oncol. 130, 132–139. doi:10.1016/j.ygyno.2013.04.048

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Singh, Gupta and Sachan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 05 November 2021
doi: 10.3389/fmolb.2021.767856

A Pan-Cancer Landscape of HOX-Related lncRNAs and Their Association With Prognosis and Tumor Microenvironment

Wei Shao¹, Qian Ding¹, Yugang Guo¹, Juan Xing², Zheng Huo¹, Zhan Wang¹, Qian Xu¹* and Yue Guo²*

¹Henan Provincial Engineering Laboratory of Insects Bio-Reactor, Nanyang Normal University, Nanyang, China

²Henan Provincial Nanyang Central Hospital, Nanyang, China

Edited by:
Florence Le Calvez-Kelm, International Agency for Research on Cancer (IARC), France

Reviewed by:
Saroj Kumari, Nation institute of immunology, India
Ali Sahin Kucukaslan, Beykent University, Turkey

* Correspondence: Qian Xu, xuqian7666@nynu.edu.cn; Yue Guo, gy33mail@126.com

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 31 August 2021
Accepted: 20 October 2021
Published: 05 November 2021

Citation: Shao W, Ding Q, Guo Y, Xing J, Huo Z, Wang Z, Xu Q and Guo Y (2021) A Pan-Cancer Landscape of HOX-Related lncRNAs and Their Association With Prognosis and Tumor Microenvironment. Front. Mol. Biosci. 8:767856. doi: 10.3389/fmolb.2021.767856

The highly conserved homology cassette family (HOX) as well as 18 referenced long non-coding antisense transcripts (HOXATs) play vital roles in the development of some cancers. Nevertheless, their expression patterns as well as their association with cancer prognosis and the tumor microenvironment (TME) in pan-cancers are still unclear. Here, based on public databases, the expression levels of HOXATs, their prognostic potentials, and correlation with tumor mutation burden (TMB), immune cell infiltration, immune subtype, immune response-related genes, and stemness scores corresponding to 33 tumor types were analyzed systematically using R language. The results of the analysis indicated that different cancer tissues show different HOXAT expression profiles. Further, HOXAT expression showed association with cancer prognosis and immune and stemness regulation. Gene set enrichment analysis also demonstrated that HOXATs participate in cancer- and immune-related pathways, and based on their expression levels, HOTAIRM1 and HOXB-AS1 showed potential involvement in oncogenesis as well as possible involvement in immune regulation across a variety of cancer types. Further investigation also confirmed a significantly higher expression of HOXB-AS1 in GBM than in lower grade glioma tissues. Importantly, in vitro cell function experiments indicated that HOXB-AS1 supports cancer stem cell and plays a fundamental role in glioma metastasis. In conclusion, our results provide valuable resources that can guide the investigation of the mechanisms related to the role of HOXATs in cancers as well as therapeutic analysis in this regard.

Keywords: homology cassette HOX, long non-coding RNA, prognostic signature, cancer immunity, immune infiltration

INTRODUCTION

For Long non-coding RNAs (lncRNAs), as one of the key regulators of cancer, function not only as important molecular markers for cancer prognosis, but also as markers of molecular characteristics at the pan-cancer level. Increasing studies have shown that several lncRNAs affect cancer tumorigenesis and contribute to cancer pathogenesis (Akbari et al., 2020; Bi et al., 2020).

The tumor microenvironment (TME), which contains multiple types of stromal cells, tumor cells, cancer stem cells (CSCs), tumor-associated immune cells and others, plays a major role in tumor growth and metastasis, and particularly, the immune microenvironment constitutes a vital element of tumor biology. Immune system dysregulation can be a major cause of cancer development (Chen et al., 2019a; Carlsten and Järås, 2019). The most recent studies suggest that CSCs represent another important component of the TME that have differentiation abilities to generate the original lineage cells for regulating tumor occurrence, development, recurrence, metastasis and drug resistance. It has also been suggested that the activities of CSCs and immune cells represent the potential mechanisms that link the TME and cancer. Recently, increasing evidence has indicated that lncRNAs play fundamental roles in the regulation of the immune microenvironment and are closely associated with the development and progression of various CSCs. For example, the lncRNA, NeST has been shown to be related to T-cell activation, and is critical for immune response regulation (Chen et al., 2019b), while the lncRNA, NRON has been shown to maintain a resting state of T cells by sequestering phosphorylated NFAT in the cytoplasm (Chen et al., 2020). Moreover, it has also been demonstrated that the lncRNA, lnc-sox5, modulates CRC tumorigenesis by unbalancing TME (Chen et al., 2016), while the lncRNA, LINK-A functions as an oncogene that promotes tumor immune evasion by sensitizing T cells (Dong et al., 2018). Additionally, it has been observed that the depletion of oncogenic HotairM1 promotes the self-renewal of CSCs via the HOXA1-Nanog signaling loop. It has also been observed that lncTCF7 is required for liver CSC self-renewal and tumor propagation (Chi et al., 2019). However, further studies on lncRNAs and their roles in the TME of different cancer types still require investigation.

Transcription factors are encoded by homeobox (HOX) genes, which are assigned to a group of 13 orthologs and paralogs in four clusters and share similar positions in the homozygous frames, with 9–11 genes per cluster (Cruz et al., 2017). Multiple reports have shown that the dysregulation of 39 HOX genes is implicated in various aspects of cancer. Similar to protein-coding transcripts, accumulating experimental evidence indicates that lncRNA transcripts in the Hox gene locus, with 18 referenced non-coding antisense transcripts (HOXATs) are implicated in cancer progression. These 18 HOXATs are located in four HOXATs clusters, with the HOXA region consisting of HOTAIRM1, HOXA-AS2, HOXA-AS3, HOXA10-AS, HOXA11-AS, and HOTTIP, the HOXB region consisting of HOXB-AS1, HOXB-AS2, HOXB-AS3, HOXB-AS4, and PRAC2, the HOXC region consisting of HOXC13-AS, HOXC-AS1, HOXC-AS2, HOXC-AS3, and HOTAIR, and the HOXD region consisting of HAGLR and HOXD-AS2. For example, it has been reported that the expression levels of HOXA10-AS, HOXA11-AS, and HOXA-AS are significantly upregulated in lung adenocarcinoma (LUAD), and their high expression levels in such a cancer type are associated with shorter survival periods in patients (Hao and Yu, 2018; Dasti et al., 2020). Further, it has also been observed that the expression levels of HOTAIRM1, HOXA10-AS, HOXA10-AS, HOXA11-AS, HOXA-AS2, HOXA-AS3, HOXB-AS1, HOXC13-AS, HOXC-AS3, and HOXD-AS2 are upregulated in glioma, and are positively correlated with malignancy (Li et al., 2017; Li et al., 2020a), and importantly, some studies have shown that HOXATs can play fundamental roles in immune system regulation (Li et al., 2020b; Liang et al., 2020). However, the different expression levels and the specific functions of the individual HOXATs in pan-cancer as well as their association with the TME still remain unclear. Therefore, an in-depth investigation and analysis of their potential roles in pan-cancer is warranted.

Hence, in this study, we attempted to explore and analyze the expression profile and prognostic significance of HOXATs across multiple cancer types and investigated the potential role of the HOXATs in the modulation of the TME as well as stemness score. Additionally, we also explored and verified the potential biological function and pathways of the HOXATs with respect to the different aspects of cancer.

MATERIALS AND METHODS

Literature Review and meta-Analysis

To identify all the publications related to HOXATs in patients with cancer, the PubMed database and Web of Science were searched for literature using the following terms, which correspond to 16 out of the 18 referenced non-coding antisense transcripts: HOXAIRM1, HOXA-AS3, HOXA10-AS, HOXA11-AS, HOTTIP, HOXB-AS1, HOXB-AS2, HOXB-AS3, HOXB-AS4, HOXC13-AS, HOXC-AS1, HOXC-AS2, HOXC-AS3, HOTAIR, HAGLR, and HOXD-AS2. The exclusion criteria were as follows: 1) repeated studies and 2) reviews, comments, letters, case reports, and conference articles. Further, subsequent meta-analysis was performed for HOTTIP and HAGLR using comprehensive Meta‐Analysis V315.

Datasets Used

In this study, datasets from the public TCGA pan-cancer data portal (https://xenabrowser.net/datapages/), containing 10,635 samples spanning 33 cancer types with clinically annotated multi-omic data (clinical data, stemness scores based on mRNA (RNAss) and DNA methylation (DNAss), and immune subtypes) were used for analyses.

Correlations Between HOXATs and Tumor Microenvironment

ESTIMATE-based immune and stromal scores were used to analyze the infiltration levels of immune and stromal cells in the different tumor types. Six immune subtypes were defined to measure the levels of immune infiltrate types in the TME based on the TCGA pan-cancer database. Further, tumor stemness features extracted from the transcriptomics and epigenetics of TCGA tumor samples were used to measure the stem-cell-like features of the tumor cells. CIBERSORT was then used for the calculation of the TMB scores of the tumor cells and the correlation between the TMB and HOXATs levels was analyzed (Lin et al., 2020). Further, the Spearman or Pearson correlation coefficients obtained were then used to evaluate the relationships between the expression levels of the HOXATs and RNAss, DNAss, immune score, estimate score, TMB, immune infiltration, and the expression of immune checkpoint genes as well as tumor stemness markers.

Gene Set Enrichment Analysis

The GSEA was performed using the Java v3.0 desktop application (http://software.broadinstitute.org/gsea/index.jsp).

Cells Culture

Two human glioma cell line samples (U251 and HS683) were obtained from Cell Culture Center, Chinese Academy of Medical Sciences (Shanghai, China). The cells were maintained in a medium containing Dulbecco’s Modified Eagles Medium (DMEM), 10% fetal bovine serum (Invitrogen, Carlsbad, CA, United States), and 1% penicillin–streptomycin solution. Thereafter, they were cultured under standard cell culture conditions consisting of 5% CO₂ and 95% relative humidity at 37 C.

lncRNA Knockdown

U251 and HS683 cells in the logarithmic growth phase were incoculated into 6-well plates, after the cells grew to 50–60% fusion. The lncRNA-HOXB-AS1 smart silencer (a mixture containing three siRNAs and three antisense oligonucleotides) and its negative control, lncRNA smart silencer, NC, synthesized by RiboBio were used (Guangzhou, China). The cells were transfected using Lipofectamine RNAiMAX (Invitrogen, Carlsbad, CA, United States) following the manufacturer’s protocol.

Sphere Formation Assay

Single human glioma cell line suspensions were washed twice with serum-free phosphate buffer solution (PBS) and transferred into a serum-free stem cell complete medium (containing 20 μg/L bFGF and 20 μg/L EGF) followed by the addition of B27. Thereafter, the cells were cultured at 37 C in 5% CO₂, and sphere formation was analyzed.

Invasion Assay and Wound Healing Assay

To realize invasion assay, transwell chambers (Corning, Tewksbury, MA, United States) were coated with Matrigel (dilution 1:8; BD Biosciences, Bedford, MA, United States). Thereafter, a 120-μL serum-free medium containing 5 × 10⁴ cells was added to the upper chamber, while a 600-μL complete medium containing 10% FBS was added to the lower chamber. After transfaction, the cells on the lower surface were fixed with methanol and stained with 0.1% crystal violet and then captured using an inverted microscope (AMG, United States). To realize the wound healing assay, U251 and HS683 cells were incoculated into 6-well plates. When the cell cultures reached 80% confluence, the cells were scraped using a 10-μL micropipette tip. Thereafter, images were captured using an inverted microscope with a camera (AMG, United States). The distance travelled by the migrating cells was quantified to assess cell migration capacity using ImageJ software version 1.46 (National Institutes of Health, Bethesda, MD, United States).

MTT Assay

Cell viability was monitored using an MTT cell viability assay kit (Sigma-Aldrich, St. Louis, MO, United States). The transfected cells were seeded in 96‐well plates. Thereafter, 10 μL MTT (5 mg/ml) was added to each well and incubation was continued for 2 h, followed by the dissolution of the precipitate formed in 100 μL of dimethyl sulfoxide (DMSO). Absorbance measurements were then performed at 570 nm under a microplate spectrophotometer.

Bioinformatics and Statistical Analyses

All computational and statistical analyses were performed using R software v4.0.3 and Prism 8 GraphPad (https://www.graphpad.com/scientific-software/prism/). Correlations between stemness markers and HOXAT expression levels were analyzed using the GEPIA online dataset (http://gepia.cancer-pku.cn/), which includes the gene expression profiles of different cancers and normal tissues in the TCGA database as well as genotype-tissue expression (GTEx). p-values < 0.05 were considered statistically significant. Further, the results were adjusted for multiple comparisons.

RESULTS

Brief Literature Review

We analyzed the connection between the expression of HOXATs and prognosis in patients with cancer by exploring all published related literature. A total of 1,802 potential studies were identified, and after title and abstract screening, 65 studies were found to be eligible for inclusion. Next, we examined the full text of these 65 articles, in which most of the relationship between HOXATs and their related genes, as well as the mechanisms of regulation were investigated. Conversely, we observed that studies in which the relationship between the HOXATs and the immune microenvironment in pan-cancers were investigated were scarce. Further, no study aimed at investigating the immune response of HOXATs in tumors was identified (Table 1).

TABLE 1 | A brief review of the literature. The related interactive genes of HOXATs and involvement in functional role in different cancers. N (T/N):number (tumor/normal). ADC: Lung adenocarcinoma; AF: Atrial fibrillation; BRCA: Breast invasive carcinoma; COAD: Colon adenocarcinoma; CRC: colorectal carcinoma; EC: esophageal carcinoma; EC: endometrial cancer; EO: epithelial ovarian cancer; HCC: hepatocellular carcinoma; HNSCC: Head and neck squamous cell carcinoma; GC: gastric carcinom; GBM: Glioblastoma multiforme; IMA: Invasive mucinous adenocarcinoma of the lung; LGG: Brain Lower Grade Glioma; LUAD: lung adenocarcinoma; MM: Multiple myeloma; NSCL: Cnonsmall-cell lung cancer; NPC: Nasopharyngeal carcinoma. NSCL: Cnonsmall-cell lung cancer; NPC: Nasopharyngeal carcinoma. OS: osteosarcoma; OSCC: Oral squamous cell carcinoma; OC: oral carcinoma; OC: oral carcinoma; PAAD: Pancreatic adenocarcinoma; PDAC: Pancreatic ductal; PTC: papillary thyroid cancer; SCLC: small cell lung cancer; UM: uveal melanoma; UCEC: Uterine Corpus Endometrial Carcinoma.

HOXATs Expression and Correlation With Pan-Cancers

To clarify the intrinsic expression pattern of HOXATs, we described the differential expression of HOXATs considering the 33 available cancer types in the TCGA pan-cancer database. Notably, most of the HOXATs showed a striking intra- and inter-tumor heterogeneity in these different cancer types. Among them, HOTAIRM1 and HAGLR showed relatively higher expression levels in all the cancer types than the other HOXATs (Figure 1A; Supplementary Figures S1–S18). Further, we systematically investigated the expression levels of all 18 genes in primary patient tumor samples corresponding 16 cancer types. This investigation also included at least five normal samples. For instance, the expression levels of HOTAIRM1 showed significant inter-tumor heterogeneity in esophageal carcinoma (ESCA), GBM, and kidney papillary cell carcinoma (KIRP), which are associated with very high HOTAIRM1 expression levels. Conversely, breast cancer (BRCA), colon adenocarcinoma (COAD), kidney chromophobe (KICH), and rectum adenocarcinoma (READ) were found to be characterized by lower HOTAIRM1 expression levels (p < 0.05; Figure 1B). Therefore, these results indicated the existence of intrinsic differences between the different tumor types with respect to the expression of HOXATs.

FIGURE 1 | Homeobox antisense transcripts (HOXATs) are abnormally expressed in human pan-cancer. Boxplot showing the distribution of the HOXATs expression across 33 cancer types (A). Heatmap showing the difference in the HOXATs expression between the primary tumor and the adjacent normal tissues, which was based on log2 (fold change) of 16 cancer types that had more than five adjacent normal samples (B). Correlation plot based on Spearman’s correlation test showing the correlation of gene expression among the 18 HOX-related long non-coding RNAs (lncRNAs) across the 33 cancer types (C).

Next, we explored the potential relationship between the HOXATs. In this regard, Pearson correlation analysis revealed the following significant correlations between the HOXATs: HOTAIRM1 with HOXA-AS2 and HOXA-AS3; HOXA-AS2 with HOXA-AS3 and HOXA10-AS; HOXA-AS3 with HOXA10-AS and HOXB-AS2; HOXA10-AS with HOXA11-AS and HOTTIP; HOXA11-AS with PRAC2; HOTTIP with HOXB-AS4 and PRAC2; HOXB-AS1 with HOXB-AS2 and HOXC-AS1; HOXB-AS2 with HOXB-AS3; HOXB-AS3 with HOXB-AS4; HOXC13-AS with HOTAIR; HOXC-AS1 with HOXC-AS2, HOXC-AS3, and HOTAIR; HOXC-AS2 with HOXC-AS3 and HOTAIR; HOXC-AS3 with HOTAIR; and HAGLR with HOXD-AS2 (R > 0.4; Figure 1C). These significant correlations indicated that several HOXATs interact with each other during the pathogenesis of pan-cancer. Interestingly, HOTAIRM1 and HOXA-AS2 showed positive correlation with the other 16 HOXATs, suggesting that they possibly exert a regulatory effect on all the other 16 HOXATs and may be critical factors in cancer development. Notably, the correlation coefficients between HOXA11-AS and HOTTIP, HOXC-AS1 and HOXC-AS2 as well as that between HOXC-AS2 and HOXC-AS3 were all 0.7, suggesting that they possibly share common features and functions.

Prognostic Analysis of HOXATs in Pan-Cancer

The prognostic significance of HOXATs in the different cancer types was analyzed using Cox regression (Figures 2A,B). In cancers, including adenoid cystic carcinoma (ACC), KIRC, LGG, and thymoma (THYM), approximately half or more than half of the HOXATs showed association with worse survival of patients. Additionally, it was also observed that certain HOXATs possibly exerted obviously different prognostic effects across the various cancer types. For instance, HOXA11-AS predicted poor prognosis in patients with ACC, KICH, KIRC, LGG, and lung adenocarcinoma (LUAD), HOTTIP predicted poor prognosis in patients with ACC, KICH, KIRC, LGG, LUAD, and uveal melanoma (UVM), and HOTAIRM1 predicted poor prognosis in patients with ACC, GBM, KICH, KIRC, LGG, THYM, and UVM patients, but was found to be associated with a survival advantage for bladder urothelial carcinoma (BLCA) and pheochromocytoma/paraganglioma (PCPG). Further, HOXC-AS1 predicted poor prognosis in patients with ACC, COAD, ESCA, acute myeloid leukemia (LAML), LGG, and THYM, but showed association with a survival advantage for KIRP. Furthermore, HOXC- AS2 predicted poor prognosis for ACC, BLCA, COAD, LGG, liver hepatocellular carcinoma (LIHC), THYM, but was associated with a survival advantage for KIRP. It was also observed that HOTAIR predicted poor prognosis for ACC, BRCA, cholangiocarcinoma (CHOL), COAD, GBM, KIRC, LGG, mesothelioma (MESO), and THYM, but was associated with a survival advantage for LAML. Hence, these results indicated that the different expression levels of the HOXATs show different prognostic associations in the different cancer types.

FIGURE 2 | Correlation between the homeobox antisense transcripts (HOXATs) expression and the overall survival (OS). Relationship between the HOXATs gene expression and the OS in patients with different cancer types. Forest plots of the hazard risk (HR) with 95% confidence intervals in the OS across different cancer types showed an advantage (HR < 1) and a disadvantage (HR > 1) in survival with increasing HOXATs expression (A, B). Statistical significance was set at p < 0.05.

Moreover, this brief review of the literature indicated the existence of an association between HOXATs and OS in different cancer types. To further validate this aforementioned results, meta-analysis involving HOTTIP and HAGLR was also performed. The results obtained indicated that elevated HOTTIP (Liu et al., 2020) and HAGLR (Liu et al., 2019) expression levels could predict poor prognosis in cancer patients, suggesting that HOTTIP and HAGLR might serve as novel prognostic biomarkers for cancer (Supplementary Figure S19). Moreover, given that several HOXATs influenced LGG prognosis, K-M survival analysis for OS and DSS (Disease Free Survival) were performed to further evaluate the prognostic value of the HOXATs in LGG, based on the TCGA database (Supplementary Figures S20,21). The results obtained showed that the overexpression of all 18 HOXATs could predict poor prognosis in patients with LGG, suggesting that HOXATs might serve as novel prognostic biomarkers for LGG.

Association Between HOXATs and the Immune Microenvironment in Pan-Cancer

We further examined the association between the expression levels of HOXATs and the infiltration levels of stromal and immune cells and tumor purity, which was defined based on stromal scores, immune scores, and estimate scores using the ESTIMATE algorithm (Figures 3A,B). The higher stromal and immune scores indicated lower tumor purity, and as expected, the degree of association between the HOXATs and stromal scores for different cancer types varied over a wide range. In cancers, including BRCA, LGG, tenosynovial giant cell tumor (TGCT), thyroid carcinoma (THCA), and THYM, approximately half or more than half of the HOXATs showed positive association with the stromal score of the patients. However, some of the HOXATs showed negative association with stromal scores in patients with ACC, BLCA, and KIRP. Additionally, certain HOXATs displayed different correlations across various cancer types. For instance, HOXB-AS1 (p < 0.001) showed the highest correlation with stromal scores across the different cancer types followed by HOTAIRM1 (p < 0.001), and HOXA-AS2 (p < 0.05). More specifically, we observed a significant positive correlation between HOXB-AS1 and the stromal scores of CHOL, ESCA, LIHC, LGG, and THCA; HOTAIRM1 and the stromal scores of CHOL, LGG, LIHC, and THCA; HOXA-AS2 and the stromal scores of BRCA, TGCT, and THCA, as well as HOXD-AS2 and the stromal score of TGCT (p < 0.0001). Conversely, there was a significant negative correlation between HOXA11-AS and the stromal scores of ACC and pancreatic adenocarcinoma (PAAD), and HOTAIR and the stromal scores of ACC and sarcoma (SARC) (p < 0.001). Further, we also analyzed the correlation between HOXATs and the immune score. Thus, we observed a significant positive correlation between HOXB-AS1 and the immune scores of CHOL, diffuse large B-cell lymphoma (DLBC), LGG, LIHC, prostate adenocarcinoma (PRAD), and THCA, and between HOTAIRM1 and the immune scores of CHOL, LGG, LIHC, and THCA (Figure 3B).

FIGURE 3 | Relationship between the expression of the homeobox antisense transcripts (HOXATs) and the tumor microenvironment. The correlation matrix of the HOXATs expression and the stromal cells (A) as well as the immune cells (B) of 33 different cancer types based on the estimation algorithm. The Spearman correlation analysis was used to test the immune scores and estimate scores. The size of each point represents the absolute value of the correlation coefficient, where the larger the size, the higher the correlation (the higher the absolute value, the higher the correlation coefficient). Relationship between the expression of the HOXATs and the immune-infiltrating subtypes across all cancer types (C). C1: wound healing, C2: INF-r dominant, C3: inflammatory, C4: lymphocyte depleted, C5: immunologically quiet, and C6: TGFβ dominant.

To further clarify the relationship between each of the HOXATs and the immune components, we determined the correlation between HOXATs and the immune infiltrates in the cancer tumors. Six classes of immune infiltrates corresponding either to tumor promotion or suppression, namely, C1 (wound healing), C2 (INF-r dominant), C3 (inflammatory), C4 (lymphocyte depleted), C5 (immunologically quiet), and C6 (TGFβ dominant), were identified. Patients characterized by C3 and C5 immune subtypes showed significantly better survival rates than those characterized by the other immune subtypes, whereas patients with C4 and C6 subtypes showed the least favorable survival, given that C1 and C2 as well as C4 and C6 showed associated with more aggressive immune subtypes (Li et al., 2019). A comparison of the expression levels of HOXATs corresponding to the different immune infiltrate subtypes showed a strong relationship between the expression of HOXATs genes and the immunity of the patients (p < 0.001; Figure 3C). Most of the HOXATs showed a lower expression level in the C5 immune subtype, indicating that they possibly play tumor promoter roles. Conversely, HAGLR and HOXD-AS2 were highly expressed in all the six immune subtypes, showing higher expression in C5, which in future, can be used as a new direction for immunosuppression. Additionally, higher levels of HOXA11-AS, HOTTIP, HOXB-AS3, HOXB-AS4, PRAC2, and HOXC13-AS were found to be associated with types 1, 2, and 6 infiltrates (i.e., C1, C2, and C6), indicating the tumor promoter role of these gene members, given that patients belonging to these categories, who showed a higher cancer cell proliferation rate as well as TGFβ enrichment, had lower survival rates (Lu et al., 2018). Conversely, HOTAIRM1, HOXA-AS2, HOXA10-AS, HOXB-AS1, HOXC-AS1, HOXC-AS2, and HOXD-AS2 showed association with C3 and C6 immune subtypes, and the decreased expression in the C5 subtype indicated that the differential expression of HOXATs in the different immune subtypes may partially explain why HOXATs played contrasting roles in the prognosis of various cancers.

Correlation Between HOXAT Expression and Immune Marker Sets

The application of cancer immunotherapy has received significant attention in recent years. Here, we analyzed the relationship between the expression of HOXATs and 47 immune checkpoint genes in different immune infiltrated cells. Notably, the expression levels of most of the HOXATs showed a close relationship with the expression of immune markers in the various immune cells (Supplementary Figures S22–S39). Specifically, the expression levels of the majority of resting T cells, NK cells, eosinophils, and mast cells exhibited a significant correlation with the expression of HOXATs. This observation suggested that in some tumors, HOXATs possibly play a role in modulating the pattern of tumor immunity by regulating the expression levels of these immune checkpoint genes. Interestingly, HOXATs, including HOXB-AS1, HOXC-AS2, HAGLR, HOTAIR, and HOTAIRM1 showed significant positive correlation with the expression levels of immune checkpoint genes, including CTLA-4 and PD-L1, suggesting that these HOXATs are involved in the regulation of immune responses in different cancer types (Figures 4A,B). Therefore, these results confirmed the specific correlation between HOXAT expression levels and immune marker sets.

FIGURE 4 | Association of the homeobox antisense transcripts (HOXATs) with immune cell marker expression and tumor mutation burden (TMB). Heatmaps represent the association of the HOXATs expression with PD-L1 and CTLA4 in the pan-cancer (A, B). For each pair, the top-right triangle represents the p-value, while the bottom-left triangle represents the correlation coefficient. *p < 0.05, **p < 0.01, ***p < 0.001. Heat map showing the correlation between the HOXAT expression and TMB (C). The numbers on the heatmap represent the Spearman correlation values, where red and blue represent positive and negative correlations, respectively. *p < 0.05, **p < 0.01, ***p < 0.001.

Correlation Between HOXAT Expression and Tumor Mutation Burden in Pan-Cancer

TMB, which is considered an essential factor that affects the occurrence and progression of tumors, has been shown to affect response to cancer immunotherapy (Mao et al., 2018). Thus, we examined its association with HOXAT expression levels (Figure 4C). Interestingly, we observed that approximately half or more than half of the HOXATs were positively associated with TMB in cancers, including ACC, LGG, ESCA, HNSC, and MESO, but negatively correlated with TMB in SKCM. Notably, the higher expression levels of HOXATs in LGG showed their greater likelihood to bring about exposure of immune checkpoints, inducing a stronger anti-tumor immune response.

Association Between HOXATs and Tumor Stemness in Pan-Cancers

To conduct a preliminary study on the association between HOXATs and cancer stemness, tumor stemness based on RNA stemness scores (RNAss) and DNA stemness score (DNAss) were utilized (Figures 5A,B); lower RNAss or higher DNAss are associated with higher immune infiltration. Thus, we observed that HOXATs were negatively associated with RNAss in BRCA, KIRC, KIRP, LGG, READ, SKCM, TGCT, and THCA, and positively associated with DNAss in CHOL, GBM, LGG, and STAD. Notably, with the exception of HOTAIRM1 and HAGLR, all the other HOXATs showed negative association with RNAss and DNAss in TGCT.

FIGURE 5 | Relationship between the homeobox antisense transcripts (HOXATs) expression levels and the tumor stemness. Correlation matrix of the HOXATs expression and the cancer RNA and DNA stemness scores, RNAss (A) and DNAss (B), respectively, based on Spearman correlation tests.

Potential Roles of HOXATs in Glioma Tumor Immunity and the TME

In this study, we also compared the expression levels of HOXAT genes across the immune subtypes in LGG and GBM based on the TCGA RNAseq database. First, the genes showed contrasting intra-tumor heterogeneities in the different immune subtypes of the different grades of glioma. Specifically, HOXB-AS3, HOXC13-AS, and HAGLR showed significant differences in expression level across C1 and C4 immune subtypes, indicating that HOXAT immune subtypes are minimally correlated with GBM (p < 0.05; Figure 6A). High HOXAT expression levels in GBM enhance immunosuppression and facilitate the escape of GBM from immune supervision. However, no significant association was observed in GBM in this regard. Similarly, our results were consistent with those of other studies, indicating that elevated expression levels of HOXATs, such as HOXA10-AS and HOXA-AS2, are associated with the malignancy of gliomas. Conversely, except for HOTTIP, HOXB-AS4, and HAGLR, the expression levels of the other 15 HOXATs were significantly different across the C1 and C4 immune subtypes in LGG, suggesting that higher HOXAT expression levels in LGG elicits a higher immune response, while a lower expression level induces immune escape (p < 0.001; Figure 6B).

FIGURE 6 | Association of the homeobox antisense transcripts (HOXATs) expression with the tumor immune microenvironment in glioblastoma (GBM) and low-grade glioma (LGG) patients based on TCGA. Association of the HOXATs expression with the immune infiltrate subtypes in GBM patients (A). Association of HOXATs expression with immune infiltrate subtypes in LGG patients (B). C1: wound healing; C2: INF-r dominant; C3: inflammatory; C4: lymphocyte depleted; C5: immunologically quiet; C6: TGFβ dominant,*p < 0.05, **p < 0.01, ***p < 0.001.

Next, an investigation of the correlation between immune checkpoint genes and the HOXATs in GBM and LGG showed the existence of a significant correlation between the majority of the immune checkpoint genes and the HOXATs in LGG compared with GBM (p < 0.001; Figures 7A,B), suggesting that HOXATs possibly regulate malignancy degree of gliomas by influencing the immune system. The correlation analysis between HOXATs and cancer stem cell-associated genes was investigated. It was found that HOXATs and CD133, SOX2 were differentially association within glioma tissues of different grades (Figure 7C). The advance of tumor grade, was related to the increasing of SOX2 and CD133 cells. To further verify the role of signaling activation in the aberrant expression of HOXATs in pan-cancer, functional annotation was performed using GSEA. Interestingly, based on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, except for HOXA-AS3 and HAGLR, 16 out of the 18 HOXATs were found to be enriched in several immune response pathways, including primary immunodeficiency, antigen processing and presentation, and the intestinal immune network corresponding to IGA.

FIGURE 7 | Association of the homeobox antisense transcripts (HOXATs) expression with stem cell and immune checkpoints in GBM and LGG. Co-expression analyses of the HOXATs and the immune checkpoints in the glioblastoma (GBM) (A) and low-grade glioma (LGG) patients (B). For each pair, the top right triangle represents the p-value, while the bottom left triangle represents the correlation coefficient. GEPIA database showing HOXATs expression correlation with stem gene in high-grade glioma tissues compared with that in low-grade glioma tissues (C). *p < 0.05, **p < 0.01, ***p < 0.001.

Critical Role of HOXB-AS1 in the Maintenance of the Properties of GSCs

To evaluate the association between HOXB-AS1 and the properties of GSCs in glioma, the expression level of HOXB-AS1 and the stemness markers that are associated with stemness in GBM and LGG were analyzed using the GEPIA online dataset (Figures 8A–C). As expected, HOXB-AS1 expression was significantly higher in GBM than in lower grade glioma tissues. Additionally, the stemness marker, CD133, which was significantly upregulated in GBM compared with normal tissues, could well distinguish between different grades of gliomas, while the other upregulated stemness marker, SOX2, was not different for the different grades of glioma. Moreover, correlation analysis demonstrated a strong correlation at transcriptional level between HOXB-AS1 and the stemness markers, CD133 and SOX2, especially in GBM (p < 0.001; Figures 8D–G). Further in vitro cell function experiments, which primarily involved cultured GBM and LGG cells in the normal culture medium with adherent culture conditions, confirmed that the established GSCs have a significantly higher neurosphere formation capacity than non-GSCs (p < 0.05; Figures 8H,I). Furthermore, sphere formation assay revealed that HOXB-AS1 knockdown inhibited spheroid formation in GSCs, indicating that HOXB-AS1 supports cancer stem cell growth. In conclusion, HOXB-AS1 was identified as being critical for the maintenance of the properties of GSCs (Figures 8J,K).

FIGURE 8 | HOXB-AS1 is Required for GSC Maintenance and Survival. Comparison of HOXB-AS1, CD133 and SOX2 expression levels between LGG and GBM data sets (A–C). The red column represents the glioma samples, and the blue column represents the normal samples. Correlations between differentially expressed HOXB-AS1 and stemness genes in GBM and LGG patients (D–G). The representative images of neurosphere-cultured GSCs and 2 established GSC cells (H, I). Silencing of HOXB-AS1 inhibits the proliferation and tumorigenicity of GSCs in U251and HS683. The effects of siRNA targeting of HOXB-AS1 on spheroid-forming ability in GBM and LGG cells were detected by sphere formation assay (K, J). Bar: 100 μm *p < 0.05, **p < 0.01, ***p < 0.001.

Inhibition of Proliferation, Migration, and Invasion in Glioma Cells Following HOXB-AS1 Knockdown

To further investigate the biological role of HOXB-AS1 in glioma cells, we depleted the expression of HOXB-AS1 using lncRNA Smart Silencer. As shown in Figures 9A,B, MTT assays were performed to detect cell viability. Thus, it was observed that U251 and HS683 cells transfected with HOXB-AS1 smart silencer exhibited a markedly increased rate of apoptosis compared with the control cells (p < 0.05). Further, transwell migration assay showed that HOXB-AS1 downregulation decreased U251 and HS683 migration (p < 0.05; Figures 9C,D), and similarly, wound healing assay indicated that HOXB-AS1 downregulation could significantly decreased invasion (p < 0.01; Figures 9E,F). These results indicated that HOXB-AS1 knockdown inhibited glioma cell proliferation, migration, and invasion.

FIGURE 9 | the homeobox antisense transcripts (HOXATs) enhanced the progression of glioma cells. Knockdown of HOXB-AS1 inhibited glioblastoma proliferation, metastasis, and invasion. Cell viability was measured by MTT assay in U251 and HS683 cell lines, and cell viability of cells with HOXB-AS1 knockdown was significantly inhibited (A, B). Transwell migration assay: HOXB-AS1 knockdown significantly decreased the migration distance in U251and HS683 cells (C, D). Invasion capacity was measured after HOXB-AS1 downregulation by transwell assay in U251 and HS683 cells (E, F). Bar: 100 μm *p < 0.05, **p < 0.01, ***p < 0.001.

DISCUSSION

Presently, it is well known that over 75% of the human genome is functional and encodes a large number of ncRNAs. A recent study provided strong evidence that lncRNAs play important roles in numerous biological processes, including chromatin modification, RNA processing, structural scaffold invasion modulation, and apoptosis (Zhou et al., 2017; Miyazato and Hayakawa, 2020). However, lncRNAs continue to be discovered and are yet to be functionally annotated. Therefore, studies with a focus on the elucidation of the biological functions of these lncRNAs and the identification of those that are disease-related are still required.

HOXATs play pivotal roles in tumor growth and metastatic spread (Meti et al., 2018). For instance, HOXB-AS1, which might function as a carcinogen and a promising therapeutic biomarker for the clinical treatment of patients with multiple myeloma, GBM, and endometrial carcinoma patients, has been shown to regulate cancer cell proliferation, migration, and invasion (Klebanov et al., 2019; Obaid et al., 2021). In this study, the expression levels of HOXATs in different cancer types were analyzed based on the TCGA database. The results of Cox regression analysis suggested that the high expression levels of half or more than half of the HOXATs was primarily associated with unfavorable outcomes in ACC, KIRC, LGG, and THYM. Additionally, certain HOXATs, including HOXA-AS2, HOXB-AS1, and HOTAIR possibly exerted distinct prognostic effects across the various cancer types (Quinn and Chang, 2016; Petermann et al., 2019).

Further, accumulating evidence also suggests that lncRNAs play important roles in immune regulation (Sharma et al., 2011; Sheng et al., 2018). For example, Li et al. identified immune lncRNAs as potential oncogenic biomarkers for pan-cancer characterization, based on the TCGA database (Sun et al., 2016). It has also been reported that the HOTAIR, influences the development of cancer invasion via immune responses (Wang et al., 2019a; Wang et al., 2019b). However, only a few examples have been identified thus far, this study represents the first comprehensive genome-wide analysis of HOXAT expression in pan-cancer, wherein the potential relationships between HOXATs and the TME in pan-cancers are explored via a bioinformatics analysis. Notably, HOXATs, such as HOTAIRM1, HOXA11-AS, HOXB-AS1, HOXB-AS4, HAGLR, and HOXC-AS1, showed significant correlation with immune cell infiltration and were likely to exhibit higher expression levels in immune cells, while the correlation between their expression levels and tumor dryness scores suggested that they possibly act primarily as tumor promoters in the TME. Additionally, as described above, we observed that HOXAT expression levels varied across the molecular subtypes within the same tumor type, and that the tumor suppression or promotion effect of the HOXATs may be subtype-specific.

The mechanisms of immune evasion by cancer cells are the targets of immunotherapies, and immune checkpoint inhibitors, including antibodies that block the programmed cell death of protein-1 (PD-1)/PD-L1 and the cytotoxic T lymphocyte associated protein 4 (CTLA-4), are receptors that attenuate T cell response. These have brought about changes in the strategies for the management of several cancers (Wang et al., 2015). Meanwhile, the co-expression of HOXATs in immune checkpoints and immune cells across 33 different cancer types indicated that the expression levels of HOXATs, such as HOXB-AS1, HOXC-AS2, HAGLR, HOTAIR, and HOTAIRM1 are significantly positively correlated with the expression of CTLA-4, PD-L1, and other immunocyte markers as well as immune cells, such as resting T cells, NK cells, eosinophils, and mast cells. Additionally, the identified signaling pathways showed association with immunity. Specifically, the results of this study indicated that these HOXATs may be important factors in the evolution of cancer development and might be involved in the regulation of immune response.

During cancer progression, tumor cells can progressively lose their differentiated phenotypes and acquire stem and progenitor-like features, which are believed to be responsible for tumor recurrence after standard chemotherapy and radiotherapy (Zhou et al., 2017). Recently, it has been hypothesized in several studies that cancer stem-like-cells, also known as tumor-initiating cells, mediate tumor initiation, progression, and metastasis (Wu et al., 2017). Interestingly, most of the HOXATs showed significant correlation with cancer stem cell-like features (DNAss and RNAss), suggesting that they likely play a role in tumor-initiating cells and are related to tumor resistance to drug treatment (Zhao et al., 2018a). HOXB-AS1 was identified as being critical for the maintenance of the properties of GSCs.

The immune microenvironment of GBM consists of anti-tumor immune response components (Yao et al., 2019; Yuan et al., 2020). Even though little is known regarding immunosuppression in this regard, a better understanding of the involvement and regulation of the immune system is essential for the treatment of glioblastoma. In this study, to observe the effects of HOXATs on the tumor immune microenvironment in gliomas, GBM and LGG were also compared in multiple ways, including immune subtype expression, immune markers, stromal scores, immune scores, and estimate scores using the ESTIMATE algorithm. The results obtained revealed that the expression levels of HOXATs are associated with diverse immune infiltration levels as well as tumor purity levels in gliomas, with high levels of intra-tumor heterogeneity in the immune infiltration corresponding to the different grades of the gliomas. Moreover, the HOXATs showed overexpression in the C4 immune subtype of GBM, i.e., the overexpression of HOXATs largely matched the immune profile of the C4 subtype, suggesting that HOXATs may play a negative role in the microenvironment, leading to an immunosuppressive TME, with poor prognosis, when compared with LGG. Further, the HOXATs also showed lower correlation with GBM than LGG in terms of both the immune and stromal scores, indicating that they are closely associated with the tumor immune microenvironment of LGG, but do not elicit a high immune response. Furthermore, the tumor immune microenvironment of GBM was altered when the glioma developed into GBM. Even though there was a high HOXAT expression that elicited an immune response, the HOXATs did not show any association with the TME. Therefore, we hypothesized that the early knockdown of HOXATs could inhibit the development of gliomas. HOXB-AS1 knockdown inhibited glioma cell proliferation, migration, and invasion.

With the continued exploration of lncRNAs (Zhang et al., 2018a)and the development of RNA-centric approaches (Zhang et al., 2018b), we will be able to identify the interaction targets of lncRNAs in a native context (Zhang et al., 2020). The identification of these targets will further improve our understanding of lncRNA-mediated pathway regulation and advance our understanding of lncRNA functions (Zhao et al., 2019). Simultaneously, the relationship between lncRNAs and TME represented an additional layer of tumor microenvironment complexity. Hence, it is important to clarify the roles of lncRNAs in TME. Additionally, the TME has a complex tissue-like structure with a rich phenotype as well as functional heterogeneity. Different concentrations of chemokines and cytokines, as well as interactions between the cells that make up the TME can activate epithelial-mesenchymal transition-related signaling pathways and control the generation of CSCs(Zhao et al., 2018b). In this study, we described the contribution of HOXATs in pan-cancer prognosis and TME modulation, demonstrating that HOXATs might serve as biomarkers for prognosis and immune response across cancers.

This study had some limitations. First, one of the major limitation of this study is that all of the experiments in this study were done in cultured cells. Second, the effects of the different HOXATs in the different cancer subtypes were not analyzed in detail, implying that further studies are needed in this regard to obtain more accurate results. These limitations notwithstanding, our results can significantly facilitate the clarification of the role of HOXATs in tumorigenesis, specifically with respect to immune response, the TME, and prognosis. This may be beneficial to researchers in this field with respect to the study of the roles of HOXATs and the development of personalized medicine for cancer treatment.

CONCLUSION

This first comprehensive analysis of HOXATs expression in 33 different cancer types, analysed the role of the tumour microenvironment, and compared results in different grades of glioma. Therefore, we hypothesized that the early knockdown of HOXATs could stop the development of gliomas. We believe that the continued investigation of the HOXATs identified here will aid in the development of better immunotherapies for human cancer and other diseases.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.

AUTHOR CONTRIBUTIONS

QX, YGG, and YG conceived and designed the study. WS and QD collected the data. YGG and WS analyzed and interpreted the data. JX, ZW, and QD verified the data. YG and JX performed all the statistical analysis. QX, WS, YGG, and ZH prepared figures and prepared the manuscript. All authors have read and approved the final version.

FUNDING

The present study was supported by the Natural Science Foundation of China (#81701185), High School Key Research Project of Henan Province (#18A320003), and the Specialized Science and Technology Key Project of Henan Province (#202102310406).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2021.767856/full#supplementary-material

REFERENCES

Akbari, F., Peymani, M., Salehzadeh, A., and Ghaedi, K. (2020). Integrative In Silico and In Vitro Transcriptomics Analysis Revealed New lncRNAs Related to Intrinsic Apoptotic Genes in Colorectal Cancer. Cancer Cel Int 20, 546. doi:10.1186/s12935-020-01633-w

Bi, Y., Mao, Y., Su, Z., Du, J., Ye, L., and Xu, F. (2020). HOXB‐AS1 Accelerates the Tumorigenesis of Glioblastoma via Modulation of HOBX2 and HOBX3 at Transcriptional and Posttranscriptional Levels. J. Cel Physiol 236, 93–106. doi:10.1002/jcp.29499

Carlsten, M., and Järås, M. (2019). Natural Killer Cells in Myeloid Malignancies: Immune Surveillance, NK Cell Dysfunction, and Pharmacological Opportunities to Bolster the Endogenous NK Cells. Front. Immunol. 10, 2357. doi:10.3389/fimmu.2019.02357

Chen, Z., He, A., Wang, D., Liu, Y., and Huang, W. (2016). Long Noncoding RNA HOTTIP as a Novel Predictor of Lymph Node Metastasis and Survival in Human Cancer: a Systematic Review and Meta-Analysis. Oncotarget 8, 14126–14132. doi:10.18632/oncotarget.12981

Chen, X., Li, L. Q., Qiu, X., and Wu, H. (2019). Long Non-Coding RNA HOXB-AS1 Promotes Proliferation, Migration and Invasion of Glioblastoma Cells via HOXB-AS1/miR-885-3p/HOXB2 axis. Neoplasma 66, 386–396. doi:10.4149/neo_2018_180606N377

Chen, R., Zhang, X., and Wang, C. (2019). LncRNA HOXB‐AS1 Promotes Cell Growth in Multiple Myeloma via FUT4 mRNA Stability by ELAVL1. J. Cel Biochem 121, 4043–4051. doi:10.1002/jcb.29573

Chen, W., Li, Q., Zhang, G., Wang, H., Zhu, Z., and Chen, L. (2020). LncRNA HOXA‐AS3 Promotes the Malignancy of Glioblastoma through Regulating miR‐455‐5p/USP3 axis. J. Cel. Mol. Med. 24, 11755–11767. doi:10.1111/jcmm.15788

Chi, Y., Wang, D., Wang, J., Yu, W., and Yang, J. (2019). Long Non-Coding RNA in the Pathogenesis of Cancers. Cells 8, 1015. doi:10.3390/cells8091015

Cruz, A. F., Fonseca, N. A., Moura, V., Simoes, S., and Moreira, J. N. (2017). Targeting Cancer Stem Cells and Non-stem Cancer Cells: the Potential of Lipid-Based Nanoparticles. Curr. Pharm. Des. 23, 43. doi:10.2174/1381612823666171115105252

Dasti, A., Cid-Samper, F., Bechara, E., and Tartaglia, G. G. (2020). RNA-centric Approaches to Study RNA-Protein Interactions In Vitro and In Silico. Methods 178, 11–18. doi:10.1016/j.ymeth.2019.09.011

Dong, C. Y., Cui, J., Li, D. H., Li, Q., and Hong, X. Y. (2018). HOXA10-AS: A Novel Oncogenic Long Non-Coding RNA in Glioma. Oncol. Rep. 40, 2573–2583. doi:10.3892/or.2018.6662

Hao, J., and Yu, J. (2018). Semaphorin 3C and its Receptors in Cancer and Cancer Stem-Like Cells. Biomedicines 6, 42. doi:10.3390/biomedicines6020042

Klebanov, N., Artomov, M., Goggins, W. B., Daly, E., Daly, M. J., and Tsao, H. (2019). Burden of Unique and Low Prevalence Somatic Mutations Correlates with Cancer Survival. Sci. Rep. 9, 4848. doi:10.1038/s41598-019-41015-5

Li, C., Liu, S., Yan, R., Han, N., Wong, K.-K., and Li, L. (2017). CD54-NOTCH1 axis Controls Tumor Initiation and Cancer Stem Cell Functions in Human Prostate Cancer. Theranostics 7, 67–80. doi:10.7150/thno.16752

Li, L., Wang, Y., Song, G., Zhang, X., Gao, S., and Liu, H. (2019). HOX Cluster-Embedded Antisense Long Non-coding RNAs in Lung Cancer. Cancer Lett. 450, 14–21. doi:10.1016/j.canlet.2019.02.036

Li, K., Luo, H., Huang, L., Luo, H., and Zhu, X. (2020). Microsatellite Instability: a Review of what the Oncologist Should Know. Cancer Cel Int 20, 16. doi:10.1186/s12935-019-1091-8

Li, Y., Jiang, T., Zhou, W., Li, J., Li, X., Wang, Q., et al. (2020). Pan-cancer Characterization of Immune-Related lncRNAs Identifies Potential Oncogenic Biomarkers. Nat. Commun. 11, 1000. doi:10.1038/s41467-020-14802-2

Liang, Y., Zhu, H., Chen, J., Lin, W., Li, B., and Guo, Y. (2020). Construction of Relapse-Related lncRNA-Mediated ceRNA Networks in Hodgkin Lymphoma. aoms 16, 1411–1418. doi:10.5114/aoms.2020.98839

Lin, S., Xu, H., Zhang, A., Ni, Y., Xu, Y., Meng, T., et al. (2020). Prognosis Analysis and Validation of m6A Signature and Tumor Immune Microenvironment in Glioma. Front. Oncol. 10, 541401. doi:10.3389/fonc.2020.541401

Liu, N., Wang, Z., Liu, D., and Xie, P. (2019). HOXC13-AS-miR-122-5p-SATB1-C-Myc Feedback Loop Promotes Migration, Invasion and EMT Process in Glioma. Onco. Targets Ther. 12, 7165–7173. doi:10.2147/OTT.S220027

Liu, D., Qiu, M., Jiang, L., and Liu, K. (2020). Long Noncoding RNA HOXB-AS1 Is Upregulated in Endometrial Carcinoma and Sponged miR-149-3p to Upregulate Wnt10b. Technol. Cancer Res. Treat. 19, 153303382096746. doi:10.1177/1533033820967462

Lu, C.-W., Zhou, D.-D., Xie, T., Hao, J.-L., Pant, O. P., Lu, C.-B., et al. (2018). HOXA11 Antisense Long Noncoding RNA (HOXA11-AS): A Promising lncRNA in Human Cancers. Cancer Med. 7 (8), 3792–3799. doi:10.1002/cam4.1571

Mao, C., Wang, X., Liu, Y., Wang, M., Yan, B., Jiang, Y., et al. (2018). A G3BP1-Interacting lncRNA Promotes Ferroptosis and Apoptosis in Cancer via Nuclear Sequestration of P53. Cancer Res. 78, 3484–3496. doi:10.1158/0008-5472.CAN-17-3454

Meti, N., Esfahani, K., and Johnson, N. (2018). The Role of Immune Checkpoint Inhibitors in Classical Hodgkin Lymphoma. Cancers 10, 204. doi:10.3390/cancers10060204

Miyazato, K., and Hayakawa, Y. (2020). Pharmacological Targeting of Natural Killer Cells for Cancer Immunotherapy. Cancer Sci. 111 (6), 1869–1875. doi:10.1111/cas.14418

Obaid, M., Udden, S., Alluri, P., and Mandal, S. S. (2021). LncRNA HOTAIR Regulates Glucose Transporter Glut1 Expression and Glucose Uptake in Macrophages during Inflammation. Sci. Rep. 11, 232. doi:10.1038/s41598-020-80291-4

Petermann, F., Pękowska, A., Johnson, C. A., Jankovic, D., Shih, H. Y., Jiang, K., et al. (2019). The Magnitude of IFN-γ Responses Is Fine-Tuned by DNA Architecture and the Non-coding Transcript of Ifng-As1. Mol. Cel 75, 1229–e5. doi:10.1038/s41598-020-80291-410.1016/j.molcel.2019.06.025

Quinn, J. J., and Chang, H. Y. (2016). Unique Features of Long Non-coding RNA Biogenesis and Function. Nat. Rev. Genet. 17, 47–62. doi:10.1038/nrg.2015.10

Sharma, S., Findlay, G. M., Bandukwala, H. S., Oberdoerffer, S., Baust, B., Li, Z., et al. (2011). Dephosphorylation of the Nuclear Factor of Activated T Cells (NFAT) Transcription Factor Is Regulated by an RNA-Protein Scaffold Complex. Proc. Natl. Acad. Sci. 108, 11381–11386. doi:10.1073/pnas.1019711108

Sheng, K., Lu, J., and Zhao, H. (2018). ELK1-induced Upregulation of lncRNA HOXA10-AS Promotes Lung Adenocarcinoma Progression by Increasing Wnt/β-Catenin Signaling. Biochem. Biophysical Res. Commun. 501, 612–618. doi:10.1016/j.bbrc.2018.04.224

Sun, T.-T., He, J., Liang, Q., Ren, L.-L., Yan, T.-T., Yu, T.-C., et al. (2016). LncRNA GClnc1 Promotes Gastric Carcinogenesis and May Act as a Modular Scaffold of WDR5 and KAT2A Complexes to Specify the Histone Modification Pattern. Cancer Discov. 6, 784–801. doi:10.1158/2159-8290.CD-15-0921

Wang, Y., He, L., Du, Y., Zhu, P., Huang, G., Luo, J., et al. (2015). The Long Noncoding RNA lncTCF7 Promotes Self-Renewal of Human Liver Cancer Stem Cells through Activation of Wnt Signaling. Cell Stem Cell 16, 413–425. doi:10.1016/j.stem.2015.03.003

Wang, L., Cho, K. B., Li, Y., Tao, G., Xie, Z., and Guo, B. (2019). Long Noncoding RNA (lncRNA)-Mediated Competing Endogenous RNA Networks Provide Novel Potential Biomarkers and Therapeutic Targets for Colorectal Cancer. Int. J. Mol. Sci. 20, 5758. doi:10.3390/ijms20225758

Wang, L., Wang, L., and Zhang, X. (2019). Knockdown of lncRNA HOXA-AS2 Inhibits Viability, Migration and Invasion of Osteosarcoma Cells by miR-124-3p/E2F3. Onco Targets Ther. 12, 10851–10861. doi:10.2147/OTT.S220072

Wu, K., Zhao, Z., Liu, K., Zhang, J., Li, G., and Wang, L. (2017). Long Noncoding RNA Lnc-Sox5 Modulates CRC Tumorigenesis by Unbalancing Tumor Microenvironment. Cell Cycle 16, 1295–1301. doi:10.1080/15384101.2017.1317416

Yao, R.-W., Wang, Y., and Chen, L.-L. (2019). Cellular Functions of Long Noncoding RNAs. Nat. Cel Biol 21, 542–551. doi:10.1038/s41556-019-0311-8

Yuan, C., Ning, Y., and Pan, Y. (2020). Emerging Roles of HOTAIR in Human Cancer. J. Cel Biochem 121, 3235–3247. doi:10.1002/jcb.29591

Zhang, H., Liu, Y., Yan, L., Zhang, M., Yu, X., Du, W., et al. (2018). Increased Levels of the Long Noncoding RNA, HOXA-AS3, Promote Proliferation of A549 Cells. Cell Death Dis 9, 707. doi:10.1038/s41419-018-0725-4

Zhang, X., Chen, W., Fan, J., Wang, S., Xian, Z., Luan, J., et al. (2018). Disrupting CD47-Sirpα axis Alone or Combined with Autophagy Depletion for the Therapy of Glioblastoma. Carcinogenesis 39, 689–699. doi:10.1093/carcin/bgy041

Zhang, X., Shao, S., and Li, L. (2020). Characterization of Class-3 Semaphorin Receptors, Neuropilins and Plexins, as Therapeutic Targets in a Pan-Cancer Study. Cancers 12, 1816. doi:10.3390/cancers12071816

Zhao, X., Li, X., Zhou, L., Ni, J., Yan, W., Ma, R., et al. (2018). LncRNA HOXA11-AS Drives Cisplatin Resistance of Human LUAD Cells via Modulating miR-454-3p/Stat3. Cancer Sci. 109, 3068–3079. doi:10.1111/cas.13764

Zhao, W., Geng, D., Li, S., Chen, Z., and Sun, M. (2018). LncRNA HOTAIR Influences Cell Growth, Migration, Invasion, and Apoptosis via the miR-20a-5p/HMGA2axis in Breast Cancer. Cancer Med. 7, 842–855. doi:10.1002/cam4.1353

Zhao, Q., Zhao, S., Li, J., Zhang, H., Qian, C., Wang, H., et al. (2019). TCF7L2 Activated HOXA-AS2 Decreased the Glucocorticoid Sensitivity in Acute Lymphoblastic Leukemia through Regulating HOXA3/EGFR/Ras/Raf/MEK/ERK Pathway. Biomed. Pharmacother. 109, 1640–1649. doi:10.1016/j.biopha.2018.10.046

Zhou, M., Zhang, Z., Zhao, H., Bao, S., Cheng, L., and Sun, J. (2017). An Immune-Related Six-lncRNA Signature to Improve Prognosis Prediction of Glioblastoma Multiforme. Mol. Neurobiol. 55, 3684–3697. doi:10.1007/s12035-017-0572-9

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Shao, Ding, Guo, Xing, Huo, Wang, Xu and Guo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 11 November 2021
doi: 10.3389/fmolb.2021.785370

Omics- and Pharmacogenomic Evidence for the Prognostic, Regulatory, and Immune-Related Roles of PBK in a Pan-Cancer Cohort

Yi Liu¹, Juan Xiang², Gang Peng¹* and Chenfu Shen¹*

¹Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China

²Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, China

Edited by:
Florence Le Calvez-Kelm, International Agency for Research on Cancer (IARC), France

Reviewed by:
Carmelina Antonella Iannuzzi, Istituto Nazionale Tumori Fondazione G. Pascale (IRCCS), Italy
Francesca Pentimalli, Istituto Nazionale Tumori Fondazione G. Pascale (IRCCS), Italy

* Correspondence: Gang Peng, penggang83403236@126.com; Chenfu Shen, scf680524@csu.edu.cnscf680524@csu.edu.cn

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 29 September 2021
Accepted: 27 October 2021
Published: 11 November 2021

Citation: Liu Y, Xiang J, Peng G and Shen C (2021) Omics- and Pharmacogenomic Evidence for the Prognostic, Regulatory, and Immune-Related Roles of PBK in a Pan-Cancer Cohort. Front. Mol. Biosci. 8:785370. doi: 10.3389/fmolb.2021.785370

PDZ-binding kinase (PBK) is known to regulate tumor progression in some cancer types. However, its relationship to immune cell infiltration and prognosis in different cancers is unclear. This was investigated in the present study by analyzing data from TCGA, GEO, GETx, TIMER, CPTAC, GEPIA2, cBioPortal, GSCALite, PROGNOSCAN, PharmacoDB, STRING, and ENCORI databases. PBK was overexpressed in most tumors including adenocortical carcinoma (hazard ratio [HR] = 2.178, p < 0.001), kidney renal clear cell carcinoma (KIRC; HR = 1.907, p < 0.001), kidney renal papillary cell carcinoma (HR = 3.024, p < 0.001), and lung adenocarcinoma (HR = 1.255, p < 0.001), in which it was associated with poor overall survival and advanced pathologic stage. PBK methylation level was a prognostic marker in thyroid carcinoma (THCA). PBK expression was positively correlated with the levels of BIRC5, CCNB1, CDC20, CDK1, DLGAP5, MAD2L1, MELK, PLK1, TOP2A, and TTK in 32 tumor types; and with the levels of the transcription factors E2F1 and MYC, which regulate apoptosis, the cell cycle, cell proliferation and invasion, tumorigenesis, and metastasis. It was also negatively regulated by the microRNAs hsa-miR-101-5p, hsa-miR-145-5p, and hsa-miR-5694. PBK expression in KIRC, liver hepatocellular carcinoma, THCA, and thymoma was positively correlated with the infiltration of immune cells including B cells, CD4+T cells, CD8⁺ T cells, macrophages, monocytes, and neutrophils. The results of the functional enrichment analysis suggested that PBK and related genes contribute to tumor development via cell cycle regulation. We also identified 20 drugs that potentially inhibit PBK expression. Thus, PBK is associated with survival outcome in a variety of cancers and may promote tumor development and progression by increasing immune cell infiltration into the tumor microenvironment. These findings indicate that PBK is a potential therapeutic target and has prognostic value in cancer treatment.

Keywords: PBK, immune infiltration, methylation, cell cycle, tumorigenesis, pan-cancer

INTRODUCTION

PDZ-binding kinase (PBK), also known as T lymphokine-activated killer cell-derived protein kinase (TOPK), is a serine-threonine kinase of the mitogen-activated protein kinase (MAPKK) family that regulates cell survival, proliferation, growth, apoptosis, and inflammation (Abe et al., 2000; Gaudet et al., 2000; Zhao et al., 2020). As a mitotic kinase, PBK is activated by the cyclin-dependent kinase 1 (CDK1)/cyclin B1 complex during mitosis and promotes cytokinesis by phosphorylating polycomb inhibitory complex 1 (PRC1) or anaphase-promoting complex (APC) ubiquitin ligase (Kraft et al., 2003; Abe et al., 2007). PBK knockdown was shown to result in cytokinesis defects in cancer cells (Park et al., 2010).

PBK is overexpressed in a variety of active proliferating cells including malignant tumor cells as well as normal sperm cells. The upregulation of PBK has diagnostic and prognostic significance in cancer. For example, increased expression of PBK has been linked to poor prognosis in patients with ovarian serous cystadenocarcinoma (OV), esophageal carcinoma (ESCA), and stomach adenocarcinoma (STAD) (Ikeda et al., 2016; Ohashi et al., 2016; Ohashi et al., 2017). PBK is also related to cell transformation induced by H-Ras, activation of c-Jun-NH2 kinase (JNK), and expression of p53 induced by ultraviolet A, ultraviolet B, and DNA damage, respectively (Oh et al., 2007; Hu et al., 2010). PBK directly phosphorylates extracellular signal-regulated kinase (ERK), histone H3 (Ser10), histone H2AX (Ser139), peroxide-reduced protein 1 (PRX1, Ser32), JNK1 (Thr183/Tyr185), and p53-associated protein kinase (PRPK, Ser250) (Zykova et al., 2010; Roh et al., 2018) and activates downstream signaling pathways including MAPK, ribosomal S6 kinase (RSK), and transcription factors (TFs) such as activating protein 1 (AP-1) or nuclear factor kappa B (NF-κB) through phosphorylation, thereby promoting cell proliferation, migration, and invasion (Aksamitiene et al., 2010; Park et al., 2014; Ishikawa et al., 2018). Additionally, PBK promotes cell migration by regulating phosphatidylinositol 3-kinase (PI3K)/phosphatase and tensin homolog (PTEN)/protein kinase B (AKT) or transforming growth factor beta 1 (TGF-β1)/mothers against decapentaplegic (Smad) signaling pathways (Shih et al., 2012; Lee et al., 2020). PBK is differentially expressed between cancer and normal tissues and modulates radiosensitivity in tumors, suggesting that patients can be treated with radiotherapy (Pirovano et al., 2017); and a recent retrospective analysis showed that PBK expression was negatively correlated with the prognosis of patients with oral squamous cell carcinoma treated with radiotherapy (Yu et al., 2021). The PBK inhibitor OTS964/OTS514 was shown to inhibit cytokinesis and cancer cell growth (Matsuo et al., 2014), and OTS964/OTS514 induced the apoptosis of lung cancer cells including small cell lung cancer (SCLC) cells (Park et al., 2017). Thus, PBK likely plays an important role in cancer development.

Previous studies on PBK were limited to a single cancer type and lacked a comparative analysis of multiple tumors. The aim of the present study was to examine the role of PBK in different cancers and elucidate the molecular mechanisms and functions of PBK and its interacting molecules in carcinogenesis. We also analyzed the relationships between PBK expression, mutation and methylation status, clinical outcome, and tumor immune infiltration in cancers, and screened drugs that can potentially inhibit PBK. Our results demonstrate that PBK plays an important role in carcinogenesis and is a potential therapeutic target in cancer treatment.

MATERIALS AND METHODS

Gene Expression Analysis

RNA expression and clinical data in The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases were downloaded from the University of California at Santa Cruz (UCSC) Xena database (https://xenabrowser.net/datapages/). The R “limma” package was used to analyze the mRNA expression of PBK in the pan-cancer cohort.

The UALCAN portal (http://ualcan.path.uab.edu/analysis-prot.html) was used to access the Clinical Proteome Oncology Analysis Alliance (CPTAC) dataset (Chen F. et al., 2019), which includes breast invasive carcinoma (BRCA), colon adenocarcinoma (COAD), OV, clear cell renal cell carcinoma, endometrial carcinoma (UCEC), lung adenocarcinoma (LUAD), and childhood brain cancer. We used these data to compare total protein expression between primary tumors and normal tissue by entering “PBK” into the search field.

We used the Gene Expression Profiling Interactive Analysis v2 (GEPIA2; http://gepia2.cancer-pku.cn/#index) “Pathological Stage Plot” module to obtain maps of PBK expression in different pathologic stages (stage I–IV) of TCGA tumors. Log2 (transcripts per million+1) expression data and an adjusted p value < 0.05 were used as cutoffs to generate the stage diagram.

Alterations in Gene Expression

We investigated the inheritance of PBK gene modifications in different cancers using the cBioPortal portal (https://www.cbioportal.org/) (Gao et al., 2013) through “TCGA Pan-Cancer Atlas Studies” in the “Quick select” section with “PBK” as query. The results of genetic changes in TCGA tumor samples were displayed in the “Cancer Types Summary” module, including the type of mutation and copy number alterations. The comparison module also returned disease specificity and overall survival (OS), progression-free survival (PFS), and disease-free survival (DFS) in TCGA cancer samples with or without PBK gene mutations. A log-rank p value < 0.05 was used as the cutoff.

PBK Regulatory Networks of TFs, microRNAs (miRNAs), and Methylation

In order to identify regulatory factors that affect PBK expression, we used Harmonizome (https://maayanlab.cloud/Harmonizome) (Rouillard et al., 2016) to predict TFs that can bind to the PBK gene promoter in the Chromatin Immunoprecipitation Enrichment Analysis (ChEA), Encyclopedia of DNA Elements (ENCODE), JASPAR, and TRANScription FACtor (TRANSFAC) databases. TFs that appeared at least twice in these databases were re-screened in the ENCORI database (http://starbase.sysu.edu.cn/panCancer.php) (p < 0.05, R = 1 to −1).

miRNAs targeting PBK were identified using the microRNA database (miRDB) (http://mirdb.org/cgi-bin/search.cgi) and TargetScan (http://www.targetscan.org/vert_72/). miRNAs that appeared at least twice were re-screened in ENCORI to determine their relationship to PBK (p < 0.05, R = 1 to −1).

Gene Set Cancer Analysis (GSCALite; http://bioinfo.life.hust.edu.cn/web/GSCALite/) is a bioinformatics platform that allows methylation, drug sensitivity, genetic, gene variation, and survival analyses of cancer datasets (Liu et al., 2018). We used the GSCALite platform to analyze the differential methylation of PBK and TFs between tumors and paired normal tissues in different types of cancer in TCGA. After logging onto the GSCALite server, we used the “TCGA Cancer–methylation” module to select 32 TCGA cancer types. In the final map generated by GSCALite, only 14 cancer types showed methylation profiles of PBK and related TFs. Using the same platform, we analyzed the relationship between methylation and the expression of PBK and related TFs in different cancers.

Survival Analysis

The PrognoScan database (http://dna00.bio.kyutech.ac.jp/PrognoScan/index.html) allows meta-analysis of the prognostic value of genes by comparing the relationship between gene expression and variables of interest in a large number of published reports (Mizuno et al., 2009). We used this platform to evaluate the relationship between PBK expression and patient prognosis in different cancer cohorts. Survival curves were generated with PrognoScan and the “survival” package in R from TCGA data. The survival results are shown as hazard ratio (HR) with 95% confidence interval (CI) and log-rank p value.

Correlation Analysis

Pearson correlation analysis was performed to evaluate the correlation between PBK and immune checkpoints, tumor mutation burden (TMB), microsatellite instability (MSI), DNA methylation, and mismatch repair (MMR). The “heatmap” package in R was used to display the results as a heatmap.

Immune Cell Infiltration Into the Tumor Microenvironment

The Tumor IMmune Estimation Resource (TIMER; http://timer.cistrome.org/) (Li et al., 2017) web server is a comprehensive resource for systematic analysis of immune cell infiltration in different cancer types. We used the immune gene module of TIMER to assess the correlation between PBK expression in six immune cell types and their infiltration in 32 TCGA tumors. We also used the Tumor-Immune System Interactions database (TISIDB) (http://cis.hku.hk/TISIDB/) to evaluate whether there was a significant difference in PBK expression between responders and non-responders to immunotherapies (e.g., anti-programmed death 1 [PD1] or anti-programmed death ligand 1 [PD-L1]).

Enrichment Analysis of PBK-Related Genes

We used the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) website (https://string-db.org/) with “PBK” and organism = Homo sapiens to construct a network of the interaction between PBK and related genes. We then used the GSCALite platform to analyze the function of PBK-related genes by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Using the ggplot2 package in R v3.6.2, a point map was generated to visualize the results (p < 0.05).

Drugs Targeting PBK

We downloaded data on PBK-related drugs from PharmacoDB (https://www.pharmacodb.pmgenomics.ca/) (Smirnov et al., 2018), with p < 0.01 and a standardized coefficient of −1 to 1 as thresholds. The “heatmap” package in R was used to display the results as a heatmap.

RESULTS

Expression of PBK in Human Cancers

We analyzed the differential expression of PBK in 33 cancers as compared to normal tissue using TCGA and GTEx databases. PBK expression was higher in adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), brain lower grade glioma (LGG), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), ovarian serous cystadenocarcinoma (OV), rectum adenocarcinoma (READ), prostate adenocarcinoma (PRAD), skin cutaneous melanoma (SKCM), stomach adenocarcinoma (STAD), thyroid carcinoma (THCA), uterine corpus endometrial carcinoma (UCEC), and uterine carcinosarcoma (UCS) but lower in acute myeloid leukemia (LAML) compared to normal tissues (all p < 0.01; Figure 1A).

FIGURE 1 | PBK expression. (A) Expression of PBK in tumor and normal tissues in TCGA + GETx dataset. (B) PBK protein level in tumor tissues compared to normal tissues. (C) PBK expression in different stages of ACC, COAD, KICH, KIRC, KIRP, LIHC, and LUAD.

Analysis of the CPTAC dataset showed that PBK protein expression was significantly higher in KIRC, COAD, OV, and UCEC tissues than in normal tissues (Figure 1B). The results of the pathologic staging map based on GEPIA2 data revealed significant associations between PBK expression and pathologic stage in ACC (p = 0.0076), COAD (p = 6.32e−09), KICH (p = 8.04e−06), KIRC (p = 0.000294), KIRP (p = 2.19e−15), LIHC (p = 1.64e−05), and LUAD (p = 0.000189) (Figure 1C). In ACC, KICH, KIRC, KIRP, and LUAD, the association was positive whereas in COAD and LIHC, there was an inverse relationship between pathologic stage and PBK expression.

PBK Mutation Profile in Different Tissues

We used cBioPortal to determine the mutation frequency of PBK in the TCGA database (10,967 samples in 32 studies). We found that PBK had a relatively high mutation frequency in PRAD, OV, and UCEC (>6%) (Figures 2A,B). A total of 40 mutations (including 31 missense, 7 truncation, 1 deletion, and 1 fusion) were detected between amino acid 0 and 322 (Figure 2C).

FIGURE 2 | Genetic alterations in PBK. (A) PBK mutation frequency in multiple TCGA pan-cancer dataset in the cBioPortal database. (B) Overall mutation count for CD96 in various TCGA cancer types in the cBioPortal database. (C) Mutation diagram of CD96 across protein domains in different cancer types. (D) OS, DSS, DFS, and PFS in cancers with genetic alterations in PBK.

PBK gene mutations were unrelated to survival and prognosis of cancers in TCGA. Using cBioPortal, we determined that there was no significant difference in OS (p = 0.163), DFS (p = 0.883), disease-specific survival (DSS; p = 0.173), or PFS (p = 0.106) between samples with vs those without PBK mutation (Figure 2D).

Association Between PBK Expression and Cancer Patient Prognosis

To evaluate the prognostic value of PBK expression, we used the PrognoScan database to analyze the relationship between PBK and survival outcomes of cancer patients. The results based on 7 cohorts including GSE13507 (BLCA, HR = 1.60, p = 0.001998) (Kim et al., 2010; Lee et al., 2010), GSE4271-GP96 (GBM, HR = 1.33, p = 0.012008) (Phillips et al., 2006; Costa et al., 2010; Lin et al., 2011), GSE4412-GP96 (GBM, HR = 1.32, p = 0.0416) (Freije et al., 2004), GSE1456-GP96 (BRCA, HR = 2.21, p = 0.00088) (Pawitan et al., 2005; Hall et al., 2006), GSE 13213 (LUAD, HR = 1.43,p = 0.000337) (Tomida et al., 2009), GSE31210 (LUAD, HR = 1.96, p = 0.000087) (Okayama et al., 2012; Yamauchi et al., 2012), and GSE19234 (SKCM, HR = 3.44, p = 0.000211) (Bogunovic et al., 2009) showed that high PBK expression was significantly associated with a worse prognosis (Figures 3A–G).

FIGURE 3 | Survival analysis comparing high and low expression of PBK in different types of cancer in the Gene Expression Omnibus and TCGA datasets. (A–G) Kaplan–Meier survival curves in 8 cancer cohorts (GSE13507, GSE4271-GP96, GSE4412-GP96, GSE1456-GP96, GSE13213, GSE31210, and GSE19234). (H, I) Relationship between PBK expression and patient prognosis (OS and DSS) of different cancers in TCGA database. (J) Survival curves of OS in 11 cancer types (ACC, DLBC, KIRC, KIRP, LGG, LIHC, LUAD, MESO, PCPG, OV, and THYM) in TCGA.

In order to further assess the prognostic value of PBK in cancer, we performed univariate Cox regression and Kaplan–Meier survival analyses using TCGA pan-cancer clinical data from the UCSC database. The results of the univariate Cox regression analysis showed that PBK expression level was associated with clinical outcome in ACC (HR = 2.178, p < 0.001), CHOL (HR = 1.941, p = 0.029), KICH (HR = 2.344, p < 0.001), KIRC (HR = 1.907, p < 0.001), KIRP (HR = 3.024, p < 0.001), LGG (HR = 1.515, p < 0.001), LIHC (HR = 1.492, p < 0.001), LUAD (HR = 1.255, p < 0.001), mesothelioma (MESO; HR = 1.898, p < 0.001), pancreatic adenocarcinoma (PAAD; HR = 1.714, p < 0.001), pheochromocytoma and paraganglioma (PCPG; HR = 3.843, p = 0.001), and sarcoma (SARC; HR = 1.245) and was a risk factor for OS in PCPG (p = 0.02), but a protective factor in OV (HR = 0.859, p = 0.031) and THYM (HR = 0.531, p = 0.015) (Figure 3H). To avoid deviations caused by non-cancer events, we performed a univariate Cox regression analysis for DSS. PBK was a risk factor for DSS in patients with ACC (HR = 2.166, p < 0.001), KICH (HR = 2.336, p < 0.001), KIRC (HR = 2.215, p < 0.001), KIRP (HR = 3.633, p < 0.001), LGG (HR = 1.559, p < 0.001), LIHC (HR = 1.361, p = 0.002), LUAD (HR = 1.225, p = 0.005), MESO (HR = 1.954, p < 0.001), PAAD (HR = 1.781, p < 0.001), PCPG (HR = 4.683, p = 0.002), and SARC (HR = 1.263, p = 0.022) but was a protective factor in COAD (HR = 0.746, p = 0.017) (Figure 3I).

The Kaplan–Meier survival analysis revealed that high PBK expression predicted worse OS in ACC, KIRC, KIRP, LGG, LIHC, LUAD, MESO, and PCPG but better OS in DLBC, OV and THYM (Figure 3J).

TFs and miRNAs Regulating PBK

Given the importance of PBK in cancer, we explored TFs and miRNAs that regulate PBK expression. We identified 153 transcriptional regulators of PBK in 4 databases including ChEA, NCODE, JASPAR, and TRANSFAC. After screening for those that were represented at least twice in these databases, 11 TFs were ultimately obtained (Figure 4A). We analyzed the expression of these TFs in different cancers using the GSCALite platform. The 11 TFs were highly expressed in most cancers, with E2F1 showing the most significant upregulation (Figure 4B). The heat map indicated that the 11 TFs in 32 cancers were associated with altered PBK expression (Figure 4C): E2F1 in THYM (cor = 0.916), tripartite motif-containing 28 (TRIM28) in THYM (cor = 0.69), MYC in uveal melanoma (UVM) (cor = 0.577), and TATA-binding protein (TBP) in LIHC (cor = 0.497) were positively correlated with PBK level whereas signal transducer and activator of transcription 5A (STAT5A) and Spi-1 proto-oncogene (SPI1) showed negative correlations. E2F1 regulates the cell cycle, DNA repair, apoptosis, and cell proliferation (Fouad et al., 2020). TRIM28 is involved in the regulation of target gene transcription, response to DNA damage, downregulation of p53 activity, stimulation of epithelial–mesenchymal transition (EMT), autophagy induction, and regulation of reverse transcription-mediated transposition (Czerwińska et al., 2017). MYC promotes tumorigenesis by regulating cell proliferation, cell cycle, and genomic instability (Casey et al., 2017). Thus, PBK may contribute to tumorigenesis through interaction with these factors.

FIGURE 4 | TF and miRNA regulatory networks. (A) TF and miRNA network. (B) Expression of TFs in 14 cancers. Red and blue dots indicate increased and decreased expression, respectively, in cancers vs normal tissues. (C, D) Heatmap of TFs (C) and miRNAs (D) altered in 32 cancers. Red means TFs or miRNAs is positively correlated with PBK expression, green means TFs or miRNAs is negatively correlated with PBK expression.

MiRNAs control gene expression at the post-transcriptional level and thereby regulate cell proliferation, apoptosis, and invasion in cancer (Li et al., 2018; Zeng et al., 2021). miRNAs targeting PBK were predicted with TargetScan and the miRDB database; a total of 45 miRNAs were obtained in the intersection of the 2 datasets. Correlations between the miRNAs and PBK were calculated using the ENCORI database (p < 0.05). Of the 7 miRNAs with significant correlations (hsa-miR-6505-5p, hsa-miR-192-3p, hsa-miR-101-5p, hsa-miR-3651, hsa-miR-302d-3p, hsa-miR-5694, and hsa-miR-145-5p) (Figure 4A), hsa-miR-101-5p in UCS (cor = −0.61), hsa-miR-145-5p in testicular germ cell tumor (TGCT) (cor = −0.357), and hsa-miR-5694 in MESO (cor = −0.368) showed the strongest inhibitory effect on PBK expression (Figure 4D). miR-101-5p is known to inhibit the progression of KIRC, BRCA, non-SCLC (NSCLC), and CESC (Chen Q. et al., 2019; Shen et al., 2019; Toda et al., 2020; Yamada et al., 2020), while miR-145-5p has this effect in GBM and PRAD (Ozen et al., 2015; Kurogi et al., 2018). Our results indicate that these miRNAs negatively regulate PBK expression, thereby affecting its function in tumorigenesis.

Changes in the Methylation State of PBK Influence Patient Prognosis

Alterations in DNA methylation state are related to gene expression changes in cancer (Masser et al., 2018). We used the GSCALite platform to analyze the methylation profiles of PBK and related TFs in various TCGA cancers. We first compared the methylation status of tumor and normal tissues in 14 cancer types and found that PBK methylation was decreased in THCA, LIHC, and LUAD (Figure 5A). We also examined the correlation between PBK methylation and expression in 32 cancer types and found that the expression levels of PBK and its TFs were negatively correlated with PBK methylation, with positive correlations observed only in a few cases (Figure 5B). Finally, we examined the association between PBK methylation and OS in 22 cancer types and found that in LGG, THCA, GBM, and PCPG, a higher methylation level was associated with a lower risk of death, whereas a positive association was observed between PBK methylation and mortality in UCEC (Figure 5C).

FIGURE 5 | PBK gene methylation in TCGA cancer tissues. (A) Bubble map of the differential methylation of PBK and TFs between tumors and corresponding normal tissues in 14 cancer types. Red and blue dots indicate increased and decreased methylation, respectively, in cancers vs normal tissues. (B) Bubble map of the association between methylation and expression of PBK and TFs across different cancer types. Red dots represent increased methylation and expression levels, and blue dots represent increased methylation and decreased expression level. (C) Bubble map showing the association between OS and methylation of PBK and TFs in different cancer types. Red and blue dots indicate high and low risk, respectively. The size of the dot reflects statistical significance, with a larger size indicating greater significance.

High PBK Expression Is Correlated With Immune Cell Infiltration in Cancers

To determine whether PBK is involved in immune cell invasion into the tumor microenvironment, we analyzed the correlation between PBK expression and the infiltration of six immune cell types in 32 cancers in the TIMR database. PBK expression level was positively correlated with the degree of immune cell infiltration in KIRC, LIHC, THCA, and THYM (Figures 6A–D). The correlations between PBK expression and immune cell infiltration in other cancers are shown in Supplementary Figure 1. Our results suggest that in some cancers such as KIRC and LIHC, PBK overexpression can affect patient prognosis by increasing immune cell infiltration.

FIGURE 6 | Correlations between PBK expression and immune cell infiltration in cancers. (A) KIRC. (B) LIHC. (C) THCA. (D) THYM.

Relationship Between Immunotherapy, Immune Checkpoints, and PBK

Immune checkpoint molecules maintain immune system activation within a normal range; abnormal expression of these molecules is closely related to the occurrence and development of some tumors. Immune checkpoint inhibitors (ICIs) are a treatment option for certain malignancies. Here we analyzed the correlation between PBK expression and immune checkpoint molecules in different cancers. In CHOL, HNSC, KICH, KIRC, LIHC, and THCA, PBK expression was positively correlated with the expression of cluster of differentiation 86 (CD86), T cell immunoreceptor with Ig and ITIM domains (TIGIT), indoleamine 2,3-dioxygenase 1 (IDO1), cytotoxic T-lymphocyte–associated protein 4 (CTLA4), inducible T cell costimulator (ICOS), lymphocyte-activating 3 (LAG3), and tumor necrosis factor superfamily member 4 (TNFSF4). On the contrary, in GBM, LUSC, and THCA, PBK expression was negatively correlated with the expression of immune checkpoint molecules such as CD86, V-set immunoregulatory receptor (VSIR), hepatitis A virus cellular receptor 2 (HAVCR2), and neuropilin 1 (NRP1) (Figure 7A). These results indicate that PBK plays an important role in tumor immune regulation and may thus impact patient survival. Although PBK expression was significant in urothelial cancer, there was no significant difference in expression level between immunotherapy responders and non-responders (Supplementary Table 1).

FIGURE 7 | Correlations between PBK and immune checkpoints and other variables of interest. (A–E) Correlations between PBK expression and confirmed immune checkpoints (A), genes involved in MMR (B), TMB (C), MSI (D), and DNA methylation (E) in multiple cancers. *p < 0.05, **p < 0.01, ***p < 0.001.

Defects in MMR and subsequent MSI can lead to the accumulation of mutations that increase TMB, which in turn stimulates the host’s antitumor immune response (Zhao et al., 2019). We examined the correlation between PBK expression and MMR signatures and found that PBK level was positively correlated with MutL homolog 1 (MLH1), MSH2, MSH6, PMS1 homolog 2 (PMS2), and epithelial cell adhesion molecule (EPCAM) levels in BLCA, BRCA, CESC, LGG, LUAD, LUSC, PAAD, PCPG, PRAD, READ, and UCEC. It was also negatively correlated with EPCAM and positively correlated with MLH1, MSH2, and MSH6 in THYM (Figure 7B). PBK score was positively correlated with TMB in BLCA, BRCA, CHOL, COAD, HNSC, KICH, KIRC, UCEC, LAML, LGG, LUAD, LUSC, MESO, PAAD, PRAD, SARC, SKCM, and STAD; but negatively correlated with TMB in THYM (p < 0.05; Figure 7C). Finally, PBK expression was positively correlated with MSI in COAD, HNSC, LIHC, MESO, READ, SARC, STAD, and UCEC (p < 0.05; Figure 7D) and with the expression of DNA methyltransferases (DNMT1, DNMT2, DNMT3A, and DNMT3B) in ACC, BLCA, BRCA, CESC, ESCA, KICH, KIRC, KIRP, LGG, LIHC, MESO, PCPG, SKCM, THYM, UCEC, and UVM (p < 0.05; Figure 7E).

Enrichment of PBK-Binding Protein Networks

We explored the functional enrichment of PBK in cancer by mapping the protein–protein interaction network of PBK with the STRING database. Ten genes including baculoviral IAP repeat-containing 5 (BIRC5), cyclin B1 (CCNB1), cell division cycle protein 20 (CDC20), cyclin-dependent kinase 1 (CDK1), DLG-associated protein 5 (DLGAP5), mitotic arrest deficient 2-like 1 (MAD2L1), maternal embryonic leucine zipper kinase (MELK), polo-like kinase 1 (PLK1), DNA topoisomerase II alpha (TOP2A), TTK protein kinase (TTK), and PDZ-binding kinase (PBK) formed an interaction network with PBK (Figure 8A). We analyzed the association between these 10 genes and PBK in 32 TCGA cancers and found that the expression of all of the genes in all of the cancers was positively correlated with PBK expression (Figure 8B). The results of the GO functional enrichment analysis showed that PBK is involved in the regulation of cell cycle, serine/threonine kinase activity, and cell division (Figure 8C). The KEGG pathway analysis results revealed enrichment in “cell cycle,” “p53 signal pathway,” and “cell senescence” (Figure 8D). Thus, PBK has potential functions in cell cycle regulation.

FIGURE 8 | PBK network. (A) PBK correlation network. (B) Correlation heatmap of 10 genes interacting with PBK in 32 cancers. (C, D) GO (biological process [BP], cellular component [CC], and molecular function [MF]) (C) and KEGG pathway enrichment (D) analyses of the PBK correlation network.

Drug and Pathway Activity Analysis

We examined the relationship between the expression of PBK and its interaction partners and the activation or inhibition of cancer-related signaling pathways using the GSCALite platform. Consistent with results of the functional enrichment analysis, BIRC5, CCNB1, CDC20, CDK1, DLGAP5, MAD2L1, MELK, PLK1, TOP2A, TTK, and PBK were related to the activation of apoptosis, cell cycle, and DNA damage response; and BIRC5, CCNB1, CDC20, CDK1, DLGAP5, MAD2L1, MELK, PLK1, TOP2A, TTK, and PBK were related to the inhibition of hormone receptors and RAS/MAPK pathways. Additionally, PBK was related to the induction of EMT as well as activation of TSC/mammalian target of rapamycin (mTOR) and inhibition of PI3K/AKT and receptor tyrosine kinase (RTK) signaling pathways (Figures 9A,B).

FIGURE 9 | Role of PBK in known cancer-related pathways (GSCALite) and analysis of drugs potentially targeting PBK based on PharmacoDB data. (A, B) Pie chart (A) and heatmap (B) of pathway activity. (C) Heatmap of drugs correlated with 10 genes (BIRC5, CCNB1, CDC20, CDK1, DLGAP5, MAD2L1, MELK, PLK1, TOP2A, and TTK) and PBK.

We searched the PharmacoDB database for drugs related to PBK with a cutoff of p < 0.01. Drugs without targets molecules or pathways were excluded based on standard coefficients >0.1 and <−0.1. PBK was found to be resistant to 31 drugs and sensitive to 20 including those targeting the cell cycle such as paclitaxel, docetaxel, doxorubicin, and irinotecan; and apoptosis regulators such as brivanib, TW37, and elesclomol (Figure 9C).

The mTOR signaling pathway regulates cell metabolism, growth, proliferation, survival, and autophagy in many cancers; mTOR inhibitors suppress inflammation, proliferation, autophagy, and apoptosis and are used in cancer treatment (Chen and Zhou, 2020). PBK was found to be sensitive to rapamycin and temsirolimus, which target the mTOR signaling pathway.

PBK overexpression was associated with resistance to linifanib, lisitinib, neratinib, erlotinib, BRD-K04800985, VX-11e, sildenafil, PD-0325901, erlotinib, gefitinib, AZD6244, VNLG/124, WZ8040, PD318088, BMS-536924, FTI-277, canertinib, PD153035, sorafenib, ML083, WZ4002, and BRD-K75293299.

DISCUSSION

PBK has been described in some human cancers and its overexpression is associated with tumor invasion, proliferation, and metastasis in OV, lung cancer, COAD, and PRAD (Shih et al., 2012; Chen et al., 2015; Ikeda et al., 2016; Zykova et al., 2017). However, the function of PBK in many other tumor types remains unclear. In this study, we provided evidence for PBK as a pan-cancer biomarker based on gene expression and survival data and analyses of immune cell infiltration, TFs, miRNAs, pharmacogenomics, and related intracellular signaling pathways.

The TCGA and GTEx data showed that PBK was highly expressed in ACC, BLCA, BRCA, CESC, CHOL, COAD, ESCA, GBM, HNSC, KICH, KIRC, KIRP, LGG, LIHC, LUAD, LUSC, OV, RAAD, PRAD, SKCM, STAD, THCA, UCEC, and UCS and downregulated in LAML compared to normal tissue. The Kaplan–Meier survival analysis revealed that high expression of PBK was a risk factor in ACC, DLBC, KIRC, KIRP, LGG, LIHC, LUAD, MESO, and PCPG and a protective factor in OV and THYM. Additionally, we found that PBK expression was positively correlated with cancer stage in ACC, KICH, KIRC, KIRP, and LUAD. PBK expression level was shown to be significantly associated with the progression of ACC and NSCLC (Lei et al., 2013; Kar et al., 2019). Thus, PBK is a prognostic biomarker in many cancers, especially in ACC and LUAD as well as in KIRC and KIRP, which has not been previously reported.

The expression of TFs related to PBK showed the highest positive correlations with UVM and THYM, while PBK-related miRNAs had the strongest inhibitory effects in UCS, TGCT, and MESO. These factors are involved in the regulation of the cell cycle, apoptosis, and cell proliferation and are linked to genome instability. For example, MYC is an oncogene in many cancers and plays an important role in regulating metabolic pathways, especially glutamine and glutamine breakdown (Tambay et al., 2021). TFs regulating PBK expression may thus influence its function in tumorigenesis.

PBK methylation was decreased in THCA, LIHC, and LUAD, which was negatively correlated with the expression level of PBK. Additionally, PBK methylation level was correlated with OS in LGG, THCA, GBM, PCPG, and UCEC cancer. The correlation was especially strong for THCA; therefore, while PBK expression cannot be used as a prognostic marker for THCA, we show here for the first time that a change in its methylation status may predict survival outcome.

There have been few studies on the function of PBK in the tumor immune microenvironment. We observed a negative correlation between PBK expression and the infiltration of immune cells including B cells, CD4⁺ T cells, CD8⁺ T cells, and macrophages in most tumors, but this association was especially strong in KIRC, LIHC, THCA, and THYM. Based on this observation combined with the survival data, we speculate that PBK affects the prognosis of cancers—particularly KIRC and LIHC—by enhancing immune cell infiltration.

ICIs are a type of immunotherapy with demonstrated efficacy and safety in multiple cancers (He and Xu, 2020). Patients with high TMB or MSI are more likely to experience long-term survival benefits from immunotherapy (Samstein et al., 2019; Zhu et al., 2021). To date there have been no studies on the relationship between TMB and MSI and PBK expression in cancers. Our results showed that PBK expression in various types of tumor was related to TMB and MSI, although additional studies are needed in order to determine the significance of this observation in terms of response to immunotherapy.

Functional enrichment analysis showed that PBK and related genes were functionally enriched in cell cycle regulation. Previous studies have shown that PBK binds to the CDK1/cyclin B1 complex on the mitotic spindle and induces its phosphorylation, leading to cell proliferation and cell cycle progression (Côté et al., 2002; Park et al., 2010). Dysregulation of the cell cycle results in abnormal cell proliferation, inhibition of apoptosis, and increased invasion and metastasis (Zaretsky et al., 2016). Thus, PBK may play an oncogenic role by interfering with cell cycle regulation. This finding can guide the development of targeted drugs for cancer treatment.

BIRC5, CCNB1, CDC20, CDK1, DLGAP5, MAD2L1, MELK, PLK1, TOP2A, TTK, and PBK formed an interaction network that was associated with activation of cell apoptosis and the DNA damage response and inhibition of RAS/MAPK signaling. PBK was also related to the induction of EMT, activation of TSC/mTOR signaling, and inhibition of the PI3K/AKT and RTK pathways. Thus, PBK and PBK-related genes promote tumorigenesis through a variety of mechanisms.

The low solubility and toxicity of the PBK inhibitors HI-TOPK-032 and OTS964TOPK limit their clinical application (Matsuo et al., 2014; Ikeda et al., 2016). As such, new PBK inhibitors for cancer treatment are needed. In this study, we screened 31 drugs that potentially target PBK and identified several candidates including linifanib, which is used in the treatment of LIHC, COAD, STAD, NSCLC, and HNSC (Hsu et al., 2014; Chen et al., 2016; Horinouchi, 2016; Chan et al., 2017; Han et al., 2021); neratinib, which is used in BRCA (Saura et al., 2021); erlotinib and gefitinib, which are used in NSCLC (Yang et al., 2017); and BMS-536924, which is used in GBM and ESCA (Adachi et al., 2014; Zhou, 2015). The anticancer effect of these drugs may be related to PBK inhibition.

CONCLUSION

We carried out a comprehensive evaluation of PBK to investigate its potential roles in promoting cancer and as a prognostic indicator through bioinformatics analyses. We identified 4 TFs and 3 miRNAs that were related to PBK upregulation and downregulation, respectively, in cancer. We also found that PBK expression is related to cancer immunity: in some cancers, PBK may affect tumor progression by increasing immune cell infiltration into the tumor microenvironment, making it a potential target for cancer immunotherapy. Functional enrichment analysis showed that PBK and its related genes regulate tumor-related signaling pathways. Finally, we found that PBK is associated with sensitivity and resistance to various anticancer drugs and is related to drug-targeted genes in tumor cells. These findings indicate that evaluating PBK expression levels may have diagnostic utility in cancer and can guide treatment decisions, although experimental studies are needed to confirm this possibility.

DATA AVAILABILITY STATEMENT

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.

ETHICS STATEMENT

Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

AUTHOR CONTRIBUTIONS

YL designed the experiment and collected and analyzed the data. YL and XJ wrote the manuscript. GP provided technical guidance. CS and GP revised the manuscript. All authors have read and approved the final manuscript.

FUNDING

This study was supported by the National Natural Science Foundation of China Youth Fund Project (no. 81801908) and Hunan Provincial Natural Science Committee Project (no. 2021JJ70074).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2021.785370/full#supplementary-material

REFERENCES

Abe, Y., Matsumoto, S., Kito, K., and Ueda, N. (2000). Cloning and Expression of a Novel MAPKK-like Protein Kinase, Lymphokine-Activated Killer T-Cell-Originated Protein Kinase, Specifically Expressed in the Testis and Activated Lymphoid Cells. J. Biol. Chem. 275 (28), 21525–21531. doi:10.1074/jbc.M909629199

Abe, Y., Takeuchi, T., Kagawa-Miki, L., Ueda, N., Shigemoto, K., Yasukawa, M., et al. (2007). A Mitotic Kinase TOPK Enhances Cdk1/Cyclin B1-Dependent Phosphorylation of PRC1 and Promotes Cytokinesis. J. Mol. Biol. 370 (2), 231–245. doi:10.1016/j.jmb.2007.04.067

Adachi, Y., Ohashi, H., Imsumran, A., Yamamoto, H., Matsunaga, Y., Taniguchi, H., et al. (2014). The Effect of IGF-I Receptor Blockade for Human Esophageal Squamous Cell Carcinoma and Adenocarcinoma. Tumor Biol. 35 (2), 973–985. doi:10.1007/s13277-013-1131-2

Aksamitiene, E., Kholodenko, B. N., Kolch, W., Hoek, J. B., and Kiyatkin, A. (2010). PI3K/Akt-sensitive MEK-independent Compensatory Circuit of ERK Activation in ER-Positive PI3K-Mutant T47D Breast Cancer Cells. Cell Signal. 22 (9), 1369–1378. doi:10.1016/j.cellsig.2010.05.006

Bogunovic, D., O'Neill, D. W., Belitskaya-Levy, I., Vacic, V., Yu, Y.-L., Adams, S., et al. (2009). Immune Profile and Mitotic index of Metastatic Melanoma Lesions Enhance Clinical Staging in Predicting Patient Survival. Proc. Natl. Acad. Sci. 106 (48), 20429–20434. doi:10.1073/pnas.0905139106

Casey, S. C., Baylot, V., and Felsher, D. W. (2017). MYC: Master Regulator of Immune Privilege. Trends Immunol. 38 (4), 298–305. doi:10.1016/j.it.2017.01.002

Chan, E., Goff, L. W., Cardin, D. B., Ancell, K., Smith, S. J., Whisenant, J. G., et al. (2017). Phase II Study of the Multikinase Inhibitor of Angiogenesis, Linifanib, in Patients with Metastatic and Refractory Colorectal Cancer Expressing Mutated KRAS. Invest. New Drugs 35 (4), 491–498. doi:10.1007/s10637-017-0458-8

Chen, Y., and Zhou, X. (2020). Research Progress of mTOR Inhibitors. Eur. J. Med. Chem. 208, 112820. doi:10.1016/j.ejmech.2020.112820

Chen, J.-H., Liang, Y.-X., He, H.-C., Chen, J.-Y., Lu, J.-M., Chen, G., et al. (2015). Overexpression of PDZ-Binding Kinase Confers Malignant Phenotype in Prostate Cancer via the Regulation of E2F1. Int. J. Biol. Macromolecules 81, 615–623. doi:10.1016/j.ijbiomac.2015.08.048

Chen, J., Guo, J., Chen, Z., Wang, J., Liu, M., and Pang, X. (2016). Linifanib (ABT-869) Potentiates the Efficacy of Chemotherapeutic Agents through the Suppression of Receptor Tyrosine Kinase-Mediated AKT/mTOR Signaling Pathways in Gastric Cancer. Sci. Rep. 6, 29382. doi:10.1038/srep29382

Chen, F., Chandrashekar, D. S., Varambally, S., and Creighton, C. J. (2019a). Pan-cancer Molecular Subtypes Revealed by Mass-Spectrometry-Based Proteomic Characterization of More Than 500 Human Cancers. Nat. Commun. 10 (1), 5679. doi:10.1038/s41467-019-13528-0

Chen, Q., Liu, D., Hu, Z., Luo, C., and Zheng, S. L. (2019b). miRNA-101-5p Inhibits the Growth and Aggressiveness of NSCLC Cells through Targeting CXCL6. Onco. Targets. Ther. 12, 835–848. doi:10.2147/ott.S184235

Côté, S., Simard, C., and Lemieux, R. (2002). Regulation of Growth-Related Genes by Interleukin-6 in Murine Myeloma Cells. Cytokine 20 (3), 113–120. doi:10.1006/cyto.2002.1988

Costa, B. M., Smith, J. S., Chen, Y., Chen, J., Phillips, H. S., Aldape, K. D., et al. (2010). ReversingHOXA9Oncogene Activation by PI3K Inhibition: Epigenetic Mechanism and Prognostic Significance in Human Glioblastoma. Cancer Res. 70 (2), 453–462. doi:10.1158/0008-5472.Can-09-2189

Czerwińska, P., Mazurek, S., and Wiznerowicz, M. (2017). The Complexity of TRIM28 Contribution to Cancer. J. Biomed. Sci. 24 (1), 63. doi:10.1186/s12929-017-0374-4

Fouad, S., Hauton, D., and D'Angiolella, V. (2020). E2F1: Cause and Consequence of DNA Replication Stress. Front. Mol. Biosci. 7, 599332. doi:10.3389/fmolb.2020.599332

Freije, W. A., Castro-Vargas, F. E., Fang, Z., Horvath, S., Cloughesy, T., Liau, L. M., et al. (2004). Gene Expression Profiling of Gliomas Strongly Predicts Survival. Cancer Res. 64 (18), 6503–6510. doi:10.1158/0008-5472.Can-04-0452

Gao, J., Aksoy, B. A., Dogrusoz, U., Dresdner, G., Gross, B., Sumer, S. O., et al. (2013). Integrative Analysis of Complex Cancer Genomics and Clinical Profiles Using the cBioPortal. Sci. Signal. 6 (269), pl1. doi:10.1126/scisignal.2004088

Gaudet, S., Branton, D., and Lue, R. A. (2000). Characterization of PDZ-Binding Kinase, a Mitotic Kinase. Proc. Natl. Acad. Sci. 97 (10), 5167–5172. doi:10.1073/pnas.090102397

Hall, P., Ploner, A., Bjöhle, J., Huang, F., Lin, C.-Y., Liu, E. T., et al. (2006). Hormone-replacement Therapy Influences Gene Expression Profiles and Is Associated with Breast-Cancer Prognosis: a Cohort Study. BMC Med. 4, 16. doi:10.1186/1741-7015-4-16

Han, Y., Zhi, W.-H., Xu, F., Zhang, C.-B., Huang, X.-Q., and Luo, J.-F. (2021). Selection of First-Line Systemic Therapies for Advanced Hepatocellular Carcinoma: A Network Meta-Analysis of Randomized Controlled Trials. World J. Gastreonterol. 27 (19), 2415–2433. doi:10.3748/wjg.v27.i19.2415

He, X., and Xu, C. (2020). Immune Checkpoint Signaling and Cancer Immunotherapy. Cell Res. 30 (8), 660–669. doi:10.1038/s41422-020-0343-4

Horinouchi, H. (2016). Anti-vascular Endothelial Growth Factor Therapies at the Crossroads: Linifanib for Non-small Cell Lung Cancer. Transl. Lung Cancer Res. 5 (1), 78–81. doi:10.3978/j.issn.2218-6751.2015.06.08

Hsu, H.-W., de Necochea-Campion, R., Williams, V., Duerksen-Hughes, P. J., Simental, A. A., Ferris, R. L., et al. (2014). Linifanib (ABT-869), Enhances Cytotoxicity with Poly (ADP-Ribose) Polymerase Inhibitor, Veliparib (ABT-888), in Head and Neck Carcinoma Cells. Oral Oncol. 50 (7), 662–669. doi:10.1016/j.oraloncology.2014.03.006

Hu, F., Gartenhaus, R. B., Eichberg, D., Liu, Z., Fang, H.-B., and Rapoport, A. P. (2010). PBK/TOPK Interacts with the DBD Domain of Tumor Suppressor P53 and Modulates Expression of Transcriptional Targets Including P21. Oncogene 29 (40), 5464–5474. doi:10.1038/onc.2010.275

Ikeda, Y., Park, J.-H., Miyamoto, T., Takamatsu, N., Kato, T., Iwasa, A., et al. (2016). T-LAK Cell-Originated Protein Kinase (TOPK) as a Prognostic Factor and a Potential Therapeutic Target in Ovarian Cancer. Clin. Cancer Res. 22 (24), 6110–6117. doi:10.1158/1078-0432.Ccr-16-0207

Ishikawa, C., Senba, M., and Mori, N. (2018). Mitotic Kinase PBK/TOPK as a Therapeutic Target for Adult T-cell L-eukemia/lymphoma. Int. J. Oncol. 53 (2), 801–814. doi:10.3892/ijo.2018.4427

Kar, A., Zhang, Y., Yacob, B. W., Saeed, J., Tompkins, K. D., Bagby, S. M., et al. (2019). Targeting PDZ-Binding Kinase Is Anti-Tumorigenic in Novel Preclinical Models of ACC. Endocr. Relat. Cancer 26 (10), 765–778. doi:10.1530/erc-19-0262

Kim, W.-J., Kim, E.-J., Kim, S.-K., Kim, Y.-J., Ha, Y.-S., Jeong, P., et al. (2010). Predictive Value of Progression-Related Gene Classifier in Primary Non-muscle Invasive Bladder Cancer. Mol. Cancer 9, 3. doi:10.1186/1476-4598-9-3

Kraft, C., Herzog, F., Gieffers, C., Mechtler, K., Hagting, A., Pines, J., et al. (2003). Mitotic Regulation of the Human Anaphase-Promoting Complex by Phosphorylation. EMBO J. 22 (24), 6598–6609. doi:10.1093/emboj/cdg627

Kurogi, R., Nakamizo, A., Suzuki, S. O., Mizoguchi, M., Yoshimoto, K., Amano, T., et al. (2018). Inhibition of Glioblastoma Cell Invasion by Hsa-miR-145-5p and Hsa-miR-31-5p Co-overexpression in Human Mesenchymal Stem Cells. J. Neurosurg. 130 (1), 44–55. doi:10.3171/2017.8.Jns1788

Lee, J.-S., Leem, S.-H., Lee, S.-Y., Kim, S.-C., Park, E.-S., Kim, S.-B., et al. (2010). Expression Signature ofE2F1and its Associated Genes Predict Superficial to Invasive Progression of Bladder Tumors. J. Clin.Oncol. 28 (16), 2660–2667. doi:10.1200/jco.2009.25.0977

Lee, Y.-J., Park, J.-H., and Oh, S.-M. (2020). TOPK Promotes Epithelial-Mesenchymal Transition and Invasion of Breast Cancer Cells through Upregulation of TBX3 in TGF-β1/Smad Signaling. Biochem. Biophysical Res. Commun. 522 (1), 270–277. doi:10.1016/j.bbrc.2019.11.104

Lei, B., Liu, S., Qi, W., Zhao, Y., Li, Y., Lin, N., et al. (2013). PBK/TOPK Expression in Non-Small-cell Lung Cancer: its Correlation and Prognostic Significance with Ki67 and P53 Expression. Histopathology 63 (5), a–n. doi:10.1111/his.12215

Li, T., Fan, J., Wang, B., Traugh, N., Chen, Q., Liu, J. S., et al. (2017). TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res. 77 (21), e108–e110. doi:10.1158/0008-5472.Can-17-0307

Li, Z., Lin, C., Zhao, L., Zhou, L., Pan, X., Quan, J., et al. (2018). Oncogene miR-187-5p Is Associated with Cellular Proliferation, Migration, Invasion, Apoptosis and an Increased Risk of Recurrence in Bladder Cancer. Biomed. Pharmacother. 105, 461–469. doi:10.1016/j.biopha.2018.05.122

Lin, K., Taylor, J. R., Wu, T. D., Gutierrez, J., Elliott, J. M., Vernes, J.-M., et al. (2011). TMEFF2 Is a PDGF-AA Binding Protein with Methylation-Associated Gene Silencing in Multiple Cancer Types Including Glioma. PLoS One 6 (4), e18608. doi:10.1371/journal.pone.0018608

Liu, C.-J., Hu, F.-F., Xia, M.-X., Han, L., Zhang, Q., and Guo, A.-Y. (2018). GSCALite: a Web Server for Gene Set Cancer Analysis. Bioinformatics 34 (21), 3771–3772. doi:10.1093/bioinformatics/bty411

Masser, D. R., Hadad, N., Porter, H., Stout, M. B., Unnikrishnan, A., Stanford, D. R., et al. (2018). Analysis of DNA Modifications in Aging Research. Geroscience 40 (1), 11–29. doi:10.1007/s11357-018-0005-3

Matsuo, Y., Park, J.-H., Miyamoto, T., Yamamoto, S., Hisada, S., Alachkar, H., et al. (2014). TOPK Inhibitor Induces Complete Tumor Regression in Xenograft Models of Human Cancer through Inhibition of Cytokinesis. Sci. Transl. Med. 6 (259), 259ra145. doi:10.1126/scitranslmed.3010277

Mizuno, H., Kitada, K., Nakai, K., and Sarai, A. (2009). PrognoScan: a New Database for Meta-Analysis of the Prognostic Value of Genes. BMC Med. Genomics 2, 18. doi:10.1186/1755-8794-2-18

Oh, S.-M., Zhu, F., Cho, Y.-Y., Lee, K. W., Kang, B. S., Kim, H.-G., et al. (2007). T-lymphokine-activated Killer Cell-Originated Protein Kinase Functions as a Positive Regulator of c-Jun-NH2-kinase 1 Signaling and H-Ras-Induced Cell Transformation. Cancer Res. 67 (11), 5186–5194. doi:10.1158/0008-5472.Can-06-4506

Ohashi, T., Komatsu, S., Ichikawa, D., Miyamae, M., Okajima, W., Imamura, T., et al. (2016). Overexpression of PBK/TOPK Contributes to Tumor Development and Poor Outcome of Esophageal Squamous Cell Carcinoma. Ar 36 (12), 6457–6466. doi:10.21873/anticanres.11244

Ohashi, T., Komatsu, S., Ichikawa, D., Miyamae, M., Okajima, W., Imamura, T., et al. (2017). Overexpression of PBK/TOPK Relates to Tumour Malignant Potential and Poor Outcome of Gastric Carcinoma. Br. J. Cancer 116 (2), 218–226. doi:10.1038/bjc.2016.394

Okayama, H., Kohno, T., Ishii, Y., Shimada, Y., Shiraishi, K., Iwakawa, R., et al. (2012). Identification of Genes Upregulated in ALK-Positive and EGFR/KRAS/ALK-negative Lung Adenocarcinomas. Cancer Res. 72 (1), 100–111. doi:10.1158/0008-5472.Can-11-1403

Ozen, M., Karatas, O. F., Gulluoglu, S., Bayrak, O. F., Sevli, S., Guzel, E., et al. (2015). Overexpression of miR-145-5p Inhibits Proliferation of Prostate Cancer Cells and Reduces SOX2 Expression. Cancer Invest. 33 (6), 251–258. doi:10.3109/07357907.2015.1025407

Park, J.-H., Nishidate, T., Nakamura, Y., and Katagiri, T. (2010). Critical Roles of T-LAK Cell-Originated Protein Kinase in Cytokinesis. Cancer Sci. 101 (2), 403–411. doi:10.1111/j.1349-7006.2009.01400.x

Park, J.-H., Jeong, Y.-J., Won, H. K., Choi, S.-Y., Park, J.-H., and Oh, S.-M. (2014). Activation of TOPK by Lipopolysaccharide Promotes Induction of Inducible Nitric Oxide Synthase through NF-Κb Activity in Leukemia Cells. Cell Signal. 26 (5), 849–856. doi:10.1016/j.cellsig.2014.01.004

Park, J.-H., Inoue, H., Kato, T., Zewde, M., Miyamoto, T., Matsuo, Y., et al. (2017). TOPK (T-LAK Cell-Originated Protein Kinase) Inhibitor Exhibits Growth Suppressive Effect on Small Cell Lung Cancer. Cancer Sci. 108 (3), 488–496. doi:10.1111/cas.13160

Pawitan, Y., Bjöhle, J., Amler, L., Borg, A.-L., Egyhazi, S., Hall, P., et al. (2005). Gene Expression Profiling Spares Early Breast Cancer Patients from Adjuvant Therapy: Derived and Validated in Two Population-Based Cohorts. Breast Cancer Res. 7 (6), R953–R964. doi:10.1186/bcr1325

Phillips, H. S., Kharbanda, S., Chen, R., Forrest, W. F., Soriano, R. H., Wu, T. D., et al. (2006). Molecular Subclasses of High-Grade Glioma Predict Prognosis, Delineate a Pattern of Disease Progression, and Resemble Stages in Neurogenesis. Cancer Cell 9 (3), 157–173. doi:10.1016/j.ccr.2006.02.019

Pirovano, G., Ashton, T. M., Herbert, K. J., Bryant, R. J., Verrill, C. L., Cerundolo, L., et al. (2017). TOPK Modulates Tumour-specific Radiosensitivity and Correlates with Recurrence after Prostate Radiotherapy. Br. J. Cancer 117 (4), 503–512. doi:10.1038/bjc.2017.197

Roh, E., Lee, M.-H., Zykova, T. A., Zhu, F., Nadas, J., Kim, H.-G., et al. (2018). Targeting PRPK and TOPK for Skin Cancer Prevention and Therapy. Oncogene 37 (42), 5633–5647. doi:10.1038/s41388-018-0350-9

Rouillard, A. D., Gundersen, G. W., Fernandez, N. F., Wang, Z., Monteiro, C. D., McDermott, M. G., et al. (2016). The Harmonizome: a Collection of Processed Datasets Gathered to Serve and Mine Knowledge about Genes and Proteins. Database (Oxford) 2016, baw100. doi:10.1093/database/baw100

Samstein, R. M., Lee, C.-H., Shoushtari, A. N., Hellmann, M. D., Shen, R., Janjigian, Y. Y., et al. (2019). Tumor Mutational Load Predicts Survival after Immunotherapy across Multiple Cancer Types. Nat. Genet. 51 (2), 202–206. doi:10.1038/s41588-018-0312-8

Saura, C., Matito, J., Oliveira, M., Wildiers, H., Brufksy, A. M., Waters, S. H., et al. (2021). Biomarker Analysis of the Phase III NALA Study of Neratinib + Capecitabine versus Lapatinib + Capecitabine in Patients with Previously Treated Metastatic Breast Cancer. Clin. Cancer Res. 27 (21), 5818–5827. doi:10.1158/1078-0432.Ccr-21-1584

Shen, W., Xie, X. Y., Liu, M. R., and Wang, L. L. (2019). MicroRNA-101-5p Inhibits the Growth and Metastasis of Cervical Cancer Cell by Inhibiting CXCL6. Eur. Rev. Med. Pharmacol. Sci. 23 (5), 1957–1968. doi:10.26355/eurrev_201903_17234

Shih, M.-C., Chen, J.-Y., Wu, Y.-C., Jan, Y.-H., Yang, B.-M., Lu, P.-J., et al. (2012). TOPK/PBK Promotes Cell Migration via Modulation of the PI3K/PTEN/AKT Pathway and Is Associated with Poor Prognosis in Lung Cancer. Oncogene 31 (19), 2389–2400. doi:10.1038/onc.2011.419

Smirnov, P., Kofia, V., Maru, A., Freeman, M., Ho, C., El-Hachem, N., et al. (2018). PharmacoDB: an Integrative Database for Mining In Vitro Anticancer Drug Screening Studies. Nucleic Acids Res. 46 (D1), D994–d1002. doi:10.1093/nar/gkx911

Tambay, V., Raymond, V.-A., and Bilodeau, M. (2021). MYC Rules: Leading Glutamine Metabolism toward a Distinct Cancer Cell Phenotype. Cancers 13 (17), 4484. doi:10.3390/cancers13174484

Toda, H., Seki, N., Kurozumi, S., Shinden, Y., Yamada, Y., Nohata, N., et al. (2020). RNA‐sequence‐based microRNA Expression Signature in Breast Cancer: Tumor‐suppressive miR‐101‐5p Regulates Molecular Pathogenesis. Mol. Oncol. 14 (2), 426–446. doi:10.1002/1878-0261.12602

Tomida, S., Takeuchi, T., Shimada, Y., Arima, C., Matsuo, K., Mitsudomi, T., et al. (2009). Relapse-related Molecular Signature in Lung Adenocarcinomas Identifies Patients with Dismal Prognosis. Jco 27 (17), 2793–2799. doi:10.1200/jco.2008.19.7053

Yamada, Y., Nohata, N., Uchida, A., Kato, M., Arai, T., Moriya, S., et al. (2020). Replisome Genes Regulation by Antitumor miR‐101‐5p in clear Cell Renal Cell Carcinoma. Cancer Sci. 111 (4), 1392–1406. doi:10.1111/cas.14327

Yamauchi, M., Yamaguchi, R., Nakata, A., Kohno, T., Nagasaki, M., Shimamura, T., et al. (2012). Epidermal Growth Factor Receptor Tyrosine Kinase Defines Critical Prognostic Genes of Stage I Lung Adenocarcinoma. PLoS One 7 (9), e43923. doi:10.1371/journal.pone.0043923

Yang, Z., Hackshaw, A., Feng, Q., Fu, X., Zhang, Y., Mao, C., et al. (2017). Comparison of Gefitinib, Erlotinib and Afatinib in Non‐small Cell Lung Cancer: A Meta‐analysis. Int. J. Cancer 140 (12), 2805–2819. doi:10.1002/ijc.30691

Yu, W.-N., Lin, H.-F., Lee, Y., Shia, W.-C., Sung, W.-W., Yeh, C.-M., et al. (2021). PBK Expression Is Associated with Prognosis of Patients with Oral Squamous Cell Carcinoma Treated with Radiotherapy: A Retrospective Study. Anticancer Res. 41 (4), 2177–2182. doi:10.21873/anticanres.14991

Zaretsky, J. M., Garcia-Diaz, A., Shin, D. S., Escuin-Ordinas, H., Hugo, W., Hu-Lieskovan, S., et al. (2016). Mutations Associated with Acquired Resistance to PD-1 Blockade in Melanoma. N. Engl. J. Med. 375 (9), 819–829. doi:10.1056/NEJMoa1604958

Zeng, Z., Zhu, Q., Zhao, Z., Zu, X., and Liu, J. (2021). Magic and Mystery of microRNA‐32. J. Cel Mol Med 25, 8588–8601. doi:10.1111/jcmm.16861

Zhao, P., Li, L., Jiang, X., and Li, Q. (2019). Mismatch Repair Deficiency/microsatellite Instability-High as a Predictor for Anti-PD-1/pd-L1 Immunotherapy Efficacy. J. Hematol. Oncol. 12 (1), 54. doi:10.1186/s13045-019-0738-1

Zhao, R., Choi, B. Y., Wei, L., Fredimoses, M., Yin, F., Fu, X., et al. (2020). Acetylshikonin Suppressed Growth of Colorectal Tumour Tissue and Cells by Inhibiting the Intracellular Kinase, T‐lymphokine‐activated Killer Cell‐originated Protein Kinase. Br. J. Pharmacol. 177 (10), 2303–2319. doi:10.1111/bph.14981

Zhou, Q. (2015). BMS-536924, an ATP-Competitive IGF-1R/IR Inhibitor, Decreases Viability and Migration of Temozolomide-Resistant Glioma Cells In Vitro and Suppresses Tumor Growth In Vivo. Onco. Targets. Ther. 8, 689–697. doi:10.2147/ott.S80047

Zhu, M., Jin, Z., and Hubbard, J. M. (2021). Management of Non-Colorectal Digestive Cancers with Microsatellite Instability. Cancers 13 (4), 651. doi:10.3390/cancers13040651

Zykova, T. A., Zhu, F., Vakorina, T. I., Zhang, J., Higgins, L. A., Urusova, D. V., et al. (2010). T-LAK Cell-Originated Protein Kinase (TOPK) Phosphorylation of Prx1 at Ser-32 Prevents UVB-Induced Apoptosis in RPMI7951 Melanoma Cells through the Regulation of Prx1 Peroxidase Activity. J. Biol. Chem. 285 (38), 29138–29146. doi:10.1074/jbc.M110.135905

Zykova, T. A., Zhu, F., Wang, L., Li, H., Bai, R., Lim, D. Y., et al. (2017). The T-LAK Cell-Originated Protein Kinase Signal Pathway Promotes Colorectal Cancer Metastasis. EBioMedicine 18, 73–82. doi:10.1016/j.ebiom.2017.04.003

GLOSSARY

ACC adrenocortical carcinoma

BLCA bladder urothelial carcinoma

BRCA breast invasive carcinoma;

CESC cervical squamous cell carcinoma and endocervical adenocarcinoma

CHOL cholangiocarcinoma

COAD colon adenocarcinoma

DLBC lymphoid neoplasm diffuse large B-cell lymphoma

ESCA esophageal carcinoma

GBM glioblastoma multiforme

HNSC head and neck squamous cell carcinoma

KICH kidney chromophobe

KIRC kidney renal clear cell carcinoma

KIRP kidney renal papillary cell carcinoma

LAML acute myeloid leukemia

LGG brain lower grade glioma

LIHC liver hepatocellular carcinoma

LUAD lung adenocarcinoma

LUSC lung squamous cell carcinoma

MESO mesothelioma

NSCLC non-small cell lung cancer

OV ovarian serous cystadenocarcinoma

PAAD pancreatic adenocarcinoma

PCPG pheochromocytoma and paraganglioma

PRAD prostate adenocarcinoma

READ rectum adenocarcinoma

SARC sarcoma

SKCM skin cutaneous melanoma

STAD stomach adenocarcinoma

TGCT testicular germ cell tumor

THCA thyroid carcinoma

THYM thymoma

UCEC uterine corpus endometrial carcinoma

UCS uterine carcinosarcoma

UVM uveal melanoma.

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Liu, Xiang, Peng and Shen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 11 November 2021
doi: 10.3389/fmolb.2021.702206

MXD3 as an Immunological and Prognostic Factor From Pancancer Analysis

Xiaoyu Zhang¹, Xiaoqin He¹, Yue Li¹, Yangtao Xu¹, Wenliang Chen¹, Xin Liu¹, Xinyao Hu¹, Lin Xiong² and Ximing Xu¹*

¹Cancer Center, Renmin Hospital of Wuhan University, Wuhan, China

²Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, China

Edited by:
Ismail Hosen, University of Dhaka, Bangladesh

Reviewed by:
Siva Koganti, Stony Brook Medicine, United States
Jian Zhang, Southern Medical University, China

* Correspondence: Ximing Xu, doctorxu120@aliyun.com

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 29 April 2021
Accepted: 27 September 2021
Published: 11 November 2021

Citation: Zhang X, He X, Li Y, Xu Y, Chen W, Liu X, Hu X, Xiong L and Xu X (2021) MXD3 as an Immunological and Prognostic Factor From Pancancer Analysis. Front. Mol. Biosci. 8:702206. doi: 10.3389/fmolb.2021.702206

MAX dimerization protein 3 (MXD3), a transcriptional regulator of the MXD3 superfamily, is a part of the MYC–MAX–MXD network. However, its role in tumors has been reported in several cancers, such as B-cell acute lymphoblastic leukemia, medulloblastoma, neuroblastoma, and glioblastoma. Based on TCGA and GEO data, our first pancancer study of MXD3 confirmed the high expression of MXD3 in cancer tissues. Our results revealed that patients suffering from cancers with higher MXD3 expression had poor OS, DSS, DFI, and PFI. We further explored the methylation status of the MXD3 gene body and gene promoter in cancer. Patients with a higher MXD3 gene body have better OS, while the prognosis of patients with a high MXD3 promoter is more complex. We also verified the differential expression of three clinical phenotypes of MXD3: age, sex, and tumor stage, in a variety of tumors, suggesting a correlation between MXD3 and clinical characteristics. We explored the negative relationship between MXD3 and TMB and MSI in most types of cancer, indicating the poor prognosis of patients with high MXD3 expression. We further investigated the relationship between MXD3 and immune infiltrating cells and identified the relationship between MXD3 and immune genes, immunosuppressive genes, and antigen-presenting genes. All of the above findings established a solid relationship between MXD3 and the immune environment and immune cells. These results demonstrated that MXD3 might also be a potential immune factor. We also found a higher expression of MXD3 and promoter according to the increasing glioma WHO grade or histologic types. Glioma patients with high MXD3 or MXD3 promoter expression had poor survival. Finally, we used IHC to verify the higher expression of MXD3 in glioma samples compared to normal samples. Our study shows that MXD3, as a poor prognostic factor, plays a significant role in many cancers, especially glioma. Although more clinical evidence for MXD3 as a clinical therapeutic target and an immunotherapy site is needed, MXD3 can play an important guiding role in multiple clinical treatments, including immunotherapy and demethylation therapy.

Keywords: MXD3, cancer, prognosis, methylation, immune, immune infiltrating cell

BACKGROUND

As a major mortality disease of humans, cancer threatens human health and affects the quality of life of patients. Additionally, cancer places a heavy economic burden on patients (Bray et al., 2018). Due to the recent increase in immunotherapy, researchers have reconsidered the role of immunity in cancer. Immune infiltrates are related to genetic alterations, clinical features, and viral infection status of patients (Pleasance et al., 2010a; Li et al., 2016). Genetic changes induce carcinogenesis, and large-scale parallel sequencing made it possible to systematically record this variation in the whole genome (Pleasance et al., 2010a; Pleasance et al., 2010b). Hence, for the development of cancer patient datasets, such as TCGA, it is easy to analyze the correlation between gene differences and clinical features and immune status.

MXD3 (MAX dimerization protein 3), a part of the MYC–MAX–MXD network, encodes a member of the Myc superfamily of basic helix–loop–helix leucine zipper transcriptional regulators. The network significantly affects the proliferation, differentiation, and apoptosis of organisms (Grandori et al., 2000). By forming the two heterocomplexes MYC–MAX and MXD–MAX, the network creates opposing effects on the combination of DNA and the E-box promoter sequences (Ayer et al., 1993). In general, recruitment of transcriptional cofactors by the MYC–MAX complex favors transcription of genes that promote proliferation (Kretzner et al., 1992). However, the MXD–MAX complex recruits transcriptional repressive factors to depress genes to promote differentiation (Ayer et al., 1996).

MXD3 has been reported as a vital factor in neuroblastoma (Barisone et al., 2012). In both mouse models and patients with medulloblastoma, MXD3 is upregulated. Knockdown of MXD3 caused a decrease in cell number in vitro (Ngo et al., 2014). MXD3 protein knockdown induced by MXD3 siRNA nanocomplexes results in apoptosis (Duong et al., 2017). These results suggest that MXD3 is an anti-apoptotic factor (Yoshida et al., 2020). Furthermore, the anti-apoptotic function of MXD3 has been identified in glioblastoma (Ngo et al., 2019) and B-cell acute lymphoblastic leukemia (Barisone et al., 2015). As a targeted therapeutic site, MXD3 enhances the killing of cancer cells and reduces the toxicity suffered by normal cells (Satake et al., 2014).

These results indicated the potential of MXD3 as a therapeutic target (Satake et al., 2014; Duong et al., 2017). Combining patient data from databases such as TCGA and GEO may elucidate the cancer landscape of MXD3 expression and eventually contribute to the precision treatment of cancer patients.

METHODS

Data Processing

From UCSC Xena (https://xena.ucsc.edu/), we downloaded clinical phenotype data for 33 tumors, along with gene expression data, tumor mutation data, and microsatellite instability data. We used R software (Version 4.0.3; https://www.R-project.org) and Strawberry Perl (5.30.0.1) to process the data.

MXD3 Expression

The exon and normal tissues of MXD3 expression data were obtained from the GTEx portal (https://www.gtexportal.org). The types of cancer with MXD3 expression data were obtained from Oncomine (https://www.oncomine.org). We obtained the mutation status from cBioPortal (http://www.cbioportal.org/). We processed TCGA MXD3 expression data by Perl and the R “limma” package and used the “ggplot2” package to draw boxplots.

Analysis of the Relationships Between MXD3, Prognosis, and Clinical Phenotype

Overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI) are classic clinical indicators that were analyzed with R. We used the Kaplan–Meier method and log-rank test for survival analyses of each cancer type. Moreover, we used the R packages “survival” and “forestplot” to uncover the indicators mentioned above. We drew survival curves with the following two R packages: “survival” and “survminer.”

We explored the relationship between the three clinical phenotypes and MXD3 mRNA expression data concerning tumor stage, age, and sex. We divided the age group of patients into two subgroups: 65 years old and above and below 65 years. The “limma” and “ggplot2” packages were used to determine the correlation.

Analysis of MXD3 Expression With OS From GEO

We obtained GEO datasets from the Biomedical Informatics Institute (BII) (http://bioinfo.henu.edu.cn/Index.html) and calculated the OS of 26 types of cancer in the two groups, divided by the median MXD3 expression. We obtained the OS of GEO datasets by PrognoScan (http://gibk21.bse.kyutech.ac.jp/PrognoScan/index.html). And we showed the results of survival analysis for those cancers with positive results where the sample size was greater than 50.

Analysis of MXD3 Expression With OS in ICI Cohort

We used the CAMOIP web tool (https://www.camoip.net/) to reveal the relationship between MXD3 expression in immune checkpoint inhibitor treatment and patient prognosis. The data of CAMOIP were coming from the GEO datasets.

Correlation of MXD3 Expression With DNA Methylation

DNA methylation is a form of DNA chemical modification, and as an essential regulator of gene transcription, it can be carcinogenic. We used the web tool MethSurv, of which the data were derived from TCGA, to analyze MXD3 methylation in OS.

Correlation of MXD3 Expression With Tumor Mutation Burden, Tumor Microsatellite Instability, and Mismatch Repair Gene Expression

We downloaded TMB and MSI data as mentioned above. We used the Pearson method to quantify the relationship between MXD3 expression and TMB and MSI for each cancer. We displayed the results in the radar plot using the “fbsm” package. We downloaded the mismatch repair gene list from the Gene Set Enrichment Analysis (GSEA) website. We analyzed the coexpression between this gene set and MXD3 by R and presented the results as a heatmap via the “ggplot2” package.

Relationship Between MXD3 Expression and Immunity

Estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE) is a method for inferring the degree of infiltration of stromal or immune cells into tumors using existing gene expression profiles. We assessed the overall immune landscape of different cancers using the “estimate” package. We presented the results as boxplots. CIBERSORT is a tool that estimates immune scores of immune cells based on gene expression status. We used this tool to capture the distribution of immune cells across cancers. We then used plots to show the correlation of MXD3 with infiltrating cells. All of the above was performed with the “cibersort” and “ggplot” packages.

Coexpression of MXD3 With Immune Pathway–Related Genes

We downloaded gene lists of apoptosis, ferroptosis, immune, immunosuppression, and major histocompatibility complex (MHC) from GSEA (https://www.gsea-msigdb.org/gsea/downloads.jsp). We processed the data in R and presented the results as heatmaps as above.

The Biological Significance of MXD3 Expression in Tumors

We used GSEA to explore the possible functional pathways of MXD3 in various tumors. We downloaded Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) gene sets from GSEA (https://www.gsea-msigdb.org/gsea/downloads.jsp). We used “clusterProfiler,” “enrichplot,” “ggplot2,” and other R packages to ascertain the GSEA function analysis process and draw the resulting graph. We selected cancers that had five or more significant functional pathways of MXD3 to generate the GSEA map.

Analysis of Glioma of Different Grades

We analyzed different grades of glioma by the Chinese Glioma Genome Atlas (CGGA; http://www.cgga.org.cn/). We obtained the differential MXD3 and promoter in different grades and histological types of gliomas. We also generated primary glioma and recurrent glioma survival curves of the MXD3 and MXD3 promoters.

Immunohistochemical Analysis of Glioma

We collected 25 pairs of vascular malformation (normal control) and glioma samples for immunohistochemistry. For IHC of glioma, following deparaffinization, hydration, and epitope retrieval, the activity of endogenous peroxidase in the slices was inhibited for 15 min by 3% hydrogen peroxide. Then, slides were incubated overnight at 4°C with an MXD3 primary antibody (1:200, Abcam, ab108525) in a humidified box and subsequently placed in secondary antibody. Finally, the slides were visualized by diaminobenzidine and counterstained with hematoxylin. Immunohistochemical sections were observed using an Olympus BX63 microscope, and the quantitative analysis of slides also used ImageJ software. Data were expressed as mean ± standard deviation. Statistical measurements were performed using SPSS 21.0 statistical software (SPSS Inc., Chicago, USA).

Statistical Analysis

All gene mRNA expression data of cancers were standardized with log2 transformation. T-test or ANOVA was used to obtain the different data of groups. If there was no special statement, 0.05 was regarded as the cutoff point of significance. We used the Cox regression model, Kaplan–Meier method, and log-rank test for survival analysis. We used Spearman’s or Pearson’s test to determine the correlation between two genes. We accomplished all statistical processes in R software. Generally, three decimal places are reserved after the decimal point.

The abbreviations of TCGA cancer used in this paper and the corresponding full names are listed in Table 1.

TABLE 1 | Cancer types from TCGA database.

RESULTS

MXD3 Had Significant Expression Differences Among Various Organs and Multiple Cancers

Among multiple organs, MXD3 had the highest expression in whole blood, followed by the spleen. MXD3 had a significantly lower expression in other organs, such as the skin, brain, heart, and liver (Figure 1E). The exon expression of MXD3 confirmed this conclusion and revealed that, in EBV-transformed lymphocytes, MXD3 also had high expression. In addition, exon 5 and exon 6 had the highest exon read counts per base among exons (Figure 1A). Using the cBioPortal tool, we found that the most common and predominant mutations in MXD3 were gene amplification, while gene deletions and mutations were less common (Figure 1B).

FIGURE 1 | MXD3 expression and mutation of normal and cancer tissues: (A) expression of the MXD3 exon; (B) MXD3 mutation; (C) Oncomine dataset of cancer and paracancerous samples; (D) TCGA dataset of cancer and paracancerous samples; (E) different organs (***p < 0.001, **p < 0.01, *p < 0.05).

We compared both specimens based on TCGA dataset. We discovered that 18 classes of cancer had a vital discrepancy between the cancer sample and its paracancerous tissue. Cancer specimens had high expression levels versus paracancerous specimens in BLCA, BRCA, CESC, COAD, ESCA, GBM, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, PRAD, READ, SARC, STAD, and UCEC (Figure 1D). We also contrasted the cancer sample with its paracancerous tissue according to the Oncomine dataset, which showed that 13 types of cancer had differences (Figure 1C). Bladder cancer, breast cancer, cervical cancer, colorectal cancer, esophageal carcinoma, gastric cancer, kidney cancer, and sarcoma collectively had a discrepancy in both datasets. BLCA and BRCA showed the largest difference among the differentially expressed genes in both datasets.

MXD3’s Prognostic Value Among Various Cancers

To clarify the clinical value of MXD3, we analyzed the classic indexes of each cancer, such as OS, DSS, DFI, and PFI. Cox regression analysis was also performed. Patients suffering from LGG (Figure 2A, p = 0.026), KIRC (Figure 2B, p < 0.001), KIRP (Figure 2C, p = 0.026), ACC (Figure 2D, p < 0.001), THYM (Figure 2E, p = 0.014), LIHC (Figure 2F, p = 0.007), and MESO (Figure 2G, p = 0.023) with high MXD3 expression had worse OS according to Kaplan–Meier survival analysis. We used a univariate Cox regression model to determine the relationship between the expression level of MXD3 and OS. The results (Figure 2H) were as follows: KIRC (CI = [2.116, 3.461], p = 2.21E-15), ACC (CI = [2.673, 7.165], p = 4.38E-09), LGG (CI = [1.348, 2.020], p = 1.19E-06), PCPG (CI = [2.795, 39.481], p < 0.001), LIHC (CI = [1.142, 1.949], p = 0.003), THYM (CI = [0.326, 0.886], p = 0.015), KIRP (CI = [1.116, 2.998], p = 0.017), HNSC (CI = [0.604, 0.954], p = 0.018), BLCA (CI = [0.585, 0.970], p = 0.028), MESO (CI = [1.021, 2.275], p = 0.039), UVM (CI = [1.018, 9.150], p = 0.046), and summary (CI = [1.153, 1.225], p < 0.001).

FIGURE 2 | OS with different MXD3 expression in LGG (A), KIRC (B), KIRP (C), ACC (D), THYM (E), LIHC (F), and MESO (G). Cox regression of MXD3 and OS (H) in cancer types.

Furthermore, the DSS of patients suffering from KIRC (Figure 3A, p < 0.001), PCPG (Figure 3B, p = 0.025), DLBC (Figure 3C, p = 0.034), KIRP (Figure 3D, p = 0.020), ACC (Figure 3E, p < 0.001), KICH (Figure 3F, p = 0.007), LGG (Figure 3G, p = 0.029), and BLCA (Figure 3H, p = 0.033) with high MXD3 expression was lower than that of patients with low expression. The expression level of MXD3 was associated with DSS (Figure 3I) in ACC (CI = [2.586, 6.997], p = 1.16E-08), KIRC (CI = [2.138, 3.710], p = 1.82E-13), LGG (CI = [1.305, 2.032], p = 1.59E-05), PCPG (CI = [4.403, 92.637], p < 0.001), KICH (CI = [1.227, 3.522], p = 0.006), MESO (CI = [1.171, 3.055], p = 0.009), KIRP (CI = [1.125, 3.362], p = 0.0.017), HNSC (CI = [0.571, 0.992], p = 0.044), PRAD (CI = [1.258, 30.317], p = 0.025), and summary (CI = [1.188, 1.310], p < 0.001) according to the univariate Cox regression model.

FIGURE 3 | DSS with different MXD3 expression in KIRC (A), PCPG (B), DLBC (C), KIRP (D), ACC (E), KICH (F), LGG (G), and BLCA (H). Cox regression of MXD3 and DSS (I) in cancer types.

The DFI of patients suffering from LIHC (Figure 4A, p = 0.015), LUAD (Figure 4B, p = 0.007), PAAD (Figure 4D, p < 0.001), ESCA (Figure 4E, p = 0.009), KIRP (Figure 4F, p = 0.019), PRAD (Figure 4G, p < 0.001), CESC (Figure 4H, p = 0.009), ACC (Figure 4I, p = 0.027), SARC (Figure 4J, p = 0.032), and UCEC (Figure 4K, p = 0.035) with high MXD3 expression was higher than that of patients with low expression. The expression level of MXD3 was associated with DSS (Figure 4C) in ESCA (CI = [1.572, 14.255], p = 0.06), KIRC (CI = [0.027, 0.684], p = 0.015), KIRP (CI = [1.012, 3.359], p = 0.046), LIHC (CI = [1.114, 1.732], p = 0.004), PRAD (CI = [2.681, 8.549], p < 0.001), and summary (CI = [1.321, 1.526], p < 0.001) according to the univariate Cox regression model.

FIGURE 4 | DFI with different MXD3 expression in LIHC (A), LUAD (B), PAAD (D), ESCA (E), KIRP (F), PRAD (G), CESC (H), ACC (I), SARC (J), and UCEC (K). Cox regression of MXD3 and DFI (C) in cancer types.

The PFI of patients suffering from KIRP (Figure 5A, p = 0.010), LIHC (Figure 5B, p = 0.009), PCPG (Figure 5D, p = 0.039), LGG (Figure 5E, p = 0.011), KIRC (Figure 5F, p = 0.008), PRAD (Figure 5G, p < 0.001), ACC (Figure 5H, p < 0.001), KICH (Figure 5I, p = 0.035), SARC (Figure 5J, p = 0.031), UVM (Figure 5K, p = 0.012), and BLCA (Figure 5L, p = 0.015) with higher MXD3 expression was longer than that of patients with lower expression. The expression level of MXD3 was associated with DSS (Figure 5C) in ACC (CI = [1.876, 4.147], p < 0.001), ESCA (CI = [1.070, 2.690], p = 0.025), KICH (CI = [1.012, 3.359], p = 0.044), KIRC (CI = [1.349, 2.214], p < 0.001), KIRP (CI = [1.173, 2.590], p = 0.004), LGG (CI = [1.229, 1.772], p < 0.001), LIHC (CI = [1.150, 1.704], p < 0.001), PCPG (CI = [1.916, 12.882], p < 0.001), PRAD (CI = [2.657, 5.324], p < 0.001), UVM (CI = [1.485, 8.479], p = 0.004), and summary (CI = [1.198, 1.293], p < 0.001) according to the univariate Cox regression model.

FIGURE 5 | PFI with different MXD3 expression in KIRP (A), LIHC (B), PCPG (D), LGG (E), KIRC (F), PRAD (G), ACC (H), KICH (I), SARC (J), UVM (K), and BLCA(L). Cox regression of MXD3 and PFI (C) in cancer types.

We generated survival curves for different types of cancers in the GEO dataset through BII, a web tool. Patients with high MXD3 expression had poorer survival than patients with low MXD3 expression in PAAD (Figure 6A; p = 0.004), BLCA (Figure 6B; p = 0.038), LUAD (Figure 6C; p < 0.0001), ESCA (Figure 6D; p = 0.024), SKCM (Figure 6E; p = 0.034), and GBM (Figure 6F; all p = 0.042). Furthermore, we identified the prognostic indicators of MXD3 in other GEO datasets using the PrognoScan web tool. UVM, at the optimal cutoff point, LGG, DLBC, and OV patients with a higher expression of MXD3 had worse OS prognosis, while UVM patients with a higher expression of MXD3 had better prognosis (Supplementary Table S1).

FIGURE 6 | OS with different MXD3 expression (A–F) in GEO in PAAD (A), BLCA (B), LUAD (C), ESCA (D), SKCM (E), and GBM (F).

Besides, we also validated the relationship between the differential expression of MXD3 and the patient outcome within the ICI cohort. Bladder cancer patients with high MXD3 expression had a better prognosis after treatment with ICIs (Supplementary Figure S6A; p = 0.005).

Relationship Between MXD3 Methylation and OS in Multiple Cancers

Alterations in DNA methylation are common in various tumors as well as during development. Hypomethylation, which inhibits transcription of tumor suppressor gene promoter regions leading to gene silencing, has been most extensively studied among all types of epigenetic modifications (Das and Singal, 2004a). Genomic methylation might be an unexpected therapeutic target for DNA methylation inhibitors, leading to the normalization of gene overexpression induced during carcinogenesis (Yang et al., 2014a). We used MethSurv to analyze the methylation data of TCGA. The cancers with better OS curves for MXD3 body hypermethylated expression were as follows: LGG (Figure 7A, p < 0.001), LIHC (Figure 7B, p = 0.048), KIRC (Figure 7C, p < 0.001), LAML (Figure 7G, p = 0.009), UVM (Figure 7I, p < 0.001), BLCA (Figure 7H, p = 0.008), CESC (Figure 7E, p = 0.002), BRCA (Figure 7F, p = 0.003), and ESCA (Figure 7D, p = 0.002). In READ (Figure 7J, p < 0.024), MXD3 body hypermethylated expression had a poor prognosis. Patients with MXD3 promoter hypermethylation had better OS in KICH (Figure 7L, p = 0.007), UCS (Figure 7K, p = 0.008), KIRP (Figure 7N, p < 0.001), and MESO (Figure 7M, p = 0.021). Conversely, patients with the same methylation status had worse OS in SARC (Figure 7P, p = 0.003), LUAD (Figure 7O, p = 0.048), and UCEC (Figure 7Q, p = 0.001).

FIGURE 7 | OS of different MXD3 gene bodies in LGG (A), LIHC (B), KIRC (C), LAML (G), UVM (I), BLCA (H), CESC (E), ESCA (D), BRCA (F), and READ (J). OS of different promoter methylation statuses in KICH (L), UCS (K), KIRP (N), MESO (M), SARC (P), LUAD (O), and UCEC (Q).

Differential Expression of MXD3 Between Different Clinical Manifestations in Various Cancers

For patients suffering from LIHC (Figure 8A, p = 0.015), LUAD (Figure 8B, p = 0.037), and THYM (Figure 8C, p = 0.005), the MXD3 expression level was higher in younger patients (<65) than in older patients (≥65). However, in most cancers, there was no significant difference in the expression of MXD3 between the two age groups (Supplementary Figure S1). Female patients suffering from MESO (Figure 8F, p = 0.034) and SARC (Figure 8G, p = 0.009) had higher MXD3 expression than males, but female patients suffering from BLCA (Figure 8D, p = 0.037) and HNSC (Figure 8E, p = 0.014) had lower expression. In the remaining cancers, there was no significant difference in the expression of MXD3 between the sexes (Supplementary Figure S2). Patients suffering from ACC (Figure 8H, p = 0.005), KIRC (Figure 8I, p = 0.002), KICH (Figure 8J, p = 0.051), and SKCM (Figure 8K, p = 0.053) with stage IV versus stage I had higher MXD3 expression. Patients suffering from ACC (p < 0.001) and KICH (p = 0.001) with stage IV versus stage II had higher MXD3 expression. In most of the remaining cancers, the expression of MXD3 was not significantly different between different tumor stages (Supplementary Figure S3).

FIGURE 8 | MXD3 expression of different clinical phenotypes of age in LIHC (A), LUAD (B), and THYM (C). MXD3 expression of different clinical phenotypes of gender in MESO (F), SARC (G), BLCA (D), and HNSC (E). MXD3 expression of different clinical phenotypes of stage in ACC (H), KIRC (I), KICH (J), and SKCM (K).

Correlations of MXD3 Expression Levels With Tumor Mutation Burden and Tumor Microsatellite Instability

In 33 types of cancer, we tested the relationship between gene expression levels and MSI and TMB, which are both beneficial for clinical immunotherapy (Das and Singal, 2004a). The outcome of TMB analysis indicated that MXD3 was correlated with TMB in STAD, LIHC, BRCA, and 12 other cancers (Figure 9A). Additionally, we determined that MXD3 was correlated with MSI in STAD, HNSC, COAD, and six other cancers (Figure 9B). Regardless of tissue cancer, numerous mutant neoantigens in mismatch repair–deficient cancers (Figure 9C) make them sensitive to immune checkpoint blockade (Le et al., 2017). MXD3 was positively correlated with mismatch repair genes overall in various cancers. In GBM and HNSC, MXD3 expression was identified more closely with mismatch repair genes. Using the web tool TISIDB, we obtained the MXD3-related expression profile of immunotherapy (Supplementary Table S2). The log-fold change of the immunotherapy response group vs. the immunotherapy non-response group was 0.215 in urothelial cancer. However, there was no significant difference in MXD3 expression between the two groups in other cancers.

FIGURE 9 | TMB (A), MSI (B), and mismatch repair genes (C) correlated with MXD3 expression.

Correlation Between the Tumor Microenvironment and the Expression of MXD3

The tumor immune microenvironment is both a consequence and a cause of cancer development and an important factor in maintaining cancer cell growth (Goodman et al., 2019). The correlation of immune score and MXD3 expression revealed a close connection between THYM, KIRC, HNSC, and GBM (Figure 10A; all p < 0.001). The connection between the stromal score and MXD3 expression was considerably close in THYM, COAD, TGCT, and GBM (Figure 10B; all p < 0.001).

FIGURE 10 | (A) The four cancers with the highest correlation between the immune score and MXD3 expression. (B) The four cancers with the highest correlation between the stromal score and MXD3 expression.

Relationship Between Immune Infiltrating Cells and the Expression of MXD3

We then analyzed the infiltration of immune cells in the tumor and combined it with the MXD3 expression level (Figure 11). Our analysis showed that MXD3 in CD4 T cells had clear relationships with nearly half of tumors. In most tumors, the numbers of CD4 T cells were negatively correlated with MXD3 expression. Similarly, in most of the cancers that had a significant relationship with MXD3 expression in dendritic cells, a type of antigen-presenting cell, the number of dendritic cells was negatively correlated with MXD3 expression. The numbers of follicular T cells and CD8 T helper cells were significantly positively correlated with MXD3 expression in tumors. The numbers of neutrophils and mast cells were significantly negatively correlated with MXD3 expression in tumors. The number of NK cells was significantly positively correlated with MXD3 expression in almost all tumors.

FIGURE 11 | Dependence analysis of MXD3 expression on infiltrating immune cells in different types of cancers.

Coexpression of MXD3 and Immune, Apoptosis, and Ferroptosis Pathway–Related Genes

In all cancers, MXD3 is associated with most apoptosis pathway–related genes (Figure 12A), confirming previous reports that MXD3 knockdown can induce apoptosis (Barisone et al., 2015; Duong et al., 2017). Except for USC, ACC, and ESCA, MXD3 in most cancers was closely related to ferroptosis (Figure 12B). The coexpression relationship between genes involved in immunosuppression pathways (Figure 12C) and MXD3 was not considerably significant, and only a few genes in a small number of tumors showed a significantly strong correlation with MXD3. PSMB6, PSMB8, and PSMB9, all genes of the MHC pathway (Figure 12D), had vital positive coexpression with MXD3. In general, MXD3 was positively coexpressed with genes of the immune-related pathway (Figure 12E) across HNSC, KIRC, and THCA.

FIGURE 12 | Coexpression of MXD3 with apoptosis (A), ferroptosis (B), immunosuppression (C), MHC (D), and immune (E) gene sets.

KEGG Analysis and GO Analysis of MXD3 Expression Levels From GSEA

To identify the pathways in which genes act in cancers, we performed GSEA and GO analysis of the MXD3 expression level, taking the intersection of the two analyses with more than five significant pathways.

The KEGG analysis showed that MXD3 expression was positively correlated with the regulation of the autophagy pathway and cytosolic DNA sensing pathway in LUSC (Figure 13B), STAD (Figure 13F), CESC (Figure 13G), and MESO (Figure 13C). Similarly, MXD3 expression was positively correlated with the RIG-I-like receptor signaling pathway in LUSC, STAD, and CESC and related to the Toll-like receptor signaling pathway in LUSC, MESO, and CESC. The antigen processing and presentation pathway was positively related to MXD3 in MESO, STAD, and CESC. Intriguingly, MXD3 expression was positively related to olfactory transduction in LUSC and STAD but negatively related to olfactory transduction in ESCA (Figure 13A). MXD3 expression was negatively related to the complement and coagulation cascades signaling pathway in READ (Figure 13D) and ACC (Figure 13E).

FIGURE 13 | KEGG and GO GSEA of MXD3 in ESCA (A, H), LUSC (B, I), MESO (C, J), READ (D, K), ACC (E, L), STAD (F, M), and CESC (G, N).

GO analysis indicated that the selected GO pathways were quite different among the selected cancers. MXD3 expression was positively correlated with epidermal development in MESO (Figure 13J) and ESCA (Figure 13H) and related to skin development in ESCA and MESO. Similarly, MXD3 expression was positively correlated with olfactory receptor activity in ESCA and STAD (Figure 13M). In LUSC (Figure 13I), MXD3 expression was positively related to cell cycle G1_S phase transition. MXD3 expression was negatively related to glutamatergic synapse in READ (Figure 13K). In ACC (Figure 13L), MXD3 expression was positively related to the integrator complex. In CESC (Figure 13N), MXD3 expression was positively related to methylation-dependent silencing and methyl-CpG binding. The GO analysis results of the remaining 25 cancers are shown in Supplementary Figure S5.

Differences in MXD3 Expression and OS of MXD3 at Different Expression Levels in Different Grades of Glioma

Different histological types of patients corresponded to different expression levels of MXD3 (Figure 14A; p = 7.8e -13). With the increase of malignancy (astrocytoma (A), recurrent astrocytoma (RA), anaplastic astrocytoma (AA), recurrent anaplastic astrocytoma (rAA), oligodendroglioma (O), recurrent oligodendroglioma (rO), anaplastic oligodendroglioma (AO), glioblastoma (GBM), and recurrent glioblastoma (rGBM)), the expression of MXD3 increased significantly. With the increase in WHO classification, the expression of MXD3 also increased (Figure 14D; P = 4e -16). The expression of MXD3 was also significantly increased in patients with IDH mutation and/or codeletion of 1p19q (Figure 14B, p = 3.5e-7; Figure 14E, p < 0.001). OS was significantly different between patients with either primary or recurrent gliomas (Figure 14I, p < 0.0001; Figure 14J, p = 0.002).

FIGURE 14 | Differential expression of MXD3 in tissues (A) and WHO subtypes (D) of glioma. Differential expression of MXD3 in 1p/19q codeletion (B) and IDH mutation status (E). Differential expression of MXD3 promoter methylation in tissues (C) and WHO subtypes (F) of glioma. Representative images of MXD3 immunohistochemistry in glioma. The image shows a 16x magnification. Scale bars = 50 μm (normal (G), glioma (H). OS of MXD3 expression in primary glioma (I) and recurrent glioma (J). OS of MXD3 promoter methylation status in primary glioma (K) and recurrent glioma (L).

The promoter methylation of MXD3 was statistically significant (Figure 14C; p = 1.5e-5) but not significantly different in patients with each degree of pathology. Similarly, there were significant differences in the promoter methylation of MXD3 among the different WHO glioma subtypes (Figure 14F; p = 2e-6). For gliomas of primary origin, lower MXD3 promoter methylation indicated worse patient outcomes (Figure 14K, p < 0.001). However, the difference in MXD3 promoter methylation across recurrent glioma patients was not significant (Figure 14L, p = 0.99).

To determine MXD3 expression in cancers, we chose glioma as the target cancer because its high expression in glioma implies meaningful poor survival in both TCGA and GEO datasets. We used hemangioma samples as controls (n = 25). IHC results showed that the nuclear localization of the MXD3 protein was significantly increased in tumor samples (Figure 14H) compared to normal tissue (Figure 14G). The cell counting of immunohistochemistry in this typical image was 72.54%.

DISCUSSION

Our study suggests that 18 types of cancer have higher MXD3 expression in tumor samples. MXD3 is a proliferation-promoting factor in neuroblastoma and B-cell lymphoma (Barisone et al., 2012; Barisone et al., 2015), which is consistent with our analysis. Our conclusion was verified by the Oncomine dataset, which is consistent with the results of a study on granular neuron precursors (Yun et al., 2007) that confirmed the proliferative effect of MXD3. MXD3 may be regulated by E2F1 as a transcriptional repressor gene (Fox and Wright, 2003).

TCGA data from Kaplan–Meier survival analysis suggested that high MXD3 expression indicates a poor prognosis for patients suffering from ACC, LIHC, KIRC, LGG, MESO, and KIRP. Patients suffering from BLCA, LUAD, ESCA, SKCM, LGG, DLBC, GBM, and OV with high MXD3 expression had a poor prognosis based on the GEO dataset. These findings suggest that MXD3 is an indicator of poor prognosis in a variety of cancers, especially gliomas, which is why we chose glioma for verification.

The prognosis of patients with high MXD3 bladder cancer in the GEO database is poor, but the bladder patients in the MXD3 group have better prognosis after treatment with the immunotherapeutic point inhibitor. It is suggested that patients with high MXD3 expression may have more benefits from ICI treatment.

For patients suffering from LIHC, LUAD, and THYM, the MXD3 expression level was higher in younger patients than in older patients. This finding might suggest age grouping of different cancers for targeted therapies. There was no significant difference in the expression of MXD3 in different cancer types between male and female patients, suggesting that there was no significant relationship between gene expression levels and sex. Patients suffering from ACC, KIRC, and KICH with stage IV versus stage I had higher MXD3 expression. These results might indicate that high MXD3 expression is suggestive of a high-grade tumor stage and may help determine patient prognosis.

Hypermethylation of the gene body is significantly and positively correlated with gene expression (Yang et al., 2014b; Seymour and Gaut, 2020). In contrast, gene promoter hypermethylation can silence current genes (Das and Singal, 2004b). In our study, the majority of individuals with hypermethylated MXD3 bodies had a better prognosis, whereas the prognosis of patients with promoter hypermethylation was complicated. This fact may be due to the relationship between the MYC pathway and the gene methylation pathway (Das and Singal, 2004a). Nevertheless, our study confirmed the role of MXD3 methylation in predicting prognosis, which plays an important role in guiding the use of the methyltransferase inhibitor decitabine and other drugs.

There is a clear correlation between high TMB and improved survival in a variety of cancer tissues, but the cutoff point varies by tissue (Samstein et al., 2019). Furthermore, TMB and not just high TMB improves patient outcomes in patients treated with immune checkpoint inhibitors (ICIs) (Samstein et al., 2019). The relationship between high TMB and ICI treatment has also been demonstrated in lung, esophageal, and triple-negative breast cancers (Fang et al., 2019; Greally et al., 2019; Barroso-Sousa et al., 2020). Previous studies with large samples of pancarcinomas have suggested that overall MSI is associated with patient outcomes (Hause et al., 2016). Overall, MXD3 expression was negatively correlated with TMB in 15 cancers and MSI in 9 cancers. These results might suggest that MXD3 expression levels will affect TMB and MSI in cancers and influence the patient response to immune checkpoint inhibition therapy. These findings provide new references for the prognosis of immunotherapy. We also found that MXD3 expression was positively correlated with MMR gene expression in most tumors. Based on the existing studies and our findings, low MXD3 expression seems to influence the efficacy of ICI therapy for tumors.

In gallbladder and gastric cancers, the TME is an indicator of prognosis and sensitivity to ICI treatment (Zeng et al., 2019; Cao et al., 2021). We performed immune and stromal scoring with ESTIMATE. Overall, MXD3 was positively correlated with the immune score in most cancers but negatively correlated with the stromal score. Immune cells within the tumor microenvironment (TME) play an important role in tumorigenesis. These tumor-associated immune cells have either antitumor or tumor-promoting functions (Lei et al., 2020). Cytotoxic CD8 T cells are the predominant cellular anticancer immune killer cells (Farhood et al., 2019). The important node in immune checkpoint therapy in a mouse model of high TMB breast cancer is the activation of B cells by follicular T helper cells (Tfhs) that mediate the antitumor immune response (Ahmadzadeh et al., 2009). Reactivation of the cellular activity of NK cells, a classical antitumor cell that is evaded by tumor cells, is an extremely promising immunotherapeutic approach (Shimasaki et al., 2020). Our results suggested a correlation between MXD3 and the relative numbers of CD8 T, Tfh, and NK cells, suggesting that MXD3 expression may contribute to the patient’s antitumor response. CD4 T cells can present signals to CD8 toxic lymphocytes and thus mediate antitumor immunity (Zhang et al., 2009). Increasing the number or activating the function of CD4 T cells is also an important direction of immunotherapy (Borst et al., 2018). Cross-presentation of antigen by DCs is thought to be the most potent activation pathway for CD8 cells to activate their cytotoxic killing effect on tumor cells (Wculek et al., 2020). In contrast, in our findings, MXD3 was significantly negatively associated with the number of DCs in almost all cancers. These results suggest that MXD3 may affect the two previously described pathways that activate CD8 T cells and reduce the anticancer immune response. However, this contradicts our previous findings that CD8 T cells were elevated with MXD3, suggesting an intricate relationship between MXD3 and the anticancer immune response activated by immune cells.

Furthermore, our study also revealed the coexpression of MXD3 with genes encoding MHC, immune activation, and immunosuppressive pathways. These results indicate that the expression of MXD3 is closely related to the immune infiltration of tumor cells, affects patient prognosis, and proposes new targets for the development of immunosuppressants.

Hypoplasia is an important pathway in the development of cancer and significantly affects cancer progression, metastasis, and drug resistance (Wong, 2011). In almost all cancers, MXD3 was significantly associated with a variety of apoptosis genes, demonstrating MXD3’s considerable impact on apoptosis. This finding verified that MXD3 inhibits apoptosis, as previously reported in the literature. Due to its remarkable iron dependence, iron-catalyzed necrosis is called ferroptosis in cancer cells. Ferroptosis inducers have been approved by the FDA for cancer treatment (Hassannia et al., 2019). MXD3 had a significant negative correlation with ferroptosis-related genes except for GSS and GPX4 in a variety of cancers, suggesting that MXD3 might have a negative correlation with iron dependence in cancer cells and might be a potential therapeutic target for ferroptosis inducers or a valid indicator of ferroptosis.

Furthermore, the enrichment analyses in 33 types of cancer indicated that MXD3 can potentially impact the regulation of autophagy, the cytosolic DNA sensing pathway, the RIG-I-like receptor, the Toll-like receptor signaling pathway, and antigen processing and presentation. Autophagy can affect cancer in different ways (Levy et al., 2017). As a type of RNA-sensing pattern recognition receptor (PRR) that can stimulate innate immunity (Rehwinkel and Gack, 2020), the role of RIG-I-like receptors in tumor therapy has been confirmed in liver cancer and other cancers (Hou et al., 2014). The cytosolic DNA sensing pathway plays a role in activating immune construction, so it is inferred that this pathway mainly plays a role in tumor inhibition (Kwon and Bakhoum, 2020). However, recent evidence suggests that the signaling pathway may also promote tumor progression and metastasis (An et al., 2019). The antigen presentation pathway can activate NK and CD8⁺ T cells, which play an important role in anticancer immunity (Sánchez-Paulete et al., 2017). These findings also verify the previous results of immune cell infiltration. These pathways further illustrate the role of MXD3 in carcinogenesis and tumor immunity.

The data from the CGGA on MXD3 in gliomas illustrate that MXD3 expression differs significantly in different grades of gliomas and glioma histologic grade. Briefly, the more malignant the glioma is, the higher the MXD3 expression is. Notably, MXD3 expression was significantly lower in patients with 1p/19q and IDH mutations, as well as in wild-type patients with comutations of both. Both are indicators of a good prognosis. Thus, high MXD3 expression indicated a poor prognosis. This is consistent with the previous expression data and the results of the subsequent survival analysis. The methylation status of the MXD3 promoter in patients with different pathological degrees was significantly different. In general, the higher the pathological grade and WHO classification of patients, the lower the methylation level of the MXD3 promoter. The lower the promoter methylation, the worse the prognosis.

Finally, we verified the high expression of MXD3 in gliomas by immunohistochemistry. The expression of MXD3 protein in gliomas was significantly higher than that in normal samples.

In brief, our first pancancer study of MXD3 confirmed the high expression of MXD3 in cancer tissues by TCGA and Oncomine. The function of MXD3 and MXD3 methylation on cancer prognosis was validated: most patients with high MXD3 gene body hypermethylation had better survival, while MXD3 may play opposite roles in different tumors. We discovered a negative relationship between MXD3 and TMB and MSI. We further investigated the relationship between MXD3 and immune infiltrating cells and identified the correlation between MXD3 and immune genes, immunosuppressive genes, and antigen-presenting genes. Finally, we also verified the expression of MXD3 in LGG using IHC. These results suggest that MXD3 is a poor prognostic factor in many cancers, especially gliomas. Although more clinical evidence is needed for MXD3 to be considered a clinical therapeutic target and immunotherapy site, this gene can play an important guiding role in a variety of clinical treatments, including immunotherapy and demethylation therapy.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Material, and further inquiries can be directed to the corresponding author.

ETHICS STATEMENT

The clinical samples were collected from patients after informed consent was obtained. Studies were performed with the approval of clinical research ethics committee of Renmin hospital of Wuhan University

AUTHOR CONTRIBUTIONS

XZ processed data and drafted the article. The remaining authors collected data and gave necessary support.

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2021.702206/full#supplementary-material

Supplementary Figure S1 | Difference in MXD3 expression in multiple tumors between older (>=65) and young (<65) patients.

Supplementary Figure S2 | Differential expression of MXD3 in multiple tumors in different sex groups.

Supplementary Figure S3 | Expression of MXD3 in different tumor stages.

Supplementary Figure S4 | Stromal score (top) and immune score (bottom) in multiple cancer types.

Supplementary Figure S5 | GO analysis of the remaining 25 types of cancer by GSEA.

Supplementary Figure S6 | Survival curve of the bladder cancer (a, b) and melanoma (c, d) in the ICI cohort.

Supplementary Table S1 | Log2 (fold change) of MXD3 between the drug response group and the non-response group across multiple drugs.

Supplementary Table S2 | Tumor MXD3 prognosis data obtained from the PrognoScan web tool.

REFERENCES

Ahmadzadeh, M., Johnson, L. A., Heemskerk, B., Wunderlich, J. R., Dudley, M. E., White, D. E., et al. (2009). Tumor Antigen-specific CD8 T Cells Infiltrating the Tumor Express High Levels of PD-1 and Are Functionally Impaired. Blood 114 (8), 1537–1544. doi:10.1182/blood-2008-12-195792

An, X., Zhu, Y., Zheng, T., Wang, G., Zhang, M., Li, J., et al. (2019). An Analysis of the Expression and Association with Immune Cell Infiltration of the cGAS/STING Pathway in Pan-Cancer. Mol. Ther. Nucleic Acids 14, 80–89. doi:10.1016/j.omtn.2018.11.003

Ayer, D. E., Kretzner, L., and Eisenman, R. N. (1993). Mad: a Heterodimeric Partner for Max that Antagonizes Myc Transcriptional Activity. Cell 72 (2), 211–222. doi:10.1016/0092-8674(93)90661-9

Ayer, D. E., Laherty, C. D., Lawrence, Q. A., Armstrong, A. P., and Eisenman, R. N. (1996). Mad Proteins Contain a Dominant Transcription Repression Domain. Mol. Cell. Biol. 16 (10), 5772–5781. doi:10.1128/mcb.16.10.5772

Barisone, G. A., Ngo, T., Tran, M., Cortes, D., Shahi, M. H., Nguyen, T. V., et al. (2012). Role of MXD3 in Proliferation of DAOY Human Medulloblastoma Cells. PLoS One 7 (7), e38508. doi:10.1371/journal.pone.0038508

Barisone, G. A., Satake, N., Lewis, C., Duong, C., Chen, C., Lam, K. S., et al. (2015). Loss of MXD3 Induces Apoptosis of Reh Human Precursor B Acute Lymphoblastic Leukemia Cells. Blood Cell Mol. Dis. 54 (4), 329–335. doi:10.1016/j.bcmd.2014.12.002

Barroso-Sousa, R., Keenan, T. E., Pernas, S., Exman, P., Jain, E., Garrido-Castro, A. C., et al. (2020). Tumor Mutational Burden and Alterations as Molecular Correlates of Response to PD-1/L1 Blockade in Metastatic Triple-Negative Breast Cancer. Clin. Cancer research:h : official J. Am. Assoc. Cancer Res. 26 (11), 2565–2572. doi:10.1158/1078-0432.ccr-19-3507

Borst, J., Ahrends, T., Bąbała, N., Melief, C. J. M., and Kastenmüller, W. (2018). CD4 T Cell Help in Cancer Immunology and Immunotherapy. Nat. reviewsImmunology 18 (10), 635–647. doi:10.1038/s41577-018-0044-0

Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., Jemal, A., et al. (2018). Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer J. Clinicians 68 (6), 394–424. doi:10.3322/caac.21492

Cao, R., Yuan, L., Ma, B., Wang, G., and Tian, Y. (2021). TumorTumour Microenvironment (TME) Characterization Identified Prognosis and Immunotherapy Response in Muscle-Invasive Bladder Cancer (MIBC). Cancer Immunology, Immunotherapy. y : CII 70 (1). doi:10.1007/s00262-020-02649-x

Das, P. M., and Singal, R. (2004). DNA Methylation and Cancer. J. Clin. oncology:y : official J. Am. Soc. Clin. Oncol. 22 (22), 4632–4642. doi:10.1200/jco.2004.07.151

Das, P. M., and Singal, R. (2004). DNA Methylation and Cancer. J. Clin. Oncol. 22 (22), 4632–4642. doi:10.1200/jco.2004.07.151

Duong, C., Yoshida, S., Chen, C., Barisone, G., Diaz, E., Li, Y., et al. (2017). Novel Targeted Therapy for Neuroblastoma: Silencing the MXD3 Gene Using siRNA. Pediatr. Res. 82 (3), 527–535. doi:10.1038/pr.2017.74

Fang, W., Ma, Y., Yin, J. C., Hong, S., Zhou, H., Wang, A., et al. (2019). Comprehensive Genomic Profiling Identifies Novel Genetic Predictors of Response to Anti-PD-(L)1 Therapies in Non-small-cell Lung CancerNon-Small Cell Lung Cancer. Clin. Cancer research:h : official J. Am. Assoc. Cancer Res. 25 (16), 5015–5026. doi:10.1158/1078-0432.ccr-19-0585

Farhood, B., Najafi, M., and Mortezaee, K. (2019). CD8 Cytotoxic T Lymphocytes in Cancer Immunotherapy: A Review. J. Cell. Physiol. 234 (6), 8509–8521. doi:10.1002/jcp.27782

Fox, E. J., and Wright, S. C. (2003). The Transcriptional Repressor Gene Mad3 Is a Novel Target for Regulation by E2F1. Biochem. J. 370 (Pt 1), 307–313. doi:10.1042/BJ20021583

Goodman, A. M., Sokol, E. S., Frampton, G. M., Lippman, S. M., and Kurzrock, R. (2019). Microsatellite-Stable Tumors with High Mutational Burden Benefit from Immunotherapy. Cancer Immunol. Res. 7 (10), 1570–1573. doi:10.1158/2326-6066.cir-19-0149

Grandori, C., Cowley, S. M., James, L. P., and Eisenman, R. N. (2000). The Myc/Max/Mad Network and the Transcriptional Control of Cell Behavior. Annu. Rev. Cel. Dev. Biol. 16, 653–699. doi:10.1146/annurev.cellbio.16.1.653

Greally, M., Chou, J. F., Chatila, W. K., Margolis, M., Capanu, M., Hechtman, J. F., et al. (2019). Clinical and Molecular Predictors of Response to Immune Checkpoint Inhibitors in Patients with Advanced Esophagogastric Cancer. Clin. Cancer research:h : official J. Am. Assoc. Cancer Res. 25 (20), 6160–6169. doi:10.1158/1078-0432.ccr-18-3603

Hassannia, B., Vandenabeele, P., and Vanden Berghe, T. (2019). Targeting Ferroptosis to Iron Out Cancer. Cancer cell 35 (6), 830–849. doi:10.1016/j.ccell.2019.04.002

Hause, R. J., Pritchard, C. C., Shendure, J., and Salipante, S. J. (2016). Classification and Characterization of Microsatellite Instability across 18 Cancer Types. Nat. Med. 22 (11), 1342–1350. doi:10.1038/nm.4191

Hou, J., Zhou, Y., Zheng, Y., Fan, J., Zhou, W., Ng, I. O., et al. (2014). Hepatic RIG-I Predicts Survival and Interferon-α Therapeutic Response in Hepatocellular Carcinoma. Cancer cell 25 (1), 49–63. doi:10.1016/j.ccr.2013.11.011

Kretzner, L., Blackwood, E. M., and Eisenman, R. N. (1992). Myc and Max Proteins Possess Distinct Transcriptional Activities. Nature 359 (6394), 426–429. doi:10.1038/359426a0

Kwon, J., and Bakhoum, S. F. (2020). The Cytosolic DNA-Sensing cGAS-STING Pathway in Cancer. Cancer Discov. 10 (1), 26–39. doi:10.1158/2159-8290.cd-19-0761

Le, D. T., Durham, J. N., Smith, K. N., Wang, H., Bartlett, B. R., Aulakh, L. K., et al. (2017). Mismatch Repair Deficiency Predicts Response of Solid Tumors to PD-1 Blockade. Science (New York, N.Y.) 357 (6349), 409–413. doi:10.1126/science.aan6733

Lei, X., Lei, Y., Li, J. K., Du, W. X., Li, R. G., Yang, J., et al. (2020). Immune Cells within the Tumor Microenvironment: Biological Functions and Roles in Cancer Immunotherapy. Cancer Lett. 470, 126–133. doi:10.1016/j.canlet.2019.11.009

Levy, J. M. M., Towers, C. G., and Thorburn, A. (2017). Targeting Autophagy in Cancer. Nat. Rev. Cancer 17 (9), 528–542. doi:10.1038/nrc.2017.53

Li, B., Severson, E., Pignon, J.-C., Zhao, H., Li, T., and Novak, J. (2016). Comprehensive Analyses of Tumor Immunity: Implications for Cancer Immunotherapy. Genome Biol. 17 (1), 174. doi:10.1186/s13059-016-1028-7

Ngo, T., Barisone, G. A., Lam, K. S., and Dίaz, E. (2014). MXD3 Regulation of DAOY Cell Proliferation Dictated by Time Course of Activation. BMC Cel. Biol. 15, 30. doi:10.1186/1471-2121-15-30

Ngo, T., Corrales, A., Bourne, T., Elmojahid, S., Lam, K. S., and Díaz, E. (2019). Alternative Splicing of MXD3 and its Regulation of MXD3 Levels in Glioblastoma. Front. Mol. Biosci. 6, 5. doi:10.3389/fmolb.2019.00005

Pleasance, E. D., Cheetham, R. K., Stephens, P. J., McBride, D. J., Humphray, S. J., Greenman, C. D., et al. (2010). A Comprehensive Catalogcatalogue of Somatic Mutations from a Human Cancer Genome. Nature 463 (7278), 191–196. doi:10.1038/nature08658

Pleasance, E. D., Stephens, P. J., O'Meara, S., McBride, D. J., Meynert, A., Jones, D., et al. (2010). A Small-Cell Lung Cancer Genome with Complex Signatures of Tobacco Exposure. Nature 463 (7278), 184–190. doi:10.1038/nature08629

Rehwinkel, J., and Gack, M. U. (2020). RIG-I-like Receptors: Their Regulation and Roles in RNA Sensing. Nat. Rev. Immunol. 20 (9), 537–551. doi:10.1038/s41577-020-0288-3

Samstein, R. M., Lee, C. H., Shoushtari, A. N., Hellmann, M. D., Shen, R., Janjigian, Y. Y., et al. (2019). Tumor Mutational Load Predicts Survival after Immunotherapy across Multiple Cancer Types. Nat. Genet. 51 (2), 202–206. doi:10.1038/s41588-018-0312-8

Sánchez-Paulete, A. R., Teijeira, A., Cueto, F. J., Garasa, S., Pérez-Gracia, J. L., Sánchez-Arráez, A., et al. (2017). Antigen Cross-Presentation and T-Cell Cross-Priming in Cancer Immunology and Immunotherapy. Ann. Oncol. 28 (Suppl. l_12), xii44–xii55. doi:10.1093/annonc/mdx237

Satake, N., Duong, C., Chen, C., Barisone, G. A., Diaz, E., Tuscano, J., et al. (2014). Targeted Therapy with MXD3 siRNA, Anti-CD22 Antibody and Nanoparticles for Precursor B-Cell Acute Lymphoblastic Leukemialeukaemia. Br. J. hemahaematology 167 (4), 487–499. doi:10.1111/bjh.13066

Seymour, D. K., and Gaut, B. S. (2020). Phylogenetic Shifts in Gene Body Methylation Correlate with Gene Expression and Reflect Trait Conservation. Mol. Biol. Evol. 37 (1), 31–43. doi:10.1093/molbev/msz195

Shimasaki, N., Jain, A., and Campana, D. (2020). NK Cells for Cancer Immunotherapy. Nat. reviewsDrug Discov. 19 (3), 200–218. doi:10.1038/s41573-019-0052-1

Wculek, S. K., Cueto, F. J., Mujal, A. M., Melero, I., Krummel, M. F., and Sancho, D. (2020). Dendritic Cells in Cancer Immunology and Immunotherapy. Nat. reviewsImmunology 20 (1). doi:10.1038/s41577-019-0210-z

Wong, R. S. (2011). Apoptosis in Cancer: from Pathogenesis to Treatment. J. Exp. Clin. Cancer research:h : CR 30 (1), 87. doi:10.1186/1756-9966-30-87

Yang, X., Han, H., De Carvalho, D. D., Lay, F. D., Jones, P. A., and Liang, G. (2014). Gene Body Methylation Can Alter Gene Expression and Is a Therapeutic Target in Cancer. Cancer cell 26 (4), 577–590. doi:10.1016/j.ccr.2014.07.028

Yang, X., Han, H., De Carvalho, D. D., Lay, F. D., Jones, P. A., and Liang, G. (2014). Gene Body Methylation Can Alter Gene Expression and Is a Therapeutic Target in Cancer. Cancer cell 26 (4), 577–590. doi:10.1016/j.ccr.2014.07.028

Yoshida, S., Duong, C., Oestergaard, M., Fazio, M., Chen, C., Peralta, R., et al. (2020). MXD3 Antisense Oligonucleotide with Superparamagnetic Iron Oxide Nanoparticles: A New Targeted Approach for Neuroblastoma. Nanomedicine:e : nanotechnology, Biol. Med. 24, 102127. doi:10.1016/j.nano.2019.102127

Yun, J. S., Rust, J. M., Ishimaru, T., and Díaz, E. (2007). A Novel Role of the Mad Family Member Mad3 in Cerebellar Granule Neuron Precursor Proliferation. Mol. Cell. Biol. 27 (23), 8178–8189. doi:10.1128/mcb.00656-06

Zeng, D., Li, M., Zhou, R., Zhang, J., Sun, H., Shi, M., et al. (2019). Tumor Microenvironment Characterization in Gastric Cancer Identifies Prognostic and Immunotherapeutically Relevant Gene Signatures. Cancer Immunol. Res. 7 (5), 737–750. doi:10.1158/2326-6066.cir-18-0436

Zhang, S., Zhang, H., and Zhao, J. (2009). The Role of CD4 T Cell Help for CD8 CTL Activation. Biochem. biophysical Res. Commun. 384 (4), 405–408. doi:10.1016/j.bbrc.2009.04.134

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Zhang, He, Li, Xu, Chen, Liu, Hu, Xiong and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 19 November 2021
doi: 10.3389/fmolb.2021.762741

Dynamically Accumulating Homologous Recombination Deficiency Score Served as an Important Prognosis Factor in High-Grade Serous Ovarian Cancer

Rongjia Su¹†, Yuan Liu²†, Xiaomei Wu¹, Jiangdong Xiang¹* and Xiaowei Xi¹*

¹Department of Obstetrics and Gynecology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China

²Reproductive Medicine Center, Department of Obstetrics and Gynecology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China

Edited by:
Hossain Shekhar, University of Dhaka, Bangladesh

Reviewed by:
Sayan Chatterjee, Guru Gobind Singh Indraprastha University, India
Rui Yang, Jining Medical University, China

* Correspondence: Xiaowei Xi, xixiaowei@shsmu.edu.cn; Jiangdong Xiang, xjd13818757959@126.com

†These authors have contributed equally to this work

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 22 August 2021
Accepted: 25 October 2021
Published: 19 November 2021

Citation: Su R, Liu Y, Wu X, Xiang J and Xi X (2021) Dynamically Accumulating Homologous Recombination Deficiency Score Served as an Important Prognosis Factor in High-Grade Serous Ovarian Cancer. Front. Mol. Biosci. 8:762741. doi: 10.3389/fmolb.2021.762741

Background: The homologous recombination (HR) pathway defects in cancers induced abrogation of cell cycle checkpoints, resulting in the accumulation of DNA damage, mitotic catastrophe, and cell death. Cancers with BRCA1/2 loss and other accumulation of similar genomic scars resulting in HRD displayed increased sensitivity to chemotherapy. Our study aimed to explore HRD score genetic mechanisms and subsequent clinical outcomes in human cancers, especially ovarian cancer.

Methods: We analyzed TCGA data of HRD score in 33 cancer types and evaluated HRD score distribution and difference among tumor stages and between primary and recurrent tumor tissues. A weighted gene co-expression network analysis (WGCNA) was performed to identify highly correlated genes representing essential modules contributing to the HRD score and distinguish the hub genes and significant pathways. We verified HRD status predicting roles in patients’ overall survival (OS) with univariate and multivariate Cox regression analyses and built the predicting model for patient survival.

Results: We found that the HRD score increased with the rise in tumor stage, except for stage IV. The HRD score tended to grow up higher in recurrent tumor tissue than in their primary counterparts (p = 0.083). We constructed 15 co-expression modules with WGCNA, identified co-expressed genes and pathways impacting the HRD score, and concluded that the HRD score was tightly associated with tumor cells replication and proliferation. A combined HRD score ≥42 was associated with shorter OS in 33 cancer types (HR = 1.010, 95% CI: 1.008–1.011, p < 0.001). However, in ovarian cancer, which ranked the highest HRD score among other cancers, HRD ≥42 cohort was significantly associated with longer OS (HR = 0.99, 95% CI: 0.98–0.99, p < 0.0001). We also built a predicting model for 3 and 5 years survival in HGSC patients.

Conclusion: A quantitative HRD score representing the accumulated genomic scars was dynamically increasing in proliferating tumor cells since the HRD score was tightly correlated to tumor cell division and replication. We highlighted HRD score biomarker role in prognosis prediction of ovarian cancer.

Keywords: BRCA, homologous recombination deficiency, ovarian cancer, PARP inhibitors, WGCNA, overall survival

INTRODUCTION

Flawed DNA damage repair and genome instability are associated with additional susceptibility to cancer and are found significantly in most cancer types (Hoeijmakers, 2009). Studies showed that the homologous recombination (HR) pathway was altered in nearly 40% of cancers, for example, in ovarian and triple-negative breast cancers. High-grade serous ovarian cancer (HGSC) is the most common and aggressive type of epithelial ovarian cancer, with homologous recombination deficiency (HRD) and germline or somatic BRCA1/2 mutation being the high risk of oncogenesis (Daly et al., 2021). HR pathway malfunction included gene mutation of the pathway in tumor cells and displayed defects in several aspects of DNA repair (Turner et al., 2004; Silver and Livingston, 2012). Many anticancer agents act by creating DNA damage, which induces cell death or senescence if unrepaired. BRCA1/2-mutated ovarian cancer, breast cancer, and pancreatic cancer were found to be sensitive to inhibitors of PARP, a single-strand break repair protein, specifically a target therapy through “synthetic lethality.” Ccells with defects in the HR pathway cannot mend DNA breaks and replication forks and are particularly susceptible to DNA damage and cell death induced by poly(ADP-ribose) polymerase inhibitors (PARPis). There were still pending questions in clinical treatment though the application of PARPis, and the related tumor biomarker tests have been advocated widely.

Some clinical phenomena have attracted our interest. First, for the patients with germline BRCA1/2 mutation who were sensitive to PARPis, human somatic cells with theoretically subsequent BRCA1/2 mutation were not globally harmed by PARPis, which meant that somatic and tumor cells demonstrated varied sensitivity to PARPis. Second, myelosuppression was present in a largely varying degree of patients receiving PARPis. Some patients could continue PARPis maintenance for years, while others have to cease due to the severe hematological toxicities. Is there other potential mechanism explaining the different extents of PARPis side effects? Third, there are always patients with germline BRCA1/2 mutation that showed no response to PARPis and short treatment free interval. In these patients, PARPis were not able to attack BRCA-mutated cells including neoplastic and non-neoplastic cells. Fourth, in VELLA and PAOLA-1 clinical trials, frontline HGSC patients showed significantly prolonged progression-free survival (PFS) in HR-deficient population compared to HR-proficient population (Coleman et al., 2019; Ray-Coquard et al., 2019). However, NOVA and ARIEL3 clinical trials illustrated that platinum-sensitive recurrent HGSC patients showed general response to PARPis, regardless of gene test results (Coleman et al., 2017; Del Campo et al., 2019). Recurrent tumors without BRCA mutation also demonstrated elevated sensitivity to PARPis. Thus, we speculated that PARPi therapeutic effects possibly depended not on BRCA mutation condition but on other mechanisms.

These questions have shed light on the mechanism of HR pathway deficiency. First, a functional HR pathway has played an essential role in ensuring tumor cell survival after defective DNA damage. HR-deficient HGSCs are P53 ectopic-expressed and have invalidation of cell cycle checkpoints resulting in the accumulation of DNA damage, mitotic catastrophe, and cell death (Evers et al., 2010; Cancer Genome Atlas Research Network, 2011). HR-deficient tumors display large (>15 Mb) sub-chromosomal deletions and lodge allelic imbalance stretching to the telomeric end of the chromosomes with or without changes in the overall DNA copy number (Konstantinopoulos et al., 2015). Second, HR deficiency may occur in epithelial ovarian cancer via multiple mechanisms, including BRCA1/2 mutations. From TCGA data, approximately 50% of HGSCs have alterations in HR repair genes and were referred to as HRD. Notably, BRCA1/2 mutation only makes up half of the HRD population (Konstantinopoulos et al., 2015). Genetic and epigenetic changes in the HR pathway other than BRCA1/2 mutation could also lead to HR deficiency like BRCA1 promoter methylation, CDK12 mutation, RAD51C promoter methylation, Fanconi gene methylation, and core RAD gene mutation (Baldwin et al., 2000; Cancer Genome Atlas Research Network, 2011; Loveday et al., 2011). Third, one method to determine if a patient has HRD is to measure genomic instability as a downstream consequence of non-precision double-strand break (DSB) repair. It evaluated the genomic footprints caused by the loss of the HR function through single-nucleotide polymorphism (SNP) array data. In the myChoice CDx assay, HRD is evaluated by a genomic instability score (GIS), characteristic of defective DNA repair. The result is made according to the calculated percentage of loss of heterozygosity (LOH), telomeric allelic imbalance (TAI), and large-scale state transitions (LSTs) (Watkins et al., 2014). A combined HRD score was the unweighted numeric sum of the three component scores (Telli et al., 2016).

Here, we analyzed the data from The Cancer Genome Atlas (TCGA) to systematically analyze genetic mechanisms of HR deficiency and the resulting consequences in 33 cancer types. We provided later the HRD score alterations and the subsequent clinical outcomes in human cancers and tried to shed light on unresolved clinical problems. Our analysis underlined the role of HRD score in precise medical therapy of HGSC.

METHODS

Data Download

The datasets including 1. signatures–HRD score and genome-wide DNA damage footprint; 2. phenotype-curated clinical data; and 3. gene expression RNAseq-TOIL RSEM TPM were downloaded from https://xenabrowser.net/. It included 12,591 cancer cases across 33 types of cancer types with clinical information, and 10,647 cases with HRD score signature. After merging, 10,619 cases were extracted. After removing cases of less than 30 days of survival, 10,171 cases were exacted for the survival analysis of 33 cancer types. The clinical information is listed in Supplementary Material S1. Additionally, gene expression RNAseq of 10,535 cases was downloaded from TPM data (Transcripts PerKilobase Million) of the website mentioned before and from which 9,335 cases with the HRD score were extracted for the WGCNA analysis.

From cases with clinical information, 7,973 cases with both the HRD score and the AJCC pathologic tumor stage or clinical stage were extracted. Next, from the 7,973 cases, 342 cases with BRCA1/2 mutation referred to HR deficiency were picked for further analysis. From the 7,973 cases, 27 cases with paired primary and recurrent cancer information (Supplementary Material S2) were picked and displayed by ggplot2 and ggpubr R packages. HRD scores from the 27 cases were compared between primary and recurrent tumor tissues by a paired-samples t test.

Clinical information of 488 cases of primary HGSC were from the attachment of the article “Integrated Genomic Analyses of Ovarian Carcinoma,” specifically 2010-09-11380C-Table_S1.2. xlsx. 473 cases of primary HSCG with an HRD score were exacted. The detailed clinical information of 473 HGSC cases is listed in Table 1. After removing cases of survival days less than 30 days and one case with missing survival information, 459 cases were extracted for survival analysis. Next, 328 cases after removing cases with missing signatures were used for predicting the model.‬‬‬‬‬‬‬‬‬‬‬‬‬

TABLE 1 | Clinical information of 473 high-grade serous ovarian adenocarcinoma cases.

Weighted Co-Expression Network Construction (WGCNA)

WGCNA is a systematic biological method used to build a gene co-expression networks to mine network modules closely associated with clinical traits (Langfelder and Horvath, 2008). In the present study, we used HRD component scores which are TAI, LST, and LOH and a combined score in every sample as target clinical traits. The top 25% genes with most median absolute division (MAD) used as a robust measure of variability were selected for WGCNA analysis. Next, an average linkage method was used for all pair-wise genes to construct a co-expression similarity matrix. The co-expression similarity matrix was then transformed into the adjacency matrix by choosing the soft-threshold parameter to ensure an unsigned scale-free network. Then we created a topological matrix using the topological overlap measure (TOM) (Li and Horvath, 2007). To classify genes with similar expression patterns into gene modules, the dynamic hybrid cut method, according to the TOM-based dissimilarity, was performed with the following major parameters: min module size of 30 and deep split of 3. Finally, a cutline of 0.25 was selected for module dendrogram. We merged some modules according to dissimilarity of estimated module eigengenes (MEs). MEs were defined as the first principal component of a given module and represented the gene expression patterns in a module (Langfelder and Horvath, 2007).

Identification of Clinical Significant Modules and Hub Genes

The interesting modules were identified by calculating the relevance between clinical traits and MEs. The module highly correlated with target clinical trait was selected for further analysis. Hub genes were defined as highly interconnected within a module and have been shown functionally significant. Two approaches were used to identify hub genes in this study. First, potential hub genes were defined by module connectivity with Pearson’s correlation of module membership >0.8 and clinical characteristic relation with Pearson’s correlation of gene significance >0.2. Module membership (MM), which quantified how close a gene stayed with a given module, was referred to as the correlation between the ME and the gene expression profile. Gene significance (GS) was defined as the log10 transformation of the p value of each gene in the linear regression between gene expression and the clinical traits. The other way to search hub genes is to use the intramodule connectivity, which represents the relationship between genes within a specific module. Potential hub genes were defined by a connectivity degree ≥5 and the connectivity weight threshold set as 0.45 in the TOM-based co-expression network. After merging the results of the two aforementioned approaches, the modules of interest were constructed using Cytoscape 3.8.0 (Shannon et al., 2003). The genes with the higher MM, GS, and connectivity degree were more possibly considered as hub genes in the module of interest.

Pathway Enrichment Analysis

To further explore the biological significance of hub genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for hub genes were conducted. Enriched pathways with a p value ≤0.05 were considered statistically significant.

Survival Analysis

Univariate and multivariate Cox regression analyses were performed to evaluate the possible factors impacting OS using survival and survminer R packages. A log-rank test was applied to make the Kaplan–Meier curve. The HRD score ≥42 compared to the HRD score <42 was an important variable in the analysis. Other impacting factors included patient age, tumor stage, and tumor residual. A stepwise regression selection with bidirectional elimination was applied in the multivariate Cox regression analysis. A p value < 0.05 was considered to demonstrate a significantly difference. In addition, the log2 (HR), 95% CI, and statistical significance were calculated and illustrated using a forest plot through the forest plot R package. We established a prognostic nonogram on the basis of the results of the stepwise regression multivariate analysis. The performance of nomogram was displayed, followed by calibration plots. A nonogram was constructed by rms and regplot R packages. The calibration plots were performed by rms R package. A decision curve analysis was used to determine the clinical usefulness of the model and was generated by the stdca. R function.

RESULTS

HRD Score Was Associated With Clinical Outcomes

We computed the HRD scores and investigated their associations with clinical outcomes in altogether 33 cancer types. We investigated the component scores, which were TAI, LST, and LOH scores, and the combined HRD score. In 342 cancer cases with BRCA1/2 mutation, which was referred to as HR deficiency, a higher tumor stage was correlated with both higher HRD component scores and a higher HRD combined score, except for the comparison between stage IV and stage III and between stage IV and stage II (Figure 1A). Next, we retrieved and compared combined HRD scores between 27 pairs of primary and recurrent tumor specimens. According to Figure 1B, combined the HRD score (Supplementary Material S2) was slightly higher in recurrent than in primary tissue although the difference was not statistically significant (p = 0.083). These results suggested that the HRD score representing the accumulation of genomic scar was probably altered in the progression and recurrent of tumor development.

FIGURE 1 | (A) HRD component scores and combined score in different tumor stage. HRD component scores and combined score were higher in stage II than in stage I, higher in stage III than in stage I, and higher in stage III than in stage II (p < 0.05). HRD component scores and combined score were similar between stage IV and stage III, and similar between stage IV and stage II (p > 0.05). (B) HRD combined score in primary and recurrent tumor samples. Recurrent tumor tissues showed similar HRD combined score with the primary counterpart (p = 0.083).

Weighted Co-Expression Network Construction (WGCNA) for HRD Mechanism

In order to understand the potential reason for the alteration of the HRD score, we performed WGCNA to identify essential modules representing highly correlated genes, contributing to both the HRD component and the combined score. The 9,335 samples with clinical information were clustered by an average linkage method and Pearson’s correlation method. The expression values of 6,386 genes were collected to construct co-expressed gene networks in the sample dendrogram and trait heat map (Figure 2A). In this study, the power of β = 6 was selected to ensure a scale-free network (Figure 2B,C) (scale-free R² = 0.96, slop = −1.79). After merging some modules through a cutline of 0.25, a total of 15 modules were identified by the dynamic tree cut method. The clustering dendrogram of genes is shown in Figure 2D. A heat map for the module–trait relationship is shown in Figure 2E,F. The brown module containing 592 genes (R = 0.5322; p = 0) was most positively correlated with both the HRD combined score and the component score in the heat map of module–trait relationship. Thus, the brown module was selected as the clinical significant module for further analysis. Scatterplots of GS (y-axis) versus MM (x-axis) for the brown module is shown in Figure 3A. The scatterplot demonstrated that GS and MM had a very significant correlation, and genes in brown module were highly associated with the HRD score.

FIGURE 2 | Weighted gene co-expression network analysis. (A) The sample dendrogram and trait heat map of 6,386 genes. (B) Analysis of scale-free topology for various soft-threshold powers (β), including the scale-free fit index and mean connectivity. (C) Scale-free network (scale-free R² = 0.96, slop = −1.79). (D) Dendrogram for the intersection of differentially expressed genes, clustered based on a dissimilarity measure, together with assigned module colors. (E) Gene significance across modules. (F) Analysis of module–trait relationships of endometriosis. Each row represents a module eigengene. Each column represents a trait. Groups indicate telomeric allelic imbalance (TAI), large-scale state transitions (LSTs), loss of heterozygosity (LOH), and HRD combined score.

FIGURE 3 | (A) Scatterplot of gene significance related to module membership in the brown module. (B,C) 76 hub genes were identified. (D) KEGG enrichment analysis of the differentially expressed genes shown as a scatterplot. The enrichment degree of KEGG was measured by the number of genes enriched to this pathway. The horizontal coordinate is the gene ratio. The greater the value of the gene ratio, greater will be the degree of enrichment. The vertical coordinate is the pathway term with high enrichment. The size of the dot indicates the number of different genes under this term; larger the dot, higher will be the number of genes. (E) GO analysis of predicted genes according to the values in the enrichment score under the themes of biological process (BP), cellular components (CC), and molecular function (MF). Color represents translational values about log10 of q value, and size represents the number of genes enrichment. The horizontal axis represents the proportion of gene enrichment. The GO enrichment analysis classifies genes according to their different functions.

Identification of Hub Genes and Construction of Co-Expression Network

The member analysis led to the identification of a total of 82 hub genes with high connectivity, with an HRD score from the brown module based on the cutoff criteria (|MM| > 0.8 and |GS| > 0.2) (Figure 3A). Meanwhile, through evaluating intramodule connectivity, 82 genes were identified with a connectivity degree ≥5 and the connectivity weight threshold set as 0.45 in the TOM-based co-expression network. Last, after merging the results of the previous two approaches, altogether 90 genes were included. The overlapped 76 genes were finally identified as the hub genes (Figure 3B and Supplementary Material S3). From Figure 3B, 76 hub genes were distributed in pink. Genes in blue were non-overlapped ones from selection of |MM| > 0.8 and |GS| > 0.2 in the first approach. Genes in green were non-overlapped ones from the TOM-based analysis in the second approach. Other connected genes were also displayed in yellow (Figure 3C). The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis of brown members are shown in Figure 3D,E. The KEGG analysis showed that the hub genes of the brown module were enriched in the cell cycle pathway (Figure 3D). GO terms such as organelle fission, nuclear division, chromosome segregation, mitotic nuclear division, and nuclear chromosome segregation for the biological process (BP), spindle, and chromosomal region for cellular components (CC), and ATPase activity for molecular function (MF) were among the statistically overrepresented terms (Figure 3E). Therefore, these bioinformatics analyses suggested that the HRD score was associated with programmed cell division and proliferation. The HRD score in tumor cells was probably dynamically increasing considering that tumor cells multiplied rapidly and were more frequently in a cell division cycle.

HRD Score Among Cancers Was a Prognostic Factor

In light of the cumulative DNA damage manifested by the HRD score in tumor cells, we tried to explore HRD score’s role in predicting patient survival. First, we tested combined HRD score associations with overall survival (OS) across 33 cancer types in 10,171 cases. The combined HRD score ≥42 was associated with shorter OS than the HRD score <42 cohort in altogether 33 cancer types from 10,171 cancer cases in TCGA (HR = 1.010, 95% CI: 1.008–1.011, p < 0.001) (Figure 4A). However, as for ovarian cancer, HRD ≥42 cohort was significantly associated with longer OS than the HRD score <42 cohort in 459 HGSC cases (HR = 0.99, 95 % CI: 0.98–0.99, p < 0.001) (Figure 4B). Next, we investigated the HRD score distribution among 33 types of cancer and found that ovarian cancer had the top highest HRD combined score and component scores, followed by lung cancer, esophagus cancer, and cervical cancer (Figure 4C, Supplementary Material S4). Ovarian cancer was the only cancer type with the median HRD combined score that outnumbered 42. Thyroid cancer was with the lowest HRD combined and component scores among the 33 cancers.

FIGURE 4 | (A) Survival analysis of OS between an HRD score ≥42 and an HRD score <42 in 10,171 cases across 33 cancer types, HR = 1.01, 95% CI = 1.008–1.011, p < 0.001. (B) Survival analysis of OS between an HRD score ≥42 and an HRD score <42 in 459 cases of ovarian cancer, HR = 0.99, 95% CI = 0.98–0.99, p < 0.001. (C) HRD combined score distribution across 33 cancer types in 10,619 cases.

Last, we tried to investigate the HRD score impact on 328 primary HGSC patient survival outcomes. First, we investigated several clinical variables impact on overall survival by univariate COX regression (Supplementary Material S5). The HRD score, tumor stage, and tumor residual after primary cytoreductive surgery made an impact on the overall survival. Next, we built multivariate COX proportional hazard models after adjusting for variables like patient age, tumor stage, tumor grade, and tumor residual (Figure 5A). We also applied stepwise regression selection with bidirectional elimination in Cox regression, and three variables including tumor grade, tumor residual, HRD scores were selected in the model (Figure 5B). From above, the HRD score was the independent factor that the higher combined HRD score was associated, with better OS in HGSC (HR 0.98, 95% CI 0.97–0.99, p < 0.001; HR 0.98, 95% CI 0.97–0.99, p < 0.001, respectively). Additionally, smaller tumor residual was statistically associated with better OS.

FIGURE 5 | (A) Forest plot displayed the association of clinical characteristics with OS by the COX regression analysis, including factors like patient age, tumor stage, tumor grade, tumor residual, and HRD score. (B) Forest plot displayed the COX regression analysis for OS with stepwise bidirectional elimination selection. (C) Nomogram to predict the 3- and 5-year survival. Pr: probability. (D,E) Calibration curve for 3- and 5-year OS nomogram models. The gold line represents the ideal nomogram, and the blue line represents the observed nomogram. (F) Decision curve analysis. Net benefit curves for model with three characteristics compared with model with all characteristics. y-axis measures net benefit which is calculated by summing the benefits (true positives) and subtracting the harms (false positives).

In order to develop a model predicting odds of OS over 3 and 5 years in HGSC patients, a nomogram (Figure 5C) was generated considering the three prognostic variables including the HRD score, tumor residual, and tumor grade on the basis of the multivariate analysis above (Figure 5B). By calculating the total score, clinicians can stratify patients into distinct risk groups and recommend closer clinical follow-up. From Figure 5, the HRD score at 42 seemed to add little impact on patient survival. The calibration plots for 3-year OS and 5-year OS rates showed an optimal agreement between prediction by the nomogram and the actual observation (Figure 5D,E). The decision curve analysis revealed that these three characteristics predicted OS and all characteristics for a large probability threshold (Figure 5F). According to the prognosis stratification, our model could improve clinical decision making, including HGSC patient postoperative tumor marker monitor, first-line chemotherapy choice, maintenance of treatment implementation, and design of clinical trials.

DISCUSSION

In this study, we first found that a higher HRD score was linked to higher tumor stages and recurrent tumor tissue demonstrated a slightly higher HRD score than the primary counterpart. We further constructed 15 co-expression modules with the WGCNA method, identified genes impacting the HRD score, and proved that the HRD score was tightly associated with cell division and proliferation. A higher HRD score across 33 cancer types was correlated with worse survival outcomes. Contrary to other cancer types, the HRD score in ovarian cancer ranked the top among 33 cancer types and was positively associated with patient survival. In HGSC, higher HRD score and smaller tumor residual predicted better survival outcomes. Meanwhile, we developed and validated a predicting model for 3- and 5-year overall survival prediction in ovarian cancer patients.

Considering the essential role played by the HR pathway in ensuring tumor cells survival after defective DNA damage, we identified the intriguing relationship between the HRD score and clinical factors by analyzing clinical data containing 33 cancer types. We showed that the HRD score climbed with the rising of tumor stage and indicated a bad prognosis. The recurrent tumor tissue displayed a higher HRD score than the primary one. These associations were compatible in correlating the accumulation of chromosomal defects burden with advanced tumor stage, tumor recurrence, and worse prognosis. The HRD score measured chromosomal abnormality including chromosomal breaks span area >10mb, the absence of paternal or maternal copies, and appearance of allelic imbalance involving telomere. These genomic scars represent a historical record of DNA damage exposure and attempt of alleviating DNA damage by the HR pathway. The disruption of DNA damage repair led to the accumulation of large-scale genome instability over time with the consequence of the increasing HRD score (Jeggo et al., 2016). Cancer progressed from the initiation of malignancy to the advanced stages coupled with the building up of genomic scar. The HRD score appeared to be one-way forward accelerating in tumor progression and metastasis (Stover et al., 2020). The lost chromosomal pieces were barely brought back due to the absence of template and the genomic scars were hard to be healed. Another study also displayed that the HRD score in ovarian cancer with BRCA1/2 mutations tended to be higher in the recurrent than in the primary sample (Patel et al., 2018). However, they did not take into account the samples of BRCA wild-type. From the dot plot they displayed, we could still see a higher HRD score in overall recurrent sample than in the primary sample. We considered that tumor distant metastasis involves more of epithelial mesenchymal transition (EMT) instead of intervened programmatic DNA replication. Hematogenous and lymphatic metastasis might be explained by other mechanisms which involved less of the accumulation of complex genome error over time. This was attributed to the reasons that tumor stage IV was not associated with a higher HRD score. Another research also found no significant genomic variations including driver genes mutation and copy number variations between primary and metastatic sites of HGSC(Lee et al., 2020).

A higher HRD score in ovarian cancer predicted a better prognosis unlike other cancer types. Ovarian cancer was found at the top of the list of the median HRD combined score and was the only cancer type with the HRD score that outnumbered 42. A higher HRD score meant less integrity of genome and rendered cancer cells vulnerable to loss of DNA repair proteins. Like every double-edged sword, ovarian cancer possessed improved sensitivity to DNA-damaging agents, namely, PARPis, by taking advantage of “synthetic lethality.” Synthetic lethality is the inability to tolerate the simultaneous loss of two important functions, literally the integrity of genome and DNA repair proteins, leading to cell death. PARPis will be most active in tumors with a higher HRD score, while tumors without HRD are unlikely to respond to PARPis (Stover et al., 2020). Ovarian cancer possessed improved sensitivity to DNA-damaging agents like platinum and PARPis, which intervened tumor cell division cycle and proliferation, since the accumulation of genomic scar summited in ovarian cancer. HGSC received platinum and PARPis as first-line standard chemotherapy, while cancer types found at the bottom list generally do not receive these as first-line standard therapy. Thus, patients who were higher HRD-scored and subsequently superior sensitive to chemotherapy and PARPis showed better survival. Our results were similar with other studies (Marquard et al., 2015; Knijnenburg et al., 2018; Takaya et al., 2020), while we dig further in the origination and alteration of the HRD score and the mechanisms behind the WGCNA method and built the model of ovarian cancer patient survival prediction with preliminary 328 cases and validated the model in several ways. Another study also showed the median number of rearranged chromosome arms was associated with cancer prognosis (Fearon and Vogelstein, 1990). According to our analysis, the HRD score hold promise an important prognostic factor in epithelial ovarian cancer. Our data strengthened the rationale for extending the clinical use of HRD biomarker’s role in predicting responses to DNA-damaging agents and warranted larger clinical research (Konstantinopoulos et al., 2015).

HGSC with BRCA mutation showed PARPis sensitivity with long treatment free interval (Tan et al., 2008; Foo et al., 2021; Kim et al., 2021). BRCA mutation seemed to be one indicator for PARPi treatment. BRCA1/2 served as a critical modulating factor in the HR pathway by co-localizing with RAD51, interacting with P53, and a set of proteins to format a remodeling complex, and in this way made fundamental impact on HR score evaluation (Lord and Ashworth, 2016). However, gene loss or mutation in the HR pathway other than BRCA1/2, such as RAD51, RAD54, ATM, TP53, MRE11, and XRS2, also brought in the genomic and chromosomal instability and contributed to cell growth arrest impacting the ultimate HRD score (Khanna and Jackson, 2001). Genetic or epigenetic changes in the HR pathway, once recognized, can also be therapeutically targeted through synthetic lethality. Cancers without BRCA1/2 loss but with accumulation of similar genomic footprints displayed increased sensitivity to DNA-damaging agents. Whether the malfunctions of other DNA damage repair like non-homologous end joining (NHEJ) and DNA single-brand break repair (SSB) would make an effect on the HRD score needed to be verified. BRCA mutation condition alone limitedly discriminated the response to DNA-damaging agents. Rapidly proliferating tumor cells and hemopoietic stem cells with a higher HRD score were more easily targeted by DNA-damaging agents than the relatively quiescent somatic cells, regardless of BRCA1/2 expression. Human somatic cells with germline BRCA1/2 mutation were not globally harmed by PARPis. Somatic non-neoplastic and neoplastic cells with germline BRCA mutation in one person demonstrated varied sensitivity to PARPis. Varying degree of anemia was observed clinically due to the different extents of HR deficiency in hemopoietic stem cells among patients. Some ovarian cancer patients with BRCA mutation developed myelodysplastic syndrome and acute myeloid leukemia upon PARPi treatment (Churpek et al., 2016; McNerney et al., 2017; Morice et al., 2021).

The clinical validity of the HRD score in cancer is better evaluated in terms of PARPi benefit (Miller et al., 2020). Several trials have demonstrated that HRD genomic scars predicted different extents of response to DNA-damaging agents in breast cancer (Isakoff et al., 2015; Telli et al., 2016; Loibl et al., 2018; Telli et al., 2018). For ovarian cancer, recently clinical trials were performed with patients being stratified by HRD status, incorporating PARPi maintenance in primary and platinum-sensitive recurrent ovarian cancer settings. An HRD score of ≥42 was determined to signify HRD (HR-deficient), and a score of <42 was considered HR-proficient in clinical trials. In PAOLA-1 and PRIMA clinical trials, HRD tests could discriminate the response to PARPis that HR-deficient populations showed great benefit compared to HR-proficient populations in primary HGSC patients. (González-Martín, 2019; Ray-Coquard et al., 2019 #167 #168) In 2021, the Society of Gynecologic and Oncologists (SGO), PAOLA-1 presentation showed the gene test by next-generation sequencing or gene panel cannot substitute the HRD score. Mutation in genes in the HR pathway did not necessarily change the effectiveness of this pathway and guarantee the PARPis response. PARP inhibition killed tumor cells by coordinating with the considerable accumulation of genomic defects by HR deficiency, rather than the mutated BRCA1/2. That explained why some patients with germline BRCA1/2 mutation showed no response to PARPis. The accumulated genomic scar has not reached the point. “Synthetic lethality” was advantageous only when DNA damage and chromosomal scars were accumulated and the HRD score was summited. We proved the HRD score accelerated as cells divided and proliferated. A continuous HRD score is more informative than binary cutoff. This is attributed to reasons that ARIEL3 and NOVA clinical trials showed platinum-sensitive recurrent HGSC patients could generally benefit from PARPis, regardless of BRCA mutation or any gene test result (Coleman et al., 2017; Del Campo et al., 2019), since the DNA damage ascended in this recurrent population. Both FDA and NCCN guidelines 2020 approved and recommended PARPi treatment in platinum-sensitive recurrent HGSC without the mandatory gene test results. The quantitative HRD score per se acted as an important predictive biomarker in personalizing the use of PARPis and platinum-based chemotherapy and patient prognosis.

Notably, archival tissue, namely, primary tumor tissue, was extracted and tested for the HRD score in AREIL3 and NOVA, instead of the pretreatment tissue, namely, recurrent tumor tissue. The median time between primary and recurrent biopsy was 2.7 years (Haunschild and Tewari, 2021). Genomic testing on initial surgery tissues in these trails did not represent the real-time genomic and chromosomal instability situation at recurrence. The ongoing proliferating tumor cells with heterogeneity and apparent adaptability under selective pressure by chemotherapy likely changed the genome scars and accumulated more genome damage and instability. A real-time sophisticated HRD assay performed on a biopsy of the recurrent tumor is able to capture tumor evolution processes and track the alteration of the HR function in response to therapy-selective pressure (Pellegrino et al., 2019). Studies comparing HRD scores between paired primary HGSC tissue and recurrent counterpart, and between neoplastic tissue and non-neoplastic tissue from the same patient, may better explain this.

CONCLUSION

In summary, we first discovered that the higher HRD score was linked to higher tumor stages, and recurrent tumor tissue demonstrated slightly higher HRD score than the primary counterpart. We demonstrated that the HRD score representing the accumulated genomic scars was dynamically increasing in proliferating tumor cells, since the HRD score was tightly correlated to tumor cell division and replication from bioinformatics analysis. We highlighted quantitative HRD score biomarker’s role in predicting therapeutic response to DNA-damaging agents and patient survival outcomes in subsets of cancers including ovarian cancer.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding authors.

ETHICS STATEMENT

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

AUTHOR CONTRIBUTIONS

RS and XX were involved in study concept and design. RS collected and analyzed the data. YL drafted the article. JX and XW revised it for important intellectual content. All authors have approved the submitted version.

FUNDING

This study was supported by grants from the National Natural Science Foundation of China (Nos. 82002738, 81802590, 81772767). The funding covers part of the publishing fees.

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

ACKNOWLEDGMENTS

We thank Dr. Xiaojie Liang (Southern Medical University) and the members of his bioinformatics team, Medical Students Study Bioinformatics, for generously sharing their experience. We thank Dr. Jianming Zeng (University of Macau), and all the members of his bioinformatics team and biotrainees, especially Xiaojie Sun, for generously sharing their experience and codes. We also thank Guozi Learn Bioinformatics (WeChat Official Accounts).

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2021.762741/full#supplementary-material

REFERENCES

Baldwin, R. L., Nemeth, E., Tran, H., Shvartsman, H., Cass, I., Narod, S., et al. (2000). BRCA1 Promoter Region Hypermethylation in Ovarian Carcinoma: a Population-Based Study. Cancer Res. 60 (19), 5329–5333.

Cancer Genome Atlas Research Network (2011). Integrated Genomic Analyses of Ovarian Carcinoma. Nature 474 (7353), 609–615. doi:10.1038/nature10166

Churpek, J. E., Marquez, R., Neistadt, B., Claussen, K., Lee, M. K., Churpek, M. M., et al. (2016). Inherited Mutations in Cancer Susceptibility Genes Are Common Among Survivors of Breast Cancer Who Develop Therapy-Related Leukemia. Cancer 122 (2), 304–311. doi:10.1002/cncr.29615

Coleman, R. L., Oza, A. M., Lorusso, D., Aghajanian, C., Oaknin, A., Dean, A., et al. (2017). Rucaparib Maintenance Treatment for Recurrent Ovarian Carcinoma after Response to Platinum Therapy (ARIEL3): a Randomised, Double-Blind, Placebo-Controlled, Phase 3 Trial. Lancet 390 (10106), 1949–1961. doi:10.1016/s0140-6736(17)32440-6

Coleman, R. L., Fleming, G. F., Brady, M. F., Swisher, E. M., Steffensen, K. D., Friedlander, M., et al. (2019). Veliparib with First-Line Chemotherapy and as Maintenance Therapy in Ovarian Cancer. N. Engl. J. Med. 381 (25), 2403–2415. doi:10.1056/NEJMoa1909707

Daly, M. B., Pal, T., Berry, M. P., Buys, S. S., Dickson, P., Domchek, S. M., et al. (2021). Genetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic, Version 2.2021, NCCN Clinical Practice Guidelines in Oncology. J. Natl. Compr. Canc Netw. 19 (1), 77–102. doi:10.6004/jnccn.2021.0001

Del Campo, J. M., Matulonis, U. A., Malander, S., Provencher, D., Mahner, S., Follana, P., et al. (2019). Niraparib Maintenance Therapy in Patients with Recurrent Ovarian Cancer after a Partial Response to the Last Platinum-Based Chemotherapy in the ENGOT-OV16/NOVA Trial. Jco 37 (32), 2968–2973. doi:10.1200/jco.18.02238

Evers, B., Helleday, T., and Jonkers, J. (2010). Targeting Homologous Recombination Repair Defects in Cancer. Trends Pharmacol. Sci. 31 (8), 372–380. doi:10.1016/j.tips.2010.06.001

Fearon, E. R., and Vogelstein, B. (1990). A Genetic Model for Colorectal Tumorigenesis. Cell 61 (5), 759–767. doi:10.1016/0092-8674(90)90186-i

Foo, T., George, A., and Banerjee, S. (2021). PARP Inhibitors in Ovarian Cancer: An Overview of the Practice‐changing Trials. Genes Chromosomes Cancer 60 (5), 385–397. doi:10.1002/gcc.22935

González-Martín, A., Pothuri, B., Vergote, I., DePont Christensen, R., Graybill, W., Mirza, M. R., et al. (2019). Niraparib in Patients With Newly Diagnosed Advanced Ovarian Cancer. N. Engl. J. Med. 381 (25), 2391–2402. doi:10.1056/NEJMoa1910962

Haunschild, C. E., and Tewari, K. S. (2021). The Current Landscape of Molecular Profiling in the Treatment of Epithelial Ovarian Cancer. Gynecol. Oncol. 160 (1), 333–345. doi:10.1016/j.ygyno.2020.09.043

Hoeijmakers, J. H. J. (2009). DNA Damage, Aging, and Cancer. N. Engl. J. Med. 361 (15), 1475–1485. doi:10.1056/NEJMra0804615

Isakoff, S. J., Mayer, E. L., He, L., Traina, T. A., Carey, L. A., Krag, K. J., et al. (2015). TBCRC009: A Multicenter Phase II Clinical Trial of Platinum Monotherapy with Biomarker Assessment in Metastatic Triple-Negative Breast Cancer. Jco 33 (17), 1902–1909. doi:10.1200/jco.2014.57.6660

Jeggo, P. A., Pearl, L. H., and Carr, A. M. (2016). DNA Repair, Genome Stability and Cancer: a Historical Perspective. Nat. Rev. Cancer 16 (1), 35–42. doi:10.1038/nrc.2015.4

Khanna, K. K., and Jackson, S. P. (2001). DNA Double-Strand Breaks: Signaling, Repair and the Cancer Connection. Nat. Genet. 27 (3), 247–254. doi:10.1038/85798

Kim, D.-S., Camacho, C. V., and Kraus, W. L. (2021). Alternate Therapeutic Pathways for PARP Inhibitors and Potential Mechanisms of Resistance. Exp. Mol. Med. 53 (1), 42–51. doi:10.1038/s12276-021-00557-3

Knijnenburg, T. A., Wang, L., Zimmermann, M. T., Chambwe, N., Gao, G. F., Cherniack, A. D., et al. (2018). Genomic and Molecular Landscape of DNA Damage Repair Deficiency across the Cancer Genome Atlas. Cell Rep 23 (1), 239. doi:10.1016/j.celrep.2018.03.076

Konstantinopoulos, P. A., Ceccaldi, R., Shapiro, G. I., and D'Andrea, A. D. (2015). Homologous Recombination Deficiency: Exploiting the Fundamental Vulnerability of Ovarian Cancer. Cancer Discov. 5 (11), 1137–1154. doi:10.1158/2159-8290.Cd-15-0714

Langfelder, P., and Horvath, S. (2007). Eigengene Networks for Studying the Relationships between Co-expression Modules. BMC Syst. Biol. 1, 54. doi:10.1186/1752-0509-1-54

Langfelder, P., and Horvath, S. (2008). WGCNA: an R Package for Weighted Correlation Network Analysis. BMC Bioinformatics 9, 559. doi:10.1186/1471-2105-9-559

Lee, S., Zhao, L., Rojas, C., Bateman, N. W., Yao, H., Lara, O. D., et al. (2020). Molecular Analysis of Clinically Defined Subsets of High-Grade Serous Ovarian Cancer. Cel Rep. 31 (2), 107502. doi:10.1016/j.celrep.2020.03.066

Li, A., and Horvath, S. (2007). Network Neighborhood Analysis with the Multi-Node Topological Overlap Measure. Bioinformatics 23 (2), 222–231. doi:10.1093/bioinformatics/btl581

Loibl, S., Weber, K. E., Timms, K. M., Elkin, E. P., Hahnen, E., Fasching, P. A., et al. (2018). Survival Analysis of Carboplatin Added to an Anthracycline/taxane-Based Neoadjuvant Chemotherapy and HRD Score as Predictor of Response-Final Results from GeparSixto. Ann. Oncol. 29 (12), 2341–2347. doi:10.1093/annonc/mdy460

Lord, C. J., and Ashworth, A. (2016). BRCAness Revisited. Nat. Rev. Cancer 16 (2), 110–120. doi:10.1038/nrc.2015.21

Loveday, C., Turnbull, C., Turnbull, C., Ramsay, E., Hughes, D., Ruark, E., et al. (2011). Germline Mutations in RAD51D Confer Susceptibility to Ovarian Cancer. Nat. Genet. 43 (9), 879–882. doi:10.1038/ng.893

Marquard, A. M., Eklund, A. C., Joshi, T., Krzystanek, M., Favero, F., Wang, Z. C., et al. (2015). Pan-cancer Analysis of Genomic Scar Signatures Associated with Homologous Recombination Deficiency Suggests Novel Indications for Existing Cancer Drugs. Biomark Res. 3, 9. doi:10.1186/s40364-015-0033-4

McNerney, M. E., Godley, L. A., and Le Beau, M. M. (2017). Therapy-related Myeloid Neoplasms: when Genetics and Environment Collide. Nat. Rev. Cancer 17 (9), 513–527. doi:10.1038/nrc.2017.60

Miller, R. E., Leary, A., Scott, C. L., Serra, V., Lord, C. J., Bowtell, D., et al. (2020). ESMO Recommendations on Predictive Biomarker Testing for Homologous Recombination Deficiency and PARP Inhibitor Benefit in Ovarian Cancer. Ann. Oncol. 31 (12), 1606–1622. doi:10.1016/j.annonc.2020.08.2102

Morice, P.-M., Leary, A., Dolladille, C., Chrétien, B., Poulain, L., González-Martín, A., et al. (2021). Myelodysplastic Syndrome and Acute Myeloid Leukaemia in Patients Treated with PARP Inhibitors: a Safety Meta-Analysis of Randomised Controlled Trials and a Retrospective Study of the WHO Pharmacovigilance Database. Lancet Haematol. 8 (2), e122–e134. doi:10.1016/s2352-3026(20)30360-4

Patel, J. N., Braicu, I., Timms, K. M., Solimeno, C., Tshiaba, P., Reid, J., et al. (2018). Characterisation of Homologous Recombination Deficiency in Paired Primary and Recurrent High-Grade Serous Ovarian Cancer. Br. J. Cancer 119 (9), 1060–1066. doi:10.1038/s41416-018-0268-6

Pellegrino, B., Mateo, J., Serra, V., and Balmaña, J. (2019). Controversies in Oncology: Are Genomic Tests Quantifying Homologous Recombination Repair Deficiency (HRD) Useful for Treatment Decision Making. ESMO Open 4 (2), e000480. doi:10.1136/esmoopen-2018-000480

Ray-Coquard, I., Pautier, P., Pignata, S., Pérol, D., González-Martín, A., Berger, R., et al. (2019). Olaparib Plus Bevacizumab as First-Line Maintenance in Ovarian Cancer. N. Engl. J. Med. 381 (25), 2416–2428. doi:10.1056/NEJMoa1911361

Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., et al. (2003). Cytoscape: a Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Res. 13 (11), 2498–2504. doi:10.1101/gr.1239303

Silver, D. P., and Livingston, D. M. (2012). Mechanisms of BRCA1 Tumor Suppression: Figure 1. Cancer Discov. 2 (8), 679–684. doi:10.1158/2159-8290.Cd-12-0221

Stover, E. H., Fuh, K., Konstantinopoulos, P. A., Matulonis, U. A., and Liu, J. F. (2020). Clinical Assays for Assessment of Homologous Recombination DNA Repair Deficiency. Gynecol. Oncol. 159 (3), 887–898. doi:10.1016/j.ygyno.2020.09.029

Takaya, H., Nakai, H., Sakai, K., Nishio, K., Murakami, K., Mandai, M., et al. (2020). Intratumor Heterogeneity and Homologous Recombination Deficiency of High-Grade Serous Ovarian Cancer Are Associated with Prognosis and Molecular Subtype and Change in Treatment Course. Gynecol. Oncol. 156 (2), 415–422. doi:10.1016/j.ygyno.2019.11.013

Tan, D. S. P., Rothermundt, C., Thomas, K., Bancroft, E., Eeles, R., Shanley, S., et al. (2008). "BRCAness" Syndrome in Ovarian Cancer: A Case-Control Study Describing the Clinical Features and Outcome of Patients with Epithelial Ovarian Cancer Associated WithBRCA1andBRCA2Mutations. Jco 26 (34), 5530–5536. doi:10.1200/jco.2008.16.1703

Telli, M. L., Hellyer, J., Audeh, W., Jensen, K. C., Bose, S., Timms, K. M., et al. (2018). Homologous Recombination Deficiency (HRD) Status Predicts Response to Standard Neoadjuvant Chemotherapy in Patients with Triple-Negative or BRCA1/2 Mutation-Associated Breast Cancer. Breast Cancer Res. Treat. 168 (3), 625–630. doi:10.1007/s10549-017-4624-7

Telli, M. L., Timms, K. M., Reid, J., Hennessy, B., Mills, G. B., Jensen, K. C., et al. (2016). Homologous Recombination Deficiency (HRD) Score Predicts Response to Platinum-Containing Neoadjuvant Chemotherapy in Patients with Triple-Negative Breast Cancer. Clin. Cancer Res. 22 (15), 3764–3773. doi:10.1158/1078-0432.Ccr-15-2477

Turner, N., Tutt, A., and Ashworth, A. (2004). Hallmarks of 'BRCAness' in Sporadic Cancers. Nat. Rev. Cancer 4 (10), 814–819. doi:10.1038/nrc1457

Watkins, J. A., Irshad, S., Grigoriadis, A., and Tutt, A. N. (2014). Genomic Scars as Biomarkers of Homologous Recombination Deficiency and Drug Response in Breast and Ovarian Cancers. Breast Cancer Res. 16 (3), 211. doi:10.1186/bcr3670

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Su, Liu, Wu, Xiang and Xi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 23 November 2021
doi: 10.3389/fmolb.2021.760031

Integrative Analysis of Immune-Related Genes in the Tumor Microenvironment of Renal Clear Cell Carcinoma and Renal Papillary Cell Carcinoma

Bin Zheng1,2,³†, Fang Xie1,⁴†, Fajuan Cheng1,5,⁶, Jianwei Wang⁷, Zhongshun Yao2,³, Wei He1,2,³* and Zhihong Niu1,2,³*

¹Cheeloo College of Medicine, Shandong University, Jinan, China

²Department of Urology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China

³Department of Urology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China

⁴Department of Urology, Weihai Municipal Hospital, Weihai, China

⁵Department of Nephrology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China

⁶Department of Nephrology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China

⁷Department of Urology, Shandong Provincial ENT Hospital Affiliated to Shandong University, Jinan, China

Edited by:
Florence Le Calvez-Kelm, International Agency For Research On Cancer (IARC), France

Reviewed by:
Imtaiyaz Hassan, Jamia Millia Islamia, India
Siddharth Shukla, Howard Hughes Medical Institute (HHMI), United States

* Correspondence: Wei He, Hewei@bjmu.edu.cn; Zhihong Niu, nzh1789@163.com

†These authors have contributed equally to this work

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 17 August 2021
Accepted: 15 October 2021
Published: 23 November 2021

Citation: Zheng B, Xie F, Cheng F, Wang J, Yao Z, He W and Niu Z (2021) Integrative Analysis of Immune-Related Genes in the Tumor Microenvironment of Renal Clear Cell Carcinoma and Renal Papillary Cell Carcinoma. Front. Mol. Biosci. 8:760031. doi: 10.3389/fmolb.2021.760031

Kidney cancer encompasses a range of primary cancers, such as clear cell renal cell carcinoma (ccRCC) and papillary renal cell carcinoma (pRCC). Our knowledge about the tumor microenvironment (TME) of kidney cancer is still limited. Therefore, we comprehensively assessed the TME of kidney cancers (including ccRCC and pRCC) using the ESTIAMTE, and CIBERSORT algorithms, and conducted distinct functional and correlation analyses with data from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), Gene Expression Omnibus (GEO), Connectivity map and CellMiner database. Next, we identified two immune-related hub genes, IGLL5 and IL2RA, which play essential roles in the TME as well as on survival in ccRCC and pRCC. Furthermore, ccRCC and pRCC samples from our medical center were collected to verify the clinical application value of these two immune-related genes. A specific enrichment analysis of immune cells related to IGLL5 and IL2RA was also conducted in two types of renal cell cancer. Based on selected genes, we predicted the drug response and uncovered novel drug candidate for RCC treatment. Considering the unfavorable outcomes of kidney cancer and emerging interest in TME-targeted treatments, our results may offer insights into immune-related molecular mechanisms and possible targets to control the kidney cancer.

Keywords: clear cell renal carcinoma, papillary renal cell carcinoma, prognosis, tumor microenvironment, immunotherapy

INTRODUCTION

Kidney or renal cancer is the sixth most common malignant cancer in men and the ninth most common in women. In 2020, it was estimated that 76,080 patients would be diagnosed with kidney cancer and 13,780 patients would die from the disease in the United States (Siegel et al., 2021). Renal cell carcinoma (RCC) is the most common form and accounts for 85% of all histological types, with ccRCC comprising 70–80% and pRCC 15–20% of all RCC (Linehan et al., 2016; Barata and Rini, 2017; Vuong et al., 2019). Although pRCC has a better prognosis than ccRCC, the overall prognosis for ccRCC and pRCC remains limited (Steffens et al., 2012). Therefore, effective therapy and accurate biomarkers need to be determined.

TME, as an integral part of tumors, is a cellular environment consisting of tumor cells and other non-malignant cells, including surrounding immune cells, lymphocytes, fibroblasts, stromal cells, and blood vessels (Wu and Dai, 2017). TME acts as a complex ecosystem, which could support tumor growth as well as metastasis while attenuating immunosurveillance. A wealth of new information has emerged which reveals how the functionality of TME determines its integral and indispensable role in various cancers (Hanahan and Coussens, 2012; Pitt et al., 2016). Within the TME, various types of immune and non-immune cell are found. Some nontumor cells, such as stromal cells, fibroblasts, may accelerate cancer cell proliferation and stimulate cancer progression (Hanahan and Coussens, 2012). However, although several immune cells, such as T cells as well as B cells also exist in TME, the exact contribution of these immune cells to the prognosis of patients is not clear (Grivennikov et al., 2010). With a variety of factors that diverse cells secrete, these lead to a chronic inflammatory, immunosuppressive environment (Osipov et al., 2019). Within that environment, cancer cells are able to adapt and grow with less possibility of detection and eradication by immunosurveillance.

Currently, RCC is considered an immunogenic and vascularized tumor. Several studies have found that immune cells could infiltrate into the TME of RCC, but these immune cells block the effective anti-tumor responses (Díaz-Montero et al., 2020). Due to the immunosuppressed state of RCC, attention has focused on immune checkpoint inhibitors. However, a large number of patients with RCC do not benefit from these immune-based treatments, and therapies are related with drug resistance because of heterogeneous and adaptive TME (Ravaud et al., 2016). In order to eradicate tumor cells, effector immune cells must be relieved from the immunosuppressive networks that constitute the TME. Moreover, with the improved understanding of interactions between immune cells, and between cancer cells and immune cells, the optimizing of our approach to treating cancers will significantly progress (Joyce and Pollard, 2009). Therefore, it is indispensable to elucidate the cross-talk process between various immune cells and the TME.

In this study, we identified the immune-related genes in the TME of ccRCC as well as pRCC, and explored their potential mechanisms, biological functions, and predict drug response as well as potential drugs for ccRCC and pRCC. These immune-genes could serve as reliable prognostic biomarkers as well as therapeutic targets.

MATERIALS AND METHODS

Data Collection

The gene expression data and clinical information of ccRCC (539 ccRCC and 72 normal samples) and pRCC (289 pRCC and 32 normal samples) were downloaded directly from TCGA (https://portal.gdc.cancer.gov/). The gene expression data and clinical information of 91 and 61 RCC samples were obtained from ICGC (https://dcc.icgc.org/) and GEO (including GSE40912 and GSE2748) (https://www.ncbi.nlm.nih.gov/geo/), separately. We eliminated samples in which clinical information was lost or unknown.

Identification and Analysis of Differentially Expressed Genes

Differentially expressed genes (DEGs) were identified using the “limma” package and visualized using the “pheatmap” package. The |logFC| > 1, p-value < 0.05, and false discovery rate (FDR) < 0.05 were used to screen for DEGs. The correlation between immune-related genes and clinical factors was performed using the “ggplot2” and “corrplot” package.

Functional Analysis

Protein–protein interaction (PPI) network construction was performed using the STRING online database (http://string-db.org/), and the network was visualized using Cytoscape (Szklarczyk et al., 2019). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses was conducted by “clusterProfiler” package. Gene set enrichment analysis (GSEA) was performed by the “gsva” R package. The Connectivity Map (Cmap) and CellMiner database were uesd to predict small molecular compounds and individual response to drugs.

Tumor Microenvironment Analysis

We used ESTIMATE algorithm (Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data) algorithm to measure stromal and immune scores in ccRCC and pRCC with data from TCGA database (https://portal.gdc.cancer.gov/) (Yoshihara et al., 2013). The prognostic-related genes were selected after performing the Univariate Cox analysis with p < 0.1. Tumor-infiltrating immune cells were calculated by the CIBERSORT algorithm. Venn diagrams as well as violin plots were visualized with the ‘VennDiagram’ and ‘vioplot’ packages.

Immunohistochemistry

Ten pairs of ccRCC, pRCC and adjacent normal tissues were collected from Shandong provincial Hospital affiliated to Shandong First University from April 2021 to June 2021. The study was approved by the Ethics Committee of Shandong Provincial Hospital (Approval no. SWYX: NO. 2021-118). IHC was performed according to previously published method (Wang et al., 2020). All samples were incubated with rabbit polyclonal anti-IL2RA (ab245687) and anti-IGLL5 (NBP2-14574) antibodies overnight at 4°C and then washed. Two pathologists independently assessed the IHC slides.

Statistical Analysis

Mann-Whitney test was utilized to measure gene expression between tumor and non-tumor tissues. The Kaplan-Meier curves with a log-rank test were adopted in the survival analysis. Statistical analysis in this study was performed with the R statistical package (R version 4.0.1).

RESULTS

Correlation of ESTIMATE Scores in ccRCC and pRCC

We first determined the association between ESTIMATE scores (including immune scores and stromal scores), prognosis as well as clinical characteristics in ccRCC and pRCC. As shown in Figures 1A,B, ccRCC patients with high immune scores had poor prognosis, but stromal scores did not have apparently association with survival. However, in pRCC patients, a decrease in overall survival was correlated with high immune scores and stromal scores (Figures 1C,D). In addition, for ccRCC, immune scores were positively associated with tumor grade, tumor stage, T stage, and M stage. Patients with advanced T stage had relatively higher stromal scores (Figures 1E–K). For pRCC, higher immune scores were associated with T stage, while patients with advanced T stage and N stage have high stromal scores (Figures 1L–N).

FIGURE 1 | The correlation analysis between ESTIMATE scores and prognosis of patients. (A-D) The K-M curves for ESTIMATE scores in ccRCC (A-B) and pRCC (C-D). (E-N) The correlation analysis between ESTIMATE scores and clinicopathological features in ccRCC (E-K) and pRCC (L-N).

Identification of Differentially Expressed Genes

Based on immune scores and stromal scores, we determined the DEGs by analyzing the expression data of all ccRCC and pRCC samples. In ccRCC, 1066 genes were upregulated and 204 genes were downregulated in samples with high immune score. Similarly, we also identified 515 genes that were highly expressed and 221 genes with low expression in ccRCC samples with high stromal score (logFC >1; p < 0.05; Figures 2A,B). In pRCC, 1155 genes had high expression levels and 174 genes had low expression in patients with high immune scores, and 1395 genes were upregulated as well as 141 downregulated in patients with high stromal scores (Figures 2C,D). Using Venn plot, we determined 218 genes with high expression and 68 genes with low expression in ccRCC patients with high scores (Figures 2E,F). pRCC patients with high scores presented 961 genes that were upregulated and 38 genes that were downregulated, respectively (Figures 2G,H).

FIGURE 2 | Identification of DEGs based on ESTIMATE scores and functional analysis of common DEGs. (A-D) Volcano plot of DEGs from the immune scores and stromal score groups in ccRCC (A-B) and pRCC (C-D). (E-H) Commonly changed genes in immune scores and stromal scores groups in ccRCC (E-F) and pRCC (G-H). (I-L) KEGG and GO enrichment analysis in ccRCC (I-J) and pRCC (K-L).

On the basis of these selected DEGs, we performed GO and KEGG enrichment analyze in two types of kidney cancers. The GO analysis showed that DEGs identified in ccRCC and pRCC cases were primarily enriched in immune-related functions, such as B cell-mediated immunity and lymphocyte-mediated immunity (Figures 2J–L). We also listed the top 10 KEGG pathways for both ccRCC and pRCC. As shown in Figures 2I–K, various common immune-related signaling pathways were enriched. Furthermore, DEGs from pRCC samples were also enriched in several pathways related to T-cell receptor and B-cell receptor signaling pathways (Figure 2K).

PPI Network and Prognostic Analysis of DEGs

Then these identified DEGs were used to construct the PPI networks. We ranked the DEGs genes by their nodes (Figures 3A,B) and identified hub genes via “Cytoscape” (Supplementary Figures S1A–D). Moreover, we further investigated prognostic genes by performing the univariate Cox analysis among all DEGs in ccRCC and pRCC, respectively. This revealed that 69 genes were related to ccRCC prognosis, and 110 genes were associated with the prognosis of pRCC patients (p < 0.1, Figures 3C,D). Then, we interrogated the top 15 prognosis-related hub genes in ccRCC and pRCC utilizing Venn algorithm (Figures 3E,F). Among 30 selected prognosis-related hub genes in ccRCC and pRCC, two common genes, IGLL5 and IL2RA, were identified, which could potentially play essential role in TME as well as survival in ccRCC and pRCC.

FIGURE 3 | Identification of the prognostic hub DEGs. (A-B) Hub DEGs ranked by the number of nodes in ccRCC (A) and Prcc (B). (C-D) Construction of univariate Cox analysis in ccRCC (C) and pRCC (D). (E-F) Identification of prognostic hub DEGs via Venn diagram in ccRCC (E) and pRCC (F).

Correlation and GSEA Enrichment Analyses of IGLL5 and IL2RA

To further assess the expression profile and clinical values of IGLL5 and IL2RA, we extracted the corresponding expression data, as well as clinical information. Both IGLL5 and IL2RA showed high expression in ccRCC samples (p < 0.05, Figures 4A,B). Furthermore, a decrease in overall survival was associated with high expression of IGLL5 and IL2RA (p < 0.05, Figures 4C,D). IGLL5 and IL2RA expression were also positively associated with the stage of ccRCC patients, including high tumor stage, tumor grade, T stage, and more lymph node metastases (p < 0.05, Figures 4E–K).

FIGURE 4 | Survival analysis and the clinic correlation analysis in ccRCC and pRCC. (A-B, L-M) Differential expression analysis of IGLL5 and IL2RA in ccRCC (A-B) and pRCC samples (L-M). (C-D) Survival analysis of IGLL5 and IL2RA groups in ccRCC and pRCC samples (N). (E-K) The correlation analyses of IGLL5 (E-H) and IL2RA (I-K, O-P) expression with clinicopathological parameters in ccRCC and pRCC samples.

The same approach was used to analyze two genes in pRCC patients. The differential expression analysis confirmed that IL2RA expressed highly in tumor as well (p < 0.05, Figure 4M), but significant differences in expression of IGLL5 between normal and tumor patients was not observed (p > 0.05, Figure 4M). Based on these results, we chose IL2RA for further analysis. In pRCC patients, poor survival was associated with high expression of IL2RA (p < 0.05, Figure 4N). Moreover, high IL2RA expression was associated with various clinical characteristics, including T and N stage indicative of disease progression. (p < 0.05, Figures 4O,P).

Next, we explored the potential regulation pathways through GSEA enrichment analysis in ccRCC and pRCC samples. Several immune-related pathways, including B cell and T cell receptor signaling pathways, chemokine signaling, and natural killer cell mediated cytotoxicity, which were closely correlated with IGLL5 and IL2RA expression (NES >1, p < 0.05, FRD <0.25, Supplementary Figures S2A–C).

Composition of Immune Cells and Correlation Analysis

To further verify the essential role of major immune cells in ccRCC and pRCC, we explored immune cell infiltration and correlation between two genes and major immune cells. The profile of the principle immune cells showed that adaptive immune cells (such as native B cells, memory B cells, native and memory CD4⁺ cell, CD8⁺ T cells) accounted for a substantial part of the immune cells in ccRCC as well as pRCC (Figures 5A,B). Next, we investigated differences in the fraction of major types of immune cells according to expression levels of IGLL5 and IL2RA. For ccRCC, high IGLL5 expression was directly proportional to most adaptive immune cells (including native B cell, CD4⁺ memory T cells, CD8⁺ T cells, T follicular helper cells, and regulatory T cells) and was inversely proportion to several innate immune cells (such as NK cell, monocytes, M2 macrophages, activated dendritic cells, and mast cells) (p < 0.05, Figure 5C). Moreover, the difference analysis between high and low expression level of IL2RA in ccRCC illustrated that the high IL2RA expression set presented a significantly larger fraction of native B cells, CD4⁺ memory T cells, and M2 macrophages, than CD8⁺ T cells, NK cells, and neutrophils (p < 0.05, Figure 5D). The same approach was used to assess IL2RA in pRCC, which revealed that high IL2RA expression set had higher fraction of native B cells, neutrophils, and a lower proportion of memory B cells, activated CD4⁺ T memory cells, activated NK cells and resting mast cells (p < 0.05, Figure 5E).

FIGURE 5 | Composition of immune cells and correlation analysis. (A,B) landscape of the 22 Immune cell in ccRCC (A) and pRCC (B). (C-E) Differences of immune cells infiltration in ccRCC (C,D) and pRCC (E). (F-I) Validation of IGLL5 and IL2RA in ICGC cohort (F-G), GSE40912 (H) and GSE2748 (I-J).

We also detected the correlation of IGLL5 and IL2RA with immune cell abundance. In ccRCC, the correlation analyses showed that IGLL5 and most adaptive immune cells had a clear linear positive correlation and had a negative correlation with the infiltration of native immune cells (p < 0.05, Supplementary Figure S3). For IL2RA, there was a positive relationship with native B cells, activated CD4⁺ memory T cells, and M0 and M2 macrophages. However, an inverse correlation was observed between IL2RA and CD8⁺ T cells, T follicular helper cells, and NK cells (p < 0.05, Supplementary Figure S4). In pRCC, IL2RA showed a positive correlativity with activated CD4⁺ T memory cells, native B cells, M1 macrophages and neutrophils but had negative relationship with resting CD4⁺ T memory cells, memory B cells, and mast cells (p < 0.05, Supplementary Figure S5). Finally, on the basis of above analyses, we further investigated the intersected immune cells associated with both IGLL5 and IL2RA in ccRCC and pRCC (Table. 1).

TABLE 1 | The intersected immune cells related to IGLL5 and IL2RA in ccRCC and pRCC.

Prognostic Value of IGLL5 and IL2RA in Validation Cohort

We utilized RCC data from the ICGC and GEO database to validate the prognostic value as well. In RCC patients from the ICGC database, a decrease in overall survival was significantly related to the high expression of IGLL5 and IL2RA (p < 0.05, Figures 5F,G). We then explored the prognostic value of IGLL5 and IL2RA using data from GSE40912 (n = 32 samples) and GSE2748 (n = 28 samples) datasets. The results indicated that high expression of IGLL5 was significantly associated with poor prognosis (p < 0.05, Figures 5H,I). Patients with high expression of IL2RA had poor survival, but the difference was not statistically significant (p = 0.112, Figure 5J)

Furthermore, the robustness of IGLL5 as well as IL2RA as biomarkers was verified using primary ccRCC and pRCC samples from Shandong provincial Hospital affiliated to Shandong First University. For ccRCC, IHC images indicated that normal renal tissue had weak staining for IGLL5 and IL2RA, but relatively strong staining patterns for IGLL5 and IL2RA were observed in the cytoplasm of tumor tissues (Figures 6A,B). Moreover, similar staining patterns for IL2RA were observed in pRCC tissues and adjacent normal tissues (Figure 6C). However, IGLL5 staining was weakly positive by IHC, and no significant differences was detected between pRCC tissues and adjacent normal tissues (Figure 6D). These unique IHC staining patterns further confirmed the above results and illustrate that the two genes could be used to predict clinical outcome and distinguish cancerous tissue from normal tissue.

FIGURE 6 | Representative IHC images of IGLL5 and IL2RA in ccRCC and pRCC samples. (A,B) IHC patterns for IL2RA (A) and IGLL5 (B) in normal and KIRC samples. (C,D) IHC patterns for IL2RA (C) and IGLL5 (D) in normal and KIRP samples.

Pharmacological Analysis

We asked whether selected regulators have ability to predict drug response and potential treatment options for ccRCC and pRCC. Through CellMiner database and Connectivity Map (Cmap), the top 16 relevant correlations between IGLL5 and IL2RA and drug activity were shown in Figure 7. The response to all anticancer drugs was negatively correlated to the expression level of IL2RA, including panobinostat, pralatrexate, AT-13387, belinostat, and ponatinib. However, except for irofulven, other anticancer drugs were positively associated with the expression of IGLL5 (p < 0.05, Supplementary Table S1). Moreover, potential drugs for ccRCC and pRCC were predicted based on the top 15 prognosis-related hub genes (Figures 3E,F). The prediction results revealed that 325 and 47 candidate drugs exhibited promising clinical applications for ccRCC and pRCC, respectively (Score ≤ −0.75, Supplementary Table S2, S3). The molecular structure of top 10 potentially active drugs for the treatment of two types of RCC was shown in Supplementary Figure S6.

FIGURE 7 | The Prediction analysis between IGLL5 and IL2RA and drug response.

DISCUSSION

RCC accounts for more than 90% of kidney cancers, which consists of over 10 histological subtypes. Among these, ccRCC and pRCC are the main subtypes with high incidence and high mortality (Comprehensive molecular c, 2013; Chen et al., 2016; Linehan et al., 2016; Hsieh et al., 2017). Given limited options of effective therapy and modest effects of immunotherapy, there is an urgent need for novel biomarkers for future clinical application and evaluation (Barata and Rini, 2017).

Several studies have established that TME is a potential regulator of cancer progression and a source of therapeutic targets (Quail and Joyce, 2013; Vitale et al., 2019; Ho et al., 2020). In complex TME, immune cells and stromal cells play a significant role in cancer development. Stromal cells are genetically stable, and are considered as promising therapeutic targets for reducing tumor recurrence and drug resistance (Quail and Joyce, 2013). In TME, immune cells are divided into tumor antagonizing and promoting-immune cells. With the ability to regulate tumor progression in various stages (Lei et al., 2020). For example, inflammation could lead to CD8⁺ T cell dysfunction and consequently promote cancer progression (Wherry, 2011). Furthermore, in breast TME, NK cells are immature and dysfunctional NK in hepatocellular TME is related with cancer development (Krneta et al., 2016; Zhang et al., 2017). Garaud et al. found that infiltration of B cells in breast TME was correlated with a favorable prognosis (Garaud et al., 2019). However, several investigations have revealed that B cells may facilitate tumorigenesis by suppressing CD8⁺ T cells and producing cytokines, which contribute to angiogenesis (de Visser et al., 2005; Inoue et al., 2006; Tsou et al., 2016). Currently, the understanding of knowledge of TME of kidney cancers is restricted to specific tumor types and there is a lack of comprehensive analysis.

Here, we used several analyses to explore the TME landscape of ccRCC and pRCC. Firstly, we calculated the immune scores as well as stromal scores and assessed the clinical relevance by integrating of clinical information, which revealed that the survival of ccRCC and pRCC patients was significantly correlated with high immune scores and/or stromal scores. Moreover, various clinical characteristics, such as tumor grade, tumor stage, T stage, M stage were associated with immune scores and stromal scores. Then, by investigating the associated biological functions and pathways via GO enrichment and KEGG pathway analyses, we uncovered diverse immune-related functions and pathways, such as B cell mediated immunity, lymphocyte mediated immunity; cytokine-cytokine receptor interaction, chemokine signaling pathways, T cell receptor and B cell receptor signaling pathways. These results are consistent with previous studies and confirmed the essential role of immune cells in ccRCC and pRCC (Xu et al., 2019; Díaz-Montero et al., 2020; Lei et al., 2020; Wan et al., 2020; Zeng et al., 2020). Subsequently, according to STRING database, univariate Cox analysis, and Venn algorithm, we identified the two prognostic hub genes, IL2RA and IGLL5 in ccRCC and pRCC. To further exploit the potential role of these genes, we validated each individual gene in TCGA ccRCC and pRCC cohorts. The results revealed that IL2RA is a DEG and is correlated with prognosis of ccRCC and pRCC. However, IGLL5 was significantly expressed in ccRCC, but not in pRCC. In the ICGC, GEO40912, and GSE2748 validation cohort, we also observed a significantly differential survival trend. However, K-M analysis of OS indicated that high expression of IL2RA had a negative impact in GSE2748 dataset, but the difference was not significant. Moreover, the clinical application of the two genes was also validated using primary KIRC and KIRP samples from our hospital, and the results further confirmed the prognostic value of the two genes in clinical application. Previous research on IGLL5 and IL2RA focused predominantly on hematological malignancies. Fusion or mutation of IGLL5 was identified in Burkitt lymphoma, multiple lymphadenopathy, diffuse large B cell lymphoma, multiple myeloma, and chronic lymphocytic leukemia (Kasar et al., 2015; White et al., 2018; Panea et al., 2019; Li et al., 2020; Mosquera Orgueira et al., 2021). It has been reported that IGLL5 may act as a risk factor and was related with metastasis and poor prognosis of multiple myeloma (White et al., 2018; Barwick et al., 2019; D'Agostino et al., 2020). IGLL5 is also correlated with glioblastoma survival and breast cancer, which could serve as a biomarker for relapse-free survival in breast cancer with more than 85% accuracy (Ascierto et al., 2012; Qin et al., 2020). IL2RA also exhibits prognostic and diagnostic value in hematopoietic cancers and solid cancers. Elevated expression of IL2RA is associated with poor prognosis and may act as a biomarker in acute myeloid leukemia (Du et al., 2019; Nguyen et al., 2019). Up-regulated IL2RA was also found in tumor of lung, prostate, breast and melanoma, and thus, could serve as a marker of prognosis and immunoreaction (Kuhn and Dou, 2005).

Based on these results, we explored underlying mechanisms by GSEA analysis and also uncovered various immune-related pathways, further confirming the role of IGLL5 and IL2RA in immune regulation. Afterwards, CIBERSORT indicated that adaptive immune cells account for most of the immune cell infiltrations in ccRCC as well as pRCC. According to difference analysis and correlation analysis, we finally identified the immune cells commonly associated with IGLL5 and IL2RA in ccRCC and pRCC, such as CD8⁺ T cells, Tregs cells and T follicular helper cells, which was consistent with prior studies (Roth et al., 2018; Cañete et al., 2019; Ohue et al., 2019). Considering the unsatisfactory outcomes of therapy, we predicted small molecular compounds and individual response to drugs. Some commonly used drugs (such as oxaliplatin, ponatinib) have been shown to be influenced by IGLL5 and IL2RA, and numerous small molecular compounds have promising potential in future treatments. Currently, drugs targeting IL2RA consist of recombinant IL2, anti-IL2RA antibodies (such as basiliximab, daclizumab), anti-IL2RA radioimmunoconjuates (such as CHT-25, ⁹⁰Y-daclizumab), anti-IL2RA immunotoxins (such as LMB-2, RFT5-SMPT-dgA) and anti-IL2RA antibody drug conjugates (Flynn and Hartley, 2017). However, the efficacy and safety of some drugs is still unclear. Furthermore, IGLL5-targeted drugs have not been developed. Thus, further investigation of drugs targeting IGLL5 and IL2RA is of the essence.

In addition, there are other potential factors which also affect clinical outcomes in kidney cancers. Firstly, the spatial distribution of TME-regulated T cells may physically exclude these immune cells from the vicinity of cancer cells. Other essential considerations are that the stages of primary tumor and potentially different constitution of TME at metastatic sites (Fridman et al., 2012). To improve the outcome of ccRCC and pRCC, accurate and efficient biomarkers are indispensable. We investigated the TME and immune-related genes to identify associated immune cells and molecular mechanism as well as clinical value. Our findings suggested that IGLL5 and IL2RA could be used to estimate prognosis and may serve as potential therapeutic targets. The limited number of patients in GEO validation cohort may partially lead to bias. Therefore, in future investigations, expression data and clinical information from our medical center will be collected and further experiments using in vivo and in vitro models will be implemented to confirm the significant role of the two genes in the TME of kidney cancer.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.

ETHICS STATEMENT

The studies involving human participants were reviewed and approved by the Shandong Provincial Hospital Ethics Committee. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

AUTHOR CONTRIBUTIONS

Conceptualization: BZ, WH; Methodology: BZ, ZN, JW, ZY; Writing—original draft preparation: BZ, FX, WH, and FC; Writing—review and editing: ZN, WH; Supervision: ZN, WH.

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2021.760031/full#supplementary-material

REFERENCES

Ascierto, M. L., Kmieciak, M., Idowu, M. O., Manjili, R., Zhao, Y., Grimes, M., et al. (2012). A Signature of Immune Function Genes Associated with Recurrence-free Survival in Breast Cancer Patients. Breast Cancer Res. Treat. 131 (3), 871–880. doi:10.1007/s10549-011-1470-x

Barata, P. C., and Rini, B. I. (2017). Treatment of Renal Cell Carcinoma: Current Status and Future Directions. CA: a Cancer J. clinicians 67 (6), 507–524. doi:10.3322/caac.21411

Barwick, B. G., Neri, P., Bahlis, N. J., Nooka, A. K., Dhodapkar, M. V., Jaye, D. L., et al. (2019). Multiple Myeloma Immunoglobulin Lambda Translocations Portend Poor Prognosis. Nat. Commun. 10 (1), 1911. doi:10.1038/s41467-019-09555-6

Cañete, P. F., Sweet, R. A., Gonzalez-Figueroa, P., Papa, I., Ohkura, N., Bolton, H., et al. (2019). Regulatory Roles of IL-10-producing Human Follicular T Cells. J. Exp. Med. 216 (8), 1843–1856. doi:10.1084/jem.20190493

Chen, F., Zhang, Y., Şenbabaoğlu, Y., Ciriello, G., Yang, L., Reznik, E., et al. (2016). Multilevel Genomics-Based Taxonomy of Renal Cell Carcinoma. Cel Rep. 14 (10), 2476–2489. doi:10.1016/j.celrep.2016.02.024

Comprehensive Molecular Characterization of clear Cell Renal Cell Carcinoma. Nature . 2013; 499 (7456): 43–49. doi:10.1038/nature12222

D'Agostino, M., Zaccaria, G. M., Ziccheddu, B., Rustad, E. H., Genuardi, E., Capra, A., et al. (2020). Early Relapse Risk in Patients with Newly Diagnosed Multiple Myeloma Characterized by Next-Generation Sequencing. Clin. Cancer Res. 26 (18), 4832–4841. doi:10.1158/1078-0432.ccr-20-0951

de Visser, K. E., Korets, L. V., and Coussens, L. M. (2005). De Novo carcinogenesis Promoted by Chronic Inflammation Is B Lymphocyte Dependent. Cancer cell 7 (5), 411–423. doi:10.1016/j.ccr.2005.04.014

Díaz-Montero, C. M., Rini, B. I., and Finke, J. H. (2020). The Immunology of Renal Cell Carcinoma. Nat. Rev. Nephrol. 16 (12), 721–735. doi:10.1038/s41581-020-0316-3

Du, W., He, J., Zhou, W., Shu, S., Li, J., Liu, W., et al. (2019). High IL2RA mRNA Expression Is an Independent Adverse Prognostic Biomarker in Core Binding Factor and Intermediate-Risk Acute Myeloid Leukemia. J. Transl Med. 17 (1), 191. doi:10.1186/s12967-019-1926-z

Flynn, M. J., and Hartley, J. A. (2017). The Emerging Role of Anti-CD25 Directed Therapies as Both Immune Modulators and Targeted Agents in Cancer. Br. J. Haematol. 179 (1), 20–35. doi:10.1111/bjh.14770

Fridman, W. H., Pagès, F., Sautès-Fridman, C., and Galon, J. (2012). The Immune Contexture in Human Tumours: Impact on Clinical Outcome. Nat. Rev. Cancer 12 (4), 298–306. doi:10.1038/nrc3245

Garaud, S., Buisseret, L., Solinas, C., Gu-Trantien, C., de Wind, A., Van den Eynden, G., et al. (2019). Tumor Infiltrating B-Cells Signal Functional Humoral Immune Responses in Breast Cancer. JCI insight 5 (18), e129641. doi:10.1172/jci.insight.129641

Grivennikov, S. I., Greten, F. R., and Karin, M. (2010). Immunity, Inflammation, and Cancer. Cell. 140 (6), 883–899. doi:10.1016/j.cell.2010.01.025

Hanahan, D., and Coussens, L. M. (2012). Accessories to the Crime: Functions of Cells Recruited to the Tumor Microenvironment. Cancer cell 21 (3), 309–322. doi:10.1016/j.ccr.2012.02.022

Ho, W. J., Jaffee, E. M., and Zheng, L. (2020). The Tumour Microenvironment in Pancreatic Cancer - Clinical Challenges and Opportunities. Nat. Rev. Clin. Oncol. 17 (9), 527–540. doi:10.1038/s41571-020-0363-5

Hsieh, J. J., Purdue, M. P., Signoretti, S., Swanton, C., Albiges, L., Schmidinger, M., et al. (2017). Renal Cell Carcinoma. Nat. Rev. Dis. Primers 3, 17009. doi:10.1038/nrdp.2017.9

Inoue, S., Leitner, W. W., Golding, B., and Scott, D. (2006). Inhibitory Effects of B Cells on Antitumor Immunity. Cancer Res. 66 (15), 7741–7747. doi:10.1158/0008-5472.can-05-3766

Joyce, J. A., and Pollard, J. W. (2009). Microenvironmental Regulation of Metastasis. Nat. Rev. Cancer 9 (4), 239–252. doi:10.1038/nrc2618

Kasar, S., Kim, J., Improgo, R., Tiao, G., Polak, P., Haradhvala, N., et al. (2015). Whole-genome Sequencing Reveals Activation-Induced Cytidine Deaminase Signatures during Indolent Chronic Lymphocytic Leukaemia Evolution. Nat. Commun. 6, 8866. doi:10.1038/ncomms9866

Krneta, T., Gillgrass, A., Chew, M., and Ashkar, A. A. (2016). The Breast Tumor Microenvironment Alters the Phenotype and Function of Natural Killer Cells. Cell Mol Immunol 13 (5), 628–639. doi:10.1038/cmi.2015.42

Kuhn, D. J., and Dou, Q. P. (2005). The Role of Interleukin-2 Receptor Alpha in Cancer. Front. Biosci. 10, 1462–1474. doi:10.2741/1631

Lei, X., Lei, Y., Li, J.-K., Du, W.-X., Li, R.-G., Yang, J., et al. (2020). Immune Cells within the Tumor Microenvironment: Biological Functions and Roles in Cancer Immunotherapy. Cancer Lett. 470, 126–133. doi:10.1016/j.canlet.2019.11.009

Li, M., Su, X., Wang, Y., Fan, L., Chai, J., Li, P., et al. (2020). Lineage-negative Lymphoma with a Helper Innate Lymphoid Cell Phenotype. Virchows Arch. 476 (2), 285–293. doi:10.1007/s00428-019-02658-x

Linehan, W. M., Linehan, W. M., Spellman, P. T., Ricketts, C. J., Creighton, C. J., Fei, S. S., et al. (2016). Comprehensive Molecular Characterization of Papillary Renal-Cell Carcinoma. N. Engl. J. Med. 374 (2), 135–145. doi:10.1056/NEJMoa1505917

Mosquera Orgueira, A., Ferreiro Ferro, R., Díaz Arias, J. Á., Aliste Santos, C., Antelo Rodríguez, B., Bao Pérez, L., et al. (2021). Detection of New Drivers of Frequent B-Cell Lymphoid Neoplasms Using an Integrated Analysis of Whole Genomes. PloS one 16 (5), e0248886. doi:10.1371/journal.pone.0248886

Nguyen, C. H., Glüxam, T., Schlerka, A., Bauer, K., Grandits, A. M., Hackl, H., et al. (2019). SOCS2 Is Part of a Highly Prognostic 4-gene Signature in AML and Promotes Disease Aggressiveness. Sci. Rep. 9 (1), 9139. doi:10.1038/s41598-019-45579-0

Ohue, Y., Nishikawa, H., and Regulatory, T. (2019). Regulatory T (Treg) Cells in Cancer: Can Treg Cells Be a New Therapeutic Target?Cancer Sci. 110 (7), 2080–2089. doi:10.1111/cas.14069

Osipov, A., Saung, M. T., Zheng, L., and Murphy, A. G. (2019). Small Molecule Immunomodulation: the Tumor Microenvironment and Overcoming Immune Escape. J. Immunotherapy Cancer 7 (1), 224. doi:10.1186/s40425-019-0667-0

Panea, R. I., Love, C. L., Shingleton, J. R., Reddy, A., Bailey, J. A., Moormann, A. M., et al. (2019). The Whole-Genome Landscape of Burkitt Lymphoma Subtypes. Blood 134 (19), 1598–1607. doi:10.1182/blood.2019001880

Pitt, J. M., Marabelle, A., Eggermont, A., Soria, J.-C., Kroemer, G., and Zitvogel, L. (2016). Targeting the Tumor Microenvironment: Removing Obstruction to Anticancer Immune Responses and Immunotherapy. Ann. Oncol. 27 (8), 1482–1492. doi:10.1093/annonc/mdw168

Qin, Z., Zhang, X., Chen, Z., and Liu, N. (2020). Establishment and Validation of an Immune-Based Prognostic Score Model in Glioblastoma. Int. immunopharmacology 85, 106636. doi:10.1016/j.intimp.2020.106636

Quail, D. F., and Joyce, J. A. (2013). Microenvironmental Regulation of Tumor Progression and Metastasis. Nat. Med. 19 (11), 1423–1437. doi:10.1038/nm.3394

Ravaud, A., Motzer, R. J., Pandha, H. S., George, D. J., Pantuck, A. J., Patel, A., et al. (2016). Adjuvant Sunitinib in High-Risk Renal-Cell Carcinoma after Nephrectomy. N. Engl. J. Med. 375 (23), 2246–2254. doi:10.1056/nejmoa1611406

Roth, T. L., Puig-Saus, C., Yu, R., Shifrut, E., Carnevale, J., Li, P. J., et al. (2018). Reprogramming Human T Cell Function and Specificity with Non-viral Genome Targeting. Nature 559 (7714), 405–409. doi:10.1038/s41586-018-0326-5

Siegel, R. L., Miller, K. D., Fuchs, H. E., and JemalCancer Statistics, A. (2021). Cancer Statistics, 2021. CA A. Cancer J. Clin. 71 (1), 7–33. doi:10.3322/caac.21654

Steffens, S., Janssen, M., Roos, F. C., Becker, F., Schumacher, S., Seidel, C., et al. (2012). Incidence and Long-Term Prognosis of Papillary Compared to clear Cell Renal Cell Carcinoma - A Multicentre Study. Eur. J. Cancer 48 (15), 2347–2352. doi:10.1016/j.ejca.2012.05.002

Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., et al. (2019). STRING V11: Protein-Protein Association Networks with Increased Coverage, Supporting Functional Discovery in Genome-wide Experimental Datasets. Nucleic Acids Res. 47 (D1), D607–D613. doi:10.1093/nar/gky1131

Tsou, P., Katayama, H., Ostrin, E. J., and Hanash, S. M. (2016). The Emerging Role of B Cells in Tumor Immunity. Cancer Res. 76 (19), 5597–5601. doi:10.1158/0008-5472.can-16-0431

Vitale, I., Manic, G., Coussens, L. M., Kroemer, G., and Galluzzi, L. (2019). Macrophages and Metabolism in the Tumor Microenvironment. Cel Metab. 30 (1), 36–50. doi:10.1016/j.cmet.2019.06.001

Vuong, L., Kotecha, R. R., Voss, M. H., and Hakimi, A. A. (2019). Tumor Microenvironment Dynamics in Clear-Cell Renal Cell Carcinoma. Cancer Discov. 9 (10), 1349–1357. doi:10.1158/2159-8290.cd-19-0499

Wan, B., Liu, B., Huang, Y., and Lv, C. (2020). Identification of Genes of Prognostic Value in the ccRCC Microenvironment from TCGA Database. Mol. Genet. Genomic Med. 8 (4), e1159. doi:10.1002/mgg3.1159

Wang, Q., Chen, C., Ding, Q., Zhao, Y., Wang, Z., Chen, J., et al. (2020). METTL3-mediated m6A Modification of HDGF mRNA Promotes Gastric Cancer Progression and Has Prognostic Significance. Gut 69 (7), 1193–1205. doi:10.1136/gutjnl-2019-319639

Wherry, E. J. (2011). T Cell Exhaustion. Nat. Immunol. 12 (6), 492–499. doi:10.1038/ni.2035

White, B. S., Lanc, I., O’Neal, J., Gupta, H., Fulton, R. S., Schmidt, H., et al. (2018). A Multiple Myeloma-specific Capture Sequencing Platform Discovers Novel Translocations and Frequent, Risk-Associated point Mutations in IGLL5. Blood Cancer J. 8 (3), 35. doi:10.1038/s41408-018-0062-y

Wu, T., and Dai, Y. (2017). Tumor Microenvironment and Therapeutic Response. Cancer Lett. 387, 61–68. doi:10.1016/j.canlet.2016.01.043

Xu, W.-H., Xu, Y., Wang, J., Wan, F.-N., Wang, H.-K., Cao, D.-L., et al. (2019). Prognostic Value and Immune Infiltration of Novel Signatures in clear Cell Renal Cell Carcinoma Microenvironment. Aging 11 (17), 6999–7020. doi:10.18632/aging.102233

Yoshihara, K., Shahmoradgoli, M., Martínez, E., Vegesna, R., Kim, H., Torres-Garcia, W., et al. (2013). Inferring Tumour Purity and Stromal and Immune Cell Admixture from Expression Data. Nat. Commun. 4, 2612. doi:10.1038/ncomms3612

Zeng, Q., Zhang, W., Li, X., Lai, J., and Li, Z. (2020). Bioinformatic Identification of Renal Cell Carcinoma Microenvironment-Associated Biomarkers with Therapeutic and Prognostic Value. Life Sci. 243, 117273. doi:10.1016/j.lfs.2020.117273

Zhang, Q.-F., Yin, W.-W., Xia, Y., Yi, Y.-Y., He, Q.-F., Wang, X., et al. (2017). Liver-infiltrating CD11b−CD27− NK Subsets Account for NK-Cell Dysfunction in Patients with Hepatocellular Carcinoma and Are Associated with Tumor Progression. Cel Mol Immunol 14 (10), 819–829. doi:10.1038/cmi.2016.28

NOMENCLATURE

Cmap Connectivity map

ccRCC Clear cell renal clear cell carcinoma,

DEGs Differentially expressed genes

ESTIMATE Estimation of STromal and Immune cells in Malignant Tumours using Expression data

GEO Gene Expression Omnibus

GO Gene Ontology

GSEA Gene set enrichment analysis

ICGC International Cancer Genome Consortium

KEGG Kyoto Encyclopedia of Genes and Genomes

pRCC Papillary renal cell carcinoma

PPI Protein–protein interaction

RCC Renal clear cell carcinoma

TCGA The Cancer Genome Atlas

TME Tumor microenvironment

IHC Immunohistochemistry.

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Zheng, Xie, Cheng, Wang, Yao, He and Niu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 24 November 2021
doi: 10.3389/fmolb.2021.762924

Bioinformatic Analyses and Experimental Verification Reveal that High FSTL3 Expression Promotes EMT via Fibronectin-1/α5β1 Interaction in Colorectal Cancer

Yuanjie Liu1,²†, Jiepin Li1,2,³†, Shuhong Zeng1,², Ying Zhang1,², Yonghua Zhang³, Zhichao Jin¹, Shenlin Liu1,²* and Xi Zou¹*

¹Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, China

²No. 1 Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, China

³Department of Oncology, Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Zhangjiagang, China

Edited by:
William C. Cho, QEH, Hong Kong SAR, China

Reviewed by:
Rishi Kumar Jaiswal, Loyola University Chicago, United States
Amirali Bukhari, University of Alberta, Canada

* Correspondence: Shenlin Liu, lsljsszyy@126.com; Xi Zou, zxvery@126.com

†These authors have contributed equally to this work

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 23 August 2021
Accepted: 09 November 2021
Published: 24 November 2021

Citation: Liu Y, Li J, Zeng S, Zhang Y, Zhang Y, Jin Z, Liu S and Zou X (2021) Bioinformatic Analyses and Experimental Verification Reveal that High FSTL3 Expression Promotes EMT via Fibronectin-1/α5β1 Interaction in Colorectal Cancer. Front. Mol. Biosci. 8:762924. doi: 10.3389/fmolb.2021.762924

Background: Colorectal cancer (CRC) is a typical cancer prevalent worldwide. Despite the conventional treatments, CRC has a poor prognosis due to relapse and metastasis. Moreover, there is a dearth of sensitive biomarkers for predicting prognosis in CRC.

Methods: This study used a bioinformatics approach combining validation experiments to examine the value of follistatin-like 3 (FSTL3) as a prognostic predictor and therapeutic target in CRC.

Results: FSTL3 was remarkably upregulated in the CRC samples. FSTL3 overexpression was significantly associated with a poor prognosis. FSTL3 was found to activate the epithelial-mesenchymal transition by promoting the binding of FN1 to α5β1. FSTL3 expression was also positively correlated with the abundance of the potent immunosuppressors, M2 macrophages.

Conclusion: FSTL3 overexpression affects CRC prognosis and thus, FSTL3 can be a prognostic biomarker and therapeutic target with potential applications in CRC.

Keywords: colorecal cancer, FSTL3 gene, EMT—epithelial to mesenchymal transformation, FN1, fibronectin 1, α5β1 integrin, actin, M2 macrophage, prognosis (carcinoma)

1 INTRODUCTION

Colorectal cancer (CRC) is the third most common cancer globally (Bray et al., 2020; Chen et al., 2020). The CRC patients undergo cancer-related mortality due to frequent metastasis in the liver, lungs, and further distant regions (Brody, 2015). The predisposing factors for CRC like smoking, unhealthy diets, obesity epidemic, and lack of exercise, are now rampant in the high-income industrialized countries (Wieszczy et al., 2017). Other risk factors, such as genetic mutations colorectal adenomatous polyposis (APC), deletion of K-RAS, p53, protooncogene serine/threonine kinase (BRAF), mismatch repair (MMR) gene, and microsatellite instability (MSI), triggering colon cancer (Harada and Morlote, 2020). The effective early diagnosis of CRC is limited, despite the conventional colonoscopy screening (The Lancet Gastroenterology and Hepatology, 2017). The early symptoms and signs of colon cancer are not distinct with most CRC patients being diagnosed in their middle to late stages. The conventional treatments for CRC include surgery, radiotherapy, and drug therapy. The latter includes chemical drugs, small molecule drugs targeting mutant genes, small molecule drugs targeting signal pathways, drugs targeting epigenetic regulation, and immune checkpoint inhibitors (Piawah and Venook, 2019). Recent studies based on the molecularly targeted drugs Bevacizumab, Cetuximab and Sorafenib have been used for treating CRC. These drugs have improved the overall survival rate of CRC patients (Garcia et al., 2020; Jeong et al., 2020; Giordano et al., 2021). However, not all patients, especially the ones with advanced and distant metastases, benefit from these targeted drugs. This necessitates the exploration of the new biomarkers or therapeutic targets, for improving the personalized systemic treatments.

FSTL3, also known as FLRG, is a protein-coding gene located in the chromosome q 13.3 region (Maguer-Satta and Rimokh, 2004). FSTL3 has been confirmed as an oncogene closely associated with the proliferation and metastasis of the tumor cell (Bloise et al., 2009; Gao et al., 2020). The rapid development of bioinformatics in recent years along with the increasing availability of transcriptomic data and clinical information have developed favorable conditions for investigating cancer pathogenesis. Our team has been involved in exploring the digestive system cancer pathogenesis (Bloise et al., 2009; Li et al., 2016). Our team previously found that FSTL3 is highly expressed in gastric cancer, promoting epithelial-mesenchymal transition (EMT) through the BMP/SMAD signaling pathway. In addition, the FSTL3 overexpression is known to promote M2 macrophage infiltration in the tumor microenvironment (Liu et al., 2021). Considering the role of FSTL3 in gastric cancer, we hypothesized that FSTL3 may have a similar molecular mechanism in CRC, which was explored in this study. We found that the expression level of FSTL3 was significantly elevated in CRC and was an independent prognostic factor for CRC patients. More importantly, FSTL3 was identified as a key factor in the remodeling of the CRC tumor microenvironment and a promising therapeutic target for blocking CRC metastasis.

2 MATERIALS AND METHODS

2.1 The Research Flowchart

Supplementary Figure S1 shows the workflow of our study and Supplementary Figure S2 illustrates the mechanism of our study.

2.2 Transcriptomic Expression Analysis

The FSTL3 expression in CRC was first investigated using the TIMER web tool (https://cistrome.shinyapps.io/timer/) (Li et al., 2017) and the Gene Expression Profiling Interactive Analysis (GEPIA) database (https://www.oncomine.org/) (Tang et al., 2017). The differential expression of FSTL3 was further confirmed using the TCGA-Colon Adenocarcinoma (COAD) cohort and GSE10950, GSE44861 datasets (Zhao et al., 2020; Chen and Ke, 2021).

2.3 Cox Model Establishment and Prognostic Significance Analysis

The raw counts of RNA-sequencing data and corresponding clinical information regarding FSTL3 were obtained from the TCGA dataset (https://portal.gdc.cancer.gov/) in January 2020 (Wang et al., 2016). The univariate and multivariate cox regression analyses were performed to access the independent prognostic values of FSTL3. The forest plot represented the p-value, hazard ratio (HRs), and 95% confidence interval (CIs) of each variable through the “forestplot” R package.

The FSTL3 expression levels were analyzed based on the various classification parameters, such as the T (Tumor) stages, N (Node) stages, M (Metastasis) stages, pathological stages, and histological grades according to the TCGA-COAD data.

To reveal the value of FSTL3 on the prognosis of CRC patients, survival analyses such as the overall survival (OS), disease-free survival (DFS), and post-progression survival (PPS) were performed through GEPIA. The automatically selected best cutoff was selected for analysis. In addition, the progression-free survival (PFS), progression-free interval (PFI), disease-specific survival (DSS), and disease-free interval (DFI) were also estimated based on the colon cancer cases in TCGA-COAD.

2.4 FSTL3-Correlated Gene Enrichment Analysis

The median cutoff of FSTL3 expression in the TCGA-COAD was used to define the groups with high and low FSTL3 expression. The “DESeq” R package was used to obtain the FSTL3 -correlated genes. The “Enrichr” database was used to perform the functional enrichment analysis to explore the potential functions of FSTL3(Kuleshov et al., 2016).

The gene set enrichment analysis (GSEA) was performed using the Broad Institute GSEA software 3.0 (Powers et al., 2018). The gene set “subset of GO” was downloaded from the Molecular Signatures Databases (http://www.gsea-msigdb.org/gsea/msigdb/index.jsp) and was used for the GO enrichment analysis (Powers et al., 2018). The FDR <0.1 was considered to be statistically significant. In addition, a single cell analysis was conducted based on the GSE146771 to seek more evidence on the potential function of FSTL3 (Cao et al., 2021).

2.5 Immune Cell and Stromal Cell Analyses

The ssGSEA algorithm was initially performed to assess the correlation between the FSTL3 expression levels and the overall immune as well as stromal infiltration levels in CRC (Liu et al., 2020). Furthermore, a high-performance computational method for quantifying cellular components from bulk tissue gene expression profiles, CIBERSORT was used to estimate immune infiltrations reliably (Chen et al., 2018). Spearman’s rank correlation coefficient was calculated for pairwise correlation comparisons and p < 0.05 was considered statistically significant. All the results from the above analyses methods and R package were implemented by the “ggplot2” and “pheatmap” packages.

2.6 Antibodies and Reagents

A complete list of reagents and antibodies is provided in Supplementary Tables. All the concentrations were chosen based on the previous studies or the manufacturer’s instructions. The experimental details are given in Supplementary material.

2.7 Cell Culture

The human CRC cell lines, SW620, SW480, RKO, HT-29, LoVo, Caco2, human monocytic cells THP-1 and normal human colonic epithelial cell line, NCM460 were purchased from the cell bank of the Chinese Academy of Sciences (Shanghai, China). The CRC and THP-1 cells were cultured in the RPMI-1640 medium with 10% fetal bovine serum (FBS). NCM460 was cultured in DMEM with 10% FBS. All the cells were incubated in 5% CO₂ at 37°C.

2.8 Western Blot Assessment

The protocol for western blotting was based on the previous studies (Hnasko and Hnasko, 2015). The Target/β-actin bands were identified using a gel image processing system (ChemiDoc XRS+). Subsequently, the relative protein levels were calculated.

2.9 Ethics Statement and Specimen Collection

The study’s protocol was approved by the ethics committee of the Jiangsu Province Hospital of Chinese Medicine, and informed consent was obtained from clinicians and patients (2020NL-107-01). The CRC tissue and the adjacent healthy tissues (margin, 5 cm) were collected during surgery from 30 previously treatment-naïve patients with CRC at the Jiangsu Provincial Hospital of Traditional Chinese Medicine. The tumors were staged and graded using the 8th edition of the American Joint Committee on Cancer tumor-node-metastasis (TNM) staging system (Tong et al., 2018). After extraction, the tissue specimens were rinsed with cold phosphate-buffered saline and immediately placed in liquid nitrogen. The flash-frozen tissues were then transferred and stored at −80°C until further examination using immunohistochemistry (IHC) and western blot analysis. The preoperative serum samples were collected from all the patients, and serum was stored at −80°C for further studies.

2.10 Immunohistochemistry

The protocol used for IHC was based on earlier studies (Nizioł et al., 2021). The images were captured using a NIKON Eclipse Ni-E microscope (NIKON, Japan) (original magnification, ×400). The H-SCORE (range 0–300, higher scores indicating stronger positive staining) was calculated as described previously (Yang et al., 2019).

2.11 Lentiviral Vector Construction and Transfection

The lentiviral vectors were used for overexpressing and knocking down FSTL3. The viruses were designed, synthesized, and produced by the GeneChem Corporation. Transfection was performed according to the supplier’s protocol. The HT-29 and RKO cells were transduced with the recombinant lentivirus using 2 μg/ml polybrene for 24 h. Subsequently, the stably transfected GFP-expressing cells were identified using 1.5 μg/ml puromycin. The FSTL3 overexpression and knockdown and transduction efficiency were assessed using western blots and GFP-expression.

2.12 CCK8 Assay

The CCK8 assay was performed using a CCK8 kit following the manufacturer’s protocol. Briefly, CRC cells were plated into 96-well plates (5 × 10³ cells per well) in 100 μl of culture medium or serum-free condition for 12, 24, 48 h at 37°C. CCK-8 solution (100 μl/well) was added for another 2 h and then incubated for 12, 24, and 48 h. Then, the optical density (OD) was measured at 450 nm with a microplate reader (BioTek Synergy HT).

2.13 Enzyme-Linked Immunosorbent Assay

The cell supernatants or patients’ serum was examined for FSTL3/FN1/α5β1 expression using the ELISA Kit based on the given instruction manual. A microplate reader (BioTek Synergy HT) was used to examine the optical density at 450 nm.

2.14 Colony Formation Assays

The clonogenic ability of the cells was assessed using a clone formation assay, performed as described previously (Grover et al., 2016). The number of colonies was counted using a compound light microscope (Olympus BX53, Japan).

2.15 Xenograft Tumor Model

All the animal experiments were approved by the ethics committee of the Jiangsu Province Hospital of Chinese Medicine (2021-5-062). Twenty-four 4-week-old male BALB/c nude mice were obtained from the Beijing Institute of Biomedicine (Beijing, China) (Certificate No. SYXK 2019-0010). The RKO cells transfected with sh-FSTL3, oe-FSTL3, and NC and control cells (4 × 10⁶ cell/mouse) were injected subcutaneously into the right armpit region (n = 6 per group). Seven days later, tumor formation was observed beneath the skin. The maximum 1) and minimum tumor diameter 2) were measured twice weekly. On day 28, the mice were euthanized and all the tumors were collected. The tumor volume was calculated (V = 1/2ab²), and the growth curves of the subcutaneous xenografts were drawn.

2.16 Wound Healing Assay

The protocol used for the wound healing assay was based on earlier studies (Han et al., 2020). Cell migration towards the scratch zone was photographed using an inverted fluorescence microscope (Olympus CKX-41, Japan) (×200 magnification).

Cells were dissociated to produce single-cell suspensions and were seeded in six-well ultralow-attachment plates at a density of 5 × 10 ³ cells/well. They were cultured in serum-free medium DMEM with FGF (20 ng/ml), EGF (20 ng/ml), and 2%B27. After culturing for 7 days, the size and number of tumor spheres were evaluated using light microscopy (Olympus BX53, Japan) (×40 magnification).

2.17 Transwell Assay

The cell migration and invasion were assessed using a transwell assay based on a previously published protocol (Wang et al., 2021). The membrane in the chamber was cut and imaged using light microscopy (Olympus BX53, Japan) (×200 magnification), and the cell counts were obtained using ImageJ software.

2.18 Immunofluorescence Staining

The protocol used for immunofluorescence staining was based on earlier studies (Donaldson, 2015). The immunofluorescence staining was observed using epifluorescence microscopy (Olympus, BX60-32FB2-A03) and different filters were used for capturing images using an Olympus, DP50 camera (×400 magnification).

2.19 Establishment of a Co-culture Unit

The THP-1 cells (1 × 10⁵ cells/ml) were treated with phorbol 12-myristate 13-acetate (PMA) (10 ng/ml) for 48 h to allow the induction of macrophage differentiation (Genin et al., 2015). The PMA-containing medium was replaced with the serum-free medium, and the cells were cultured for 24 h. Two days before the co-culture experiment, the cells (1 × 10⁵ cells/ml) from the control, knock-down (sh-FSTL3), overexpression (oe-FSTL3), and negative control (NC) groups were seeded onto the 0.4 μM transwell inserts. For co-culture, the culture medium in the inserts with the CRC cells was removed and transferred to the top of the pates with differentiated THP-1 cells. After 48 h of further co-culturing, the cells were obtained, and immunofluorescence staining was performed.

2.20 Statistical Analysis

Data were reported as mean ± standard deviation. The t-tests and one-way ANOVA were used to perform comparisons between the two groups and among the multiple groups, respectively. All data were analyzed using the SPSS 26.0 (SPSS Inc., USA) and illustrated using the GraphPad Prism 8.0 (GraphPad Software, Inc., USA). All the experiments were carried out at least thrice. **p < 0.01 and *p < 0.05 were defined to be statistically significant.

3 RESULTS

3.1 Follistatin-Like 3 Expression in Colorectal Cancer

The TCGA-COAD data showed that the FSTL3 expression is higher in the CRC tissues than in the normal tissues (Figure 1A; p < 0.05). Moreover, the TIMER and TCGA data analyses also showed higher FSTL3 expression in the CRC than in the normal tissues (Figure 1B).

FIGURE 1 | FSTL3 levels in the colorectal cancer (CRC) tissues. (A) Expression levels of FSTL3 in CRC based on the data of the Cancer Genome Atlas (TCGA)-COAD. (B) The FSTL3 mRNA levels in the CRC and normal tissues based on the TIMER database. (C) Public datasets from the Gene Expression Omnibus (GSE10950, GSE44861) were used to verify the FSTL3 mRNA levels in CRC. (D,E) The FSTL3 protein expression in the normal human colonic epithelial cells as well as the CRC cells. (F) FSTL3 expression in the CRC tissues (T) and paired non-tumorous tissue (N) evaluated using western blotting (n = 30). (G) The intensity of FSTL3 immunohistochemistry staining and FSTL3 expression levels in the paracancerous and CRC tissues (n = 30). (H) The FSTL3 immunohistochemistry in the CRC tissues is based on data from the Human Protein Atlas (THPA). *p < 0.05, **p < 0.01, ***p < 0.001.

The expression of FSTL3 was further explored in the CRC tissues using data from the Gene Expression Omnibus (GEO) database. Data from the GSE10950 and GSE44861 datasets indicated a significant difference in the FSTL3 expression between the CRC tissues and adjacent tissues (Figure 1C). Western blot, ELISA, and IHC staining revealed that FSTL3 was overexpressed in the CRC cells and tissues. The mean H-SCOREs for FSTL3 expression in the CRC and paracancerous tissues were 95.53 ± 15.96 and 11.70 ± 3.55, respectively (Figures 1D–G) (p < 0.01, ANOVA). The FSTL3 protein expression in CRC was further verified using the IHC data from The Human Protein Atlas, which revealed that FSTL3 to be primarily expressed in the cell membrane and cytoplasm (Figure 1H).

3.2 Prognostic Value of the Follistatin-Like 3 Expression in Colorectal Cancer

The multivariate hazard ratios for the different variables were then calculated using a Cox regression model based on the TCGA-COAD. Univariate analysis results (OS) demonstrated that the FSTL3 overexpression (p = 0.002), T classification (p = 0.004), N classification (p < 0.001), and M classification (p < 0.001) were all closely correlated with a poor prognosis (Figure 2A). The multivariate analyses revealed that the T classification (p = 0.021) and M classification (p < 0.001) were all independent predictors of an unfavorable prognosis (Figure 2D).

FIGURE 2 | Univariate and multivariate Cox regression analyses of FSTL3 expression and clinicopathological factors related to the prognosis of colorectal cancer (CRC). (A–C) Univariate Forest plot illustrating FSTL3 expression and the clinicopathological factors related to the (A) Overall Survival (OS), (B) Disease-Specific Survival (DSS), and (C) Progression Free Interval (PFI) in CRC. (D–F) Multivariate Forest plot showing FSTL3 expression and the clinicopathological factors related to (D) OS, (E) DSS, and (F) PFI in CRC. (G) Association of the FSTL3 mRNA expression with the T/N/M stage, pathological stage, BMI, weight, age, sex, and race. (H) Overall survival (OS) and Disease-free survival (DFS) from the GEPIA database. (I,J) The OS, disease-free interval (DFI), DSS, and PFI from The Cancer Genome Atlas-COAD database based on the FSTL3 expression levels. (I) KRAS mutated; (J) KRAS wild-type. Hazard ratios and p-values are shown. NS: not significant, *p < 0.05, **p < 0.01, ***p < 0.001.

Furthermore, the results of the univariate analysis (DSS) demonstrated that FSTL3 overexpression (p < 0.001), T classification (p = 0.004), N classification (p < 0.001), and M classification (p = 0.004) were in close correlation with a poor prognosis (Figure 2B). The multivariate analyses revealed soverexpression (p = 0.039), and M classification (p < 0.01) to independently predict unfavorable prognosis (Figure 2E).

Finally, the results of the univariate analysis (PFI) demonstrated FSTL3 overexpression (p < 0.001), T classification (p < 0.001), N classification (p < 0.001), and M classification (p < 0.001) to be closely correlated with a poor prognosis (Figure 2C). The multivariate analyses revealed FSTL3 overexpression (p = 0.012), and M classification (p < 0.001) to independently predict the unfavorable prognosis (Figure 2F).

The FSTL3 levels are related to the clinicopathological characteristics of the CRC patients, including the T/N/M stage, pathological stage, BMI, weight, age, sex, and race, as illustrated in Figure 2G.

To evaluate the association of FSTL3 expression levels with the survival in CRC patients, the GEPIA database was used. Interestingly, the expression of FSTL3 showed a significant negative correlation with the patient survival (GEPIA: Overall survival (OS), p = 0.006; Disease-Free Survival (DFS), p = 0.018) (Figure 2H).

The results based on the TCGA-COAD revealed the increasing FSTL3 expression levels to be associated with a worse prognosis (OS, p = 0.01; PFI, p = 0.0073; DSS, p = 0.0045) in the KRAS wild type (Figure 2I) and mutant type (Figure 2J).

3.3 Functional Enrichment Analysis of Follistatin-Like 3

The genes positively or negatively correlated with FSTL3 in TCGA-COAD were obtained based on the “DESeq” R package (Figure 3A). A total of 21 differentially expressed genes (DEGs) were imported into the DEGs PPI, and then the Cytotype cytoHubba was applied for further analysis demonstratingFN1 to be the highest-scoring hub gene (Figure 3B). The correlation analyses between the serum FN1, ITGA5 (gene symbol for α5β1), and serum FSTL3 in the patients by comparing between the two groups (area within the purple box) and TCGA-COAD-based correlation analyses between FN1, ITGA5, and FSTL3 (green box). The results showed a highly significant positive correlation between the expression of FSTL3 and FN1 and ITGA5 (Figure 3C). FN1 was overexpressed in COAD (Figure 3D) but was not correlated with the prognosis of COAD patients (Figures 3E–G). The analysis of the functional enrichment showed FSTL3 to be involved in “extracellular matrix organization,” “extracellular structure organization,” “collagen-containing extracellular matrix,” “platelet-derived growth factor binding,” “Protein digestion and absorption,” ECM-receptor interaction,” and “Focal adhesion.” Most results have been correlated to the EMT or extracellular matrix (ECM) leading to the invading phenotype of the carcinoma cells. (Figures 3H–K).

FIGURE 3 | Protein-protein interaction (PPI) network and enrichment analysis. (A) Volcano map of the genes showing differential expression after a change in the FSTL3 levels. Red dots, upregulated genes; blue dots, downregulated genes; abscissa, differences in the gene expression (log2 fold change); and ordinate, the significance of these differences (−log10 padj). (B) The protein-protein interaction (PPI) network of FSTL3 and its related genes. (C) TCGA-based correlation analysis between FN1, ITGA5, and FSTL3 (area within the green box). Analysis of the correlation between the serum FN1, ITGA5, and serum FSTL3 in the patients based on the comparison between the two groups (area within the purple box). (D) Expression of FN1 in the normal and tumor tissues based on TCGA-COAD data. (E–G) The OS, DSS, and PFI from The Cancer Genome Atlas-COAD database are based on the FN1 expression levels. (H–K) Functional enrichment analysis of the FSTL3-related genes. (H) Biological Processes (BP), (I) Cellular Components (CC), (J) Molecular Functions (MF), and (K) Kyoto Encyclopedia of Genes and Genomes (KEGG).(LM) Uniform manifold approximation and projection (UMAP) plots illustrating the expression of (L) FSTL3 and (M) FN1 clusters. (N) UMAP plots illustrating the CRC cell landscape. We found 13 cell types across all cells after quality control, dimensionality reduction, and clustering. (O) Enrichment score for the genes from the hallmark hypoxia gene set in each cell was obtained using the gene set variation analysis. (P,Q) Violin plots for CRC cell cluster marker genes and (P) FSTL3, (Q) FN1 in different cell types. Expression was measured as log 2 (TP10K + 1). NS: not significant, *p < 0.05, **p < 0.01, ***p < 0.001.

In the single-cell level study, the FSTL3 and FN1 were more inclined to express on the fibroblasts (Figures 3L–Q), which are important players in the EMT.

3.4 Relationship Between Follistatin-Like 3 and Epithelial-Mesenchymal Transition and Its Underlying Mechanism

The expression level of FSTL3 was the highest in the RKO and HT-29 cell lines, therefore these were selected for further experiments. The transfection efficiency was verified by GFP expression and western blot ((Figure 4A) (p < 0.01). FSTL3 (either overexpression or knockdown) does not have an obvious effect on cell proliferation as well as cell viability under either with serum or without serum condition for 48 h (Supplementary Figure S3). FSTL3 silencing decreased the capacity of forming the tumor cell clone as well as the sphere-forming abilities (Figures 4B,C). Moreover, the stable FSTL3 overexpression in the RKO cells promoted the formation of the subcutaneous xenograft tumors in vivo (Figures 4D–F) (p < 0.01). Based on the TIMER, the data, and patient’s serum (n = 30) in Figure 3 C was the FSTL3 expression was positively correlated with that of FN1, and ITGA5. Subsequent the in vitro experiments using the western blot revealed a reduction in the levels of FN1, ITGA5 after FSTL3 silencing (p < 0.05) (Figure 4G), and the opposite trend was observed when FSTL3 was overexpressed. The GSEA for FSTL3 revealed the potential role of FSTL3 in “epithelial-mesenchymal transition in CRC” (Figure 4H). Based on this result, the EMT markers and EMT-related phenotypes were studied and the FSTL3 overexpression was found to increase the EMT-related phenotypes. However, this effect was attenuated after treatment with the integrin ITGA5 -specific inhibitor, ATN-161 (Figures 4I–N). The cell viability assays showed the cell proliferation to be unaffected by the selected concentration of ATN-161 (Supplementary material).

FIGURE 4 | FSTL3 overexpression promotes a malignant phenotype in colorectal cancer (CRC). (A) The transfection efficiency was verified by the GFP expression as well as the western blot. Transfection efficiency is represented as percentages. (B) Clone formation capacity of the CRC cells transfected with the NC, sh- FSTL3, and oe-FSTL3 constructs assessed using the clone formation assay. (C) Representative images from the sphere-forming assay. The number of tumorspheres was counted and plotted, and percentage of tumorspheres with diameters of 50–100 μm, 100–150 μm, or >150 μm was calculated and plotted. The scale bar represents 100 μm. (D) The xenograft tumors from the nude mice. (D–F) (E) Tumor volume and (F) weight of xenografts from the nude mice. (G) FN1, ITGA5 levels in CRC cells transfected with NC, sh-FSTL3, and oe-FSTL3 were examined using western blot. (H) Gene Set Enrichment Analysis (GSEA) of FSTL3. (I,J) The invasion ability of CRC cells after transfection; the relative invasive cell number is shown towards the right. (K,L) The migratory ability of the different groups of CRC cells [(K): RKO, (L): HT-29] was examined using the wound-healing assays. (M,N) Expression of the EMT-related proteins was examined using western blots after the transfection of the CRC cells with NC, sh- FSTL3, and oe-FSTL3 constructs and treatment with 10 μM ATN-161, an antagonist of integrin α5β1. The statistical analysis of the western blot result is shown towards the right. *p < 0.05, **p < 0.01, ***p < 0.001.

3.5 Relationship of the Follistatin-Like 3 Expression With the Cytoskeletal Remodeling in the Colorectal Cancer Cells

GSEA also revealed the functional enrichment for FSTL3 under the “regulation of actin cytoskeleton” and “regulation of microtubule cytoskeleton” domains (Figure 5A). Subsequent in vitro experiments revealed that FSTL3 overexpression makes the cell pseudopodia longer and more obvious (Figures 5B,C) and the upregulation of F-actin (Figures 5D,E). Consistent with the prior experiments, this effect was attenuated after treatment with ATN-161 (p < 0.01).

FIGURE 5 | Relationship between the FSTL3 and the F-actin cytoskeleton. (A) Gene Set Enrichment Analysis (GSEA) of FSTL3. (B–D) The cytoskeleton of colorectal cancer (CRC) cells (control cells and CRC cells transfected with the NC, sh-FSTL3, and oe-FSTL3 constructs) treated with 5 μM ATN-161 detected using phalloidin staining and immunofluorescence staining. [(B): RKO, (C): HT-29] Yellow arrowheads point to filopodia/larger lamellipodia/pseudopodia-like protrusions. (E) Immunofluorescence intensities expressed as mean intensity ±SD. *p < 0.05, **p < 0.01, ***p < 0.001. Arrowheads indicate pseudopodia.

3.6 Relationship of Follistatin-Like 3 Expression With the M2 Macrophage Infiltration

The ssGSEA algorithm was used to calculate the correlation between the FSTL3 expression and infiltration degree of several immune cells (Figure 6A). Considering FSTL3 to be associated with the abundance of macrophages, the relationship between the FSTL3 and macrophage abundance was further assessed using the GSE10950 and GSE44861 datasets. After excluding the normal samples from these two datasets, 22 immune cell profiles were obtained for the CRC samples (Figure 6B). Subsequently, the GSE10950 and GSE44861 datasets were used to assess the relationship between the FSTL3 expression and the macrophage infiltration The FSTL3 expression levels were positively correlated with the M2 macrophage abundance (Figure 6C). Therefore, the correlation between the FSTL3 and the M2 surface markers were calculated using the TIMER database and a positive correlation was observed between the FSTL3 expression and MRC1 (CD206) (R = 0.36, p < 0.001) and CD163 (R = 0.47, p < 0.001) expression (Figure 6D). This series of results suggested a positive association between the FSTL3 expression and M2 macrophage infiltration. To further investigate the influence of FSTL3 overexpression on the M2 macrophage abundance in CRC, a tumor–macrophage cell co-culture model was established using a transwell non-contact co-culture unit (Figure 6E). The FSTL3 overexpression was found to significantly upregulate the surface markers of the M2 tumor-associated macrophages (TAMs) (CD206 and CD163) (Figures 6F,G).

FIGURE 6 | Immune landscape of the colorectal cancer (CRC) patients with the different expression levels of FSTL3 and the relationship between the FSTL3 expression and the abundance of M2 tumor-associated macrophage infiltration. (A) Spearman correlation between FSTL3 and 24 types of immune cells; positive correlation, red lollipop, and negative correlation, blue lollipop. (B) Proportion of 22 types of Tumor-initiating cells (TICs) in the CRC tumor samples based on GSE10950 and GSE44861. (C) Correlation of the FSTL3 expression levels with macrophage abundance based on GSE10950 and GSE44861. (D) Correlation of the FSTL3 expression levels with M2 macrophage markers. (E) Schematic diagram for the tumor–macrophage cell co-culture. (F) Immunofluorescence staining for CD206 (red) and CD163 (green). (G) Immunofluorescence intensities are expressed as mean intensity ±SD. *p < 0.05, **p < 0.01, ***p < 0.001.

Hence, the findings of this study confirmed the FSTL3 levels to be positively correlated with the abundance of M2 macrophage infiltration.

4 DISCUSSION

The CRC patients demonstrate high incidence and poor prognosis due to postoperative metastasis and local recurrence (Dekker et al., 2019). Nearly half of the CRC patients die within 5 years from diagnoses (Kumar et al., 2021). CRC is a highly complex and heterogeneous disease evolving not from the dysfunction of a single gene but the synergistic behavior of numerous genes in a complex network (Denlinger and Barsevick, 2009). This necessitates the identification of the key genes involved in this network. It can, therefore, provide new ideas for the targeted therapy and assessment of prognosis. The previous studies of our group have demonstrated a coactivating relationship between FSTL3 and the BMP/SMAD signaling, showing that it can regulate the SMAD phosphorylation and promote EMT in gastric cancer cells (Liu et al., 2021). Since FSTL3 is a potentially useful target gene for tumor therapy, it not only directly affects the biological characteristics of the tumor cells but also remodels the tumor microenvironment (TME) and influences the prognosis of the patient.

The extracellular matrix (ECM) is a protein scaffold defining a part of the extracellular microenvironment and forming the non-cellular component of the cancer tissues (Pickup et al., 2014). ECM is associated with a variety of functions, including mechanical support and biochemical signaling (Theocharis et al., 2016). ECM degradation is considered the first step in cancer invasion and metastasis (Bonnans et al., 2014). Therefore, the tumor therapeutic strategies targeting the ECM are being increasingly developed and utilized at present (Abyaneh et al., 2020). The results of the enrichment analysis suggested a possibly close link between FSTL3 and ECM stating that FN1 is a core factor in the FSTL3-related network. Fibronectin-1 (FN1) is a large ECM protein with an important role in cell adhesion, cell migration, invasion, EMT as well as TME, mediated through integrin signaling (Efthymiou et al., 2020). Integrins are the heterodimeric cell surface glycoprotein receptors with 2 non-covalently -associated subunits linking the ECM to the intracellular cytoskeleton (Barczyk et al., 2010). They mediate the cell-cell and cell-matrix adhesion (Huttenlocher and Horwitz, 2011). The binding of integrins to FN1, transduce signals to the intracellular interior, with simultaneous reception of intracellular signals that regulate their ligand-binding affinity (Giancotti and Ruoslahti, 1999). Integrin α5β1 is a well-known major receptor for FN1(Miroshnikova et al., 2017) and was demonstrated to bind to its receptor integrin α5β1 activating the PI3K/Akt signaling pathway, thus, promoting the progression of breast cancers (Veevers-Lowe et al., 2011). Based on the results of the enrichment analysis, a single-cell level analysis was performed suggesting that FSTL3 and FN1 are predominantly expressed on the fibroblasts, inducing the production of the ECM components. Integrins are the key mediators of the cell–ECM interaction, linking the ECM to the actin cytoskeleton (Kechagia et al., 2019). FSTL3 overexpression has been subsequently demonstrated experimentally to significantly upregulated the FN1 and α5β1 expression. Then, the rhodamine staining was used to visualize the F-actin showing that the FSTL3-overexpressed cells tended to have more pseudopods. The results also showed that FSTL3 overexpression upregulates the F-actin participating in the cytoskeletal remodeling in the CRC cells.

Cytoskeletal remodeling is involved in the EMT process, constituting the reorganization and reconstruction of the actin cortical cytoskeleton, which is an important hallmark of EMT (Yilmaz and Christofori, 2009). The cross-talk between FN1 and α5β1 triggers a cellular architectural change, with protrusion of different structures such as the pseudopodia, filopodia, or lamellipodia (Revach et al., 2020). We observed that FSTL3 upregulation significantly promoted the EMT phenotype, yielding cells with greater invasive capacity. As a proof-of-concept experiment, the integrin α5β1 specific inhibitor was used, which rescued the EMT phenotype and cytoskeleton remodeling caused by FSTL3 overexpression.

The advent of immunotherapy using immune checkpoint (ICP) inhibitors in recent years has indicated the evaluation of the tumor immune microenvironment (TIME) landscape heterogeneity and reshaping TIME as promising approaches for prospective CRC treatment (Li et al., 2019). The tumor-associated macrophages (TAMs) are important components of the TIME (Pathria et al., 2019), mainly derived from circulating monocyte populations, exhibiting characteristics similar to that of the M2 macrophages (Yamaguchi et al., 2016). The M2 macrophages are known to be involved in ECM remodeling, angiogenesis, and immunosuppression (Najafi et al., 2019). Nearly 70% of the patients receiving ICP inhibitors therapy are non-responders or quickly attain drug resistance, primarily due to the M2 macrophage infiltration. Recent studies have found the M2 macrophages to express high levels of TGF-β1, promoting the ECM deposition and EMT (Liu et al., 2018; Zeng et al., 2019). However, the cancer cells undergoing EMT also promote the M2 macrophage infiltration by secreting the tumor metabolites (Dongre et al., 2017), forming a vicious circle. Analysis of the TCGA-COAD data by the ssGSEA algorithm showed a significant positive correlation between the expression level of FSTL3 and the level of macrophage infiltration. Further calculations indicated an excellent correlation between FSTL3 and the M2 macrophages. Furthermore, the macrophages with the CRC cells were co-cultured and immunofluorescence staining indicated the CRC cells to overexpress FSTL3 with a greater ability to induce the M2 macrophage proliferation. These results revealed an important role for FSTL3 in remodeling the CRC TIME.

To sum up, the results of the study indicated that FSTL3 is increased in CRC cells and tissues and that a high FSTL3 level is related to the clinicopathological features and poor prognosis of colorectal cancer. These results suggest FSTL3 be an important player in the complex gene regulatory mechanisms triggering CRC through the processes such as promoting the binding of FN1 to α5β1, activating the EMT phenotype, and M2 macrophage infiltration, ultimately promoting tumor progression. We initiated animal experiments after in vitro validation of the cell phenotype. The subcutaneous graft tumour model was chosen to allow more visualisation of the changes in tumour size, a higher rate of tumour formation and easy measurement of tumour changes. In fact, during the final animal autopsy, we found that some mice in the NC group, control group and the oe-FSTL3 groups had liver metastases (scattered and different in size), which were not quantified for statistical comparison because the sample size was too small (Supplementary Figure S4). Therefore, based on the results of this animal study in which some mice developed liver metastases, we will use the venous metastatic tumour model, a model of tumour formation in one or more organs of the animal after injection of tumour cells into the tail vein, in our subsequent study of FSTL3 on the tumour stromal microenvironment. This is more appropriate for studying metastasis in vivo. The use of Luciferase labelling of tumour cells in combination with in vivo imaging allows a clearer study of the effect of FSTL3 on metastasis in vivo. In addition, there are some limitations to this study. The in vivo studies are still in infancy and most of the data analyzed in this study are derived from online databases and that the clinical sample size for this study was small, hence, further larger sample studies are necessary to substantiate the findings. In conclusion, the findings of this study provided exciting novel clues which require further focused studies for the elucidation of the mole.

DATA AVAILABILITY STATEMENT

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.

ETHICS STATEMENT

The studies involving human participants were reviewed and approved by the Ethics Committee of the Jiangsu Province Hospital of Chinese Medicine (2020NL-107-01). The patients/participants provided their written informed consent to participate in this study. The animal study was reviewed and approved by the Ethics Committee of the Jiangsu Province Hospital of Chinese Medicine (2021-5-062).

AUTHOR CONTRIBUTIONS

SL and XZ developed the experimental plan. YL and JL performed all experiments. S-HZ and YZ analyzed the data. YZ and Z-CJ support and providing equipment/instrumentation. YL and JL wrote the manuscript. All authors have read and approved the final manuscript.

FUNDING

The present study was supported by the Youth Science and Technology Project of Suzhou (No. KJXW2019059); the Suzhou Science and Technology Development Plan (No. SYSD2019006); Advantageous Disciplines Program of Nanjing University of Chinese Medicine (ZYX03KF022 and ZYX03KF019), Science and Technology Project of Affiliated Hospital of Nanjing University of Chinese Medicine (Y2020CX62), State Administration of Chinese Medicine Project (20085-9-3), Jiangsu Provincial Science and Technology Department Project (BE2019771), Jiangsu Province Postgraduate Research Innovation Program Project (KYCX21-1677).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2021.762924/full#supplementary-material

REFERENCES

Abyaneh, H. S., Regenold, M., Mckee, T. D., Allen, C., and Gauthier, M. A. (2020). Towards Extracellular Matrix Normalization for Improved Treatment of Solid Tumors. Theranostics 10, 1960–1980. doi:10.7150/thno.39995

Barczyk, M., Carracedo, S., and Gullberg, D. (2010). Integrins. Cell Tissue Res 339, 269–280. doi:10.1007/s00441-009-0834-6

Bloise, E., Couto, H. L., Massai, L., Ciarmela, P., Mencarelli, M., Borges, L. E., et al. (2009). Differential Expression of Follistatin and FLRG in Human Breast Proliferative Disorders. BMC Cancer 9, 320. doi:10.1186/1471-2407-9-320

Bonnans, C., Chou, J., and Werb, Z. (2014). Remodelling the Extracellular Matrix in Development and Disease. Nat. Rev. Mol. Cel Biol. 15, 786–801. doi:10.1038/nrm3904

Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., and Jemal, A. (2020). Erratum: Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 70, 313. doi:10.3322/caac.21609

Brody, H. (2015). Colorectal Cancer. Nature 521, S1. doi:10.1038/521s1a

Cao, Y., Jiao, N., Sun, T., Ma, Y., Zhang, X., Chen, H., et al. (2021). CXCL11 Correlates with Antitumor Immunity and an Improved Prognosis in Colon Cancer. Front. Cel Dev. Biol. 9, 646252. doi:10.3389/fcell.2021.646252

Chen, B., Khodadoust, M. S., Liu, C. L., Newman, A. M., and Alizadeh, A. A. (2018). Profiling Tumor Infiltrating Immune Cells with CIBERSORT. Methods Mol. Biol. 1711, 243–259. doi:10.1007/978-1-4939-7493-1_12

Chen, L., and Ke, X. (2021). MMP7 as a Potential Biomarker of Colon Cancer and its Prognostic Value by Bioinformatics Analysis. Medicine (Baltimore) 100, e24953. doi:10.1097/md.0000000000024953

Chen, X., Yang, J., and Wang, Y. (2020). LncRNA JPX Promotes Cervical Cancer Progression by Modulating miR-25-3p/SOX4 Axis. Cancer Cel Int 20, 441. doi:10.1186/s12935-020-01486-3

Dekker, E., Tanis, P. J., Vleugels, J. L. A., Kasi, P. M., and Wallace, M. B. (2019). Colorectal Cancer. Lancet 394, 1467–1480. doi:10.1016/s0140-6736(19)32319-0

Denlinger, C. S., and Barsevick, A. M. (2009). The Challenges of Colorectal Cancer Survivorship. J. Natl. Compr. Canc. Netw. 7, 883–894. doi:10.6004/jnccn.2009.0058

Donaldson, J. G. (2015). Immunofluorescence Staining. Curr. Protoc. Cel Biol. 69, 582218. doi:10.1002/0471143030.cb0403s69

Dongre, A., Rashidian, M., Reinhardt, F., Bagnato, A., Keckesova, Z., Ploegh, H. L., et al. (2017). Epithelial-to-Mesenchymal Transition Contributes to Immunosuppression in Breast Carcinomas. Cancer Res. 77, 3982–3989. doi:10.1158/0008-5472.can-16-3292

Efthymiou, G., Saint, A., Ruff, M., Rekad, Z., Ciais, D., and Van Obberghen-Schilling, E. (2020). Shaping up the Tumor Microenvironment with Cellular Fibronectin. Front. Oncol. 10, 641. doi:10.3389/fonc.2020.00641

Gao, L., Chen, X., Wang, Y., and Zhang, J. (2020). Up-Regulation of FSTL3, Regulated by lncRNA DSCAM-AS1/miR-122-5p Axis, Promotes Proliferation and Migration of Non-Small Cell Lung Cancer Cells. Onco. Targets Ther. 13, 2725–2738. doi:10.2147/ott.s236359

Garcia, J., Hurwitz, H. I., Sandler, A. B., Miles, D., Coleman, R. L., Deurloo, R., et al. (2020). Bevacizumab (Avastin) in Cancer Treatment: A Review of 15 Years of Clinical Experience and Future Outlook. Cancer Treat. Rev. 86, 102017. doi:10.1016/j.ctrv.2020.102017

Genin, M., Clement, F., Fattaccioli, A., Raes, M., and Michiels, C. (2015). M1 and M2 Macrophages Derived from THP-1 Cells Differentially Modulate the Response of Cancer Cells to Etoposide. BMC Cancer 15, 577. doi:10.1186/s12885-015-1546-9

Giancotti, F. G., and Ruoslahti, E. (1999). Integrin Signaling. Science 285, 1028–1033. doi:10.1126/science.285.5430.1028

Giordano, G., Parcesepe, P., Bruno, G., Piscazzi, A., Lizzi, V., Remo, A., et al. (2021). Evidence-Based Second-Line Treatment in RAS Wild-Type/Mutated Metastatic Colorectal Cancer in the Precision Medicine Era. Int. J. Mol. Sci. 22, 7717. doi:10.3390/ijms22147717

Grover, A., Sanjuan-Pla, A., Thongjuea, S., Carrelha, J., Giustacchini, A., Gambardella, A., et al. (2016). Single-Cell RNA Sequencing Reveals Molecular and Functional Platelet Bias of Aged Haematopoietic Stem Cells. Nat. Commun. 7, 11075. doi:10.1038/ncomms11075

Han, J. H., Yoon, J. S., Chang, D. Y., Cho, K. G., Lim, J., Kim, S. S., et al. (2020). CXCR4-STAT3 Axis Plays a Role in Tumor Cell Infiltration in an Orthotopic Mouse Glioblastoma Model. Mol. Cell 43, 539–550. doi:10.14348/molcells.2020.0098

Harada, S., and Morlote, D. (2020). Molecular Pathology of Colorectal Cancer. Adv. Anat. Pathol. 27, 20–26. doi:10.1097/pap.0000000000000247

Hnasko, T. S., and Hnasko, R. M. (2015). The Western Blot. Methods Mol. Biol. 1318, 87–96. doi:10.1007/978-1-4939-2742-5_9

Huttenlocher, A., and Horwitz, A. R. (2011). Integrins in Cell Migration. Cold Spring Harbor Perspect. Biol. 3, a005074. doi:10.1101/cshperspect.a005074

Jeong, K. Y., Park, M., Sim, J. J., and Kim, H. M. (2020). Combination Antitumor Effect of Sorafenib via Calcium-Dependent Deactivation of Focal Adhesion Kinase Targeting Colorectal Cancer Cells. Molecules 25, 5299. doi:10.3390/molecules25225299

Kechagia, J. Z., Ivaska, J., and Roca-Cusachs, P. (2019). Integrins as Biomechanical Sensors of the Microenvironment. Nat. Rev. Mol. Cel Biol. 20, 457–473. doi:10.1038/s41580-019-0134-2

Kuleshov, M. V., Jones, M. R., Rouillard, A. D., Fernandez, N. F., Duan, Q., Wang, Z., et al. (2016). Enrichr: A Comprehensive Gene Set Enrichment Analysis Web Server 2016 Update. Nucleic Acids Res. 44, W90–W97. doi:10.1093/nar/gkw377

Kumar, R., Harilal, S., Carradori, S., and Mathew, B. (2021). A Comprehensive Overview of Colon Cancer- A Grim Reaper of the 21st Century. Curr. Med. Chem. 28, 2657–2696. doi:10.2174/0929867327666201026143757

Li, B., Chan, H. L., and Chen, P. (2019). Immune Checkpoint Inhibitors: Basics and Challenges. Curr. Med. Chem. 26, 3009–3025. doi:10.2174/0929867324666170804143706

Li, J., Teng, Y., Liu, S., Wang, Z., Chen, Y., Zhang, Y., et al. (2016). Cinnamaldehyde Affects the Biological Behavior of Human Colorectal Cancer Cells and Induces Apoptosis via Inhibition of the PI3K/Akt Signaling Pathway. Oncol. Rep. 35, 1501–1510. doi:10.3892/or.2015.4493

Li, T., Fan, J., Wang, B., Traugh, N., Chen, Q., Liu, J. S., et al. (2017). TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res. 77, e108–e110. doi:10.1158/0008-5472.can-17-0307

Liu, Y.-J., Li, J.-P., Zhang, Y., Nie, M.-J., Zhang, Y.-H., Liu, S.-L., et al. (2021). FSTL3 Is a Prognostic Biomarker in Gastric Cancer and Is Correlated with M2 Macrophage Infiltration. Onco Targets Ther. 14, 4099–4117. doi:10.2147/ott.s314561

Liu, Z., Kuang, W., Zhou, Q., and Zhang, Y. (2018). TGF-β1 Secreted by M2 Phenotype Macrophages Enhances the Stemness and Migration of Glioma cells via the SMAD2/3 Signalling Pathway. Int. J. Mol. Med. 42, 3395–3403. doi:10.3892/ijmm.2018.3923

Liu, Z., Mi, M., Li, X., Zheng, X., Wu, G., and Zhang, L. (2020). A lncRNA Prognostic Signature Associated with Immune Infiltration and Tumour Mutation burden in Breast Cancer. J. Cel. Mol. Med. 24, 12444–12456. doi:10.1111/jcmm.15762

Maguer-Satta, V., and Rimokh, R. (2004). FLRG, Member of the Follistatin Family, a New Player in Hematopoiesis. Mol. Cell Endocrinol. 225, 109–118. doi:10.1016/j.mce.2004.07.009

Miroshnikova, Y. A., Rozenberg, G. I., Cassereau, L., Pickup, M., Mouw, J. K., Ou, G., et al. (2017). α5β1-Integrin Promotes Tension-Dependent Mammary Epithelial Cell Invasion by Engaging the Fibronectin Synergy Site. Mol. Biol. Cel 28, 2958–2977. doi:10.1091/mbc.e17-02-0126

Najafi, M., Farhood, B., and Mortezaee, K. (2019). Extracellular Matrix (ECM) Stiffness and Degradation as Cancer Drivers. J. Cel Biochem. 120, 2782–2790. doi:10.1002/jcb.27681

Nizioł, M., Zińczuk, J., Zaręba, K., Guzińska-Ustymowicz, K., and Pryczynicz, A. (2021). Immunohistochemical Analysis of the Expression of Adhesion Proteins: TNS1, TNS2 and TNS3 in Correlation with Clinicopathological Parameters in Gastric Cancer. Biomolecules 11, 640. doi:10.3390/biom11050640

Pathria, P., Louis, T. L., and Varner, J. A. (2019). Targeting Tumor-Associated Macrophages in Cancer. Trends Immunol. 40, 310–327. doi:10.1016/j.it.2019.02.003

Piawah, S., and Venook, A. P. (2019). Targeted Therapy for Colorectal Cancer Metastases: A Review of Current Methods of Molecularly Targeted Therapy and the Use of Tumor Biomarkers in the Treatment of Metastatic Colorectal Cancer. Cancer 125, 4139–4147. doi:10.1002/cncr.32163

Pickup, M. W., Mouw, J. K., and Weaver, V. M. (2014). The Extracellular Matrix Modulates the Hallmarks of Cancer. EMBO Rep. 15, 1243–1253. doi:10.15252/embr.201439246

Powers, R. K., Goodspeed, A., Pielke-Lombardo, H., Tan, A.-C., and Costello, J. C. (2018). GSEA-InContext: Identifying Novel and Common Patterns in Expression Experiments. Bioinformatics 34, i555–i564. doi:10.1093/bioinformatics/bty271

Revach, O. Y., Grosheva, I., and Geiger, B. (2020). Biomechanical Regulation of Focal Adhesion and Invadopodia Formation. J. Cel Sci. 133, jcs244848. doi:10.1242/jcs.244848

Tang, Z., Li, C., Kang, B., Gao, G., Li, C., and Zhang, Z. (2017). GEPIA: A Web Server for Cancer and normal Gene Expression Profiling and Interactive Analyses. Nucleic Acids Res. 45, W98–w102. doi:10.1093/nar/gkx247

The Lancet Gastroenterology and Hepatology (2017). Improving Uptake of Colorectal Cancer Screening. Lancet Gastroenterol. Hepatol. 2, 767. doi:10.1016/s2468-1253(17)30298-4

Theocharis, A. D., Skandalis, S. S., Gialeli, C., and Karamanos, N. K. (2016). Extracellular Matrix Structure. Adv. Drug Deliv. Rev. 97, 4–27. doi:10.1016/j.addr.2015.11.001

Tong, G.-J., Zhang, G.-Y., Liu, J., Zheng, Z.-Z., Chen, Y., Niu, P.-P., et al. (2018). Comparison of the Eighth Version of the American Joint Committee on Cancer Manual to the Seventh Version for Colorectal Cancer: A Retrospective Review of Our Data. World J. Clin. Oncol. 9, 148–161. doi:10.5306/wjco.v9.i7.148

Veevers-Lowe, J., Ball, S. G., Shuttleworth, A., and Kielty, C. M. (2011). Mesenchymal Stem Cell Migration Is Regulated by Fibronectin through α5β1-Integrin-Mediated Activation of PDGFR-β and Potentiation of Growth Factor Signals. J. Cel Sci. 124, 1288–1300. doi:10.1242/jcs.076935

Wang, X., Wang, H., Xu, J., Hou, X., Zhan, H., and Zhen, Y. (2021). Double-Targeting CDCA8 And E2F1 Inhibits The Growth And Migration Of Malignant Glioma. Cell Death Dis. 12 (2), 146. doi:10.1038/s41419-021-03405-4

Wang, Z., Jensen, M. A., and Zenklusen, J. C. (2016). A Practical Guide to the Cancer Genome Atlas (TCGA). Methods Mol. Biol. 1418, 111–141. doi:10.1007/978-1-4939-3578-9_6

Wieszczy, P., Regula, J., and Kaminski, M. F. (2017). Adenoma Detection Rate and Risk of Colorectal Cancer. Best Pract. Res. Clin. Gastroenterol. 31, 441–446. doi:10.1016/j.bpg.2017.07.002

Yamaguchi, T., Fushida, S., Yamamoto, Y., Tsukada, T., Kinoshita, J., Oyama, K., et al. (2016). Tumor-associated Macrophages of the M2 Phenotype Contribute to Progression in Gastric Cancer with Peritoneal Dissemination. Gastric Cancer 19, 1052–1065. doi:10.1007/s10120-015-0579-8

Yang, Y., Xiao, M., Song, Y., Tang, Y., Luo, T., Yang, S., et al. (2019). H-Score of 11β-Hydroxylase and Aldosterone Synthase in the Histopathological Diagnosis of Adrenocortical Tumors. Endocrine 65, 683–691. doi:10.1007/s12020-019-02022-8

Yilmaz, M., and Christofori, G. (2009). EMT, the Cytoskeleton, and Cancer Cell Invasion. Cancer Metastasis Rev. 28, 15–33. doi:10.1007/s10555-008-9169-0

Zeng, X.-Y., Xie, H., Yuan, J., Jiang, X.-Y., Yong, J.-H., Zeng, D., et al. (2019). M2-like Tumor-Associated Macrophages-Secreted EGF Promotes Epithelial Ovarian Cancer Metastasis via Activating EGFR-ERK Signaling and Suppressing lncRNA LIMT Expression. Cancer Biol. Ther. 20, 956–966. doi:10.1080/15384047.2018.1564567

Zhao, Q. Q., Jiang, C., Gao, Q., Zhang, Y. Y., Wang, G., Chen, X. P., et al. (2020). Gene Expression and Methylation Profiles Identified CXCL3 and CXCL8 as Key Genes for Diagnosis and Prognosis of colon Adenocarcinoma. J. Cel Physiol. 235, 4902–4912. doi:10.1002/jcp.29368

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Liu, Li, Zeng, Zhang, Zhang, Jin, Liu and Zou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 29 November 2021
doi: 10.3389/fmolb.2021.775743

SPARC Overexpression Promotes Liver Cancer Cell Proliferation and Tumor Growth

Zhao-wei Gao, Chong Liu, Lan Yang, Ting He, Xia-nan Wu, Hui-zhong Zhang and Ke Dong*

Department of Clinical Laboratory, Tangdu Hospital, Air Force Medical University, xi’an, China

Edited by:
Hossain Shekhar, University of Dhaka, Bangladesh

Reviewed by:
Metin Çalışkan, Uşak University, Turkey
Can Gan, Mayo Clinic, United States
Xiongwen Lv, Anhui Medical University, China

* Correspondence: Ke Dong, tdjyk3@fmmu.edu.cn

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 14 September 2021
Accepted: 03 November 2021
Published: 29 November 2021

Citation: Gao Z-w, Liu C, Yang L, He T, Wu X-n, Zhang H-z and Dong K (2021) SPARC Overexpression Promotes Liver Cancer Cell Proliferation and Tumor Growth. Front. Mol. Biosci. 8:775743. doi: 10.3389/fmolb.2021.775743

Background: Secreted protein acidic and rich in cysteine (SPARC) plays an important role in cancer development. The roles of SPARC in the liver hepatocellular carcinoma (LIHC) are unclear.

Methods: GEPIA2 and UALCAN were used to analyze the SPARC mRNA expression levels in LIHC based on the TCGA database. The GEO database was used to verify the analysis results. Immunohistochemical (IHC) analysis was used to investigate the SPARC protein levels in LIHC tissues. The Kaplan–Meier (KM) plotter was used to analyze the correlation between SPARC and prognosis. The serum SPARC levels were measured by ELISA. CCK8 and murine xenograft models were used to investigate the effect of SPARC on the liver cancer growth in vitro and in vivo. SPARC-correlated genes were screened by LinkedOmics.

Results: Based on the TCGA and GEO databases, the analysis showed that the SPARC mRNA expression levels were increased in tumor tissues and peripheral blood mononuclear cell (PBMC) from LIHC compared to normal controls. The IHC analysis showed an increased level of SPARC in LIHC tissues compared to adjacent non-tumor tissues. However, we found that the serum SPARC levels were lower in LIHC than those in healthy controls. The KM plotter showed that there was no significant correlation between the SPARC mRNA levels and overall survival. However, in sorafenib-treated LIHC patients, the high SPARC expression predicts favorable prognosis. Furthermore, the endogenous SPARC overexpression promotes liver cancer cell proliferation in vitro and tumor growth in vivo, while there was no significant effect of exogenous SPARC treatment on liver cancer cell proliferation. Function enrichment analysis of SPARC-correlated genes indicated a critical role of interaction with an extracellular matrix in SPARC-promoting cancer cell proliferation.

Conclusion: SPARC mRNAs were increased in LIHC tumor tissues, and SPARC overexpression may promote the liver cancer growth. Further studies are needed to clarify the potential prognostic value of SPARC, both in tissues and in circulation.

Keywords: SPARC, liver cancer, proliferation, tumor growth, bioinformatics

INTRODUCTION

According to the Global Cancer Observatory (GLOBOCAN) data from 2020 (Sung et al., 2021), primary liver cancer is currently the sixth most commonly diagnosed cancer (4.7%) and the third cause of cancer death (8.3%) worldwide. Notably, the incidence rate (6.3%) and the mortality rate (10.5%) of primary liver cancer are higher in males. Liver hepatocellular carcinoma (LIHC) is the most common subtype (75–85%) of primary liver cancer (Sung et al., 2021). The risk factors for LIHC include hepatitis virus (HBV and HCV) infection, aflatoxin, excessive drinking, and obesity (Sung et al., 2021). However, the molecular mechanism involvement in LIHC progression is still unclear.

SPARC (secreted protein acidic and cysteine rich; Gene ID: 6678) encodes a cysteine-rich acidic matrix-associated protein, which belongs to the extracellular matrix protein family. SPARC is required for the collagen in bone to become calcified, and is also involved in regulating the cross talk between the extracellular matrix and cell biological behavior, such as cell shape, proliferation, and migration (Bornstein and Sage, 2002). Thus, SPARC has been found to play an important role in tumor progression (Tai and Tang, 2008). First, the SPARC expression has been found to be either increased or decreased in different tumor types. For example, the higher levels of SPARC expression have been reported in breast cancer (Lien et al., 2007) and glioblastomas (Rempel et al., 1998), while the lower levels of SPARC expression have been found in ovarian (Yiu et al., 2001), colorectal (Yang et al., 2007; Cheetham et al., 2008), and pancreatic cancers (Puolakkainen et al., 2004). And moreover, the functions of SPARC in different cancers are controversial. The studies have shown that SPARC overexpression could act as a tumor suppressor gene, which inhibits cancer proliferation, invasion, and angiogenesis in certain types of cancer, including gastric cancer (Zhang et al., 2012), medulloblastoma (Bhoopathi et al., 2010; Bhoopathi et al., 2011), ovarian cancer (Said and Motamed, 2005), and prostate cancer (Wong et al., 2007). In contrast, the high SPARC expression showed a correlation with the highly aggressive tumor phenotype, through promoting tumor cell survival, proliferation, and invasion, such as glioma (Shi et al., 2004) and melanoma (Ledda et al., 1997; Alvarez et al., 2005).

The roles of SPARC in LIHC are unclear. In this study, we first analyzed the difference of SPARC expression between LIHC and normal controls, and then investigated the effects of SPARC overexpression on liver cancer cell proliferation in vitro and in vivo. The function enrichment analysis of SPARC-correlated genes was used to explore the potential mechanism of SPARC in LIHC. This study provided evidence for the involvement of SPARC in LIHC progression.

MATERIAL AND METHOD

SPARC Expression-Level Analysis

The GEPIA2 (Tang et al., 2019), UALCAN (Chandrashekar et al., 2017), and GEO databases were used to analyze the difference of SPARC expression levels between LIHC tumor and non-tumor tissues. By using the “Expression DIY” module of GEPIA2 and the “TCGA Analysis” module of UALCAN, differential expression of SPARC mRNA was obtained. UALCAN was also used to analyze the promoter methylation level of SPARC. GSE14323 (Levy et al., 2016), GSE14520 (Roessler et al., 2010; Wang et al., 2021), and GSE121248 (Wang et al., 2007) datasets contain the gene expression profiling of LIHC tumor and their matched normal adjacent tissues, which were used to validate the analysis results based on GEPIA2 and UALCAN. GSE58208 and GSE49515 (Shi et al., 2014) datasets contain the gene expression profiling of PBMC from LIHC patients and healthy subjects, which were used to evaluate the difference of SPARC expression in PBMC.

Tissue Microarrays and Immunohistochemical Analysis

Formalin-fixed tissue microarray which contained 48 LIHC and 48 matched adjacent controls (D097Lv01, http://www.bioaitech.com/chip-design/13c0de19d7e94d4a99952af585089847.html) was purchased from Bioaitech (Xi’an, China). A 1:200 dilution of anti-SPARC mAb (D10F10, Cell Signaling Technology) was used as the primary antibody. PANNORAMIC and CaseViewer 2.4 software (3DHISTECH, Hungary) were used for image acquisition and analysis. IHC analysis was conducted by two independent pathologists. The intensity was scored as 0 (negative), 1 (weak), 2 (moderate), and 3 (strong). The extent of positive cells less than 5% was scored as 0, 6%–25% was scored as 1, 26%–50% was 2, 51%–75% was 3, and >75% was 4. The final score was determined by multiplying the intensity and extent positivity scores. The detail information of tissue microarrays and IHC scores is available in Supplementary Table S1.

Kaplan–Meier Plotter Analysis

The KM plotter bioinformatic tools (Menyhárt et al., 2018) were used to evaluate the potential prognostic value of SPARC expression in LIHC patients. We chose the “auto select best cutoff” model, which means that all possible cutoff values were computed and the best performing threshold was used as a cutoff.

Serum SPARC Level Detection

Ethical approval was obtained from the Ethics Committee of Tangdu Hospital, Fourth Military Medical University (TDLL-202106-04). Informed consent was exempted for this study. Serum from 39 LIHC patients and 32 healthy controls was collected. PBMC from 10 LIHC patients and 10 healthy individuals was collected. PBMC was obtained by using Ficoll separation according to the operation manual (Dakewe Biotech, China).

Serum SPARC levels were measured by the ELISA assay (ELH-SPARC-1, RayBiotech, United States). In brief, 100 μl diluted serum were added into test wells and incubated for 2.5 h at room temperature. Then, the solution was discarded and washed 4 times, and 100 μl biotinylated antibody was added and incubated for 1 h. After washing 4 times, streptavidin was added and incubated for 45 min. The TMB reagent was added into wells, incubated for 30 min, followed by adding stop solution and absorbance determination at 450 nm.

Cell Culture and Cell Codel Construction

HepG2 cells were cultured in the DMEM (Gibco, United States) plus 10% heat-inactivated fetal bovine serum (FBS, Sijiqing Biotec, China) at 37°C with 5% CO₂ in a humidified incubator. The SPARC coding gene was cloned into the pcDNA3.1 + expression vector. Then the SPARC/pcDNA3.1+ and control plasmids were transfected into HepG2 cells using Lipofectamine 2000 (Invitrogen, United States) according to the manufacturer’s instruction. Briefly, HepG2 cells were seeded in 6-well plate and cultured until 70–80% confluence. Premixed lipofection and plasmid DNA (10 μl: 4 μg) were added into the wells and incubated for 24 h. The stable cell models were screened by G418. The expression levels of SPARC in cell models were investigated by the quantitative real-time PCR (qRT-PCR). The primers used for qRT-PCR are listed in Table 1.

TABLE 1 | Primer sequences.

Cell Proliferation Detection

Cell proliferation was investigated by the CCK8 assay. In short, cells were seeded into 96-cell plate at a density of 2 × 10³ cells/well, with six replicate wells per group. After 24 and 48h, a 10 µl CCK8 reagent (KeyGEN BioTECH, Nanjing, China) was added, followed by incubation for 2 h. The absorbance value was measured at 450 nm by using the microplate reader (BIO-TEK Epoch, United States).

Cell Cycle and Apoptosis Detection

Flow cytometry (FCM) was used to analyze the cell cycle and apoptosis. The cell cycle was analyzed by ethanol-fixed cells stained with propidium iodide (PI) in buffer containing RNase A. The DNA content was assessed by using the FACS calibur flow cytometer (BD Biosciences). Cell apoptosis was detected using the annexin V–FITC apoptosis detection kit (KeyGEN BioTECH, Nanjing, China). Briefly, cells were collected and washed with PBS twice, and stained for 15 min at room temperature with annexin V-FITC and (PI). Following that, cell apoptosis was examined using FACS calibur. The percentage of apoptotic cells was calculated with CellQuest 6.0 (BD Biosciences).

Murine Xenograft Models

All procedures of animal experiments were processed according to animal welfare guidelines approved by Air Force Medical University’s Institutional Animal Care and Use Committee. Male BALB/c nude mice were used. The effect of high SPARC expression on tumor growth in vivo was assessed by subcutaneous injection of 2×10⁶ HepG2SPARC or HepG2NC cells into a BALB/c nude mouse. Nude mice were purchased from Animal Experiment Center, Fourth Military Medical University (age: 6–8 weeks; body weight: 20–25 g). Tumor size was measured every 2 days with a vernier caliper. The volume of tumor was calculated according to the formula: a×b² × 0.5 (a, largest diameter and b, perpendicular diameter).

Function Enrichment Analysis of SPARC Correlated Genes

The gene set which was positively or negatively correlated to SPARC in LIHC was screened by the LinkedOmics analysis tool (http://www.linkedomics.org) based on TCGA data (Vasaikar et al., 2018). The gene ontology (GO) and KEGG pathway analyses were performed by DAVID Bioinformatics Resources (https://david.ncifcrf.gov/tools.jsp) (Huang et al., 2009). R Bubble diagrams were used for visualization of the enriched GO term and KEGG pathway.

Statistical Analysis

Student’s t-test was used to analyze the difference of IHC scores between tumor tissues and controls. Student’s t-test was used to analyze the difference of SPARC expression between the LIHC and control, both in tissues and in PBMC. Serum SPARC levels were expressed as the median (inter quartile range, IQR). Wilcoxon’s test was used to analyze the difference of serum SPARC levels between the LIHC and healthy controls. Student’s t-test was used to analyze the difference of the cell proliferation rate between different groups. p < 0.05 was considered to be statistically significant.

RESULTS

SPARC Expression Were Increased in LIHC Tissues

We first analyzed the SPARC expression levels in LIHC patients based on the TCGA and GEO databases. GEPIA2 and UALCAN analyses showed an increased mRNA expression level of SPARC (Figures 1A,B). And moreover, the results were validated in GSE121248, GSE14520, and GSE14323 datasets (Figures 1C–E). These data showed that SPARC expression levels were higher in LIHC tumor tissues than those in control normal tissues. Notably, there was no significance of the SPARC expression between LIHC patients with different stage or grade (Figures 1F,G). And moreover, the UALCAN analysis showed a lower promoter methylation level of SPARC in LIHC than that in normal controls (Figure 1H), which indicated demethylation might contribute to an increased expression of SPARC in LIHC.

FIGURE 1 | SPARC mRNA expression in LIHC tissues and normal controls. (A) Differential SPARC mRNA expression between the LIHC and normal live tissues in the GEPIA2 database. (B) Differential SPARC mRNA expression between the LIHC and normal controls in the UALCAN database. (C–E) Differential SPARC expression between the LIHC tissues and non-tumor tissues in GSE121248, GSE14520, and GSE14323 datasets. (F, G) Boxplot of SPARC expression based on the tumor stage and grade. (H) Boxplot of SPARC promoter methylation level. (I) IHC analysis of LIHC tissue microarray.

Furthermore, the IHC analysis of tissue microarrays was used to investigate the protein expression levels of SPARC in LIHC tissue. The IHC analysis revealed an increased level of the SPARC protein in LIHC tissues compared to adjacent non-tumor tissues (Figure 1I). Taken together, these results demonstrated that SPARC was increased in LIHC tissues.

The Potential Prognostic Value of SPARC

Furthermore, the relationship between the SPARC expression and prognosis of LIHC patients was analyzed by using the KM plotter. The results showed that there was no significant correlation between the SPARC mRNA expression and LIHC patients’ overall survival (OS) (Supplementary Figure S1A), whether or not the patients were with hepatitis virus (Supplementary Figures S1B,C) or alcohol consumption (Supplementary Figures S1D,E). However, in LIHC patients treated with sorafenib, although the number of patients was small (n = 29), high SPARC expression showed a favorable prognosis (HR = 0.19, logrank p = 0.0053; Supplementary Figure.S1F).

Serum SPARC Levels Decreased in LIHC Patients

SPARC was expressed in a variety of cells, including PBMC. The human protein atlas (HPA) database showed that SPARC was expressed in total PBMC, monocytes, neutrophil, basophil, eosinophil, etc (Figure 2A). Thus, in addition to analyzing the SPARC expression in tumor tissues, we also analyzed the difference of SPARC mRNA expression levels in PBMC from LIHC patients and normal controls based on GSE58208 and GSE49515 datasets. These datasets showed a higher SPARC expression in LIHC’s PBMC than in control’s PBMC (Figures 2B,C).

FIGURE 2 | SPARC levels in serum and PBMC from LIHC. (A) Human protein atlas (HPA) database showed the SPARC expression profile in blood. (B, C) Differential SPARC mRNA expression in PBMC between LIHC and normal controls in GSE49515 and GSE58208 datasets. (D) Serum SPARC protein levels in LIHC and healthy controls (HC). (E) SPARC mRNA expression levels in PBMC from our sample in this study.

As SPARC is a secreted protein, we further investigated the SPARC levels in serum from LIHC patients. As shown in Figure 2D, the serum SPARC levels were significantly decreased in LIHC patients compared to healthy controls [86.19 (IQR: 49.16-140.46) vs. 293.53 (IQR: 245.97-356.66) ng/ml, p < 0.001)]. Furthermore, by using qRT-PCR, we detected the SPARC mRNA expression in PBMC from LIHC patients and healthy individuals. The results showed a lower expression level of SPARC in LIHC’s PBMC than that in healthy individuals (Figure 2E), which is consistent with our results of serum SPARC detection. However, these data were inconformity with the published data in GSE58208 and GSE49515 datasets.

Endogenous SPARC Overexpression Promotes Liver Cancer Cell Growth

We investigated the endogenous SPARC overexpression on HepG2 cell proliferation in vitro. First, the SPARC overexpressed cell model was constructed and named as HepG2SPARC. The higher expression level of SPARC in HepG2SPARC than that in HepG2NC control cells was identified by qRT-PCR (Figure 3A). Then, by using the CCK8 assay, HepG2SPARC cells showed an enhanced proliferation rate compared to HepG2NC (Figure 3B). And moreover, the FCM analysis showed that there was a significant decrease of HepG2SPARC cells in the G1 phase, while there was an increase in G2 and S phases (Figure 3C). There was no significant difference of cell apoptosis between HepG2SPARC and HepG2NC cells (Figure 3D).

FIGURE 3 | Endogenous SPARC overexpression promote liver cancer cells proliferation and tumor growth. (A) qRT-PCR showed the higher SPARC expression level in HepG2SPARC. (B) SPARC overexpression promotes liver cancer cell proliferation. (C) FCM showed the effects of SPARC overexpression on cell cycle. (D) FCM showed the effects of SPARC overexpression on cell apoptosis. (E) SPARC overexpression promotes tumor growth in vivo.

In addition to the cell experiments in vitro, we further investigated the effect of SPARC overexpression on tumor growth in vivo. By using nude xenografted models, we found that SPARC overexpression promoted liver cancer growth (Figure 3E). Taken together, these results demonstrated a promoted effect of high SPARC expression on liver cancer growth.

The Effect of Exogenous SPRAC Treatment on HepG2 Proliferation, Cell Cycle, and Apoptosis

Furthermore, we evaluated the effects of exogenous SPARC treatment on the HepG2 cell biological behavior. The gradient concentration of recombination SPARC protein was added into the cell cultured medium. Then, CCK8 results showed that exogenous SPARC treatment has no significant effect on HepG2 cell proliferation (Supplementary Figure S2A). The FCM analysis results showed that there was no significant effect of exogenous SPARC on cell cycle and apoptosis (Supplementary Figures S2B,C).

The Function Enrichment Analysis of SPARC-Correlated Genes in LIHC

We used the LinkedOmics bioinformatic tool to screen the genes which positively or negatively correlated to SPARC in LIHC (Supplementary Table S2). As shown in the heatmap (Figure 4A), SPARC strongly and positively correlated to the collagen gene family (such as COL4A2, COL4A1, COL3A1, COL6A3, and COL1A2; r > 0.8, p < 0.0001), peroxidasin-PXDN (r = 0.893, p < 0.0001), and junctional adhesion molecule 3-JAM3 (r = 0.871, p < 0.0001). Furthermore, the KEGG pathway and GO enrichment analyses of SPARC association genes were performed (Figures 4B–E; Supplementary Table S2). The results showed that SPARC-correlated gene enrichment in GO_BP (biological process) was mainly related to the extracellular matrix (ECM) organization (GO:0030198), collagen catabolic process (GO:0030574), collagen fibril organization (GO:0030199), and cellular response to amino acid stimulus (GO:0071230) (Figure 4C). GO_CC (cell component) enrichment showed that these genes were mainly expressed in the extracellular matrix (GO:0031012), endoplasmic reticulum lumen (GO:0005788), proteinaceous extracellular matrix (GO:0005578), and extracellular region (GO:0005576) (Figure 4D). The GO_MF (molecular function) analysis showed an enrichment in the extracellular matrix structural constituent (GO:0005201), calcium ion binding (GO:0005509), and platelet-derived growth factor binding (GO:0048407) (Figure 4E). And moreover, the KEGG pathway analysis showed that SPARC-correlated genes were involved in protein digestion and absorption (hsa04974), ECM-receptor interaction (hsa04512), and PI3K-Akt signaling pathway (hsa04151). Taken together, these results indicated a critical role of interaction with the extracellular matrix in SPARC-promoting cancer cell proliferation.

FIGURE 4 | SPARC-associated genes and pathway enrichment in LIHC. (A) Heatmaps showing top 50 genes positively correlated (left) and top 50 genes negatively correlated with SPARC in LIHC (right). (B) Enriched KEGG pathways of SPARC-associated genes. (C–E) Enriched GO terms of SPARC-associated genes.

DISCUSSION

In this study, we aim to evaluate the expression change and potential function of SPARC in LIHC. First, based on TCGA and GEO databases, we found the mRNA expression levels of SPARC were increased in LIHC tumor tissues compared to non-tumor tissues. And moreover, hypomethylation in SPARC promoter might be a cause of its increased expression in LIHC tumor tissues. The IHC analysis of tissue microarray showed an increased level of the SPARC protein in LIHC tumor tissues compared to adjacent non-tumor tissues.

Second, for the prognosis value of SPARC in LIHC patients, the KM plotter analysis showed that there was no significant correlation between SPARC mRNA expression and overall survival of LIHC patients, except in sorafenib-treated patients. The relationship between the higher SPARC mRNA level and favorable prognosis for sorafenib treatment needs to be further verified in a large number of patients. Additionally, the study by Ju MJ et al. suggested that higher expression of SPARC in peritumoral activated hepatic stellate cells is an independent predictor of more early recurrence and poorer survival of LIHC patients (Ju et al., 2009).

SPARC was expressed in a variety of cells, including PBMC. The HPA database showed that SPARC was expressed in total PBMC, monocytes, neutrophil, basophil, eosinophil, etc. In this study, we found that the SPARC mRNA expression was lower in PBMC from LIHC patients than that in healthy individuals. As shown in Supplementary Table S3, in our samples, the counts of lymphocyte and eosinophil were significantly lower in LIHC patients than those in healthy controls. Thus, the decreased SPARC mRNA expression in PBMC could be due to the decreased number of lymphocyte and eosinophil. And moreover, serum SPARC protein levels were also lower in LIHC patients than those in healthy individuals. However, the study by Zhang J et al. showed that serum SPARC in LIHC patients was significantly higher than that in healthy controls, while it was significantly lower than that in benign liver disease patients (Zhang et al., 2017a). These inconsistent results need further verification in larger cohorts. The study by Liu et al. showed that lower serum SPARC levels were associated with better prognosis for LIHC patients who have undergone transarterial chemoembolization-TACE (Liu et al., 2020). Thus, serum SPARC might be served as a potential predictor for prognosis of LIHC patients with certain characteristics. Of note, the association between the serum SPARC and prognosis might be conflicting in patients with different cancer types. For example, Akutsu T’s study (Akutsu et al., 2020) showed that digestive tract cancer patients with serum SPARC levels lower than the median level had a significantly higher risk for death than those with higher levels.

Furthermore, our experiments showed a promoted effect of endogenous SPARC overexpression on liver cancer cell proliferation in vitro and tumor growth in vivo, which might be associated with an increased cell ratio in S and G2 phases. Consistent with this result, the previous study reported that down-regulation of SPARC contributed to the inhibition of proliferation of HuH-7 liver cancer cells (Liu et al., 2020). The study by Jiang et al. found SPARC overexpression promoted tumor growth via enhancing the acquisition of stem cell phenotypes in liver cancer cells (Jiang et al., 2019). And moreover, the previous study showed that microRNA-211 could suppress liver cancer cell proliferation and invasion by targeting SPARC, which indicated that SPARC overexpression might drive progression of LIHC (Deng et al., 2016). However, by using a non-alcoholic fatty liver (NAFLD)-related HCC murine model, Onorato et al. (2021) found that the absence of SPARC accelerated HCC development, which is associated with an altered hepatic lipid metabolism. And moreover, the study by Atorrasagasti et al. also showed that SPARC overexpression in hepatocellular carcinoma cells results in a reduced tumorigenicity partially (Atorrasagasti et al., 2010). Taken together, these data demonstrated the important roles of endogenous SPARC during LIHC progression, and SPARC could be a double-edged sword. And moreover, the SPARC-correlated genes were enriched in the biological process which related to ECM organization, collagen catabolic process, collagen fibril organization, etc. Thus, the potential mechanism of SPARC in LIHC might be associated with these biological processes. Notably, the gene expression characteristics of patients are only one of the factors that affect their survival. Thus, the function of gene in the biological process is not equal to its prognostic value. For example, the study by Zhang QY et al. showed that FAM46C repressed LIHC cell proliferation (Zhang et al., 2017b). However, the KM plotter showed that there were significant effects of FAM46C on the survival of LIHC patients (HR (95% CI) = 0.74 (0.5-1.09), logrank p = 0.13).

In conclusion, we demonstrate that SPARC expression is increased in LIHC patients. And moreover, endogenous SPARC overexpression may promote LIHC progression. Our study showed that SPARC could be a potential therapeutic target for LIHC.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Material, and further inquiries can be directed to the corresponding author.

ETHICS STATEMENT

The studies involving human participants were reviewed and approved by the Ethics Committee of Tangdu Hospital, Fourth Military Medical University. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. The animal study was reviewed and approved by the Air Force Medical University’s Institutional Animal Care and Use Committee.

AUTHOR CONTRIBUTIONS

Z-wG and KD designed and supervised this work; CL, TH, and LY performed the experiments and analyzed data; Z-wG and CL analyzed the online databases; CL and X-nW performed the animal experiments; Z-wG, H-zZ, and KD wrote the manuscript.

FUNDING

This work was supported by the National Natural Science Fund of China (No. 81702732).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2021.775743/full#supplementary-material

REFERENCES

Akutsu, T., Ito, E., Narita, M., Ohdaira, H., Suzuki, Y., and Urashima, M. (2020). Effect of Serum SPARC Levels on Survival in Patients with Digestive Tract Cancer: A Post Hoc Analysis of the AMATERASU Randomized Clinical Trial. Cancers 12, 1465. doi:10.3390/cancers12061465

Alvarez, M. J., Prada, F., Salvatierra, E., Bravo, A. I., Lutzky, V. P., Carbone, C., et al. (2005). Secreted Protein Acidic and Rich in Cysteine Produced by Human Melanoma Cells Modulates Polymorphonuclear Leukocyte Recruitment and Antitumor Cytotoxic Capacity. Cancer Res. 65, 5123–5132. doi:10.1158/0008-5472.CAN-04-1102

Atorrasagasti, C., Malvicini, M., Aquino, J. B., Alaniz, L., Garcia, M., Bolontrade, M., et al. (2009). Overexpression of SPARC Obliterates Thein Vivotumorigenicity of Human Hepatocellular Carcinoma Cells. Int. J. Cancer 126, 2726–2740. doi:10.1002/ijc.24966

Bhoopathi, P., Chetty, C., Gujrati, M., Dinh, D. H., Rao, J. S., and Lakka, S. S. (2010). The Role of MMP-9 in the Anti-angiogenic Effect of Secreted Protein Acidic and Rich in Cysteine. Br. J. Cancer 102, 530–540. doi:10.1038/sj.bjc.6605538

Bhoopathi, P., Gondi, C. S., Gujrati, M., Dinh, D. H., and Lakka, S. S. (2011). SPARC Mediates Src-Induced Disruption of Actin Cytoskeleton via Inactivation of Small GTPases Rho-Rac-Cdc42. Cell Signal. 23, 1978–1987. doi:10.1016/j.cellsig.2011.07.008

Bornstein, P., and Sage, E. H. (2002). Matricellular Proteins: Extracellular Modulators of Cell Function. Curr. Opin. Cel. Biol. 14, 608–616. doi:10.1016/s0955-0674(02)00361-7

Chandrashekar, D. S., Bashel, B., Balasubramanya, S. A. H., Creighton, C. J., Ponce-Rodriguez, I., Chakravarthi, B. V. S. K., et al. (2017). UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia 19, 649–658. doi:10.1016/j.neo.2017.05.002

Cheetham, S., Tang, M. J., Mesak, F., Kennecke, H., Owen, D., and Tai, I. T. (2008). SPARC Promoter Hypermethylation in Colorectal Cancers Can Be Reversed by 5-Aza-2′deoxycytidine to Increase SPARC Expression and Improve Therapy Response. Br. J. Cancer 98, 1810–1819. doi:10.1038/sj.bjc.6604377

Deng, B., Qu, L., Li, J., Fang, J., Yang, S., Cao, Z., et al. (2016). MiRNA-211 Suppresses Cell Proliferation, Migration and Invasion by Targeting SPARC in Human Hepatocellular Carcinoma. Sci. Rep. 6, 26679. doi:10.1038/srep26679

Huang, D. W., Sherman, B. T., and Lempicki, R. A. (2009). Bioinformatics Enrichment Tools: Paths toward the Comprehensive Functional Analysis of Large Gene Lists. Nucleic Acids Res. 37, 1–13. doi:10.1093/nar/gkn923

Jiang, X., Liu, F., Wang, Y., and Gao, J. (2019). Secreted Protein Acidic and Rich in Cysteine Promotes Epithelial-Mesenchymal Transition of Hepatocellular Carcinoma Cells and Acquisition of Cancerstem Cell Phenotypes. J. Gastroenterol. Hepatol. 34, 1860–1868. doi:10.1111/jgh.14692

Ju, M.-J., Qiu, S.-J., Fan, J., Xiao, Y.-S., Gao, Q., Zhou, J., et al. (2009). Peritumoral Activated Hepatic Stellate Cells Predict Poor Clinical Outcome in Hepatocellular Carcinoma after Curative Resection. Am. J. Clin. Pathol. 131, 498–510. doi:10.1309/AJCP86PPBNGOHNNL

Ledda, M. F., Adris, S., Bravo, A. I., Kairiyama, C., Bover, L., Chernajovsky, Y., et al. (1997). Suppression of SPARC Expression by Antisense RNA Abrogates the Tumorigenicity of Human Melanoma Cells. Nat. Med. 3, 171–176. doi:10.1038/nm0297-171

Levy, G., Habib, N., Guzzardi, M. A., Kitsberg, D., Bomze, D., Ezra, E., et al. (2016). Nuclear Receptors Control Pro-viral and Antiviral Metabolic Responses to Hepatitis C Virus Infection. Nat. Chem. Biol. 12, 1037–1045. doi:10.1038/nchembio.2193

Lien, H. C., Hsiao, Y. H., Lin, Y. S., Yao, Y. T., Juan, H. F., Kuo, W. H., et al. (2007). Molecular Signatures of Metaplastic Carcinoma of the Breast by Large-Scale Transcriptional Profiling: Identification of Genes Potentially Related to Epithelial-Mesenchymal Transition. Oncogene 26, 7859–7871. doi:10.1038/sj.onc.1210593

Liu, Y., Feng, Y., Wang, X., Yang, X., Hu, Y., Li, Y., et al. (2020). SPARC Negatively Correlates with Prognosis after Transarterial Chemoembolization and Facilitates Proliferation and Metastasis of Hepatocellular Carcinoma via ERK/MMP Signaling Pathways. Front. Oncol. 10, 813. doi:10.3389/fonc.2020.00813

Menyhárt, O., Nagy, Á., and Győrffy, B. (2018). Determining Consistent Prognostic Biomarkers of Overall Survival and Vascular Invasion in Hepatocellular Carcinoma. R. Soc. Open Sci. 5, 181006. doi:10.1098/rsos.181006

Onorato, A., Fiore, E., Bayo, J., Casali, C., Fernandez‐Tomé, M., Rodríguez, M., et al. (2021). SPARC Inhibition Accelerates NAFLD‐associated Hepatocellular Carcinoma Development by Dysregulating Hepatic Lipid Metabolism. Liver Int. 41, 1677–1693. doi:10.1111/liv.14857

Puolakkainen, P. A., Brekken, R. A., Muneer, S., and Sage, E. H. (2004). Enhanced Growth of Pancreatic Tumors in SPARC-Null Mice Is Associated with Decreased Deposition of Extracellular Matrix and Reduced Tumor Cell Apoptosis. Mol. Cancer Res. 2, 215–224.

Rempel, S. A., Golembieski, W. A., Ge, S., Lemke, N., Elisevich, K., Mikkelsen, T., et al. (1998). SPARC: A Signal of Astrocytic Neoplastic Transformation and Reactive Response in Human Primary and Xenograft Gliomas. J. Neuropathol. Exp. Neurol. 57, 1112–1121. doi:10.1097/00005072-199812000-00002

Roessler, S., Jia, H.-L., Budhu, A., Forgues, M., Ye, Q.-H., Lee, J.-S., et al. (2010). A Unique Metastasis Gene Signature Enables Prediction of Tumor Relapse in Early-Stage Hepatocellular Carcinoma Patients. Cancer Res. 70, 10202–10212. doi:10.1158/0008-5472.CAN-10-2607

Said, N., and Motamed, K. (2005). Absence of Host-Secreted Protein Acidic and Rich in Cysteine (SPARC) Augments Peritoneal Ovarian Carcinomatosis. Am. J. Pathol. 167, 1739–1752. doi:10.1016/S0002-9440(10)61255-2

Shi, M., Chen, M.-S., Sekar, K., Tan, C.-K., Ooi, L. L., and Hui, K. M. (2014). A Blood-Based Three-Gene Signature for the Non-invasive Detection of Early Human Hepatocellular Carcinoma. Eur. J. Cancer 50, 928–936. doi:10.1016/j.ejca.2013.11.026

Shi, Q., Bao, S., Maxwell, J. A., Reese, E. D., Friedman, H. S., Bigner, D. D., et al. (2004). Secreted Protein Acidic, Rich in Cysteine (SPARC), Mediates Cellular Survival of Gliomas through AKT Activation. J. Biol. Chem. 279, 52200–52209. doi:10.1074/jbc.M409630200

Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., et al. (2021). Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA A. Cancer J. Clin. 71 (3), 209–249. doi:10.3322/caac.21660

Tai, I. T., and Tang, M. J. (2008). SPARC in Cancer Biology: its Role in Cancer Progression and Potential for Therapy. Drug Resist. Updates 11, 231–246. doi:10.1016/j.drup.2008.08.005

Tang, Z., Kang, B., Li, C., Chen, T., and Zhang, Z. (2019). GEPIA2: an Enhanced Web Server for Large-Scale Expression Profiling and Interactive Analysis. Nucleic Acids Res. 47, W556–W560. doi:10.1093/nar/gkz430

Vasaikar, S. V., Straub, P., Wang, J., and Zhang, B. (2018). LinkedOmics: Analyzing Multi-Omics Data within and across 32 Cancer Types. Nucleic Acids Res. 46, D956–D963. doi:10.1093/nar/gkx1090

Wang, C., Liao, Y., He, W., Zhang, H., Zuo, D., Liu, W., et al. (2021). Elafin Promotes Tumour Metastasis and Attenuates the Anti-metastatic Effects of Erlotinib via Binding to EGFR in Hepatocellular Carcinoma. J. Exp. Clin. Cancer Res. 40, 113. doi:10.1186/s13046-021-01904-y

Wang, S. M., Ooi, L. L. P. J., and Hui, K. M. (2007). Identification and Validation of a Novel Gene Signature Associated with the Recurrence of Human Hepatocellular Carcinoma. Clin. Cancer Res. 13, 6275–6283. doi:10.1158/1078-0432.CCR-06-2236

Wong, S. Y., Haack, H., Kissil, J. L., Barry, M., Bronson, R. T., Shen, S. S., et al. (2007). Protein 4.1B Suppresses Prostate Cancer Progression and Metastasis. Proc. Natl. Acad. Sci. 104, 12784–12789. doi:10.1073/pnas.0705499104

Yang, E., Kang, H. J., Koh, K. H., Rhee, H., Kim, N. K., and Kim, H. (2007). Frequent Inactivation of SPARC by Promoter Hypermethylation in colon Cancers. Int. J. Cancer 121, 567–575. doi:10.1002/ijc.22706

Yiu, G. K., Chan, W. Y., Ng, S.-W., Chan, P. S., Cheung, K. K., Berkowitz, R. S., et al. (2001). SPARC (Secreted Protein Acidic and Rich in Cysteine) Induces Apoptosis in Ovarian Cancer Cells. Am. J. Pathol. 159, 609–622. doi:10.1016/S0002-9440(10)61732-4

Zhang, J.-L., Chen, G.-W., Liu, Y.-C., Wang, P.-Y., Wang, X., Wan, Y.-L., et al. (2012). Secreted Protein Acidic and Rich in Cysteine (SPARC) Suppresses Angiogenesis by Down-Regulating the Expression of VEGF and MMP-7 in Gastric Cancer. PLoS One 7, e44618. doi:10.1371/journal.pone.0044618

Zhang, J., Hao, N., Liu, W., Lu, M., Sun, L., Chen, N., et al. (2017a). In-depth Proteomic Analysis of Tissue Interstitial Fluid for Hepatocellular Carcinoma Serum Biomarker Discovery. Br. J. Cancer 117, 1676–1684. doi:10.1038/bjc.2017.344

Zhang, Q.-Y., Yue, X.-Q., Jiang, Y.-P., Han, T., and Xin, H.-L. (2017b). FAM46C Is Critical for the Anti-proliferation and Pro-apoptotic Effects of Norcantharidin in Hepatocellular Carcinoma Cells. Sci. Rep. 7 (1), 396. doi:10.1038/s41598-017-00313-6

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Gao, Liu, Yang, He, Wu, Zhang and Dong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 30 November 2021
doi: 10.3389/fmolb.2021.736272

A Novel Diagnostic Biomarker, PZP, for Detecting Colorectal Cancer in Type 2 Diabetes Mellitus Patients Identified by Serum-Based Mass Spectrometry

Jiayue Yang¹†, Weigang Fang²†, Wenjun Wu¹, Zhen Tian³, Rong Gao³, Lu Yu¹, Dayang Chen², Xiaohua Weng¹, Shengwei Zhu¹* and Cheng Yang²*

¹Department of Endocrinology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China

²Department of Gastroenterology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China

³Department of Clinical Laboratory, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China

Edited by:
Florence Le Calvez-Kelm, International Agency For Research On Cancer (IARC), France

Reviewed by:
Sandra Anjo, University of Coimbra, Portugal
Rodney Hull, University of Pretoria, South Africa

* Correspondence: Cheng Yang, yangchengds@163.com; Shengwei Zhu, zhusw1978@163.com

†These authors have contributed equally to this work

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 05 July 2021
Accepted: 16 November 2021
Published: 30 November 2021

Citation: Yang J, Fang W, Wu W, Tian Z, Gao R, Yu L, Chen D, Weng X, Zhu S and Yang C (2021) A Novel Diagnostic Biomarker, PZP, for Detecting Colorectal Cancer in Type 2 Diabetes Mellitus Patients Identified by Serum-Based Mass Spectrometry. Front. Mol. Biosci. 8:736272. doi: 10.3389/fmolb.2021.736272

Background: Growing evidence has confirmed that populations with type 2 diabetes mellitus (T2DM) have an increasing risk of developing colorectal cancer (CRC). Thus, convenient and effective screening strategies for CRC should be developed for the T2DM population to increase the detection rate of CRC.

Methods: Twenty serum samples extracted from five healthy participants, five T2DM patients, five CRC patients and five T2DM patients with CRC (T2DM + CRC) were submitted to data-independent acquisition mass spectrometry (DIA-MS) analysis to discover unique differentially altered proteins (DAPs) for CRC in patients with T2DM. Then, the diagnostic value of pregnancy zone protein (PZP) was validated by ELISA analysis in the validated cohort.

Results: Based on DIA-MS analysis, we found eight unique proteins specific to T2DM patients with CRC. Among these proteins, four proteins showed different expression between the T2DM + CRC and T2DM groups, and PZP exhibited the largest difference. Next, the diagnostic value of serum PZP was validated by ELISA analysis with an AUC of 0.713. Moreover, the combination of PZP, CA199 and CEA exhibited encouraging diagnostic value, and the AUC reached 0.916.

Conclusion: Overall, our current research implied that PZP could be regarded as a newfound serum biomarker for CRC medical diagnosis in T2DM patients.

Keywords: biomarker, colorectal cancer, type 2 diabetes mellitus, mass spectrum, PZP

INTRODUCTION

Diabetes mellitus is a widespread chronic disease, especially type 2 diabetes mellitus (T2DM), whose incidence is increasing gradually. According to the latest statistics published by the International Diabetes Federation (ninth edition), the global diabetes prevalence in 2019 was expected to be 9.3%, which will increase to 10.2% by 2030 and 10.9% by 2045 (Saeedi et al., 2019). In China, the estimated prevalence of diabetes was expected to be 11.6% in the adult population, and the prevalence of prediabetes was 50.1% (Xu et al., 2013). In addition, T2DM accounts for 90–95% of total diabetes cases (DeFronzo et al., 2015). Increasing studies have demonstrated that T2DM is associated not only with micro- and/or macrovascular complications (DeFronzo et al., 2015) but also with carcinogenesis and progression of various types of malignancies, such as lung cancer, gynecological cancers and gastrointestinal cancers (Shlomai et al., 2016; Abudawood, 2019; Pearson-Stuttard et al., 2021).

Colorectal cancer (CRC) is one of the most widespread malignant tumors in the digestive system; the third highest incidence rate of all cancers and ranks as one of the cancers with the highest mortality rates (Siegel et al., 20192019). Because the prognosis of CRC primarily depends on the tumor stage, early detection plays a decisive role in the treatment and prognosis of CRC patients (Messersmith, 2019). However, a large number of patients are still diagnosed at progressive stages, and the 5-years survival rate is only approximately 30%, even after standardized systemic treatment (Siegel et al., 2020). Increasing evidence has confirmed that T2DM patients face an enhanced threat of suffering from CRC compared to a population without the disease (Larsson et al., 2005; Elwing et al., 2006; Yu et al., 2016). Therefore, the T2DM population should be regarded as a high-risk population prone to CRC, and convenient and effective screening strategies for CRC should be developed for the T2DM population to increase the detection rate of CRC and improve the prognosis of these patients.

In this study, data-independent acquisition mass spectrometry (DIA-MS) was used to characterize serum protein profiles of healthy participants, T2DM patients, CRC patients and T2DM patients complicated with CRC (T2DM + CRC). In addition, the unique differentially altered proteins (DAPs) in the serum of T2DM + CRC patients were determined, and one of these proteins, PZP was further verified by enzyme-linked immunosorbent assay (ELISA) analysis in the validated cohort. In summary, pregnancy zone protein (PZP) was identified as a newfound serum indicator for monitoring CRC in patients with T2DM, which will boost the noninvasive diagnosis rate for CRC in clinical practice.

MATERIALS AND METHODS

Patients and Sample Description

The following two patient cohorts were recruited to identify and verify serum indicators for screening CRC in T2DM patients (Figure 1): In the discovery cohort, a total of 20 serum samples from five healthy participants, five T2DM patients, five CRC patients and five T2DM + CRC patients, were collected for DIA-MS analysis. The baseline information in the discovery cohort was exhibited in Supplementary Table S1. In the validation cohort, 40 serum specimens from T2DM patients and 32 serum samples from T2DM + CRC patients were submitted to ELISA analysis. The baseline information of patients in the validation cohort was exhibited in Table 1. Before collecting serum samples, patients or healthy participants did not receive any treatment other than hypoglycemic therapy. The serum specimens were preserved at -80 °C until use. The general information of the patients and the results of tumor biomarkers (AFP, CEA, CA125 and CA199) were obtained by retrospective review of medical records. The Ethical Committee at Wuxi People’s Hospital Affiliated to Nanjing Medical University gave the ethical license to the study.

FIGURE 1 | The overall design of the current study. Schematic illustration of the discovery procedure for the screening of potential diagnostic biomarkers for CRC in T2DM patients based on the DIA-MS method (A) and the validated procedure based on ELISA analysis (B).

TABLE 1 | Comparison of general information between two groups.

DIA-MS Analysis

DIA-MS analysis was performed on each serum sample from the discovery set to obtain differential serum proteins. The main steps were as follows: protein extraction and FASP enzymatic hydrolysis, high PH RP classification, DDA-MS library construction and DIA analysis, these techniques were described in our previous publication (Yang et al., 2021). DAPs were extracted using R language 4.0.0 with a criterion of fold change (FC) ≥ 1.50 or ≤0.67 and a unpaired t-test p value ≤0.05.

ELISA Analysis

The serum levels of PZP were quantified using a commercial ELISA kit (Cat no. DY8280-05; R&D Systems) based on the manufacturer’s protocol. An 8-point standard curve was fitted to the OD values of each standard sample after subtraction of the blank sample. The concentration of PZP in serum was reached using the regression equation and multiplied by the dilution ratio (with a dilution of 1:2). All specimens were examined in duplicate, and the average levels were used in all analyses in this research.

Statistical Analysis

Most statistical analyses were performed using SPSS (version 26.0) and GraphPad Prism (version 8.0). Figure exhibition was performed using R language 4.0.0 and GraphPad Prism (version 8.0). The difference between the two groups was assessed by Student’s t-test or Mann-Whitney test. Receiver operating characteristic (ROC) analysis was plotted to evaluate the specificity and sensitivity of the candidate indicator, and the area under the ROC curve (AUC) was generated for diagnostic biomarkers. Binary logistic regression analysis was devoted to evaluating the combined diagnostic performance. For all analyses, a p value ≤0.05 was considered to be statistically significant.

RESULTS

Clinical Features of T2DM and T2DM + CRC Patients

The general clinicopathological characteristics in the two groups in the validation cohort consisting of 40 T2DM patients and 32 T2DM + CRC patients were compared. Of the 40 patients in the T2DM group, 23 were males and 17 were females, with an average age of 61.05 ± 9.78 years and an average fasting plasma glucose (FPG) of 7.95 ± 1.91 mmol/L. In the T2DM + CRC group, there were 15 males and 17 females with an average age of 68.69 ± 8.87 years and an average FPG of 6.53 ± 2.09 mmol/L. No statistically significant difference was found between the sex of the two groups (p = 0.370), but the differences in age (p = 0.001) and FPG (p = 0.004) were statistically significant (Table 1). Next, we evaluated the differences in the levels of the widely applied tumor biomarkers between these two groups. The results showed that there were no remarkable differences in serum AFP (p = 0.311) and CA125 (p = 0.377) levels between the T2DM + CRC group and the T2DM group, but CEA (p < 0.001) and CA199 (p = 0.016) levels were notably enhanced in the T2DM + CRC group (Table 1).

Discovery of Differentially Expressed Proteins by DIA-MS Analysis

Based on DIA-MS analysis, the overall protein changes in serum specimens from 20 patients (5 healthy participants, five T2DM patients, five CRC patients and five T2DM + CRC patients) were analyzed. The levels of 67 serum proteins were deemed remarkably different between these disease groups and the healthy participant group (Figures 2A–C). To redistribute the specimens based on similarities in the patterns of serum protein levels, we conducted a hierarchical cluster analysis of the 67 DAPs according to previous research (Huang et al., 2010; Yang et al., 2021). Cluster analysis revealed a clear separation of the four groups (Figure 2D). Next, three protein subgroups from the afore mentioned analysis (T2DM vs healthy participant, CRC vs healthy participant and T2DM + CRC vs healthy participant) were differentiated to identify a subgroup of proteins that were differentially expressed only in the T2DM + CRC group. In total, eight distinct proteins were regarded as specific candidates in patients with T2DM + CRC (Figures 3A–I). Furthermore, the levels of four candidates were remarkably different between the T2DM + CRC and T2DM groups, namely, B2MG (FC = 1.257, p = 0.031), LV218 (FC = 2.067, p = 0.015), MDN1 (FC = 2.274, p = 0.032) and PZP (FC = 3.032, p = 0.027), and PZP exhibited the largest discrimination level (Table 2).

FIGURE 2 | Serum protein profiles in patient cohorts in the current study. (A–C) Volcano plot exhibiting the DAPs in the T2DM, CRC and T2DM + CRC patients in comparison with healthy participants. DAPs were extracted using R language 4.0.0 with a criterion of fold change (FC) ≥ 1.50 or ≤0.67 and unpaired t-test p value ≤0.05. (D) Hierarchical cluster analysis of the 67 DAPs in the groups of healthy participants, T2DM, CRC, and T2DM + CRC. There were five samples in each group. Red represents for high expression and blue represents for low expression.

FIGURE 3 | Extraction of specific serum proteins in T2DM + CRC patients. (A) Venn diagram of the 67 DAPs in the serum specimens from the four comparisons (T2DM vs healthy participants, CRC vs healthy participants and T2DM + CRC vs healthy participants). (B–I) Statistical graphs of eight specific proteins levels. Unpaired t-test was used to compare the potential difference.

TABLE 2 | Unpaired t-test of the quantitative proteomic results for protein expression in the T2DM + CRC group in comparison with the control and T2DM groups.

Validation of the Diagnostic Value of PZP and Combined Diagnostic Analysis

We subsequently validated the changes in PZP protein abundance using a commercial ELISA kit. The concentration-dependent standard curve is shown in Supplementary Figure S1. In addition, the level of PZP between the T2DM + CRC and T2DM groups was notably different in the validated cohort (Figure 4A), and the ROC analysis suggested an AUC of 0.713 (Figure 4B). To exclude the influence of confounding factors on PZP level, including age and sex, we compared the PZP level in male and female participants and assessed its correlation with age. There were no notable differences in PZP levels between males and females (p = 0.192), and no notable correlation was observed between PZP levels and patient age (p = 0.179) (Supplementary Figure S2). Considering the notable diagnostic value of CA199 and CEA in identifying CRC in T2DM populations, we next conducted a combined diagnostic performance test. Encouragingly, the combination of PZP, CA199 and CEA exhibited excellent diagnostic value compared with CA199, CEA and combined CA199&CEA, and the AUC reached 0.916 (Figure 4B).

FIGURE 4 | Diagnostic value of PZP and combined diagnostic analysis. (A) Serum levels of PZP in the T2DM and T2DM + CRC groups. Unpaired t-test was used to compare the potential difference. (B) ROC analysis of the diagnostic values of PZP, CA199, CEA and the combined score.

DISCUSSION

Previous studies have shown that T2DM patients have a superior risk of suffering from CRC in comparison with the general population (Yuhara et al., 2011; Sinagra et al., 2017). Hsieh et al. (Hsieh et al., 2012) previously examined the impact of T2DM on cancer risk using a logistic regression model. They found that T2DM patients have a notably higher risk of CRC. Chiu et al. (Chiu et al., 2013) applied the Cox proportional hazard regression model to assess the impacts of T2DM on the morbidities of gastrointestinal malignancies. They pointed out that T2DM was correlated remarkably with an enhanced threat of developing CRC. In addition, studies have shown that the application of the hypoglycemic drug metformin could significantly decrease the risk of CRC (Chen et al., 2020). Apart from this, metformin having the ability to mediate alterations to multiple critical events, including CRC cell proliferation, stemness maintenance, epithelial-mesenchymal transition (EMT), and transformational protumor cellular metabolic conditions (Jaromy and Miller, 2021). Moreover, CRC patients with T2DM receiving medical treatment with metformin experienced fewer distant metastases, associated with slower progression of CRC (Powell et al., 2020). A retrospective study pointed out that patients in the T2DM + CRC group were older than those in the T2DM group in a randomly selected cohort, which was consistent with our research (Feng et al., 2020). Therefore, it is very important to carry out CRC screening in elderly T2DM patients.

With the development of medical technology, there are increasing numbers of strategies available for CRC screening, consisting of both invasive strategies (colonoscopy, sigmoidoscopy and capsule colonoscopy) and noninvasive strategies (stool DNA testing and blood tumor biomarker testing) (Nee et al., 2020). Accumulating evidence based on observational studies has suggested that colonoscopy screening has a remarkably high success rate resulting in decreased (Manser et al., 2012; Nishihara et al., 2013; Brenner et al., 2014). The risk of T2DM patients developing CRC is significantly increased, and regular screening should be carried as they are a high-risk group. The latest research pointed out that through colonoscopy, the detection rate of colonic adenoma in T2DM patients was notably higher than that of the general population (Ottaviano et al., 2020). Although colonoscopy and other invasive screening modalities are generally regarded as safe, the threats of bowel preparation, the procedure, and sedation medications are all enhanced in older patients (Nee et al., 2020). In addition, colonoscopies are very difficult to carry out, especially in primary hospitals, due to the high technical requirements of staff, low patient acceptance, and high medical costs. Therefore, it is very important to find noninvasive diagnostic methods, especially serum biomarkers, to accurately identify CRC in T2DM patients.

The discovery and validation of tumor biomarkers involve a variety of modalities and platforms. Among them, MS-based strategies are widely utilized (Whetton et al., 2020; Ponzini et al., 2021). DIA-MS is a fairly simplified and convenient method to conduct high-throughput, quantitative and highly renewable proteomic analyses of clinical specimens (Guo et al., 2015). Based on the DIA-MS method, an increasing number of novel tumor indicators have been progressively discovered (Ortea et al., 2016; Ku et al., 2020). In the current research, to identify effective serum biomarker for CRC in T2DM population, we characterized the serum protein profiles of healthy participants, T2DM patients, CRC patients and T2DM + CRC patients. We identified a subgroup of proteins that were differentially expressed specifically in the T2DM + CRC group. A total of eight precise proteins were found to be specific to T2DM + CRC patients. In addition, the levels of four candidate proteins were prominently different between the T2DM + CRC and T2DM groups, and PZP exhibited the largest difference. To further validate the diagnostic value of PZP in screening CRC in T2DM populations, the validated validation cohort consisting of 40 T2DM patients and 32 T2DM + CRC patients was applied, and the results confirmed that promising diagnostic value.

PZP has been regarded as a pregnancy-associated protein that is remarkably overexpressed in the decidua of recurrent and spontaneous miscarriage (Lin and Halbert, 1976; Lob et al., 2021). Previous research suggested that serum PZP levels are notably elevated in patients with ovarian cancer in comparison with those with benign ovarian tumors and in those with cervical or endometrial cancer in comparison with those with benign uterine myomas (Teng et al., 1994). PZP has been identified as an unprecedented noninvasive indicator for screening lung adenocarcinoma in T2DM in our previous research (Yang et al., 2021). Furthermore, several tumor biomarkers already in clinical use also exhibited valuable discriminatory activity in screening CRC, the combination of PZP, CA199 and CEA exhibited encouraging diagnostic value, and the AUC reached 0.916.

In addition, the current research also provided a clue for the discovery of general CRC biomarkers. Namely, these nine proteins shared between the T2DM-CRC and CRC groups may be effective biomarkers for CRC regardless of diagnosing with T2DM or not. As shown in Supplementary Figure S4, we could found that FUBP1/2, PON3 and FHR1 were increased in general CRC (CRC and T2DM + CRC) patients compared with non-CRC patients (NC + T2DM), while KV104 and FRPD2 were decreased (Supplementary Figure S4). Recent research reported that FUBP1 promoted CRC stemness and metastasis by activating Wnt/β-catenin signaling (Yin et al., 2021). Whether increased FUBP1 was derived from tumor tissues and whether FUBP1 could be potential biomarker for CRC diagnosis needed to be further studied.

Whatever, there were some limitations in the current study. The major limitation is the small sample size. The patients diagnosed with T2DM + CRC are not common in our hospital but could not be ignored on a larger scale. Thus, multi-centric observational studies with a larger sample size should be conducted before PZP could be applied in clinical practice. Besides, in our previous research, PZP was identified as a serum indicator for screening lung adenocarcinoma in T2DM. We also compared the serum level of PZP in T2DM, T2MD + CRC and T2DM with lung adenocarcinoma (T2MD + LAC) patients, and the results showed that PZP was increased in T2MD + CRC and T2MD + LAC patients, but no difference was observed in these two groups (Supplementary Figure S3). Thus, whether PZP is a pan-cancer or pan-adenocarcinoma biomarker in T2DM needs to be further explored.

CONCLUSION

In summary, we successfully identified PZP as a potential diagnostic classifier for CRC screening in T2DM patients through DIA-MS combined with ELISA. In addition, the combined application of PZP with conventional tumor biomarkers CA199 and CEA has stronger diagnostic efficiency. However, the above results still need to be verified by multiple centers and larger samples.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.

ETHICS STATEMENT

The studies involving human participants were reviewed and approved by the Ethical Committee at Wuxi People’s Hospital Affiliated to Nanjing Medical University. The patients/participants provided their written informed consent to participate in this study.

AUTHOR CONTRIBUTIONS

CY and SZ designed the research. JY and WF collected the serum samples. JY, WF, WW, ZT, RG, LY, DC, and XW collected the data and performed the computational and experimental analyses. JY and WF drafted the manuscript. CY and SZ revised the manuscript. CY and SZ supervised and coordinated the research. All authors contributed to the article and approved the final version.

FUNDING

The current research was supported by the General Project of Wuxi Health Commission (M202016), the Major Project of Wuxi Health Commission (Z202017) and the Taihu Talent Program (BJ2020011).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2021.736272/full#supplementary-material

REFERENCES

Abudawood, M. (2019). Diabetes and Cancer: A Comprehensive Review. J. Res. Med. Sci. 24, 94. doi:10.4103/jrms.JRMS_242_19

Brenner, H., Chang-Claude, J., Jansen, L., Knebel, P., Stock, C., and Hoffyangster, M. (2014). Reduced Risk of Colorectal Cancer up to 10 Years after Screening, Surveillance, or Diagnostic Colonoscopy. Gastroenterology 146 (3), 709–717. doi:10.1053/j.gastro.2013.09.001

Chen, C.-H., Lin, C.-L., Hsu, C.-Y., and Kao, C.-H. (2020). Insulin Enhances and Metformin Reduces Risk of Colorectal Carcinoma in Type-2 Diabetes. QJM 113 (3), 194–200. doi:10.1093/qjmed/hcz253

Chiu, C.-C., Huang, C.-C., Chen, Y.-C., Chen, T.-J., Liang, Y., Lin, S.-J., et al. (2013). Increased Risk of Gastrointestinal Malignancy in Patients with Diabetes Mellitus and Correlations with Anti-diabetes Drugs: a Nationwide Population-Based Study in Taiwan. Intern. Med. 52 (9), 939–946. doi:10.2169/internalmedicine.52.8276

DeFronzo, R. A., Ferrannini, E., Groop, L., Henry, R. R., Herman, W. H., Holst, J. J., et al. (2015). Type 2 Diabetes Mellitus. Nat. Rev. Dis. Primers 1, 15019. doi:10.1038/nrdp.2015.19

Elwing, J. E., Gao, F., Davidson, N. O., and Early, D. S. (2006). Type 2 Diabetes Mellitus: the Impact on Colorectal Adenoma Risk in Women. Am. J. Gastroenterol. 101 (8), 1866–1871. doi:10.1111/j.1572-0241.2006.00651.x

Feng, L. H., Su, T., Bu, K. P., Ren, S., Yang, Z., Deng, C. E., et al. (2020), A Clinical Prediction Nomogram to Assess Risk of Colorectal Cancer Among Patients with Type 2 Diabetes. Sci. Rep. 10(1), 14359. doi:10.1038/s41598-020-71456-2

Guo, T., Kouvonen, P., Koh, C. C., Gillet, L. C., Wolski, W. E., Rost, H. L., et al. (2015). Rapid Mass Spectrometric Conversion of Tissue Biopsy Samples into Permanent Quantitative Digital Proteome Maps. Nat. Med. 21 (4), 407–413. doi:10.1038/nm.3807

Hsieh, M.-C., Lee, T.-C., Cheng, S.-M., Tu, S.-T., Yen, M.-H., and Tseng, C.-H. (2012). The Influence of Type 2 Diabetes and Glucose-Lowering Therapies on Cancer Risk in the Taiwanese. Exp. Diabetes Res. 2012, 1–6. doi:10.1155/2012/413782

Huang, H., Dong, X., Kang, M. X., Xu, B., Chen, Y., Zhang, B., et al. (2010). Novel Blood Biomarkers of Pancreatic Cancer-Associated Diabetes Mellitus Identified by Peripheral Blood-Based Gene Expression Profiles. Am. J. Gastroenterol. 105 (7), 1661–1669. doi:10.1038/ajg.2010.32

Jaromy, M., and Miller, J. D. (2021). Pharmacologic Mechanisms Underlying Antidiabetic Drug Metformin's Chemopreventive Effect against Colorectal Cancer. Eur. J. Pharmacol. 897, 173956. doi:10.1016/j.ejphar.2021.173956

Ku, X., Sun, Q., Zhu, L., Gu, Z., Han, Y., Xu, N., et al. (2020). Deciphering Tissue-Based Proteome Signatures Revealed Novel Subtyping and Prognostic Markers for Thymic Epithelial Tumors. Mol. Oncol. 14 (4), 721–741. doi:10.1002/1878-0261.12642

Larsson, S. C., Orsini, N., and Wolk, A. (2005). Diabetes Mellitus and Risk of Colorectal Cancer: a Meta-Analysis. J. Natl. Cancer Inst. 97 (22), 1679–1687. doi:10.1093/jnci/dji375

Lin, T. M., and Halbert, S. P. (1976). Placental Localization of Human Pregnancy-Aassociated Plasma Proteins. Science 193 (4259), 1249–1252. doi:10.1126/science.60782

Lob, S., Vattai, A., Kuhn, C., Schmoeckel, E., Mahner, S., Wockel, A., et al. (2021). Pregnancy Zone Protein (PZP) Is Significantly Upregulated in the Decidua of Recurrent and Spontaneous Miscarriage and Negatively Correlated to Glycodelin A (GdA). J. Reprod. Immunol. 143, 103267. doi:10.1016/j.jri.2020.103267

Manser, C. N., Bachmann, L. M., Brunner, J., Hunold, F., Bauerfeind, P., and Marbet, U. A. (2012). Colonoscopy Screening Markedly Reduces the Occurrence of colon Carcinomas and Carcinoma-Related Death: a Closed Cohort Study. Gastrointest. Endosc. 76 (1), 110–117. doi:10.1016/j.gie.2012.02.040

Messersmith, W. A. (2019). NCCN Guidelines Updates: Management of Metastatic Colorectal Cancer. J. Natl. Compr. Canc Netw. 17 (55), 599–601. doi:10.6004/jnccn.2019.5014

Nee, J., Chippendale, R. Z., and Feuerstein, J. D. (2020). Screening for Colon Cancer in Older Adults: Risks, Benefits, and when to Stop. Mayo Clin. Proc. 95 (1), 184–196. doi:10.1016/j.mayocp.2019.02.021

Nishihara, R., Wu, K., Lochhead, P., Morikawa, T., Liao, X., Qian, Z. R., et al. (2013). Long-term Colorectal-Cancer Incidence and Mortality after Lower Endoscopy. N. Engl. J. Med. 369 (12), 1095–1105. doi:10.1056/NEJMoa1301969

Ortea, I., Rodriguez-Ariza, A., Chicano-Galvez, E., Arenas Vacas, M. S., and Jurado Gamez, B. (2016). Discovery of Potential Protein Biomarkers of Lung Adenocarcinoma in Bronchoalveolar Lavage Fluid by SWATH MS Data-independent Acquisition and Targeted Data Extraction. J. Proteomics 138, 106–114. doi:10.1016/j.jprot.2016.02.010

Ottaviano, L. F., Li, X., Murray, M., Frye, J. T., Lung, B. E., Zhang, Y. Y., et al. (2020). Type 2 Diabetes Impacts Colorectal Adenoma Detection in Screening Colonoscopy. Sci. Rep. 10 (1), 7793. doi:10.1038/s41598-020-64344-2

Pearson-Stuttard, J., Papadimitriou, N., Markozannes, G., Cividini, S., Kakourou, A., Gill, D., et al. (2021). Type 2 Diabetes and Cancer: An Umbrella Review of Observational and Mendelian Randomization Studies. Cancer Epidemiol. Biomarkers Prev. 30, 1218–1228. doi:10.1158/1055-9965.EPI-20-1245

Ponzini, E., Santambrogio, C., De Palma, A., Mauri, P., Tavazzi, S., and Grandori, R. (2021). Mass Spectrometry-Based Tear Proteomics for Noninvasive Biomarker Discovery. Mass. Spectrom. Rev. doi:10.1002/mas.21691

Powell, M. K., Cempirkova, D., Dundr, P., Grimmichova, T., Trebicky, F., Brown, R. E., et al. (2020), Metformin Treatment for Diabetes Mellitus Correlates with Progression and Survival in Colorectal Carcinoma. Transl Oncol. 13(2), 383–392. doi:10.1016/j.tranon.2019.10.011

Saeedi, P., Petersohn, I., Salpea, P., Malanda, B., Karuranga, S., Unwin, N., et al. (2019). Global and Regional Diabetes Prevalence Estimates for 2019 and Projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th Edition. Diabetes Res. Clin. Pract. 157, 107843. doi:10.1016/j.diabres.2019.107843

Shlomai, G., Neel, B., LeRoith, D., and Gallagher, E. J. (2016). Type 2 Diabetes Mellitus and Cancer: The Role of Pharmacotherapy. Jco 34 (35), 4261–4269. doi:10.1200/JCO.2016.67.4044

Siegel, R. L., Miller, K. D., Goding Sauer, A., Fedewa, S. A., Butterly, L. F., Anderson, J. C., et al. (2020). Colorectal Cancer Statistics, 2020. CA A. Cancer J. Clin. 70 (3), 145–164. doi:10.3322/caac.21601

Siegel, R. L., Miller, K. D., and Jemal, A. (20192019). Cancer Statistics, 2019. CA A. Cancer J. Clin. 69 (1), 7–34. doi:10.3322/caac.21551

Sinagra, E., Guarnotta, V., Raimondo, D., Mocciaro, F., Dolcimascolo, S., Rizzolo, C. A., et al. (2017). Colorectal Cancer in Patients with Type 2 Diabetes Mellitus: a Single-center Experience. J. Biol. Regul. Homeost Agents 31 (4), 1101–1107.

Teng, H., Zhang, W. Y., and Zhu, F. Q. (1994). A Study on the Serum Pregnancy Zone Protein Levels in Pregnant Women and Patients with Gynecological Tumors. Chin. Med. J. (Engl) 107 (12), 910–914.

Whetton, A. D., Preston, G. W., Abubeker, S., and Geifman, N. (2020). Proteomics and Informatics for Understanding Phases and Identifying Biomarkers in COVID-19 Disease. J. Proteome Res. 19 (11), 4219–4232. doi:10.1021/acs.jproteome.0c00326

Xu, Y., Wang, L., He, J., Bi, Y., Li, M., Wang, T., et al. (2013). Prevalence and Control of Diabetes in Chinese Adults. JAMA 310 (9), 948–959. doi:10.1001/jama.2013.168118

Yang, J., Yang, C., Shen, H., Wu, W., Tian, Z., Xu, Q., et al. (2021). Discovery and Validation of PZP as a Novel Serum Biomarker for Screening Lung Adenocarcinoma in Type 2 Diabetes Mellitus Patients. Cancer Cel Int 21 (1), 162. doi:10.1186/s12935-021-01861-8

Yin, H., Gao, T., Xie, J., Huang, Z., Zhang, X., Yang, F., et al. (2021). FUBP1 Promotes Colorectal Cancer Stemness and Metastasis via DVL1-Mediated Activation of Wnt/beta-Catenin Signaling. Mol. Oncol. doi:10.1002/1878-0261.13064

Yu, F., Guo, Y., Wang, H., Feng, J., Jin, Z., Chen, Q., et al. (2016). Type 2 Diabetes Mellitus and Risk of Colorectal Adenoma: a Meta-Analysis of Observational Studies. BMC Cancer 16, 642. doi:10.1186/s12885-016-2685-3

Yuhara, H., Steinmaus, C., Cohen, S. E., Corley, D. A., Tei, Y., and Buffler, P. A. (2011). Is Diabetes Mellitus an Independent Risk Factor for colon Cancer and Rectal Cancer?Am. J. Gastroenterol. 106 (11), 1911–1921. doi:10.1038/ajg.2011.301

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Yang, Fang, Wu, Tian, Gao, Yu, Chen, Weng, Zhu and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 01 December 2021
doi: 10.3389/fmolb.2021.751842

Development and Validation of Ten-RNA Binding Protein Signature Predicts Overall Survival in Osteosarcoma

Jian Zhang1,², Xinxin Miao1,², Tianlong Wu1,³, Jingyu Jia¹ and Xigao Cheng1,2,³*

¹Department of Orthopedics, The Second Affiliated Hospital of Nanchang University, Nanchang, China

²Institute of Orthopedics of Jiangxi Province, Nanchang, China

³Institute of Minimally Invasive Orthopedics, Nanchang University, Nanchang, China

Edited by:
Florence Le Calvez-Kelm, International Agency For Research On Cancer (IARC), France

Reviewed by:
Anupam Nath Jha, Tezpur University, India
Tamer Saad Kaoud, University of Texas at Austin, United States
Udhaya Kumar S., Vellore Institute of Technology, India

* Correspondence: Xigao Cheng, 228206846@qq.com

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 02 August 2021
Accepted: 16 November 2021
Published: 01 December 2021

Citation: Zhang J, Miao X, Wu T, Jia J and Cheng X (2021) Development and Validation of Ten-RNA Binding Protein Signature Predicts Overall Survival in Osteosarcoma. Front. Mol. Biosci. 8:751842. doi: 10.3389/fmolb.2021.751842

Osteosarcoma is a malignant tumor that originates in the bones with the characteristics of high malignancy, predisposition to metastasis, and poor prognosis. RNA binding proteins (RBPs) are closely related to various tumors, but their relationship with osteosarcoma remains unclear. Based on GTEx and TARGET RNA sequencing data, we applied differential analysis to obtain RBP genes that are differentially expressed in osteosarcoma, and analyzed the functions of these RBPs. After applying univariate and LASSO Cox regression analysis, 10 key prognostic RBPs (TDRD6, TLR8, NXT2, EIF4E3, RPS27L, CPEB3, RBM34, TERT, RPS29, and ZC3HAV1) were screened, and an RBP prognostic risk assessment model for patients with osteosarcoma was established. The independent cohort GSE21257 was used for external verification, and the results showed that the signature has an excellent ability to predict prognosis. In addition, a nomogram that can be used for clinical evaluation was constructed. Finally, the expression levels of 10 prognostic RBPs in osteosarcoma cells and tissues were confirmed through experiments. Our study identified a ten-gene prognostic marker related to RBP, which is of great significance for adjusting the treatment strategy of patients with osteosarcoma and exploring prognostic markers.

Keywords: RNA-binding protein, osteosarcoma, prognostic signature, overall survival, nomogram

INTRODUCTION

Osteosarcoma is most common in adolescents (Arndt and Crist, 1999; Gianferante et al., 2017). It is highly malignant, progresses quickly, and has a poor prognosis, which seriously affects family and social health (Stiller et al., 2006). Early treatment of osteosarcoma was mostly based on amputation, but the prognosis was poor (Fletcher et al., 2002). Subsequent chemotherapy improved the patient’s prognosis (Ritter and Bielack, 2010). So far, surgery combined with chemotherapy has become an effective method for the treatment of osteosarcoma. However, the 5 years survival rate of patients with metastatic osteosarcoma is <20% (Kempf-Bielack et al., 2005). Therefore, research to find new treatments to improve the prognosis of osteosarcoma patients is ongoing (Kansara et al., 2014).

RNA binding proteins (RBPs) are important molecules with RNA binding domains that are widely expressed in organisms (Lunde et al., 2007). RBPs combine with their targeted mRNA to form a ribonucleoprotein (RNP) complex, and regulate genes at the post-transcriptional level by various mechanisms, thereby rapidly and effectively changing mRNA expression (Gerstberger et al., 2014). In the past few decades, many studies have revealed that RBPs are abnormally expressed in tumors, affecting the conversion of mRNA to protein and participating in tumor occurrence (Chatterji and Rustgi, 2018; Moore et al., 2018; Wang et al., 2018). The RBP CPEB4 has an important connection with the progress of liver cancer, melanoma, and pancreatic cancer (Ortiz-Zapater et al., 2011; Calderone et al., 2016; Pérez-Guijarro et al., 2016). In addition, the RBP Musashi has a cancer-promoting effect in several cancer types, including medulloblastoma (Vo et al., 2012; Kudinov et al., 2017) and colorectal cancer (Li et al., 2015; Wang et al., 2015). Although more and more studies have proved that RBPs are directly involved in the occurrence and development of many tumors, their relationship with osteosarcoma remains unclear, and there is no reliable RBP-related prognostic signature for osteosarcoma.

In this study, we extracted transcriptome sequencing information from TARGET and GTEx databases, and screened RBP genes related to the prognosis of osteosarcoma, an RBP-related risk model was constructed in the TARGET database, and its predictive ability in osteosarcoma was verified. Our research may provide valuable molecular targets for the future treatment and prognosis of osteosarcoma and open up new ideas for research into RBPs in osteosarcoma.

MATERIALS AND METHODS

Data Collection and Processing

The gene sequencing data and clinical information of 84 osteosarcoma patients were screened from the TARGET database (https://ocg.cancer.gov/programs/target). Gene expression data of musculoskeletal samples from 396 healthy humans were collected from the GTEx database (https://gtexportal.org/). To eliminate the platform data difference between TCGA and GTEx databases, the gene transcriptional expression data of each sample were transformed into log2 (FPKM value +1). Subsequently, the combat function from the “sva” R package was used to integrate the GTEx and TARGET datasets into one dataset.

The microarray dataset, named the GSE21257, was obtained from the high-throughput microarray expression profile database (Gene Expression Omnibus database, GEO, https://www.ncbi.nlm.nih.gov/geo/). GSE21257 contained the gene expression data and related clinical information of 53 osteosarcoma patients, which were used as the verification cohort for follow-up analysis and model verification.

The RNA-seq data was converted from FPKM to transcripts per million (TPM) using the algorithm described in the previous study for subsequent analysis (Li et al., 2010; Wagner et al., 2012). The 1,542 RBPs were obtained from previously published research (Gerstberger et al., 2014). The research process is shown in Figure 1A.

FIGURE 1 | Identification of differentially expressed RBPs and functional analysis (A) The flow chart of this study (B) The heatmap of 142 differentially expressed RBPs (C,D) GO analysis and KEGG pathway analysis of the differentially expressed RBPs (E) PPI network diagram composed of differentially expressed RBPs.

Identification of Differentially Expressed RBPs (DERBPs) and Functional Enrichment Analysis

The “limma” package was used to screen the RBPs that were differentially expressed between osteosarcoma and normal tissues. The false discovery rate (FDR) < 0.05 and log2 fold change >1 were set as critical values. Then Gene Ontology (GO) function enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway analyses were performed to explore the potential molecular mechanisms of the DERBPs. Analyzed the interaction between the DERBPs by the STRING online tool (http://www.string-db.org/) (Udhaya Kumar et al., 2020). Cytoscape software (version 3.7.2) was used to construct and visualize the PPI network. The expression levels of ZC3HAV1 in several common cancers were visualized by using the TIMER online tool (https://cistrome.shinyapps.io/timer/) and GEPIA online tool (Gene Expression Profiling Interactive Analysis, http://gepia.cancer-pku.cn/) (Kumar et al., 2019).

Identification and Construction of Prognostic Signatures

Taking the TARGET dataset as the training cohort, the “survival” package in R was used to perform univariate Cox regression analysis to screen genes related to prognosis, which were then used as candidate genes for constructing the model. Based on the above-mentioned prognostic-related genes, we performed LASSO Cox regression analysis through the “glmnet” package to determine the best penalty value, and the corresponding gene was selected as the modeling gene. The formula for calculating the risk value of each patient was:

Here, represents the RBPs coefficient, and represents the RBPs expression.

Evaluation and Verification of the Prognostic Signature

The above formula was used to calculate the risk score of each patient and divide them into high-risk groups and low-risk groups accordingly. Using the “survminer” package in R, we drawn the Kaplan–Meier (KM) curve. The “ggplot2” package was used to draw the distribution map of the risk score and survival status of the two patient groups and the modeled gene expression heatmap. The time-dependent ROC curve was produced through the “survivalROC” package in R. In parallel, combined with clinical information, such as age, gender, and tumor metastasis of osteosarcoma patients in the training cohort, the univariate and multivariate Cox regression model was used to analyze whether the risk score was an independent factor for judging the poor prognosis of osteosarcoma. The “rms” package in R was used to build and verify the nomogram model. The “calibrate” function of the rms software package was used to draw the calibration curve.

Gene Set Enrichment Analysis (GSEA)

To verify the functional differences between the low-risk and high-risk groups, we used GSEA to compare the enrichment of tumor characteristic gene sets (hallmark gene sets) between the two groups. The tumor characteristic gene set comes from the Molecular Signatures database (MSigDB) database and contains 50 sets of genes involved in inflammation and hypoxia.

Cell Lines and Cell Culture

The osteosarcoma cell lines (U2OS, 143B) and the osteoblast hFOB 1.19 cell line were purchased from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). The cells were grown in DMEM medium containing 10% fetal bovine serum (Gibco, United States) and 1% penicillin/streptomycin (solarbio, China). Osteosarcoma cells were grown at 37°C and 5% CO₂. Osteoblasts were cultured at 34°C with a volume fraction of 5% CO₂.

Clinical Specimens

We collected six osteosarcoma tissues and six matched adjacent normal tissues. The samples came from patients who underwent surgery at The Second Affiliated Hospital of Nanchang University and were pathologically diagnosed with osteosarcoma. After the surgical resection, the sample is immediately stored in liquid nitrogen until the RNA or protein is extracted. All patients signed an informed consent form, and the study was approved by the Research Ethics Committee of the Second Affiliated Hospital of Nanchang University.

RNA Extraction and Quantitative Real-Time PCR

Using TRIzol™ Reagent (Thermo Fisher Scientific, United States) to extract total RNA from cells and tissues, and using PrimeScript™ RT reagent Kit (Perfect Real Time) reverse transcription kit (Takara, Japan) to reverse transcription into cDNA, GAPDH as internal control, and real-time PCR was performed. According to the 2−ΔΔCt method for relative quantitative analysis. The primer sequence was shown in Supplementary Table S1.

Western Blotting

RIPA lysis buffer (Beyotime, China) was used to extract proteins from cells and osteosarcoma tissues. The equal amount of protein was separated by 10% SDS-PAGE gel electrophoresis, and then transferred to PVDF membrane by electroblotting (BioRad, United States). Then it was blocked with 5% skim milk, and incubated overnight with anti-ZC3HAV1 (1:1,000, Proteintech, China) and anti-GAPDH (1:2000, Proteintech, China) primary antibodies at 4 °C. After washing with TBST, incubated with secondary antibody (1:2000, Proteintech, china) for 1 h at room temperature. The blot was observed using ECL (enhanced chemiluminescence) and analyzed using ImageJ software.

Statistical Analysis

All statistical analyses were conducted using R. 4.0.4 (https://www.r-project.org/) and SPSS Statistics 25 (https://www.ibm.com/products/software). Two groups were compared with Student’s t-tests. p-value < 0.05 is considered to be statistically significant.

RESULTS

Screening and Functional Analysis of DERBPs

We extracted the RBPs expression matrix from the three datasets, and the Venn diagram displayed 1,093 intersected RBPs in all datasets (Figure 1A). Based on the dataset merged by TARGET and GTEx, we identified 142 DERBPs using the “limma” package and visualized by heatmap (Figure 1B). Next, functional analysis was performed on these DERBPs. GO analysis showed that, in terms of biological process (BP), DERBPs were mainly enriched in RNA splicing, regulation of translation, and mRNA metabolic process. In terms of cellular component (CC), cytoplasmic ribonucleoprotein granule, ribosome, and nuclear speck were enriched. In terms of molecular function (MF), DERBPs were mainly related to translation regulator activity, acting on RNA catalytic activity, and mRNA 3′-UTR binding (Figure 1C). Enrichment analysis of the KEGG pathway showed that the DERBPs mainly act on RNA transport (Figure 1D). We also constructed a PPI network. Figure 1E shows the interaction between the DERBPs. The darker the node, the more interacting proteins.

Establishment of RBPs Prognostic Signature

First, in the TARGET cohort, 142 DERBPs were analyzed using univariate regression analysis to obtain 11 RBPs related to prognosis (Figure 2A). LASSO regression analysis was performed on these 11 RBP genes (Figures 2B,C). When the log lambda reached the minimum, the parameters corresponding to the best modeling parameters and 10 model genes (TDRD6, TLR8, NXT2, EIF4E3, RPS27L, CPEB3, RBM34, TERT, RPS29, and ZC3HAV1), and their coefficients were obtained (Figure 2D).

FIGURE 2 | Construction of the RBPs prognostic signature (A) Univariate Cox regression revealed 11 RBPs associated with the prognosis of osteosarcoma (B,C) LASSO Cox regression analysis screened out 10 best genes to build the prognostic signature (D) The coefficients of 10 prognostic-related RBPs.

Evaluation and Verification of the RBPs Prognostic Signature

We evaluated and verified the signature in the TARGET cohort and the GSE21257 cohort. Survival analysis showed a significant difference in survival between the high- and low-risk groups of the training and validation datasets (Figures 3A,B). For predictions of 1–5 years survival, the AUC values ​​in the training and test cohorts were 0.84, 0.87, and 0.88 and 0.72, 0.75, and 0.81, respectively (Figures 3C,D). As the risk score increased, the number of deaths gradually increased, and the survival time was significantly reduced (Figures 3E,F). PCA analysis showed that the distribution patterns of patients in different risk groups were significantly different (Figures 3G,H). These results show that the RBP signature has excellent forecasting capabilities for patient prognosis and has been verified in independent data.

FIGURE 3 | Survival analysis of RBPs prognostic signature in TARGET cohort and validation in GSE21257 cohort (A,B) Kaplan-Meier curves in the two cohorts (C,D) Risk score analysis of the signature in the two cohorts (E,F) The AUC for the prediction of 1, 3, 5 years survival rate (G,H) PCA based on the RBP-related signature.

Relationship Between Risk Model and Clinical Characteristics

In the TARGET cohort, univariate and multivariate Cox regression analysis showed that gender and age do not predict the prognosis of osteosarcoma patients, although metastasis status and risk score can be used as an independent factor in prognosis (Figures 4A,B). The difference in clinical characteristics and RBP gene expression patterns between the two groups is revealed by the heatmap (Figures 4C,D). The risk score was significantly related to metastasis, and high-risk patients were more likely to have tumor metastasis, as shown by the box plot in Figures 4E–J.

FIGURE 4 | Clinical correlation analyses (A,B) Univariate and multivariate cox analysis showed the RBP genes signature and metastasis were two independent predictors of prognosis in osteosarcoma (C,D) The heatmap of the expression pattern of RBP is associated with the RBP signature and other clinical features (E–G) Relationship between risk score and clinical pathological factor in the TARGET cohort (H–J) Relationship between risk score and clinical pathological factor in the GSE21257 cohort.

Establishment of a Clinical Nomogram

The expression levels of the 10 prognostic RBPs were displayed as boxplots, grouped according to the status of metastasis and risk (Figures 5A–D). Most RBPs showed obvious differential expression between the two groups, and the trends were consistent in the training and validation cohorts. We also established a clinical nomogram to clinically predict the survival of patients (Figure 5E). The C-index was used to evaluate the nomograms of the two datasets, and both sets of results showed robust predictive power (the C index of the TARGET dataset was 0.86, and the GSE21257 dataset was 0.74). A calibration plot showed that the predicted 3–5 years overall survival was in good agreement with the overall survival observed in the TARGET cohort (Figures 5F,G) and GSE21257 cohort (Supplementary Figure S1A,B). These data indicate that the nomogram is stable in predicting the survival of patients with osteosarcoma.

FIGURE 5 | (A–D) The expression level of the 10 RBP genes, the patients were grouped according to metastasis and risk score (E) Nomogram based on gender, age, metastasis and risk in the TARGET cohort (F,G) Calibration plots of the nomogram for predicting the 3 and 5 years survival of osteosarcoma.

Signal Pathway Enrichment Analysis of Characteristic Gene Sets

GSEA results showed that in the TARGET dataset, the signal pathways or biological processes related to the tumor and immunity were enriched in the high-risk group. Such as allograft rejection, complement, inflammatory response, IL6-JAK-STAT3 signaling, and the interferon-gamma response had higher negative enrichment scores (NES) in the high-risk groups (Figure 6).

FIGURE 6 | GSEA of osteosarcoma patients based on the RBPs prognostic signature in the TARGET cohort.

Verification of the Expression Level of RBP-Related Prognostic Genes

We verified the expression levels of these prognostic RBPs genes, by using qRT-PCR and western blot. Our results showed that compared with osteoblasts, CPEB3, EIF4E3, RBM34, RPS27L, RPS29 and TDRD6 were down-regulated in U2OS and 143B, while NXT2, TERT, TLR8 and ZC3HAV1 were up-regulated in osteosarcoma cells (Figure 7A). The RT-qPCR results of the 10 prognostic RBPs genes expression levels are consistent with the RNA-sequence data (Figure 7B). Among these 10 RBPs, ZC3HAV1 is an antiviral protein, recent studies have shown that it is related to the occurrence of a variety of cancers (Lin et al., 2014; Todorova et al., 2015). It is up-regulated in breast cancer, cervical squamous cell carcinoma, cholangiocarcinoma, esophageal cancer, glioma, head and neck squamous cell carcinoma and other malignant tumors (Supplementary Figure S1C, D). However, the role of ZC3HAV1 in osteosarcoma has not been reported yet. Therefore, we further quantified the expression of ZC3HAV1 in normal osteoblasts (hFOB1.19 cells) and 143B and U2OS osteosarcoma cell lines. Western blotting showed that compared with normal osteoblasts, ZC3HAV1 protein levels in osteosarcoma cells were significantly up-regulated (Figure 7C). Consistently, ZC3HAV1 mRNA and protein levels in osteosarcoma tissues were significantly higher than adjacent normal tissues (Figures 7D,E).

FIGURE 7 | The expression levels of the prognostic RBP genes (A) The qRT-PCR result of the 10 RBP genes was evaluated by the 2-ΔΔCT method (B) The similarity of qRT-PCR and RNA-sequence analysis results of the 10 RBP genes. The data are expressed as mean ± standard deviation (C) The protein expression of ZC3HAV1 in osteoblast cell line and osteosarcoma cell lines (D,E) The mRNA and protein expression of ZC3HAV1 in six paired osteosarcoma tissues and adjacent normal tissues. *p < 0.05, **p < 0.01 and ***p < 0.001.

DISCUSSION

With the vigorous development of biotechnology and bioinformatics, genome analysis and various bioinformatics tools have been widely used to find cancer biomarkers (Tu et al., 2020; Udhaya Kumar et al., 2021). RBPs play a key role in regulating various RNA processes (Köster et al., 2017). Recent studies have shown that RBPs are not only involved in normal cell functions but are also major participants in the development and spread of tumors and so have great potential for the treatment of cancer (Pereira et al., 2017; Mohibi et al., 2019). Therefore, it is of great significance to study the clinical value and potential molecular mechanisms of RBP-related genes in osteosarcoma.

Although several signatures have recently been developed that can predict patient prognosis (Guan et al., 2020; Li et al., 2021), there are still some shortcomings in experimental verification. In addition, these studies lack corresponding clinical relevance studies and analyses based on clinical information (age, gender, metastasis), which affects the widespread application of signatures. Our study identified 142 DERBPs between tumor tissues and normal tissues based on GTEx and TARGET RNA sequencing data. Following systematic analysis of relevant biological pathways, a PPI network of these DERBPs was constructed. Single-factor and LASSO Cox regression analysis of abnormally expressed RBPs, resulted in 10 key prognostic RBPs (TDRD6, TLR8, NXT2, EIF4E3, RPS27L, CPEB3, RBM34, TERT, RPS29, and ZC3HAV1), and an RBP prognostic risk assessment model for patients with osteosarcoma was successfully constructed. The model was verified in the independent dataset GSE21257, and the results showed that the model has a good ability to predict prognosis. The constructed nomogram further visually and quantitatively describes the 3–5 years survival rate of patients with osteosarcoma. These results indicate that the RBPs’ prognostic signature established in this study is of great significance for the adjustment of treatment strategies and the exploration of prognostic markers for patients with osteosarcoma.

We have identified 10 key prognostic RBP genes, some of them are closely related to tumors. Telomerase reverse transcriptase (TERT) is a part of telomerase closely related to cancer (Yuan et al., 2019), and higher TERT expression in tumors can predict the poor prognosis of various cancers (Liu et al., 2016; Barthel et al., 2017; Ma et al., 2019). Cytoplasmic polyadenylation element-binding protein 3 (CPEB3) is a sequence-specific RBP whose overexpression inhibits the proliferation and migration of tumor cells, which has been proven in many studies (Tang et al., 2017; Luo and Wang, 2020; Zhang et al., 2020; Zhong et al., 2020). Ribosomal protein S27-like (RPS27L) is an evolutionarily preserved ribosomal protein. Xiong et al. showed that RPS27L can regulate genome stability and has potential tumor suppressor functions (Xiong et al., 2014). Eukaryotic translation initiation factor 4E family member 3 (EIF4E3) is a transformation initiating factor that can act as a tumor suppressor (Osborne et al., 2013; Volpon et al., 2013). Toll-like receptor (TLR) is a critical component of the innate immune response, and TLR8 is one of its subtypes (Khan et al., 2016). The in vivo study by Li et al. clearly shows that the metabolic reprogramming of regulatory T (Treg) cells mediated by TLR8 enhances anti-tumor immunity (Li et al., 2019). The 40S RP S29 coded by the RPS29 gene is a component of the small 40S ribosomal subunit, which is essential for rRNA processing and ribosomal biological production (O'Donohue et al., 2010). Compared with normal tissues, its expression is down-regulated in head and neck squamous cell carcinoma (Lallemant et al., 2009). But so far, TDRD6, NXT2, RBM34 have not been described in cancer. Zinc finger CCCH-type containing, antiviral 1 (ZC3HAV1), also known as zinc-finger antiviral protein (ZAP), has been shown to limit the replication of certain viruses, thereby preventing virus-related cancers such as liver cancer (Mao et al., 2013) and leukemia (Gao et al., 2002). For the first time, we found that ZC3HAV1 was up-regulated in osteosarcoma cell lines and also osteosarcoma tissues. Our results provide new ideas for exploring the role of RBPs in the development of osteosarcoma and may provide valuable perspectives for future cancer diagnosis and treatment.

The results of GSEA showed that patients in the high-risk group had higher NES in biological functions such as allogeneic rejection, complement system, inflammation, and IL6-JAK-STAT3 signaling. Aguirre et al. showed that transplant rejection and cancer immunomodulation have overlapping or even mutually exclusive mechanisms of action (Aguirre et al., 2019). The complement system is an important part of the inflammatory response, and inflammation involves all stages of tumorigenesis and cancer progression (Coussens and Werb, 2002). Moreover, supplemental activation regulates the adaptive immune response and may play a role in regulating the response of T cells to tumors (Carroll and Isenman, 2012). The IL-6/JAK/STAT3 pathway plays a key role in the growth and development of many human cancers (Johnson et al., 2018), and elevated IL-6 levels stimulate the overactivation of JAK/STAT3 signaling, which is usually related to poor patient outcomes (Ludwig et al., 1991; Kusaba et al., 2006; Chen et al., 2013).

It must be noted, however, that our research has some limitations. First, there are currently few public gene expression databases that contain prognostic information for patients with osteosarcoma, resulting in a small sample size for our study. In the future, larger sample size will be needed to build a more accurate prognostic model. Second, the clinical information of the dataset is not complete, and richer clinical data is needed to evaluate the relationship between genes and the clinic. Finally, we did not use in vitro or in vivo experiments to thoroughly verify our findings, which means that the exact mechanism of ZC3HAV1 involved in osteosarcoma is still unclear. This is an important subject that requires further research. It is necessary to conduct research on tissue and cell-type specificity loss and function gain to deepen our understanding.

In summary, this study systematically explored the expression and prognostic value of RBPs in osteosarcoma. By constructing a prognostic model of 10 RBP genes, our study can positively guide the future treatment and prognosis of osteosarcoma. The results of this study provide clear evidence for revealing the pathogenesis of osteosarcoma, developing new diagnostic ideas, finding new therapeutic targets and prognostic molecular markers.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Material further inquiries can be directed to the corresponding author.

ETHICS STATEMENT

The studies involving human participants were reviewed and approved by The Research Ethics Committee of the Second Affiliated Hospital of Nanchang University. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

AUTHOR CONTRIBUTIONS

JZ and XC designed the study. XM and TW searched the data from the database. JZ and TW performed the analysis of the data. JZ and XM wrote the original draft of the manuscript. JJ and XC supervised this work revised the manuscript. All authors had read and approved the final manuscript.

FUNDING

This work was supported by the Natural Science Foundation of Jiangxi Province of China under Grant No.20202ACBL206012.

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

ACKNOWLEDGMENTS

This is a short text to acknowledge the contributions of specific colleagues et al, institutions, or agencies that aided the efforts of the authors.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2021.751842/full#supplementary-material

REFERENCES

Aguirre, L. E., Guzman, M. E., Lopes, G., and Hurley, J. (2019). Immune Checkpoint Inhibitors and the Risk of Allograft Rejection: A Comprehensive Analysis on an Emerging Issue. Oncologist 24 (3), 394–401. doi:10.1634/theoncologist.2018-0195

Arndt, C. A. S., and Crist, W. M. (1999). Common Musculoskeletal Tumors of Childhood and Adolescence. N. Engl. J. Med. 341 (5), 342–352. doi:10.1056/nejm199907293410507

Barthel, F. P., Wei, W., Tang, M., Martinez-Ledesma, E., Hu, X., Amin, S. B., et al. (2017). Systematic Analysis of Telomere Length and Somatic Alterations in 31 Cancer Types. Nat. Genet. 49 (3), 349–357. doi:10.1038/ng.3781

Calderone, V., Gallego, J., Fernandez-Miranda, G., Garcia-Pras, E., Maillo, C., Berzigotti, A., et al. (2016). Sequential Functions of CPEB1 and CPEB4 Regulate Pathologic Expression of Vascular Endothelial Growth Factor and Angiogenesis in Chronic Liver Disease. Gastroenterology 150 (4), 982–997. doi:10.1053/j.gastro.2015.11.038

Carroll, M. C., and Isenman, D. E. (2012). Regulation of Humoral Immunity by Complement. Immunity 37 (2), 199–207. doi:10.1016/j.immuni.2012.08.002

Chatterji, P., and Rustgi, A. K. (2018). RNA Binding Proteins in Intestinal Epithelial Biology and Colorectal Cancer. Trends Mol. Med. 24 (5), 490–506. doi:10.1016/j.molmed.2018.03.008

Chen, Y., Wang, J., Wang, X., Liu, X., Li, H., Lv, Q., et al. (2013). STAT3, a Poor Survival Predicator, Is Associated with Lymph Node Metastasis from Breast Cancer. J. Breast Cancer 16 (1), 40–49. doi:10.4048/jbc.2013.16.1.40

Coussens, L. M., and Werb, Z. (2002). Inflammation and Cancer. Nature 420 (6917), 860–867. doi:10.1038/nature01322

Fletcher, C., Unni, K., and Mertens, F. (2002). “WHO Classification of Tumours,” in Pathology and Genetics of Tumours of Soft Tissue and Bone . Lyon, France: IARC Press.

Gao, G., Guo, X., and Goff, S. P. (2002). Inhibition of Retroviral RNA Production by ZAP, a CCCH-type Zinc finger Protein. Science 297 (5587), 1703–1706. doi:10.1126/science.1074276

Gerstberger, S., Hafner, M., and Tuschl, T. (2014). A Census of Human RNA-Binding Proteins. Nat. Rev. Genet. 15 (12), 829–845. doi:10.1038/nrg3813

Gianferante, D. M., Mirabello, L., and Savage, S. A. (2017). Germline and Somatic Genetics of Osteosarcoma - Connecting Aetiology, Biology and Therapy. Nat. Rev. Endocrinol. 13 (8), 480–491. doi:10.1038/nrendo.2017.16

Guan, X., Guan, Z., and Song, C. (2020). Expression Profile Analysis Identifies Key Genes as Prognostic Markers for Metastasis of Osteosarcoma. Cancer Cel Int. 20, 104. doi:10.1186/s12935-020-01179-x

Johnson, D. E., O'Keefe, R. A., and Grandis, J. R. (2018). Targeting the IL-6/JAK/STAT3 Signalling axis in Cancer. Nat. Rev. Clin. Oncol. 15 (4), 234–248. doi:10.1038/nrclinonc.2018.8

Kansara, M., Teng, M. W., Smyth, M. J., and Thomas, D. M. (2014). Translational Biology of Osteosarcoma. Nat. Rev. Cancer 14 (11), 722–735. doi:10.1038/nrc3838

Kempf-Bielack, B., Bielack, S. S., Jürgens, H., Branscheid, D., Berdel, W. E., Exner, G. U., et al. (2005). Osteosarcoma Relapse after Combined Modality Therapy: an Analysis of Unselected Patients in the Cooperative Osteosarcoma Study Group (COSS). J. Clin. Oncol. 23 (3), 559–568. doi:10.1200/jco.2005.04.063

Khan, A. A., Khan, Z., and Warnakulasuriya, S. (2016). Cancer-associated Toll-like Receptor Modulation and Insinuation in Infection Susceptibility: Association or Coincidence?Ann. Oncol. 27 (6), 984–997. doi:10.1093/annonc/mdw053

Köster, T., Marondedze, C., Meyer, K., and Staiger, D. (2017). RNA-binding Proteins Revisited - the Emerging Arabidopsis mRNA Interactome. Trends Plant Sci. 22 (6), 512–526. doi:10.1016/j.tplants.2017.03.009

Kudinov, A. E., Karanicolas, J., Golemis, E. A., and Boumber, Y. (2017). Musashi RNA-Binding Proteins as Cancer Drivers and Novel Therapeutic Targets. Clin. Cancer Res. 23 (9), 2143–2153. doi:10.1158/1078-0432.CCR-16-2728

Kumar, S. U., Kumar, D. T., Siva, R., Doss, C. G. P., and Zayed, H. (2019). Integrative Bioinformatics Approaches to Map Potential Novel Genes and Pathways Involved in Ovarian Cancer. Front. Bioeng. Biotechnol. 7, 391. doi:10.3389/fbioe.2019.00391

Kusaba, T., Nakayama, T., Yamazumi, K., Yakata, Y., Yoshizaki, A., Inoue, K., et al. (2006). Activation of STAT3 Is a Marker of Poor Prognosis in Human Colorectal Cancer. Oncol. Rep. 15 (6), 1445–1451. doi:10.3892/or.15.6.1445

Lallemant, B., Evrard, A., Combescure, C., Chapuis, H., Chambon, G., Raynal, C., et al. (2009). Reference Gene Selection for Head and Neck Squamous Cell Carcinoma Gene Expression Studies. BMC Mol. Biol. 10, 78. doi:10.1186/1471-2199-10-78

Li, B., Fang, L., Wang, B., Yang, Z., and Zhao, T. (2021). Identification of Prognostic RBPs in Osteosarcoma. Technol. Cancer Res. Treat. 20, 153303382110049. doi:10.1177/15330338211004918

Li, B., Ruotti, V., Stewart, R. M., Thomson, J. A., and Dewey, C. N. (2010). RNA-seq Gene Expression Estimation with Read Mapping Uncertainty. Bioinformatics 26 (4), 493–500. doi:10.1093/bioinformatics/btp692

Li, L., Liu, X., Sanders, K. L., Edwards, J. L., Ye, J., Si, F., et al. (2019). TLR8-Mediated Metabolic Control of Human Treg Function: A Mechanistic Target for Cancer Immunotherapy. Cel Metab. 29 (1), 103–123. doi:10.1016/j.cmet.2018.09.020

Li, N., Yousefi, M., Nakauka-Ddamba, A., Li, F., Vandivier, L., Parada, K., et al. (2015). The Msi Family of RNA-Binding Proteins Function Redundantly as Intestinal Oncoproteins. Cel Rep. 13 (11), 2440–2455. doi:10.1016/j.celrep.2015.11.022

Lin, Y., Zhang, H., Liang, J., Li, K., Zhu, W., Fu, L., et al. (2014). Identification and Characterization of Alphavirus M1 as a Selective Oncolytic Virus Targeting ZAP-Defective Human Cancers. Proc. Natl. Acad. Sci. USA 111 (42), E4504–E4512. doi:10.1073/pnas.1408759111

Liu, T., Yuan, X., and Xu, D. (2016). Cancer-Specific Telomerase Reverse Transcriptase (TERT) Promoter Mutations: Biological and Clinical Implications. Genes 7 (7), 38. doi:10.3390/genes7070038

Ludwig, H., Nachbaur, D., Fritz, E., Krainer, M., and Huber, H. (1991). Interleukin-6 Is a Prognostic Factor in Multiple Myeloma [letter] [see Comments]. Blood 77 (12), 2794–2795. doi:10.1182/blood.v77.12.2794

Lunde, B. M., Moore, C., and Varani, G. (2007). RNA-binding Proteins: Modular Design for Efficient Function. Nat. Rev. Mol. Cel Biol. 8 (6), 479–490. doi:10.1038/nrm2178

Luo, N., and Wang, Z. (2020). TRIM11 Stimulates the Proliferation of Gastric Cancer through Targeting CPEB3/EGFR axis. J. BUON 25 (4), 2097–2104.

Ma, R., Liu, C., Lu, M., Yuan, X., Cheng, G., Kong, F., et al. (2019). The TERT Locus Genotypes of Rs2736100-CC/CA and Rs2736098-AA Predict Shorter Survival in Renal Cell Carcinoma. Urol. Oncol. Semin. Original Invest. 37 (5), e1–301. doi:10.1016/j.urolonc.2019.01.014

Mao, R., Nie, H., Cai, D., Zhang, J., Liu, H., Yan, R., et al. (2013). Inhibition of Hepatitis B Virus Replication by the Host Zinc finger Antiviral Protein. Plos Pathog. 9 (7), e1003494. doi:10.1371/journal.ppat.1003494

Mohibi, S., Chen, X., and Zhang, J. (2019). Cancer the'RBP'eutics-RNA-binding Proteins as Therapeutic Targets for Cancer. Pharmacol. Ther. 203, 107390. doi:10.1016/j.pharmthera.2019.07.001

Moore, S., Järvelin, A. I., Davis, I., Bond, G. L., and Castello, A. (2018). Expanding Horizons: New Roles for Non-canonical RNA-Binding Proteins in Cancer. Curr. Opin. Genet. Develop. 48, 112–120. doi:10.1016/j.gde.2017.11.006

O’Donohue, M.-F., Choesmel, V., Faubladier, M., Fichant, G., and Gleizes, P.-E. (2010). Functional Dichotomy of Ribosomal Proteins during the Synthesis of Mammalian 40S Ribosomal Subunits. J. Cel Biol. 190 (5), 853–866. doi:10.1083/jcb.201005117

Ortiz-Zapater, E., Pineda, D., Martínez-Bosch, N., Fernández-Miranda, G., Iglesias, M., Alameda, F., et al. (2011). Key Contribution of CPEB4-Mediated Translational Control to Cancer Progression. Nat. Med. 18 (1), 83–90. doi:10.1038/nm.2540

Osborne, M. J., Volpon, L., Kornblatt, J. A., Culjkovic-Kraljacic, B., Baguet, A., and Borden, K. L. B. (2013). eIF4E3 Acts as a Tumor Suppressor by Utilizing an Atypical Mode of Methyl-7-Guanosine Cap Recognition. Proc. Natl. Acad. Sci. 110 (10), 3877–3882. doi:10.1073/pnas.1216862110

Pereira, B., Billaud, M., and Almeida, R. (2017). RNA-binding Proteins in Cancer: Old Players and New Actors. Trends Cancer 3 (7), 506–528. doi:10.1016/j.trecan.2017.05.003

Pérez-Guijarro, E., Karras, P., Cifdaloz, M., Martínez-Herranz, R., Cañón, E., Graña, O., et al. (2016). Lineage-specific Roles of the Cytoplasmic Polyadenylation Factor CPEB4 in the Regulation of Melanoma Drivers. Nat. Commun. 7, 13418. doi:10.1038/ncomms13418

Ritter, J., and Bielack, S. S. (2010). Osteosarcoma. Ann. Oncol. 21 (Suppl. 7), vii320–vii325. doi:10.1093/annonc/mdq276

Stiller, C. A., Bielack, S. S., Jundt, G., and Steliarova-Foucher, E. (2006). Bone Tumours in European Children and Adolescents, 1978-1997. Report from the Automated Childhood Cancer Information System projectReport from the Automated Childhood Cancer Information System Project. Eur. J. Cancer 42 (13), 2124–2135. doi:10.1016/j.ejca.2006.05.015

Tang, H., Zhang, J., Yu, Z., Ye, L., Li, K., Ding, F., et al. (2017). Mir-452-3p: A Potential Tumor Promoter that Targets the CPEB3/EGFR Axis in Human Hepatocellular Carcinoma. Technol. Cancer Res. Treat. 16 (6), 1136–1149. doi:10.1177/1533034617735931

Todorova, T., Bock, F. J., and Chang, P. (2015). Poly(ADP-ribose) Polymerase-13 and RNA Regulation in Immunity and Cancer. Trends Mol. Med. 21 (6), 373–384. doi:10.1016/j.molmed.2015.03.002

Tu, Z., Wu, L., Wang, P., Hu, Q., Tao, C., Li, K., et al. (2020). N6-Methylandenosine-Related lncRNAs Are Potential Biomarkers for Predicting the Overall Survival of Lower-Grade Glioma Patients. Front. Cel Dev. Biol. 8, 642. doi:10.3389/fcell.2020.00642

Udhaya Kumar, S., Madhana Priya, N., Thirumal Kumar, D., Anu Preethi, V., Kumar, V., Nagarajan, D., et al. (2021). An Integrative Analysis to Distinguish between Emphysema (EML) and Alpha-1 Antitrypsin Deficiency-Related Emphysema (ADL)-A Systems Biology Approach. Adv. Protein Chem. Struct. Biol. 127, 315–342. doi:10.1016/bs.apcsb.2021.02.004

Udhaya Kumar, S., Thirumal Kumar, D., Bithia, R., Sankar, S., Magesh, R., Sidenna, M., et al. (2020). Analysis of Differentially Expressed Genes and Molecular Pathways in Familial Hypercholesterolemia Involved in Atherosclerosis: A Systematic and Bioinformatics Approach. Front. Genet. 11, 734. doi:10.3389/fgene.2020.00734

Vo, D. T., Subramaniam, D., Remke, M., Burton, T. L., Uren, P. J., Gelfond, J. A., et al. (2012). The RNA-Binding Protein Musashi1 Affects Medulloblastoma Growth via a Network of Cancer-Related Genes and Is an Indicator of Poor Prognosis. Am. J. Pathol. 181 (5), 1762–1772. doi:10.1016/j.ajpath.2012.07.031

Volpon, L., Osborne, M. J., Culjkovic-Kraljacic, B., and Borden, K. L. B. (2013). eIF4E3, a New Actor in mRNA Metabolism and Tumor Suppression. Cell Cycle 12 (8), 1159–1160. doi:10.4161/cc.24566

Wagner, G. P., Kin, K., and Lynch, V. J. (2012). Measurement of mRNA Abundance Using RNA-Seq Data: RPKM Measure Is Inconsistent Among Samples. Theor. Biosci. 131 (4), 281–285. doi:10.1007/s12064-012-0162-3

Wang, S., Li, N., Yousefi, M., Nakauka-Ddamba, A., Li, F., Parada, K., et al. (2015). Transformation of the Intestinal Epithelium by the MSI2 RNA-Binding Protein. Nat. Commun. 6, 6517. doi:10.1038/ncomms7517

Wang, Z.-L., Li, B., Luo, Y.-X., Lin, Q., Liu, S.-R., Zhang, X.-Q., et al. (2018). Comprehensive Genomic Characterization of RNA-Binding Proteins across Human Cancers. Cel Rep. 22 (1), 286–298. doi:10.1016/j.celrep.2017.12.035

Xiong, X., Zhao, Y., Tang, F., Wei, D., Thomas, D., Wang, X., et al. (2014). Ribosomal Protein S27-like Is a Physiological Regulator of P53 that Suppresses Genomic Instability and Tumorigenesis. eLife 3, e02236. doi:10.7554/eLife.02236

Yuan, X., Larsson, C., and Xu, D. (2019). Mechanisms Underlying the Activation of TERT Transcription and Telomerase Activity in Human Cancer: Old Actors and New Players. Oncogene 38 (34), 6172–6183. doi:10.1038/s41388-019-0872-9

Zhang, H., Zou, C., Qiu, Z., E, F., Li, Q., Chen, M., et al. (2020). CPEB3-mediated MTDH mRNA Translational Suppression Restrains Hepatocellular Carcinoma Progression. Cell Death Dis. 11 (9), 792. doi:10.1038/s41419-020-02984-y

Zhong, Q., Fang, Y., Lai, Q., Wang, S., He, C., Li, A., et al. (2020). CPEB3 Inhibits Epithelial-Mesenchymal Transition by Disrupting the Crosstalk between Colorectal Cancer Cells and Tumor-Associated Macrophages via IL-6R/STAT3 Signaling. J. Exp. Clin. Cancer Res. 39 (1), 132. doi:10.1186/s13046-020-01637-4

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Zhang, Miao, Wu, Jia and Cheng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 06 December 2021
doi: 10.3389/fmolb.2021.687319

SEMA6B Overexpression Predicts Poor Prognosis and Correlates With the Tumor Immunosuppressive Microenvironment in Colorectal Cancer

Tiegang Li†,, Zheng Yan†,, Weiqi Wang, Rixin Zhang, Wenqiang Gan, Silin Lv, Zifan Zeng, Yufang Hou and Min Yang*

State Key Laboratory of Bioactive Substances and Function of Natural Medicine, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

Edited by:
Hossain Shekhar, University of Dhaka, Bangladesh

Reviewed by:
Mengzhe Guo, Xuzhou Medical University, China
Quanqing Zhang, University of California, Riverside, United States

* Correspondence: Min Yang, minyang@imm.ac.cn

†These authors have contributed equally to this work

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 29 March 2021
Accepted: 03 November 2021
Published: 06 December 2021

Citation: Li T, Yan Z, Wang W, Zhang R, Gan W, Lv S, Zeng Z, Hou Y and Yang M (2021) SEMA6B Overexpression Predicts Poor Prognosis and Correlates With the Tumor Immunosuppressive Microenvironment in Colorectal Cancer. Front. Mol. Biosci. 8:687319. doi: 10.3389/fmolb.2021.687319

Background: Semaphorin 6b (SEMA6B) is a member of the semaphorin axon-guidance family and has been demonstrated to both induce and inhibit tumor progression. However, the role of SEMA6B in colorectal cancer (CRC) has remained unclear. This study sought to explore the promising prognostic biomarker for CRC and to understand the expression pattern, clinical significance, immune effects, and biological functions of SEMA6B.

Methods: SEMA6B expression in CRC was evaluated via multiple gene and protein expression databases and we identified its prognostic value through The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Correlations between SEMA6B expression and components of the tumor immune microenvironment were analyzed by packages implemented in R, Tumor Immune Estimation Resource (TIMER), Gene Expression Profiling Interactive Analysis (GEPIA), and Tumor-Immune System Interactions database (TISIDB). RNA interference was performed to silence the expression of SEMA6B to explore its biological roles in the colon cancer cell lines HCT116 and LoVo.

Results: The messenger RNA (mRNA) level of SEMA6B and the protein expression were higher in CRC tissues than adjacent normal tissues from multiple CRC datasets. High SEMA6B expression was significantly associated with dismal survival. Multivariate Cox regression analysis demonstrated that SEMA6B was an independent prognostic factor for progression-free survival (PFS). The nomogram showed a favorable predictive ability in PFS. Functional enrichment analysis and the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm revealed that the gene cluster associated with the high SEMA6B group were prominently involved in immune responses and inflammatory activities. Notably, SEMA6B expression was positively correlated with infiltrating levels of CD4⁺ T cells, macrophages, myeloid-derived suppressor cells (MDSCs), regulatory T cells (Tregs), neutrophils, and dendritic cells. Moreover, SEMA6B expression displayed strong correlations with diverse marker sets of immunosuppressive cells in CRC. Integrative analysis revealed that immunosuppressive molecules and immune checkpoints were markedly upregulated in CRC samples with high SEMA6B expression. Furthermore, knockdown of SMEA6B in colon cancer cells significantly inhibited cell proliferation, migration, invasion and reduced the mRNA levels of immunosuppressive molecules.

Conclusion: Our findings provide evidence that high SEMA6B expression correlated with adverse prognosis and the tumor immunosuppressive microenvironment in CRC patients. Therefore, SEMA6B may serve as a novel prognostic biomarker for CRC, which offers further insights into developing CRC-targeted immunotherapies.

Keywords: SEMA6B, colorectal cancer, prognosis, tumor microenvironment, immune response, immune checkpoint

INTRODUCTION

Colorectal cancer (CRC) is a major cause of cancer mortality around the word. Approximately 20% of patients occurred metastases at diagnosis (Garborg et al., 2013; Siegel et al., 2014). Although many advances in systemic therapy and liver-directed treatments made thus far, 5 years survival rate is only 12–14% in patients with metastatic CRC (Siegel et al., 2017; Bray et al., 2018). Over the past few years, full recognition of the complex interactions between cancer cells and the immune system has led to a rapid development in immunotherapeutic approaches. Immunotherapeutic strategies include immune checkpoint inhibitors, cancer vaccines, adoptive cell transfer, oncolytic viral therapy, and carcinoembryonic antigen (CEA) T-cell bispecific antibodies, which focus on selectively enhancing the host immune system to fight cancer (Kakimi et al., 2017; Szeto and Finley 2019; Wrobel and Ahmed 2019).

Immune checkpoint inhibitors currently represent the main domain of immunotherapy and have achieved clinical benefits to patients with advanced cancer including renal cell carcinoma, non-small cell lung cancer and malignant melanoma (Reck et al., 2016; Atkins et al., 2017; Gettinger et al., 2018; Ribas and Wolchok 2018). Recent success in using antibodies against various immune checkpoints such as cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4), programmed death 1 (PD-1), and programmed death ligand 1 (PD-L1) for cancer immunotherapy has brought this approach being implemented as a new treatment modality for CRC, especially in terms of targeting the microsatellite instability-high (MSI-H) phenotype (Le et al., 2015; Overman and McDermott 2017; Overman et al., 2018; Le et al., 2020). However, clinical immunotherapeutic trials have revealed that anti-CTLA-4 monoclonal antibodies (mAbs) yield unsatisfactory clinical efficacies in unselective CRC patients (Chung et al., 2010), and anti-PD-L1 mAbs and anti-PD-1 have shown little or no response rates in metastatic CRC (mCRC) (Le, Uram and Wang 2015; Overman and McDermott 2017). Although there is clear clinical evidence for a therapeutic role of immune checkpoint inhibitors in deficient mismatch repair (dMMR) or MSI-H mCRC, the majority of mCRC patients with proficient MMR (pMMR) or microsatellite stable (MSS) phenotypes do not benefit from this type of immunotherapy (Koi and Carethers 2017; Ciardiello and Vitiello 2019; Ganesh et al., 2019; Judge et al., 2020; Morse et al., 2020). Furthermore, previously described molecular features, such as immunoscore, PD-1, PD-L1, MSI, mutational load, and consensus molecular subtypes have not been identified in predicting responses to immune checkpoint inhibitors based on immunotherapy. (Emambux and Tachon 2018; Sveen et al., 2020). Therefore, it is required to discover novel biomarkers with latent prognostic value and screen immune-based therapeutic targets for CRC patients.

Semaphorin family members were initially characterized as axon-guidance factors with functions in axonal navigation, but have subsequently also been linked to the pathology of various diseases, such as cancer, immune disease and neurodegenerative disease (Műzes and Sipos 2014; Neufeld and Mumblat 2016; Franzolin and Tamagnone 2019). Accumulated studies have shown that some semaphorins—including semaphorin 3E (SEMA3E), SEMA4D, SEMA5A, SEMA6D, and SEMA7A—play vital roles in tumorigenesis and tumor development by promoting angiogenesis and tumor-cell migration, as well as the epithelial-mesenchymal transition (EMT); in contrast, SEMA3A, SEMA3B, and SEMA3F exhibit tumor-inhibitory effects (Neufeld et al., 2012; Neufeld and Mumblat 2016; Gurrapu and Tamagnone 2019). Mechanisms that account for the diversity of semaphorin signaling responses in different cellular contexts can profoundly affect these different biological activities. SEMA6B, a member of the semaphorin axon-guidance family, has recently been investigated in terms of human SEMA6B gene expression and its roles in cancer. In breast cancer tissues, the SEMA6B promoter undergoes abnormal methylation, and downregulation of SEMA6B messenger RNA (mRNA) has been found in tumor samples (D'Apice and Costa 2013; Kuznetsova et al., 2007). In CRC patients, miR-30b could mediate axon guidance and is significantly negatively correlated with SEMA6B (Coebergh van den Braak et al., 2018). Among different human cell lines, high levels of SEMA6B mRNA have been observed in MCF-7 breast adenocarcinoma cells, and these levels have been found to be downregulated by 9-cis-retinoic acid, an anti-proliferative and differentiation-promoting agent (Murad and Collet 2006). Functionally, SEMA6B has been found to exert complex roles in the development and progression of tumors such as breast cancer (D'Apice and Costa 2013; Murad and Collet 2006), glioblastoma (Kigel and Rabinowicz 2011), gastric cancer (Ge and Li 2013), and testicular cancer (Ji and Wang 2020). However, the prognostic value of SEMA6B in CRC and the relationship between SEMA6B and immune responses remain elusive.

At present study, we assessed SEMA6B expression and clarified its potential prognostic value in CRC patients using The Cancer Genome Atlas (TCGA), Human Protein Atlas (HPA), and Gene Expression Omnibus (GEO) databases. Moreover, we explored the underlying biological functions and relevant pathways of SEMA6B and investigated correlations of SEMA6B with a variety of tumor-infiltrating immune cells (TIICS) as well as tumor-immunity status via comprehensive bioinformatic analyses. Taken together, our present findings may help to uncover prominent immunoregulatory roles of SEMA6B in the CRC microenvironment, and provide a promising biomarker and target for CRC diagnosis and immunotherapy.

MATERIALS AND METHODS

Data Resources

RNA-sequencing data for the TCGA-colon adenocarcinoma (TCGA-COAD) and TCGA-rectal adenocarcinoma (TCGA-READ) cohorts, including 638 CRC samples and 51 normal tissue samples, were downloaded from public databases (https://portal.gdc.cancer.gov/). Corresponding clinicopathological characteristics for each patient—including age, gender, race, tumor location, disease type, tumor stage, tumor-node-metastasis (TNM) classification, venous invasion, lymphatic invasion, pretreatment CEA level, and survival information—were also retrieved from the TCGA data portal. Only patients with both survival information and expression data were included in the present study. Another mRNA expression profile for 308 normal tissue samples was obtained in transcripts-per-million (TPM) format from the Genotype-Tissue Expression (GTEx) project (https://www.gtexportal.org/home/datasets), which is another large-scale repository cataloging gene expression from healthy individuals. Then, Ensembl gene IDs were mapped to human gene SYMBOL in terms of GENCODE V22 annotations for human datasets through the use of R/Bioconductor packages. As the raw mRNA sequence datasets from TCGA were normalized in terms of fragments per kilobase million (FPKM) via log₂(FPKM+1), these datasets were scaled to a total depth of 10⁶ fragments per sample and were interpreted as TPM in order to more easily compare the proportion of reads that was aligned to a given gene in each sample. Subsequently, any gene with a mean expression of ≤0.3 across all samples was deleted from the final mRNA expression matrices for subsequent analysis. Six independent datasets from the GEO database were used for external validation in the present study, including GSE41258, GSE44076, GSE37182, GSE20842, GSE83889, and GSE39582, together with survival information. A normalized expression matrix from GEO database was applied directly for the analyses. The protein expression levels of SEMA6B in clinical specimens from CRC patients and normal tissues were examined using immunohistochemical data from the HPA database (http://www.proteinatlas.org/). Since the data used in the present study were provided by TCGA and GEO, informed consent or ethical approval was not required. Furthermore, the present study fully adhered to all TCGA publication guidelines.

Survival Analysis

Kaplan-Meier (KM) curves were plotted to compare overall survival (OS), disease-free survival (DFS), progression-free survival (PFS), and relapse-free survival (RFS). These curves were generated with an optimum cut-off value for SEMA6B mRNA expression using the survfit function from the R package ‘survminer’, and a log-rank test was conducted to compare differences between survival status. Univariate and multivariate analyses of Cox proportional-hazards regression models were performed to obtain hazard ratios (HRs) with 95% confidence intervals (CIs) and statistical significance; the results were illustrated using a forest plot via GraphPad Prism 8.0. PFS-related nomogram models were established based on the multivariate Cox regression results. Calibration curves were drawn, and the concordance index (C-index) was computed to assess the prediction power of the nomogram.

Additionally, the prognostic values of SEMA6B expression in breast, esophageal, stomach, liver, lung, and ovarian cancers were assessed by the best cut-off values via Kaplan-Meier plotter (www.kmplot.com). HRs with 95% CIs and log-rank p values were also computed on the Kaplan-Meier plotter web page.

DNA Mutation and Methylation Analyses

To investigate the regulation of expression associated with the expression profile of SEMA6B, DNA mutation and methylation analyses were explored via online databases. Specifically, somatic mutation information was identified by the cBioPortal platform (www.cbioportal.org), which is a comprehensive web resource for exploring, visualizing, and analyzing multidimensional cancer-genomic data. Methylation changes in SEMA6B in CRC and adjacent normal tissues were compared using UALCAN (http://ualcan.path.uab.edu/index.html) (Chandrashekar et al., 2017) and Wanderer (http://maplab.imppc.org/wanderer/) (Díez-Villanueva et al., 2015) databases, which are web tools that can be employed to analyze DNA methylation profiles and gene expression from TCGA.

ROC Analysis

Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic accuracy for both OS and PFS; areas under the curve (AUCs) as well as p values were calculated via SPSS 25.0 software.

Identification of Differentially Expressed Genes

In accordance with the optimum cutoff value in KM survival analysis for OS, patients were classified into two groups (low and high SEMA6B expression) across TCGA datasets. Linear models were used to screen differentially expressed genes (DEGs) between these two groups by using the R package, “limma”. The threshold for identifying DEGs was set as the false discovery rate (FDR)-adjusted p value <0.01 and absolute value of log2 (fold change) ≥ 1. With the R package, “gghplot2”, a volcano plot was generated to visualize fold changes and t-test criteria.

GO and KEGG Pathway Enrichment Analyses of DEGs

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses using 1,789 overexpressed DEGs were performed using R software with the aid of the “clusterProfiler” package. Biological processes (BP) and molecular functions (MF) were included in the GO enrichment analysis. Only terms with an FDR-adjusted p value <0.01 were considered to be statistically enriched. The top-15 enriched terms ordered by q value, from small to large, are shown in the corresponding plot.

Gene Set Enrichment Analysis of DEGs

GSEA (version 4.1.0) was used to evaluate correlations between SEMA6B expression (high vs low) using the TCGA dataset. The annotated gene set was c2. cp.kegg.v6.2. symbols.gmt. Standard settings with 1,000 runs of gene permutations were employed for each analysis to determine the enriched pathways. Normalized enrichment scores (NES) and FDR-adjusted p values were obtained to indicate significantly enriched gene sets and pathways.

Gene Set Variation Analysis and Functional Annotation

To investigate the difference on biological pathways and processes according to the expression patterns of SEMA6B, GSVA was employed with the “GSVA” R package. GSVA is a non-parametric unsupervised method to explore the variation of pathway activity over samples (Hänzelmann et al., 2013). The annotated gene set was also “c2. cp.kegg.v6.2. symbols” downloaded from the MSigDB database (https://www.gsea-msigdb.org/gsea/index.jsp). An adjusted p value ≤0.05 was considered statistically significance. A heatmap was drawn to display the enriched score value of each sample using the “pheatmap” R package.

Generation of Immune and Stromal Scores

The Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm (Yoshihara and Shahmoradgoli 2013) was applied to calculate immune scores, stromal scores, ESTIMATE scores, and tumor purity for each patient from the TCGA dataset via R software loaded with the “estimate” package with default parameters.

TIMER Database Analysis

The Tumor Immune Estimation Resource (TIMER) is a web-based platform for systematic analysis of immune infiltrates across diverse cancer types from the TCGA (http://timer.comp-genomics.org/) (Li et al., 2017), which adopts a deconvolution of previously published computational approaches to infer TIICs from gene expression profiles. In the present study, correlations between SEMA6B expression and TIICs (B cells, CD4⁺T cells, CD8⁺T cells, neutrophils, macrophages, and dendritic cells) were investigated separately in TCGA-COAD and TCGA-READ. Meanwhile, correlations between SEMA6B expression and gene markers of TIICs were also analyzed via the “Gene_Corr” module.

Tumor-Immune System Interactions Database Analysis

To further determine the relationship between SEMA6B mRNA expression and the abundance of TIICs, the Tumor-Immune System Interactions database (TISIDB) was used to determine the correlation between SEMA6B mRNA expression and tumor-infiltrating lymphocytes (TILs). TISIDB is a web portal for assessing tumor and immune system interactions, which integrates multiple heterogeneous data types (http://cis.hku.hk/TISIDB/index.php) (Ru et al., 2019).

Gene Correlation Analysis

The Gene Expression Profiling Interactive Analysis (GEPIA2) online database (http://gepia2.cancer-pku.cn/#index) (Tang et al., 2019) was used to determine correlations between SEMA6B mRNA expression and gene markers of TIICs in CRC and adjacent normal tissues. Pearson correlation coefficients between SEMA6B expression and immunoinhibitor gene sets were visualized using R software with the “corrplot” package.

Heatmaps and Hierarchical Clustering Analysis

The “complexHeatmap” package from Bioconductor was used to plot heatmaps in terms of the expression of immunosuppressive gene sets as well as immune and stromal scores in different subgroup samples with R software. Specifically, Z-score normalization was applied in the expression dataset matrix, and then Euclidean distance was used to determine hierarchically clustered.

Analysis of Cell Viability

The mRNA expression level of SEMA6B in different colon cell lines was examined using the Cancer Cell Line Encyclopedia (CCLE) website (https://sites.broadinstitute.org/ccle). According to the survey, we selected two cell lines with high SEMA6B levels for subsequent research, i.e., HCT116 and LoVo cells.

A cell-counting kit 8 (CCK8) was used to determine cell proliferation. HCT116 and LoVo cells were plated and cultured in a 96-well plate with 1,500 cells per well, and then were interfered with by SEMA6B- and NC-siRNA for 0, 24, 48, or 72 h. After the interference, the supernatant was removed, and 100 μl DMEM or DMEM F12K was added in the presence of 10 μl of CCK8, and the cells were incubated at 37°C for 4 h. Then, the absorbance at 450 nm was assayed.

Cell Migration and Invasion Assay

Cells were transfected with NC- or SEMA6B-siRNA for 48 h. An invasion chamber with 8 μ pores (Matrigel invasion chamber; Corning, Corning, NY, United States) was used to evaluate the potency of cells in the migration and invasion stages. For the invasion assay, 2 × 10⁵ cells in serum-free medium were added to the upper chamber. Then, 500 μl of 10% FBS DMEM or DMEM F12K was added to the lower chamber, and the number of cells that had migrated after 48 h was quantified by counting five random fields under a microscope (IX70; Olympus Corp., Tokyo, Japan). Similar methods were adopted for the migration assay, except that 1 × 10⁵ cells were added to the upper chamber without the Matrigel coating. Five random fields were counted for each chamber.

RNA Extraction and Real-Time Polymerase Chain Reaction

Total RNA was isolated from cells using an RNA isolation kit (Beyotime, Shanghai, China) following the manufacturer’s instructions. The concentration of total RNA was quantified using a microplate reader; then, 1 μg of total RNA was reverse-transcribed to complementary DNA using the PrimeScript RT reagent kit (Takara Bio, Kusatsu, Japan). Quantitative real-time PCR (qRT-PCR) was applied using a TB Green Premix Ex Taq II kit (Takara Bio). Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as an internal control. Each experiment was performed in triplicate. Differences in gene expression level, expressed as fold changes, were calculated using the 2−ΔΔCt method. The following primers were used: interleukin 6 signal transducer (IL6ST), forward: 5′-GGA​AGC​TCA​GCC​AAC​TCG​AA-3′, reverse: 5′-CCC​AAG​CAG​CCT​TTC​CAT​GA-3′; B and T lymphocyte attenuator (BTLA), forward: 5′-CTG​AGG​TTT​TGT​GGT​GGA​GAG​A-3′, reverse: 5′-TTG​CAC​CCC​CAA​ATC​TAA​GGA-3′; CD274 (PD-L1), forward: 5′-GGA​AAT​TCC​GGC​AGT​GTA​CC-3′, reverse: 5′-TGA​CAG​CTG​GTG​GCA​TTC​AA-3′; Galectin 1 (LGALS1), forward: 5′-CGC​TAA​GAG​CTT​CGT​GCT​GA-3′, reverse: 5′- CGT​TGA​AGC​GAG​GGT​TGA​AG-3′; interleukin 1, beta (IL1B), forward: 5′-CCT​GAG​CTC​GCC​AGT​GAA​AT-3′, reverse: 5′-GTC​GGA​GAT​TCG​TAG​CTG​GA-3′; intercellular adhesion molecule 1(ICAM1), forward: 5′-GGC​CCC​ACA​GAC​TTA​CAG​AA-3′, reverse: 5′-RGTCAGGAAGTGTGGGCCTTT-3′; hepatitis A virus cellular receptor 2 (HAVCR2), forward: 5′- CTA​CTG​CTG​CCG​GAT​CCA​AA-3′, reverse: 5′- GTC​CCC​TGG​TGG​TAA​GCA​TC-3′; endothelin receptor type B (EDNRB), forward: 5′- AGG​TGC​TAT​CGT​TCA​ACT​TCA-3′, reverse: 5′- TAG​CCA​CTT​TAG​GCA​ACC​AA-3′; and transforming growth factor β2 (TGF-β2), forward: 5′-TAC​CAC​CTT​TCC​GAT​TGC​CC-3′, reverse: 5′-TGG​CCT​GAC​TCT​TGT​GCT​TT-3′.

Statistical Analysis

Data analysis and visualization were performed using R software (version 4.0.0) with appropriate packages, as well as with SPSS25.0 (IBM Corp., Armonk, New York, United States) and GraphPad Prism 8.0 (GraphPad Software Inc., San Diego, CA, United States). Pearson’s correlations were used to analyze pairwise relationships between mRNA levels of different genes. For continuous variables, differences among groups were analyzed using one-way analysis of variance (ANOVA) for comparisons of more than two groups, and via t-tests for comparisons between only two groups. Chi-squared tests (χ ²) were used to evaluate associations between SEMA6B expression and clinicopathological parameters. A two-sided p < 0.05 was considered statistically significant.

RESULTS

SEMA6B Expression is Upregulated in CRC Tissues

Using RNA-sequencing data from the TCGA-CRC dataset and GTEx project, we found that SEMA6B mRNA expression in CRC tissues (n = 638) was significantly higher than that in normal colorectal tissues (n = 359; p < 0.001). Meanwhile, upregulated SEMA6B mRNA levels in CRC tissues were also validated in GSE datasets, including GSE41258 (p < 0.001), GSE44076 (p < 0.001), GSE37182 (p < 0.001), GSE20842 (p = 0.003), and GSE83889 (p = 0.015). (Figure 1A). Moreover, immunohistochemical staining obtained from the HPA database demonstrated that protein expression levels of SEMA6B were consistent with their transcriptional levels in comparison with those in normal tissues (Figure 1B). We further detected the function of methylation in regulating the expression of SEMA6B and found that the DNA methylation levels of SEMA6B were dramatically downregulated in CRC tissues compared with those in normal colorectal samples (p < 0.001) using the UALCAN web tool (Supplementary Figure S1A). As shown in Supplementary Figure S1B, data from the Wanderer web tool were similar (p < 0.05; normal n = 44, tumor n = 400), with most of the SEMA6B probes in the 450 methylation array exhibiting significant differences between CRC and normal specimens. Otherwise, few mutational or somatic copy-number alterations of SEMA6B were observed in CRC tissues (Supplementary Figure S2).

FIGURE 1 | SEMA6B mRNA and protein expression in CRC tissues and normal tissues. (A) The expression of SEMA6B mRNA in normal and tumor samples derived from TCGA and GEO databases. (B) Representative immunohistochemical images of SEMA6B in CRC tissues and corresponding normal tissues using the HPA database. Scale bars = 200 µm.

High SEMA6B Expression Predicts Poor Prognosis of CRC Patients

We next assessed associations of SEMA6B expression with clinicopathological features of CRC patients using the TCGA-CRC dataset. As shown in Figure 2, the expression levels of SEMA6B were significantly associated with venous invasion, T stage, MSI, KRAS mutation, and CMS. However, SEMA6B expression in CRC was not significantly correlated with other clinicopathological characteristics, including age, gender, tumor site, lymphatic invasion, M stage, N stage, pathological stage, and patient status.

FIGURE 2 | Cluster heatmap of correlations between SEMA6B and molecular subtypes in the TCGA dataset. The relationships between SEMA6B level and each clinicopathological characteristic were measured with the χ² test. *p < 0.05, **p < 0.01, and ***p < 0.001.

To evaluate the prognostic value of SEMA6B, CRC patients were divided into high and low SEMA6B expression groups according to the optimal cut-off value of SEMA6B levels. Kaplan-Meier survival curve analysis showed that CRC patients with high SEMA6B expression had shorter OS (p = 0.040), 5 year survival (p = 0.042), DFS (p = 0.002), and PFS (p < 0.001) compared to those with low SEMA6B expression (Figure 3A). To validate these findings, we analyzed the prognostic significance of SEMA6B using another GEO cohort (GSE39582). In line with results in the TCGA dataset, the high SEMA6B expression group exhibited poor survival (OS: p = 0.022; RFS: p = 0.050) (Figure 3B). Subsequently, the Kaplan-Meier plotter database was used to analyze the prognostic potential of SEMA6B in different cancers. Interestingly, high SEMA6B expression levels were associated with poor prognosis of OS in lung adenocarcinoma and lung squamous cell carcinoma (Supplementary Figure S3), while SEMA6B expression had less influence on the prognoses within other types of cancers (Supplementary Figure S3).

FIGURE 3 | Kaplan-Meier survival analysis comparing high and low expression of SEMA6B in patients with CRC. (A) Survival curves of overall survival (OS), 5 year survival, disease-free survival (DFS), and progression-free survival (PFS) in the TCGA dataset. (B) Survival curves of OS and relapse-free survival (RFS) in the validated GEO dataset (GSE39582).

Furthermore, to explore the clinical prognostic significance of SEMA6B in CRC, Cox regression analysis was performed to determine PFS and OS. Univariate Cox regression analysis showed that SEMA6B, venous invasion, N stage, pretreatment CEA, T stage, and M stage were significantly associated with PFS in CRC patients sourced from the TCGA dataset (p < 0.05; Figure 4A). Meanwhile, multivariate Cox regression analysis showed that SEMA6B, T stage, and M stage were independent prognostic factors for PFS among CRC patients (p < 0.05; Figure 4A). For OS, univariate Cox regression analysis showed that SEMA6B, age, venous invasion, pretreatment CEA, T stage, N stage, and M stage have statistical significance; however, SEMA6B expression was not an independent prognostic factor in the multivariate analysis for OS (p > 0.05; Supplementary Figure S4). Moreover, in the GSE17538 validation CRC dataset, SEMA6B was an independent prognostic factor for DFS in multivariate Cox regression analysis (p < 0.05; Figure 4B).

FIGURE 4 | Univariate and multivariate Cox regression analyses to evaluate the prognostic value of the SEAM6B in CRC patients. (A) PFS for TGGA dataset. (B) DFS for GSE17538 dataset. Forest plots visualizing the hazard ratios (HRs) and 95% confidence intervals (CI) for each variable. Differences with p < 0.05 were considered significant.

Prognostic Nomogram Models Based on SEMA6B for CRC

According to the results of multivariate Cox regression analysis, the independent prognostic signature of SEMA6B, M stage, and N stage was enrolled to establish a nomogram model to predict the 3- and 5 year PFS probabilities of each patient for clinical practice (Figure 5A). The C-index of this model was 0.72 (95% CI, 0.70–0.75). Moreover, the calibration curve revealed that the 3- and 5 year PFS rates predicted by the nomogram were highly consistent with the actual observation outcomes (Figure 5B). These results demonstrated that the nomogram models show a favorable prognostic ability for predicting PFS. In addition, ROC analysis demonstrated that the AUC values for OS or PFS of the prediction model—including pathological M stage, N stage, T stage, and SEMA6B expression—were significantly improved from 0.639 to 0.759 for OS and from 0.641 to 0.719 for PFS (Figures 5C,D), indicating the additive predictive value of SEMA6B compared with that of known prognostic factors.

FIGURE 5 | Nomogram model and ROC for survival prediction of CRC members. (A) Nomogram model to predict 3, and 5 years associated PFS probability. (B) The calibration curve of the nomogram when predicting 3- and 5 years PFS probability. ROC curves for predicting OS (C) and PFS (D) in CRC patients via prognostic models from the TCGA dataset. The x-axis indicates the false-positive rate, which is presented as ‘1-Specificity’. The y-axis indicates the true-positive rate, which is designated as “Sensitivity”.

SEMA6B-Related Biological Processes

According to the aforementioned results, SEMA6B may play an essential role in the biological functions of CRC. To clarify the biological roles of SEMA6B expression in CRC, DEGs were detected between high and low SEMA6B expression groups. Volcano plot analysis identified 1,794 DEGs (Figure 6A). Among them, 1,789 genes were upregulated and 5 genes were downregulated (Figure 6A). Then, the biological functions of these DEGs were explored by KEGG signaling pathway and GO annotation analysis. The top-15 pathways from the KEGG results showed that the most significantly altered pathways in the SEMA6B high expression group were those involving cytokine-cytokine receptor interactions, chemokine signaling pathways, rheumatoid arthritis, viral protein interactions with cytokines and cytokine receptors, and complement and coagulation cascades (Figure 6B). Furthermore, GSEA was conducted to determine the potential mechanism underlying SEMA6B in CRC. According to NES, we selected the most highly enriched signaling pathways. As shown in Figure 6C, the genes in the SEMA6B high expression group were mainly enriched in inflammatory activities and immune-related processes such as leukocyte transendothelial migration, chemokine signaling pathways, cytokine receptor interactions, and complement cascades. Furthermore, tumor-aggressiveness feature-related gene sets including those involving the Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway, cancer pathways, and vascular endothelial growth factor (VEGF) signaling pathway were also significantly enriched in the high SEMA6B expression group. Meanwhile, the results of GO analysis revealed that many biological functions of these DEGs were primarily associated with inflammatory responses and adaptive immune responses from categories of biological processes and molecular functions (Figure 7A), respectively. To determine the biological behaviors based on distinct SEMA6B profiles in CRC patients, GSVA was performed. As expected, we found that the SEMA6B high-risk group was markedly enriched in tumor cell proliferation, immune response, EMT-related pathways, such as the JAK-STAT signaling pathway, antigen processing and presentation, natural killer cell mediated cytotoxicity, the chemokine signaling pathway, cytokine-cytokine receptor interaction, ECM receptor interaction, and focal adhesion (Figure 7B).

FIGURE 6 | Identification differentially expressed genes (DEGs) in CRC patients from the TCGA dataset as well as KEGG and GSEA pathway enrichment analysis. (A) Volcano plot of aberrantly altered gene profiles between high and low SEMA6B expression groups. A total of 1,794 DEGs were identified. The red dots (n = 1,789) represent upregulated mRNAs, while the blue dots (n = 5) refer to downregulated mRNAs. The x-axis indicates the log2-fold change in gene expression, and the y-axis denotes the adjusted p values plotted in −Log10. (B) Top-15 KEGG pathways enriched by the overexpressed DEGs in the high SEMA6B expression group. p values were adjusted by the false-discovery rate (FDR) via R software. The size of each circle indicates the number of enriched genes, and the color denotes the adjusted p value. (C) GSEA enrichment plots of the TCGA-CRC dataset between high and low SEMA6B expression. A normalized enrichment score (NES) > 1 and adjusted p value (FDR) < 0.05 were used to determine significant gene sets.

FIGURE 7 | GO and GSVA enrichment analysis. (A) The 15-most significantly enriched GO terms in terms of upregulated mRNAs in the high SEMA6B expression group are listed according to their biological processes (BP) and molecular functions (MF). The length of each bar indicates the number of enriched genes. (B) The heatmap was used to visualize the result of GSVA enrichment analysis in high and low SEMA6B expression groups. The color changes from green to red, indicating an increase in the value of the enriched score. Brown represents the high SEMA6B group and green represents the low SEMA6B group.

SEMA6B Expression is Correlated With Immune Infiltration Levels

Based on the ESTIMATE algorithm, patients’ stromal scores (range from −2634.54 to 1608.69), immune scores (range from −1272.49 to 2656.79), ESTIMATE scores (range from −3716.94 to 3710.24), and tumor purities (range from 0.409 to 0.998) are listed in Supplementary Table S1. As shown in Figure 8A, SEMA6B expression was positively correlated with stromal scores, immune scores, and ESTIMATE scores, while SEMA6B expression was negatively correlated with tumor purities.

FIGURE 8 | SEMA6B expression is correlated with immune infiltration in CRC patients. (A) Heatmaps of immune scores, stromal scores, ESTIMATE scores, and tumor purities for each patient between the high and low SEMA6B expression groups. (B) Correlations of SEMA6B expression with tumor purity and immune-cell infiltration levels in COAD and READ from the TIMER database.

Given that TILs are a prognostic indicator for CRC (Reissfelder et al., 2015; Berntsson et al., 2017), associations between SEMA6B gene expression and TILs infiltration levels in CRC were analyzed via the TIMER database. The results revealed that SEMA6B was negatively correlated with tumor purity in COAD (r = −0.39, p = 2.86E-16) and READ (r = −0.39, p = 1.94E-06), whereas it was strongly positively correlated with infiltrating levels of CD4⁺ T cells (r = 0.504, p = 2.65E-27), macrophages (r = 0.465, p = 4.53E-23), neutrophils (r = 0.542, p = 5.37E-32), and dendritic cells (r = 0.545, p = 1.90E-32) in COAD (Figure 8B). Similar trends in terms of correlations between SEMA6B and TILs infiltrating levels were also observed in READ (Figure 8B). Additionally, we also investigated the relationship between the expression levels of SEMA6B and immune infiltration in TISIDB database so as to further verify the role of SEMA6B in TME. Spearman’s correlation analysis illustrated that SEMA6B was strongly related to immune infiltration in COAD (Supplementary Figure S5A) and READ (Supplementary Figure S5B), especially for the five most significant infiltrators of immune cells, as follows: macrophages (r = 0.611 in COAD, r = 0.527 in READ), myeloid-derived suppressor cells (MDSCs) (r = 0.666 in COAD, r = 0.585 in READ), natural killer (NK) cells (r = 0.677 in COAD, r = 0.562 in READ), regulatory T cells (Tregs) (r = 0.619 in COAD, r = 0.586 in READ), and type-1 T helper (Th1) cells (r = 0.609 in COAD, r = 0.474 in READ).

To further determine correlations between SEMA6B and TILs, we analyzed relationships between SEMA6B and marker genes of different immune cells in COAD and READ via the TIMER and GEPIA databases. After correlation adjustments by purity, the findings demonstrated that expression levels of most marker sets of CD4⁺ T cells, Tregs, exhausted T cells, monocytes, tumor-associated macrophages (TAMs), M2 macrophages, and dendritic cells presented strong correlations with SEMA6B expression in COAD and READ (Table 1). In the GEPIA database, the analyses revealed that SEMA6B was strongly related to TILs infiltration in CRC tissues compared to that in normal colorectal samples, especially in terms of important signatures of pro-cancer immune cells, such as chemokine (C-C motif) ligand (CCL)-2, CD68, interleukin 10 (IL10) of TAMs, CD163, V-set and immunoglobulin domain-containing (VSIG4), membrane-spanning 4-domain subfamily A, member 4A (MS4A4A) of M2 macrophages, forkhead box protein P3 (FOXP3), transforming growth factor beta (TGFβ), C-C chemokine receptor 8 (CCR8), signal transducer and activator of transcription 5B (STAT5B) of Tregs, PD-1, CTLA4, and T-cell immunoglobulin mucin 3 (TIM-3) of T cell exhaustion (Table 2).

TABLE 1 | Correlation analysis between SEMA6B and markers of immune cells in TIMER.

TABLE 2 | Correlation analysis between SEMA6B expression and related genes and markers of immune cells in normal tissues and CRC tissues by GEPIA.

SEMA6B Overexpression is Indicative of the Tumor Immunosuppressive Microenvironment

To further determine the influence of SEMA6B on the tumor microenvironment, we investigated correlations between SEMA6B and expression of genes negatively regulating the cancer-immunity cycle, which consists of a cycle of processes involving eradication of cancer by the immune system (Chen and Mellman 2013). Gene signatures were downloaded from the Tracking Tumor Immunophenotype website (http://biocc.hrbmu.edu.cn/TIP/index.jsp) (Xu et al., 2018). Unsupervised hierarchical clustering analyses revealed that genes involved in the negative regulation of the cancer-immunity cycle were mostly upregulated in the high SEMA6B expression group (Figure 9A). A correlation coefficient heatmap showed that SEMA6B had a significant positive correlation with immunosuppressive molecules, including colony stimulating factor 1 receptor (CSF1R), PD-L2, FOXP3, CD86, and TGFβ1 (Figure 9B). As expected, the expression levels of immune checkpoints—such as PD-L1, PD-1, CTLA-4, T cell immunoreceptor with Ig and ITIM domains (TIGIT), TIM-3, and lymphocyte activation gene 3 (LAG-3)—in the high SEMA6B expression group were significantly higher than those in the low SEMA6B expression group (Figure 9C). These results suggest that SEMA6B plays a vital role in immune escape via promoting the tumor immunosuppressive microenvironment.

FIGURE 9 | High SEMA6B expression indicates the tumor immunosuppressive microenvironment. (A) Heatmap of the gene profiles involved in the negative regulation of the cancer-immunity cycle in the high and low SEMA6B expression groups using the TCGA-CRC cohort. (B) Heatmap displaying correlations between SEMA6B expression and immunosuppressive genes. The number in each small box indicates the Pearson correlation coefficient between the two corresponding genes. (C) Comparisons of the expression levels of representative immune checkpoint genes in the high and low SEMA6B expression groups.

SEMA6B Knockdown Blocks Cell Proliferation, Migration, Invasion, and the mRNA Expression of Immunosuppressive Molecules in Colon Cancer Cells

The mRNA level of SEMA6B in different colon cancer cell lines was investigated through CCLE datasets. Bar plots show that HCT116, SW480, LoVo, SW620, GP2D, and LS513 are the top six colon cancer cell lines with the highest SEMA6B expression levels (Figure 10A). Therefore, HCT116 and LoVo cell lines were chosen for further study. QRT-PCR analysis revealed that SEMA6B silencing can not only significantly reduce the expression of SEMA6B, but also decrease the mRNA levels of immunosuppressive molecules, EDNRB, TGFB2, IL1B, IL6ST, BTLA, PD-L1, LGALS1, ICAM1, HAVCR2 in the selected cells lines compared to the corresponding controls (Figure 10B). Knockdown of SEMA6B significantly reduced the proliferation in both cell lines according to the results of the CCK-8 assay (Figure 10C). Furthermore, the cell numbers that migrated through the membrane were significantly reduced in the SEMA6B-silenced groups of both cell lines according to the results of the Transwell migration assay (Figure 10D). Transwell invasion assay also showed that SEMA6B silencing significantly decreased the degree of invasiveness in both selected colon cancer cell lines (Figure 10E). These data demonstrate that SEMA6B knockdown reduces the growth and progression of colon cancer cells as well as suppresses the formation of an immunosuppressive microenvironment.

FIGURE 10 | SEMA6B knockdown retards the malignant biological behavior of colon cancer cells and the expression of immunosuppressive molecules. HCT116 and LoVo cells were transfected with NC-siRNA or SEMA6B-siRNA. (A) The mRNA expression level in different colon cancer cells from the CCLE website. (B) QRT-PCR analysis was performed to examine the knockdown efficiency of SEMA6B and the relative mRNA expression of immunosuppressive genes. (C) CCK-8 assay was applied to evaluate cell viability. Transwell assay was employed to count the number of migrated (D) and invaded (E) cells. *p < 0.05, **p < 0.01, and ***p < 0.001.

DICUSSION

Accumulating evidence has indicated that the intra-tumoral immune contexture (i.e., type, functional orientation, density, and location of immune cells) of solid tumors may be a dominant determinant of clinical outcomes (Becht et al., 2016; Fridman et al., 2017). Despite CRC being one of the major cancer types for which new immune-based cancer treatments are currently in development, the prognosis of patients with advanced CRC remains poor (Emambux and Tachon 2018; Ciardiello and Vitiello 2019). Therefore, it has been necessary to further identify immune-related biomarkers and better elucidate the underlying molecular mechanisms of CRC to improve patient prognosis and guide the development of immunomodulation for CRC treatments. In the present study, we focused on SEMA6B, a member of the semaphorin axon-guidance family, which exhibits immune functions related to the control of cellular movements and cell-cell communication (Tawarayama et al., 2010; Koivisto et al., 2020). Given that our knowledge of the role of SEMA6B in cancers is limited, in the present study, we aimed at investigating its prognostic value in CRC and revealed its associated biological functions by performing a comprehensive analysis of population databases.

Here, we found that the mRNA expression levels of SEMA6B were significantly increased in CRC compared with those in normal colorectal tissues using the TCGA and GEO databases. A similar trend in the protein expression levels of SEMA6B was also observed in the HPA database. In addition, our data demonstrated that gene hypomethylation was influential in upregulation of SEMA6B expression in CRC. These findings imply that enhanced SEMA6B expression had considerable effects on CRC progression in a manner that may be due to epigenetic alterations. Of note, SEMA6B levels were related with the disease type, venous invasion, T stage, MSI, KRAS mutation, and CMS of CRC patients. Moreover, high SEMA6B expression was found to predict worse survival in all cohorts, and was further shown to be an independent prognostic factor of PFS in CRC patients. The nomogram based on SEMA6B, M stage, and N stage could facilitate accurate prediction of the 3- and 5 year PFS probabilities in CRC patients. To our knowledge, this is the first study to report a consistent association between increased SEMA6B levels and poor prognosis in CRC. These results indicate a malignant biological influence of high SEMA6B levels in CRC.

Studies based on the roles of SEMA6B have also been reported for other malignant human tumors. In breast cancer, high levels of SEMA6B in human MCF-7 cells exhibit an in vitro invasive capacity, and show potential as a key regulator of tumor progression (Murad and Collet, 2006). Recently, SEMA6B has been shown to exhibit higher expression levels in testicular cancer tissues than in normal tissues and is considered to be a predictor of poor prognosis in patients with testicular germ-cell tumors (Ji and Wang, 2020). On the contrary, SEMA6B has been reported to be downregulated in human glioblastoma cells upon prolonged treatment with the anti-tumor action of all-trans retinoic acid (ATRA) (Correa et al., 2001). Increasing evidence has indicated that SEMA6B is related to macrophages and correlates with a favorable prognosis in glioma patients (Sun et al., 2019). Thus, these conflicting findings suggest that SEMA6B may play differential roles in different kinds of human cancers, and that discrepancies between SEMA6B expression and prognostic values may derive from underlying mechanisms pertinent to specific biological properties in various cancers. Exploring and revealing the mechanisms of SEMA6B in CRC may facilitate the discovery of novel therapeutic approaches for CRC patients.

Biological pathway analysis and functional enrichment analysis in our present study illustrated that immune-related processes, inflammatory activities, and cancer signaling pathways were significantly enriched in the high SEMA6B expression group. Previous studies have indicated that immune infiltrating levels in tumor sites influence prognosis and the response rate of chemotherapy and immunotherapy in CRC patients (Waniczek et al., 2017). The ESTIMATE algorithm was first reported by Yoshihara et al. to assess immune-cell infiltration and the presence of stromal cells based on gene expression data (Yoshihara and Shahmoradgoli, 2013). In the present study, we revealed that high SEMA6B expression was positively associated with higher stromal scores, immune scores, and ESTIMATE scores but was negatively association with tumor purity. Another noteworthy finding of the present study was that SEMA6B expression was correlated with diverse immune infiltration levels in CRC. Our results showed that there were moderate-to-strong positive relationships between SEMA6B expression levels and infiltration levels of macrophages, MDSCs, NK cells, Tregs, and Th1 cells, as well as significantly positive correlations between infiltrating levels of CD4⁺ T cells, neutrophils, and dendric cells and SEMA6B expression in CRC. Likewise, the relationships between gene markers of different immune cells and SEMA6B expression are suggestive of a pivotal role of SEMA6B in regulating the tumor immune microenvironment. In addition to identifying markers of CD8⁺ T-cell activation, we also found that NK cells were more active in tumors with high SEMA6B expression, which indicates that these tumors may have an antitumor immune microenvironment (Qu et al., 2018). However, the above-mentioned immune cells do not serve as the key effectors that destroy tumor cells in CRC patients. This phenomenon may be due to the following possibilities. First, immunosuppressive cells such as Tregs, M2 macrophages, and MDSCs (Qu et al., 2018; Xiang and Gilkes 2019) are known to dominate the immune microenvironment in tumors with high SEMA6B expression. Second, SEMA6B has the potential to induce CD8⁺ T-cell exhaustion, as reflected by positive correlations with T-cell exhaustion markers. TIM-3, a crucial surface protein on exhausted T cells (Anderson 2012), was highly correlated with SEMA6B expression in CRC in the present study. Third, SEMA6B expression represents weak correlations with gene markers of M1 macrophages, whereas M2 macrophage indicators exhibited strong correlations in the present study. Previous studies have underscored the dualistic roles of macrophages in tumors: the M1 phenotype exhibits proinflammatory and tumoricidal activities, whereas the M2 phenotype exhibits anti-inflammatory activities and exerts functions in immunosuppression and malignant progression of tumors (Biswas and Mantovani 2010; Mantovani and Sica 2010). Our present results suggest a potential regulatory role of SEMA6B in polarization of TAMs with the M2 phenotype for evading immune surveillance.

The cancer-immunity cycle is a series of functional stepwise events required to obtain an efficient control of cancer growth by the immune system (Chen and Mellman 2013; Horton et al., 2019). This process consists of the following seven steps (Chen and Mellman 2013): 1) releasing of cancer cell antigens; 2) cancer-antigen presentation; 3) priming and activation; 4) trafficking of immune cells to tumors; 5) infiltration of immune cells into tumors; 6) recognition of cancer cells by T cells; and 7) killing of cancer cells. Immune checkpoint molecules—such as PD-1, PD-L1, and CTLA4—can inhibit the development of an active immune response by acting primarily at the level of T-cell development and proliferation (step 3) (Chen and Mellman 2013; Chen and Mellman 2017). Negative gene sets in the cancer-immunity cycle—including PD-1, PD-L1, LAG-3, TIM-3, and TIGIT—can have an inhibitory function that primarily acts to modulate active immune responses in the tumor bed (step 7) (Chen and Mellman 2013; Chen and Mellman 2017). Therefore, tracking tumor immunophenotypes may be essential for further elucidating the mechanisms of cancer immunity and progressing the development of biomarkers of responses to immunotherapy. Our present study demonstrated that in CRC patients with high SEMA6B expression, the genes involved in the prevention of T-cell priming and the induction of tolerance were increased, and immune checkpoints—such as PD1, PD-L1, CTLA-4, LAG3, TIM-3, and TIGIT—were also upregulated in this group. Hence, accumulating evidence suggests that SEMA6B may contribute to tumor development by both attenuation of tumor-specific cytotoxic T-cell responses and induction of an immunosuppressive state (Kigel and Rabinowicz 2011; Ge and Li 2013; Cui and Bian 2021).

We conducted SEMA6B knockdown experiments to further confirm the biological role of SEMA6B in two colon cancer cell lines, i.e., HCT116 and LoVo. The results demonstrate that SEMA6B silencing enables a reduction in proliferation, migration and invasion in vitro; meanwhile, the mRNA expression levels of immunosuppressive molecules were also diminished in silenced colon cancer cells by qRT-PCR. Gui et al. reported that siRNA-mediated knockdown of SEMA6B weakened gallbladder cancer cell invasion and migration (Cui and Bian 2021). Ge et al. also discovered that SEMA6B may promote gastric cancer invasion and metastasis and represents a reliable biomarker for the clinical diagnosis and therapy of gastric cancer (Ge and Li 2013). Overexpression of SEMA6B in BHK-21 cells could promote cell proliferation, and the inhibition of SEMA6B signaling suppresses tumor formation for the sake of abrogation of bFGF and VEGF signaling (Kigel and Rabinowicz 2011). Overall, our findings reveal that SEMA6B may play a key role in regulating CRC progression and helps shape the immunosuppressive microenvironment. However, further studies are required to validate the biological functions of SEMA6B in CRC.

There were some limitations of our present study. First, only transcriptomic expression of SEMA6B expression with clinical data was analyzed to predict prognosis in CRC from public databases; thus, our results should be validated in larger sample size. Second, this was a retrospective study with selection biases inherent in the cohorts; thus a prospective study is also needed. Third, the molecular mechanisms of SEMA6B in CCR remain unclear, despite a series of functional annotations and enrichment analysis being investigated. Thus, further study is required to recognize the potential biological mechanisms of SEMA6B using additional experimental approaches.

In conclusion, our present study explored the clinical value and biological processes of SEMA6B, using CRC samples from the TCGA and GEO databases on a large scale. Our data revealed that high SEMA6B expression was significantly correlated with cancer progression, poor survival, and immune infiltration in patients with CRC. Moreover, high SEMA6B expression was correlated with increased immunosuppressive cellular infiltration and the expression of immune checkpoints. Moreover, in vitro cell studies validate that overexpress SEMA6B may promote proliferation and metastasis in two colon cancer cell lines, and help to foster an immunosuppressive microenvironment. Our present findings therefore offer novel insights into how SEMA6B affects prognosis and the immune microenvironment in CRC and suggests that SEMA6B may serve as a potential biomarker for developing immunotherapeutic strategies for assessing the efficacy and responsiveness of CRC treatments.

DATA AVAILABILITY STATEMENT

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.

AUTHOR CONTRIBUTIONS

The study concept and design were done by MY and TL. Administrative support was provided by MY. Provision of study materials or patients was done by TL, ZY, WW, and RZ. Collection and assembly of data were done by TL, ZY, WW, and RZ. Analysis and interpretation of data were done by TL, ZY, WG, SL, and ZZ. In vitro experiments were performed by ZY. Statistical advice and technical support were provided by YH and ZY. Funding support was provided by MY and TL. Manuscript writing and final approval were done by all authors.

FUNDING

This work was supported by the Natural Science Foundation of China (NSFC) Grant (No. 81773750), Beijing Natural Science Foundation Grant (No. 7194298), and CAMS Innovation Fund for Medical Sciences (CIFMS) Grant (No. 2021-1-I2M-028).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

ACKNOWLEDGMENTS

The authors would like to thank the public databases for the availability of the data.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2021.687319/full#supplementary-material

REFERENCES

Anderson, A. C. (2012). Tim-3, a Negative Regulator of Anti-tumor Immunity. Curr. Opin. Immunol. 24, 213–216. Epub 2012/01/10. doi:10.1016/j.coi.2011.12.005

Atkins, M. B., Clark, J. I., and Quinn, D. I. (2017). Immune Checkpoint Inhibitors in Advanced Renal Cell Carcinoma: Experience to Date and Future Directions. Ann. Oncol. 28, 1484–1494. Epub 2017/04/07. doi:10.1093/annonc/mdx151

Becht, E., Giraldo, N. A., Dieu-Nosjean, M.-C., Sautès-Fridman, C., and Fridman, W. H. (2016). Cancer Immune Contexture and Immunotherapy. Curr. Opin. Immunol. 39, 7–13. Epub 2015/12/29. doi:10.1016/j.coi.2015.11.009

Berntsson, J., Svensson, M. C., Leandersson, K., Nodin, B., Micke, P., Larsson, A. H., et al. (2017). The Clinical Impact of Tumour-Infiltrating Lymphocytes in Colorectal Cancer Differs by Anatomical Subsite: A Cohort Study. Int. J. Cancer 141, 1654–1666. Epub 2017/07/06. doi:10.1002/ijc.30869

Biswas, S. K., and Mantovani, A. (2010). Macrophage Plasticity and Interaction with Lymphocyte Subsets: Cancer as a Paradigm. Nat. Immunol. 11, 889–896. Epub 2010/09/22. doi:10.1038/ni.1937

Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., and Jemal, A. (2018). Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer J. Clinicians 68, 394–424. Epub 2018/09/13. doi:10.3322/caac.21492

Chandrashekar, D. S., Bashel, B., Balasubramanya, S. A. H., Creighton, C. J., Ponce-Rodriguez, I., Chakravarthi, B. V. S. K., et al. (2017). UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia 19, 649–658. Epub 2017/07/22. doi:10.1016/j.neo.2017.05.002

Chen, D. S., and Mellman, I. (2017). Elements of Cancer Immunity and the Cancer-Immune Set point. Nature 541, 321–330. Epub 2017/01/20. doi:10.1038/nature21349

Chen, D. S., and Mellman, I. (2013). Oncology Meets Immunology: the Cancer-Immunity Cycle. Immunity 39, 1–10. Epub 2013/07/31. doi:10.1016/j.immuni.2013.07.012

Chung, K. Y., Gore, I., Fong, L., Venook, A., Beck, S. B., Dorazio, P., et al. (2010). Phase II Study of the Anti-cytotoxic T-Lymphocyte-Associated Antigen 4 Monoclonal Antibody, Tremelimumab, in Patients with Refractory Metastatic Colorectal Cancer. Jco 28, 3485–3490. Epub 2010/05/26. doi:10.1200/jco.2010.28.3994

Ciardiello, D., Vitiello, P. P., Cardone, C., Martini, G., Troiani, T., Martinelli, E., et al. (2019). Immunotherapy of Colorectal Cancer: Challenges for Therapeutic Efficacy. Cancer Treat. Rev. Immunother. colorectal Cancer Challenges Ther. efficacy. Cancer Treat Rev. Jun 76, 22–32. Epub 2019/05/13. doi:10.1016/j.ctrv.2019.04.003

Coebergh van den Braak, R. R. J., Sieuwerts, A. M., Sieuwerts, A. M., Lalmahomed, Z. S., Smid, M., Wilting, S. M., et al. (2018). Confirmation of a Metastasis-specific microRNA Signature in Primary colon Cancer. Sci. Rep. 8, 5242. doi:10.1038/s41598-018-22532-1

Correa, R. G., Sasahara, R. M., Bengtson, M. H., Katayama, M. L. H., Salim, A. C. M., Brentani, M. M., et al. (2001). Human Semaphorin 6B [(HSA)SEMA6B], a Novel Human Class 6 Semaphorin Gene: Alternative Splicing and All-Trans-Retinoic Acid-dependent Downregulation in Glioblastoma Cell Lines. Genomics 73, 343–348. Epub 2001/05/15. doi:10.1006/geno.2001.6525

Cui, K., and Bian, X. (2021). The microRNA Cluster miR-30b/-30d Prevents Tumor Cell Switch from an Epithelial to a Mesenchymal-like Phenotype in GBC. Mol. Ther. - Methods Clin. Dev. 20 (20), 716–725. Epub 2021/03/20. doi:10.1016/j.omtm.2020.11.019

D'Apice, L., Costa, V., Valente, C., Trovato, M., Pagani, A., Manera, S., et al. (2013). Analysis of SEMA6B Gene Expression in Breast Cancer: Identification of a New Isoform. Biochim. Biophys. Acta (Bba) - Gen. Subjects 1830, 4543–4553. Epub 2013/05/15. doi:10.1016/j.bbagen.2013.05.003

Díez-Villanueva, A., Mallona, I., and Peinado, M. A. (2015). Wanderer, an Interactive Viewer to Explore DNA Methylation and Gene Expression Data in Human Cancer. Epigenetics & Chromatin 8, 22. Epub 2015/06/27. doi:10.1186/s13072-015-0014-8

Emambux, S., Tachon, G., Junca, A., and Tougeron, D. (2018). Results and Challenges of Immune Checkpoint Inhibitors in Colorectal Cancer. Expert Opin. Biol. Ther. 18, 561–573. Epub 2018/02/24. doi:10.1080/14712598.2018.1445222

Franzolin, G., and Tamagnone, L. (2019). Semaphorin Signaling in Cancer-Associated Inflammation. Int. J. Mol. Sci. 20, 20. Epub 2019/01/20. doi:10.3390/ijms20020377

Fridman, W. H., Zitvogel, L., Sautès–Fridman, C., and Kroemer, G. (2017). The Immune Contexture in Cancer Prognosis and Treatment. Nat. Rev. Clin. Oncol. 14, 717–734. Epub 2017/07/26. doi:10.1038/nrclinonc.2017.101

Ganesh, K., Stadler, Z. K., Cercek, A., Mendelsohn, R. B., Shia, J., Segal, N. H., et al. (2019). Immunotherapy in Colorectal Cancer: Rationale, Challenges and Potential. Nat. Rev. Gastroenterol. Hepatol. 16, 361–375. Epub 2019/03/20. doi:10.1038/s41575-019-0126-x

Garborg, K., Holme, Ø., Løberg, M., Kalager, M., Adami, H. O., and Bretthauer, M. (2013). Current Status of Screening for Colorectal Cancer. Ann. Oncol. 24, 1963–1972. Epub 2013/04/27. doi:10.1093/annonc/mdt157

Ge, C., Li, Q., Wang, L., and Xu, X. (2013). The Role of Axon Guidance Factor Semaphorin 6B in the Invasion and Metastasis of Gastric Cancer. J. Int. Med. Res. 41, 284–292. Epub 2013/06/20. doi:10.1177/0300060513476436

Gettinger, S., Horn, L., Jackman, D., Spigel, D., Antonia, S., Hellmann, M., et al. (2018). Five-Year Follow-Up of Nivolumab in Previously Treated Advanced Non-small-cell Lung Cancer: Results from the CA209-003 Study. Jco 36, 1675–1684. Epub 2018/03/24. doi:10.1200/jco.2017.77.0412

Gurrapu, S., and Tamagnone, L. (2019). Semaphorins as Regulators of Phenotypic Plasticity and Functional Reprogramming of Cancer Cells. Trends Mol. Med. 25, 303–314. Epub 2019/03/03. doi:10.1016/j.molmed.2019.01.010

Hänzelmann, S., Castelo, R., and Guinney, J. (2013). GSVA: Gene Set Variation Analysis for Microarray and RNA-Seq Data. BMC Bioinformatics 14, 7. Epub 2013/01/18. doi:10.1186/1471-2105-14-7

Horton, B. L., Fessenden, T. B., and Spranger, S. (2019). Tissue Site and the Cancer Immunity Cycle. Trends Cancer 5, 593–603. Epub 2019/11/11. doi:10.1016/j.trecan.2019.07.006

Ji, C., Wang, Y., Wang, Y., Luan, J., Yao, L., Wang, Y., et al. (2020). Immune-related Genes Play an Important Role in the Prognosis of Patients with Testicular Germ Cell Tumor. Ann. Transl Med. 8, 866. Epub 2020/08/15. doi:10.21037/atm-20-654

Judge, S. J., Ji, J., Liu, J., Kaur, M., Kim, E., Gong, J., et al. (2020). The Role of Palliative Surgery for Malignant Bowel Obstruction and Perforation in Advanced Microsatellite Instability-High Colorectal Carcinoma in the Era of Immunotherapy: Case Report. Front. Oncol. 10, 581. Epub 2020/05/07. doi:10.3389/fonc.2020.00581

Kakimi, K., Karasaki, T., Matsushita, H., and Sugie, T. (2017). Advances in Personalized Cancer Immunotherapy. Breast Cancer 24 (1), 16–24. Epub 2016/03/24. doi:10.1007/s12282-016-0688-1

Kigel, B., Rabinowicz, N., Varshavsky, A., Kessler, O., and Neufeld, G. (2011). Plexin-A4 Promotes Tumor Progression and Tumor Angiogenesis by Enhancement of VEGF and bFGF Signaling. Blood 118, 4285–4296. Epub 2011/08/13. doi:10.1182/blood-2011-03-341388

Koi, M., and Carethers, J. M. (2017). The Colorectal Cancer Immune Microenvironment and Approach to Immunotherapies. Future Oncol. 13, 1633–1647. Epub 2017/08/23. doi:10.2217/fon-2017-0145

Koivisto, O., Hanel, A., and Carlberg, C. (2020). Key Vitamin D Target Genes with Functions in the Immune System. Nutrients 12, 12. Epub 2020/04/25. doi:10.3390/nu12041140

Kuznetsova, E. B., Kekeeva, T. V., Larin, S. S., Zemliakova, V. V., Babenko, O. V., Nemtsova, M. V., et al. (2007). Novel Methylation and Expression Markers Associated with Breast Cancer. Mol. Biol. (Mosk) 41, 624–633. Epub 2007/10/17. doi:10.1134/s0026893307040061

Le, D. T., Uram, J. N., Wang, H., Bartlett, B. R., Kemberling, H., Eyring, A. D., et al. (2015). PD-1 Blockade in Tumors with Mismatch-Repair Deficiency. N. Engl. J. Med. 372 (372), 2509–2520. Epub 2015/06/02. doi:10.1056/NEJMoa1500596

Le, D. T., Kim, T. W., Van Cutsem, E., Geva, R., Jäger, D., Hara, H., et al. (2020). Phase II Open-Label Study of Pembrolizumab in Treatment-Refractory, Microsatellite Instability-High/Mismatch Repair-Deficient Metastatic Colorectal Cancer: KEYNOTE-164. Jco 38, 11–19. Epub 2019/11/15. doi:10.1200/jco.19.02107

Li, T., Fan, J., Wang, B., Traugh, N., Chen, Q., Liu, J. S., et al. (2017). TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res. 77, e108–e110. Epub 2017/11/03. doi:10.1158/0008-5472.CAN-17-0307

Mantovani, A., and Sica, A. (2010). Macrophages, Innate Immunity and Cancer: Balance, Tolerance, and Diversity. Curr. Opin. Immunol. 22, 231–237. Epub 2010/02/11. doi:10.1016/j.coi.2010.01.009

Morse, M. A., Hochster, H., and Benson, A. (2020). Perspectives on Treatment of Metastatic Colorectal Cancer with Immune Checkpoint Inhibitor Therapy. Oncol. 25, 33–45. Epub 2019/08/07. doi:10.1634/theoncologist.2019-0176

Murad, H., Collet, P., Huin-Schohn, C., Al-Makdissy, N., Kerjan, G., Chedotal, A., et al. (2006). Effects of PPAR and RXR Ligands in Semaphorin 6B Gene Expression of Human MCF-7 Breast Cancer Cells. Int. J. Oncol. 28, 977–984. Epub 2006/03/10. doi:10.3892/ijo.28.4.977

Műzes, G., and Sipos, F. (2014). Relation of Immune Semaphorin/plexin Signaling to Carcinogenesis. Eur. J. Cancer Prev. 23, 469–476. Epub 2014/06/13. doi:10.1097/CEJ.0000000000000059

Neufeld, G., Mumblat, Y., Smolkin, T., Toledano, S., Nir-Zvi, I., Ziv, K., et al. (2016). The Role of the Semaphorins in Cancer. Cell Adhes. Migration 10, 652–674. Epub 2016/08/18. doi:10.1080/19336918.2016.1197478

Neufeld, G., Sabag, A. D., Rabinovicz, N., and Kessler, O. (2012). Semaphorins in Angiogenesis and Tumor Progression. Cold Spring Harbor Perspect. Med. 2, a006718. Epub 2012/02/09. doi:10.1101/cshperspect.a006718

Overman, M. J., Lonardi, S., Wong, K. Y. M., Lenz, H.-J., Gelsomino, F., Aglietta, M., et al. (2018). Durable Clinical Benefit with Nivolumab Plus Ipilimumab in DNA Mismatch Repair-Deficient/Microsatellite Instability-High Metastatic Colorectal Cancer. Jco 36, 773–779. Epub 2018/01/23. doi:10.1200/jco.2017.76.9901

Overman, M. J., McDermott, R., Leach, J. L., Lonardi, S., Lenz, H.-J., Morse, M. A., et al. (2017). Nivolumab in Patients with Metastatic DNA Mismatch Repair-Deficient or Microsatellite Instability-High Colorectal Cancer (CheckMate 142): an Open-Label, Multicentre, Phase 2 Study. Lancet Oncol. 18, 1182–1191. Epub 2017/07/25. doi:10.1016/s1470-2045(17)30422-9

Qu, X., Tang, Y., and Hua, S. (2018). Immunological Approaches towards Cancer and Inflammation: A Cross Talk. Front. Immunol. 9, 563. Epub 2018/04/18. doi:10.3389/fimmu.2018.00563

Reck, M., Rodríguez-Abreu, D., Robinson, A. G., Hui, R., Csőszi, T., Fülöp, A., et al. (2016). Pembrolizumab versus Chemotherapy for PD-L1-Positive Non-small-cell Lung Cancer. N. Engl. J. Med. 10375, 1823–1833. Epub 2016/10/11. doi:10.1056/nejmoa1606774

Reissfelder, C., Stamova, S., Gossmann, C., Braun, M., Bonertz, A., Walliczek, U., et al. (2015). Tumor-specific Cytotoxic T Lymphocyte Activity Determines Colorectal Cancer Patient Prognosis. J. Clin. Invest. 125, 739–751. Epub 2015/01/07. doi:10.1172/jci74894

Ribas, A., and Wolchok, J. D. (2018). Cancer Immunotherapy Using Checkpoint Blockade. Science 359 (359), 1350–1355. Epub 2018/03/24. doi:10.1126/science.aar4060

Ru, B., Wong, C. N., Tong, Y., Zhong, J. Y., Zhong, S. S. W., Wu, W. C., et al. (2019). TISIDB: an Integrated Repository portal for Tumor-Immune System Interactions. Bioinformatics 35, 4200–4202. Epub 2019/03/25. doi:10.1093/bioinformatics/btz210

Siegel, R., Desantis, C., and Jemal, A. (2014). Colorectal Cancer Statistics, 2014. CA A Cancer J. Clinicians 64, 104–117. Epub 2014/03/19. doi:10.3322/caac.21220

Siegel, R. L., Miller, K. D., Fedewa, S. A., Ahnen, D. J., Meester, R. G. S., Barzi, A., et al. (2017). Colorectal Cancer Statistics, 2017. CA Cancer J. Clin. 67 (67), 177–193. Epub 2017/03/02. doi:10.3322/caac.21395

Sun, X., Liu, X., Xia, M., Shao, Y., and Zhang, X. D. (2019). Multicellular Gene Network Analysis Identifies a Macrophage-Related Gene Signature Predictive of Therapeutic Response and Prognosis of Gliomas. J. Transl Med. 17, 159. Epub 2019/05/18. doi:10.1186/s12967-019-1908-1

Sveen, A., Kopetz, S., and Lothe, R. A. (2020). Biomarker-guided Therapy for Colorectal Cancer: Strength in Complexity. Nat. Rev. Clin. Oncol. 17, 11–32. Epub 2019/07/11. doi:10.1038/s41571-019-0241-1

Szeto, G. L., and Finley, S. D. (2019). Integrative Approaches to Cancer Immunotherapy. Trends Cancer 5, 400–410. Epub 2019/07/18. doi:10.1016/j.trecan.2019.05.010

Tang, Z., Kang, B., Li, C., Chen, T., and Zhang, Z. (2019). GEPIA2: an Enhanced Web Server for Large-Scale Expression Profiling and Interactive Analysis. Nucleic Acids Res. 47, W556–w560. Epub 2019/05/23. doi:10.1093/nar/gkz430

Tawarayama, H., Yoshida, Y., Suto, F., Mitchell, K. J., and Fujisawa, H. (2010). Roles of semaphorin-6B and Plexin-A2 in Lamina-Restricted Projection of Hippocampal Mossy Fibers. J. Neurosci. 30, 7049–7060. Epub 2010/05/21. doi:10.1523/jneurosci.0073-10.2010

Waniczek, D., Lorenc, Z., Śnietura, M., Wesecki, M., Kopec, A., and Muc-Wierzgoń, M. (2017). Tumor-Associated Macrophages and Regulatory T Cells Infiltration and the Clinical Outcome in Colorectal Cancer. Arch. Immunol. Ther. Exp. 65, 445–454. Epub 2017/03/28. doi:10.1007/s00005-017-0463-9

Wrobel, P., and Ahmed, S. (2019). Current Status of Immunotherapy in Metastatic Colorectal Cancer. Int. J. Colorectal Dis. 34, 13–25. Epub 2018/11/23. doi:10.1007/s00384-018-3202-8

Xiang, L., and Gilkes, D. M. (2019). The Contribution of the Immune System in Bone Metastasis Pathogenesis. Int. J. Mol. Sci. 20, 20. Epub 2019/03/03. doi:10.3390/ijms20040999

Xu, L., Deng, C., Pang, B., Zhang, X., Liu, W., Liao, G., et al. (2018). TIP: A Web Server for Resolving Tumor Immunophenotype Profiling. Cancer Res. 78, 6575–6580. Epub 2018/08/30. doi:10.1158/0008-5472.can-18-0689

Yoshihara, K., Shahmoradgoli, M., Martínez, E., Vegesna, R., Kim, H., Torres-Garcia, W., et al. (2013). Inferring Tumour Purity and Stromal and Immune Cell Admixture from Expression Data. Nat. Commun. 4, 2612. Epub 2013/10/12. doi:10.1038/ncomms3612

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Li, Yan, Wang, Zhang, Gan, Lv, Zeng, Hou and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 07 December 2021
doi: 10.3389/fmolb.2021.756085

Identification and Validation of CYBB, CD86, and C3AR1 as the Key Genes Related to Macrophage Infiltration of Gastric Cancer

Haiyan Chen¹†, Qi Sun²†, Cangang Zhang³†, Junjun She⁴†, Shuai Cao⁵, Meng Cao¹, Nana Zhang¹, Ayarick Vivian Adiila¹, Jinjin Zhong¹, Chengyun Yao⁶*, Yili Wang¹*, Hongping Xia2,⁴* and Linhua Lan⁷*

¹Institute for Cancer Research, School of Basic Medical Science of Xi’an Jiaotong University, Xi’an, China

²Department of Pathology, School of Basic Medical Sciences and Sir Run Run Hospital and Key Laboratory of Antibody Technique of National Health Commission, Nanjing Medical University, Nanjing, China

³Department of Pathogenic Microbiology and Immunology, School of Basic Medical Sciences, Xi’an Jiaotong University, Xi’an, China

⁴Department of High Talent and General Surgery and Center for Gut Microbiome Research and Med-X Institute, the First Affiliated Hospital of Xi’an Jiao Tong University, Xi’an, China

⁵Department of Orthopedics, Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China

⁶Jiangsu Cancer Hospital and the Affiliated Cancer Hospital of Nanjing Medical University and Jiangsu Institute of Cancer Research, Nanjing, China

⁷Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China

Edited by:
Ismail Hosen, University of Dhaka, Bangladesh

Reviewed by:
Yunbao Pan, Zhongnan Hospital of Wuhan University, China
Pedro José Carlos Rondot Radío, University of Buenos Aires, Argentina

* Correspondence: Chengyun Yao, yaochengyun@jszlyy.com.cn; Yili Wang, wangyili@mail.xjtu.edu.cn; Hongping Xia, xiahongping@njmu.edu.cn; Linhua Lan, paullee90@wmu.edu.cn

†These authors have contributed equally to this work

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 10 August 2021
Accepted: 08 November 2021
Published: 07 December 2021

Citation: Chen H, Sun Q, Zhang C, She J, Cao S, Cao M, Zhang N, Adiila AV, Zhong J, Yao C, Wang Y, Xia H and Lan L (2021) Identification and Validation of CYBB, CD86, and C3AR1 as the Key Genes Related to Macrophage Infiltration of Gastric Cancer. Front. Mol. Biosci. 8:756085. doi: 10.3389/fmolb.2021.756085

Gastric cancer (GC) is rampant around the world. Most of the GC cases are detected in advanced stages with poor prognosis. The identification of marker genes for early diagnosis is of great significance. Studying the tumor environment is helpful to acknowledge the process of tumorigenesis, development, and metastasis. Twenty-two kinds of immune cells were calculated by CIBERSORT from Gene Expression Omnibus (GEO) database. Subsequently, higher infiltration of macrophages M0 was discovered in GC compared with normal tissues. WGCNA was utilized to construct the network and then identify key modules and genes related to macrophages in TCGA. Finally, 18 hub genes were verified. In the PPI bar chart, the top 3 genes were chosen as hub genes involved in most pathways. On the TIMER and THPA websites, it is verified that the expression levels of CYBB, CD86, and C3AR1 genes in tumor tissues were higher than those in normal tissues. These genes may work as biomarkers or targets for accurate diagnosis and treatment of GC in the future. Our findings may be a new strategy for the treatment of GC.

Keywords: macrophage, gastric cancer, WGCNA, immune infiltration, hub genes

INTRODUCTION

Gastric cancer (GC) has the third mortality rate among cancers worldwide (Siegel et al., 2019). Although the incidence of GC is declining in many countries, its dismal clinical outcome still threatens the health and lives of thousands of people (Siegel et al., 2020). There are disparities between the 5-years survival rates due to various factors, but the survival rates remain very low (Youn et al., 2010; Fields et al., 2011). Besides, it is worth noting that both the morbidity and mortality of GC are still extraordinarily high in China. The majority (95%) of stomach cancers are adenocarcinomas, and no obvious symptoms are observed in the early stage (Zhao et al., 2020; Ma et al., 2016). Surgical resection is the most common treatment for GC, but with a poor prognosis (Liu et al., 2012). In recent years, immunotherapy has been given high expectations. Although immunotherapy has gradually been used as the first or second-line treatment in most kinds of cancers, it was not considered as the preferential treatment of GC. Lacking effective treatment is another problem of advanced GC. Thus, it is urgent to find effective methods for early diagnosis and treatment of GC.

The tumor microenvironment (TME) influences the occurrence and progression of tumors. The complex interaction between tumor cells and tumor-related immune cells occurred in TME (Sperlich et al., 2017). It is mainly composed of tumor-related fibroblasts, immune cells, extracellular matrix, various growth factors, inflammatory factors, special physical and chemical characteristics (such as low oxygen and low pH) and cancer cells. TME plays an essential role in the diagnosis, prognosis, and clinical treatment sensitivity of tumors. The cells in the microenvironment can be grouped into different categories and have complex and significant interactions with each other, and there are some robust cell infiltration patterns (Shaw et al., 2013; Tamborero et al., 2018). It is closely related to clinical prognosis. Thus, the infiltrated immune cells could be used as drug targets to improve the survival rates of cancer patients, which have been recognized in current immunotherapy strategies. By understanding immune cell infiltration, we are capable of decrypting the proportion and functional potential of immune cells in tumor tissues.

With the development of bioinformatics, many algorithms and tools are utilized to explore TME (Lin et al., 2020). Weighted gene co-expression network analysis (WGCNA) is used to find co-expressed gene modules and to explore the association between the gene network and the phenotypes of interest, as well as the core genes in the network (Langfelder and Horvath, 2008). Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) deconvolutes the expression matrix of immune cell subtypes based on the principle of linear support vector regression. RNA-Seq data can be used to estimate immune cell infiltration in various cancers (Newman et al., 2015). In this study, the WGCNA co-expression network in GC was constructed, and 18 critical genes associated with macrophages were determined. Subsequently, the hub genes and signal pathways were found out in PPI (protein-to-protein interaction). Moreover, we verified that these hub genes were highly expressed in GC at the gene and protein levels. Finally, immunohistochemistry (IHC) results of the collected clinical samples also showed that these genes were highly expressed in GC. In conclusion, CYBB, CD86, and C3AR1 were identified as potential biomarkers in GC.

MATERIALS AND METHODS

Data From the GEO Cohort and Preprocessing

From the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/), we downloaded publicly available raw microarray expression data of GSE13911 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13911) to acquire the transcriptional data of GC. Among the datasets of GC in GEO, GSE13911 has paired normal tissues, containing 31 normal samples and 38 tumor tissues. The chip standardization method was RMA (Robust Multichip Average algorithm), referring to the chip tutorial. The standardization process is mainly divided into three steps: Background correction (removing array auto-fluorescence), Quantile normalization (making all intensity distributions identical), and Probeset summarization (calculating one representative value per probeset).

Quantification of Immune Tissue Cells

CIBERSORT, a deconvolution algorithm, can calculate the cell composition of complex tissues based on the standardized gene expression data and change the method to energize the abundance of specific cell types. The composition of immune cells in breast and liver cancer tissues was verified and successfully evaluated by flow cytometry (Hackl et al., 2016). It provides 22 kinds of common infiltrating cell expression data LM22 as a reference. Then, we employed the “CIBERSORT” packages in the R to evaluate the infiltration scores of 22 types of immune cells (Langfelder and Horvath, 2008). CIBERSORT was constructed based on microarray data. Thus we applied CIBERSORT for quantitative analysis of immune cells in GEO data.

Data Preprocessing of GC in TCGA

RNA-seq and corresponding clinical data of GC from the TCGA database were acquired. The RNA-seq data was in the form of HTSeq-FPKM (fragments per kilobase of transcript per million). Thirty-two normal tissues and 381 tumor tissues were screened for further study.

Co-Expression Network Construction

A weight co-expression network was constructed by using the R package “WGCNA” (12). WGCNA is suitable for complex data analysis in multiple samples. It can calculate the expression connection between genes, identify gene modules with similar expression patterns, analyze the relationship between the gene set and the sample phenotype, and plot the regulation between the genes in the gene set, and appraise key regulatory genes. Through this analysis, we were able to identify the co-expressed gene set, which is called modules. We also associated modules with phenotype data for further investigation and discovered potential marker genes.

The first step was to filter the gene expression data. Missing values or genes with low expression were removed. The samples with many missing values were also deleted. WGCNA has a built-in test gene and sample function, and a basic filter could be performed through this function. After filtering, we proceeded to check if there were samples of outliers and judged the clustering tree of the samples.

When constructing a co-expression network, the correlation coefficients between genes were multiplied to characterize their correlation. The power value was determined by the soft threshold, or what we call the power value and the beta value.

Differentially Expressed mRNAs Between GC and Non-Tumorous Tissues

“Limma” package was used to screen out the differentially expressed mRNA. FDR-Filter = 0.01 and logFC-filter = 1 were used as the critical values. Differentially expressed genes (DEGs) were screened out for follow-up research.

Correlation Analysis of Immune Cell Infiltration and Clinical Information

The corresponding clinical information organized in TCGA was analyzed. The clinical information matrix was combined with the immune cell expression matrix. Xcell (Rscript, web tool) (https://xcell.ucsf.edu/) calculated the content of 22 kinds of immune cells to visualize the changes of macrophages in different clinical stages based on ssGSEA (Aran et al., 2017). Through the survival package in R, survival analysis of immune cells and the evaluation of their effects on tumors were attached.

Functional Enrichment Analysis

With the “clusterProfiler” R package, Gene Ontology (GO) enrichment analysis was realized to explore the molecular functions (MF), biological processes (BP), and cellular components (CC) related to DEGs. From the Kyoto Encyclopedia of Genes and Genomes (KEGG), numerous functions of hub genes were presented.

Protein-Protein Interaction Network Analysis

STRING is an online tool used to predict the functional interactions between proteins, which is essential for recognizing the mechanisms of cell activities at the molecular levels in cancer progression (Szklarczyk et al., 2019). Using Search Tool for the Retrieval of Interacting Genes (STRING, version 11.5, http://string-db.org) database, a Protein-Protein Interaction (PPI) network was constructed based on 18 genes that positively correlated with macrophage infiltration in GC. The disconnected nodes were hidden in the network, and the PPI network was visualized by the Cytoscape software (version 3.7.2, http://www.cytoscape.org/).

The Validation in Tumor Immune Estimation Resource

TIMER (https://cistrome.shinyapps.io/timer/) uses RNA-Seq expression profiling data to detect immune cell infiltration in tumor tissues (Li et al., 2017). It provides the infiltration scores of 6 kinds of immune cells (B cells, CD4⁺ T cells, CD8⁺ T cells, Neutrophils, Macrophages, and Dendritic cells). SCNA (somatic copy number alteration) module explores the relationship between somatic cell copy number variation and immune infiltration. TIMER uses GISTIC2.0 data to examine the effect of different gene copy states on immune infiltration compared with normal tissues. The SCNA module is grouped according to the CNA (copy number alteration) of a certain gene. This module explores the different levels of infiltration in 6 kinds of immune cells between the groups. The grouping of CNA is divided into five situations: deep deletion (−2), arm-level deletion (−1), diploid/normal (0), arm-level gain (1) and high amplification (2).

Gene Expression Level in Gene Expression Profiling Interactive Analysis

GEPIA (http://gepia.cancer-pku.cn/) generates a gene expression boxplot and stages plot based on user-defined input. The specific gene expression of the normal tissues was compared with tumor tissues. Based on the section of the pathological stage, the violin diagram was generated according to the pathological stages of the cases (Zhou et al., 2019).

The Human Protein Atlas Database

The Human Protein Atlas project (https://www.proteinatlas.org/) contains the protein expression of normal cells, tissues, and cancers. More than 50% of all human protein-encoding genes in line with 11,200 unique proteins were included (Zhou et al., 2018). Through this website, IHC was used to compare the expression of different proteins between human normal tissues and cancer tissues.

Immunohistochemistry

Paraffin-embedded GC tissues were collected from the First Affiliated Hospital of Xi’an Jiaotong University. Supplementary Table S2 shows the characteristics of patients. IHC was performed by using the EnVision immunohistochemistry kit (Dako, Denmark, k5007). Firstly, the slides were toasted at 60°C overnight. Secondly, the slides were deparaffinized with xylene and rehydrated in gradient ethanol baths. 0.01 M citrate buffer was applied to retrieve the antigen. A 3% H₂O₂ solution was dropped in methanol to block endogenous peroxidase. Then, slides were treated with 5% normal goat serum (AR0009) (BOSTER Biological Technology Co. Ltd.) and incubated in the wet box at 37°C for 30 min to block non-specific antigen. Later, commercially available primary antibodies: CD86 (sc-28347), CYBB (sc-130543) (both from Santa Cruz Biotechnology, Inc.), and C3aR (ER 1904-90) (Hangzhou HuaAn Biotechnology Co., Ltd.) were incubated at 4°C overnight (dilution, 1:500). Sections were washed with TBST (Tris Borate Saline Tween-20) 5 min for 3 times. The tablets were incubated with a biotinylated secondary antibody against mouse and rabbit at 37°C for 30 min 3,3′-diaminobenzidine (DAB) was used as the chromogen. The slices were dyed with hematoxylin and mounted after dehydration. Images were digitally captured by ImageView software.

RESULTS

Data Processing

As shown in Figure 1, the flowchart presented the process. Firstly, we analyzed the GEO dataset GSE13911. Then, we found that macrophage infiltration was different in GC and normal tissues. To verify this phenomenon, we downloaded related data from TCGA. In this section, we identified three hub genes relevant to macrophages by using WGCNA. Finally, the former results were verified by various websites and IHC.

FIGURE 1 | The flowchart of the data processing. GSE13911 was used to analyze the immune cell profiles by CIBERSORT. Corresponding TCGA gene expression and clinical data were used to find the genes related to tumor-infiltrating macrophages by WGCNA. Through PPI analysis, 3 hub genes were identified. The relationship between hub genes and tumor-infiltrating macrophages was explored by TIMER. The expression of hub genes in tumor and normal tissues was identified in GEPIA. HPA was utilized to verify the expression of hub genes between adjacent non-tumorous tumor and tumor tissues. This result was validated by IHC.

Figure 2A (barplot) showed the ratio of immune cell infiltration in 69 samples. Thirty-one samples on the left are normal tissues, 38 samples are tumor tissues on the right. A heat map (Figure 2B) of the cell infiltration of about 69 samples presented that there was a significant difference in the distribution of some cells in normal and tumor tissues. For example, the proportion of resting memory CD4⁺ T cells was high in the tumor group, while plasma cells and CD8⁺ T cells have a higher proportion in the normal group. CorHeatmap (Figure 2C) was used to observe the co-expression (red) or co-inhibition (blue) of various cells in tumor tissues. We could summarize from the picture that neutrophils and activated dendritic cells had the highest correlation and the correlation coefficient was 0.65. In contrast, resting and activated mast cells were most negatively correlated and the coefficient was −0.59. Then, 31 pairs of samples could be matched in the GSE13911 data set for pairwise comparison. The violin plot was a variation of the box plot, which combines the distribution kernel density estimation curve with the box plot. Follicular helper T cells and M0 macrophages activated dendritic cells, and activated mast cells have a high proportion in the tumor group (Figure 2D). The difference was statistically significant. In the normal group, plasma cells and CD8⁺ T cells and resting mast cells were highly expressed. The most important finding was that the proportion of M0 macrophages in the tumor was significantly higher than the normal group. Besides, the trends of M1 and M2 macrophages were also consistent with the general knowledge in immunology. Moreover, the paired plot result was unanimous in the result of the violin plot but merely looked at a certain cell more directly (Supplementary Figure S1). The M0 macrophages were exclusively selected. In the 31 pairs of matched samples, it could be seen that except for individual cases, most of the M0 macrophage infiltration was higher in the tumor group than in the normal group.

FIGURE 2 | CIBERSORT algorithm for immune infiltration analysis of GEO data. (A) Relative percent of 22 kinds of immune cells in GEO. (B) A heat map of the cell infiltration data in 69 samples. (C) CorHeatmap of 22 immune cells in normal and tumor tissues. (D) A comparison of normal and tumor tissues. Blue represent normal controls. Red represent tumor group.

Immune Infiltration of GC in TCGA

Combined with TCGA, the Xcell website was used to analyze the infiltration of immune cells. The changes of macrophages in different clinical stages were observed. In KS multi-group test, the p-value indicated the overall situation. The significant discrepancy stated the difference between each group. There was an upward trend in Figure 3, which meant the macrophage fraction was increasing as the clinical-grade increased.

FIGURE 3 | Clinical stage of macrophage in TCGA. (A) Macrophage fraction in the I-IV stage. (B) Macrophage fraction in G1-G3 grade. (C) Macrophage fraction in the T1-T4 stage. (D) Macrophage fraction in N0-N3 stage.

Differential Analysis of Genes in TCGA Samples

To understand the difference caused by infiltration difference, we used WGCNA to identify the key genes that affect macrophage infiltration. In TCGA, 32 samples were in the normal group and 381 samples were in the tumor group. WilcoxTest was used to analyze the differences between the genes in TCGA samples. A total of 6227 DEGs were picked out.

WGCNA Co-Expression Network Analysis and Module Mining

WGCNA achieved the goal of quickly locking core genes by grouping genes (modules) and associating gene modules with phenotypes. Next, it was utilized to divide these genes into various modules. Macrophages and neutrophils were used as independent variables to calculate the modules related to macrophages. Finally, Module Membership (MM) > 0.8 and the Gene-Significance (GS) > 0.5 worked as condition to screen out 18 genes that were positively associated with macrophages.

Sample clustering in WGCNA was to screen out the samples with outlier expression and cluster those with a similar expression level. It could be delineated from Figure 4A that the TCGA-BR-6710-01A-11R-1884-13 sample was distinct from other samples. The filtering principle of the soft threshold is to make the network more scale-free. Filtered soft threshold, undirected networks with power less than 15 or directed networks with power less than 30 could not lead to the scale-free network structure R² reaching 0.8 or the average connectivity dropping to below 100. This may result from batch effects, sample heterogeneity, and complicated experimental conditions on expression, which needed to be removed.

FIGURE 4 | The determination of soft threshold power. (A) Screening out the samples with outlier expression. (B) By hierarchical clustering, genes were divided into different modules. Different colors equal to different modules. (C) Scale-free fit index analysis of 1–20 soft threshold power (β). The horizontal axis was the soft threshold (power), and the vertical axis was the evaluation parameter of the scale-free network. The higher the value, the more the network conforms to the non-scale feature (non-scale). (D) The average connectivity of soft threshold power was analyzed.

WGCNA gene clustering was to cluster genes with similar expression trends. By using a dynamic tree cut, a hierarchical clustering tree was formed. Its rationale was a module (pathway) based analysis (Wang et al., 2019). On the tree, each leaf represented a gene. The genes with similar expression data were tightly connected, formed a branch of the tree, and represented a gene module. After that, several modules were generated (Figure 4B).

The next step was to choose a suitable cutting position. We calculated all integers from 1 to 20 as thresholds to test the optimal threshold. Among them, power Estimate was the best power value, while fit Indices saved the characteristics of the network corresponding to each power and k is the connection degree value. The average value of the connection was visualized and then generated Figures 4C,D. When the y-axis was equal to 0.9, the intersection with the curve was exactly equal to 4. Therefore, we chose 4 as the power value.

To merge the clustered gene modules and make the modules with similar expressions into a large module (Figure 5A), we used 4 as a beta value to build a gene module. The key step was to associate the genes of different modules with the content of macrophages and pick out the modules that were positively correlated with the content of macrophages. The MEpink module in Figure 5B was selected as most positively correlated with macrophages (correlation coefficient = 0.69, p = 2e-52) and the MEbrown module was negatively correlated with macrophages. This research focused on the modules that had a poor survival expectation and the MEpink module was positively correlated with the content of macrophages based on this module.

FIGURE 5 | The identification of key modules and genes. (A) Co-expression gene modules were acquired from a hierarchical cluster dendrogram. (B) Heatmap displays correlations of module eigengenes with macrophages. (C) In the MEpink module, each dot represented a gene. The upper right corner of the scatter plot showed genes under the condition of MM > 0.8 and GS > 0.5. (D) The correlation heat map of 18 genes. (E) Core genes in PPI. (F) Co-expression of key genes and upstream genes.

In Figure 5C, the MEpink module (cor = 0.85, p = 9.3e-43) had a strong positive correlation with macrophages. Among them, there were 149 genes in the pink module. In case of MM > 0.8 and GS > 0.5, 18 genes most relevant to macrophage infiltration were selected for following research. Table 1 exhibited the function of 18 genes. A barplot was drawn (Supplementary Figure S2), and these genes were highly expressed in tumors. In the heat map, a total of 18 genes were up-regulated in tumor tissues (Supplementary Figure S3). From the correlation heat map (Figure 5D), we could know the co-expression relation between these genes. All the values were greater than 0.5, indicating that these genes were correlated with each pair, which was also confirmed by WGCNA. After finding the modules associated with the phenotypes, the PPI bar chart (Figure 5E) was obtained. Genes were at the core position, and they could be screened according to different conditions. Cytoscape software was used to form Figure 5F. CD86, CYBB, and C3AR1 were identified as hub genes.

TABLE 1 | Functional roles of 18 hub genes.

Verifying the Tissue Expression Level in THPA and GEPIA Database

We obtained the transcription level of real central genes from THPA. As Supplementary Figure S4A exhibited, the expression level of three hub genes was remarkably high in cancer tissues, which also supported our suppose. CYBB, C3AR1, and CD86 were highly correlated and had high expression levels in GC (Supplementary Figures S4C–E). There was a rising trend in Supplementary Figure S4B, but taking out any of them had little effect on survival (p > 0.05), indicating that they might work together.

Validation of the Protein Expression Level by IHC

After IHC staining, it was mostly intuitive that the protein level of CD86 was highly up-regulated in GC tissues (Figure 6A). Figure 6B showed a rise in CYBB expression level in tumor tissues compared to adjacent non-tumorous tissues. The positive cell density of C3AR1 was also higher in GC tissues than that in normal tissues (Figure 6C). These results were consistent with THPA. Collectively, these genes regulate the increase of macrophage infiltration in the tumor by affecting several key pathways. In addition, these genes affect monocyte migration.

FIGURE 6 | IHC of CD86, CYBB, and C3AR1 in GC and normal tissues. (A) The expression level of CD86 in normal and GC tissues. (B) Images of CYBB IHC (Original magnification, × 20). (C) IHC for C3AR1.

Exploring Immune Infiltration by TIMER

Using the TIMER website, we found the expression level of CD86, CYBB, and C3AR1 had a positive correlation with CD8⁺T Cells, CD4⁺T Cells, Macrophages, Neutrophils, and Dendritic cells (Figure 7B). The degree of macrophage infiltration significantly affected the prognosis. Hence it is worthy of further research and exploration. These genes affected macrophages and had an impact on survival (5-years survival rate, 10-years survival rate and long-term survival rate) (Figure 7A). To explore the relationship between gene copy number variation and immune infiltration abundance, SCNA module was used to analyze the effect of different somatic CNA of CD86, CYBB, and C3AR1 on the immune cell infiltration in GC. It showed that these genes had a great influence on immune cell infiltration (Figure 7C). These three representative genes identified were associated with tumor-infiltrating macrophages.

FIGURE 7 | Six kinds of immune cells infiltration situation in TIMER. (A) The effect of CD86, CYBB, C3AR1 expression on six kinds of immune cells. (B) The survival of immune cells. (C) Mutations influence the immune infiltration (*p < 0.05, **p < 0.01, ***p < 0.001).

Finding the Biological Pathway Through Gene Function Analysis

From the GO enrichment pathway map, we acquainted these 18 genes that were mainly enriched in several pathways (Figure 8A). These genes mainly regulated the production of tumor necrosis superfamily cytokines. Table 2 exhibits the details. Another GO-BP enrichment analysis of DEGs was displayed in Supplementary Table S1. It was delineated that these 18 genes were mainly enriched in the phagosome pathway in the KEGG enrichment pathway map (Figure 8B). GO-CC, GO-MF, and KEGG pathway enrichment analysis of DEGs in GC samples are shown in Table 3.

FIGURE 8 | Functional enrichment of key genes. (A) The blue module was significantly enriched in GO annotations. (B) The blue module was significantly enriched in KEGG pathways. (C) Filtering the path with ClueGO.

TABLE 2 | Top 20 GO-Biological Process enrichment analysis of DEGs in GC samples.

TABLE 3 | GO-Cellular Components, GO-Molecular Function, and KEGG pathway enrichment analysis of DEGs in GC samples.

ClueGO, as a Cytoscape plug-in that integrated GO terminology and KEGG/BioCarta pathway was used to create a functionally ordered GO/pathway network (Bindea et al., 2009). After screening (p < 0.05), we found C3AR1 participates in the regulation of mononuclear cell migration and monocyte chemotaxis. CD86 partakes in the regulation of cytokine biosynthetic process and interleukin-2 production. CYBB is also involved in the regulation of cytokine biosynthesis (Figure 8C).

DISCUSSION

Although the incidence of GC continues to decline in Western countries, it is still a common tumor in developing countries. At present, surgery is still the main treatment for localized GC. However, most GC is diagnosed at advanced stages and the recurrence rate remains very high. Combined chemotherapy for patients with GC usually causes drug resistance. In recent years, immunotherapy has become one of the most promising cancer treatment strategies and has had significant effects on several types of tumors (Yang, 2015; Granier et al., 2016; Kerr and Chisholm, 2019). Immunotherapy for GC also has promising prospects (Wu et al., 2019).

Tumor-associated macrophages (TAMs) are one of the most abundant immune cell populations in the TME (Solinas et al., 2009; Franklin and Li, 2016; Pang et al., 2017). TAMs are associated with poor prognosis. While the mechanisms of TAMs in GC are still unclear, further research is urgently needed.

In our study, we calculated the infiltration scores of various immune cells in GC tissues and matched normal tissues (GSE13911) by using CIBERSORT. We found that the M0 macrophage infiltration score in the tumor tissues was significantly higher than the normal tissues (p = 0.001). We suspected that tumor progression was related to the degree of macrophage infiltration. To verify our hypothesis, the WGCNA algorithm was performed to find 18 genes that were positively related to the degree of macrophage infiltration in GC. We conducted GO and KEGG enrichment analysis on these 18 genes and found these genes were related to the production of TNF and IL-2 and the formation of phagosomes. Furthermore, we identified three hub genes: CD86, CYBB, and C3AR1, based on the PPI network for further studies. We used TIMER to explore the effect of various immune cell infiltration levels on the survival of GC patients. Moreover, we found that these three genes affected various tumor-infiltrating immune cells, especially tumor-infiltrating macrophages. Again, these hub genes were found to be highly expressed in tumor tissues than normal tissues by using the THPA database. As the tumor progressed, these hub genes expressions were confirmed to rise in GEPIA. Finally, we collected some tumor tissues and adjacent tissue samples in the clinic. Through IHC, we also confirmed that these genes were highly expressed in tumor tissues.

Cytochrome B-245 Beta Chain (CYBB) is expressed in eosinophils, neutrophils (Nauseef and Borregaard, 2014), and B lymphocytes et al., responding to many inflammatory cytokines and stimuli such as IFN-γ, LPS, and TNF-α (Newburger et al., 1991; Frazão et al., 2015). It has been proposed as a primary component of the microbicidal oxidase system of phagocytes (Belambri et al., 2018). CYBB can lead to Immunodeficiency 34 (IMD34) and Granulomatous Disease. The terminal of a respiratory chain needs it. Cellular pH can be regulated by CYBB.

Complement C3a Receptor 1(C3AR1) is activated by its natural ligand, C3a, which is a 77 amino acid split product converted by C3 protein and is traditionally considered to be mainly pro-inflammatory (Coulthard and Woodruff, 2015). After stimulation, chemotaxis, granule enzyme, and superoxide anion were produced (Gaudet et al., 2011). It is widely expressed in a variety of differentiated hematopoietic cell lines in the lung, spleen, ovary, placenta, small intestine, whole brain, heart, and endothelial cells. Also, it was mainly expressed in lymphoid tissue (Brennan et al., 2019).

CD86 is a member of the immunoglobulin superfamily. It works as a ligand for two proteins-CD28 antigen and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4). It is widely known that the CD86 molecule (B7-2) belongs to the B-7 family. Together with the CD80 molecule (B7-1), it is expressed on antigen-presenting cells (APCs, like dendritic cells, macrophages, and B-cells) (Takács et al., 2019). Diseases related to CD86 include acute myocarditis and myocarditis. CD86, as a costimulatory molecule, participates in the formation and regulation of immune synapses (Huemer et al., 2014). Dai et al. showed that CD86 expression in CLL was lower than that in normal B cells, but its role in CLL cell survival was not clear (Dai et al., 2009; Brzostek et al., 2016).

In general, C3AR1 is related to complement function. CYBB and TNF are pro-inflammatory genes. CYBB is related to interaction between neutrophils and macrophages. CD86 is a macrophage activation marker. Its high expression indicates that macrophages are activated. Numerous researches spring up about the identification of prognosis-related genes about GC by WGCNA. For example, one research explored the prognosis of GC but was not related to macrophages (Zhao et al., 2016). Another study aimed to identify potential vital miRNAs and modules associated with the progression of GC (Gong et al., 2019). Our study revealed the important genes and pathways associated with macrophages in GC for the first time.

However, our study had limitations that should be acknowledged. Whether macrophages can convert to each other has always been controversial. The intrinsic response of TAM is the anti-cancer effect of M1 macrophage activation in the process of definite tumor or early carcinogenesis. Subsequently, the tumor cell microenvironment was filled with abundant growth factors and inflammatory mediators [colony stimulating factor-1 (CSF-1), IL-4, IL-10, and TGF-β], which changed the phenotype of macrophages into M2-type macrophages with the function of promoting tumor growth (O’Sullivan et al., 2012). Last but not least, we analyzed the results based on bioinformatics methods and only predicted the relevant results. Therefore, further molecular biological experiments were required to confirm the function of the hub genes associated with GC.

CONCLUSION

We identified and verified that the expression levels of CYBB, CD86, and C3AR1 in tumor tissues were higher than those in normal tissues. Together they affect macrophage infiltration. These genes may work as biomarkers or targets for accurate diagnosis and treatment of GC in the future. Our findings may be a new strategy for the treatment of GC.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are openly available in TCGA. The data that support the findings of this study are openly available in GEO at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13911.

ETHICS STATEMENT

The studies involving human participants were reviewed and approved by the Xi’an Jiaotong University. The patients/participants provided their written informed consent to participate in this study.

AUTHOR CONTRIBUTIONS

HC and CZ designed the study concept and took responsibility for the integrity of the data. HC analyzed all data and drafted the manuscript. SC, MC, NZ, AA, and JZ help the study and revised the manuscript. JS, YW, and HX revised the manuscript and supervised the study. QS, CY, and LL supported clinical samples and funding.

FUNDING

This work was supported by the grants from the National Natural Science Foundation of China (82072739), The Recruitment Program of Overseas High-Level Young Talents, “Innovative and Entrepreneurial Team” (No. (2018)2015), Science and Technology Grant (BE2019758) of Jiangsu Province; Beijing Xisike Clinical Oncology Research Foundation (Y-HS202102-0177, Y-BMS2019-071); Shaanxi Province Hundred Talents and “Young Top Talent” of Xi’an Jiaotong University and China Scholarship Council.

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

ACKNOWLEDGMENTS

We acknowledge TCGA and GEO databases for providing their platforms and contributors for uploading their meaningful datasets. A preprint version has been uploaded in https://www.researchsquare.com/article/rs-56342/v1.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2021.756085/full#supplementary-material

REFERENCES

Aran, D., Hu, Z., and Butte, A. J. (2017). xCell: Digitally Portraying the Tissue Cellular Heterogeneity Landscape. Genome Biol. 18 (1), 220. doi:10.1186/s13059-017-1349-1

Belambri, S. A., Rolas, L., Raad, H., Hurtado-Nedelec, M., Dang, P. M.-C., and El-Benna, J. (2018). NADPH Oxidase Activation in Neutrophils: Role of the Phosphorylation of its Subunits. Eur. J. Clin. Invest. 48, e12951. doi:10.1111/eci.12951

Bindea, G., Mlecnik, B., Hackl, H., Charoentong, P., Tosolini, M., Kirilovsky, A., et al. (2009). ClueGO: a Cytoscape Plug-In to Decipher Functionally Grouped Gene Ontology and Pathway Annotation Networks. Bioinformatics 25 (8), 1091–1093. doi:10.1093/bioinformatics/btp101

Brennan, F. H., Jogia, T., Gillespie, E. R., Blomster, L. V., and Ruitenberg, M. J. (2019). Complement Receptor C3aR1 Controls Neutrophil Mobilization Following Spinal Cord Injury through Physiological Antagonism of CXCR2. JCI Insight 4 (9), e98254. doi:10.1172/jci.insight.98254

Brzostek, J., Gascoigne, N. R. J., and Rybakin, V. (2016). Cell Type-specific Regulation of Immunological Synapse Dynamics by B7 Ligand Recognition. Front. Immunol. 7, 24. doi:10.3389/fimmu.2016.00024

Coulthard, L. G., and Woodruff, T. M. (2015). Is the Complement Activation Product C3a a Proinflammatory Molecule? Re-evaluating the Evidence and the Myth. J.I. 194 (8), 3542–3548. doi:10.4049/jimmunol.1403068

Dai, Z.-s., Chen, Q.-f., Lu, H.-z., and Xie, Y. (2009). Defective Expression and Modulation of B7-2/CD86 on B Cells in B Cell Chronic Lymphocytic Leukemia. Int. J. Hematol. 89 (5), 656–663. doi:10.1007/s12185-009-0320-7

Fields, R. C., Strong, V. E., Gönen, M., Goodman, K. A., Rizk, N. P., Kelsen, D. P., et al. (2011). Recurrence and Survival after Pathologic Complete Response to Preoperative Therapy Followed by Surgery for Gastric or Gastrooesophageal Adenocarcinoma. Br. J. Cancer 104 (12), 1840–1847. doi:10.1038/bjc.2011.175

Franklin, R. A., and Li, M. O. (2016). Ontogeny of Tumor-Associated Macrophages and its Implication in Cancer Regulation. Trends Cancer 2 (1), 20–34. doi:10.1016/j.trecan.2015.11.004

Frazão, J. B., Thain, A., Zhu, Z., Luengo, M., Condino-Neto, A., and Newburger, P. E. (2015). Regulation ofCYBBGene Expression in Human Phagocytes by a Distant Upstream NF-Κb Binding Site. J. Cel. Biochem. 116 (9), 2008–2017. doi:10.1002/jcb.25155

Gaudet, P., Livstone, M. S., Lewis, S. E., and Thomas, P. D. (2011). Phylogenetic-based Propagation of Functional Annotations within the Gene Ontology Consortium. Brief. Bioinform. 12 (5), 449–462. doi:10.1093/bib/bbr042

Gong, C., Hu, Y., Zhou, M., Yao, M., Ning, Z., Wang, Z., et al. (2019). Identification of Specific Modules and Hub Genes Associated with the Progression of Gastric Cancer. Carcinogenesis 40 (10), 1269–1277. doi:10.1093/carcin/bgz040

Granier, C., Karaki, S., Roussel, H., Badoual, C., Tran, T., Anson, M., et al. (2016). Immunothérapie des cancers : rationnel et avancées récentes. La Revue de Médecine Interne 37 (10), 694–700. doi:10.1016/j.revmed.2016.05.023

Hackl, H., Charoentong, P., Finotello, F., and Trajanoski, Z. (2016). Computational Genomics Tools for Dissecting Tumour-Immune Cell Interactions. Nat. Rev. Genet. 17 (8), 441–458. doi:10.1038/nrg.2016.67

Huemer, M., Rebhandl, S., Zaborsky, N., Gassner, F. J., Hainzl, S., Weiss, L., et al. (2014). AID Induces Intraclonal Diversity and Genomic Damage in CD86 + Chronic Lymphocytic Leukemia Cells. Eur. J. Immunol. 44 (12), 3747–3757. doi:10.1002/eji.201344421

Kerr, W. G., and Chisholm, J. D. (2019). The Next Generation of Immunotherapy for Cancer: Small Molecules Could Make Big Waves. J.I. 202 (1), 11–19. doi:10.4049/jimmunol.1800991

Langfelder, P., and Horvath, S. (2008). WGCNA: an R Package for Weighted Correlation Network Analysis. BMC bioinformatics 9 (1), 559. doi:10.1186/1471-2105-9-559

Li, T., Fan, J., Wang, B., Traugh, N., Chen, Q., Liu, J. S., et al. (2017). TIMER: a Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res. 77 (21), e108–e110. doi:10.1158/0008-5472.can-17-0307

Lin, J., Yu, M., Xu, X., Wang, Y., Xing, H., An, J., et al. (2020). Identification of Biomarkers Related to CD8+ T Cell Infiltration with Gene Co-expression Network in clear Cell Renal Cell Carcinoma. Aging 12 (4), 3694–3712. doi:10.18632/aging.102841

Liu, C., Pan, C., Shen, J., Wang, H., and Yong, L. (2012). Identification of Serum Amyloid A in the Serum of Gastric Cancer Patients by Protein Expression Profiling. Oncol. Lett. 3 (6), 1259–1262. doi:10.3892/ol.2012.664

Ma, J., Shen, H., Kapesa, L., and Zeng, S. (2016). Lauren Classification and Individualized Chemotherapy in Gastric Cancer. Oncol. Lett. 11 (5), 2959–2964. doi:10.3892/ol.2016.4337

Nauseef, W. M., and Borregaard, N. (2014). Neutrophils at Work. Nat. Immunol. 15 (7), 602–611. doi:10.1038/ni.2921

Newburger, J. W., Burns, J. C., Beiser, A. S., and Loscalzo, J. (1991). Altered Lipid Profile after Kawasaki Syndrome. Circulation 84 (2), 625–631. doi:10.1161/01.cir.84.2.625

Newman, A. M., Liu, C. L., Green, M. R., Gentles, A. J., Feng, W., Xu, Y., et al. (2015). Robust Enumeration of Cell Subsets from Tissue Expression Profiles. Nat. Methods 12 (5), 453–457. doi:10.1038/nmeth.3337

O’Sullivan, T., Saddawi-Konefka, R., Vermi, W., Koebel, C. M., Arthur, C., White, J. M., et al. (2012). Cancer Immunoediting by the Innate Immune System in the Absence of Adaptive Immunity. J. Exp. Med. 209 (10), 1869–1882. doi:10.1084/jem.20112738

Pang, L., Han, S., Jiao, Y., Jiang, S., He, X., and Li, P. (2017). Bu Fei Decoction Attenuates the Tumor Associated Macrophage Stimulated Proliferation, Migration, Invasion and Immunosuppression of Non-small Cell Lung Cancer, Partially via IL-10 and PD-L1 Regulation. Int. J. Oncol. 51 (1), 25–38. doi:10.3892/ijo.2017.4014

Shaw, K. R. M., Weinstein, J. N., Collisson, E. A., Mills, G. B., Shaw, K. R., Ozenberger, B. A., et al. (2013). The Cancer Genome Atlas Pan-Cancer Analysis Project. Nat. Genet. 45 (10), 1113–1120. doi:10.1038/ng.2764

Siegel, R. L., Miller, K. D., and Jemal, A. (2019). Cancer Statistics, 2019. CA A. Cancer J. Clin. 69 (1), 7–34. doi:10.3322/caac.21551

Siegel, R. L., Miller, K. D., and Jemal, A. (2020). Cancer Statistics, 2020. CA A. Cancer J. Clin. 70 (1), 7–30. doi:10.3322/caac.21590

Solinas, G., Germano, G., Mantovani, A., and Allavena, P. (2009). Tumor-associated Macrophages (TAM) as Major Players of the Cancer-Related Inflammation. J. Leukoc. Biol. 86 (5), 1065–1073. doi:10.1189/jlb.0609385

Sperlich, J., Kerr, R., and Teusch, N. (2017). The Marine Natural Product Pseudopterosin Blocks Cytokine Release of Triple-Negative Breast Cancer and Monocytic Leukemia Cells by Inhibiting NF-Κb Signaling. Mar. Drugs 15 (9), 262. doi:10.3390/md15090262

Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., et al. (2019). STRING V11: Protein-Protein Association Networks with Increased Coverage, Supporting Functional Discovery in Genome-wide Experimental Datasets. Nucleic Acids Res. 47 (D1), D607–d613. doi:10.1093/nar/gky1131

Takács, F., Tolnai-Kriston, C., Hernádfői, M., Szabó, O., Szalóki, G., Szepesi, Á., et al. (2019). The Effect of CD86 Expression on the Proliferation and the Survival of CLL Cells. Pathol. Oncol. Res. 25 (2), 647–652. doi:10.1007/s12253-018-0512-7

Tamborero, D., Rubio-Perez, C., Muiños, F., Sabarinathan, R., Piulats, J. M., Muntasell, A., et al. (2018). A Pan-Cancer Landscape of Interactions between Solid Tumors and Infiltrating Immune Cell Populations. Clin. Cancer Res. 24 (15), 3717–3728. doi:10.1158/1078-0432.ccr-17-3509

Wang, Y., Chen, L., Wang, G., Cheng, S., Qian, K., Liu, X., et al. (2019). Fifteen Hub Genes Associated with Progression and Prognosis of clear Cell Renal Cell Carcinoma Identified by Coexpression Analysis. J. Cel Physiol 234 (7), 10225–10237. doi:10.1002/jcp.27692

Wu, X., Gu, Z., Chen, Y., Chen, B., Chen, W., Weng, L., et al. (2019). Application of PD-1 Blockade in Cancer Immunotherapy. Comput. Struct. Biotechnol. J. 17, 661–674. doi:10.1016/j.csbj.2019.03.006

Yang, Y. (2015). Cancer Immunotherapy: Harnessing the Immune System to Battle Cancer. J. Clin. Invest. 125 (9), 3335–3337. doi:10.1172/jci83871

Youn, H. G., An, J. Y., Choi, M. G., Noh, J. H., Sohn, T. S., and Kim, S. (2010). Recurrence after Curative Resection of Early Gastric Cancer. Ann. Surg. Oncol. 17 (2), 448–454. doi:10.1245/s10434-009-0772-2

Zhao, L.-L., Huang, H., Wang, Y., Wang, T.-B., Zhou, H., Ma, F.-H., et al. (2020). Lifestyle Factors and Long-Term Survival of Gastric Cancer Patients: A Large Bidirectional Cohort Study from China. Wjg 26 (14), 1613–1627. doi:10.3748/wjg.v26.i14.1613

Zhao, X., Cai, H., Wang, X., and Ma, L. (2016). Discovery of Signature Genes in Gastric Cancer Associated with Prognosis. neo 63 (2), 239–245. doi:10.4149/209_150531n303

Zhou, L., Liu, S., Li, X., Yin, M., Li, S., and Long, H. (2019). Diagnostic and Prognostic Value of CEP55 in clear Cell Renal Cell Carcinoma as Determined by Bioinformatics Analysis. Mol. Med. Rep. 19 (5), 3485–3496. doi:10.3892/mmr.2019.10042

Zhou, Z., Cheng, Y., Jiang, Y., Liu, S., Zhang, M., Liu, J., et al. (2018). Ten Hub Genes Associated with Progression and Prognosis of Pancreatic Carcinoma Identified by Co-expression Analysis. Int. J. Biol. Sci. 14 (2), 124–136. doi:10.7150/ijbs.22619

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Chen, Sun, Zhang, She, Cao, Cao, Zhang, Adiila, Zhong, Yao, Wang, Xia and Lan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 09 December 2021
doi: 10.3389/fmolb.2021.790433

The Involvement of TRIB3 and FABP1 and Their Potential Functions in the Dynamic Process of Gastric Cancer

Songyi Liu1,2,³, Chuxuan Ni1,2,³, Yizhi Li1,2,³, Honghao Yin1,2,³, Chengzhong Xing1,2,³, Yuan Yuan1,2,³* and Yuehua Gong1,2,³*

¹Tumor Etiology and Screening Department of Cancer Institute and General Surgery, The First Hospital of China Medical University, Shenyang, China

²Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, The First Hospital of China Medical University, Shenyang, China

³Key Laboratory of GI Cancer Etiology and Prevention in Liaoning Province, The First Hospital of China Medical University, Shenyang, China

Edited by:
Ismail Hosen, University of Dhaka, Bangladesh

Reviewed by:
Zhiyuan Ke, Experimental Drug Development Centre (EDDC), Singapore
Shilpita Karmakar, JAX Cancer Center-Jackson Laboratory, United States

* Correspondence: Yuan Yuan, yuanyuan@cmu.edu.cn; Yuehua Gong, yhgong@cmu.edu.cn

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 06 October 2021
Accepted: 18 November 2021
Published: 09 December 2021

Citation: Liu S, Ni C, Li Y, Yin H, Xing C, Yuan Y and Gong Y (2021) The Involvement of TRIB3 and FABP1 and Their Potential Functions in the Dynamic Process of Gastric Cancer. Front. Mol. Biosci. 8:790433. doi: 10.3389/fmolb.2021.790433

Background: Dysregulated expression of TRIB3 and FABP1 have been previously observed in human cancer tissues. However, there are little information as to their expression change in dynamic gastric diseases and the functional roles.

Methods: Tissues from a total of 479 patients, including 89 GS, 102 IM-GA, 144 EGC, and 144 AGC were collected. The protein expressions of TRIB3 and FABP1 were detected by immunohistochemical staining. Meanwhile, the potential functions of TRIB3 and FABP1 in GC were further analyzed by R software and some internet public databases, such as TCGA and DAVID.

Results: During this multi-stage process that go through GS to EGC, the expression trend of TRIB3 and FABP1 protein was GS > IM-GA > EGC. Besides, the expression of TRIB3 protein continued to decrease in AGC, while the expression of FABP1 was abnormally increased. Hp infection was significantly associated with the decreased expression of TRIB3 and FABP1. In addition, the diagnostic efficiency of the combination of these two indicators to diagnose EGC was higher than that of a single indicator. Survival analysis showed that higher expression of TRIB3 or FABP1 could indicate a better prognosis of GC. The protein expressions of TRIB3 and FABP1 were significantly positively correlated. Moreover, CEACAM5 and PRAP1 were positively correlated with both TRIB3 and FABP1 expressions, while GABRP and THBS4 were negatively correlated. The macrophages M0 infiltration was positively correlated with both TRIB3 and FABP1 expressions.

Conclusion: The protein expressions of TRIB3 and FABP1 gradually decreased with the gastric disease progress, and was positively correlated. Hp infection may reduce the protein expression of TRIB3 and FABP1. Combing TRIB3 and FABP1 expressions can improve the diagnostic efficiency for EGC. Either a high expression of TRIB3 or FABP1 indicates a better prognosis for GC. TRIB3 and FABP1 may interact with CEACAM5, PRAP1, GABRP and THBS4, and affect tumor immune microenvironment by regulating immune cells, and participate in the development and progression of GC.

Keywords: TRIB3, FABP1, expression, gastric cancer, biomarker

1 BACKGROUND

Gastric cancer (GC) is one of the most common malignant tumors of the digestive tract and ranked fourth in morbidity and second in mortality among all tumor types (Topi et al., 2020). Generally, GC arises from a series of diseases such as chronic superficial gastritis (GS), intestinal metaplasia atrophic gastritis (IM-GA), and dysplasia (Valenzuela et al., 2015). With the rapid progress in molecular biology, more and more evidence has shown that some crucial genes might be involved in this multi-stage process (Joo et al., 2001). Therefore, it is very necessary to identify key molecules or prognostic biomarkers during the dynamic development of gastric diseases.

TRIB3, which belongs to the tribbles pseudokinase family, is encoded by the six exons (20p13-p12.2) located on human chromosome 20. It’s a protein of about 65 kDa containing 358 amino acids (Huang et al., 2003; Wu et al., 2003; Kiss-Toth et al., 2004). Studies have shown that TRIB3 might be an oncogene or a tumor suppressor depending on the specific tumor types (Mondal et al., 2016). The overexpression of TRIB3 in lung and colorectal cancer can regulate some important cancer signaling pathways (Wennemers et al., 2011a; Izrailit et al., 2013). Wennemer et al found that the high expression of TRIB3 was significantly associated with better prognosis in breast cancer (Wennemers et al., 2011b).

Liver fatty acid binding protein (FABP1) is a soluble protein of 14 kDa. It is widely distributed in the cytoplasm of hepatocytes, but seldom detected in the nucleus or mitochondrial outer membrane (Börchers et al., 1989; Bordewick et al., 1989; Fahimi et al., 1990). FABP1 also presents in many other cell types, such as intestinal cells, renal tubular cells, and alveolar epithelial cells (Ho and Storch, 2001; Haunerland and Spener, 2004; Chmurzynska, 2006; Furuhashi and Hotamisligil, 2008), and plays a key role in the storage and degradation of fatty acids (Georgiadi and Kersten, 2012). It has been demonstrated that FABP1 was downregulated in nearly 10% of hepatocellular carcinomas (Inoue et al., 2014). The low expression of FABP1 in adenoma was related to the occurrence and development of colorectal cancer (Lee et al., 2006). In addition, FABP1 was found significantly downregulated in kidney cancer and its low expression indicated poor disease-free survival (Wu et al., 2020).

Our previous studies have suggested that TRIB3 and FABP1 were differentially expressed in GA vs GC, and they may interact at protein level. These results suggested that they might participate in the progress of gastric diseases (Liu et al., 2020). However, the correlation between TRIB3 and FABP1 remains undefined, and the expression characteristics during gastric carcinogenesis, as well as the clinical value and potential functions of these two proteins, and remain largely unknown.

Therefore, our study detected the protein expression of TRIB3 and FABP1 in GS, IM-GA, early gastric cancer (EGC) and advanced gastric cancer (AGC) by immunohistochemical staining, aimed to clarify their correlation and roles in gastric carcinogenesis, including their associations with clinicopathological features of GC, and their diagnostic and prognostic values. We further explored the potential functions of TRIB3 and FABP1 by bioinformatics. Our study would lay firm foundations for further exploring the underlying mechanism of the involvement of TRIB3 and FABP1 in the initiation and development of GC.

2 MATERIALS AND METHODS

2.1 Patients and Tissue Specimens

A total of 479 patients who were diagnosed in the First Affiliated Hospital of China Medical University from December 2012 to December 2018 were enrolled, including 89 patients with GS, 102 patients with IM-GA, 144 patients with EGC and 144 patients with AGC. Tumor tissues and their corresponding adjacent IM and distal normal (NO) tissue specimens were obtained from 47 AGC patients. The Helicobacter pylori (Hp) infection status was detected using ASSURE Hp rapid test (MP DiagnosticsTM). Histology classification was determined according to the updated Sydney System for gastritis (Dixon et al., 1996). The diagnosis of GC was based on the World Health Organization criteria (4th edition, 2010). The TNM staging of GC was determined in accordance with the NCCN Clinical Practice Guidelines (2nd Edition, 2018).

The follow-up was continued until November 2019 and the time of it ranged from 11 months to 83 months. Totally, 112 cases were included for prognosis analysis and 58 of them died at data cut-off date, with a median survival time of 30.1 months. Approval was obtained from the Research Medical Ethics Committee of the First Affiliated Hospital of China Medical University, and each subject provided written informed consent.

2.2 Immunohistochemistry Staining

IHC was performed mainly as previously described (Yang et al., 2018). 4-µm-thick tissues were mounted in poly-l-lysine-coated glass slides and baked overnight at 65°C. After deparaffinization with xylene and hydration with graded ethanol, tissue sections were heated in EDTA buffer for 20 min for antigen retrieval. The 10% normal goat serum was used for incubation for 15 min to reduce non-specific binding. Then sections were then incubated with primary antibody for 60 min at room temperature (24–27°C) and secondary antibody for 10 min. At last, the slides were stained with DAB (DAB-1031, Maxim Inc., Fujian, China) and counterstained with hematoxylin. The anti-TRIB3 (ab-137526, 1:1,000 dilution; Abcam, Cambridge, United Kingdom) and anti-FABP1 (ab-171739, 1:4,000 dilution; Abcam, Cambridge, United Kingdom) antibodies were used in our experiment.

2.3 Evaluation of IHC Staining

Two pathologists individually evaluated the protein expression of TRIB3 and FABP1 in different tissues through multiplying the staining intensity by the proportion of stained cells to get the final immunoreactivity score (IS) without knowing their clinical information. The staining intensity of cells was classified into 0 (no staining), 1 (light brown), 2 (brown staining), and 3 (heavy brown staining); the proportion of stained cells were recorded as 0 (0–5%), 1 (6–25%), 2 (26–50%), 3 (51–75%), and 4 (76–100%). We used IS = 0 (the median of immunohistochemical staining scores) to distinguish between the negative and positive expression of TRIB3 and FABP1.

2.4 Analysis of Biological Functions

R software (R 4.0.3) was applied for co-expression analysis utilizing TCGA data normalized by the log2 (TPM (Transcripts per million) +1] transformation. DAVID (https://david.ncifcrf.gov/home.jsp) website was chosen for GO and KEGG pathway analysis. Cibersort was applied to estimate the percentage of different infiltrative immunocytes of each tumor sample of TCGA STAD dataset.

2.5 Statistical Analysis

All statistical analyses were performed using SPSS software (version 26.0) or R software (R 4.0.3). χ2 test was applied to assess the differences in age, gender and Hp infection between groups. Non-parametric test was applied to evaluate the differential expression of TRIB3 and FABP1 among different gastric diseases and the relationship of their expression with clinicopathological parameters. Paired non-parametric test was selected for comparing the expression of TRIB3 and FABP1 among GC, adjacent IM-GA, and distal NO of the same person. The relationship between TRIB3 and FABP1 protein expressions was assessed by Spearman correlation analysis. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic values of these two proteins. Survival analysis was evaluated by univariate (Log-rank) and multivariate (Cox model) analysis. The p-value < 0.05 was considered statistically significant.

3 RESULTS

3.1 The Protein Expression of TRIB3 and FABP1 in Different Gastric Diseases

The baseline clinical characteristics of GS, IM-GA, EGC, and AGC patients were listed in Table 1. We analyzed the differential expression of TRIB3 and FABP1 in different disease groups as well as in the cancer and adjacent tissues.

TABLE 1 | Clinicopathological parameters of GS, IM-GA, EGC, AGC, and survival in AGC.

3.1.1 TRIB3 Protein Expression in Different Gastric Diseases

The comparison of TRIB3 expression between different gastric diseases was shown in Figure 1. TRIB3 was mainly expressed in the epithelial cytoplasm. Representative staining images were shown in Figure 1A. Compared with GS, TRIB3 expression in the IM-GA, EGC, and AGC groups was significantly decreased (p < 0.001, p < 0.001, and p < 0.001, respectively); compared with IM-GA, TRIB3 expression was decreased in EGC and AGC (p < 0.001), though the difference in IM-GA vs EGC was not significant (p = 0.174). Specifically, TRIB3 expression in the AGC group was significantly lower than that in the EGC group (p < 0.001). Besides, the expression of TRIB3 protein in EGC + AGC group (Cancer group, CG) was significantly lower than that in GS + IM-GA group (Non cancer group, NCG) (p < 0.001). Overall, the protein expression trends of TRIB3 among different gastric disease were GS > IM-GA > EGC > AGC and NCG > CG (Figure 1B). Furthermore, the expression of TRIB3 showed a significant decrease from distal NO tissue, adjacent IM-GA to GC in the same patients (p < 0.001) (Figure 1C).

FIGURE 1 | The protein expressions of TRIB3 (A–C) and FABP1 (D–F) in different gastric tissues as well as in AGC and non-tumor adjacent tissue. Magnification ×200.

3.1.2 FABP1 Protein Expression in Different Gastric Disease

The comparison of FABP1 expression between different gastric diseases was shown in Figure 1. FABP1 was also mainly expressed in the epithelial cytoplasm. Representative staining images were shown in Figure 1D. The expression pattern of FABP1 was similar with that of TRIB3 from GS, IM-GA to EGC (p < 0.001, p < 0.001, and p < 0.001, respectively). However, compared with EGC group, FABP1 expression was significantly increased in AGC group. Similarly, the protein expression of FABP1 in CG was significantly lower than that in NCG (p < 0.001). Overall, the protein expression trends of FABP1 were GS > IM-GA > EGC, EGC < AGC, and NCG > CG. (Figure 1E). Similar to TRIB3, the protein expression of FABP1 showed a significant decrease from distal NO tissue, and adjacent IM-GA to GC in the same patients (p < 0.001) (Figure 1F).

3.2 The Relation of the TRIB3 and FABP1 Expression With Clinical Parameters

We separately assessed the association of the protein expression of TRIB3 and FABP1 with clinical parameters, such as gender, age, Hp infection status, differentiation degree, and TNM stage, etc.

3.2.1 TRIB3 Protein Expression and Clinical Parameters

TRIB3 protein expression in the Hp⁺ group were significantly lower than that in the Hp⁻ group (p = 0.038). This lower expression was also observed in patients with GS, IM-GA or EGC though without significance (p = 0.285, p = 0.428, and p = 0.630, respectively) (Figure 2A). We also found that among the overall or the IM-GA patients, the expression of TRIB3 was significantly different in patients with age ≥60 years and those with age <60 (p = 0.029, p = 0.049, respectively) (Figure 2B). Besides, the AGC patients with well differentiated/mucinous histology had significantly higher TRIB3 expression than those with well/moderate differentiated histology (p = 0.007) (Figure 2C). Other clinicopathological parameters didn’t indicate the significantly differential expression of TRIB3 (Supplementary Figure S1).

FIGURE 2 | Association between the protein expressions of TRIB3 and FABP1 and clinicopathological parameters. TRIB3 was correlated with Hp infection status (A) and age (B). Besides, it was significantly correlated with the differentiation degree in AGC (C). FABP1 was also correlated with Hp infection status (D).

3.2.2 FABP1 Protein Expression and Clinical Parameters

Consistent with TRIB3, FABP1 showed significantly lower expression in the Hp⁺ group than in the Hp⁻ group (p = 0.031). Likewise, this lower expression was observed in patients with GS, IM-GA or EGC though without significance (p = 0.320, p = 0.193, and p = 0.600, respectively) (Figure 2D). Other clinicopathological parameters didn’t indicate the significantly differential expression of FABP1 (Supplementary Figure S2).

3.3 The Diagnostic/Prognostic Value of TRIB3 and FABP1 for GC

We evaluated the diagnostic value of TRIB3 and FABP1 protein expressions, separately or combined, for GC and EGC. Their prognostic value in AGC, separately or combined, and was also analyzed in this study.

3.3.1 The Diagnostic/Prognostic Value of TRIB3 for GC

The ROC results showed that the expression of TRIB3 had significant diagnostic value for GC (AUC = 0.705, p < 0.001), but had no diagnostic value for EGC (AUC = 0.507, p = 0.821) (Figures 3A,B).

FIGURE 3 | The diagnostic value of TRIB3 and FABP1 for GC. TRIB3 had significant diagnostic value for GC (A), but had no diagnostic value for EGC (B). FABP1 had significant diagnostic value for both GC (C) and EGC (D). Combined TRIB3 with FABP1 had significant diagnostic value for both GC (E) and EGC (F).

The Kaplan-Meier curve showed that AGC patients positive for TRIB3 expression tended to have better survival in comparison to those negative (p = 0.043) (Figure 4A). However, multivariate survival analysis didn’t reveal a prognostic significance considering the expression level of TRIB3 in AGC (p = 0.144) (Figure 4D; Table 2).

FIGURE 4 | The prognostic value of TRIB3 and FABP1 for AGC. The protein expressions of TRIB3 (A) and FABP1 (B) tended to have better survival in comparison to those negative. Besides, patients with both positive expression of TRIB3 and FABP1 tended to have superior survival time compared with the both negative patients (C).

TABLE 2 | Correlation between the expression of FABP1 and TRIB3 and survival in GC.

3.3.2 The Diagnostic/Prognostic Value of FABP1 for GC

The expression of FABP1 had significant diagnostic value for both GC and EGC, with an AUC of 0.646 for EGC (p < 0.001), and an AUC of 0.625 for GC (p < 0.001) (Figures 3C,D).

Moreover, AGC patients with positive FABP1 expression achieved better prognosis than those with negative FABP1 expression (p = 0.039) (Figure 4B); whereas the multivariate survival analysis suggested that FABP1 was not an independent prognostic factor of AGC (p = 0.279) (Figure 4D; Table 2).

3.3.3 Combined Diagnostic/Prognostic Value for GC

The value of TRIB3 combined with FABP1 to diagnose GC (AUC = 0.684, p < 0.001) was lower than that of TRIB3 alone (Figure 3E). However, the diagnostic efficiency of the combination of these two indicators to diagnose EGC was higher than that of a single indicator (AUC = 0.673, p < 0.001) (Figure 3F).

Interestingly, AGC patients with both positive expression of TRIB3 and FABP1 tended to have superior survival time compared with the both negative patients (p = 0.015) (Figure 4C).

3.4 Significantly Positive Correlation Between the Protein Expression of TRIB3 and FABP1

The spearman correlation analysis was used to evaluate the relationship between TRIB3 and FABP1 protein expressions (Figure 5). The protein expressions of TRIB3 and FABP1 were significantly positively correlated in the overall patients (r = 0.474, p < 0.001) (Figure 5A) as well as in each disease group. Interestingly, during the development and progression of gastric disease, and the correlation coefficient gradually decreased (GS: r = 0.806, p < 0.001; IM-GA: r = 0.533, p < 0.001; EGC: r = 0.365, p < 0.001; AGC: r = 0.265, p < 0.01) (Figures 5B–E).

FIGURE 5 | The protein expressions of TRIB3 and FABP1 were significantly positively correlated in the overall patients (A), GS (B), IM-GA (C), EGC (D), and AGC (E).

3.5 Identification of Genes Co-expressed With TRIB3 and FABP1

We searched for genes co-expressed with TRIB3 (Figure 6A) and FABP1 (Figure 6C) respectively based on |logFC| ≥ 0.5 and adjust p value ≤0.05, with the top 20 positively or negatively regulated genes of TRIB3 (Figure 6B) or FABP1 (Figure 6D) visualized by the heat plot. We found that CEACAM5 and PRAP1 were positively correlated with both TRIB3 and FABP1 expressions, while GABRP and THBS4 were negatively correlated with both TRIB3 and FABP1 expressions.

FIGURE 6 | Genes co-expressed with TRIB3 (A) and FABP1 (C) were searched, with the top 20 positively or negatively regulated genes of TRIB3 (B) or FABP1 (D) visualized by the heat plot.

3.6 Potential Functions and Pathways of TRIB3 and FABP1 by GO and KEGG Analyses

The genes shared by the two co-expression analyses were used for further GO and KEGG analyses. The GO functions enriched in biological processes (BP) were antigen processing and presentation, immune response, and T cell co-stimulation, etc. The significantly enriched cell components (CC) terms were MHC class II protein complex, cell surface and extracellular exosome, etc. The molecular function (MF) significantly related to TRIB3 and FABP1 were MHC class II receptor activity, peptide antigen binding and MHC class II protein complex binding (Figure 7A). Moreover, the KEGG pathway analysis suggested that the shared co-expression genes were mainly enriched in cell adhesion molecules, inflammatory bowel disease and intestinal immune network for IgA production (Figure 7B).

FIGURE 7 | Potential functions and pathways of TRIB3 and FABP1 by GO and KEGG analyses. The genes shared by the two co-expression analyses were used for further GO (A) and KEGG (B) analyses.

3.7 Correlation Between Immune Infiltration and Expression of TRIB3 and FABP1

Due to the potential relevance of TRIB3 and FABP1 in immune-related process as suggested by the GO and KEGG analysis, we further evaluated their associations with immunocyte infiltrations in GC. TRIB3 expression was indicative of the infiltrations of T cells CD4 memory resting, NK cell resting, macrophages M0, macrophages M1, dendritic cells activated and mast cells resting (Figure 8A), while FABP1 expression was associated with the infiltrations of B cells memory, plasma cells, T cells CD8, T cells CD4 memory resting, T cells CD4 memory activated, macrophages M0, macrophages M1, and mast cells activated and neutrophils (Figure 8B). Notably, the infiltration of T cells CD4 memory resting was negatively correlated with TRIB3 but positively associated with FABP1 expression, while the infiltration of macrophages M1 was positively correlated with TRIB3 but negatively associated with FABP1 expression. Besides, the macrophages M0 infiltration was positively correlated with both TRIB3 and FABP1 expressions.

FIGURE 8 | The associations of TRIB3 (A) and FABP1 (B) with immunocyte infiltrations in GC were further evaluated.

4 DISCUSSION

In this study, we elucidated the protein expression of TRIB3 and FABP1 in different stages of gastric disease, and identified the correlation between their expressions. Their diagnostic and prognosis values, as well as their associations with clinicopathological parameters were also analyzed. Furthermore, the potential functions of TRIB3 and FABP1 were explored through TCGA and David database. As far as we know, this is the first study assessing the expression trend of TRIB3 and FABP1 in the dynamic process of gastric disease development, and also the first report correlating TRIB3 with FABP1. This study identified candidate diagnostic and prognostic biomarkers for the early detection of GC, and provided evidence and insight for further study revealing the molecular mechanism underlying the GC development and progression.

The occurrence of GC is a multi-stage process that go through GS and IM-GA. As the disease developed, EGC progressed to AGC (Liu et al., 2020). Our results showed that during this multi-stage process, the expression trend of TRIB3 and FABP1 protein was GS > IM-GA > EGC. Besides, TRIB3 and FABP1 was significantly lower expressed in GC tissues than in adjacent IM-GA or distal NO tissue. These results indicated that the expression of TRIB3 and FABP1 was downregulated when GC occurred. Some relevant studies have reported that TRIB3 played a key role in the anti-cancer activity of cannabinoids, and the gene inactivation of TRIB3 promoted the occurrence of cancer (Vara et al., 2013). Supriya Srivastava et al. observed that the protein expression of FABP1 in esophageal carcinoma was significantly lower than that in esophageal dysplasia, which could be used as a key auxiliary diagnostic index to determine the status of disease progress (Srivastava et al., 2017). Takeaki Hashimoto et al. reported that FABP1 protein was highly expressed in IM but lacking in most GC patients (Hashimoto et al., 2004). Specifically, we found that compared with EGC, the expression of TRIB3 protein continued to decrease in AGC, and while the expression of FABP1 was abnormally increased. The above results showed that TRIB3 expression was successively decreased during the process of GC initiation and development; whereas FABP1 was expressed in a two-stage pattern: its expression was decreased at first, and then increased during the progress of a disease. Previous studies based on several cancer cells and animal models had found that the inactivation of TRIB3 regulated Akt pathway and enhanced tumor development (Salazar et al., 2015), while FABP1 was significantly downregulated in some subtypes of colorectal cancer due to the influence of immune microenvironment (Wood et al., 2017). These implied the important role of TRIB3 and FABP1 in GC development. Similar to FABP1, the two-stage expression pattern of CDX2 and SOX9 during the initiation and development of GC was also reported previously (Uozaki et al., 2011; Sun et al., 2012), suggesting that these molecules might play different roles in different stages of gastric disease progression.

It’s commonly accepted that Hp infection was closely related to the occurrence of GC (Uozaki et al., 2011; Liu et al., 2020). We observed that Hp infection was significantly associated with the decreased expression of TRIB3 and FABP1, indicating that the decreased expression of TRIB3 and FABP1 in GC lesions was partly due to the Hp infection. A prior meta-analysis revealed that Hp infection probably caused hypermethylation of CDH1, which was significantly associated with the risk of GC (Zeng et al., 2015). And TRIB3 was reported to be ubiquitinated by CDH1 and then degraded (Ohoka et al., 2010). Based on these studies, we speculate that the infection of Hp may regulate TRIB3 expression through the ubiquitination-proteasome pathway mediated degradation. In addition, studies have reported that antibacterial drugs could alleviate the decrease in FABP1 expression (Brown et al., 2016). Furthermore, we explored the relationship between the expressions of TRIB3 and FABP1 and their associations with clinicopathological parameters of AGC. AGCs that were poor differentiated/mucinous presented lower TRIB3 expressions than the well/moderate differentiated ones. Our result was consistent with the previous reports that TRIB3 was significantly correlated with the degree of bone marrow differentiation in patients with acute promyelocytic leukemia (Li et al., 2017). Since differentiation is a key factor reflecting the invasiveness of tumors, the low expression of TRIB3 may indicate a higher malignant degree of GC.

In addition, we found that the expression of TRIB3 were of diagnostic value for GC, while FABP1 were of diagnostic value for both GC and EGC, and as suggested by the previous research results in renal cancer and breast cancer (Wennemers et al., 2011a; Wu et al., 2020). Moreover, combining these two proteins was proved to improve the diagnostic efficiency for EGC. These results suggested that TRIB3 can be used as a diagnostic biomarker for GC, while FABP1 is more effective in diagnosing EGC. The diagnostic efficiency for EGC was higher when combining TRIB3 and FABP1.

Studies have shown that the high expression of TRIB3 in breast cancer and oral squamous cell carcinoma indicated a better prognosis (Wennemers et al., 2011a; Zhang et al., 2011), but whether it can affect GC prognosis has not been confirmed. Takeaki Hashimoto et al. reported that the expression level of FABP1 was not associated with GC prognosis (Hashimoto et al., 2004); while another study revealed that it was a reliable prognostic indicator of GC (Satoh et al., 2012). Until now, no definite conclusion formed on the relationship between the expression of FABP1 and the prognosis of GC patients. Our study evaluated the prognostic roles of TRIB3 and FABP1 in GC by univariate and multivariate survival analyses. And the results suggested that a higher expression of TRIB3 or FABP1 could indicate a better prognosis of GC, but it was not significant in multivariate survival analyses. It had been demonstrated that TRIB3 was involved in the normal death process of tumor cells (Ohoka et al., 2005; Örd et al., 2007; Salazar et al., 2009). The high protein expression of TRIB3 could inhibit the ability of tumor immune apoptosis, cause tumor cell death and prolong the survival of patients (Wennemers et al., 2011b). Besides, our results were consistent with those of Yumiko Satoh et al., and which indicated that FABP1 was a reliable prognostic indicator of GC (Satoh et al., 2012).

We also evaluated the correlation between the expressions of TRIB3 and FABP1. The expressions of TRIB3 and FABP1 were positively correlated regardless of the diseases the patients harbored. Of note, the correlation coefficients between TRIB3 and FABP1 expressions gradually weakened as the gastric disease progressed from GS to GC, suggesting that there might be direct or indirect interactions between TRIB3 and FABP1 and the strength of this interaction can be regulated during GC development. Interestingly, the correlation between TRIB3 and FABP1 was stronger in gastric tissues that tend to be normal, and which was worthy of further study.

To further explore the potential functions of TRIB3 and FABP1, we conducted bioinformatics mining. Co-expression and correlation analyses suggested that CEACAM5 and PRAP1 expressions were positively correlated with both TRIB3 and FABP1 expressions, while GABRP and THBS4 expressions were negatively correlated with them. CEACAM5 was a human epidermal cell adhesion molecule, which could interact with Hp adhesion protein HopQ and transfer CagA protein to the host gastric epithelial cells, thus triggering inflammation (Xia et al., 2019). PRAP1 was a proline-rich acidic protein that could down-regulate MAD1 and inhibit mitotic checkpoint signaling in hepatocellular carcinoma (Sze et al., 2014). GABRP has been reported to play an immunomodulatory role in pancreatic cancer in a neurotransmitter independent manner, promoting macrophage infiltration by inducing the expression of CXCL5 and CCL20, and thereby affecting tumor growth and metastasis (Jiang et al., 2019). Studies demonstrated that THBS4, an extracellular glycoprotein, was involved in wound healing and tissue remodeling. It was found to be expressed on fibroblasts in the microenvironment of GC and was related to the metastasis of cancer cells (Kuroda et al., 2019). Our findings suggested that they might be active molecules involved in the interaction between TRIB3 and FABP1.

GO and KEGG enrichment analyses revealed the possible role of TRIB3 and FABP1 in immune related process, and further immune infiltration analysis elucidated that TRIB3 and FABP1 may play a role in the inhibition of CD4⁺ T cells infiltration and the differentiation of macrophages. Existing studies have found that TRIB3 participated in the differentiation of CD4⁺ T cells and played an important role in the apoptosis of macrophages induced by oxidized low density lipoprotein (Shang et al., 2009; Kiuchi et al., 2021). FABP1 had not been reported to be related to CD4⁺ T cells, but it could affect the function of tumor-associated macrophages by participating in tumor internal metabolic pathways, and thereby exerting anti-tumor effects (Xu et al., 2020). The above results suggest that TRIB3 and FABP1 may affect the tumor immune microenvironment by acting on immune cells, and then participate in the process of GC development.

However, there are still some limitations in our study. Although we have tried to reveal the functional role of TRIB3 and FABP1 by exploring co-expressed genes and immune infiltration, there is currently no direct evidence support the hypothesis, and which needs to be further investigated.

5 CONCLUSION

In conclusion, the protein expressions of TRIB3 and FABP1 gradually decreased with the gastric disease progress, and was positively correlated. Hp infection may reduce the protein expression of TRIB3 and FABP1. In addition, the low expression of TRIB3 protein is associated with the malignant biological behavior of GC. Combing TRIB3 and FABP1 expressions can improve the diagnostic efficiency for EGC. Either a high expression of TRIB3 or FABP1 indicates a better prognosis for GC. Bioinformatics analysis showed that TRIB3 and FABP1 may interact with CEACAM5, PRAP1, GABRP, and THBS4, and affect tumor immune microenvironment by regulating immune cells, and participate in the development and progression of GC.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.

ETHICS STATEMENT

The studies involving human participants were reviewed and approved by the Ethics Committee of Medical Science Research in the First Hospital of China Medical University. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

AUTHOR CONTRIBUTIONS

SL, YG, and YY performed the histological examination of the gastric cancer, and were major contributor in writing the manuscript. CN, YL, HY, and CX analyzed and interpreted the patient data. All authors read and approved the final manuscript.

FUNDING

This work was supported by the National Natural Science Foundation of China (Award No.81970501), the National Key R and D Program of China (Grant 2018YFC1311600) and Liaoning Province Science and Technology Plan Project (Award No 20180550049).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2021.790433/full#supplementary-material

ABBREVIATIONS

AGC, advanced gastric cancer; BP, biological processes; CC, cell components; CG, Cancer group; EGC, early gastric cancer; FABP1, Liver fatty acid binding protein; GC, gastric cancer; GS, superficial gastritis; Hp, Helicobacter pylori; IHC, immunohistochemistry; IM-GA, intestinal metaplasia atrophic gastritis; IS, immunoreactivity score; MF, molecular function; NCG, Non cancer group; NO, normal; ROC, Receiver operating characteristic; TCGA, The Cancer Genome Atlas; TRIB3, Tribbles Pseudokinase 3.

REFERENCES

Börchers, T., Unterberg, C., Rüdel, H., Robenek, H., and Spener, F. (1989). Subcellular Distribution of Cardiac Fatty Acid-Binding Protein in Bovine Heart Muscle and Quantitation with an Enzyme-Linked Immunosorbent Assay. Biochim. Biophys. Acta (BBA) - Lipids Lipid Metab. 1002, 54–61. doi:10.1016/0005-2760(89)90063-5

Bordewick, U., Heese, M., Börchers, T., Robenek, H., and Spener, F. (1989). Compartmentation of Hepatic Fatty-Acid-Binding Protein in Liver Cells and its Effect on Microsomal Phosphatidic Acid Biosynthesis. Biol. Chem. Hoppe-Seyler 370, 229–238. doi:10.1515/bchm3.1989.370.1.229

Brown, K., Zaytsoff, S. J. M., Uwiera, R. R. E., and Inglis, G. D. (2016). Antimicrobial Growth Promoters Modulate Host Responses in Mice with a Defined Intestinal Microbiota. Sci. Rep. 6, 38377. doi:10.1038/srep38377

Chmurzynska, A. (2006). The Multigene Family of Fatty Acid-Binding Proteins (FABPs): Function, Structure and Polymorphism. J. Appl. Genet. 47, 39–48. doi:10.1007/BF03194597

Dixon, M. F., Genta, R. M., Yardley, J. H., and Correa, P. (1996). Classification and Grading of Gastritis. The Updated Sydney System. International Workshop on the Histopathology of Gastritis, Houston 1994. Am. J. Surg. Pathol. 20, 1161–1181. doi:10.1097/00000478-199610000-00001

Fahimi, H. D., Voelkl, A., Vincent, S. H., and Muller-Eberhard, U. (1990). Localization of the Heme-Binding Protein in the Cytoplasm and of a Heme-Binding Protein-like Immunoreactive Protein in the Nucleus of Rat Liver Parenchymal Cells: Immunocytochemical Evidence of the Subcellular Distribution Corroborated by Radioimmunoassay and Immunoblotting. Hepatology 11, 859–865. doi:10.1002/hep.1840110522

Furuhashi, M., and Hotamisligil, G. S. (2008). Fatty Acid-Binding Proteins: Role in Metabolic Diseases and Potential as Drug Targets. Nat. Rev. Drug Discov. 7, 489–503. doi:10.1038/nrd2589

Georgiadi, A., and Kersten, S. (2012). Mechanisms of Gene Regulation by Fatty Acids. Adv. Nutr. 3, 127–134. doi:10.3945/an.111.001602

Hashimoto, T., Kusakabe, T., Watanabe, K., Sugino, T., Fukuda, T., Nashimoto, A., et al. (2004). Liver-type Fatty Acid-Binding Protein Is Highly Expressed in Intestinal Metaplasia and in a Subset of Carcinomas of the Stomach without Association with the Fatty Acid Synthase Status in the Carcinoma. Pathobiology 71, 115–122. doi:10.1159/000076465

Haunerland, N. H., and Spener, F. (2004). Fatty Acid-Binding Proteins - Insights from Genetic Manipulations. Prog. Lipid Res. 43, 328–349. doi:10.1016/j.plipres.2004.05.001

Ho, S.-Y., and Storch, J. (2001). Common Mechanisms of Monoacylglycerol and Fatty Acid Uptake by Human Intestinal Caco-2 Cells. Am. J. Physiology-Cell Physiol. 281, C1106–C1117. doi:10.1152/ajpcell.2001.281.4.c1106

Huang, J., Teng, L., Liu, T., Li, L., Chen, D., Li, F., et al. (2003). Identification of a Novel Serine/threonine Kinase that Inhibits TNF-Induced NF-Κb Activation and P53-Induced Transcription. Biochem. Biophysical Res. Commun. 309, 774–778. doi:10.1016/j.bbrc.2003.08.069

Inoue, M., Takahashi, Y., Fujii, T., Kitagawa, M., and Fukusato, T. (2014). Significance of Downregulation of Liver Fatty Acid-Binding Protein in Hepatocellular Carcinoma. Wjg 20, 17541–17551. doi:10.3748/wjg.v20.i46.17541

Izrailit, J., Berman, H. K., Datti, A., Wrana, J. L., and Reedijk, M. (2013). High Throughput Kinase Inhibitor Screens Reveal TRB3 and MAPK-ERK/TGF Pathways as Fundamental Notch Regulators in Breast Cancer. Proc. Natl. Acad. Sci. 110, 1714–1719. doi:10.1073/pnas.1214014110

Jiang, S.-H., Zhu, L.-L., Zhang, M., Li, R.-K., Yang, Q., Yan, J.-Y., et al. (2019). GABRP Regulates Chemokine Signalling, Macrophage Recruitment and Tumour Progression in Pancreatic Cancer through Tuning KCNN4-Mediated Ca2+ Signalling in a GABA-independent Manner. Gut 68, 1994–2006. doi:10.1136/gutjnl-2018-317479

Joo, Y.-E., Rew, J.-S., Kim, H.-S., Choi, S.-K., Park, C.-S., and Kim, S.-J. (2001). Changes in the E-Cadherin-Catenin Complex Expression in Early and Advanced Gastric Cancers. Digestion 64, 111–119. doi:10.1159/000048849

Kiss-Toth, E., Bagstaff, S. M., Sung, H. Y., Jozsa, V., Dempsey, C., Caunt, J. C., et al. (2004). Human Tribbles, a Protein Family Controlling Mitogen-Activated Protein Kinase Cascades. J. Biol. Chem. 279, 42703–42708. doi:10.1074/jbc.m407732200

Kiuchi, M., Onodera, A., Kokubo, K., Ichikawa, T., Morimoto, Y., Kawakami, E., et al. (2021). The Cxxc1 Subunit of the Trithorax Complex Directs Epigenetic Licensing of CD4+ T Cell Differentiation. J. Exp. Med. 218, e20201690. doi:10.1084/jem.20201690

Kuroda, K., Yashiro, M., Sera, T., Yamamoto, Y., Kushitani, Y., Sugimoto, A., et al. (2019). The Clinicopathological Significance of Thrombospondin-4 Expression in the Tumor Microenvironment of Gastric Cancer. PLoS One 14, e0224727. doi:10.1371/journal.pone.0224727

Lee, S., Bang, S., Song, K., and Lee, I. (2006). Differential Expression in normal-adenoma-carcinoma Sequence Suggests Complex Molecular Carcinogenesis in colon. Oncol. Rep. 16, 747–754. doi:10.3892/or.16.4.747

Li, K., Wang, F., Cao, W.-B., Lv, X.-X., Hua, F., Cui, B., et al. (2017). TRIB3 Promotes APL Progression through Stabilization of the Oncoprotein PML-Rarα and Inhibition of P53-Mediated Senescence. Cancer Cell 31, 697–710. doi:10.1016/j.ccell.2017.04.006

Liu, S., Yin, H., Zheng, S., Chu, A., Li, Y., Xing, C., et al. (2020). Differentially Expressed mRNAs and Their Long Noncoding RNA Regulatory Network with Helicobacter Pylori-Associated Diseases Including Atrophic Gastritis and Gastric Cancer. Biomed. Res. Int. 2020, 3012193. doi:10.1155/2020/3012193

Mondal, D., Mathur, A., and Chandra, P. K. (2016). Tripping on TRIB3 at the junction of Health, Metabolic Dysfunction and Cancer. Biochimie 124, 34–52. doi:10.1016/j.biochi.2016.02.005

Ohoka, N., Sakai, S., Onozaki, K., Nakanishi, M., and Hayashi, H. (2010). Anaphase-promoting Complex/cyclosome-Cdh1 Mediates the Ubiquitination and Degradation of TRB3. Biochem. Biophysical Res. Commun. 392, 289–294. doi:10.1016/j.bbrc.2009.12.175

Ohoka, N., Yoshii, S., Hattori, T., Onozaki, K., and Hayashi, H. (2005). TRB3, a Novel ER Stress-Inducible Gene, Is Induced via ATF4-CHOP Pathway and Is Involved in Cell Death. EMBO J. 24, 1243–1255. doi:10.1038/sj.emboj.7600596

Örd, D., Meerits, K., and Örd, T. (2007). TRB3 Protects Cells against the Growth Inhibitory and Cytotoxic Effect of ATF4. Exp. Cel Res. 313, 3556–3567. doi:10.1016/j.yexcr.2007.07.017

Salazar, M., Carracedo, A., Salanueva, Í. J., Hernández-Tiedra, S., Lorente, M., Egia, A., et al. (2009). Cannabinoid Action Induces Autophagy-Mediated Cell Death through Stimulation of ER Stress in Human Glioma Cells. J. Clin. Invest. 119, 1359–1372. doi:10.1172/jci37948

Salazar, M., Lorente, M., García-Taboada, E., Pérez Gómez, E., Dávila, D., Zúñiga-García, P., et al. (2015). Loss of Tribbles Pseudokinase-3 Promotes Akt-Driven Tumorigenesis via FOXO Inactivation. Cell Death Differ 22, 131–144. doi:10.1038/cdd.2014.133

Satoh, Y., Mori, K., Kitano, K., Kitayama, J., Yokota, H., Sasaki, H., et al. (2012). Analysis for the Combination Expression of CK20, FABP1 and MUC2 Is Sensitive for the Prediction of Peritoneal Recurrence in Gastric Cancer. Jpn. J. Clin. Oncol. 42, 148–152. doi:10.1093/jjco/hyr179

Shang, Y.-y., Wang, Z.-h., Zhang, L.-p., Zhong, M., Zhang, Y., Deng, J.-t., et al. (2009). TRB3, Upregulated by Ox-LDL, Mediates Human Monocyte-Derived Macrophage Apoptosis. FEBS J. 276, 2752–2761. doi:10.1111/j.1742-4658.2009.06998.x

Srivastava, S., Kern, F., Sharma, N., McKeon, F., Xian, W., Yeoh, K. G., et al. (2017). FABP1 and Hepar Expression Levels in Barrett's Esophagus and Associated Neoplasia in an Asian Population. Dig. Liver Dis. 49, 1104–1109. doi:10.1016/j.dld.2017.06.014

Sun, M., Uozaki, H., Hino, R., Kunita, A., Shinozaki, A., Ushiku, T., et al. (2012). SOX9 Expression and its Methylation Status in Gastric Cancer. Virchows Arch. 460, 271–279. doi:10.1007/s00428-012-1201-7

Sze, K. M.-F., Chu, G. K.-Y., Mak, Q. H.-Y., Lee, J. M.-F., and Ng, I. O.-L. (2014). Proline-rich Acidic Protein 1 (PRAP1) Is a Novel Interacting Partner of MAD1 and Has a Suppressive Role in Mitotic Checkpoint Signalling in Hepatocellular Carcinoma. J. Pathol. 233, 51–60. doi:10.1002/path.4319

Topi, S., Santacroce, L., Bottalico, L., Ballini, A., Inchingolo, A. D., Dipalma, G., et al. (2020). Gastric Cancer in History: A Perspective Interdisciplinary Study. Cancers (Basel) 12, 264. doi:10.3390/cancers12020264

Uozaki, H., Barua, R. R., Minhua, S., Ushiku, T., Hino, R., Shinozaki, A., et al. (2011). Transcriptional Factor Typing with SOX2, HNF4aP1, and CDX2 Closely Relates to Tumor Invasion and Epstein-Barr Virus Status in Gastric Cancer. Int. J. Clin. Exp. Pathol. 4, 230–240.

Valenzuela, M. A., Canales, J., Corvalan, A. H., and Quest, A. F. (2015). Helicobacter Pylori-Induced Inflammation and Epigenetic Changes during Gastric Carcinogenesis. Wjg 21, 12742–12756. doi:10.3748/wjg.v21.i45.12742

Vara, D., Morell, C., Rodríguez-Henche, N., and Diaz-Laviada, I. (2013). Involvement of PPARγ in the Antitumoral Action of Cannabinoids on Hepatocellular Carcinoma. Cell Death Dis 4, e618. doi:10.1038/cddis.2013.141

Wennemers, M., Bussink, J., Grebenchtchikov, N., Sweep, F. C. G. J., and Span, P. N. (2011). TRIB3 Protein Denotes a Good Prognosis in Breast Cancer Patients and Is Associated with Hypoxia Sensitivity. Radiother. Oncol. 101, 198–202. doi:10.1016/j.radonc.2011.05.057

Wennemers, M., Bussink, J., Scheijen, B., Nagtegaal, I. D., van Laarhoven, H. W., Raleigh, J. A., et al. (2011). Tribbles Homolog 3 Denotes a Poor Prognosis in Breast Cancer and Is Involved in Hypoxia Response. Breast Cancer Res. 13, R82. doi:10.1186/bcr2934

Wood, S. M., Gill, A. J., Brodsky, A. S., Lu, S., Friedman, K., Karashchuk, G., et al. (2017). Fatty Acid-Binding Protein 1 Is Preferentially Lost in Microsatellite Instable Colorectal Carcinomas and Is Immune Modulated via the Interferon γ Pathway. Mod. Pathol. 30, 123–133. doi:10.1038/modpathol.2016.170

Wu, G., Zhang, Z., Tang, Q., Liu, L., Liu, W., Li, Q., et al. (2020). Study of FABP's Interactome and Detecting New Molecular Targets in clear Cell Renal Cell Carcinoma. J. Cel Physiol 235, 3776–3789. doi:10.1002/jcp.29272

Wu, M., Xu, L.-G., Zhai, Z., and Shu, H.-B. (2003). SINK Is a P65-Interacting Negative Regulator of NF-κb-dependent Transcription. J. Biol. Chem. 278, 27072–27079. doi:10.1074/jbc.m209814200

Xia, R., Zhang, B., Wang, X., and Jia, Q. (2019). Pathogenic Interactions between Helicobacter pylori Adhesion Protein HopQ and Human Cell Surface Adhesion Molecules CEACAMs in Gastric Epithelial Cells. Iran J. Basic Med. Sci. 22, 710–715. doi:10.22038/ijbms.2019.34237.81

Xu, L., Xia, H., Ni, D., Hu, Y., Liu, J., Qin, Y., et al. (2020). High-Dose Dexamethasone Manipulates the Tumor Microenvironment and Internal Metabolic Pathways in Anti-tumor Progression. Int. J. Mol. Sci. 21, 1846. doi:10.3390/ijms21051846

Yang, H., Liu, J., Jing, J., Wang, Z., Li, Y., Gou, K., et al. (2018). Expression of DDB2 Protein in the Initiation, Progression, and Prognosis of Colorectal Cancer. Dig. Dis. Sci. 63, 2959–2968. doi:10.1007/s10620-018-5224-z

Zeng, W., Zhu, J., Shan, L., Li, H., Han, Z., Aeixiding, P., et al. (2015). The Clinicopathological Significance of CDH1 in Gastric Cancer: a Meta-Analysis and Systematic Review. Dddt 9, 2149–2157. doi:10.2147/dddt.s75429

Zhang, J., Wen, H.-j., Guo, Z.-m., Zeng, M.-s., Li, M.-z., Jiang, Y.-e., et al. (2011). TRB3 Overexpression Due to Endoplasmic Reticulum Stress Inhibits AKT Kinase Activation of Tongue Squamous Cell Carcinoma. Oral Oncol. 47, 934–939. doi:10.1016/j.oraloncology.2011.06.512

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Liu, Ni, Li, Yin, Xing, Yuan and Gong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 10 December 2021
doi: 10.3389/fmolb.2021.763958

Comprehensive Analysis of the Potential Immune-Related Biomarker Transporter Associated With Antigen Processing 1 That Inhibits Metastasis and Invasion of Ovarian Cancer Cells

Xiaoxue Li, Shiyu Zeng, Yiling Ding, Yanting Nie and Mengyuan Yang*

Department of Obstetrics and Gynecology, The Second Xiangya Hospital of Central South University, Changsha, China

Edited by:
Ismail Hosen, University of Dhaka, Bangladesh

Reviewed by:
Ya-Wen Wang, Shandong University, China
Fan Chao, Fudan University, China
Liming Gu, Shantou University, China

* Correspondence: Mengyuan Yang, yangmengyuan_xy@csu.edu.cn

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 24 August 2021
Accepted: 19 November 2021
Published: 10 December 2021

Citation: Li X, Zeng S, Ding Y, Nie Y and Yang M (2021) Comprehensive Analysis of the Potential Immune-Related Biomarker Transporter Associated With Antigen Processing 1 That Inhibits Metastasis and Invasion of Ovarian Cancer Cells. Front. Mol. Biosci. 8:763958. doi: 10.3389/fmolb.2021.763958

Transporter associated with antigen processing 1 (TAP1) is a protein related immune regulation and plays a role in several malignant tumors. However, the effect of TAP1 on immune infiltration, immunotherapy, and metastasis in different cancers has not been reported till date. The cancer genome atlas database, the tumor immune estimation resource database, and the estimation of stromal and immune cells in malignant tumors using expression (ESTIMATE) algorithm were used to determine the correlation between TAP1 expression and the prognosis of a variety of cancers, immune infiltration, immune checkpoint genes, DNA methylation, and neoantigens. Various enrichment analyses were used to study the correlation between TAP1 and key transcription factors using the Kyoto encyclopedia of genes and genomes (KEGG) pathway in ovarian cancer. Immunological methods were used to evaluate the expression of TAP1 protein in ovarian and cervical cancer, and Kaplan–Meier analysis was used to analyze the prognostic value of TAP1. RNA interference (RNAi) was used to verify the effect of TAP1 on ovarian cancer. Compared with normal tissues, cancer tissues showed a significant increase in the expression of TAP1, and TAP1 expression was related to the poor prognosis of cancers such as ovarian cancer. The expression level of TAP1 was correlated with immune checkpoint genes, DNA methylation, tumor mutation burden, microsatellite instability, and neoantigens in various cancers. Our results showed that TAP1 was upregulated in ovarian cancer cell lines and was associated with poor prognosis. Further, we verified the expression of TAP1-related transcription factors (MEF2A and LEF1) and found that TAP1 was closely related to ovarian cancer metastasis in vitro and in vivo. These results indicated that TAP1 could be used as a biomarker for the diagnosis and prognosis of cancer and as a new therapeutic target.

Keywords: pan-cancer, ovarian cancer, cervical cancer, transporter associated with antigen processing 1, tumor-infiltrating immune cells, immune checkpoint, metastasis

INTRODUCTION

Cancer is a disease with the highest mortality rate worldwide. The incidence and fatality rate of cancer is increasing gradually every year and poses a serious threat to the lives and health of the people (Sung et al., 2021). Prostate cancer, colorectal cancer, and melanoma are the most common tumors among men. Breast cancer, cervical cancer (CC), and ovarian cancer (OC) are very common among women (Miller et al., 2019). Diagnosis of OC in their early stage is very difficult, and <70% of the patients are at late stages when their condition is diagnosed as OC (Torre et al., 2018). Early diagnosis and appropriate treatment can effectively improve the prognosis of cancer patients. Recent studies on tumorigenesis, diagnosis, and therapy have shown the importance of tumor immunity and the role of anti-programmed death 1/programmed death ligand 1 (anti-PD-1/PDL-1) and polyadenosine diphosphate-ribose polymerase (PARP) inhibitors (Mateo et al., 2019; Puri and Shafique, 2020). Thus, tumor immunity has become an emerging and popular direction of tumor research.

Transporter associated with antigen processing 1 (TAP1) belongs to a class of proteins closely related to antigen processing and immune regulation. TAP1 is responsible for processing exogenous pathogenic microorganisms and then presenting them to immune cells, thereby functioning as a target for tumor cells (Meng et al., 2018). Recent studies have reported the function of TAP1 in some cancers, but the role of TAP1 in immune infiltration, immunotherapy, and metastasis across various cancers has not been reported thus far.

We used the bioinformatics methods to determine the function of TAP1 in predicting the prognosis, immune infiltration, and immune checkpoint genes in various cancers. Additionally, we analyzed the correlation between TAP1 expression and tumor mutation burden (TMB) and microsatellite instability (MSI) and tumor neoantigens. Then, we determined the relationship between TAP1, pivotal transcription factors (TFs), and biological pathways in OC through gene enrichment analysis. In addition, we examined the expression of TAP1 protein in OC and CC cell lines by using western blot assays and its role in tumor metastasis in vivo and vitro. TAP1 protein expression in OC and CC tissues was determined by immunohistochemical staining, while Kaplan–Meier (KM) analysis was used to determine prognostic prediction function of TAP1. Our results showed that TAP1 could play an important role in immunotherapy across various cancers, and we determined the expression of TAP1 in cell lines and tissues to explain its role in the prognosis of CC and OC.

MATERIALS AND METHODS

Processing Public Sequencing Data

The sequencing data of TAP1 in various tumor specimens and cell lines was derived from the cancer genome atlas (TCGA) database and cancer cell line encyclopedia (CCLE) database (Barretina et al., 2012). We enrolled samples pertaining to 20 tumors from the TCGA database (Weinstein et al., 2013) (https://www.cancer.gov/tcga), which included 8,624 tissues (7,900 tumor tissues and 724 adjacent normal tissues). Considering that some tumors had few adjacent samples, which may cause errors in the analysis, we imported 6678 healthy tissue and organ samples from the genotype-tissue expression (GTEx) database (Carithers and Moore, 2015) (https://www.gtexportal.o-rg/home/). Tumors with missing data were not included in the calculation of the difference in gene expression between tumor and healthy tissues. The Kruskal-Wallis test was used to compare TAP1 expression differences between different tumors or cells. We used the combat function of the R software package “SVA” to remove the batch effect and normalized the expression of the rest samples. After that, Wilcoxon test was utilized to illustrate the expression of TAP1 in tumor tissues and the corresponding normal tissues. A value of p < 0.05 was considered to be statistically significant.

The Analysis of TAP1 in Predicting the Prognosis of Cancer Patients

The survival data of 7,900 patients with cancer were derived from the TCGA database, which included data on overall survival (OS), disease-specific survival (DSS), disease-free survival (DFS), and progression-free survival (PFS). We used the KM survival analysis to determine the predictive value of TAP1 in various cancers, and the log-rank test was used to calculate the significance. The results were considered to be statistically significant when p < 0.05.

Correlation Analysis Between Immune Infiltration and TAP1 Expression

The tumor immune estimation resource (TIMER) database (Li et al., 2020) (http://timer.cistrome.org/) used a method known as deconvolution, which applied sequencing data to calculate the abundance of tumor infiltrating immune cells (TIICs) in tumor tissues. The TIMER database was used to systematically analyze the TIICs in 32 kinds of cancers using 10,897 tissues from the TCGA. Using this database, we determined the correlation between the TAP1 gene expression and the abundance of TIICs (neutrophils, CD4⁺ T cells, B cells, CD8⁺ T cells, macrophages, and dendritic cells) in 32 tumors.

Analysis of the Relationship Between Estimation of Stromal and Immune Cells in Malignant Tumors Using Expression Scores and TAP1 Expression Level

Estimation of stromal and immune cells in malignant tumors using expression (ESTIMATE) (https://bioinformatics.mdanderson.org/publicsoftware/estimate/) was an algorithm that applied sequencing profiles to infer the extent of immune infiltration in a tumor microenvironment, including stromal and immune cells. The sequencing profiles of 8,624 tissues from 20 types of cancers were used to estimate the proportion of immune and stromal cells in each tissue through limma packages. STROMAL score, IMMUNE score, and ESTIMATE score were three ESTIMATE scores which described the immune infiltration. Then, we integrated the profiles of TAP1 and ESTIMATE scores to calculate the correlations. The results were considered to be statistically significant when p < 0.05.

The Correlation Between TAP1 Expression and TMB and MSI

TMB and MSI were the 2 metrics which could influence the treatment effect of tumor immunotherapy. The gene mutation profiles of 10,114 tissues of 33 cancers from the TCGA database were used to calculate gene variation in every tissue. The Spearman correlation test was used to determine the relationship between TAP1 and TMB using the fmsb package (https://CRAN.R-project.org/package=fmsb), and then, we drew the correlation radar map. A procedure similar to that for TMB scores was followed for the MSI scores.

Correlation of TAP1 With Immune Checkpoint Genes and Methyltransferase

After analyzing the correlation of TAP1 with ESTIMATE scores and TIICs, we further investigated the relationship between TAP1 and immune checkpoint genes. We summarized 47 immune checkpoint genes mentioned in the literature, and then, we used the limma package to determine the relationship between TAP1 and the 47 genes in 33 tumors. The Spearman correlation test was applied to calculate the significant difference. We used the reshape2 package (http://www.jstatsoft.org/v21/i12/) to draw the correlation heat plot. DNA methylation was a form of DNA chemical modification which could alter genetic performance without changing the DNA sequence by 4 methyltransferases (DNMT1, DNMT2, DNMT3A, and DNMT3B). The relationship between TAP1 and the 4 methyltransferases was calculated using the method same as above.

Gene Set Enrichment/Variation Analysis

To determine the signaling pathways and biological functions of TAP1 in OC, we divided the OC tissues into high-expression and low-expression group according to the level of expression of TAP1, which was determined using limma,org.Hs.eg.db, clusterProfiler (http://www.bioconductor.org/packages/release/bioc/html/clusterProfiler.html). We employed the enrich-plot package based on the c5.all.v7.1.symbols file to perform gene set enrichment analysis (GSEA) or gene set variation analysis (GSVA) on the sequencing data of the OC samples from the TCGA database. Then, we selected the top TFs and KEGG terms to draw the enrichment maps. The results were statistically significant when p < 0.05.

Quantification of Mutation/Neoantigens Load

Neoantigen was an abnormal protein produced by somatic cell mutation. After being degraded into short peptides, it could be identified by antigen-presenting cells (APCs), which were mainly dendritic cells (DC cells). According to the tumor neoantigen defined by Rooney et al. (2015), we counted the mutations in each sample and annotated somatic mutations with variant effect predictor (VEP). We used the NetMHCpan (v2.4) algorithm to predict whether the tumor cells in 33 types of tumors would produce new antigens that were different from ordinary cells. The NetMHCpan software was used to predict the affinity between peptides and major histocompatibility complex (MHC) type I molecules. On the basis of the artificial neural network algorithm, the training set was constructed with a complex of binding affinity profiles and MHC-eluted ligand data.

Immunohistochemistry and KM Analysis

We randomly selected 41 human OC specimens and 32 CC specimens from patients undergoing surgery during 2013–2015 after obtaining their consent; the specimens were collected from the department of pathology at The Second Xiangya Hospital of Central South University. The experiment was approved by the Human Ethics Committee of The Second Xiangya Hospital of Central South University. The average survival time of patients with OC in our 5-years analysis was 43 months, while the longest survival time was 89 months. The average survival time of patients with CC was 57 months, while the longest survival time was 85 months. The survival data of each patient was confirmed by phone or questionnaire. The tissue obtained from the patients was embedded in paraffin and slices were prepared. After the sections were deparaffinized and dehydrated, the Tris-EDTA buffer was used for antigen retrieval. Subsequent incubation with antibody and staining using diaminobenzidine (DAB) were performed according to the non-biotin detection kit (ZSGB-BIO, PV-9001, China). The method was similar to that described previously (Li et al., 2019).

Western Blotting

We used radioimmunoprecipitation assay (RIPA) buffer to lyse the cell lines cultured in 6-cm dishes to prepare protein samples. Then, we centrifuged the samples at 12,000 g for 5 min at 4°C in a 1.5-ml tube and then transferred the cell extract to a new tube, added 5*sodium dodecyl sulfate (SDS) loading buffer and heated it for 5 min at 95°C. The electrophoresis conditions were 120 V, 50 min. After electrophoresis, a polyvinylidene difluoride (PVDF) membrane was used for protein transfer at conditions of 400 mA, 45 min. The PVDF membrane was blocked using 5% skim milk diluted with Tris-buffered saline Tween 20 (TBST). The membrane was incubated with the following primary antibodies TAP1 (1:3,000), MEF2A (1:2000, #12382-1-AP, Proteintech, United States), LEF1 (1:1000, #14972-1-AP, Proteintech), E-cadherin (1:10000, #20874-1-AP, Proteintech), vimentin (1:2000, #10366-1-AP, Proteintech), and beta actin (1:1000, # 20536-1-AP, Proteintech). After overnight incubation, the membrane was labeled using horseradish peroxide (HRP)-conjugated secondary antibody. Then, we washed the PVDF membrane and applied electrochemiluminescence (ECL) luminescent solution to develop the pictures.

Small Interfering RNA Transfection

We designed 2 different sequences to knock down TAP1 mRNA in OC cell lines. The small interfering RNAs (siRNAs) (si-TAP1_01 and si-TAP1_02) and negative control siRNA (si-NC) were obtained from RiboBio (Guangzhou, China). The cells were seeded into 35-mm cell culture dishes at a density of 2 × 10⁴ cells/dish and transfected with siRNA using riboFECTTM CP (Guangzhou, China) after reaching 70% confluence. After 48 h of incubation, the efficiency of siRNAs was assessed using quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting analysis. The target sequences of the TAP1 siRNAs in TAP1 were as follows: Si-TAP1_01, 5′-CGG​GAT​CTA​TAA​CAA​CAC​CAT-3′; Si-TAP1_02, 5′-CCG​TGT​GTA​CTT​ATC​CTG​GAT-3′.

Cell Migration and Wound Healing Assays

Cell migration assays were conducted using transwell inserts with 8-μm pore filters (Corning, NY, United States). After transfecting the OC cell lines with TAP1 siRNAs or negative control siRNA, the cells were digested and resuspended in 200-μL serum-free medium, and then inoculated in the upper chamber of the transwell chamber in a 24-well plate. RPMI-1640 (Gibco, United States) containing 20% fetal bovine serum (FBS; Gibco) was added to the bottom well. After 24 h of incubation, the cells remaining on the surface of the upper chamber were removed, and the cells entering the lower chamber were fixed with 10% formalin and stained with crystal violet staining solution (Beyotime, Shanghai, China). Then, the stained cells were photographed at a 100× magnification. For the wound healing assays, the transfected cells were seeded into 12-well plates and cultured until almost overgrowth. Then, we created a wound by scratching the cells using 100-μL pipette tips. Cell migration was evaluated by measuring the gap area in multiple fields after 0 and 24 h at a magnification of 100×.

Cell Proliferation Assays and Colony Formation Assays

In cell proliferation assays, we seeded SKOV3 and OVCAR3 in 96-well plates at a density of 1 × 10⁴/well and transfected with si-TAP1_01, si-TAP1_02 or si-NC for 24, 48, 72 or 96 h at 37°C with 5% CO₂. Then 10 μL Cell Counting Kit-8 solution (CCK-8, #C0037, Beyotime, Shanghai, China) was added and incubated for 60 min. The absorbance of formazan produced was detected at 450 nm under microplate reader (Thermo Scientific, CA, United States). In colony formation assays, the transfected SKOV3 and OVCAR3 cells were seeded in 6-well plates at a density of 1×10³/well and kept for 14 days. Then the clonies were stained with crystal violet staining solution (Beyotime, Shanghai, China) and imaged.

Xenograft Experiments

All animal experiments were approved by the Scientific Investigation Board of the Central South University (Changsha, China) and conducted in according to the National Institute of Health Guide for the Care and Use of Laboratory Animals. For in vivo tumor metastasis assays, eight female BALB/c nude mice (6 weeks old) were purchased from SJA Laboratory Animal (Changsha, China) and maintained in a specific pathogen-free facility. All nude mice were injected intraperitoneally (i.p.) with 5 × 10⁶ cells in 200 μL of phosphate-buffered saline (PBS). Mice were randomly selected for treatment with cholesterol conjugated si-TAP1_01 or si-NC (n = 4/group) provided by RIBOBIO (Guangzhou, China) and 25 nmol RNA in 0.1 ml PBS was injected i.p. twice a week. The body weight of mice were measured each week. Four weeks after inoculation the mice were sacrificed and visceral organs (liver, intestine, mesentery, kidney, ovary and diaphragm) were observed. Visible tumors were harvested for weight and western blotting assays.

Statistical Analysis

We used the limma package to determine the expression level of TAP1 in tumors and the Wilcoxon test to calculate the significant difference. KM analysis was used to analyze the prognostic value of TAP1 using the log-rank test. Spearman method was applied to analyze the correlations between TAP1 and immune cells and immune genes. The R version 3.6.2 software (https://www.r-project.org/) was used for data analysis. p < 0.05 was considered statistically significant.

RESULTS

Pan-Cancer Expression of TAP1

Analysis of the sequencing data of TAP1 from 31 normal tissues derived from the GTEx dataset showed overexpression of TAP1 mRNA in many tissues, including blood, spleen, and lungs (Figure 1A). The expression profiles of 21 cancer cell lines derived from the CCLE dataset showed high expression levels of TAP1 in the kidney, pleura, and lymphoid cancer cell lines (Figure 1B). We collected the transcriptome sequencing data of 20 cancers vs. adjacent normal tissue from the TCGA dataset, consisting of 8,624 samples. The results showed that compared with normal tissues, tissues of various cancers, including head and neck squamous cell (HNSC), kidney renal clear cell carcinoma (KIRC), and stomach adenocarcinoma (STAD), showed increased levels of TAP1 expression (Figure 1C). To date, however, the TCGA database does not have sufficient data on TAP1 expression in different kinds of tumors, including OC and its adjacent tissues. Thus, we matched the normal samples obtained from GTEx with the corresponding samples in TCGA and analyzed the expression data of 27 cancers. Our results showed that TAP1 was upregulated in cervical squamous cell carcinoma (CESC), esophageal carcinoma (ESCA), OC, pancreatic adenocarcinoma (PAAD), acute myeloid leukemia (LAML), etc. (Figure 1D), indicating the role of TAP1 as a tumor marker across various cancers.

FIGURE 1 | The expression of transporter associated with antigen processing 1 (TAP1) in pan-cancer. (A) TAP1 expression of 31 kinds of normal tissues downloaded from genotype-tissue expression (GTEx) database. (B) TAP1 expression in 21 kinds of cancer cell lines derived from the cancer cell line encyclopedia (CCLE). (C) TAP1 expression in tumor samples and the adjacent normal samples of 20 cancers derived from the TCGA. (D) TAP1 expression in tumor samples and normal samples of 27 cancers derived from the cancer genome atlas (TCGA) and GTEx database. *p < 0.05. **p < 0.01. ***p < 0.001.

The Predictive Ability of TAP1 in the Prognosis of Various Cancers

To explore the ability of TAP1 in predicting the prognosis of various cancers, we used the KM analysis to calculate the relationship between TAP1 expression and survival status in 33 tumors using the patient information in the TCGA. The results showed that the TAP1 expression level was correlated with DSS (Figures 2D–F), PFS (Figures 2G–I), and OS (Figures 2J–L) in kidney renal papillary cell carcinoma (KIRP) (DSS: HR = 1.01, p < 0.0001; PFS: HR = 1.01, p < 0.0001; OS: HR = 1.01, p < 0.0001), uveal melanoma (UVM) (DSS: HR = 1.01, p < 0.0001; PFS: HR = 1.00, p = 0.00057; OS: HR = 1.00, p < 0.0001), and OC (DSS: HR = 1.01, p < 0.0001; PFS: HR = 1.00, p = 0.0011; OS: HR = 1.00, P = 5e-04). High levels of TAP1 expression were related with shorter DFS in KIRP (HR = 1.00, p < 0.0001), mesothelioma (MESO) (HR = 1.00, p = 0.0069), and PAAD (HR = 1.00, p = 0.0087) (Figures 2A–C). In addition, our results showed that TAP1 was associated with DSS, PFS, OS in different kinds of tumors such as brain lower grade glioma (LGG), skin cutaneous melanoma (SKCM), and bladder urothelial carcinoma (BLCA) (Supplementary Figures S1–S3). Thus, our results indicated further studies should be performed to establish the role of TAP1 as a prognostic marker in many cancers.

FIGURE 2 | The prediction ability of transporter associated with antigen processing 1 (TAP1) in pan-cancer prognosis. (A–C) The top three cancers correlated with TAP1 in disease-free survival (DFS). (D–F) The top three cancers correlated with TAP1 in disease-specific survival (DSS). (G–I) The top three cancers correlated with TAP1 in progression-free survival (PFS). (J–L) The top three cancers correlated with TAP1 in Overall survival (OS).

The Correlation of TAP1 With Immune Infiltration in Multiple Cancers

TAP1 was an important regulator involved in immune response, while immune infiltration could influence the development and treatment effect of tumors. The TIMER database was used to analyze the correlation of TAP1 with TIICs in 27 tumors. Results of this analysis showed that in some tumors, TAP1 was significantly correlated with 6 TIICs, including B cells, CD4+T cells, CD8+T cells, neutrophils, macrophages, and dendritic cells. The top 3 tumors were colon adenocarcinoma (COAD), KIRC, and liver hepatocellular carcinoma (LIHC) (Figure 3). Additionally, TAP1 was positively related with B cells (R = 0.341, p = 1.99e-11), CD4+T cells (R = 0.419, p < 0.0001), CD8+T cells (R = 0.488, p < 0.0001), neutrophils (R = 0.616, p < 0.0001), macrophages (R = 0.137, p = 0.0001), and dendritic cells (R = 0.592, p < 0.0001) in OC. Similarly, in CESC, TAP1 was positively related with B cells (R = 0.240, p = 5.28e-08), CD4+T cells (R = 0.252, p = 1.12e-08), CD8+T cells (R = 0.515, p = 2.63e-35), neutrophils (R = 0.491, p = 9.4e-32), macrophages (R = 0.167, p = 0.0002), and dendritic cells (R = 0.511, p < 0.0001) (Supplementary Figure S4). The ESTIMATE algorithm was used to determine the correlation of TAP1 with immune cells and stromal cells in 33 tumors. The ESTIMATE algorithm included 3 immune-related scores: ESTIMATE score, STROMAL score, and IMMUNE score. The results demonstrated that the expression of TAP1 had a significant correlation with immune-related scores. TAP1 was positively correlated with ESTIMATE scores in CESC (R = 0.493, p < 0.0001), OC (R = 0.448, p < 0.0001), UVM (R = 0.667, p < 0.0001), testicular germ cell tumors (TGCT) (R = 0.805, p < 0.0001), and thyroid carcinoma (THCA) (R = 0.743, p < 0.0001), etc. (Figure 4). Furthermore, TAP1 was positively correlated with IMMUNE score and STROMAL score in different kinds of tumors, including OC and CESC (Supplementary Figures S5, S6).

FIGURE 3 | The correlation of transporter associated with antigen processing 1 (TAP1) with immune infiltration in multiple cancers. The top three cancers with the strongest correlation with 6 immune infiltration cells which included B cells, CD4+T cells, CD8+T cells, neutrophils, macrophages, and dendritic cells. The cancers were colon adenocarcinoma (COAD), kidney renal clear cell carcinoma (KIRC), and breast invasive carcinoma (BRCA), respectively.

FIGURE 4 | The relationship between TAP1 and ESTIMATE score in multiple cancers. The ESTIMATE score was used to determine the correlation of TAP1 with immune cells and stromal cells in 33 tumors. TAP1 was positively correlated with ESTIMATE scores in cervical squamous cell carcinoma (CESC), ovarian cancer (OC), uveal melanoma (UVM), testicular germ cell tumors (TGCT), and thyroid carcinoma (THCA) (p < 0.05).

The Relationship Between TAP1 and Immune Checkpoint Therapy in Multiple Cancers

Immune checkpoints referred to a group of molecules present in immune cells that could participate in preventing the activation of the immune system. Tumor cells could inhibit the function of T cells by activating immune checkpoints, thereby achieving tumor immune escape. Typically, anti- PD-1, anti- PD-L1, and anti-cytotoxic T lymphocyte antigen 4(CTLA-4) therapy was administered in the treatment of tumors. We analyzed the expression of TAP1 with 47 immune checkpoint genes through the expression data in 33 tumors. The results demonstrated that TAP1 was expressed together with many immune checkpoint genes in various tumors. For example, in OC and CESC, TAP1 was positively correlated with CD274, TIGHT, CTLA4, and LAG3 (Figure 5A). These results indicated that TAP1 may affect immune therapy in tumor through immune checkpoint genes. TMB was an index that reflects the abundance of gene mutations in cancer. MSI was a phenomenon in which a new microsatellite allele appeared at a certain site in the tumor because of the insertion or deletion of a repeat unit. Both were important metrics correlated with the effect of immune checkpoint therapy such as PD-1 and PDL-1. The results indicated that the expression TAP1 was positively related to TMB in CESC, breast invasive carcinoma (BRCA), COAD, lung adenocarcinoma (LUAD), PAAD, sarcoma (SARC), SKCM, and STAD, whereas the highest score was observed for the correlation of TAP1 and COAD. Further, TAP1 was negatively correlated with TMB in BLCA (Figure 5B). In addition, TAP1 negatively correlated with MSI in OC, MESO, lung squamous cell carcinoma (LUSC), LUAD, diffuse large B-cell lymphoma (DLBC), and TGCT, while the correlation in TGCT had the strongest score. The expression of TAP1 was positively related to MSI in COAD and KIRC (Figure 5C). Overall, we found that TAP1 could possibly affect the tumor immunotherapy.

FIGURE 5 | The relationship between transporter associated with antigen processing 1 (TAP1) and immune checkpoint therapy in multiple cancers. (A) The relationship between TAP1 and 47 immune checkpoint genes. Color green represented negative correlation with TAP1 and color red represented positive correlation with TAP1. (B) The correlation of TAP1 with tumor mutation burden (TMB). (C) The correlation of TAP1 with microsatellite instability (MSI). *p < 0.05. **p < 0.01. ***p < 0.001.

The Correlation of TAP1 With Neoantigens and Methyltransferases Across Various Cancers

Neoantigens referred to a series of protein with specific amino acid sequences produced by tumor cells during gene mutation. These proteins attracted autoimmune cells and caused a series of immune responses, which were more beneficial for the efficacy of tumor-targeted therapy. We analyzed the relationship between TAP1 and neoantigens across various cancers as summarized by Rooney [7]. The results showed that TAP1 was positively related to neoantigens in OC, CESC, LUAD, BRCA, SKCM, and LGG (p < 0.05), whereas it was negatively correlated in COAD and rectum adenocarcinoma (READ) (Figure 6). DNA methylation was a form of DNA chemical modification that was commonly observed in tumors. DNA methyltransferases prevented changing the DNA sequence while changing the genetic performance. DNMT1, DNMT2, DNMT3A, and DNMT3B were 4 important methyltransferases, and we analyzed the correlation between TAP1 and these methyltransferases in 33 tumors. TAP1 was positively correlated with DMNT1 in OC, CESC, PAAD, pheochromocytoma and paraganglioma; (PCPG), prostate adenocarcinoma (PRAD), STAD, UVM, BLCA, BRCA, COAD, DLBC, HNSC, kidney chromophobe (KICH), KIRC, KIRP, LAML, LGG, and LIHC (p < 0.05). TAP1 was positively related to DMNT2 in PCPG, READ, STAD, TGCT, BLCA, BRCA, DLBC, KICH, KIRC, KIRP, LGG, and LIHC (p < 0.05), while it was negatively correlated in OC, CESC, TGCT, and THCA (p < 0.05). The remaining results were shown in Supplementary Figure S7.

FIGURE 6 | The relationship between transporter associated with antigen processing 1 (TAP1) and neoantigen in various cancers. TAP1 was positively related to neoantigens in ovarian cancer (OC), cervical squamous cell carcinoma (CESC), lung adenocarcinoma (LUAD), breast invasive carcinoma (BRCA), skin cutaneous melanoma (SKCM), and brain lower grade glioma (LGG) (p < 0.05), whereas it was negatively correlated in colon adenocarcinoma (COAD) and rectum adenocarcinoma (READ).

Overexpression of TAP1 in OC Patients With Poor Prognosis

On the basis of previous results, we speculated that TAP1 may play a role in predicting the prognosis of patients with OC. Additionally, we wanted to understand the potential role of TAP1 in CC, which was another common gynecological cancer. Therefore, we analyzed the expression of TAP1 in clinical samples obtained from patients with OC and CC. We collected 41 OC tissue samples and 32 CC tissue samples and performed immunohistochemical staining to determine the expression of TAP1. Moderate or strong staining, indicating TAP1 expression, was observed in 39.02% (16/41) of OC specimens, whereas 60.98% (25/41) of the samples showed weak staining, indicating negative TAP1 expression. The representative images of OC samples with strong, moderate, or weak TAP1 expression were shown in Figure 7A. Further, we classified the 41 OC specimens into 2 groups according to negative or positive TAP1 expression. The relationship between OS of patients with OC and TAP1 was analyzed using KM method; results of this analysis showed that patients with positive expression of TAP1 had lower 5-years OS than those with negative expression (p = 0.0172) (Figure 7C). In CC tissues, 43.75% (14/32) of the samples showed moderate or strong staining, indicating positive TAP1 expression and 56.23% (18/32) of the samples showed weak or negative staining, indicating negative TAP1 expression. The representative images of CC samples were shown in Figure 7B. The KM analysis showed no significant difference between the OS of CC patients and TAP1 expression (p = 0.1099) (Figure 7D).

FIGURE 7 | Correlation of transporter associated with antigen processing 1 (TAP1) expression with the prognosis of ovarian cancer (OC) and cervical cancer (CC). (A) Immunohistochemical (IHC) analysis of transporter associated with antigen processing 1 (TAP1) protein expression in OC tissues. Original magnifications:×200 and ×400. i represents strong positive staining, ii represents moderately positive staining, and iii represents weakly positive staining. (B) IHC analysis of TAP1 protein expression in CC tissues. Original magnifications:×200 and ×400. i represents strong positive staining, ii represents moderately positive staining, and iii represents weakly positive staining. Scale bars 200 μm. (C) 5-Year Kaplan–Meier analysis of overall survival in OC patients on the basis of TAP1 protein expression. (D) 5-Year Kaplan–Meier analysis of overall survival in CC patients on the basis of the TAP1 protein expression. (E) The protein-protein interaction (PPI) network of TAP1 and top 26 correlated transcription factors (TFs) were identified using STRING (https://string-db.org/cgi/input.pl). Color blue represented TFs negatively correlated with TAP1, color red represented TFs positively correlated with TAP1, and color white represented TFs significantly correlated with the top 26 TFs in enrichment analysis. (F) Gene set enrichment analysis of transporter associated with antigen processing 1 (TAP1) and pivotal TFs in OC. (G) TAP1, MEF2A, LEF1, and β-actin protein expression in normal cell lines and cancer cell lines. Error bars represent the SD of triplicate measurements. *p < 0.05; **p < 0.01; ***p < 0.001.

GSEA of TAP1 in OC

GSEA was used to further investigate the function of TAP1 in OC. We determined the relationship between TAP1 and pivotal TFs and selected the TFs with high normal enrichment score (NES). Then, all the TFs with high NES were added to combine with chip-seq in the chipbase database (http://rna.sysu.edu.cn/chipbase/), and the network diagram (Figure 7E). We found that some well-known TFs associated with OC could probably regulate TAP1 directly, such as signal transducer and activator of transcription 3 (STAT3) and paired box 3 PAX3 (Han et al., 2019; Liang et al., 2020). However, some TFs did not play a role in OC such as myocyte specific enhancer factor 2A (MEF2A) and lymphoid enhancer-binding factor 1 (LEF1) (Figure 7F). Then, we used western blotting to determine the expression of TAP1, MEF2A, and LEF1 in OC cell lines (SKOV3 and OVCAR3) and compared it with that in normal ovarian epithelial cell IOSE80. Further, we analyzed the expression of TAP1, MEF2A, and LEF1 in CC cell lines (Hela and Caski) and normal cervical cell line H8 (Figure 7G). Our results showed that the expression levels of these 3 genes were higher in SKOV3 and OVCAR3 cell lines than in IOSE80 and in Caski and Hela cell lines than in H8. Our results showed that the expression levels of TAP1, MEF2A, and LEF1 were similar in OC and CC.

Downregulation of TAP1 Reduces Invasion and Migration in OC Cells

Through KEGG pathway analysis by using GSEA according to TAP1 expression in OC specimens from the TCGA database, we found that TAP1 could influence the cell adhesion molecules pathway, apoptosis pathway, JAK-STAT signaling pathway, and epithelial cell signaling (Supplementary Figure S8). We performed GSVA on all survival-related genes in TCGA OC specimens. On the basis of the expression of TAP1, we then analyzed the different pathways to find that TAP1 could promote the cell adhesion molecules pathway and illustrated through the volcano map (Figure 8A). Further, comparison of the differences in survival-related genes in the TAP1 high and low expression groups and enriching the pathways of differential genes showed that TAP1 could promote the cell adhesion molecules pathway (Figure 8B).

FIGURE 8 | Downregulation of transporter associated with antigen processing 1 (TAP1) reduces the invasion and migration in ovarian cancer cells. (A) Then volcano map of TAP1-related pathways by gene set variation analysis. (B) Enriching pathway analysis in ovarian cancer of TAP1. (C) The migration abilities of OVCAR3 and SKOV3 were photographed (i, ii) and measured (iii) by examining the wound closure after TAP1 knockdown using wound healing assays. Original magnifications, ×100. Scale bars 150 μm. (D) Transwell assays were photographed (i) and measured (ii) to detect the migration abilities after TAP1 knockdown in SKOV3 and OVCAR3 cells. Original magnifications, ×200. Scale bars 100 μm. (E) Colony formation assays were photographed (i) and measured (ii) to detect the proliferation abilities after TAP1 knockdown in SKOV3 and OVCAR3 cells. (F) Effects of TAP1 knockdown on migration-associated protein (E-cadherin and vimentin) and transcription factor (MEF2A and LEF1) were analyzed by western blotting in SKOV3 and OVCAR3 cells (i). Error bars of histogram (ii) represented the SD of triplicate measurements. *p < 0.05; **p < 0.01; ***p < 0.001.

On the basis of the above findings, we wanted to further explore the potential role of TAP1 in the metastasis and migration of OC cells. Thus, we designed 2 different siRNA products (si-TAP1_01 and si-TAP1_02) and a scrambled siRNA as the negative control (NC). The siRNA products were transfected into OVCAR3 and SKOV3 cells, and western blot analysis was performed. The results showed that both si-TAP1 could effectively knock down TAP1 expression. Then, we studied the impact of TAP1 on cell migration and invasion using cell scratch-wound healing assays and transwell cell migration assays. Scratch-wound healing assays revealed that silencing TAP1 in OVCAR3 and SKOV3 cells decreased the wound healing ability (Figure 8C). The results of transwell cell migration assays demonstrated that knockdown of TAP1 significantly reduced the number of cells on membrane filters compared with controls (Figure 8D). Further, we measured the protein levels of cancer cell metastasis-related markers (E-cadherin and vimentin) and the genes of interest (MEF2A and LEF1) were measured. The results showed that TAP1 deficiency decreased vimentin, MEF2A, and LEF1 levels, whereas the levels of E-cadherin protein increased in OVCAR3 and SKOV3 cells (Figure 8F). Through cell proliferation assays (Supplementary Figure S9) and colony formation assays (Figure 8E), we found that TAP1 had no significant effect on the proliferation of ovarian cancer cells. Thus we eliminated the potential effect of proliferation on the aforementioned scratch-wound healing and transwell assays. To further confirm the metastasis-promoting effects of TAP1, we performed in vivo xenograft tumor experiments in nude mice. Accordingly, we injected SKOV3 cells into nude mice intraperitoneally and treated them with TAP1 siRNA (si-TAP1_01) or scrambled NC RNA (si-NC). The results demonstrated that the number and weight of metastatic tumors in peritoneal cavity were lower in the si-TAP1_01 group than in the si-NC group (Figures 9A–C), while the weight growth rates were higher in the si-NC group (Figure 9D). Higher level of TAP1 in metastatic tumors were associated with higher levels of vimentin and LEF1, while associated with lower level of E-cadherin. However the enhancement of TAP1 in vivo did not seem to notably effect the expression of MEF2A (Figure 9E). These results demonstrated that TAP1 could play a role in the invasion and metastasis of OC cells and TAP1 may regulate the transcription factor LEF1 in OC. Further studies were necessary to confirm these findings.

FIGURE 9 | To establish the model of tumor metastasis, SKOV3 cells were injected into nude mice by the i.p. route and randomly treated with si-TAP1_01 or si-NC (n = 4/group). (A) Representative images of metastatic tumors in the peritoneal cavity. Yellow arrows and rectangles highlighted visible tumor nodes and liver metastasis. (B,C) Number of metastasis and total weight of all metastases were shown (mean ± SEM). (D) Body weight growth of all mice were shown (mean ± SEM). (E) The protein expression of migration-associated protein (E-cadherin and vimentin) and transcription factor (MEF2A and LEF1) in the tumor of si-TAP1_01 group and si-NC group (i). Error bars of histogram (ii) represented the SD of quadruplicate measurements. *p < 0.05; **p < 0.01; ***p < 0.001.

DISCUSSION

TAP includes TAP1 and TAP2, which belong to the ABC transporter superfamily and consist of 2 transmembrane domains, consisting of a substrate binding site and 2 nucleotide binding domains (Praest et al., 2018). TAP1 and TAP2 are responsible for transporting antigen peptides from the cytoplasm to the lumen of the endoplasmic reticulum, then loading them on the MHC class I molecules of the MHC. TAP1 and TAP2 can provide endogenous protein peptides for CD8⁺ cytotoxic T cells, so the TAP family plays a role in maintaining the normal function of the immune system (Meng et al., 2018).

Recent studies showed that TAP1 was closely related to a variety of tumors, which may be related to tumor cells evading the recognition of cytotoxic T cells by shutting down peptide delivery. Chow et al. found that increased copy number of TAP1 was closely related to histological duct variation in prostate cancer (Chow et al., 2021). TAP1 was involved in IL-27-mediated anti-PD-1 antibody immunotherapy in small cell lung cancer (Carbotti et al., 2017). The level of TAP1 could affect the survival time of patients with oral squamous cell carcinoma (SCCOT) and breast cancer, and may serve as a tumor marker for the early diagnosis and follow-up of SCCOT (Henle et al., 2017; Attaran et al., 2020). Seliger et al. reported that miR-200a-5p reduced HLA-I expression on the membrane of melanoma tumor cells by downregulating the level of TAP1, causing the tumor cells to escape the immune system and reducing the survival time of tumor patients (Lazaridou et al., 2020). In this study, we analyzed the data derived from TCGA database and GTEx database and found that TAP1 expression was elevated in lung cancer, breast cancer, pancreatic cancer, renal cell carcinoma, and other tumors and was correlated with the survival state of patients, which was consistent with the findings reported previously (Xu et al., 2013; Xu et al., 2019). Moreover, our results showed that TAP1 expression levels were increased in gynecological tumors, including OC, CC, and endometrial cancer. Our results showed that TAP1 was significantly correlated with the prognosis of patients with OC, including data in TCGA and clinical specimens. The results indicated that TAP1 could function as a prognostic marker for OC.

TAP1 plays a role in tumor development and resistance to treatment mainly by affecting tumor immune infiltration. Marcoto et al. found that the levels of TAP1 and CD80 decreased in breast cancer stem cells after decitabine treatment, thereby reducing the recognition and killing effect of T cells and promoting tumor cell growth (Sultan et al., 2018). Ling et al. confirmed that TAP1 in colorectal cancer was positively related to TIICs, including CD3⁺ T cells and FOXP3+ T cells (Ling et al., 2017). Loss of TAP1 expression in melanoma was correlated with the increase of Treg cells and neutrophils in cancer, which could change the immune microenvironment and participate in the reversal of resistance to anti-PD1 therapy (Zhang et al., 2021). The results of analysis of the TIMER database showed a relationship between TAP1 and the infiltration of a variety of TIICs, including CD4⁺ T cells, CD8⁺ T cells, B cells, neutrophils, dendritic cells, and macrophages. The results showed that TAP1 was related to all of these 6 immune cells in KIRC, COAD, BRCA, and LUSC. Further, we analyzed the correlation between TAP1, IMMUNE score, STROMAL score, and ESTIMATE score in a variety of tumors. Then, we identified the top 3 tumors—BRCA, COAD, and KIRC. We directly explained the significant correlation between TAP1 and tumor immune cell infiltration in OC as well as immune scores, which suggested that TAP1 could influence the abundance of TIICs in OC.

OC is one of the most common causes of death in women with cancers, with more than 120,000 deaths worldwide every year (Vaughan et al., 2011). Approximately 70% of women with OC are in the late stage during the initial diagnosis, while 5-years survival rate is less than 50% (Torre et al., 2018). Immunotherapy includes anti-PDL-1 therapy and PARP inhibitor therapy has shown promising results in the treatment of OC; however, many patients may relapse after receiving first-line treatment. The therapeutic effect is related to the tumor heterogeneity and its surrounding tumor microenvironment (Baci et al., 2020). Thus, it is very important to find effective prognostic markers of OC and related genes of immune regulation. Based on the aforementioned research, we analyzed the correlation between TAP1 and 47 recognized immune checkpoint genes in pan-cancers through Spearman correlation test. We found that TAP1 was significantly correlated with genes such as CD274 (PDL-1), CTLA4, CD244, and LAG3 in OC, in which the above genes were all related to the occurrence and development and the curative effect of existing immunotherapy (Huang et al., 2015; Macgregor et al., 2019; Marth et al., 2019). Chen et al. found that the degree of methylation of TAP1 was correlated with the degree of CD8⁺ T cell infiltration, while the reduction of CD8⁺ T cells infiltration was related to the recurrence of high-grade serous epithelial OC (Wang et al., 2014). By analyzing the correlation between TAP1 and methyltransferases in pan-cancers, we found that TAP1 had a significant correlation with the 4 methyltransferases in KICH and DLBC. In OC, TAP1 had a significant correlation with DNMT1, which was involved in the metastasis and cisplatin resistance of OC (Cao et al., 2017). Therefore, we speculated that DNMT1 changed the methylation status of TAP1 and affected the infiltration of tumor immune cells thereby changing the prognosis of OC, which was worthy of further verification.

In order to further analyze the relationship between TAP1 and OC, we performed GSEA. The results of GSEA showed that TAP1 was associated with the apoptosis signaling, JAK/STAT signaling, TOLL-like receptor signaling and cell adhesion signaling in OC, which indicated that TAP1 may affect the formation of tumor, proliferation, and migration in OC in addition to immune-related pathways. Analysis of transcription factors in GSEA showed that some core TFs may have a relation with TAP1 in OC, including ETS transcription factor (ELK1), interferon regulatory factor (IRF), STAT3, and MEF2A, which affected metastasis, cisplatin resistance, and apoptosis in OC [(Li et al., 2019), (Praest et al., 2018), (Gu et al., 2021), (Pavan et al., 2013)]. We found that MEF2A and LEF1 may participate in OC and regulate TAP1. MEF2A could promote the proliferation and metastasis of tumor cells by inducing epithelial-mesenchymal transition (EMT) and activating the WNT/β-catenin signaling pathway (Xiao et al., 2021). LEF1 could promote tumor cell metastasis by participating in EMT and regulating the intracellular reactive oxygen species (ROS). These results provided a research basis for our follow-up study of the mechanism of TAP1 in OC. However, further studies are required to understand the function of TAP1 in OC.

Our results showed that the expression of TAP1 was increased in many tumors. High expression level of TAP1 was positively related to the poor prognosis of various tumors and immune cell infiltration. Additionally, we found that TAP1 affected the development of OC in different ways. According to the analysis of tumor neoantigen load and immune checkpoint, TAP1 had the potential to become a novel tumor-targeted drug. Thus, TAP1 may be a new therapeutic target for OC and other kinds of cancers. Thus, our results showed that TAP1 could function as a biomarker for cancer diagnosis and prognosis.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are openly available in the Cancer Genome Atlas (TCGA) database at https://www.cancer.gov/t-cga, the Genotype-Tissue Expression (GTEx) database at https://www.gtexportal.org/home/, the Cancer Cell Line Encyclopedia (CCLE) database at https://portals.broadinstitute.org/ccle, and the TIMER database at http://timer.cistrome.org/. The data that support the research about cancer neoantigens are openly available in “Molecular and genetic properties of tumors associated with local immune cytolytic activity“ at https://pubmed.ncbi.nlm.nih.gov/25-594174/.

ETHICS STATEMENT

The studies involving human participants were reviewed and approved by Human Ethics Committee of The Second Xiangya Hospital of Central South University, 2017S109. The patients/participants provided their written informed consent to participate in this study.

AUTHOR CONTRIBUTIONS

XL, SZ, and MY conducted the research. YD, XL, and MY analyzed bioinformation data. YN helped with implementation. XL and SZ drafted the manuscript. MY and YD reviewed the manuscript. All authors read and approved the submitted manuscript.

FUNDING

This work was supported by the Hunan Provincial Key Research and Development Program (Grant no. 2018SK 2067).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

ACKNOWLEDGMENTS

The Medical Experiment Center of the Second Xiangya Hospital of Central South University provided assistance in equipment and experimental design for this article.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2021.763958/full#supplementary-material

REFERENCES

Attaran, N., Gu, X., Coates, Pj., Fåhraeus, R., Boldrup, L., Wilms, T., et al. (2020). Downregulation of TAP1 in Tumor-free Tongue Contralateral to Squamous Cell Carcinoma of the Oral Tongue, an Indicator of Better Survival. Int. J. Mol. Sci. 21, 21. doi:10.3390/ijms21176220

Baci, D., Bosi, A., Gallazzi, M., Rizzi, M., Noonan, Dm., Poggi, A., et al. (2020). The Ovarian Cancer Tumor Immune Microenvironment (TIME) as Target for Therapy: A Focus on Innate Immunity Cells as Therapeutic Effectors. Int. J. Mol. Sci. 21, 21. doi:10.3390/ijms21093125

Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, Aa., Kim, S., et al. (2012). The Cancer Cell Line Encyclopedia Enables Predictive Modelling of Anticancer Drug Sensitivity. Nature 483, 603–607. doi:10.1038/nature11003

Cao, Y.-L., Zhuang, T., Xing, B.-H., Li, N., and Li, Q. (2017). Exosomal DNMT1 Mediates Cisplatin Resistance in Ovarian Cancer. Cell Biochem Funct 35, 296–303. doi:10.1002/cbf.3276

Carbotti, G., Nikpoor, A. R., Vacca, P., Gangemi, R., Giordano, C., Campelli, F., et al. (2017). IL-27 Mediates HLA Class I Up-Regulation, Which Can Be Inhibited by the IL-6 Pathway, in HLA-Deficient Small Cell Lung Cancer Cells. J. Exp. Clin. Cancer Res. 36, 140. doi:10.1186/s13046-017-0608-z

Carithers, L. J., and Moore, H. M. (2015). The Genotype-Tissue Expression (GTEx) Project. Biopreservation and Biobanking 13, 307–308. doi:10.1089/bio.2015.29031.hmm

Chow, K., Bedő, J., Ryan, A., Agarwal, D., Bolton, D., Chan, Y., et al. (2021). Ductal Variant Prostate Carcinoma Is Associated with a Significantly Shorter Metastasis-free Survival. Eur. J. Cancer 148, 440–450. doi:10.1016/j.ejca.2020.12.030

Gu, H., Lin, R., Zheng, F., and Zhang, Q. (2021). ELK1 Activated-Long Noncoding RNA LBX2-AS1 Aggravates the Progression of Ovarian Cancer through Targeting miR-4784/KDM5C axis. J. Mol. Histol. 52, 31–44. doi:10.1007/s10735-020-09921-5

Han, G. H., Chay, D. B., Nam, S., Cho, H., Chung, J.-Y., and Kim, J.-H. (2019). Prognostic Implications of Forkhead Box Protein O1 (FOXO1) and Paired Box 3 (PAX3) in Epithelial Ovarian Cancer. BMC Cancer 19, 1202. doi:10.1186/s12885-019-6406-6

Henle, A. M., Nassar, A., Puglisi-Knutson, D., Youssef, B., and Knutson, K. L. (2017). Downregulation of TAP1 and TAP2 in Early Stage Breast Cancer. PLOS ONE 12, e0187323. doi:10.1371/journal.pone.0187323

Huang, R.-Y., Eppolito, C., Lele, S., Shrikant, P., Matsuzaki, J., and Odunsi, K. (2015). LAG3 and PD1 Co-inhibitory Molecules Collaborate to Limit CD8+ T Cell Signaling and Dampen Antitumor Immunity in a Murine Ovarian Cancer Model. Oncotarget 6, 27359–27377. doi:10.18632/oncotarget.4751

Lazaridou, M.-F., Gonschorek, E., Massa, C., Friedrich, M., Handke, D., Mueller, A., et al. (2020). Identification of miR-200a-5p Targeting the Peptide Transporter TAP1 and its Association with the Clinical Outcome of Melanoma Patients. Oncoimmunology 9, 1774323. doi:10.1080/2162402x.2020.1774323

Li, T., Fu, J., Zeng, Z., Cohen, D., Li, J., Chen, Q., et al. (2020). TIMER2.0 for Analysis of Tumor-Infiltrating Immune Cells. Nucleic Acids Res. 48, W509–W514. doi:10.1093/nar/gkaa407

Li, X., Zhang, Y., Chai, X., Zhou, S., Zhang, H., He, J., et al. (2019). Overexpression of MEF2D Contributes to Oncogenic Malignancy and Chemotherapeutic Resistance in Ovarian Carcinoma. Am. J. Cancer Res. 9, 887–905.

Liang, R., Chen, X., Chen, L., Wan, F., Chen, K., Sun, Y., et al. (2020). STAT3 Signaling in Ovarian Cancer: a Potential Therapeutic Target. J. Cancer 11, 837–848. doi:10.7150/jca.35011

Ling, A., Löfgren-Burström, A., Larsson, P., Li, X., Wikberg, M. L., Oberg, A., et al. (2017). TAP1 Down-Regulation Elicits Immune Escape and Poor Prognosis in Colorectal Cancer. Oncoimmunology 6, e1356143. doi:10.1080/2162402x.2017.1356143

Macgregor, H. L., Sayad, A., Elia, A., Wang, B. X., Katz, S. R., Shaw, P. A., et al. (2019). High Expression of B7-H3 on Stromal Cells Defines Tumor and Stromal Compartments in Epithelial Ovarian Cancer and Is Associated with Limited Immune Activation. J. Immunotherapy Cancer 7, 357. doi:10.1186/s40425-019-0816-5

Marth, C., Wieser, V., Tsibulak, I., and Zeimet, A. G. (2019). Immunotherapy in Ovarian Cancer: Fake News or the Real deal?. Int. J. Gynecol. Cancer 29, 201–211. doi:10.1136/ijgc-2018-000011

Mateo, J., Lord, C. J., Serra, V., Tutt, A., Balmaña, J., Castroviejo-Bermejo, M., et al. (2019). A Decade of Clinical Development of PARP Inhibitors in Perspective. Ann. Oncol. 30, 1437–1447. doi:10.1093/annonc/mdz192

Meng, J., Li, W., Zhang, M., Hao, Z., Fan, S., Zhang, L., et al. (2018). An Update Meta-Analysis and Systematic Review of TAP Polymorphisms as Potential Biomarkers for Judging Cancer Risk. Pathol. - Res. Pract. 214, 1556–1563. doi:10.1016/j.prp.2018.07.018

Miller, K. D., Nogueira, L., Mariotto, A. B., Rowland, J. H., Yabroff, K. R., Alfano, C. M., et al. (2019). Cancer Treatment and Survivorship Statistics, 2019. CA A. Cancer J. Clin. 69, 363–385. doi:10.3322/caac.21565

Pavan, S., Olivero, M., Corà, D., and Di Renzo, M. F. (2013). IRF-1 Expression Is Induced by Cisplatin in Ovarian Cancer Cells and Limits Drug Effectiveness. Eur. J. Cancer 49, 964–973. doi:10.1016/j.ejca.2012.09.024

Praest, P., Luteijn, R. D., Brak-Boer, I. G. J., Lanfermeijer, J., Hoelen, H., Ijgosse, L., et al. (2018). The Influence of TAP1 and TAP2 Gene Polymorphisms on TAP Function and its Inhibition by Viral Immune Evasion Proteins. Mol. Immunol. 101, 55–64. doi:10.1016/j.molimm.2018.05.025

Puri, S., and Shafique, M. (2020). Combination Checkpoint Inhibitors for Treatment of Non-small-cell Lung Cancer: an Update on Dual Anti-CTLA-4 and Anti-PD-1/pd-L1 Therapies. Drugs Context 9. doi:10.7573/dic.2019-9-2

Rooney, M. S., Shukla, S. A., Wu, C. J., Getz, G., and Hacohen, N. (2015). Molecular and Genetic Properties of Tumors Associated with Local Immune Cytolytic Activity. Cell 160, 48–61. doi:10.1016/j.cell.2014.12.033

Sultan, M., Vidovic, D., Paine, A. S., Huynh, T. T., Coyle, K. M., Thomas, M. L., et al. (2018). Epigenetic Silencing of TAP1 in Aldefluor+Breast Cancer Stem Cells Contributes to Their Enhanced Immune Evasion. Stem Cells 36, 641–654. doi:10.1002/stem.2780

Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., et al. (2021). Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA A. Cancer J. Clin. 71, 209–249. doi:10.3322/caac.21660

Torre, L. A., Trabert, B., Desantis, C. E., Miller, K. D., Samimi, G., Runowicz, C. D., et al. (2018). Ovarian Cancer Statistics, 2018. CA: A Cancer J. Clinicians 68, 284–296. doi:10.3322/caac.21456

Vaughan, S., Coward, J. I., Bast, R. C., Berchuck, A., Berek, J. S., Brenton, J. D., et al. (2011). Rethinking Ovarian Cancer: Recommendations for Improving Outcomes. Nat. Rev. Cancer 11, 719–725. doi:10.1038/nrc3144

Wang, C., Cicek, M. S., Charbonneau, B., Kalli, K. R., Armasu, S. M., Larson, M. C., et al. (2014). Tumor Hypomethylation at 6p21.3 Associates with Longer Time to Recurrence of High-Grade Serous Epithelial Ovarian Cancer. Cancer Res. 74, 3084–3091. doi:10.1158/0008-5472.can-13-3198

Weinstein, J. N., Collisson, E. A., Collisson, E. A., Mills, G. B., Shaw, K. R. M., Ozenberger, B. A., et al. (2013). The Cancer Genome Atlas Pan-Cancer Analysis Project. Nat. Genet. 45, 1113–1120. doi:10.1038/ng.2764

Xiao, Q., Gan, Y., Li, Y., Fan, L., Liu, J., Lu, P., et al. (2021). MEF2A Transcriptionally Upregulates the Expression of ZEB2 and CTNNB1 in Colorectal Cancer to Promote Tumor Progression. Oncogene 40, 3364–3377. doi:10.1038/s41388-021-01774-w

Xu, M., Li, L., Liu, Z., Jiao, Z., Xu, P., Kong, X., et al. (2013). ABCB2 (TAP1) as the Downstream Target of SHH Signaling Enhances Pancreatic Ductal Adenocarcinoma Drug Resistance. Cancer Lett. 333, 152–158. doi:10.1016/j.canlet.2013.01.002

Xu, Y., Morales, A. J., Cargill, M. J., Towlerton, A. M. H., Coffey, D. G., Warren, E. H., et al. (2019). Preclinical Development of T-Cell Receptor-Engineered T-Cell Therapy Targeting the 5T4 Tumor Antigen on Renal Cell Carcinoma. Cancer Immunol. Immunother. 68, 1979–1993. doi:10.1007/s00262-019-02419-4

Zhang, X., Sabio, E., Krishna, C., Ma, X., Wang, J., Jiang, H., et al. (2021). Qa-1b Modulates Resistance to Anti-PD-1 Immune Checkpoint Blockade in Tumors with Defects in Antigen Processing. Mol. Cancer Res. 19, 1076–1084. doi:10.1158/1541-7786.mcr-20-0652

GLOSSARY

ACC adrenocortical carcinoma

BLCA bladder urothelial carcinoma

BRCA breast invasive carcinoma

CESC cervical squamous cell carcinoma and endocervical adenocarcinoma

CHOL cholangiocarcinoma

COAD colon adenocarcinoma

READ rectum adenocarcinoma esophageal carcinoma rectum adenocarcinoma

DLBC lymphoid neoplasm diffuse large B-cell lymphoma

ESCA esophageal carcinoma

GBM glioblastoma multiforme

HNSC head and neck squamous cell carcinoma

KICH kidney chromophobe

KIPAN pan-kidney cohort (KICH + KIRC + KIRP)

KIRC kidney renal clear cell carcinoma

KIRP kidney renal papillary cell carcinoma

LAML acute myeloid leukemia

LGG brain lower grade glioma

LIHC liver hepatocellular carcinoma

LUAD lung adenocarcinoma

LUSC lung squamous cell carcinoma

MESO mesothelioma

OC ovarian serous cystadenocarcinoma

PAAD pancreatic adenocarcinoma

PCPG pheochromocytoma and paraganglioma

PRAD prostate adenocarcinoma

READ rectum adenocarcinoma esophageal carcinoma rectum adenocarcinoma

SARC sarcoma

SKCM skin cutaneous melanoma

STAD stomach adenocarcinoma

STES stomach and esophageal carcinoma

TGCT testicular germ cell tumors

THCA thyroid carcinoma

THYM thymoma

UCEC uterine corpus endometrial carcinoma

UCS uterine carcinosarcoma

UVM uveal melanoma

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Li, Zeng, Ding, Nie and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 14 December 2021
doi: 10.3389/fmolb.2021.766609

An Integrated Fibrosis Signature for Predicting Survival and Immunotherapy Efficacy of Patients With Hepatocellular Carcinoma

Long Liu1,2,³†, Zaoqu Liu⁴†, Lingfang Meng⁵†, Lifeng Li⁶, Jie Gao1,2,³, Shizhe Yu1,2,³, Bowen Hu1,2,³, Han Yang1,2,³, Wenzhi Guo1,2,³ and Shuijun Zhang1,2,³*

¹Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China

²Henan Research Centre for Organ Transplantation, Zhengzhou, China

³Henan Key Laboratory of Digestive Organ Transplantation, Zhengzhou, China

⁴Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China

⁵Department of Infection Management, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, China

⁶Internet Medical and System Applications of National Engineering Laboratory, Zhengzhou, China

Edited by:
Florence Le Calvez-Kelm, International Agency for Research on Cancer (IARC), France

Reviewed by:
Rupkatha Mukhopadhyay, Johns Hopkins Medicine, United States
Mingjian Lu, Institute of Guangzhou Medical University, China

* Correspondence: Shuijun Zhang, Zhangshuijun@zzu.edu.cn

†These authors have contributed equally to this work

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 29 August 2021
Accepted: 16 November 2021
Published: 14 December 2021

Citation: Liu L, Liu Z, Meng L, Li L, Gao J, Yu S, Hu B, Yang H, Guo W and Zhang S (2021) An Integrated Fibrosis Signature for Predicting Survival and Immunotherapy Efficacy of Patients With Hepatocellular Carcinoma. Front. Mol. Biosci. 8:766609. doi: 10.3389/fmolb.2021.766609

Introduction: Fibrosis, a primary cause of hepatocellular carcinoma (HCC), is intimately associated with inflammation, the tumor microenvironment (TME), and multiple carcinogenic pathways. Currently, due to widespread inter- and intra-tumoral heterogeneity of HCC, the efficacy of immunotherapy is limited. Seeking a stable and novel tool to predict prognosis and immunotherapy response is imperative.

Methods: Using stepwise Cox regression, least absolute shrinkage and selection operator (LASSO), and random survival forest algorithms, the fibrosis-associated signature (FAIS) was developed and further validated. Subsequently, comprehensive exploration was conducted to identify distinct genomic alterations, clinical features, biological functions, and immune landscapes of HCC patients.

Results: The FAIS was an independent prognostic predictor of overall survival and recurrence-free survival in HCC. In parallel, the FAIS exhibited stable and accurate performance at predicting prognosis based on the evaluation of Kaplan–Meier survival curves, receiver operator characteristic curves, decision curve analysis, and Harrell’s C-index. Further investigation elucidated that the high-risk group presented an inferior prognosis with advanced clinical traits and a high mutation frequency of TP53, whereas the low-risk group was characterized by superior CD8⁺ T cell infiltration, a higher TIS score, and a lower TIDE score. Additionally, patients in the low-risk group might yield more benefits from immunotherapy.

Conclusion: The FAIS was an excellent scoring system that could stratify HCC patients and might serve as a promising tool to guide surveillance, improve prognosis, and facilitate clinical management.

Keywords: hepatocellular carcinoma, fibrosis, immunotherapy, prognosis, immune

INTRODUCTION

Worldwide, liver cancers are the sixth most prevalent cancer and rank second in cancer-related mortality (Sung et al., 2021). Hepatocellular carcinoma (HCC) is the major histologic category of primary liver cancers, which mainly arise from chronic liver diseases, mostly as a result of HBV/HCV infection, alcoholic liver disease, and liver cirrhosis (Villanueva, 2019). Although the diagnosis and treatment of HCCs are increasingly diversified, the clinical benefits are limited, and the 5-year relapse rate exceeds 70% (Brown et al., 2019). In clinical practice, the Barcelona Clinic Liver Cancer (BCLC) staging system is a basic measurement to evaluate risk and manage the prognosis of HCC patients (Villanueva, 2019). However, the current staging system is restricted to stratifying patients and may hinder optimal clinical decisions as HCC patients present significant heterogeneity (Craig et al., 2020). The clinical heterogeneity of HCC is actually reflected from molecular heterogeneity. Therefore, searching for a new tool via multiple gene panels to stratify patients and further improve clinical management is imperative.

Fibrosis is a highly dynamic process and is associated with spatiotemporal regulation, the involvement of multiple carcinogenic signaling pathways, and the interaction of inflammation (Koyama and Brenner, 2017; Henderson et al., 2020). For instance, proteins involved in the mTOR signaling pathway, mTOR complex 1 (mTORC1) and mTOR complex 2 (mTORC2), have pronounced impacts on fibrogenesis (Huang et al., 2020). The mTORC1/4E-BP1 axis is a universal molecular pathway and is involved in the fibrosis progression of different tissues (Woodcock et al., 2019). Moreover, liver fibrosis could progress to cirrhosis and is a primary cause of HCC, playing an important role in the premalignant environment (Affo et al., 2017). Previous studies demonstrated that both the essential mediator of fibrogenesis transforming growth factor-β (TGF-β) and the aberrant glycolysis metabolism could activate hepatic stellate cells (HSCs), aggravating liver fibrosis (Koyama and Brenner, 2017; Chang and Yang, 2019). Various cells, cytokines, and the extracellular matrix are significant and consist of a fibrotic microenvironment, leading to the development of HCC (Filliol and Schwabe, 2019). Cancer-associated fibroblasts (CAFs) in HCC facilitate tumor metastasis via hedgehog and TGF-β pathways (Liu et al., 2016b). Inflammation is also involved in the process of fibrogenesis and regulates immune cells in the tumor microenvironment (TME) (Mack, 2018). Different phenotypic and functional macrophages are key players in fibrogenesis, such as pro-inflammatory M1 macrophages and anti-inflammatory M2 macrophages, which accelerate the occurrence and development of fibrosis (Wynn and Vannella, 2016; Schuppan et al., 2018). Exploring the biological characteristics and immune landscape of each patient might help increase the understanding of fibrogenesis.

HCC has a high relapse rate and adverse long-term prognosis, with a 5-year survival of approximately 18% (Jemal et al., 2017). New therapeutic strategies are desperately needed to improve overall survival (OS). Personalized immunotherapy is currently an encouraging and revolutionized treatment for solid tumors (Chai et al., 2020). Nevertheless, the therapeutic efficacy is disappointing as only a small proportion of individuals yield prominent benefits (Finn et al., 2020). The inter- and intra-tumoral heterogeneity of HCC may be responsible for different treatment effects (Craig et al., 2020). Many immune cells are critical components of the TME and display tight interactions with tumor cells, which are likely to involve the development of tumor heterogeneity (Hinshaw and Shevde, 2019; Liu et al., 2021d). Previous studies have suggested that TME markers exhibit striking advantages in assessing immunotherapy in HCC patients (Zhang et al., 2019). As a common approach of immunotherapy, immune checkpoint inhibitor (ICI) treatment has made encouraging progress, which aims to help immune surveillance and target specific immune checkpoints, such as programmed death-ligand 1 (PD-L1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) (Ribas and Wolchok, 2018). With the deep investigation of the TME, patients with objective and durable immunotherapy responses may be identified, which is conducive to improving clinical management.

Advances in bioinformatics and machine learning have made it possible to explore tumor heterogeneity at the genomic level and identify a robust multigene signature for HCC patients. In this context, we performed three machine learning algorithms, including stepwise Cox regression, LASSO, and random survival forest (RSF) algorithms, to construct and validate a stable signature. Based on the expression of fibrosis-associated genes (FAGs), an individualized fibrosis-associated signature (FAIS) for HCC patients was identified. Furthermore, the clinical and molecular characteristics, underlying biological functions, immune cell infiltration, and immune checkpoint profiles of FAIS were explored. Using the T-cell inflammatory signature (TIS) and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms, the immunotherapy efficacy was evaluated for each patient. The initial establishment of the FAIS for stratifying risk could facilitate clinical benefits, help improve therapeutic strategies, and promote prognostic management of HCC patients. Collectively, this study might support optimized decision making in immunotherapy and further facilitate better clinical outcomes for HCC patients.

MATERIALS AND METHODS

Public Data Collection and Processing

In this study, gene expression data were retrieved from TCGA (https://portal.gdc.cancer.gov/) and GEO (https://www.ncbi.nlm.nih.gov/geo/). All samples enrolled were available for mRNA sequencing. In the TCGA-LIHC cohort, the corresponding clinical information, copy number alteration (CNA), and somatic mutation were obtained from the online portal cBioPortal (http://www.cbioportal.org/), which contained 363 HCC patients with survival information. The GSE14520 cohort integrated 221 HCC patients who possessed comprehensive clinical information, including overall survival (OS) and recurrence-free survival (RFS). All the gene expression data are from the patients at diagnosis. The detailed baseline data from TCGA-LIHC and GSE14520 are summarized in Supplementary Table S1. OS and RFS were regarded as primary outcome events for prognostic factor analysis. OS was measured from the time of surgery to death, and RFS was estimated from the time of surgery to cancer recurrence.

Construction and Evaluation of Signatures

To obtain a stable and valuable signature, we used three machine learning algorithms for analysis, including stepwise Cox regression, LASSO, and RSF algorithms. These algorithms were fitted based on 10-fold cross-validations. First, some fibrosis-associated genes (FAGs) were retrieved from the Molecular Signatures Database (MSigDB, http://www.gsea-msigdb.org/gsea/msigdb/search.jsp). The other FAGs were extracted from the study of Job-S et al. (Job et al., 2020). Second, the limma package was used for differential expression analysis to identify candidate FAGs. Then, the overlapping differentially expressed genes (DEGs) were screened between the TCGA-LIHC and GSSE14520 cohorts. Kaplan–Meier analysis and univariate Cox regression were applied to further assess the prognostic value of these genes. Finally, we constructed three signatures, which were evaluated by the Kaplan–Meier survival curve, receiver operator characteristic (ROC) curve, decision curve analysis (DCA), and Harrell’s C-index. The Kaplan–Meier survival curve was utilized to display the predictive ability by the Survival R package. Using the time ROC package, time-dependent ROC curves for survival variables were created to assess the performance of signatures based on the area under the ROC curves (AUCs). DCA was employed to evaluate the clinical utility of the three signatures by the ggDCA R package. The Harrell’s C-index of three signatures was calculated and compared to reflect their accuracy and stability by the compareC R package.

Clinical and Molecule Characteristics

According to the median of the signature’s risk score, the HCC patients were assigned into high-risk and low-risk groups. The heatmap with multiple clinical traits was generated by the Pheatmap R package. The survival status, age, gender, BMI, AJCC stage, histology grade, and vascular invasion were displayed as patient annotations. Univariate and multivariate Cox regression analyses were used to identify the independent prognostic factors for OS and RFS. For genomic data, the maftools R package was utilized to explore the landscape of somatic mutations (Mayakonda et al., 2018). Additionally, the CNA status was compared between the high-risk and low-risk groups with chi-square tests.

Biological Function Enrichment Analysis

To further decipher potential biological functions between the high-risk and low-risk groups, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed with the clusterProfiler R package. The normalized enrichment score (NES) was calculated according to 1,000 permutations. A false discovery rate (FDR) < 0.05 was defined as statistically significant. In addition, the DEGs of the two groups were ranked in accordance with the log2-foldchange (logFC) value, and the gene set enrichment analysis (GSEA) algorithm was applied to decode the potential molecular mechanisms. The hallmark pathway gene sets were obtained from MSigDB and submitted to identify dramatically carcinogenic pathways using the GSVA algorithm implemented in the GSVA R package.

Evaluation of Immune Cell Infiltration and Immune Checkpoint Expression

According to the research by Charoentong et al.(Charoentong et al., 2017), immune gene signatures were collected from 28 immune cell subgroups, including innate immune cells and adaptive immune cells (Supplementary Table S2). The gene expression profiles and immune gene signatures were utilized to evaluate the infiltration abundance of 28 immune cells. Using a single sample gene set enrichment analysis (ssGSEA) algorithm (Zuo et al., 2020), the scores were calculated and applied to quantify the infiltration abundance of immune cells in the TME. To elucidate immunotherapy implications and facilitate clinical applications, we retrieved 27 co-stimulatory and 14 co-inhibitory immune checkpoints from the literature Xiao Y et al (Xiao et al., 2019). Based on expression profiles, the heatmaps and boxplots were generated with the ggplot2 R package. The correlations between the risk score and immune infiltration abundance and immune checkpoint expression were assessed by Spearman’s correlation analysis.

Estimation of Immunotherapy Efficacy

To evaluate the latent response to ICI treatment for HCC patients, two approaches were conducted, encompassing the T-cell inflammatory signature (TIS) and Tumor Immune Dysfunction and Exclusion (TIDE) analysis. Ayers et al. first proposed TIS and further predicted clinical response to PD-1 blockade (Ayers et al., 2017). The signature consists of 18 inflammatory genes conspicuously associated with antigen presentation, chemokine expression, cytotoxic activity, and adaptive immune resistance. The TIDE (http://tide.dfci.harvard.edu/) algorithm is used to model tumor immune evasion, which gathers two primary mechanisms of immune evasions: T cell dysfunction and T cell exclusion. TIDE is a prevalent tool that has been applied in mass studies to evaluate immunotherapy efficacy (Liu et al., 2021a; Liu et al., 2021b; Liu et al., 2021c). Patients with higher TIDE scores portended stronger potential for tumor immune evasion and worse immunotherapy response (Jiang et al., 2018).

Validation of the FAIS via the In-House Cohort

To further verify the robustness of FAIS, quantitative real-time PCR was performed to quantify the expression level of 11 genes. A total of 58 HCC tissues were obtained from patients who underwent surgical treatment at The First Affiliated Hospital of Zhengzhou University, and all the patients had written informed consent. None of the patients received any preoperative chemotherapy or radiotherapy. The detailed baseline is shown in Supplementary Table S1. All the tissue samples were immediately obtained and frozen in liquid nitrogen and subsequently stored at −80°C. This project was approved by the Ethics Committee Board of The First Affiliated Hospital of Zhengzhou University. RNA extraction and reverse transcription were conducted based on the manufacturer’s protocol. The sequences of quantitative real-time PCR primers are summarized in Supplementary Table S3. See Supplementary Material for details.

Statistical Analysis

The Survival, glmnet, and randomForestSRC R packages were used to perform stepwise Cox regression, LASSO, and RSF, respectively. The Kaplan–Meier method and log-rank test were applied to evaluate the different OS and RFS rates between the two groups. The Wilcoxon rank-sum test was adopted to estimate the difference of continuous variables when comparing two groups. For comparison of more than two groups, the Kruskal–Wallis test was conducted. Fisher’s exact test or Pearson’s chi-squared test was carried out to compare categorical variables. All statistical tests were two-sided tests, and p < 0.05 was considered as statistically significant. All data cleaning, statistical analyses, and plots were handled in R 4.0.5 software.

RESULTS

Multiple Machine Learning Algorithms Construct Robust Signatures

A total of 351 FAGs were retrieved in this study. Using the limma R package, there were 95 and 52 DEGs between normal and tumor samples in TCGA-LIHC and GSE14520, respectively. The overlapping DEGs were further screened as candidate genes, including 15 downregulated genes and 20 upregulated genes. To further explore the underlying prognostic value of these 35 FAGs, Kaplan–Meier analysis was carried out, and the results exhibited that 11 FAGs had a potential prognostic value (p < 0.05) (Figure 1A). In addition, univariate Cox regression revealed that FCN3 and GADD45B were protective prognostic indicators, while the remaining genes were all risk factors (Figure 1B). Based on the expression of these genes in TCGA-LIHC, we developed signatures for predicting prognosis by three machine learning algorithms. Stepwise Cox regression based on the Akaike information criterion (AIC) showed that signature-1 containing four FAGs was optimal, including FCN3, SPP1, IGFBP3, and MCM7 (Figure 2A). The coefficients of signature-1 were -0.13, 0.11, 0.12, and 0.16, respectively (Figure 2B). Another algorithm LASSO identified that signature-2 contained eight FAGs, encompassing RPN2, MCM7, HMGA1, CAPG, SPP1, FCN3, IGFBP3, and GADD45B (Figure 2C). The optimal lambda was obtained when the partial likelihood deviance reached the minimum value. The results suggested that the optimal lambda value was 0.014, and then signature-2 was constructed (Figure 2D). Using RSF, signature-3 contained 11 FAGs, and the error rate is exhibited in Figure 2E. The importance of each variable was estimated and is shown in Figure 2F. Taken together, we obtained three signatures to predict the OS of each HCC patient in the TCGA-LIHC cohort.

FIGURE 1 | Identification of FAGs with a significantly prognostic value. (A) Kaplan–Meier curves of OS according to the 11 FAGs in TCGA-LIHC. (B) Univariate Cox regression of 11 FAGs regarding OS in TCGA-LIHC.

FIGURE 2 | Establishment of prognostic signatures by three machine learning algorithms. (A) AIC of stepwise Cox regression analyses. (B) Coefficient of four genes finally obtained in stepwise Cox regression analyses. (C) LASSO coefficient profiles of the candidate genes for the construction of prognostic signature. (D) Determination of the optimal lambda is obtained when the partial likelihood deviance reached the minimum value and further generated the key genes with nonzero coefficients. The dotted vertical line is drawn at the optimal lambda value. (E) Relationship between the error rate and the number of classification trees. (F) Relative importance values of 11 out-of-bag genes.

The Comparison and Validation of Multiple Signatures

For OS as an endpoint event, Kaplan–Meier analysis suggested that the three signatures presented a significant prognostic value in the TCGA-LIHC cohort (Figures 3A–C). Similar results were displayed in the GSE14520 cohort (Supplementary Figures S2A–C). We assessed the three signatures across multiple dimensions and screened the optimal signature. The discrimination was evaluated by ROC and C-index, and clinical utility was assessed by DCA. In the TCGA-LIHC cohort, the ROC curves of the three signatures exhibited that the AUCs for predicting OS at 1–5 years were 0.759, 0.712, 0.719, 0.724, and 0.688 in signature-1; 0.761, 0.709, 0.718, 0.723, and 0.690 in signature-2; and 0.900, 0.877, 0.875, 0.888, and 0.889 in signature-3, respectively (Figures 3D–F). Moreover, the 95% confidence interval (CI) of the C-index and clinical utility value were calculated and compared. The C-index values were 0.699 (95% CI: 0.674–0.724), 0.701 (95% CI: 0.676–0.726), and 0.839 (95% CI: 0.824–0.854) among the three signatures, respectively (Supplementary Figure S1A). As instructed, signature-3 performed superior accuracy and clinical benefits compared with the other signatures (Figures 3G–I and Supplementary Figure S1A). In the GSE14520 cohort, the ROC curves of the three signatures exhibited that the AUCs for predicting OS at 1–5 years were 0.672, 0.720, 0.708, 0.699, and 0.686 in signature-1; 0.673, 0.722, 0.704, 0.692, and 0.681 in signature-2; and 0.699, 0.718, 0.700, 0.655, and 0.652 in signature-3, respectively (Supplementary Figures S2D–F). The C-index values were 0.664 (95% CI: 0.633–0.695), 0.660 (95% CI: 0.629–0.691), and 0.643 (95% CI: 0.611–0.675) among the three signatures, respectively (Supplementary Figure S1B). The three signatures had similar performance in predicting OS (Supplementary Figures S1B, S2G–I). After rigorous comparison and validation, signature-3 presented the highest predictive accuracy and the best clinical utility and was defined as the fibrosis-associated signature (FAIS) for subsequent analysis.

FIGURE 3 | Evaluation and comparison of signatures in the TCGA-LIHC cohort. (A–C) Kaplan–Meier curves of OS according to signature-1 (A), signature-2 (B), and signature-3 (C). (D–F) Time-dependent ROC analysis for predicting OS at 1–5 years according to signature-1 (D), signature-2 (E), and signature-3 (F). (G–I) DCA curves of signatures for evaluating 1- (G), 2- (H), and 3-year (I) OS.

The Clinical Characteristics and Implications

To further characterize and reveal the clinical significance of the FAIS, we combined clinical factors for assessing the performance at predicting OS and RFS. Univariate and multivariate Cox regression analyses suggested that the FAIS was an independent predictive factor for OS (Figures 4A,B). The Kaplan–Meier analysis of RFS indicated that the low-risk group had a favorable prognosis (Figures 4C,D). All results demonstrated that the FAIS was also an independent predictive factor for RFS (Figures 4E,F). The above results elucidated that FAIS may be an excellent clinical management and translation tool.

FIGURE 4 | Prognosis significance of overall survival and recurrence-free survival for the FAIS. (A) Univariate Cox regression analyses of OS in the TCGA-LIHC cohort. (B) Multivariate Cox regression analyses of OS in the TCGA-LIHC cohort. (C) Kaplan–Meier curve of RFS according to the FAIS in the TCGA-LIHC cohort. (D) Kaplan–Meier curve of RFS according to the FAIS in the GSE14520 cohort. (E) Univariate Cox regression analyses of RFS in the TCGA-LIHC cohort. (F) Multivariate Cox regression analyses of RFS in the TCGA-LIHC cohort.

To confirm the clinical implication of the FAIS, a clinical in-house cohort was used to evaluate its performance. Based on the median of risk score, patients were divided into high- and low-risk groups. As expected, patients in the low-risk group exhibited more favorable prognosis (Figure 5A). In the in-house cohort, the AUCs for predicting OS at 1–5 years were 0.902, 0.701, 0.821, 0.911, and 0.857 in FAIS, respectively (Figure 5B). The Kaplan–Meier analysis of RFS also indicated that the low-risk group had a favorable prognosis (Figure 5C). Multivariate Cox regression analyses verified that the FAIS was an independent predictive factor for OS and RFS (Figures 5D,E). All the results validated the excellent performance and reconfirmed the clinical applicability of FAIS.

FIGURE 5 | Validation of the FAIS via in-house cohort. (A) Kaplan–Meier curve of OS according to the FAIS in the in-house cohort. (B) Time-dependent ROC analysis for predicting OS at 1–5 years according to the FAIS. (C) Kaplan–Meier curve of RFS according to the FAIS in the in-house cohort. (D,E) Multivariate Cox regression analyses of OS (D) and RFS (E) in the in-house cohort.

Apart from the prognostic value, there were also differences in the distribution of clinicopathological characteristics and molecular alternations in the two groups. Compared to the low-risk group, most FAGs had higher expression in the high-risk group, whereas FCN3 and GADD45B exhibited the opposite pattern (Supplementary Figure S3A). High expression of FCN3 and GADD45B corresponded to a favorable prognosis, which was consistent with the above and previous results (Zhao et al., 2018; Wang et al., 2021). For clinicopathological characteristics, there were no conspicuously different distributions of age, gender, and BMI (Supplementary Figure S3B). The high-risk group mainly presented in patients with an advanced AJCC stage, a superior histological grade, and vascular invasion, predominantly related to the malignant phenotype and adverse prognosis (Supplementary Figure S3B). With the advancement of the AJCC stage and T stage, the FAIS score showed an increasing tendency, which presumed that the FAIS could stratify HCC patients, improving clinical outcomes (Supplementary Figures S3C,D). Overall, the FAIS could serve as an independent predictor of OS and RFS and help promote the prognostic management of HCC patients.

The Landscape of Somatic Mutation and Copy Number Alteration in the Two Groups

We depicted and explored genomic alterations to identify distinct molecular characteristics in the two groups. A summary of somatic mutations for all HCC patients is exhibited in Figure 6A. The most universal features of variant classification, variant type, and single nucleotide variant (SNV) class were missense mutations, single-nucleotide polymorphisms (SNPs), and “C > T”. The variant numbers for each sample and the mutation frequency of the top 10 genes were rendered intuitively (Figure 6A). Using the maftools R package, the top 15 mutated genes were identified and defined as significantly mutated genes (SMGs) between the two groups (Figures 6B,C). Eight overlapping SMGs were present in the two groups, encompassing TP53, CTNNB1, TTN, MUC16, MUC4, PCLO, ALB, and OBSCN, implying that these alterations were prevalent in HCC. Notably, the tumor suppressor gene TP53, proto-oncogene CTNNB1, and the largest known protein-encoded gene TTN were the top three SMGs in both groups. The alterations of these genes might be important drivers of HCC progression. Except for the core gene of the cell cycle, TP53, there were no significant mutation differences of common SMGs between the two groups (p-value > 0.05), but the metabolic gene ALB showed a lower mutation frequency in the high-risk group relative to the low-risk group (Supplementary Figure S4). The mutation of TP53 plays a key role in hepatocarcinogenesis and is associated with adverse clinical outcomes (Gao et al., 2019). The ALB gene is involved in both inflamed and metabolic pathway networks and impacts the prognosis of HCC (Chen et al., 2020; Wang et al., 2020). Thus, diverse mutations with specific molecules were associated with opposite clinical outcomes in the two groups. The amplification and deletion of copy number alteration (CNA) were further decoded. The top 20 genes were recruited based on copy number amplification and deletion for each patient. Both amplification and deletion were not pronounced (all p-value > 0.05), meaning that similar CNA events existed in the two groups (Figures 6D,E). These results suggested that somatic mutation might have more crucial impacts on distinct prognosis and HCC progression compared to CNA.

FIGURE 6 | Genomic alterations of high-risk and low-risk groups in the TCGA cohort. (A). Summary of somatic mutations for all HCC patients. (B,C) Top 15 significantly mutated genes in a high-risk group (B) and low-risk group (C). The percentage on the right showed the proportion of samples with mutations. (D,E) Top 20 genes with significant amplification and deletion of copy number alteration (CNA). Between the high-risk group and low-risk groups, the difference of amplification (D) and deletion (E) rates are further compared. “Amp” means amplification of CNA. “DEL” means deletion of CNA.

Distinct Biological Functions in the Two Groups

The initiation and development of HCC are regulated via multiple biological pathways. Using the clusterProfiler and GSVA R package, we depicted the specific biological characteristics of the two groups. Based on DEGs, the GO enrichment analysis indicated that cell division and biosynthetic processes were conspicuously enriched (Figure 7A). The KEGG enrichment analysis suggested that cell cycle and metabolism-associated pathways were identified (Figure 7B). Due to the significant heterogeneity of HCC, the two groups might have various functional characteristics. Enrichment analysis (GSEA) was further conducted, and we discovered that the significantly enriched cell cycle pathway was a crucial feature of the high-risk group (Figures 7C,D). Consistent with previous studies, the cell cycle and metabolism were key biological functions in HCC (Wu et al., 2020). Moreover, we investigated the underlying carcinogenic characteristics of the two groups with the GSVA algorithm. The high-risk group was characterized as cell cycle-associated pathways, and the low-risk group was characterized as metabolism-associated functions (Figure 7E). These results indicated that the two groups displayed different biological characteristics and were influenced by multiple pathways.

FIGURE 7 | Distinct biological functions of the two groups. (A,B) GO (A) and KEGG (B) enrichment analysis of differentially expressed genes between the high-risk group and low-risk group. The top 30 significantly enriched pathways extracted with adjusted p-value < 0.05. (C,D) Enrichment plots depicted by gene set enrichment analysis (GSEA) based on GO (C) and KEGG (D) gene sets, respectively. (E) Heatmap of 50 Hallmark gene sets between the high-risk group and low-risk group using the GSVA algorithm.

The Estimation of Immune Infiltration by the Fibrosis-Associated Signature

Since inflammation and immune cells are key factors for the process of fibrosis (Mack, 2018) and the FAIS was established based on FAGs, we hypothesized that there were distinct immune characteristics in the two risk groups. The ssGSEA algorithm was utilized to assess the infiltration abundance of 28 immune cells. As instructed, the heatmap depicted the relative infiltration of different immune cell populations (Supplementary Figure S5A). Superior infiltration of active CD8⁺ T cells was the immune feature in the low-risk group, whereas pronounced higher levels of active CD4⁺ T cells and dendritic cells (DCs) were the immune characteristics of the high-risk group (Figure 8A). A previous study proved that CD8⁺ T cells, named cytotoxic T cells, were the most crucial factor in killing tumor cells (Maimela et al., 2019). Therefore, compared to the high-risk group, patients in the low-risk group had superior CD8⁺ cell infiltration and might benefit more from ICI treatment. In addition, we discovered that FAIS exhibited a broad positive association with different immune cell categories, except for eosinophil cells (Figure 8B and Supplemetary Figure S5B). The most significant correlation was found between FAIS and CD4⁺ T cells (Figure 8B).

FIGURE 8 | Landscape of immune cell infiltration and profiles of immune checkpoint. (A) Distribution of 28 immune cell infiltrations between two risk groups in the TCGA-LIHC cohort. (B) Correlations between six specific immune cells and risk score using Spearman analysis. (C) Expression heatmap of immune checkpoints between two risk groups in the TCGA-LIHC cohort. ns p >0.05; *p <0.05, **p < 0.01, ***p < 0.001.

Immune Checkpoint Profiles of Fibrosis-Associated Signature and Prediction of Immunotherapy

The landscape of immune checkpoints in the two groups was decoded to gain new insights into facilitating clinical practice. Both co-stimulatory and co-inhibitory immune checkpoints (ICPs) exhibited higher expression in the high-risk group (Figure 8C). Prior studies suggested that the higher the expression of ICPs is the more immune suppression there is, further resulting in immune tolerance and unfavorable prognosis (Das and Johnson, 2019). Screening specific ICPs in the high-risk group might yield more benefit from ICI therapy. For most co-inhibitory ICPs, patients in the high-risk group displayed superior expression, such as CTLA4 and TIGIT (Figure 9A). Moreover, the risk score was broad and positively correlated with the expression of co-inhibitory ICPs (Figure 9B). Consistent with this finding, patients in the high-risk group also exhibited higher expression of most co-inhibitory ICPs (Supplementary Figure S5C). A strongly positive correlation was also found between the risk score and most co-inhibitory ICPs (Figure 9C). Notably, the tumor necrosis factor receptor superfamily (TNFRSF) showed conspicuously higher expression in the high-risk group. Targeting these specific molecules might have stronger potential benefits for patients in the high-risk group. To further evaluate the immunotherapeutic efficacy of the two groups, we applied TIS and TIDE approaches. The results indicated that a higher TIS score and lower TIDE score were characteristics of the low-risk group, implying that these patients might obtain more considerable benefits and improved clinical outcomes (Figure 9D). The patients in the low-risk group had a higher proportion of responders to immunotherapy (Figure 9E). Overall, the patients in the low-risk group demonstrated a better immunotherapy response.

FIGURE 9 | Deep exploration of immune checkpoints and potential immunotherapy predictor of FAIS. (A) Distribution of co-inhibitory ICPs between two risk groups in the TCGA-LIHC cohort. (B) Correlations between co-inhibitory ICPs and risk score using Spearman analysis. (C) Correlations between co-stimulatory ICPs and risk score using Spearman analysis. (D) Distribution difference of TIDE and TIS prediction scores between the high-risk group and low-risk group. (E) Immunotherapy response ratio of FAIS in the TCGA-LIHC cohort. ns p > 0.05; *p < 0.05, **p < 0.01, ***p < 0.001.

DISCUSSION

Hepatocellular carcinoma (HCC) is a complex disease with elevated incidence and mortality (Villanueva, 2019; Craig et al., 2020). A previous study reported that there are three distinct TME phenotypes in HCC that are prominently associated with prognosis, immune surveillance, immune escape, and genetic alterations (Liu et al., 2021e). Liver fibrosis indicates tremendous potential in tumorigenesis and tightly correlates with prognosis (Kariyama et al., 2019). Inflammation bursts and the immune responses activate hepatic stellate cells (HSCs) and favor extracellular matrix (ECM) deposition, leading to liver fibrosis (Koyama and Brenner, 2017; Mack, 2018). For instance, Th17 cells produce IL-17 cytokines and implicate fibrogenesis, and dendritic cells (DCs) induce HSCs to mediate inflammation and fibrogenesis (Koyama and Brenner, 2017). In parallel, immune cells are a major component of TME that have profound impacts on prognosis, carcinogenesis, and immunotherapy (Liu et al., 2021e). Therefore, liver fibrosis might interfere with TME phenotypes and the shape of HCC heterogeneity and even has implications for the immunotherapy response. Revolutionized and thrilling progress has been achieved in immunotherapy, but prominent therapeutic efficacy is limited to a small subset of populations. It is imperative to increase the understanding of HCC heterogeneity and search for a stable tool for identifying patients with excellent immunotherapy response.

Using three machine learning algorithms, a robust FAIS was ultimately constructed based on FAGs. The advantage of integrative analysis and comparison by multiple approaches arises in making the model more applicable and translational. This study shows that the FAIS was an independent factor for OS and RFS, which could serve as a prognostic biomarker. According to previous research, most of the 11 genes included in the FAIS displayed pivotal roles in the prognosis and progression of HCC (Liu et al., 2016a; Qu et al., 2017; Wang et al., 2021). FCN3 was reported to mediate apoptosis, activate the complement lectin pathway, regulate the immune system, and be a potential immunotherapy target and prognosticator for HCC (Wang et al., 2021). SPP1 was highly expressed in HCC and contributed to tumorigenesis by promoting a stem-like phenotype (Liu et al., 2016a). MCM7 regulated the initiation of DNA replication and driven HCC progression by cyclin D1-dependent signaling (Qu et al., 2017). There was strong attraction to further decipher the characteristics of HCC patients, and we comprehensively explored genomic alterations, clinical features, biological functions, and the immune landscape.

Patients were assigned into two groups by FAIS, the high-risk group with the features of an advanced AJCC stage, superior histological grade, and vascular invasion and the low-risk group with the traits of an early AJCC stage, inferior histological grade, and vascular invasion. Mostly, patients in the high-risk group were linked with poor prognosis, thus facilitating clinical management. Furthermore, metabolic disorders and aberrant proliferation are ubiquitous in liver diseases (Piccinin et al., 2019). The underlying biological pathways of the two groups are depicted in detail. The high-risk group was mainly enriched in metabolism-associated pathways, and the low-risk group was significantly enriched in proliferation-related pathways. Notably, patients in the high-risk group with adverse prognosis possessed the highest somatic mutation frequency, 43%, for the TP53 mutation. The DNA-replication kinase CDC7 inhibitor selectively treated liver cancer cells with TP53 mutation, inducing cell senescence (Wang et al., 2019). Molecular targeted therapies are increasingly prevalent in cancer treatment (Klinke, 2010). Distinct molecular alterations could be used as potential therapeutic targets, particularly for patients with poor prognosis and more molecular variations.

As an emerging and potential therapeutic approach, immunotherapy is gradually being incorporated into the treatment armamentarium in HCC (Llovet et al., 2018). Currently, because immunotherapy response lacks effective assessments, a promising indicator is urgently needed to improve the predictive performance of immunotherapy in HCC. The immune landscape and immune checkpoint profiles were dissected to identify the populations that may benefit. Our results suggested that patients in the low-risk group might yield more benefit from immunotherapy due to superior CD8⁺ T cell infiltration. ICI therapy is the backbone of immunotherapy aimed at enhancing the durable response and survival of patients with HCC and employs monoclonal antibodies targeting immune checkpoints, such as PD1 and PD-L1 (Cheng et al., 2020). Using TIDE and TIS, we found that patients in the low-risk group had a better immunotherapy response. Co-stimulatory and co-inhibitory ICPs are critical immune molecules that participate in T cell activation. The co-inhibitory ICPs attenuated anti-tumoral immune responses and facilitated tumor immune escape by suppressing T cell functions (Sangro et al., 2020). In the high-risk group, the expression of co-inhibitory ICPs was more highly and widely positively associated with FAIS. The aberrant distribution of co-inhibitory ICPs not only supported that these patients had worse prognosis but also implied that targeting these specific molecules via ICI treatment may improve clinical outcomes. Notably, the co-stimulatory ICPs were significantly elevated in the low-risk group and exhibited an extensive positive correlation with FAIS, especially for TNF superfamily members. TNFRSF9 (also known as 4-1BB) is conducive to T cell activation and augments tumor immunity. The agonist anti-4-1BB mAbs demonstrates the capacity to enhance CD8⁺ T cell responses to promote tumor rejection (Buchan et al., 2018; Pourakbari et al., 2021). TNFRSF18 (also known as GITR) combines with the GITR ligand to favor T cell proliferation, and an anti-GITR antibody agonist (TRX518) displays potent anti-tumor activity (Zappasodi et al., 2019). In addition, with the development of co-stimulation agonist therapy, combination therapy with ICIs is a booming immunotherapy approach. For patients in the high-risk group, co-stimulation agonists would be a sensible option. These results indicated that FAIS could stratify patients and guide precise treatment to some degree as a promising biomarker.

Although FAIS displayed many strengths, there are also some limitations. First, all cohorts retrieved in this study were retrospective, and further validation of this risk scoring system should be applied to prospective cohorts. Second, our study used bioinformatics algorithms to indirectly evaluate the immunotherapy response rather than large-scale immunotherapy clinical trials. Third, patients in the high-risk group had inferior prognosis and poor immunotherapy response, which needed more attention. In the further research, we will focus on these populations to develop more effective therapy strategies.

CONCLUSION

From the perspective of fibrosis, an independent predictor of OS and RFS, the FAIS was constructed by three machine learning algorithms. The FAIS could stratify HCC patients who had distinct genomic alterations, clinical features, biological functions, and immune landscapes. Our findings also revealed that FAIS had critical implications for immunotherapy and might be a useful tool to favor clinical surveillance and management for individual patients.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Material, and further inquiries can be directed to the corresponding author.

ETHICS STATEMENT

The studies involving human participants were reviewed and approved by the Ethics Committee Board of The First Affiliated Hospital of Zhengzhou University. The patients/participants provided their written informed consent to participate in this study.

AUTHOR CONTRIBUTIONS

LoL, ZL, and SZ designed this work. LoL, LM, LiL, and JG integrated and analyzed the data. LoL, SY, BH, and HY wrote this manuscript. LoL, WG, and SZ edited and revised the manuscript. All authors approved this manuscript.

FUNDING

This study was supported by Henan Province Medical Science and Technology Research Plan (Joint Construction) Project (SBGJ2018002) and the Scientific Research Project of Education Department of Henan Province (20A320014).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at https://www.frontiersin.org/articles/10.3389/fmolb.2021.766609/full#supplementary-material

Supplementary Table S2 | The gene sets for marking 28 immune cell types.

Supplementary Table S3 | The forward and reverse primers for quantitative real-time PCR.

REFERENCES

Affo, S., Yu, L.-X., and Schwabe, R. F. (2017). The Role of Cancer-Associated Fibroblasts and Fibrosis in Liver Cancer. Annu. Rev. Pathol. Mech. Dis. 12, 153–186. doi:10.1146/annurev-pathol-052016-100322

Ayers, M., Lunceford, J., Nebozhyn, M., Murphy, E., Loboda, A., Kaufman, D. R., et al. (2017). IFN-γ-related mRNA Profile Predicts Clinical Response to PD-1 Blockade. J. Clin. Invest. 127 (8), 2930–2940. doi:10.1172/JCI91190

Brown, Z. J., Greten, T. F., and Heinrich, B. (2019). Adjuvant Treatment of Hepatocellular Carcinoma: Prospect of Immunotherapy. Hepatology 70 (4), 1437–1442. doi:10.1002/hep.30633

Buchan, S. L., Dou, L., Remer, M., Booth, S. G., Dunn, S. N., Lai, C., et al. (2018). Antibodies to Costimulatory Receptor 4-1BB Enhance Anti-tumor Immunity via T Regulatory Cell Depletion and Promotion of CD8 T Cell Effector Function. Immunity 49 (5), 958–970. doi:10.1016/j.immuni.2018.09.014

Chai, L. F., Prince, E., Pillarisetty, V. G., and Katz, S. C. (2020). Challenges in Assessing Solid Tumor Responses to Immunotherapy. Cancer Gene Ther. 27 (7-8), 528–538. doi:10.1038/s41417-019-0155-1

Chang, M.-L., and Yang, S.-S. (2019). Metabolic Signature of Hepatic Fibrosis: From Individual Pathways to Systems Biology. Cells 8 (11), 1423. doi:10.3390/cells8111423

Charoentong, P., Finotello, F., Angelova, M., Mayer, C., Efremova, M., Rieder, D., et al. (2017). Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cel Rep. 18 (1), 248–262. doi:10.1016/j.celrep.2016.12.019

Chen, S., Ma, W., Cao, F., Shen, L., Qi, H., Xie, L., et al. (2020). Hepatocellular Carcinoma within the Milan Criteria: A Novel Inflammation-Based Nomogram System to Assess the Outcomes of Ablation. Front. Oncol. 10, 1764. doi:10.3389/fonc.2020.01764

Cheng, A.-L., Hsu, C., Chan, S. L., Choo, S.-P., and Kudo, M. (2020). Challenges of Combination Therapy with Immune Checkpoint Inhibitors for Hepatocellular Carcinoma. J. Hepatol. 72 (2), 307–319. doi:10.1016/j.jhep.2019.09.025

Craig, A. J., von Felden, J., Garcia-Lezana, T., Sarcognato, S., and Villanueva, A. (2020). Tumour Evolution in Hepatocellular Carcinoma. Nat. Rev. Gastroenterol. Hepatol. 17 (3), 139–152. doi:10.1038/s41575-019-0229-4

Das, S., and Johnson, D. B. (2019). Immune-related Adverse Events and Anti-tumor Efficacy of Immune Checkpoint Inhibitors. J. Immunotherapy Cancer 7 (1), 306. doi:10.1186/s40425-019-0805-8

Filliol, A., and Schwabe, R. F. (2019). Contributions of Fibroblasts, Extracellular Matrix, Stiffness, and Mechanosensing to Hepatocarcinogenesis. Semin. Liver Dis. 39 (3), 315–333. doi:10.1055/s-0039-1685539

Finn, R. S., Qin, S., Ikeda, M., Galle, P. R., Ducreux, M., Kim, T.-Y., et al. (2020). Atezolizumab Plus Bevacizumab in Unresectable Hepatocellular Carcinoma. N. Engl. J. Med. 382 (20), 1894–1905. doi:10.1056/NEJMoa1915745

Gao, Q., Zhu, H., Dong, L., Shi, W., Chen, R., Song, Z., et al. (2019). Integrated Proteogenomic Characterization of HBV-Related Hepatocellular Carcinoma. Cell 179 (2), 561–577. doi:10.1016/j.cell.2019.08.052

Henderson, N. C., Rieder, F., and Wynn, T. A. (2020). Fibrosis: from Mechanisms to Medicines. Nature 587 (7835), 555–566. doi:10.1038/s41586-020-2938-9

Hinshaw, D. C., and Shevde, L. A. (2019). The Tumor Microenvironment Innately Modulates Cancer Progression. Cancer Res. 79 (18), 4557–4566. doi:10.1158/0008-5472.CAN-18-3962

Huang, E., Peng, N., Xiao, F., Hu, D., Wang, X., and Lu, L. (2020). The Roles of Immune Cells in the Pathogenesis of Fibrosis. Ijms 21 (15), 5203. doi:10.3390/ijms21155203

Jemal, A., Ward, E. M., Johnson, C. J., Cronin, K. A., Ma, J., Ryerson, A. B., et al. (2017). Annual Report to the Nation on the Status of Cancer, 1975-2014, Featuring Survival. J. Natl. Cancer Inst. 109 (9), djx030. doi:10.1093/jnci/djx030

Jiang, P., Gu, S., Pan, D., Fu, J., Sahu, A., Hu, X., et al. (2018). Signatures of T Cell Dysfunction and Exclusion Predict Cancer Immunotherapy Response. Nat. Med. 24 (10), 1550–1558. doi:10.1038/s41591-018-0136-1

Job, S., Rapoud, D., Dos Santos, A., Gonzalez, P., Desterke, C., Pascal, G., et al. (2020). Identification of Four Immune Subtypes Characterized by Distinct Composition and Functions of Tumor Microenvironment in Intrahepatic Cholangiocarcinoma. Hepatology 72 (3), 965–981. doi:10.1002/hep.31092

Kariyama, K., Nouso, K., Toyoda, H., Tada, T., Hiraoka, A., Tsuji, K., et al. (2019). Utility of FIB4-T as a Prognostic Factor for Hepatocellular Carcinoma. Cancers 11 (2), 203. doi:10.3390/cancers11020203

Klinke, D. J. (2010). A Multiscale Systems Perspective on Cancer, Immunotherapy, and Interleukin-12. Mol. Cancer 9, 242. doi:10.1186/1476-4598-9-242

Koyama, Y., and Brenner, D. A. (2017). Liver Inflammation and Fibrosis. J. Clin. Invest. 127 (1), 55–64. doi:10.1172/JCI88881

Liu, G., Fan, X., Tang, M., Chen, R., Wang, H., Jia, R., et al. (2016a). Osteopontin Induces Autophagy to Promote Chemo-Resistance in Human Hepatocellular Carcinoma Cells. Cancer Lett. 383 (2), 171–182. doi:10.1016/j.canlet.2016.09.033

Liu, J., Chen, S., Wang, W., Ning, B.-F., Chen, F., Shen, W., et al. (2016b). Cancer-associated Fibroblasts Promote Hepatocellular Carcinoma Metastasis through Chemokine-Activated Hedgehog and TGF-β Pathways. Cancer Lett. 379 (1), 49–59. doi:10.1016/j.canlet.2016.05.022

Liu, Z., Liu, L., Jiao, D., Guo, C., Wang, L., Li, Z., et al. (2021a). Association of RYR2 Mutation with Tumor Mutation Burden, Prognosis, and Antitumor Immunity in Patients with Esophageal Adenocarcinoma. Front. Genet. 12, 669694. doi:10.3389/fgene.2021.669694

Liu, Z., Liu, L., Lu, T., Wang, L., Li, Z., Jiao, D., et al. (2021b). Hypoxia Molecular Characterization in Hepatocellular Carcinoma Identifies One Risk Signature and Two Nomograms for Clinical Management. J. Oncol. 2021, 1–20. doi:10.1155/2021/6664386

Liu, Z., Lu, T., Li, J., Wang, L., Xu, K., Dang, Q., et al. (2021c). Development and Clinical Validation of a Novel Six-Gene Signature for Accurately Predicting the Recurrence Risk of Patients with Stage II/III Colorectal Cancer. Cancer Cel Int 21 (1), 359. doi:10.1186/s12935-021-02070-z

Liu, Z., Wang, L., Liu, L., Lu, T., Jiao, D., Sun, Y., et al. (2021d). The Identification and Validation of Two Heterogenous Subtypes and a Risk Signature Based on Ferroptosis in Hepatocellular Carcinoma. Front. Oncol. 11, 619242. doi:10.3389/fonc.2021.619242

Liu, Z., Zhang, Y., Shi, C., Zhou, X., Xu, K., Jiao, D., et al. (2021e). A Novel Immune Classification Reveals Distinct Immune Escape Mechanism and Genomic Alterations: Implications for Immunotherapy in Hepatocellular Carcinoma. J. Transl Med. 19 (1), 5. doi:10.1186/s12967-020-02697-y

Llovet, J. M., Montal, R., Sia, D., and Finn, R. S. (2018). Molecular Therapies and Precision Medicine for Hepatocellular Carcinoma. Nat. Rev. Clin. Oncol. 15 (10), 599–616. doi:10.1038/s41571-018-0073-4

Mack, M. (2018). Inflammation and Fibrosis. Matrix Biol. 68-69, 106–121. doi:10.1016/j.matbio.2017.11.010

Maimela, N. R., Liu, S., and Zhang, Y. (2019). Fates of CD8+ T Cells in Tumor Microenvironment. Comput. Struct. Biotechnol. J. 17, 1–13. doi:10.1016/j.csbj.2018.11.004

Mayakonda, A., Lin, D.-C., Assenov, Y., Plass, C., and Koeffler, H. P. (2018). Maftools: Efficient and Comprehensive Analysis of Somatic Variants in Cancer. Genome Res. 28 (11), 1747–1756. doi:10.1101/gr.239244.118

Piccinin, E., Villani, G., and Moschetta, A. (2019). Metabolic Aspects in NAFLD, NASH and Hepatocellular Carcinoma: the Role of PGC1 Coactivators. Nat. Rev. Gastroenterol. Hepatol. 16 (3), 160–174. doi:10.1038/s41575-018-0089-3

Pourakbari, R., Hajizadeh, F., Parhizkar, F., Aghebati-Maleki, A., Mansouri, S., and Aghebati-Maleki, L. (2021). Co-stimulatory Agonists: An Insight into the Immunotherapy of Cancer. EXCLI J. 20, 1055–1085. doi:10.17179/excli2021-3522

Qu, K., Wang, Z., Fan, H., Li, J., Liu, J., Li, P., et al. (2017). MCM7 Promotes Cancer Progression through Cyclin D1-dependent Signaling and Serves as a Prognostic Marker for Patients with Hepatocellular Carcinoma. Cell Death Dis 8 (2), e2603. doi:10.1038/cddis.2016.352

Ribas, A., and Wolchok, J. D. (2018). Cancer Immunotherapy Using Checkpoint Blockade. Science 359 (6382), 1350–1355. doi:10.1126/science.aar4060

Sangro, B., Chan, S. L., Meyer, T., Reig, M., El-Khoueiry, A., and Galle, P. R. (2020). Diagnosis and Management of Toxicities of Immune Checkpoint Inhibitors in Hepatocellular Carcinoma. J. Hepatol. 72 (2), 320–341. doi:10.1016/j.jhep.2019.10.021

Schuppan, D., Surabattula, R., and Wang, X. Y. (2018). Determinants of Fibrosis Progression and Regression in NASH. J. Hepatol. 68 (2), 238–250. doi:10.1016/j.jhep.2017.11.012

Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., et al. (2021). Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA A. Cancer J. Clin. 71 (3), 209–249. doi:10.3322/caac.21660

Villanueva, A. (2019). Hepatocellular Carcinoma. N. Engl. J. Med. 380 (15), 1450–1462. doi:10.1056/NEJMra1713263

Wang, C., Vegna, S., Jin, H., Benedict, B., Lieftink, C., Ramirez, C., et al. (2019). Inducing and Exploiting Vulnerabilities for the Treatment of Liver Cancer. Nature 574 (7777), 268–272. doi:10.1038/s41586-019-1607-3

Wang, S., Song, Z., Tan, B., Zhang, J., Zhang, J., and Liu, S. (2021). Identification and Validation of Hub Genes Associated with Hepatocellular Carcinoma via Integrated Bioinformatics Analysis. Front. Oncol. 11, 614531. doi:10.3389/fonc.2021.614531

Wang, Y., Wu, D., Wu, G., Wu, J., Lu, S., Lo, J., et al. (2020). Metastasis-on-a-chip Mimicking the Progression of Kidney Cancer in the Liver for Predicting Treatment Efficacy. Theranostics 10 (1), 300–311. doi:10.7150/thno.38736

Woodcock, H. V., Eley, J. D., Guillotin, D., Platé, M., Nanthakumar, C. B., Martufi, M., et al. (2019). The mTORC1/4E-BP1 axis Represents a Critical Signaling Node during Fibrogenesis. Nat. Commun. 10 (1), 6. doi:10.1038/s41467-018-07858-8

Wu, H., Reizel, Y., Wang, Y. J., Lapiro, J. L., Kren, B. T., Schug, J., et al. (2020). A Negative Reciprocal Regulatory axis between Cyclin D1 and HNF4α Modulates Cell Cycle Progression and Metabolism in the Liver. Proc. Natl. Acad. Sci. USA 117 (29), 17177–17186. doi:10.1073/pnas.2002898117

Wynn, T. A., and Vannella, K. M. (2016). Macrophages in Tissue Repair, Regeneration, and Fibrosis. Immunity 44 (3), 450–462. doi:10.1016/j.immuni.2016.02.015

Xiao, Y., Ma, D., Zhao, S., Suo, C., Shi, J., Xue, M.-Z., et al. (2019). Multi-Omics Profiling Reveals Distinct Microenvironment Characterization and Suggests Immune Escape Mechanisms of Triple-Negative Breast Cancer. Clin. Cancer Res. 25 (16), 5002–5014. doi:10.1158/1078-0432.CCR-18-3524

Zappasodi, R., Sirard, C., Li, Y., Budhu, S., Abu-Akeel, M., Liu, C., et al. (2019). Rational Design of Anti-GITR-based Combination Immunotherapy. Nat. Med. 25 (5), 759–766. doi:10.1038/s41591-019-0420-8

Zhang, J., Shi, Z., Xu, X., Yu, Z., and Mi, J. (2019). The Influence of Microenvironment on Tumor Immunotherapy. FEBS J. 286 (21), 4160–4175. doi:10.1111/febs.15028

Zhao, Z., Gao, Y., Guan, X., Liu, Z., Jiang, Z., Liu, X., et al. (2018). GADD45B as a Prognostic and Predictive Biomarker in Stage II Colorectal Cancer. Genes 9 (7), 361. doi:10.3390/genes9070361

Zuo, S., Wei, M., Wang, S., Dong, J., and Wei, J. (2020). Pan-Cancer Analysis of Immune Cell Infiltration Identifies a Prognostic Immune-Cell Characteristic Score (ICCS) in Lung Adenocarcinoma. Front. Immunol. 11, 1218. doi:10.3389/fimmu.2020.01218

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Liu, Liu, Meng, Li, Gao, Yu, Hu, Yang, Guo and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 20 December 2021
doi: 10.3389/fmolb.2021.784553

Identification and Validation of a Prognostic Prediction Model of m6A Regulator-Related LncRNAs in Hepatocellular Carcinoma

Chen Jin¹†, Rui Li²†, Tuo Deng3,⁴, Jialiang Li3,⁴, Yan Yang¹, Haoqi Li¹, Kaiyu Chen3,⁴, Huihua Xiong²*, Gang Chen3,⁴* and Yi Wang¹*

1Department of Epidemiology and Biostatistics, School of Public Health and Management, Wenzhou Medical University, Wenzhou, China

2Department of Oncology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China

3Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China

4Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China

Edited by:
Ismail Hosen, University of Dhaka, Bangladesh

Reviewed by:
Cecilia Battistelli, Sapienza University of Rome, Italy
Vinay Kumar, The Ohio State University, United States

* Correspondence: Huihua Xiong, xionghuihua@hotmail.com; Gang Chen, chen.gang@wmu.edu.cn; Yi Wang, wang.yi@wmu.edu.cn

†These authors have contributed equally to this work

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 28 September 2021
Accepted: 08 November 2021
Published: 20 December 2021

Citation: Jin C, Li R, Deng T, Li J, Yang Y, Li H, Chen K, Xiong H, Chen G and Wang Y (2021) Identification and Validation of a Prognostic Prediction Model of m6A Regulator-Related LncRNAs in Hepatocellular Carcinoma. Front. Mol. Biosci. 8:784553. doi: 10.3389/fmolb.2021.784553

Hepatocellular carcinoma (HCC) is a highly invasive malignancy prone to recurrence, and patients with HCC have a low 5-year survival rate. Long non-coding RNAs (lncRNAs) play a vital role in the occurrence and development of HCC. N6-methyladenosine methylation (m6A) is the most common modification influencing cancer development. Here, we used the transcriptome of m6A regulators and lncRNAs, along with the complete corresponding clinical HCC patient information obtained from The Cancer Genome Atlas (TCGA), to explore the role of m6A regulator-related lncRNA (m6ARlnc) as a prognostic biomarker in patients with HCC. The prognostic m6ARlnc was selected using Pearson correlation and univariate Cox regression analyses. Moreover, three clusters were obtained via consensus clustering analysis and further investigated for differences in immune infiltration, immune microenvironment, and prognosis. Subsequently, nine m6ARlncs were identified with Lasso-Cox regression analysis to construct the prognostic signature m6A-9LPS for patients with HCC in the training cohort (n = 226). Based on m6A-9LPS, the risk score for each case was calculated. Patients were then divided into high- and low-risk subgroups based on the cutoff value set by the X-tile software. m6A-9LPS showed a strong prognosis prediction ability in the validation cohort (n = 116), the whole cohort (n = 342), and even clinicopathological stratified survival analysis. Combining the risk score and clinical characteristics, we established a nomogram for predicting the overall survival (OS) of patients. To further understand the mechanism underlying the m6A-9LPS-based classification of prognosis differences, KEGG and GO enrichment analyses, competitive endogenous RNA (ceRNA) network, chemotherapeutic agent sensibility, and immune checkpoint expression level were assessed. Taken together, m6A-9LPS could be used as a precise prediction model for the prognosis of patients with HCC, which will help in individualized treatment of HCC.

Keywords: hepatocellular carcinoma, m6A regulators, long non-coding RNA, prognosis prediction, therapy

INTRODUCTION

According to the latest data released by the International Agency for Research on Cancer (IARC), hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide, with a high mortality rate and an increasing annual morbidity trend (Siegel et al., 2020), posing a tremendous threat to public health (Lin et al., 2020). In recent years, significant advances have been made in terms of treatments such as partial hepatectomy, liver transplantation, interventional surgery, and systemic treatment for HCC (Wu et al., 2020), which significantly improves the prognosis of patients with early-stage HCC. However, the long-term outcome of late-stage HCC, which is found in half of the patients at the time of diagnosis, is still devastating (Jung et al., 2017). At present, extensively applied staging systems using clinicopathological factors, such as histological grade, lymph node metastasis, and tumor-node-metastasis (TNM), provide relatively vague predictions in evaluating the options for treatment and the prognostic outcomes for patients with HCC (Cao et al., 2014; Yoo et al., 2018). In fact, there is a lack of a highly sensitive and specific tumor signature to accurately evaluate the prognosis of patients. Therefore, it is imperative to identify novel and precise biomarkers, for both prognosis and improvement of individualized treatments for patients.

RNA modification is the post-transcriptional regulation of gene expression, which is widely present in various RNA types (Agris et al., 2017; Roundtree et al., 2017). To date, more than 100 types of RNA modifications have been identified; among them, RNA methylation accounts for more than 60% of all modifications (Zhang S. et al., 2020). N6-methyladenosine (m6A), the most common type of RNA methylation modification in eukaryotes, plays an important role in the biological processes of immunity, metabolism, proliferation, and apoptosis (Meyer and Jaffrey 2017; Huang et al., 2018). The methylation level of m6A in tumors is mainly modulated by three types of regulators: methyltransferases, demethylases, and methyl-binding proteins (Robinson et al., 2019; Wang et al., 2021). Various studies have shown that the abnormal regulation of m6A is correlated with the occurrence and development of various human tumors such as cervical cancer, breast cancer, prostate cancer, lung cancer, pancreatic cancer, hepatocellular carcinoma, and glioma (Zhang B. et al., 2020; Ma X. et al., 2020; Dong and Cui 2020; Hou et al., 2020; Yuan et al., 2020; Zhao et al., 2020). For example, the impaired autophagic degradation of ARHGAP5-AS1 by METT12 promotes chemoresistance in gastric cancer patients, which is associated with a poor prognosis (Zhu et al., 2019); LINC00662 regulates the radioresistance of oral squamous carcinoma cells by affecting AK4 (Chen Y. et al., 2020); upregulation of PVT1 mediated by ALKBH5 affects the prognosis of osteosarcoma (Chen S. et al., 2020).

Long non-coding RNA (lncRNA) is a non-protein-encoding RNA with a transcript length more than 200 bp. LncRNAs can perform cis or trans transcriptional regulation or RNA molecular regulation. It is known that lncRNAs regulate various aspects of tumors (Kung and Lee 2013; Tran et al., 2016; Kopp and Mendell 2018). For example, CHRF is an lncRNA associated with the epithelial-to-interstitial transformation (EMT), which participates in the occurrence and development of gastric cancer (Gong J. et al., 2020); upregulation of the TUC338 may activate the MAPK pathway to promote lung cancer invasion (Zhang et al., 2018); many studies have reported that lncRNAs are related to the progression and prognosis of liver cancer (Li et al., 2018; Pan et al., 2019; Hong et al., 2020). However, few studies have explored the relationship between lncRNA and the expression of m6A regulators with the occurrence or development of HCC. In addition to the potential applications, value, and mechanisms of lncRNAs regulating the expression of m6A regulators, the relationship between lncRNAs and the prognosis of patients with HCC is still unclear.

Therefore, in this study, we collected transcriptomic data and clinicopathological data of patients with HCC and conducted a series of bioinformatic analyses to understand the m6A regulator-related lncRNA expression and its impact in HCC, and to shed light on the underlying mechanisms for the prognosis of patients. The results of this study may help identify biomarkers that could be used as novel therapeutic targets for HCC.

MATERIALS AND METHODS

Data Collection and Pre-processing to Identify Prognostic lncRNAs Associated With m6A Regulators

FPKM-masked sequencing data of 374 primary hepatocyte malignant tissues and 50 normal tissues were obtained from The Cancer Genome Atlas (TCGA; https://portal.gdc.cancer.gov) database and annotated with human gene annotation files from the Ensembl official website (http://asia.ensembl.org/index.html; GRCh38.103. chr.gtf.gz). GTF files (GRCh38.103. chr.gtf.gz) were used to annotate and distinguish the mRNAs and lncRNAs for subsequent analysis. Corresponding clinicopathological data were downloaded concomitantly. Patients with pathologically confirmed HCC and complete clinical information were included in this study; those with a survival time inferior or equal to 30 days were excluded to avoid any bias originating from demise due to unrelated factors. A total of 342 HCC cases were included in the subsequent analysis.

By systematically searching and reviewing representative m6A-related literature, 23 universally acknowledged m6A regulators, namely, 8 methyltransferases, 2 demethylases, and 13 binding proteins, were selected. The details of these genes [14, 31–35] are listed in Supplementary Table S1. We calculated the correlation coefficient between the expression levels of m6A regulators and the expression level of lncRNA and selected the one with |Pearson r| > 0.4 and p < 0.001 as the m6A regulator-related lncRNA (m6ARlnc). Then, the expression matrix of the m6ARlnc genes was extracted. A flow chart of this study is illustrated in Supplementary Figure S1.

To identify prognostic m6ARlncs, univariate Cox regression analysis was conducted to screen the differentially expressed lncRNAs between the tumor and normal groups, with significance set at p ≤ 5 × 10⁻⁵, using the limma R package.

Comprehensive Analysis of Patients With HCC Based on the Consensus Clustering of Prognostic m6ARlnc

Based on the differentially expressed prognostic m6ARlncs in tumor and normal tissues, unsupervised consensus clustering was performed using the ConsensusClusterPlus R package. Based on the similarity between the expression levels of prognostic m6ARlncs and the proportion of fuzzy clustering measurements, the cluster number with the best stability was determined. In addition, CIBERSORT analysis was performed to determine the proportion of 22 types of immune cells in each sample. The ESTIMATE algorithm was used to evaluate the tumor microenvironment (TME) score (including stromal cell, immune cell, and comprehensive scores).

Establishment and Validation of an m6ARlncs Prognostic Prediction Model

To fully understand the predictive value of prognostic m6ARlnc, 342 HCC cases were randomly divided into a training cohort (n = 226) and a validation cohort (n = 116) in a 2:1 ratio. In this study, all subsequent analyses were performed in the training cohort, except for model validation, hierarchical analysis of the model, and enrichment analysis of the signature, which were performed on the entire cohort. First, the glmnet, survminer, and survival R packages were used to conduct Lasso-Cox regression analysis to construct a prognosis prediction model of patients with HCC by screening the prognostic m6ARlncs. The model was optimized by cross-validation, and lncRNAs used to construct the prognostic signature were screened. A survival plot was constructed using the survival R package. The risk score of each patient was calculated using the following formula:

Coefi represents the coefficient of the corresponding m6ARlnc from the Cox regression model, and Xi is the expression level of m6ARlnc. The X-tile software was used to determine the optimal cutoff value of the risk score for the training cohort, and the same cutoff value was used in the validation and entire cohorts. Thus, all patients were divided into high- and low-risk groups. Second, Kaplan–Meier (K-M) survival curve analysis was conducted to compare the survival rates between groups using the survival R package.

The receiver operator characteristic (ROC) curves for 1, 3, and 5 years were constructed using the timeROC R package to evaluate the predictive accuracy of this signature, and the areas under the curve (AUC) were calculated. In addition, to confirm that the signature has consistent predictions across patients with different clinical characteristics such as sex, age, and tumor status, we conducted subgroup stratification survival analysis based on clinicopathological features using the K-M plot.

Construction of a Nomogram Integrating the Prognostic Signature and Clinical Characteristics

To improve the clinical application of the prognostic signature, we constructed a comprehensive prediction model combining the signature risk score and clinicopathological factors. First, we conducted a univariate Cox regression analysis, and variables with p < 0.0001 were subsequently included in the multivariate Cox regression analysis to screen independent factors in predicting the prognosis of patients with HCC. Second, we evaluated whether there were differences among clinicopathological factors, classification characteristics of prognostic m6ARlnc, stromal cell score, immune cell score, comprehensive score, and risk score between the differential expression of lncRNAs in the prognostic signature. The stromal cell, immune cell, and comprehensive scores were calculated using the ESTIMATE algorithm. Finally, a nomogram was established, according to the independent prognostic factors, to predict overall survival (OS) of patients using the rms R package.

Identification of Enriched Signaling Pathways and Function Between High- and Low-Risk Patients Classified by Prognostic Signature

To identify potential signaling pathways related to high- and low-risk patients determined by prognostic signature, we performed a gene set enrichment analysis (GSEA) using the GSEA software (version: 4.1.0). The gene set c2. cp.kegg.v7.4. symbols.gmt was downloaded from Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and the gene set used for Gene Ontology (GO) enrichment analysis was c5. go.bp.v7.3. symbols.gmt.

Construction of a ceRNA Network and Functional Enrichment Analysis

To explore the regulatory relationship between lncRNA and m6A regulators, constructing a ceRNA network is a good choice. The miRcode database (http://www.mircode.org/) was used to predict miRNAs that interact with prognostic m6ARlncs. To identify the target mRNAs, the relationship between miRNA and target mRNA was characterized using miRDB (http://www.mirdb.org/), miRTarBase (https://mirtarbase.cuhk.edu.cn/), and TargetScan (http://www.targetscan.org/). In addition, the Cytoscape (version 3.6.0) software was used to visualize the lncRNA–miRNA–mRNA ceRNA network. Subsequently, GO and KEGG enrichment analyses of target mRNAs were performed using the clusterProfiler R package to explore the potential signaling pathways of m6ARlncs.

Evaluation of Chemotherapeutic Agent Sensitivity and Expression of Immune Checkpoints Among Patients With HCC in High- and Low-Risk Groups

To predict the sensitivity of high- and low-risk patients to chemotherapeutic agents, we used the pRRophetic (https://github.com/paulgeeleher/pRRophetic) R package to predict the half-maximal inhibitory concentration (IC50) of chemotherapeutic agents for each patient. The R package was based on pre-treatment gene expression and drug sensitivity data of cancer cell lines to predict the chemotherapeutic response (Geeleher et al., 2014). Thirty-three immune checkpoints (Supplementary Table S2) were used to evaluate the performance of the model for immunotherapy and to analyze the differences in immune checkpoint expression levels between high- and low-risk groups.

Statistical Analysis

The Wilcoxon rank-sum test was used to compare the differences between two groups of continuous variables, and the Kruskal–Wallis test was used to examine the difference in TEM scores between the different subtypes. The chi-square test was used to examine the expression differences between lncRNAs. The K-M curve, combined with the log-rank test, was used to evaluate the OS of the different groups. Independent predictive factors were identified using Cox proportional hazards regression analysis. Based on the results of multivariate Cox analysis, a nomogram was established and evaluated for consistency using the C-index and calibration curve. All statistical analyses were carried out using the R software (version 3.6.3). Unless indicated otherwise, p < 0.05 was considered statistically significant.

RESULTS

Identification of m6ARlnc in Patients With HCC

A total of 644 lncRNAs that were significantly correlated with 23 m6A regulators were identified in the TCGA dataset and defined as m6ARlnc. The co-expression network between these 23 m6A regulators and the related lncRNAs is shown in Figure 1A. We conducted a univariate Cox regression analysis, and the results indicated that out of these 644 lncRNAs, 31 were associated with the OS of patients with HCC (Figure 1B). We further compared the expression levels of these 31 lncRNAs between tumor and normal tissues and found that the tumor group presented a significantly higher expression level (Figures 1C,D).

FIGURE 1 | Identification of m6ARlnc and prognostic m6ARlnc. (A) The co-expression network between m6A regulators and m6A regulator-related lncRNAs. (B) Forest plot of predictive ability of 31 prognostic lncRNAs associated with m6A regulators. (C) The heatmap of the expression of 31 m6A regulator-related prognostic lncRNAs in cancer and normal groups in the TCGA cohort (green: low expression level, red: high expression level). (D) Box plot graph of the expression of 31 prognostic lncRNAs related to m6A regulators in the TCGA cohort (green: low expression level, red: high expression level; p-value was: ***p < 0.001, *p < 0.05).

Association of Consensus Clustering of Prognostic m6ARlnc With Characteristics and Survival of Patients With HCC

To identify heterogeneity, unsupervised consensus clustering was performed on all samples based on the expression levels of prognostic m6ARlncs. We determined that k = 3 had the best clustering stability among the range k = 2 to 9 (Figures 2A–D). Thus, patients with HCC were divided into three subgroups: Cluster1 (n = 80), Cluster2 (n = 243), and Cluster3 (n = 19). To evaluate the prognostic value of m6ARlncs, survival analysis was performed on the different subgroups; significant differences were observed in the clinical outcomes. The OS of Cluster2 was the highest, while that of Cluster3 was the lowest (Figure 2E). The heatmap shows the differences in the expression of prognostic m6ARlncs in different subgroups, and the expression levels of most prognostic m6ARlncs were elevated in Cluster3; contrastingly, their expression levels were lowest in Cluster2. The differences in the expression of prognostic m6ARlncs in concomitance of various clinicopathological factors are also described in the above-mentioned heatmap. There was no difference in the expression of these lncRNAs among the clinicopathological factors (Figure 2F). Our results indicate a close relationship between the aggregation subgroup defined by the expression levels of prognostic m6ARlncs and the heterogeneity of patients with HCC.

FIGURE 2 | Consensus clustering of prognostic lncRNAs related to m6A regulators. (A) Consensus clustering matrix for k = 3. (B) Consensus clustering cumulative distribution function (CDF), (C) the relative change of area under the CDF curve and (D) tracking plot from k = 2 to 9. (E) Kaplan–Meier curve of OS for three clusters in HCC. (F) The heatmap for expression levels of 31 m6A regulator-related prognostic lncRNAs in the TCGA cohort, based on the relationship between three clusters and clinicopathological features.

Immunocyte Infiltration Landscape of Prognostic m6ARlncs

Increasing evidence shows that m6ARlncs are involved in the tumor immune microenvironment. CIBERSORT analysis showed that T cells regulatory (p = 0.014) and B cells naive (p = 0.011) were significantly enriched among the three subgroups (Figure 3A). Then, the stromal cell score, immune cell score, and comprehensive score of each sample were obtained using the ESTIMATE algorithm. Results revealed that the stromal cell score of Cluster2 was higher than that of Cluster1 (p < 0.001) and Cluster3 (p = 0.028), and the comprehensive score of Cluster2 was also higher than Cluster1 (p = 0.042), indicating that Cluster2 contained more immune-related cells and tumor cells of lower purity (Figures 3B–D).

FIGURE 3 | The immune characteristics of the prognostic lncRNA patterns associated with three m6A regulators. (A) The difference of 22 kinds of immune cell infiltration levels under the three kinds of prognosis m6A regulator-related lncRNA. (B) The expression of immune score, (C) stromal cell score, and (D) comprehensive score in lncRNA patterns of three kinds of prognosis m6A regulator-related lncRNA.

Establishment of a Prognostic Signature Based on m6ARlncs and Performance Evaluation and Validation of the Signature

Nine lncRNAs (LINC01224, C2orf27A, AL158166.1, AL117336.2, MKLN1-AS, AL031985.3, LINC01138, POLH-AS1, and AL442125.2) were finally selected to construct a prognostic signature (m6A-9LPS) using LASSO Cox regression (Figures 4A,B). The risk score for each HCC patient was calculated based on the expression levels and coefficients of these nine lncRNAs, using the following equation (Table 1):

FIGURE 4 | Risk model of m6A regulator-related lncRNA. (A) Lasso Cox regression analysis of 31 m6A regulator-related prognostic lncRNAs. (B) Partial likelihood deviation for different number of variables. (C) The optimal cutoff value of risk score identified by X-tile analysis. (D) Kaplan–Meier curve of HCC patients with the best cutoff value in X-tile analysis. The picture is generated by X-tile software. (E) The distribution of risk scores and (F) survival status of HCC patients in the training cohort. (G) The expression matrix of nine lncRNAs included in the model in the training cohort of HCC patients. (H) The distribution of risk scores and (I) survival status of HCC patients in the validation cohort. (J) The expression matrix of nine lncRNAs included in the model in the validation cohort of HCC patients. Kaplan–Meier curve of OS of high-risk and low-risk HCC patients in the (K) training and (L) validation cohort.

TABLE 1 | Nine lncRNAs significantly correlated with the overall survival of hepatocellular carcinoma.

Patients in the training cohort were assigned to the high- or low-risk group according to the best cutoff value of 2.9, which was determined via X-tile software analysis (Figures 4C,D). When analyzing the survival time and status of patients with HCC according to the distribution of risk scores, patients in the high-risk group showed a worse prognosis than those in the low-risk group. The nine lncRNAs were differentially expressed in the high- and low-risk groups, with a higher expression level in the high-risk group (Figures 4E–G). Patients in the validation cohort were also divided into high- and low-risk groups using the same cutoff value of the training cohort (Figures 4H–J). Furthermore, the prognosis of the two groups was compared using the K-M curve, and the results revealed that patients with a high-risk score had a poor prognosis both in the training and validation cohorts (p < 0.001, Figures 4K,L). In the training cohort, the 3-year survival rates of the high- and low-risk groups were 31.8 and 72.8%, respectively; in the validation cohort, these rates were 29.9 and 80.2%, respectively.

In addition, we evaluated the predictive ability of the established m6A-9LPS signature in the training, validation, and entire cohorts. Regardless of the ROC curve analysis in the three cohorts, m6A-9LPS showed strong predictive capabilities (Figures 5A–C). Moreover, the AUC values of m6A-9LPS were higher than those of single signatures in the training, validation, and entire cohorts, which was confirmed by time-dependent ROC analysis (Figures 5D–F). These results suggest that m6A-9LPS has a strong ability to predict the prognosis of patients with HCC.

FIGURE 5 | M6A-9LPS predicts the efficiency of OS in patients with HCC in training, validation, and the entire cohort. (A–C) The time-dependent ROC curve analysis verified the efficiency of m6A-9LPS in predicting 1-year, 3-year, and 5-year survival rates in the training, validation, and entire TCGA cohort. (D–F) The time-dependent AUC value graph compared the predictive efficiency between m6A-9LPS and known prognostic markers in the training cohort, validation, and entire TCGA cohort.

Stratified Survival Analysis of m6A-9LPS in TCGA Cohort

To confirm the stability of this model in predicting the prognosis of patients with HCC under different clinicopathological conditions, we performed a stratified analysis on the entire cohort of patients divided into different subgroups based on their different clinical characteristics. Survival analysis revealed significant differences in prognosis between high-risk and low-risk groups classified by the m6A-9LPS model, regardless of sex, age, grade, or AJCC staging. In patients over 65 years of age (n = 126) and patients aged 65 or younger (n = 216), m6A-9LPS could accurately divide the high- and low-risk groups (p < 0.001) (Figures 6A,B); similarly, the male (n = 233, p < 0.001) and female (n = 109, p = 0.02) patients were also accurately divided into high- and low-risk groups (Figures 6C,D). In addition, with regard to grade and AJCC staging, the K-M curve showed that the low-risk group had a better OS (Figures 6E–P). However, for patients with grade G4 (n = 12, p = 0.79), stage N1 (n = 3), stage M1 (n = 3), and stage IV (n = 3), there was no significant difference in prognosis between the high-risk and low-risk groups (Supplementary Figure S2). These results suggest that m6A-9LPS may be a powerful prognostic signature for patients with HCC, and that it is not affected by changes in traditional clinicopathological features.

FIGURE 6 | The KM curve of patients with OS stratified by age, gender, and clinical stage according to m6A-9LPS. (A, B) KM curve of the elderly and young patients. (C, D) KM curve of female and male patients. (E–G) KM curve of G1, G2, and G3 stage patients. (H) KM curve of patients with the M0 stage group. (I) KM curve of patients with the N0 stage group. (J–L) KM curve of stage I, stage II, and stage III groups. (M–P) KM curves of T1, T2, T3, and T4 stage groups.

Establishment of a Nomogram Combining the m6A-9LPS Signature and Clinicopathological Factors for Predicting the Prognosis of Patients With Hepatocellular Carcinoma

Independent prognostic analysis helped us understand whether m6A-9LPS could be independent of other clinicopathological factors that may affect the prognosis of patients with HCC. Univariate and multivariate Cox regression analyses showed that AJCC staging and m6A-9LPS risk scores were significantly correlated with the prognosis of patients (both p < 0.001). This indicates that, the m6A-9LPS risk score is an independent prognostic indicator of HCC (Figures 7A–D). The heatmap shows that the nine lncRNAs were upregulated in the high-risk group. Except for the cluster group (p < 0.001), there was no significant difference in the expression of the nine lncRNAs between the high- and low-risk groups (Figure 8A).

FIGURE 7 | Cox proportional hazards regression analysis of risk score of m6A-9LPS combined with clinical factors. The forest map of (A) univariate and (B) multivariate Cox regression analysis in the training cohort. The forest map of (C) univariate and (D) multivariate Cox regression analysis in the validation cohort.

FIGURE 8 | Establishment of nomogram for predicting prognosis of HCC patients. (A) The expression of nine lncRNAs in m6A-9LPS was correlated with the clustering characteristics, stromal cell score, immune cell score, comprehensive score, and clinical factors. (B) Nomogram for predicting OS of HCC patients constructed based on training cohort patients and significant clinical factors of multivariate Cox regression analysis. (C–E) The calibration curves of the Nomogram predicted 1-year, 3-year, and 5-year OS in the training, validation, and entire cohort.

We then constructed a nomogram to predict the OS of patients with HCC based on all the significant prognostic factors from the multivariate Cox regression analysis. The nomogram was used to evaluate the 1-year, 3-year, and 5-year OS rates. The total score was calculated based on the scores of all the variables in the nomogram. The OS rate of each patient could be predicted by drawing a vertical line from the total score to the survival prediction axis (Figure 8B). In addition, the C-index of the nomogram was 0.726 (95% CI, 0.663–0.788). The results of the calibration curve analysis showed that, when predicting the OS of patients with HCC at 1 year, 3 years, and 5years, the nomogram had precise predictive ability, and the OS rate was close to reality in the training, validation, and entire cohorts (Figures 8C–E).

Construction of a ceRNA Network and Enrichment Analysis of Target mRNA Functional Signals

We constructed a ceRNA network based on prognostic m6ARlncs to illustrate how m6ARlnc can regulate target mRNA expression in HCC via sponging miRNA. Four prognostic m6ARlncs were extracted from the miRcode database, and 98 pairs of interactions between the four lncRNAs and 11 miRNAs were identified. Based on the results of 11 miRNAs, 352 target mRNAs were identified. Interestingly, these mRNAs contained the m6A regulator FMR1 (Edupuganti et al., 2017), which confirmed that in HCC, m6ARlnc can act as a ceRNA by targeting m6A regulators through miRNA. Finally, we constructed a ceRNA network of lncRNA–miRNA–mRNA containing four lncRNAs, 11 miRNAs, and 352 mRNAs (Figure 9A). Enrichment analysis of 352 target mRNAs showed that these genes were correlated with enriched Ras protein signal transduction, negative regulation of G1/S transition of mitotic cell cycle, negative regulation of cell cycle G1/S phase transition, cell cycle arrest, and positive regulation of cell morphogenesis involved in differentiation (the top five terms of GO biological processes). The top five KEGG signaling pathways were in hepatitis B, pancreatic cancer, endocytosis, axon guidance, and Kaposi sarcoma-associated herpesvirus infection (Figures 9B,C). This information may provide a reference for further exploration of the function of m6ARlnc in HCC.

FIGURE 9 | The ceRNA network containing m6ARlncs and their target miRNAs and mRNAs. (A) A ceRNA network containing four m6ARlncs (green), 11 miRNAs (purple), and 352 mRNAs (red). Visualization of the results of target mRNA functional signal enrichment, the top five terms of GO (B) and the top 30 pathways of KEGG (C).

Enrichment Analysis of m6A-9LPS

To investigate the biological processes and pathways underlying the molecular heterogeneity between the high- and low-risk m6A-9LPS divided subgroups, we performed KEGG and GO enrichment analyses on the entire cohort. In the GO enrichment analysis, we selected the biological process (BP), combined with the results of the KEGG enrichment analysis, to explore the main biological processes involving lncRNAs in patients with HCC. In the KEGG enrichment analysis, the cell cycle, DNA replication, fatty acid metabolism, homologous recombination, and mismatch repair pathways were significantly enriched in the tumors of the m6A-9LPS-defined high-risk group. In the low-risk group, the significantly enriched pathways were mainly related to various metabolic processes (including primary bile acid biosynthesis, tryptophan metabolism, and acetone metabolism), spliceosomes, and the degradation of certain amino acids (valine, leucine, and isoleucine) (Figure 10A). The results of GO enrichment analysis showed that in the high-risk group, cell cycle DNA replication, DNA-dependent replication, mRNA export from the nucleus, ncRNA export from the nucleus, and tRNA transport played important roles, while no significant biological processes were enriched in the low-risk group (Figure 10B).

FIGURE 10 | The potential mechanism of m6A-9LPS in TCGA cohort was studied by gene set enrichment analysis. (A) Five representative pathways in the high-risk group and five representative pathways in the low-risk group by KEGG enrichment analysis. (B) Five representative biological processes in the high-risk group of BP by GO enrichment analysis. The difference of IC50 of axitinib (C), dasatinib (D), erlotinib (E), sorafenib (F), sunitinib (G), and gemcitabine (H) between high- and low-risk groups. Box plot of 33 immune checkpoints in entire cohort (green: low-risk group, red: high-risk group; p-values were: ***p < 0.001, **p < 0.01, *p < 0.05) (I).

Responses of Patients to Chemotherapy and Immunotherapy Based on the Classification of m6A-9LPS

The IC50 of six commonly used chemotherapeutic agents was predicted in high- and low-risk groups for HCC, which was based on the algorithm provided in the pRRophetic R package. The IC50 values of axitinib, dasatinib, erlotinib, sorafenib, and sunitinib were higher in the high-risk group, indicating that patients in the low-risk group were more sensitive to these five drugs; at the same time, the IC50 of gemcitabine was higher in low-risk patients, which meant that high-risk patients were more sensitive to gemcitabine (Figures 10C–H). In addition, the relationship between the gene expression levels of immune checkpoints and the risk group was further analyzed. The expression levels of 33 immune checkpoints in the high-risk group were significantly higher than those in the low-risk group (p < 0.001, Figure 10I).

DISCUSSION

The change in lncRNAs can be used as a target or biomarker for disease diagnosis and stage definition. Many studies have reported that lncRNAs play an essential role in the invasion, migration, proliferation, apoptosis, and drug resistance of a variety of tumors (Wei and Wang 2017; Zhao et al., 2018; Luo et al., 2019; Zhuang et al., 2019; Shi et al., 2020). More importantly, lncRNAs are involved in the occurrence and development of HCC (Wong et al., 2018; Huang et al., 2020), with m6A modification strongly regulating the RNA life cycle. The m6A modification in tumors is primarily encoded by “writers” that catalyze the formation of m6A (such as METL16, RBM15, and ZC3H13), “erasers” that selectively remove m6A (FTO and ALKBH5), and “readers” that decode the methylation of m6A (such as YTHDC1, FMR1, and RBMX) (Jung and Goldman 2018; Meng et al., 2020; Wang et al., 2020). m6A has been shown to be involved in the progression of various cancers (Alarcon et al., 2015; Zhao et al., 2017; Liu J. et al., 2019), but there are few studies on the regulation of m6A regulators on lncRNAs in tumors. Our study attempted to focus on the relationship between m6A regulator-related lncRNAs and their association with the prognosis of patients with HCC, aiming to establish a new prediction signature of m6ARlnc, explore its potential underlying molecular mechanisms, and evaluate its clinical application in foreseeing the prognosis of patients and determining the most suitable treatment.

In the entire cohort, the results of the co-expression network suggested that many lncRNAs were related to m6A regulators, which stimulated our interest in exploring the expression and role of m6ARlncs in HCC. At the same time, we further explored the relationship between the subgroups enriched with prognostic m6ARlnc and the OS, tumor cell immune infiltration, and TME of patients through consensus clustering analysis. The results suggested that prognostic m6ARlncs have a potential predictive effect. CIBERSORT analysis showed that regulatory T cells and naïve B cells were highly infiltrated in Cluster2; in the results provided by the ESTIMATE algorithm, stromal cell score of Cluster2 was higher. Therefore, the activated regulatory T cells and naïve B cells infiltration in the TME may be related to the poor prognosis of HCC, which is consistent with the results of previous studies on immunity in other tumors (Ma C. et al., 2020; Song and Wu 2020), providing novel insights for patients with HCC on immunotherapy choice.

A total of 342 patients from the TCGA dataset were randomly divided into training and validation cohorts to explore the prognostic value of m6ARlnc. Finally, nine lncRNAs were selected to establish the risk signature, and survival analysis showed that our model could strongly predict the OS of patients in the training, validation, and entire cohorts. To the best of our knowledge, lncRNA-regulated miRNA, chemotherapeutic agent sensitivity, lncRNA mutation, and immunotherapy are closely related to tumor progression. LINC01224, LINC01138, and MKLN1-AS affect the progression of HCC by regulating miRNAs; the upregulation of C2orf27 A was found to be related to resistance to sorafenib in HCC; POLH-AS1 mutation is associated with the occurrence and development of many diseases, but it has not been reported in HCC (Flanagan et al., 2007; Jiang et al., 2019; Gong D. et al., 2020; Gao et al., 2020; Kong et al., 2020; Yuan et al., 2021). Thus, we can say that the lncRNAs in m6A-9LPS are closely related to the progression of cancer and its therapy. In addition, three of these lncRNAs have never been reported; hence, we speculate that further research may be helpful for the early diagnosis of HCC and the development of new prognostic signatures.

Additionally, a nomogram was constructed and validated. The c-index of the nomogram and the 1-year, 3-year, and 5-year calibration curves of patients with HCC showed that the nomogram prediction was highly consistent with the actual survival. Additionally, in the validation and entire cohorts, the nomogram showed excellent predictive ability, providing clinicians with an easy-to-develop prognosis prediction tool.

Through KEGG and GO enrichment analyses, we revealed the potential biological processes and pathways underlying m6A-9LPS. It is worth noting that the scores of patients in the high-risk group were closely related to the cell cycle, DNA replication, and RNA transport. The cell cycle is a regulatory process closely associated with cell reproductive capacity, and its repetition leads to exponential cell proliferation (Coffman 2004). An imbalance in the cell cycle is very common in tumors, and its expression can result in abnormal proliferation or apoptosis (Evan and Vousden 2001). DNA replication is a critical process in cell proliferation, and many studies have shown that it is an important hallmark of cancer (Evan and Vousden 2001; Masai et al., 2010). RNA modification can affect RNA transport through RNA interference (Witkin et al., 2015); therefore, we speculate that the type of RNA modification involved in HCC may be m6A, confirming the rationale of our study. The metabolism-related pathways and amino acid degradation were more prominent in the low-risk group. Metabolic reprogramming plays an important role in tumor cells, which, in the presence of abundant nutrients, can promote the transformation of carbon into biomacromolecules via the activation of metabolic pathways (Boroughs and DeBerardinis 2015). At the same time, the imbalance of amino acid degradation also affects the state of tumor cells and the metabolism of patients (Sivanand and Vander Heiden, 2020). In addition, m6ARlncs may play the role of ce-RNA, targeting m6A regulators to affect the occurrence and development of HCC. At the same time, the results of target mRNA enrichment of m6ARlncs were similar to those of the previous enrichment analyses of patients in the high- and low-risk groups, mainly related to the cell cycle and certain malignant processes. In summary, m6A-9LPS may mediate the occurrence and development of HCC through the above-mentioned pathways; however, further experimental research is needed to explain the potential role of these lncRNAs in HCC. Moreover, in the follow-up research, more attention should be paid to the interaction between lncRNAs and m6A regulators, in order to gain a deeper understanding of the potential mechanisms underlying the occurrence and development of HCC.

It is well known that HCC is resistant to chemotherapeutic drugs; hence, it is very important to choose individualized chemotherapeutic agents (Feng et al., 2020; Xu et al., 2020). The predictive results on the sensitivity to six chemotherapeutic agents suggested that gemcitabine is an ideal choice for patients with HCC in the high-risk group, whereas axitinib, dasatinib, erlotinib, sorafenib, and sunitinib may have a better effect in patients in the low-risk group. However, further experiments are necessary to validate these results.

Immunotherapy has become a promising method to cure HCC, according to emerging studies (Liu Z. et al., 2019). The immune checkpoints play an indispensable role in tumors in maintaining self-tolerance and regulating the duration and intensity of the immune response. Blocking the immune checkpoints has become a new method for eliminating the immunosuppressive microenvironment, thus achieving tumor immunotherapy (Ribas and Wolchok 2018). Our results showed that the expression levels of 33 immune checkpoints were significantly different between the m6A-9LPS-defined high- and low-risk groups, with an upregulation in the high-risk group. The use of immunosuppressive agents in high-risk patients may be effective.

However, our study had some limitations. First, this was a retrospective analysis based on open-source databases, so to avoid the possible deviation of the data from different platforms, we used the internal validation method in the TCGA cohort; if possible, it should also be validated in other independent cohorts. Additionally, the potential mechanism of m6A-9LPS may need to be further validated through in vitro and in vivo studies.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.

AUTHOR CONTRIBUTIONS

YW, GC, and HX conceptualized and designed the study. CJ, RL, TD, JL, YY, HL, and KC collected the data and performed the analysis. All authors contributed to results interpretation. CJ and RL drafted the initial version of the manuscript. All authors critically reviewed many revisions of the manuscript and contributed important intellectual content. YW, GC, and HX had full access to all the data in the study and had responsibility for the integrity of the data, the accuracy of the analyses, and the final decision to submit the manuscript for publication.

FUNDING

This study was supported by the National Natural Science Foundation of China (81703310).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

ACKNOWLEDGMENTS

We sincerely thank the scientists who shared their data on the public database.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2021.784553/full#supplementary-material

REFERENCES

Agris, P. F., Narendran, A., Sarachan, K., Väre, V. Y. P., and Eruysal, E. (2017). The Importance of Being Modified. Enzymes 41, 1–50. doi:10.1016/bs.enz.2017.03.005

Alarcón, C. R., Lee, H., Goodarzi, H., Halberg, N., and Tavazoie, S. F. (2015). N6-methyladenosine marks Primary microRNAs for Processing. Nature 519 (7544), 482–485. doi:10.1038/nature14281

Boroughs, L. K., and DeBerardinis, R. J. (2015). Metabolic Pathways Promoting Cancer Cell Survival and Growth. Nat. Cel Biol. 17 (4), 351–359. doi:10.1038/ncb3124

Cao, Y.-W., Li, W.-Q., Wan, G.-X., Li, Y.-X., Du, X.-M., Li, Y.-C., et al. (2014). Correlation and Prognostic Value of SIRT1 and Notch1 Signaling in Breast Cancer. J. Exp. Clin. Cancer Res. 33, 97. doi:10.1186/s13046-014-0097-2

Chen, S., Zhou, L., and Wang, Y. (2020a). ALKBH5-mediated m6A Demethylation of lncRNA PVT1 Plays an Oncogenic Role in Osteosarcoma. Cancer Cel Int. 20, 34. doi:10.1186/s12935-020-1105-6

Chen, Y., Bao, C., Zhang, X., Lin, X., and Fu, Y. (2020b). Knockdown of LINC00662 Represses AK4 and Attenuates Radioresistance of Oral Squamous Cell Carcinoma. Cancer Cel Int. 20, 244. doi:10.1186/s12935-020-01286-9

Coffman, J. A. (2004). Cell Cycle Development. Develop. Cel 6 (3), 321–327. doi:10.1016/s1534-5807(04)00067-x

Dong, Z., and Cui, H. (2020). The Emerging Roles of RNA Modifications in Glioblastoma. Cancers 12 (3), 736. doi:10.3390/cancers12030736

Edupuganti, R. R., Geiger, S., Lindeboom, R. G. H., Shi, H., Hsu, P. J., Lu, Z., et al. (2017). N6-methyladenosine (m6A) Recruits and Repels Proteins to Regulate mRNA Homeostasis. Nat. Struct. Mol. Biol. 24 (10), 870–878. doi:10.1038/nsmb.3462

Evan, G. I., and Vousden, K. H. (2001). Proliferation, Cell Cycle and Apoptosis in Cancer. Nature 411 (6835), 342–348. doi:10.1038/35077213

Feng, J., Dai, W., Mao, Y., Wu, L., Li, J., Chen, K., et al. (2020). Simvastatin Re-sensitizes Hepatocellular Carcinoma Cells to Sorafenib by Inhibiting HIF-1α/PPAR-γ/PKM2-Mediated Glycolysis. J. Exp. Clin. Cancer Res. 39 (1), 24. doi:10.1186/s13046-020-1528-x

Flanagan, A., Rafferty, G., O'Neill, A., Rynne, L., Kelly, J., McCann, J., et al. (2007). The Human POLH Gene Is Not Mutated, and Is Expressed in a Cohort of Patients with Basal or Squamous Cell Carcinoma of the Skin. Int. J. Mol. Med. 19 (4), 589–596. doi:10.3892/ijmm.19.4.589

Gao, W., Chen, X., Chi, W., and Xue, M. (2020). Long Noncoding RNA MKLN1AS Aggravates Hepatocellular Carcinoma Progression by Functioning as a Molecular Sponge for miR6543p, Thereby Promoting Hepatoma Derived Growth Factor Expression. Int. J. Mol. Med. 46 (5), 1743–1754. doi:10.3892/ijmm.2020.4722

Geeleher, P., Cox, N., and Huang, R. S. (2014). pRRophetic: an R Package for Prediction of Clinical Chemotherapeutic Response from Tumor Gene Expression Levels. PLoS One 9 (9), e107468. doi:10.1371/journal.pone.0107468

Gong, D., Feng, P.-C., Ke, X.-F., Kuang, H.-L., Pan, L.-L., Ye, Q., et al. (2020a). Silencing Long Non-coding RNA LINC01224 Inhibits Hepatocellular Carcinoma Progression via MicroRNA-330-5p-Induced Inhibition of CHEK1. Mol. Ther. Nucleic Acids 19, 482–497. doi:10.1016/j.omtn.2019.10.007

Gong, J., Wang, Y., and Shu, C. (2020b). LncRNA CHRF Promotes Cell Invasion and Migration via EMT in Gastric Cancer. Eur. Rev. Med. Pharmacol. Sci. 24 (3), 1168–1176. doi:10.26355/eurrev_202002_20168

Hong, W., Liang, L., Gu, Y., Qi, Z., Qiu, H., Yang, X., et al. (2020). Immune-Related lncRNA to Construct Novel Signature and Predict the Immune Landscape of Human Hepatocellular Carcinoma. Mol. Ther. Nucleic Acids 22, 937–947. doi:10.1016/j.omtn.2020.10.002

Hou, J., Wang, Z., Li, H., Zhang, H., and Luo, L. (2020). Gene Signature and Identification of Clinical Trait-Related M6 A Regulators in Pancreatic Cancer. Front. Genet. 11, 522. doi:10.3389/fgene.2020.00522

Huang, T., Gao, Q., Feng, T., Zheng, Y., Guo, J., and Zeng, W. (2018). FTO Knockout Causes Chromosome Instability and G2/M Arrest in Mouse GC-1 Cells. Front. Genet. 9, 732. doi:10.3389/fgene.2018.00732

Huang, Z., Zhou, J.-K., Peng, Y., He, W., and Huang, C. (2020). The Role of Long Noncoding RNAs in Hepatocellular Carcinoma. Mol. Cancer 19 (1), 77. doi:10.1186/s12943-020-01188-4

Jiang, H., Shi, X., Ye, G., Xu, Y., Xu, J., Lu, J., et al. (2019). Up-regulated Long Non-coding RNA DUXAP8 Promotes Cell Growth through Repressing Krüppel-like Factor 2 Expression in Human Hepatocellular Carcinoma. Onco. Targets Ther. 12, 7429–7436. doi:10.2147/OTT.S214336

Jung, H. I., Jeong, D., Ji, S., Ahn, T. S., Bae, S. H., Chin, S., et al. (2017). Overexpression of PD-L1 and PD-L2 Is Associated with Poor Prognosis in Patients with Hepatocellular Carcinoma. Cancer Res. Treat. 49 (1), 246–254. doi:10.4143/crt.2016.066

Jung, Y., and Goldman, D. (2018). Role of RNA Modifications in Brain and Behavior. Genes Brain Behav. 17 (3), e12444. doi:10.1111/gbb.12444

Kong, W., Wang, X., Zuo, X., Mao, Z., Cheng, Y., and Chen, W. (2020). Development and Validation of an Immune-Related lncRNA Signature for Predicting the Prognosis of Hepatocellular Carcinoma. Front. Genet. 11, 1037. doi:10.3389/fgene.2020.01037

Kopp, F., and Mendell, J. T. (2018). Functional Classification and Experimental Dissection of Long Noncoding RNAs. Cell 172 (3), 393–407. doi:10.1016/j.cell.2018.01.011

Kung, J. T. Y., and Lee, J. T. (2013). RNA in the Loop. Develop. Cel 24 (6), 565–567. doi:10.1016/j.devcel.2013.03.009

Li, B., Mao, R., Liu, C., Zhang, W., Tang, Y., and Guo, Z. (2018). LncRNA FAL1 Promotes Cell Proliferation and Migration by Acting as a CeRNA of miR-1236 in Hepatocellular Carcinoma Cells. Life Sci. 197, 122–129. doi:10.1016/j.lfs.2018.02.006

Lin, Z., Ni, X., Dai, S., Chen, H., Chen, J., Wu, B., et al. (2020). Screening and Verification of Long Noncoding RNA Promoter Methylation Sites in Hepatocellular Carcinoma. Cancer Cel Int. 20, 311. doi:10.1186/s12935-020-01407-4

Liu, J., Harada, B. T., and He, C. (2019a). Regulation of Gene Expression by N-Methyladenosine in Cancer. Trends Cel Biol. 29 (6), 487–499. doi:10.1016/j.tcb.2019.02.008

Liu, Z., Lin, Y., Zhang, J., Zhang, Y., Li, Y., Liu, Z., et al. (2019b). Molecular Targeted and Immune Checkpoint Therapy for Advanced Hepatocellular Carcinoma. J. Exp. Clin. Cancer Res. 38 (1), 447. doi:10.1186/s13046-019-1412-8

Luo, H., Xu, C., Le, W., Ge, B., and Wang, T. (2019). lncRNA CASC11 Promotes Cancer Cell Proliferation in Bladder Cancer through miRNA‐150. J. Cel Biochem. 120 (8), 13487–13493. doi:10.1002/jcb.28622

Ma, C., Luo, H., Cao, J., Zheng, X., Zhang, J., Zhang, Y., et al. (2020a). Identification of a Novel Tumor Microenvironment-Associated Eight-Gene Signature for Prognosis Prediction in Lung Adenocarcinoma. Front. Mol. Biosci. 7, 571641. doi:10.3389/fmolb.2020.571641

Ma, X., Li, Y., Wen, J., and Zhao, Y. (2020b). m6A RNA Methylation Regulators Contribute to Malignant Development and Have a Clinical Prognostic Effect on Cervical Cancer. Am. J. Transl. Res. 12 (12), 8137–8146.

Masai, H., Matsumoto, S., You, Z., Yoshizawa-Sugata, N., and Oda, M. (2010). Eukaryotic Chromosome DNA Replication: where, when, and How?Annu. Rev. Biochem. 79, 89–130. doi:10.1146/annurev.biochem.052308.103205

Meng, Z., Yuan, Q., Zhao, J., Wang, B., Li, S., Offringa, R., et al. (2020). The m6A-Related mRNA Signature Predicts the Prognosis of Pancreatic Cancer Patients. Mol. Ther. Oncolytics 17, 460–470. doi:10.1016/j.omto.2020.04.011

Meyer, K. D., and Jaffrey, S. R. (2017). Rethinking m6A Readers, Writers, and Erasers. Annu. Rev. Cel Dev. Biol. 33, 319–342. doi:10.1146/annurev-cellbio-100616-060758

Pan, W., Li, W., Zhao, J., Huang, Z., Zhao, J., Chen, S., et al. (2019). Lnc RNA ‐ PDPK 2P Promotes Hepatocellular Carcinoma Progression through the PDK 1/AKT/Caspase 3 Pathway. Mol. Oncol. 13 (10), 2246–2258. doi:10.1002/1878-0261.12553

Ribas, A., and Wolchok, J. D. (2018). Cancer Immunotherapy Using Checkpoint Blockade. Science 359 (6382), 1350–1355. doi:10.1126/science.aar4060

Robinson, M., Shah, P., Cui, Y.-H., and He, Y.-Y. (2019). The Role of Dynamic m6A RNA Methylation in Photobiology. Photochem. Photobiol. 95 (1), 95–104. doi:10.1111/php.12930

Roundtree, I. A., Evans, M. E., Pan, T., and He, C. (2017). Dynamic RNA Modifications in Gene Expression Regulation. Cell 169 (7), 1187–1200. doi:10.1016/j.cell.2017.05.045

Shi, Q., Li, Y., Li, S., Jin, L., Lai, H., Wu, Y., et al. (2020). LncRNA DILA1 Inhibits Cyclin D1 Degradation and Contributes to Tamoxifen Resistance in Breast Cancer. Nat. Commun. 11 (1), 5513. doi:10.1038/s41467-020-19349-w

Siegel, R. L., Miller, K. D., and Jemal, A. (2020). Cancer Statistics, 2020. CA A. Cancer J. Clin. 70 (1), 7–30. doi:10.3322/caac.21590

Sivanand, S., and Vander Heiden, M. G. (2020). Emerging Roles for Branched-Chain Amino Acid Metabolism in Cancer. Cancer Cell 37 (2), 147–156. doi:10.1016/j.ccell.2019.12.011

Song, J., and Wu, L. (2020). Friend or Foe: Prognostic and Immunotherapy Roles of BTLA in Colorectal Cancer. Front. Mol. Biosci. 7, 148. doi:10.3389/fmolb.2020.00148

Tran, N. T., Su, H., Khodadadi‐Jamayran, A., Lin, S., Zhang, L., Zhou, D., et al. (2016). The AS‐RBM15 lncRNA Enhances RBM15 Protein Translation during Megakaryocyte Differentiation. EMBO Rep. 17 (6), 887–900. doi:10.15252/embr.201541970

Wang, H., Zhao, X., and Lu, Z. (2021). m6A RNA Methylation Regulators Act as Potential Prognostic Biomarkers in Lung AdenocarcinomaA RNA Methylation Regulators Act as Potential Prognostic Biomarkers in Lung Adenocarcinoma. Front. Genet. 12, 622233. doi:10.3389/fgene.2021.622233

Wang, T., Kong, S., Tao, M., and Ju, S. (2020). The Potential Role of RNA N6-Methyladenosine in Cancer Progression. Mol. Cancer 19 (1), 88. doi:10.1186/s12943-020-01204-7

Wei, G. H., and Wang, X. (2017). lncRNA MEG3 Inhibit Proliferation and Metastasis of Gastric Cancer via P53 Signaling Pathway. Eur. Rev. Med. Pharmacol. Sci. 21 (17), 3850–3856.

Witkin, K. L., Hanlon, S. E., Strasburger, J. A., Coffin, J. M., Jaffrey, S. R., Howcroft, T. K., et al. (2015). RNA Editing, Epitranscriptomics, and Processing in Cancer Progression. Cancer Biol. Ther. 16 (1), 21–27. doi:10.4161/15384047.2014.987555

Wong, C.-M., Tsang, F. H.-C., and Ng, I. O.-L. (2018). Non-coding RNAs in Hepatocellular Carcinoma: Molecular Functions and Pathological Implications. Nat. Rev. Gastroenterol. Hepatol. 15 (3), 137–151. doi:10.1038/nrgastro.2017.169

Wu, X., Zhang, X., Tao, L., Dai, X., and Chen, P. (2020). Prognostic Value of an m6A RNA Methylation Regulator-Based Signature in Patients with Hepatocellular Carcinoma. Biomed. Res. Int. 2020, 1–11. doi:10.1155/2020/2053902

Xu, W.-P., Liu, J.-P., Feng, J.-F., Zhu, C.-P., Yang, Y., Zhou, W.-P., et al. (2020). miR-541 Potentiates the Response of Human Hepatocellular Carcinoma to Sorafenib Treatment by Inhibiting Autophagy. Gut 69 (7), 1309–1321. doi:10.1136/gutjnl-2019-318830

Yoo, J.-J., Chung, G. E., Lee, J.-H., Nam, J. Y., Chang, Y., Lee, J. M., et al. (2018). Sub-classification of Advanced-Stage Hepatocellular Carcinoma: A Cohort Study Including 612 Patients Treated with Sorafenib. Cancer Res. Treat. 50 (2), 366–373. doi:10.4143/crt.2017.126

Yuan, W., Tao, R., Huang, D., Yan, W., Shen, G., and Ning, Q. (2021). Transcriptomic Characterization Reveals Prognostic Molecular Signatures of Sorafenib Resistance in Hepatocellular Carcinoma. Aging 13 (3), 3969–3993. doi:10.18632/aging.202365

Yuan, Y., Du, Y., Wang, L., and Liu, X. (2020). The M6A Methyltransferase METTL3 Promotes the Development and Progression of Prostate Carcinoma via Mediating MYC Methylation. J. Cancer 11 (12), 3588–3595. doi:10.7150/jca.42338

Zhang, B., Gu, Y., and Jiang, G. (2020a). Expression and Prognostic Characteristics of M6 A RNA Methylation Regulators in Breast Cancer. Front. Genet. 11, 604597. doi:10.3389/fgene.2020.604597

Zhang, S., Liu, F., Wu, Z., Xie, J., Yang, Y., and Qiu, H. (2020b). Contribution of m6A Subtype Classification on Heterogeneity of Sepsis. Ann. Transl. Med. 8 (6), 306. doi:10.21037/atm.2020.03.07

Zhang, Y. X., Yuan, J., Gao, Z. M., and Zhang, Z. G. (2018). LncRNA TUC338 Promotes Invasion of Lung Cancer by Activating MAPK Pathway. Eur. Rev. Med. Pharmacol. Sci. 22 (2), 443–449. doi:10.26355/eurrev_201801_14193

Zhao, B. S., Roundtree, I. A., and He, C. (2017). Post-transcriptional Gene Regulation by mRNA Modifications. Nat. Rev. Mol. Cel Biol. 18 (1), 31–42. doi:10.1038/nrm.2016.132

Zhao, W., Geng, D., Li, S., Chen, Z., and Sun, M. (2018). LncRNA HOTAIR Influences Cell Growth, Migration, Invasion, and Apoptosis via the miR-20a-5p/HMGA2axis in Breast Cancer. Cancer Med. 7 (3), 842–855. doi:10.1002/cam4.1353

Zhao, Z., Yang, L., Fang, S., Zheng, L., Wu, F., Chen, W., et al. (2020). The Effect of m6A Methylation Regulatory Factors on the Malignant Progression and Clinical Prognosis of Hepatocellular Carcinoma. Front. Oncol. 10, 1435. doi:10.3389/fonc.2020.01435

Zhu, L., Zhu, Y., Han, S., Chen, M., Song, P., Dai, D., et al. (2019). Impaired Autophagic Degradation of lncRNA ARHGAP5-AS1 Promotes Chemoresistance in Gastric Cancer. Cell Death Dis. 10 (6), 383. doi:10.1038/s41419-019-1585-2

Zhuang, C., Ma, Q., Zhuang, C., Ye, J., Zhang, F., and Gui, Y. (2019). LncRNA GClnc1 Promotes Proliferation and Invasion of Bladder Cancer through Activation of MYC. FASEB J. 33 (10), 11045–11059. doi:10.1096/fj.201900078RR

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Jin, Li, Deng, Li, Yang, Li, Chen, Xiong, Chen and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 23 December 2021
doi: 10.3389/fmolb.2021.780329

An Unfolded Protein Response Related Signature Could Robustly Predict Survival Outcomes and Closely Correlate With Response to Immunotherapy and Chemotherapy in Bladder Cancer

Facai Zhang1,²†, Dechao Feng¹†, Xiaoming Wang¹†, Yiwei Gu¹†, Zhiyong Shen³, Yubo Yang¹, Jiahao Wang¹, Quliang Zhong², Dengxiong Li¹, Huan Hu⁴* and Ping Han1,⁵*

¹Department of Urology/Institute of Urology, West China Hospital, Sichuan University, Sichuan, China

²Department of Urology, the Affiliated Hospital of Guizhou Medical University, Guizhou, China

³Department of Urology, the Affiliated Cancer Hospital of Guizhou Medical University, Guizhou, China

⁴School of Clinical Medicine, Guizhou Medical University, Guizhou, China

⁵Department of Urology, the Second People’s Hospital of Yibin, Sichuan, China

Edited by:
Ismail Hosen, University of Dhaka, Bangladesh

Reviewed by:
Lei Fu, Beijing Normal University, China
Anirban Chakraborty, University of Texas Medical Branch at Galveston, United States

* Correspondence: Huan Hu, 403329423@qq.com; Ping Han, hanpingwch@163.com

†These authors have contributed equally to this work and share first authorship

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 20 September 2021
Accepted: 06 December 2021
Published: 23 December 2021

Citation: Zhang F, Feng D, Wang X, Gu Y, Shen Z, Yang Y, Wang J, Zhong Q, Li D, Hu H and Han P (2021) An Unfolded Protein Response Related Signature Could Robustly Predict Survival Outcomes and Closely Correlate With Response to Immunotherapy and Chemotherapy in Bladder Cancer. Front. Mol. Biosci. 8:780329. doi: 10.3389/fmolb.2021.780329

Background: The unfolded protein response (UPR) plays a significant role in maintaining protein hemostasis in tumor cells, which are crucial for tumor growth, invasion, and resistance to therapy. This study aimed to develop a UPR-related signature and explore its correlation with immunotherapy and chemotherapy in bladder cancer.

Methods: The differentially expressed UPR-related genes were put into Lasso regression to screen out prognostic genes, which constituted the UPR signature, and were incorporated into multivariate Cox regression to generate risk scores. Subsequently, the predictive performance of this signature was estimated by receiver operating characteristic (ROC) curves. The CIBERSORTx, the maftool, and Gene set enrichment analysis (GSEA) were applied to explore infiltrated immune cells, tumor mutational burden (TMB), and enriched signaling pathways in both risk groups, respectively. Moreover, The Cancer Immunome Atlas (TCIA) and Genomics of Drug Sensitivity in Cancer (GDSC) databases were used to predict responses to chemotherapy and immunotherapy.

Results: Twelve genes constituted the UPR-related signature. Patients with higher risk scores had worse overall survival (OS) in training and three validation sets. The UPR-related signature was closely correlated with clinicopathologic parameters and could serve as an independent prognostic factor. M0 macrophages showed a significantly infiltrated difference in both risk groups. TMB analysis showed that the risk score in the wild type and mutation type of FGFR3 was significantly different. GSEA indicated that the immune-, extracellular matrix-, replication and repair associated pathways belonged to the high risk group and metabolism-related signal pathways were enriched in the low risk group. Prediction of immunotherapy and chemotherapy revealed that patients in the high risk group might benefit from chemotherapy, but had a worse response to immunotherapy. Finally, we constructed a predictive model with age, stage, and UPR-related risk score, which had a robustly predictive performance and was validated in GEO datasets.

Conclusion: We successfully constructed and validated a novel UPR-related signature in bladder cancer, which could robustly predict survival outcomes and closely correlate with the response to immunotherapy and chemotherapy in bladder cancer.

Keywords: unfolded protein response, immunotherapy, chemotherapy, bladder cancer, TCGA

INTRODUCTION

Bladder cancer (BC) is a common malignancy in the urinary system, with an estimated 500,000 new cases diagnosed and 200,000 deaths worldwide each year (Antoni et al., 2017; Lenis et al., 2020). It includes a spectra of diseases, including non-muscle-invasive bladder cancer (NMIBC), which is characterized by easy recurrence to aggressive muscle-invasive bladder cancer (MIBC), requiring radical cystectomy (Patel et al., 2020; Richters et al., 2020). Although minimally invasive technology, enhanced endoscopy with narrow-band, neoadjuvant chemotherapy, targeted therapy, and immunotherapy have been introduced into clinical practice in recent years, the 5 years survival rates of BC patients have not improved in the past 30 years (Berdik, 2017; Grayson, 2017). Moreover, to predict BC patients’ survival accurately, the AJCC-stage system based on classical pathological parameters also needs to improve (Sun et al., 2020; Zhang et al., 2021a). Therefore, with the widespread application of next generation sequencing (NGS), an increasing number of studies have focused on biomarkers and molecular subtypes of cancers to provide a theoretical basis for the development of new targeted drugs or to more accurately predict the prognosis of patients (Zhang et al., 2021b).

Unfolded protein response (UPR) is a significant signal pathway that surveils the status and fidelity of proteins in the endoplasmic reticulum (ER) and could be activated when unfolded or misfolded protein accumulates in ER (Hetz et al., 2020). UPR restored correct protein conformation and reduced total protein burden in acute and moderate stress, but it could also induce cell apoptosis in chronic or intense stress (Hetz and Papa, 2018; Hetz et al., 2020; Oakes, 2020). Tumor cells often proliferate rapidly and are exposed to intrinsic and external stimuli, which could disrupt the homeostasis of protein and evoke UPR to alleviate endoplasmic reticulum stress (ERS) (Urra et al., 2016; Cubillos-Ruiz et al., 2017; Chen and Cubillos-Ruiz, 2021). Due to the significance of UPR in tumors, many researchers have paid more attention to UPR related biomarkers. It has been reported that OTUB1, P4HB, and EHMT2 could regulate the malignant phenotype of bladder cancer cells via the ERS related URP pathway (Cui et al., 2015; Wang et al., 2020; Zhang et al., 2021c; Wu et al., 2021).

Although several URP-related genes have been identified as biomarkers that influence tumor progression, there are still no UPR-related multigene signatures that can be used to predict patients’ prognosis in BC. To examine this, the present study first constructed and validated a robust UPR-related signature and investigated its correlation with clinicopathological parameters and response to immunotherapy and chemotherapy.

MATERIALS AND METHODS

Data Collection

The RNA-seq and clinical data of bladder cancer patients were downloaded from the TCGA database, which was used as the training set to develop a UPR-related gene signature. Similarly, GSE13507, GSE32548, and GSE48075 in the GEO dataset were retrieved and used as validation sets to validate the UPR-related signature. The UPR-related genes were obtained from hallmark genes from the Molecular Signature database.

Identification of Differentially Expressed UPR Genes in the TCGA Dataset

We first used the “VennDiagram” package in R software to screen out the co-expressed URP-related genes in the TCGA dataset, GSE13507, GSE32548, and GSE48075. Then, the “limma” package was used to identify the differentially expressed genes between 19 normal samples and 414 tumor samples with a False Discovery Rate (FDR) of < 0.05 in the training set.

Gene Ontology(GO) Enrichment Analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Analysis

All differentially expressed genes were used to explore potentially enriched function and pathways with the “clusterProfiler” package in R software. The enriched KEGG pathways were screened out using p < 0.05 and Q < 0.05, and were presented in bubble plots. Similarly, GO terms with p < 0.05 and Q < 0.05 were selected and presented respectively according to molecular function (MF), biological process (BP), and cellular component (CC) categories in bar plots.

Development and Validation of a UPR-Related Signature

All differentially expressed UPR genes were incorporated into the lasso regression model, in which penalties were applied to all differentially expressed UPR genes for preventing the overfitting effects of the model. The penalty parameter (λ) for the model was determined by 10 fold cross-validation following the minimum criteria. After that, the prognostic UPR genes selected by lasso were used to construct the UPR signature and risk scores were generated in a multivariate Cox regression model with the following formula:

According to the median risk score, all patients with different risk scores could be divided into high and low risk groups. Kaplan-Meier analysis was used to compare the survival outcomes of patients in the high and low risk groups in the TCGA database. Patients in GSE13507, GSE32548, and GSE48075 also had risk scores based on the formula above and these three cohorts were used to validate the signature’s prognostic performance.

Correlation of the UPR Signature With Clinical Parameters

To investigate the relationship between the UPR signature and clinical parameters, patients in the training and validation sets were classified into different subgroups in light of age, gender, T-stage, nodule, metastasis, AJCC-stage, and molecular subtypes, etc, and risk scores in different subgroups were compared. Then, the Kaplan-Meier method was used to explore the survival outcomes of patients with high or low risk scores in different subgroups. Moreover, UPR-related risk scores and these clinicopathological parameters were incorporated into the univariate and multivariate Cox regression model to screen out independent prognostic factors in the training and validation dataset, which were used to constitute a nomogram in the following study. Multivariate ROC curves at different points of time were used to evaluate the predictive performance of the risk score and other clinical parameters.

Gene Set Enrichment Analysis, Immune Cell Infiltration, and Tumor Mutational Burden Analysis

We uploaded the RNA-seq data of patients in the high and low risk groups to the GSEA website. The differentially enriched pathways in both groups were screened with p < 0.05 and FDR<25%. After that, the top enriched pathways in the high- and low risk groups were analyzed and presented in multi-GSEA plots to explore the potential activated pathways in the two risk groups.

Then, the transcriptome data in training and validation sets were normalized and we estimated the contents of 22 immune cells in patients with different risk scores on the CIBERSORTx website. Subsequently, we compared the contents of infiltrated immune cells between the high and low risk groups and presented them in violin plots.

We downloaded the tumor mutational data from the TCGA dataset. The Maftools package in R software was used to analyze these mutational data in both risk groups and TMB was calculated with the tumor specific mutation genes. After that, we listed the top mutational genes in the high and low risk groups individually and compared the risk scores in the wild and mutational groups. A scatter plot was then drawn to explore the correlation between TMB and risk scores. p < 0.05 was considered statistically significant.

Prediction of Immunotherapy Response

As TMB, programmed cell death 1 ligand 1 and 2 (PD-L1 and PD-L2), and microsatellite instable (MSI) associated mismatched repair genes in tumor tissue are believed to be potent biomarkers for predicting immunotherapy response, the transcriptome data of CD274 (PD-L1), PDCD1LG2 (PD-L2), MSH2, MSH6, PMS2, and MLH1 were extracted and compared in both risk groups. Moreover, The Cancer Immunome Atlas (TCIA), a database providing comprehensive immunogenomic analyses based on the TCGA, was used to evaluate the immunotherapy response by generating the immunophenoscore (IPS) in each sample. After that, IPS were compared between the high and the low risk groups. p < 0.05 was considered statistically significant.

Prediction of Chemotherapy Response

Genomics of Drug Sensitivity in Cancer (GDSC), a public pharmacogenomics database, was used to predict the chemotherapy response for bladder cancer patients with different UPR-related risk scores. The chemotherapy response and drug sensitivity were evaluated by the half-maximal inhibitory concentration (IC50) with the “pRRophetic” package in R software, and were compared between the high and low risk groups. p < 0.05 was considered statistically significant.

Development and Validation of a Predictive Nomogram Based on Clinical Parameters and the UPR-Related Risk Score

We selected independent prognostic factors screened by multivariate Cox regression model to establish a nomogram. Age, stage, and UPR-related risk scores were incorporated into the nomogram in the TCGA dataset. An enhanced bootstrap strategy of 1,000 times was used to validate this nomogram internally, while GSE13507 and GSE48075 datasets were applied to validate this model externally. AUR, Brier scores, and calibration plots were used to assess the performance of the nomogram in 1, 3, 5 years.

Statistical Analysis

All statistical analyses were performed in R software (Version 4.0.5) and GraphPad Prism 9. Quantitative data in two groups were compared using Student-t test and quantitative data in three or more groups were compared with one-way analysis of variance (ANOVA) or Welch’s test. p < 0.05 was regarded as statistically significant.

RESULTS

Selection of UPR-Related Genes and Construction of UPR-Related Signature

All procedures and analyses are presented in the flowchart (Figure 1). Table 1 and Figure 2A show the basic characteristics and survival outcomes of the training set and three validation sets. First, we used the Venn diagram to screen out 97 co-expressed genes in the training and validation sets (Figure 2B). After that, the “limma” package in R software was applied to filter out differentially expressed genes among these 97 co-expressed genes between normal samples and tumors with FDR < 0.05 in the TCGA dataset and 53 differentially expressed UPR-related genes were selected. Second, GO and KEGG enrichment analyses were made with these 53 differentially expressed genes and terms such as cell response to unfolded protein, endoplasmic reticulum unfolded protein response were significantly enriched, which were in accordance with the characteristics of UPR-related genes (Figure 2C). Meanwhile, KEGG pathway enrichment analysis showed that those differentially expressed genes closely correlated with RNA degradation and protein processing in the endoplasmic reticulum (Figure 2D). Third, all 53 differential UPR-related genes were incorporated into the lasso regression and 12 prognostic UPR-related genes (CEBPG, HYOU1, IMP3, KDELR3, MTHFD2, PDIA6, POP4, PREB, SRPRB, TATDN2, YIF1A, ZBTB17) were identified and used to construct the UPR-related signature (Figures 2E,F; Supplementary Table S1). Subsequently, we put the 12 prognostic UPR-related genes into the multivariate Cox regression model to generate risk scores according to the formula above (Figure 2G; Supplementary Table S2). Finally, we uploaded these 12 prognostic UPR-related genes to the STRING website and explored the interaction of the potential protein with a minimum required interaction score = 0.4 (Figure 2H).

FIGURE 1 | The flowchart of the study.

TABLE 1 | Baseline clinical characteristics of four databases.

FIGURE 2 | Construction of UPR-related signature utilizing Lasso regression analysis. (A) The outcomes variables of TCGA, GSE13507, GSE32548, and GSE48075. (B) The Venn diagram showed that 97 UPR genes coexisted in the training and three validation sets. (C) GO enrichment analysis revealed that terms of cell response to unfolded protein and endoplasmic reticulum unfolded protein response were significantly enriched, while KEGG enrichment analysis showed that DNA degradation and protein processing in endoplasmic reticulum closely correlated with the differentially expressed UPR-related genes (D). (E and F) Differentially expressed UPR-related genes were incorporated into Lasso regression analysis to screen out prognostic UPR genes. (G) 12 prognosis related UPR-related genes were incorporated into the multivariate regression model to generate risk scores. (H) Protein-protein interaction network was established by STRING with a minimum required interaction score = 0.4.

Prognostic Performance of the UPR-Related Signature in the Training Set and Validation Sets

According to the median risk score in the TCGA dataset, all patients with risk scores in the training dataset and 3 validation datasets could be divided into high and low risk groups. As shown in Figures 3A–D, the distribution of patients in the high and low risk groups were quite different in the training and validation datasets, which meant that the risk score had a robust separating capacity. Kaplan-Meier survival curves showed that patients in the high risk group tended to have worse overall survival compared with ones in the low risk group, which were validated by GSE13507, GSE32548, and GSE48075 (Figures 3E–H). Moreover, the scatter points revealed that the mortality of patients increased and survival time decreased along with the increment of risk scores in the training set (Figure 3I), which were also verified by the 3 validation sets (Figures 3J–L). Finally, the multi-group heatmaps were used to present the tendency of signature gene expression and clinicopathological parameters in the training and validation sets (Figures 3M–P).

FIGURE 3 | Prognostic performance of the UPR-related signature in the training set and validation sets. (A–D) The UPR-related signature could significantly distinguish patients with different risk scores, and patients with higher risk scores had a worse survival time than ones with lower risk scores (E–H). (I–L) Along with the increment of risk scores, the mortality rates of patients increased, while overall survival of patients was shortened. (M–P) Multi-group heatmaps showed the tendency of signature gene expression and clinicopathological parameters in the training and validation sets.

Relationship Between the UPR-Related Signature With Clinical Parameters

To investigate the relationship between UPR-related signature and clinical parameters, patients with different risk scores were first stratified by clinicopathologic parameters and risk scores were compared among different subgroups. Patients of older age, with high pathological grade, advanced AJCC-stage, and metastasis tended to have a higher risk score in the TCGA dataset (Figures 4A–E). Similarly, this significant tendency was also validated in GSE13507 (Figures 4F–H), GSE32548 (Figures 4I,J), and GSE48075 (Figures 4K–P), which indicated that the UPR-related signature might be significantly correlated with come classical clinical parameters. It is worth noting that the basal molecular subtype of bladder cancer had a significantly higher risk score compared with the luminal molecular subtypes, which were in accordance with the fact that the luminal subtype of bladder cancer usually had a better prognosis compared with the basal subtype.

FIGURE 4 | The risk scores of patients in different subgroups were stratified by clinicopathological parameters. The red, blue, green, and orange dashed boxes corresponded to the subgroups in the TCGA, GSE13507, GSE32548, and GSE48075, respectively. The risk scores were significantly different in subgroups of (A) age, (B) gender, (C) pathological grade, (D) AJCC-stage, and (E) metastasis in the TCGA dataset. Similarly, the risk scores were still different in subgroups of (F) age, (G) pathological grade, and (H) T-stage in GSE13507; in subgroups of (I) pathological grade and (J) T-stage in GSE32548; in subgroups (K) T-stage, (L) nodule, (M) pathological grade, (N) invasiveness, (O) molecular subtypes in GSE48075. Interestingly, the risk score did not correlate with smoking (p). *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001; ns: not significance significant.

We also used the Kaplan-Meier method to further analyze the survival outcomes of patients with different risk scores in different subgroups. Patients with higher risk scores had poor survival outcomes compared with ones with lower risk scores in the subgroups of age <70 years (Figure 5A), age ≥ 70 (Figure 5B), female (Figure 5C), male (Figure 5D), high grade (Figure 5E), advanced stage (III + IV) (Figure 5F), low T-stage (T1 + T2) (Figure 5G), high T-stage (T3 + T4) (Figure 5H), nodule-free (Figure 5I), nodule metastasis (Figure 5J), metastasis-free (Figure 5K). Similarly, we also validated the signature’s performance in the subgroups stratified by clinical parameters in GSE13507, GSE32548, and GSE48075. Patients in the high risk group still had significantly worse survival outcomes compared with those in the low risk group in the subgroups of age <70 years (Figure 5L), male (Figure 5M), nodule-free (Figure 5N), and metastasis-free (Figure 5O) in GSE13507. The overall survival time of the low risk group was significantly longer than that of the high risk group in the subgroup of age <70 years (Figure 6A), age >70 years (Figure 6B), female (Figure 6C), male (Figure 6D), high grade (Figure 6E), and high T-stage (≥T2) (Figure 6F) in GSE32548. While the survival difference in both risk groups was still significant in subgroups of female (Figure 6G), male (Figure 6H), high grade (Figure 6I), muscle-invasiveness (T2 + T3 + T4) (Figure 6J), nodule free (Figure 6K), nodule metastasis (Figure 6L), metastasis (Figure 6M), luminal subtype (Figure 6N), and invasiveness (Figure 6F) in GSE48075.

FIGURE 5 | The survival outcomes of bladder cancer patients with different risk scores in subgroups of clinicopathological parameters in the TCGA and GSE13507. The red and green, dashed boxes corresponded to the subgroups in the TCGA and GSE13507, respectively. Patients with higher risk scores had worse overall survival compared with those with lower risk scores in subgroups for (A) age < 70, (B) age ≥ 70, (C) female, (D) male, (E) high grade, (F) advanced-stage, (G) low T-stage, (H) high T-stage, (I) nodal metastasis-free, (J) nodal metastasis, and (K) metastasis-free in the TCGA; and in the subgroups (L) age < 70, (M) male, (N) nodal metastasis-free, (O) metastasis-free in GSE13507.

FIGURE 6 | The survival outcomes of bladder cancer patients with different risk scores in subgroups of clinicopathological parameters in GSE32548and GSE48075. The red and green, dashed boxes corresponded to the subgroups in GSE32548 and GSE48075, respectively. Patients with higher risk scores had worse overall survival compared with those with lower risk scores in subgroups of (A) age < 70, (B) age ≥ 70, (C) female, (D) male, (E) high grade, and (F) high T-stage in GSE32548; in the subgroups of (G) female, (H) male, (I) high grade, (J) muscle-invasive bladder cancer, (K) nodal metastasis-free, (L) nodal metastasis, (M) metastasis-free, (N) luminal subtype, and (O) invasiveness in GSE48075.

Selection of Independent Prognostic Factors and Predictive Performance of Clinical Parameters

We put all the classical clinical parameters and risk scores into the univariate and multivariate Cox regression analysis to screen out independent prognostic factors. Interestingly, the UPR-related risk score was deemed as a significant independent predictor with p < 0.05 in the TCGA dataset (Figures 7A,B), GSE32548 (Figures 7E,F), and GSE48075 (Figures 7G,H). Although the risk score cannot be identified as an independent prognostic factor with p = 0.058 in GSE13507 (Figures 7C,D), we believed that the small sample size could account for this insignificance to a certain extent.

FIGURE 7 | The predictive performance of the risk score and other clinicopathological parameters. (A–H) The univariate and multivariate Cox regression suggested that the risk score was an independent prognostic factor in the training and validation sets. The red, blue, green, and yellow dashed boxes corresponded to the TCGA, GSE13507, GSE32548, and GSE48075, respectively. I, J, K, and L showed that the 1-year multiple ROC curves of the risk score and other clinicopathological parameters in the training set, GSE13507, GSE32548, and GSE48075, respectively. M, N, O, and P showed that the 3-years multiple ROC curves of the risk score and other clinicopathological parameters in the training set, GSE13507, GSE32548, and GSE48075, respectively. Q, R, S, and T showed that the 5-years multiple ROC curves of the risk score and other clinicopathological parameters in the training set, GSE13507, GSE32548, and GSE48075, respectively.

Multivariate ROC curves were used to evaluate the predictive performance of clinical parameters and the risk score in 1, 3, and 5 years. Unexpectedly, the UPR-related risk scores have the highest discrimination with the highest AUC value compared with traditional clinicopathological parameters used to evaluate tumor progression in the TCGA dataset (Figures 7I,M,Q). Similarly, the discrimination of the UPR-related risk score was validated with a higher AUC value in GSE13507 (Figures 7J,N,R), GSE32548 (Figures 7K,O,S), and GSE48075 (Figures 7L,P,T), which demonstrated the robust predictive performance of the risk score.

Gene Set Enrichment Analysis

The transcriptome data of patients in the high and low risk group was uploaded to GSEA, and the top 50 KEGG pathways were enriched with the differentially expressed genes in both risk groups. Interestingly, we found that immune and extracellular matrix associated pathways were enriched in the high risk group in the TCGA dataset, such as antigen processing and presentation, graft versus host disease, ECM receptor interaction, cell adhesion molecules cams, cytokine and cytokine receptor interaction, and so forth. Meanwhile, some metabolism associated pathways belonged to the low risk group, including alpha linolenic acid metabolism, arachidonic acid metabolism, fatty acid metabolism, ether lipid metabolism, and so forth (Figures 8A,B). In the same way, the GSEA analysis showed that replication and repairmen associated pathways were enriched in the high risk groups of GSE13507, GSE32548, and GSE48075 (Figures 8C,E,G), while metabolism associated pathways still belonged to the low risk group, which were in accordance with results of the TCGA dataset (Figures 8D,F,H).

FIGURE 8 | Gene set enrichment analysis. The red, blue, green, and yellow dashed boxes corresponded to the TCGA, GSE13507, GSE32548, and GSE48075, respectively. GSEA results showed significant enrichment of immune-, extracellular matrix-, replication and repair-related signaling pathways in the high-risk group (A,C,E, and G), and significant enrichment of metabolism signaling pathways in the low-risk group (B,D,F, and H).

Immune Cells Infiltration Analysis

We used CIBERSORTx to evaluate 22 immune cell types in the training set and three validation sets. Patients with low risk scores had a higher infiltrated content of naive B cells, naive CD4⁺ T cells, and regulatory T cells compared with those with high risk scores, while the contents of infiltrated M0 macrophages were significantly higher in the high risk groups in the training datasets (Figure 9A). There were also 5 immune cell types significantly different between both risk groups in GSE13507, which included plasma cells, resting memory CD4⁺ T cells, follicular helper T cells, M0 macrophages, and M1 macrophages (Figure 9B). Activated memory CD4⁺ T cells and M0 macrophages were significantly different between both groups in GSE32548 (Figure 9C), and B cells naïve, gamma delta T cells, M0 macrophages, and neutrophils significantly differently infiltrated in both risk groups in GSE48075 (Figure 9D). Collectively, only M0 macrophages were all significantly different in these four datasets, which were significantly enriched in the high risk group.

FIGURE 9 | The vioplots of 22 immune cells contents of the high-risk and low-risk group in (A) TCGA dataset, (B) GSE13507, (C) GSE32548, and (D) GSE48075. M0 macrophages were all significantly different in these four datasets and M0 macrophages had higher infiltration in the high-risk group.

Tumor Mutational Burden Analysis

The “Maftools” package in R software was used to analyze the mutational data in the TCGA dataset. Figures 10A,B show the top 20 mutational genes in the high and low risk groups respectively, and TP53, TTN, MUC16, KMT2D, SYNE1, ARID1A, MACF1, KMT2C, PIK3CA, FLG, HMCN1, RYR2, KDM6A, FAT4, were all top mutational genes and widely presented in both the high and low risk groups. In addition, RB1, EP300, CSMD3, AKAP9, ERBB2, and CREBBP belonged to the top 20 frequent mutational genes in the high risk group, while FGFR3, OBSCN, ATM, AHNAK2, ZFHX4, and LRP1B were part of the top 20 frequent mutational genes in the low risk group. Moreover, we also compared the risk scores between the wild- and mutational types of top mutational genes and found that the risk score in the EGFR-mutation type was significantly lower than that in the EGFR-wild type (Figure 10C). Furthermore, the scatter plot was used to explore the correlation between risk scores and TMB, and there was a slightly negative correlation between risk scores and TMB, although the correlation was not statistically significant (Figure 10D). Finally, the Kaplan–Meier curves showed that patients with higher TMB and low risk scores tended to have the best overall survival, and those with low TMB and high risk scores usually had a worse probability of survival (Figure 10E).

FIGURE 10 | Tumor mutational burden analysis. The top 20 mutational genes were listed in the high risk group (A) and low risk group (B). The risk scores between the wild type and the mutation type of the top frequent mutational genes were compared(C). The risk scores were higher in the wild types of FGFR3 compared with the mutational types of FGFR3. TMB decreased slightly with R = −0.07 and p = 0.16 (D), and the Kaplan–Meier curves showed that patients with higher TMB and low-risk scores tended to have the best overall survival and those with low TMB and high risk scores usually had worse survival probabilities (E). *p < 0.05; **p < 0.01; ***p < 0.001; ns: no significance.

Prediction of Immunotherapy Response

Apart from TMB, Programmed cell death ligand 1 and ligand 2 (PD-L1 and PD-L2) and microsatellite instable (MSI) associated mismatch repair genes in tumor samples were identified as significant biomarkers to predict patients’ responses to immunotherapy. We then compared the transcriptional data of CD274 (PD-L1), PDCD1LG2 (PD-L2), MSH2, MSH6, PMS2, and MLH1 in the high and low risk groups. Interestingly, PD-L1 and PD-L2 in the high risk group were higher than that in the low risk group, while the expression of mismatch repair genes, MSH6 and MLH1, were significantly higher in the high risk group compared with that in the low risk group, which signified that the microsatellites might be more stable in the high-risk group (Figure 11A). Similarly, the expression of CD274 (PD-L1) was still higher in the high risk groups in GSE13507 and GSE32548, and one or more mismatch repair genes had higher expression in the high risk groups in GSE13507, GSE32548, and GSE48075 (Figures 11B–D).

FIGURE 11 | Prediction of immunotherapy and chemotherapy response. The expressions of PD-L1 and/or PD-L2 were significantly higher in the high risk group compared with that in the low risk group in training and validation sets (A–D). At least one of the four mismatch repair genes, MLH1, MSH2, MSH6, and PMS2, in tumor samples expressed significantly higher in the high-risk group, which signified that microsatellites might be more stable in the high risk group. (E) The overall IPS and IPS for CTLA-4 blocker in the high risk group was significantly lower than that in the low risk group, predicting that patients with higher risk scores had a worse immunotherapy response. (F) Moreover, IC50 of gemcitabine, cisplatin, vinblastine, doxorubicin, camptothecin, docetaxel, bortezomib, thapsigargin, and salubrinal in the high-risk group were significantly lower than that in the low risk group, which meant that application of these drugs could be more beneficial to patients with higher risk scores.

TCIA, a database providing comprehensive immunogenomic analyses based on the TCGA, was employed to evaluate the immunotherapy response of patients with different risk scores by IPS. The total IPS and IPS for CTLA-4 blocker in the high-risk group was significantly lower than that in the low-risk group (Figure 11E), which powerfully predicted that patients with higher risk scores had a worse immunotherapy response, especially for CTLA-4 blockers. While there was no significant difference in the IPS for PD1/PD-L1/PD-L2 blocker and PD1/PD-L1/PD-L2 plus CTLA-4 blocker between both risk groups (Figure 11E). Taken together, TMB, MSI associated mismatch repair genes, and IPS all predicted that patients in the high risk score group might have a poor immunotherapy response, although the expression of PD-L1/PL-L2 was higher in tumor samples with high risk scores.

Prediction of Chemotherapy Response

The “pRRophetic” package in R software was used to analyze the therapeutic biomarkers in the GDSC database and to investigate whether the UPR-related risk scores could predict the chemotherapy response of patients with different risk scores in the TCGA dataset. We first selected gemcitabine (G), cisplatin (C), methotrexate (M), vinblastine (V), and doxorubicin (A) to evaluate chemotherapy response in both risk groups, for they constituted the first-line chemotherapy regimen (GC or MVAC) in muscle-invasive bladder cancer. Moreover, camptothecin, docetaxel, bortezomib, thapsigargin, and salubrinal were also screened out to evaluate response, for bortezomib, thapsigargin, and salubrinal could induce UPR-related endoplasmic reticulum stress, and camptothecin and docetaxel were widely used in NMIBC. It is remarkable that the IC50 of almost all chemotherapy drugs mentioned above in the high risk group was significantly lower than that in the low risk group, which indicated that the application of these drugs could be more beneficial to patients with higher risk scores (Figure 11F).

Predictive Nomogram Construction

Age, AJCC-stage and the UPR-related risk score, as independent prognostic factors in the training set, were incorporated into the multivariate Cox regression model, which was presented in a nomogram to predict patients’ survival probabilities (Figure 12A). In the training set, the area under ROC (AUR) and Brier scores of the nomogram in 1, 3, 5 years were 76.8% [70.9%; 62.6%] and 14.0% [11.4%; 16.5%], 74.8% [68.4%; 81.1%] and 20.5% [18.3%; 22.6%], and 73.5% [65.6%; 81.4%] and 20.5% [17.7%; 23.2%], respectively (Figure12B–D). Subsequently, the enhanced bootstrap strategy was employed to validate the model internally. The optimism adjusted C statistics and the optimism adjusted Brier score in 1-, 3-, 5-years were 0.7648 and 0.1414, 0.7444 and 0.2081, 0.7306 and 0.1437 in internal validation, respectively (Table 2). Due to a lack of AJCC stage in GSE32548, only GSE13507 and GSE48075 were utilized as external validation sets to validate the nomogram. The AUR and Brier scores in 1-, 3-, 5-years were 88.3% [81.0%; 95.6%] and 8.8% [5.3%; 12.3%], 84.6% [77.8%; 91.4%] and 15.1% [11.4%; 18.8%], 80.0% [71.4%; 88.7%] and 17.3% [13.5%; 21.0%] in GSE13507 (Figure12E–G), which corresponded to 78.9% [68.3%; 89.4%] and 13.8% [9.3%; 18.3%], 65.7% [53.9%; 77.4%] and 22.4% [18.9%; 26.0%], 70.1% [58.9%; 81.4%] and 21.8% [18.3%; 25.3%] in GSE48075 (Figure12H–J).

FIGURE 12 | The nomogram of the UPR-related gene signature and its performance in the training set, and external validation sets. The predictive model was presented with a nomogram (A). The red, blue and green dashed boxes corresponded to the TCGA dataset, GSE13507, and GSE48075. The AUC and Brier scores of the nomogram in years 1, 3, and 5 in the training set (B–D), GSE13507 (E–G), and GSE48075 (H–J).

TABLE 2 | Basic parameters of enhanced bootstrap in internal validation.

DISCUSSION

Bladder cancer is a common malignancy with high recurrence and invasiveness in the urinary system, with more than 80,500 new cases and 32,900 deaths in China each year (Chen et al., 2016; Siegel et al., 2017; Babjuk et al., 2019; Witjes et al., 2021). In recent years neoadjuvant chemotherapy, immunotherapy, and targeted therapy have been introduced into clinical practice, improving survival outcomes of BC patients to an extent, but the low response rate, non-negligible side effects, and heavy economic burden for patients prompted us to explore some useful biomarkers to screen for patients with good responses to immunotherapy or chemotherapy (Sievert et al., 2009; Pichler et al., 2018; Bednova and Leyton, 2020). To date, ERS associated UPR has attracted much attention from researchers and oncologists, as it could influence tumor proliferation and aggravation, and determine cell fate via three pathway branches initiated by IRE1α, PERK, and ATF6 (Urra et al., 2016; Oakes, 2020). A large number of UPR-related studies have been made in BC, including UPR-related biomarkers and drugs inducing ERS. Wang once reported that targeted inhibition of P4HB promoted cell sensitivity to gemcitabine and induced cell apoptosis via PERK/eIF2α/ATF4/CHOP signaling in urothelial carcinoma of the bladder (Wang et al., 2020), and Zhang discovered that OTUB1 facilitated bladder cancer progression by stabilizing ATF6 in response to endoplasmic reticulum stress (Zhang et al., 2021c). It has also been reported that some drugs have antitumor effects via activation of the ERS pathways, including nelfinavir-ritonavir combination (Sato et al., 2018), lopinavir-ritonavir combination (Okubo et al., 2019), Flaccidoxide-13-Acetate (Wu et al., 2019), Sulforaphane (Jo et al., 2014), and cantharidin (Su et al., 2015), etc.

Taking the significant roles of UPR into account, the present study constructed a UPR-related multigene signature to predict patients’ survival outcomes and the responses to immunotherapy and chemotherapy in the era of genomics and precision medicine. Herein, CEBPG, HYOU1, IMP3, KDELR3, MTHFD2, PDIA6, POP4, PREB, SRPRB, TATDN2, YIF1A, and ZBTB17 constituted a UPR signature with robust predictive performance. The ROC of the UPR signature in years 1, 3, 5 were 0.738, 0.648, and 0.667 respectively, which is much higher than that of classical clinicopathological parameters, such as age, grade, and AJCC-stage in the training set (Figure 7). Similarly, the good performance of the UPR signature was also validated in three validation sets, which illustrated the signature’s stability and wide applicability.

For a better understanding of the UPR-related signature, we investigated each gene involved in this signature. It was reported that CEBPG promoted esophageal squamous cell carcinoma progression, and the downregulation of CEBPG resulted in differentiation arrest in acute myeloid leukemia (Alberich-Jordà et al., 2012; Huang et al., 2020), although its function has still not been explored in BC. Asahi once discovered that the expression of HYOU1 was overexpressed and positively correlated with stage in BC (Asahi et al., 2002). IMP3 was widely studied and deemed as a biomarker with poor survival in both NMIBC and MIBC (Sitnikova et al., 2008; Szarvas et al., 2012; Yang et al., 2019). Zhang once revealed that KDELR3, as a risk gene, constituted a hypoxia signature that could robustly predict BC patients’ prognosis in the TCGA dataset (Zhang et al., 2021b). A Single nucleotide polymorphism study once revealed that increased risk in BC was associated with variants in MTHFD2 with OR = 1.7 (Andrew et al., 2009). Cheng once demonstrated that PDIA6 downregulation inhibited BC cell proliferation and invasion via the Wnt/β-catenin signaling pathway (Cheng et al., 2017). Meanwhile, Zhang discovered that lncRNA PCAT6 could regulate BC progression via the microRNA-143-3p/PDIA6 axis (Zhang et al., 2021d). Furthermore, POP4, PREB, SRPRB, TATDN2, YIF1A, and ZBTB17 were seldom studied in BC and perhaps researchers need to pay more attention to them in the future.

To investigate the relationship between the UPR-related signature and clinicopathological parameters, we first stratified patients with clinical parameters, and then the risk scores of patients in each subgroup were compared in the training and three validation sets. Interestingly, the risk scores in the subgroups of elder age, male, high grade, advanced stage, and metastasis were significantly higher than that in the other subgroups, which was also validated in the GEO datasets (Figure 4). Moreover, what attracted our attention was that the risk scores in the subgroups of basal type are significantly higher than that in luminal type, which indicates that our UPR-related risk score was closely related to the molecular subtypes used to predict treatment response and prognosis of BC patients (Robertson et al., 2017; Seiler et al., 2017; Kamoun et al., 2020). Secondly, we compared the survival outcomes of patients with higher or lower risk in the subgroups of age<70, age ≥ 70, female, male, high pathological grade, advanced stage, low T-stage, high T-stage, nodal metastasis-free, nodal metastasis, and metastasis-free, and found that patients in the high risk group still had a worse overall survival than ones in the low risk group (Figure 5), which demonstrated the stability and universality of the URP-related signature. In addition, the signature’s prognostic performance was also validated in the subgroups stratified by clinical parameters in GSE13507, GSE32548, and GSE48075 (Figure 6).

GSEA analysis was made with differentially expressed genes in the high- and low risk groups to seek out the enriched KEGG pathways in both groups. Immune, extracellular matrix, replication, and repair associated pathways were enriched into the high risk group in the training and validation sets, while metabolism related pathways belonged to the low risk group significantly. Extracellular matrix related pathways in the high risk groups included ECM receptor interaction, cell adhesion molecules, cytokine and cytokine receptor interaction, and so forth. These extracellular interactions led to direct or indirect control of tumor cellular activities such as adhesion, migration, metastasis, differentiation, proliferation, angiogenesis, and local immunosuppression in the tumor microenvironment (Lippitz, 2013; Hamidi and Ivaska, 2018; Läubli and Borsig, 2019), which uncovered the reason why patients with higher risk scores had worse survival to a certain extent. The replication and repair associated pathways enriched in the high risk group indicated that the tumors had high proliferation ability, strong mismatch repair ability, and stable microsatellite (Fishel, 2015; Li et al., 2016; Baretti and Le, 2018), which were in accordance with poor survival outcomes and poor immunotherapy response in the high risk group. Immune related pathways in the high risk groups included autoimmune disease pathways and inflammatory pathways, which had little correlation with immunosuppressive phenotype. As for the metabolism pathways enriched into the low risk groups, we believed that it revealed a status that high metabolic demand, oxidative stress, and disturbance of the protein-folding in the tumor microenvironment, which might induce ERS-related apoptosis in chronic or intense stress.

Immune cell infiltration in the tumor microenvironment plays a significant role in regulating the proliferation, invasion, and migration of cancer cells (Petrova et al., 2018; Liu et al., 2020). We compared the contents of 22 immune cells in both different risk groups and found that only M0 macrophage cells were significantly higher in the high risk groups in TCGA datasets and two validation datasets. Macrophages could be divided into 3 classes: M0, M1, and M2 macrophages (Locati et al., 2020). M0 macrophage was a kind of inactive macrophages that have the potential to differentiate M1 or M2 macrophages. M1 macrophages usually expressed MHC-II, CD68, CD80, and CD86 and had the effect of antitumor and pro-inflammation, while the M2 microphage in the tumor microenvironment could play a role in pro-tumor, immunosuppression, and anti-inflammation (Malfitano et al., 2020; Mantovani et al., 2021). In our study, M1 or M2 macrophages did not significantly enrich in the low or high risk groups respectively, but the contents of M0 macrophages were significantly higher in the high risk group, which indicates that M0 macrophages might correlate with poor prognosis. In most tumor microenvironments, tumor-associated macrophage (TAM) resembled M2-like macrophages and played a role in cancer progression, metastasis, and immunotherapy resistance (Liu et al., 2017). Li once reported that the low expression of M0 macrophage in tumor samples was associated with a better clinical prognosis in bladder cancer (Li et al., 2020). The two studies mentioned above agreed with our results.

TMB, PD-L1 and PD-L2, and MSI in tumor tissue are believed to be potent biomarkers for predicting immunotherapy response, but the relationship between these biomarkers is complex and it remains unclear whether employing a combination of biomarkers is superior to relying on a single marker (Luchini et al., 2019; Ren et al., 2020). In the present study, we first analyzed the difference of TMB in both risk groups. It was believed that tumors with more mutational genes tended to generate more mutational RNA and protein, which could be more easily recognized by the immune system and had a good response to immunotherapy (Rizvi et al., 2015). The top 20 mutational genes in both risk groups were listed to analyze the different mutational genes (Figures 10A,B), and then the risk scores between the wild types and the mutation types were compared. The risk scores in the wild type of FGFR3 were significantly higher than that in the mutation type of FGFR3 (Figure 10C). It has been reported that FGFR3 has a common mutational gene in bladder cancer and its mutation was associated with a favorable BC prognosis, which was in accordance with the low UPR-related risk score in the mutational type of EGFR3.

We also explored the correlation between UPR-related risk scores and TMB and found a negative correlation with R = −0.07. Subsequently, the transcriptome data of significant DNA mismatch repair genes (MLH1, MSH2, MSH6, and PMS2) were compared, and we found that one or more mismatch repair genes had higher expression in the high risk groups, which signified that microsatellites might be more stable in the high-risk group. After that, the immune-comprehensive analysis from the TCIA database also showed that overall IPS and IPS for CTLA4 blocker were significantly higher in the low risk group, which indicated that patients with lower risk scores had a better immunotherapy response. Interestingly, we discovered that the expression of PD-L1 and/or PD-L2 were significantly higher in the high risk group than that in the low risk group, and whether it meant that patients with higher risk scores had a better response to immunotherapy, at least anti-PD-1/PD-L immune checkpoint therapy (Figures 11A–D). This contradicted the TMB and MSI analysis, and IPS for PD-1/PD-L1/PD-L2 also showed no significant difference in both risk groups. Keenan once explored the genomic correlates of response to immune checkpoint blockade and found that as a predictive biomarker for PD-l/PD-L1 therapy, PD-L1 lacked diagnostic accuracy in selecting patients with good responses (Keenan et al., 2019). The predictive performance of PD-L1 seemed to depend on tumor type and specific immune checkpoint inhibitors (Chan et al., 2019). It was based on the fact mentioned above that MSI was regarded as a premier biomarker for predicting the response to immunotherapy in current clinical practice, irrespective of tumor origin (Duffy and Crown, 2019).

Prediction of chemotherapy response showed that nearly all first-line chemotherapeutic had a better response to patients with higher risk scores, which indicated that the UPR-related signature closely correlated with chemotherapy response. Finally, a predictive model for risk score, age, and stage was constructed in the training set and presented in a nomogram. The Enhanced bootstrap with 1,000 times was used to validate the nomogram internally, and GSE13507 and GSE48075 were utilized as external validation sets to validate the nomogram. Whatever the training dataset, the internal validation dataset, or the external validation dataset, the performance of this model is still steady and robust (Figure 12).

This is the first UPR-related signature in bladder cancer, which could accurately predict the survival outcomes of BC patients compared with the traditional pathological parameters. Moreover, the molecular signature has close relationships with clinicopathological parameters and closely correlates with the response to immunotherapy and chemotherapy.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.

ETHICS STATEMENT

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

AUTHOR CONTRIBUTIONS

FZ, DF, and XW designed this study. YG, ZS, and YY collected the transcriptome and clinical data. YG, JW, QZ, DL, and HH took part in the data collection. FZ drafted the manuscript, and PH revised the final manuscript.

FUNDING

This work was supported by the 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University (ZY2016104), Pillar Program from Department of Science and Technology of Sichuan Province (2018SZ0219), and Science and technology projects of Guizhou Province (2021) General 060.

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

ACKNOWLEDGMENTS

We thank all patients who participated in this study and donated samples.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2021.780329/full#supplementary-material

REFERENCES

Alberich-Jordà, M., Wouters, B., Balastik, M., Shapiro-Koss, C., Zhang, H., Di Ruscio, A., et al. (2012). C/EBPγ Deregulation Results in Differentiation Arrest in Acute Myeloid Leukemia. J. Clin. Invest. 122 (12), 4490–4504. doi:10.1172/JCI65102

Andrew, A. S., Gui, J., Sanderson, A. C., Mason, R. A., Morlock, E. V., Schned, A. R., et al. (2009). Bladder Cancer SNP Panel Predicts Susceptibility and Survival. Hum. Genet. 125 (5-6), 527–539. doi:10.1007/s00439-009-0645-6

Antoni, S., Ferlay, J., Soerjomataram, I., Znaor, A., Jemal, A., and Bray, F. (2017). Bladder Cancer Incidence and Mortality: A Global Overview and Recent Trends. Eur. Urol. 71 (1), 96–108. doi:10.1016/j.eururo.2016.06.010

Asahi, H., Koshida, K., Hori, O., Ogawa, S., and Namiki, M. (2002). Immunohistochemical Detection of the 150-kDa Oxygen-Regulated Protein in Bladder Cancer. BJU Int. 90 (4), 462–466. doi:10.1046/j.1464-410x.2002.02915.x

Babjuk, M., Burger, M., Compérat, E. M., Gontero, P., Mostafid, A. H., Palou, J., et al. (2019). European Association of Urology Guidelines on Non-Muscle-Invasive Bladder Cancer (TaT1 and Carcinoma In Situ) - 2019 Update. Eur. Urol. 76 (5), 639–657. doi:10.1016/j.eururo.2019.08.016

Baretti, M., and Le, D. T. (2018). DNA Mismatch Repair in Cancer. Pharmacol. Ther. 189, 45–62. doi:10.1016/j.pharmthera.2018.04.004

Bednova, O., and Leyton, J. V. (2020). Targeted Molecular Therapeutics for Bladder Cancer-A New Option beyond the Mixed Fortunes of Immune Checkpoint Inhibitors. Int. J. Mol. Sci. 21 (19):7268. doi:10.3390/ijms21197268

Berdik, C. (2017). Unlocking Bladder Cancer. Nature 551 (7679), S34–S35. doi:10.1038/551s34a

Chan, T. A., Yarchoan, M., Jaffee, E., Swanton, C., Quezada, S. A., Stenzinger, A., et al. (2019). Development of Tumor Mutation burden as an Immunotherapy Biomarker: Utility for the Oncology Clinic. Ann. Oncol. 30 (1), 44–56. doi:10.1093/annonc/mdy495

Chen, W., Zheng, R., Baade, P. D., Zhang, S., Zeng, H., Bray, F., et al. (2016). Cancer Statistics in China, 2015. CA: a Cancer J. clinicians 66 (2), 115–132. doi:10.3322/caac.21338

Chen, X., and Cubillos-Ruiz, J. R. (2021). Endoplasmic Reticulum Stress Signals in the Tumour and its Microenvironment. Nat. Rev. Cancer. 21 (2), 71–88. doi:10.1038/s41568-020-00312-2

Cheng, H.-P., Liu, Q., Li, Y., Li, X.-D., and Zhu, C.-Y. (2017). The Inhibitory Effect of PDIA6 Downregulation on Bladder Cancer Cell Proliferation and Invasion. Oncol. Res. 25 (4), 587–593. doi:10.3727/096504016x14761811155298

Cubillos-Ruiz, J. R., Bettigole, S. E., and Glimcher, L. H. (2017). Tumorigenic and Immunosuppressive Effects of Endoplasmic Reticulum Stress in Cancer. Cell 168 (4), 692–706. doi:10.1016/j.cell.2016.12.004

Cui, J., Sun, W., Hao, X., Wei, M., Su, X., Zhang, Y., et al. (2015). EHMT2 Inhibitor BIX-01294 Induces Apoptosis through PMAIP1-Usp9x-MCL1 axis in Human Bladder Cancer Cells. Cancer Cel Int. 15 (1), 4. doi:10.1186/s12935-014-0149-x

Duffy, M. J., and Crown, J. (2019). Biomarkers for Predicting Response to Immunotherapy with Immune Checkpoint Inhibitors in Cancer Patients. Clin. Chem. 65 (10), 1228–1238. doi:10.1373/clinchem.2019.303644

Fishel, R. (2015). Mismatch Repair. J. Biol. Chem. 290 (44), 26395–26403. doi:10.1074/jbc.r115.660142

Grayson, M. (2017). Bladder Cancer. Nature 551 (7679), S33. doi:10.1038/551s33a

Hamidi, H., and Ivaska, J. (2018). Every Step of the Way: Integrins in Cancer Progression and Metastasis. Nat. Rev. Cancer 18 (9), 533–548. doi:10.1038/s41568-018-0038-z

Hetz, C., and Papa, F. R. (2018). The Unfolded Protein Response and Cell Fate Control. Mol. Cel. 69 (2), 169–181. doi:10.1016/j.molcel.2017.06.017

Hetz, C., Zhang, K., and Kaufman, R. J. (2020). Mechanisms, Regulation and Functions of the Unfolded Protein Response. Nat. Rev. Mol. Cel Biol. 21 (8), 421–438. doi:10.1038/s41580-020-0250-z

Huang, Y., Lin, L., Shen, Z., Li, Y., Cao, H., Peng, L., et al. (2020). CEBPG Promotes Esophageal Squamous Cell Carcinoma Progression by Enhancing PI3K-AKT Signaling. Am. J. Cancer Res. 10 (10), 3328–3344.

Jo, G. H., Kim, G.-Y., Kim, W.-J., Park, K. Y., and Choi, Y. H. (2014). Sulforaphane Induces Apoptosis in T24 Human Urinary Bladder Cancer Cells through a Reactive Oxygen Species-Mediated Mitochondrial Pathway: the Involvement of Endoplasmic Reticulum Stress and the Nrf2 Signaling Pathway. Int. J. Oncol. 45 (4), 1497–1506. doi:10.3892/ijo.2014.2536

Kamoun, A., de Reyniès, A., Allory, Y., Sjödahl, G., Robertson, A. G., Seiler, R., et al. (2020). A Consensus Molecular Classification of Muscle-Invasive Bladder Cancer. Eur. Urol. 77 (4), 420–433. doi:10.1016/j.eururo.2019.09.006

Keenan, T. E., Burke, K. P., and Van Allen, E. M. (2019). Genomic Correlates of Response to Immune Checkpoint Blockade. Nat. Med. 25 (3), 389–402. doi:10.1038/s41591-019-0382-x

Läubli, H., and Borsig, L. (2019). Altered Cell Adhesion and Glycosylation Promote Cancer Immune Suppression and Metastasis. Front. Immunol. 10, 2120.doi:10.3389/fimmu.2019.02120

Lenis, A. T., Lec, P. M., Chamie, K., and Mshs, M. (2020). Bladder Cancer. Jama 324 (19), 1980–1991. doi:10.1001/jama.2020.17598

Li, W., Zeng, J., Luo, B., Mao, Y., Liang, Y., Zhao, W., et al. (2020). High Expression of Activated CD4+ Memory T Cells and CD8+ T Cells and Low Expression of M0 Macrophage Are Associated with Better Clinical Prognosis in Bladder Cancer Patients. Xi Bao Yu Fen Zi Mian Yi Xue Za Zhi 36 (2), 97–103.

Li, Z., Pearlman, A. H., and Hsieh, P. (2016). DNA Mismatch Repair and the DNA Damage Response. DNA repair 38, 94–101. doi:10.1016/j.dnarep.2015.11.019

Lippitz, B. E. (2013). Cytokine Patterns in Patients with Cancer: a Systematic Review. Lancet Oncol. 14 (6), e218–e228. doi:10.1016/s1470-2045(12)70582-x

Liu, X., Wu, S., Yang, Y., Zhao, M., Zhu, G., and Hou, Z. (2017). The Prognostic Landscape of Tumor-Infiltrating Immune Cell and Immunomodulators in Lung Cancer. Biomed. Pharmacother. 95, 55–61. doi:10.1016/j.biopha.2017.08.003

Liu, Y., Wu, J., Huang, W., Weng, S., Wang, B., Chen, Y., et al. (2020). Development and Validation of a Hypoxia-Immune-Based Microenvironment Gene Signature for Risk Stratification in Gastric Cancer. J. Transl Med. 18 (1), 201. doi:10.1186/s12967-020-02366-0

Locati, M., Curtale, G., and Mantovani, A. (2020). Diversity, Mechanisms, and Significance of Macrophage Plasticity. Annu. Rev. Pathol. Mech. Dis. 15, 123–147. doi:10.1146/annurev-pathmechdis-012418-012718

Luchini, C., Bibeau, F., Ligtenberg, M. J. L., Singh, N., Nottegar, A., Bosse, T., et al. (2019). ESMO Recommendations on Microsatellite Instability Testing for Immunotherapy in Cancer, and its Relationship with PD-1/pd-L1 Expression and Tumour Mutational burden: a Systematic Review-Based Approach. Ann. Oncol. 30 (8), 1232–1243. doi:10.1093/annonc/mdz116

Malfitano, A. M., Pisanti, S., Napolitano, F., Di Somma, S., Martinelli, R., and Portella, G. (2020). Tumor-Associated Macrophage Status in Cancer Treatment. Cancers (Basel) 12 (7): 1987. doi:10.3390/cancers12071987

Mantovani, A., Marchesi, F., Jaillon, S., Garlanda, C., and Allavena, P. (2021). Tumor-Associated Myeloid Cells: Diversity and Therapeutic Targeting. Cell Mol Immunol. 18 (3), 566–578. doi:10.1038/s41423-020-00613-4

Oakes, S. A. (2020). Endoplasmic Reticulum Stress Signaling in Cancer Cells. Am. J. Pathol. 190 (5), 934–946. doi:10.1016/j.ajpath.2020.01.010

Okubo, K., Isono, M., Asano, T., and Sato, A. (2019). Lopinavir-Ritonavir Combination Induces Endoplasmic Reticulum Stress and Kills Urological Cancer Cells. Anticancer Res. 39 (11), 5891–5901. doi:10.21873/anticanres.13793

Patel, V. G., Oh, W. K., and Galsky, M. D. (2020). Treatment of Muscle‐invasive and Advanced Bladder Cancer in 2020. CA A. Cancer J. Clin. 70 (5), 404–423. doi:10.3322/caac.21631

Petrova, V., Annicchiarico-Petruzzelli, M., Melino, G., and Amelio, I. (2018). The Hypoxic Tumour Microenvironment. Oncogenesis 7 (1), 10. doi:10.1038/s41389-017-0011-9

Pichler, R., Loidl, W., and Pichler, M. (2018). High Economic burden of Immunotherapy Underlines the Need of Predictive Biomarkers for the Individual Therapy Algorithm in Metastatic Bladder Cancer. Transl Androl. Urol. 7 (Suppl. 6), S738–s40. doi:10.21037/tau.2018.08.15

Ren, Q., Zhu, P., Zhang, H., Ye, T., Liu, D., Gong, Z., et al. (2020). Identification and Validation of Stromal-Tumor Microenvironment-Based Subtypes Tightly Associated with PD-1/pd-L1 Immunotherapy and Outcomes in Patients with Gastric Cancer. Cancer Cel Int. 20, 92. doi:10.1186/s12935-020-01173-3

Richters, A., Aben, K. K. H., and Kiemeney, L. A. L. M. (2020). The Global burden of Urinary Bladder Cancer: an Update. World J. Urol. 38 (8), 1895–1904. doi:10.1007/s00345-019-02984-4

Rizvi, N. A., Hellmann, M. D., Snyder, A., Kvistborg, P., Makarov, V., Havel, J. J., et al. (2015). Mutational Landscape Determines Sensitivity to PD-1 Blockade in Non-Small Cell Lung Cancer. Science 348 (6230), 124–128. doi:10.1126/science.aaa1348

Robertson, A. G., Kim, J., Al-Ahmadie, H., Bellmunt, J., Guo, G., Cherniack, A. D., et al. (2017). Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer. Cell 171 (3), 540–e25. doi:10.1016/j.cell.2017.09.007

Sato, A., Asano, T., Okubo, K., Isono, M., and Asano, T. (2018). Nelfinavir and Ritonavir Kill Bladder Cancer Cells Synergistically by Inducing Endoplasmic Reticulum Stress. Oncol. Res. 26 (2), 323–332. doi:10.3727/096504017x14957929842972

Seiler, R., Ashab, H. A. D., Erho, N., van Rhijn, B. W. G., Winters, B., Douglas, J., et al. (2017). Impact of Molecular Subtypes in Muscle-Invasive Bladder Cancer on Predicting Response and Survival after Neoadjuvant Chemotherapy. Eur. Urol. 72 (4), 544–554. doi:10.1016/j.eururo.2017.03.030

Siegel, R. L., Miller, K. D., and Jemal, A. (2017). Cancer Statistics. CA: A Cancer J. Clinicians 67 (1), 7–30. doi:10.3322/caac.21387

Sievert, K. D., Amend, B., Nagele, U., Schilling, D., Bedke, J., Horstmann, M., et al. (2009). Economic Aspects of Bladder Cancer: what Are the Benefits and Costs. World J. Urol. 27 (3), 295–300. doi:10.1007/s00345-009-0395-z

Sitnikova, L., Mendese, G., Liu, Q., Woda, B. A., Lu, D., Dresser, K., et al. (2008). IMP3 Predicts Aggressive Superficial Urothelial Carcinoma of the Bladder. Clin. Cancer Res. 14 (6), 1701–1706. doi:10.1158/1078-0432.ccr-07-2039

Su, C. C., Liu, S. H., Lee, K. I., Huang, K. T., Lu, T. H., Fang, K. M., et al. (2015). Cantharidin Induces Apoptosis through the Calcium/PKC-Regulated Endoplasmic Reticulum Stress Pathway in Human Bladder Cancer Cells. Am. J. Chin. Med. 43 (3), 581–600. doi:10.1142/S0192415X15500366

Sun, Z., Jing, C., Xiao, C., and Li, T. (2020). An Autophagy-Related Long Non-Coding RNA Prognostic Signature Accurately Predicts Survival Outcomes in Bladder Urothelial Carcinoma Patients. Aging 12 (15), 15624–15637. doi:10.18632/aging.103718

Szarvas, T., vom Dorp, F., Niedworok, C., Melchior-Becker, A., Fischer, J. W., Singer, B. B., et al. (2012). High Insulin-Like Growth Factor mRNA-Binding Protein 3 (IMP3) Protein Expression Is Associated with Poor Survival in Muscle-Invasive Bladder Cancer. BJU Int. 110 (6 Pt B), E308–E317. doi:10.1111/j.1464-410X.2012.11149.x

Urra, H., Dufey, E., Avril, T., Chevet, E., and Hetz, C. (2016). Endoplasmic Reticulum Stress and the Hallmarks of Cancer. Trends Cancer 2 (5), 252–262. doi:10.1016/j.trecan.2016.03.007

Wang, X., Bai, Y., Zhang, F., Yang, Y., Feng, D., Li, A., et al. (2020). Targeted Inhibition of P4HB Promotes Cell Sensitivity to Gemcitabine in Urothelial Carcinoma of the Bladder. OncoTargets Ther. 13, 9543–9558. doi:10.2147/ott.s267734

Witjes, J. A., Bruins, H. M., Cathomas, R., Compérat, E. M., Cowan, N. C., Gakis, G., et al. (2021). European Association of Urology Guidelines on Muscle-Invasive and Metastatic Bladder Cancer: Summary of the 2020 Guidelines. Eur. Urol. 79 (1), 82–104. doi:10.1016/j.eururo.2020.03.055

Wu, Y., Peng, Y., Guan, B., He, A., Yang, K., He, S., et al. (2021). P4HB: A Novel Diagnostic and Prognostic Biomarker for Bladder Carcinoma. Oncol. Lett. 21 (2), 95. doi:10.3892/ol.2020.12356

Wu, Y. J., Su, T. R., Dai, G. F., Su, J. H., and Liu, C. I. (2019). Flaccidoxide-13-Acetate-Induced Apoptosis in Human Bladder Cancer Cells Is through Activation of p38/JNK, Mitochondrial Dysfunction, and Endoplasmic Reticulum Stress Regulated Pathway. Mar. Drugs 17 (5):287. doi:10.3390/md17050287

Yang, F., Zhou, Q., Meng, L., and Xing, N. (2019). IMP3 Is a Biomarker for Non-Muscle-Invasive Urothelial Carcinoma of the Bladder Associated with an Aggressive Phenotype. Medicine 98 (27), e16009. doi:10.1097/md.0000000000016009

Zhang, F., Wang, X., Bai, Y., Hu, H., Yang, Y., Wang, J., et al. (2021). Development and Validation of a Hypoxia-Related Signature for Predicting Survival Outcomes in Patients with Bladder Cancer. Front. Genet. 12, 670384. doi:10.3389/fgene.2021.670384

Zhang, F., Wang, X., Hu, H., Yang, Y., Wang, J., Tang, Y., et al. (2021). A Hypoxia Related Long Non-Coding RNA Signature Could Accurately Predict Survival Outcomes in Patients with Bladder Cancer. Bioengineered 12 (1), 3802–3823. doi:10.1080/21655979.2021.1948781

Zhang, H. H., Li, C., Ren, J. W., Liu, L., Du, X. H., Gao, J., et al. (2021). OTUB1 Facilitates Bladder Cancer Progression by Stabilizing ATF6 in Response to Endoplasmic Reticulum Stress. Cancer Sci. 112 (6), 2199–2209. doi:10.1111/cas.14876

Zhang, Y., Chen, L., and Luo, G. (2021). Long Non-coding RNA PCAT6 Regulates Bladder Cancer Progression via the microRNA-143-3p/PDIA6 axis. Exp. Ther. Med. 22 (3), 947. doi:10.3892/etm.2021.10379

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Zhang, Feng, Wang, Gu, Shen, Yang, Wang, Zhong, Li, Hu and Han. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 04 January 2022
doi: 10.3389/fmolb.2021.762541

BTBD10 is a Prognostic Biomarker Correlated With Immune Infiltration in Hepatocellular Carcinoma

Jianhui Li1,²†, Xiaojuan Tian³†, Ye Nie1,²†, Ying He¹, Wenlong Wu², Xinjun Lei¹, Tianchen Zhang¹, Yanfang Wang¹, Zhenzhen Mao¹, Hong Zhang², Xuan Zhang²* and Wenjie Song²*

¹Xi’an Medical University, Xi’an, China

²Department of Hepatobiliary Surgery, Xijing Hospital, Fourth Military Medical University, Xi’an, China

³Operating Room, Xijing Hospital, Fourth Military Medical University, Xi’an, China

Edited by:
William C. Cho, QEH, Hong Kong SAR, China

Reviewed by:
Harpreet Kaur, Pine Biotech, United States
Ratnakar Potla, Genentech, Inc., United States

* Correspondence: Wenjie Song, wjsong@fmmu.edu.cn; Xuan Zhang, zhangxuantj@163.com

†These authors have contributed equally to this work

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 22 August 2021
Accepted: 03 December 2021
Published: 04 January 2022

Citation: Li J, Tian X, Nie Y, He Y, Wu W, Lei X, Zhang T, Wang Y, Mao Z, Zhang H, Zhang X and Song W (2022) BTBD10 is a Prognostic Biomarker Correlated With Immune Infiltration in Hepatocellular Carcinoma. Front. Mol. Biosci. 8:762541. doi: 10.3389/fmolb.2021.762541

Background: BTBD10 serves as an activator of Akt family members through decreasing the protein phosphatase 2A-mediated dephosphorylation. The present study attempted to investigate the prognostic value of BTBD10 in hepatocellular carcinoma (HCC), specially, its relationship with tumor-infiltrating lymphocytes (TILs).

Methods: BTBD10 expression was evaluated in HCC using The Cancer Genome Atlas (TCGA) and Xijing Hospital database, and verified in HCC cell lines. Cox analyses were performed to analyze independent prognostic risk factors for HCC. The optimal cut-off value of BTBD10 was calculated, by which all patients were divided into two groups to compare the overall survival (OS). The signaling pathways were predicted, by which BTBD10 may affect the progression of HCC. To investigate the impact of BTBD10 on HCC immunotherapy, correlations between BTBD10 and TILs, immune checkpoints, m6A methylation-related genes and ferroptosis-related genes were assessed. The distribution of half-maximal inhibitory concentration (IC50) of diverse targeted drugs was observed based on the differential expression of BTBD10.

Results: BTBD10 expression was higher in HCC tissues and cell lines than that of normal liver tissues and cells. The patients with high expression of BTBD10 showed a worse OS, as compared to that of BTBD10 low-expressing group. Cox analyses indicated that BTBD10 was an independent prognostic risk factor for HCC. Several molecular pathways of immune responses were activated in HCC patients with high-expressing of BTBD10. Furthermore, BTBD10 expression was demonstrated to be positively correlated with tumor-infiltrating B cells, T cells, macrophages, neutrophils and dendritic cells. Meanwhile, the expression of BTBD10 was synchronized with that of several m6A methylation-related genes, ferroptosis-related genes and immune checkpoints. The IC50 scores of Sorafenib, Navitoclax, Veliparib, Luminespib, and Imatinib were found to be lower in BTBD10 high-expressing HCC group.

Conclusion: BTBD10 negatively regulates tumor immunity in HCC and exhibits adverse effect on the prognosis of HCC, which could be a potential target for immunotherapy.

Keywords: BTBD10, hepatocellular carcinoma, prognosis, biomarker, immune infiltration

INTRODUCTION

The high mortality and recurrence of hepatocellular carcinoma (HCC) made it the second leading cause of cancer-related death in the world (Chen et al., 2016; Sung et al., 2021). Although current treatments for HCC, including interventional or radiofrequency ablation (Pérez-Romasanta et al., 2021), chemotherapy or targeted therapy (Cheon et al., 2021), surgical resection (Gunasekaran et al., 2021), and liver transplantation (Hughes and Humar, 2021), etc., exhibit respective therapeutic effect, the overall survival (OS) of HCC is still unsatisfied (D'Avola et al., 2021). Therefore, there is an urgent need to explore new effective biomarkers and/or potential molecular targets for anti-tumor therapy to improve the treatment strategy of HCC patients with different pathological stages.

BTB/POZ domain-containing protein 10 (BTBD10) is located on human chromosome 11p15.2 and consists of nine exons spanning a 1,428 bp open reading frame (ORF) that encodes a 475-amino acid protein (Chen et al., 2004). BTBD10 plays a major role as an activator of AKT family members by inhibiting PPP2A-mediated dephosphorylation, thereby keeping AKTs activated (Nawa et al., 2008). For example, BTBD10 overexpression inhibited protein phosphatase 2A-mediated Akt dephosphorylation and G93A-superoxide dismutase 1-induced motor neuron death (Nawa et al., 2012). BTBD10 regulated pancreatic beta cell proliferation and apoptosis via activation of Akt signaling pathway (Wang et al., 2011). Specially, BTBD10 has been found to be associated with tumor progression. The BTBD10 subunit (KCTD20) enhanced the proliferation and invasion of non-small cell lung (NSCLC) cancer by increasing the phosphorylation level of Akt (Zhang et al., 2017a). However, the possible biological function of BTBD10 in HCC and its prognostic value have not been investigated yet.

In this study, we explored the expression of BTBD10 in HCC and analyzed the relationship of BTBD10 expression with the prognosis of HCC patients. Furthermore, we unraveled the potential molecular mechanisms, by which BTBD10 exhibited an obvious impact on the targeting and/or immunotherapy of HCC.

PATIENTS AND METHODS

HCC Sample Collection and Follow-Up Study

Sixty patients with HCC who underwent surgical excision at Xijing Hospital in 2017 were randomly selected for this study. Three patients with missing data were excluded, leaving 57 patients for analysis. None of the patients had received preoperative radiotherapy or chemotherapy. The pathological results of all patients were HCC. Data from Xijing Hospital included 57 tumor samples and 20 normal tissue samples. This study was approved by the Xijing Hospital Ethics Committee (KY20172013-1) in accordance with the criteria of the Helsinki Declaration.

RNA Extraction and Gene Expression Measurement

RNA was extracted from all tissue samples, normal human liver cell lines (MIHA) and the human hepatocellular carcinoma cell lines (HepG2, Hep3B, HUH7, LM3, MHCC97H, SNU-368 and SNU-739), according to the instructions of the kit. Real-time quantitative fluorescence PCR (qRT-PCR) assay to detect the expression levels of BTBD10, PD-1, PD-L1, PD-L2, CTLA4 and GAPDH. Using GAPDH as an internal control, the 2−ΔΔCt method was used to calculate the relative expression levels of each gene in the samples. All cell lines were purchased from the National Collection of Authenticated Cell Cultures (Shanghai, China). Primer sequences used for amplification are shown in Table 1.

TABLE 1 | Primer sequences of quantitative real-time polymerase chain reaction (qRT-PCR).

Downloading and Screening Data

UALCAN (http://ualcan.path.uab.edu/index.html) was used to predict the expression difference of BTBD10, the relationship between BTBD10 and tumor stage, grade, TP53 mutation and DNA methylation. The sequencing data and corresponding clinical data of HCC patients were downloaded from TCGA database. Patients with a survival time less than 30 days and those missing clinicopathological parameters were excluded, leaving 343 tumor patients and 50 normal patients for analysis.

Survival Analysis

The optimal cut-off value of BTBD10 was calculated according to the Youden index [(sensitivity + specificity) −1], and the HCC samples were divided into two groups: high expression and low expression of BTBD10. Kaplan-Meier (K-M) method was used to compare the survival differences between the two groups and determined by log-rank test. p < 0.05 was considered significant.

Enrichment Analysis

In order to understand the carcinogenic mechanism of BTBD10, we used GSEA software (4.1.0 version) to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis to explore the role of BTBD10 in the progression of HCC (p < 0.05).

Immunoassay

To determine the association between BTBD10 and TILs, we used the Tumor IMmune Estimation Resource (TIMER, https://cistrome.shinyapps.io/timer/) and TCGA data to analyze the relationship between BTBD10 expression and TILs. The relationship between BTBD10 and immune checkpoints in different groups was evaluated to further analyze the effect of BTBD10 on TILs. Potential ICIs response was predicted with Tumor Immune Dysfunction and Exclusion (TIDE) algorithm. The Wilcoxon test was used to explore the relationship between BTBD10 expression with m6A methylation-related genes and ferroptosis-related genes to guide clinical immunotherapy.

IC50 Scores

The half-maximal inhibitory concentration (IC50) is an important indicator for evaluating the efficacy of a drug or the response of a sample to treatment. Using the largest publicly available pharmacogenomics database, Genomics of Drug Sensitivity in Cancer (GDSC), the sample-based transcriptome predicts the response of each sample to the targeting and/or immunotherapy of HCC.

Statistical Analysis

Unpaired t test was performed for the expression differences of BTBD10 using GraphPad Prism 6 software. Cox analysis was used to determine the independent prognostic risk factors of HCC patients. Survival differences were expressed by K-M curve and determined by log-rank test. Spearman correlation analysis was used for correlation analysis. Related R packages included “forestplot”, “rms”, “pheatmap”, “ timeROC ”, “GSEABase ”, “ggstatsplot” and “ggplot2”. A p-value of less than 0.05 was considered statistically significant.

RESULTS

Identification of BTBD10 Expression in HCC Tissues and Cell Lines

According to the prediction of TIMER data, the expression of BTBD10 mRNA in 11 types of tumor tissues (including HCC tissues) was significantly higher than that in normal tissues (Figure 1). Meanwhile, we confirmed the higher expression of BTBD10 mRNA in HCC tissues with the data from UALCAN and TCGA database (Figures 2A,B). Furthermore, qRT-PCR was performed to verify the expression of BTBD10 mRNA in HCC tissue and cell lines. The result demonstrated the higher expression of BTBD10 mRNA in HCC tissues obtained from Xijing Hospital (Figure 2C) and HCC cell lines (HepG2, Hep3B, HUH7, LM3, MHCC97H, SNU-368 and SNU-739) (Figure 2D). Immunohistochemical results of Human Protein Atlas (HPA) also indicated the higher expression of BTBD10 protein in HCC tissues than that in normal liver tissues (Figures 2E,F).

FIGURE 1 | BTBD10 expression levels in various cancer tissues determined using the TIMER analysis. p < 0.001 = ***, p < 0.01 = **, and p < 0.05 = *.

FIGURE 2 | Differences in expression of BTBD10: (A) UALCAN data; (B) TCGA data; (C) Xijing Hospital data; (D) cell lines. Representative images of immunohistochemical staining for BTBD10 in normal liver tissues and HCC tissues from the Human Protein Atlas: (E) normal tissues; (F) tumor tissues.

BTBD10 Expression Affects the Prognosis of HCC Patients

The area under ROC curve (AUC) of BTBD10 expression based on TCGA data and Xijing Hospital data were 0.689 and 0.683, respectively, suggesting that BTBD10 has a strong predictive ability for the survival of HCC patients (Figures 3A,B). In the distribution of survival status of HCC patients, we observed that with the increase of BTBD10 expression, the number of patients dying increased (Figures 3C,D). K-M curve confirmed that BTBD10 expression was positively correlated with poor prognosis of HCC patients (Figures 3E,F). Univariate and multivariate Cox analysis showed that BTBD10 was an independent prognostic risk factor for HCC patients, with hazard ratios (HR, the high expression divided by the low expression) of 1.750 and 5.088, respectively (Table 2, 3). The expression level of BTBD10, an independent prognostic risk factor, provides a quantitative method for clinicians to predict the likelihood of progression-free survival at 1, 3, and 5 years for HCC patients (Figures 4A,B).

FIGURE 3 | The predictive ability of the BTBD10 for 1, 2, and 3 years: (A) ROC curves drawn using TCGA data; (B) ROC curves drawn using Xijing Hospital data. In the TCGA data, the expression of BTBD10 was related to the survival time and survival status of patients: (C) risk curves; (D)survival status distribution map. Kaplan-Meier curve in patients with hepatocellular carcinoma: (E) TCGA data; (F) Xijing Hospital data.

TABLE 2 | TCGA data: Univariate and multivariate cox regression analyses of risk factors associated with overall survival.

TABLE 3 | Xijing hospital data: Univariate and multivariate cox regression analyses of risk factors associated with overall survival.

FIGURE 4 | (A) Nomogram to predict the 1-year, 3-years and 5-years progression-free survival (PFS) of HCC patients; (B) Calibration curve for the PFS nomogram model with dashed diagonal line represents the ideal nomogram.

In addition, we investigated the correlation between BTBD10 expression and TNM stage and/or grade, TP53 mutation and DNA methylation in HCC to elucidate the potential causes of adverse impact of BTBD10 on the prognosis of HCC patients. The expression of BTBD10 in HCC tissues were associated with different TNM stages and grades of HCC patients (Supplementary Figure S1A). The level of BTBD10 was found to be increased along with the increase of clinical stage and pathological grade of HCC patients (Figures 5A,B). Surprisingly, TP53 mutant group exhibited higher expression of BTBD10 than that of non-mutant group (Figure 5C). Previous studies have shown that gene bodies hypermethylation and gene promoter hypomethylation can lead to the occurrence of tumors, and gene promoter methylation level is negatively correlated with gene expression (Keller et al., 2016; Sun et al., 2018). Our results showed that the expression of BTBD10 promoter methylation in tumor tissues was lower than that in normal tissues, and the level of BTBD10 promoter methylation gradually decreased with the increase of tumor grade (Figures 5D,E). However, the methylation level of BTBD10 promoter did not change with tumor stage and TP53 mutation (Figures 5F,G). These results reversely proved that high expression of BTBD10 can promote the progression of HCC.

FIGURE 5 | Association between expression of BTBD10 mRNA and clinical characteristics in HCC from the UALCAN database. (A) tumor grades; (B) tumor stages; (C) TP53 mutation. (D) Promoter methylation level of BTBD10 in HCC. Association between expression of Promoter methylation level of BTBD10 mRNA and clinical characteristics in HCC from the UALCAN database. (E) tumor grades; (F) tumor stages; (G) TP53 mutation.

Enrichment Analysis

Exploration of the mechanism by which BTBD10 affects HCC progression will help guide clinical treatment. GO and KEGG analysis of TCGA data showed that BTBD10 affected the progression of HCC by regulating cell cycle, transcription, translation and immune response, and the BTBD10 high expression group mainly regulated immune-related pathways (Supplementary Figure S1B).

Relationship Between TILs and BTBD10

TIMER and TCGA analysis revealed that BTBD10 expression was positively correlated with intratumoral B cells, CD4⁺ T cells, CD8⁺ T cells, macrophages, neutrophils and dendritic cells (DCs) (Figures 6A,B). In order to provide a more accurate target for immunotherapy of HCC, the correlation between BTBD10 and TIL surface markers was further studied (Figures 6C,D). Statistical analysis results are shown in additional file1.

FIGURE 6 | Correlation analysis between BTBD10 expression and levels of infiltrating immune cells in HCC. (A) From the TIMER website; (B) TCGA data from ACLBI website; (C,D) A Heatmap of the correlation between BTBD10 and surface molecules of tumor-infiltrating lymphocytes (in the diagram, red represents positive correlation, blue represents negative correlation).

Immunotherapy

Immunotherapy has gradually replaced surgical resection in the treatment of advanced HCC patients. In the TCGA and Xijing Hospital data, expression levels of PD-1, PD-L1, CTLA4 and PD-L2 in the high BTBD10 expression group were significantly higher than those in the low BTBD10 expression group and the normal group (Figures 7A,B). Subsequently, the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm revealed that the response to ICIs was poorer in the high BTBD10 expression group than in the low BTBD10 expression group (high TIDE score, worse response to ICIs, and short survival after ICIs treatment) (Jiang et al., 2018a) (Figure 7C).

FIGURE 7 | Expression distribution of immune checkpoint genes in different BTBD10 expression groups (A)TCGA data; (B)Xijing Hospital data; (C) TIDE algorithm was used to obtain the statistical table of immune response and the distribution of immune response scores in different groups.

Correlation Analysis of Between BTBD10 and m6A Methylation

In recent years, m6A methylation has become increasingly prominent in anti-tumor immunotherapy. We found that BTBD10 was positively proportional to m6A methylation related genes, including methylation transferases (METTL3, METTL14 and WTAP, etc.), Demethylase (FTO and ALKBH5) and methylated reading proteins (YTHDC1, YThDF1-YTHDF3, IGF2BP1-IGF2BP3, etc.) (Figure 8A). This correlation provides a new perspective for improving the response rate of targeted drugs.

FIGURE 8 | (A) Heatmaps of m6A methylation-related genes expression in different BTBD10 expression groups using TCGA data (B) Heatmaps of ferroptosis-related genes expression in different BTBD10 expression groups using TCGA data.

Correlation Analysis of Between BTBD10 and Ferroptosis

Increased ferroptosis in tumor cells can accelerate cell death and increase the efficacy of immunotherapy. Our results demonstrate that BTBD10 is positively correlated with NFE2L2, NCOA4, LPCAT3, CS, ATL1, FDFT1, CDKN1A, ALOX15, HSPA5, ACSL4, FANCD2, TFRC, SLC1A5, CARS1, EMC2 and SLC7A11, while SAT1 was negatively correlated with BTBD10 (Figure 8B). Patients with high expression of CARS1, FANCD2, SLC1A5, SLC7A11 and TFRC had worse prognosis than those with low expression, prognosis of patients in the SAT1 high expression group was better than that in the low expression group (Supplementary Figure S2). Therefore, low BTBD10 expression group is more suitable for immunization combined with ferroptosis-related treatment.

IC50 Score

IC50 is an important indicator to evaluate patients’ response to targeted drug therapy. We used GDSC data to predict differences in IC50 scores of chemotherapy agents between BTBD10 expression groups. The IC50 of Sorafenib, Navitoclax, Veliparib, Luminespib and Imatinib was lower in the BTBD10 high-expression group, while the IC50 of Saracatinib was higher (Figure 9). Therefore, these results confirm that the distribution of IC50 of targeted agents in different BTBD10 expression groups is statistically significant.

FIGURE 9 | Distribution of IC50 scores of targeted drugs in different BTBD10 expression groups via ACLBI website. (A) Sorafenib (B) Navitoclax (C) Veliparib (D) Luminespib (E) Imatinib (F) Saracatinib.

DISCUSSION

The KCTD family includes tetramerization (T1) domain containing proteins with diverse biological effects. BTBD10 is a novel member of the KCTD family, which has been found to be an Akt activator, activating Akt by decreasing the protein phosphatase 2A-mediated dephosphorylation (Nawa and Matsuoka, 2013). Studies suggested that BTBD10 inhibited apoptosis of neuronal and islet beta cells via the Akt pathway (Wang et al., 2013). The diseases associated with BTBD10 also includes progressive myoclonus epilepsy 1A and progressive myoclonus epilepsy 3. An important paralog of this gene is KCTD20 (Nawa and Matsuoka, 2013). Recently, scientists found that KCTD20) enhanced the proliferation and invasion of NSCLC via increasing the phosphorylation level of Akt (Zhang et al., 2017b), indicating a potential role of BTBD10 in tumor diseases. Our study unraveled that BTBD10 was high expressed in HCC tumor and showed an adverse impact on the prognosis of patients with HCC. we confirmed that BTBD10 was associated with tumor clinical stage and pathological grade and can serve as an independent prognostic risk factor for HCC patients.

As TILs in tumor microenvironment (TME) is increasingly regarded as an effective tumor immunotherapy target, revealing its mechanism of action is the premise of individualized precision therapy at present (Wan et al., 2021). Our data suggested that high expression of BTBD10 significantly affected immune response-related pathways, which could reveal the mechanism by which tumor cells evade immune response (Sturm et al., 2019). Analyses of data obtained from TIMER and TCGA data showed that BTBD10 was positively correlated with B cells, CD4⁺ T cells, CD8⁺ T cells, macrophages, DCs and neutrophils in the tumor. BTBD10 interferes with TME homeostasis by affecting the composition ratio of TILs, and this “two-way communication” information exchange promotes tumor progression and metastasis (Postow et al., 2018). TAMs play a mainstay role in the TME and increase tumor invasiveness, which is mainly related to the polarization of M1 and M2 macrophages (Wu et al., 2021). Interferon-γ and other factors trigger the production of inflammatory and immune-stimulating factors by stimulating the polarization of M1 macrophages (Wu et al., 2021). M1 macrophage secretion promotes the epithelial-mesenchymal transition (EMT) of HCC via the IL-35 activation of STAT3 (He et al., 2021). IL-35 can also stimulate the polarization of M2 macrophages to induce a mesenchymal-epithelial transition (MET) in HCC cells (He et al., 2021). However, IL-1β secreted by M1 macrophages can increase the expression of PD-L1 in HCC cells, promote tumor progression, and affect the prognosis of tumor patients (Zong et al., 2019). Increasing evidence shows that inflammation is necessary for the initiation and progression of HCC, and when neutrophils are elevated in the TME, HCC is more aggressive and patients have a worse prognosis (Gong et al., 2016). This finding may be related to neutrophil activation of Toll-like receptor 4/9 signaling to increase HCC metastasis in vivo (Yang et al., 2020). Furthermore, the C-X-C motif chemokine ligand (CXCL)-2, CXCL8 and CCL25 released by HCC cells can in turn increase the neutrophil ratio in the TME, forming an immunosuppressive microenvironment and leading to tumor progression (Zhu et al., 2020; Xu et al., 2021). In addition, our prediction concluded that BTBD10 may significantly affect the expression of Tregs (FOXP3, CCR8, STAT5B, TGFβ) and T cell exhaustion (PD-1, CTLA4, LAG3, TIM-3) in HCC. These results suggest that BTBD10 may inhibit the recognition of tumor cells by the immune system and help the immune escape of HCC cells. In conclusion, BTBD10 can cause an imbalance in the TME by regulating the ratio of TILs and increase the expression of PD-1/PD-L1 to cause the metastasis and invasion of HCC.

Although many prognostic markers for HCC have been identified, treatment for HCC is not ideal because drug response is usually a complex feature, often influenced by many genomic and environmental factors (Lu et al., 2019). Our results showed that the expression levels of PD-1 and PD-L1 in the high expression group of BTBD10 were significantly higher than those in the low expression group, and patients in the high expression group of BTBD10 had better ORR of targeted drugs than those in the low expression group. However, TIDE score of patients with high expression of BTBD10 was higher than that of patients with low expression of BTBD10 (the higher the TIDE score, the worse the efficacy of ICIs) (Jiang et al., 2018b), which may be because the increased binding between PD-1 and PD-L1 on T cells in TME reduces the immune capacity of T cells and accelerates the depletion of T cells (Abd El Aziz et al., 2020). PD-L1 and CTLA-4 are highly expressed in DCs and B cells in HCC and downregulate the T-cell-mediated immune response through the function of immunosuppression (Zhou et al., 2017). In addition, high PD-L1 expression can increase AFP and TIL levels in patients, providing a new perspective for the regulation of the PD-L1/PD-1 pathway in HCC immunotherapy (Liu et al., 2019). Although targeted drugs have been the treatment standard for patients with advanced HCC, greatly improving the tumor resection rate and survival rate of patients with cancer, but the effective rate of single drugs is only 12–20%, thus limiting their clinical application (Kudo et al., 2018; Abd El Aziz et al., 2020; Pinter et al., 2021).

Therefore, it is urgent to predict and evaluate the efficacy of immunotherapy more accurately in clinical practice to improve the efficacy of precise individualized therapy. Studies have shown that dual immune checkpoint blockade can enhance anti-tumor immunity to HCC(Du et al., 2021). For example, Imatinib reversed Sorafenib induced autophagy, and the combination of Imatinib and Sorafenib had a synergistic effect on HCC cells compared to monotherapy (Xiao et al., 2021). Meanwhile, m6A methyltransferase METTL3 also enhanced Sorafenib’s ability to inhibit HCC by decreasing the autophagy pathway (Lin et al., 2020). However, the demethylase FTO can reduce the expression of METTL3 by demethylating the m6A modified base, and the up-regulation of methyltransferase METTL4 and down-regulation of demethylase FTO can increase the ferroptosis of cells (Shen et al., 2021). Ferroptosis refers to the metabolic disorder of lipid oxides in cells, producing toxic lipids that induce cell death, but this mode of death can be blocked by iron-chelating agents, presenting iron dependence (Li et al., 2021). Sorafenib, as a promoter of ferroptosis, can cause the accumulation of ROS by inhibiting system XC-, leading to the depletion of intracellular glutathione (GSH), thus causing ferroptosis and increasing the response rate of patients to sorafenib (Li et al., 2021). Previous studies have shown that SAT1 participates in the regulation of p53-mediated ROS response and ferroptosis, and enhances the role of p53 in promoting tumor cell apoptosis (Ou et al., 2016). Our study showed that the expression of SAT1 in the high expression group of BTBD10 was lower than that in the low expression group, and TIDE results suggested that the high expression group had a lower ORR to ICIs. Therefore, patients with low BTBD10 expression are more suitable for targeted drugs combined with ferroptosis-related therapy, while patients with high BTBD10 expression are more suitable for multiple targeted drugs combination or combined m6A-related therapy, which may bring hope for the treatment of HCC patients. In summary, BTBD10 showed an advantage over other prognostic markers. However, there are still many unknown factors that limit the clinical application of ICIs, so we need to collect more data in the future to screen out more sensitive ICIs and increase the survival time of HCC patients.

CONCLUSION

We demonstrate that BTBD10 is associated with TILs and immune checkpoints, and we illustrate the molecular mechanism by which BTBD10 acts as an independent risk factor in disease progression in HCC patients. Our results suggest that BTBD10 may serve as a prognostic biomarker for HCC.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.

ETHICS STATEMENT

The studies involving human participants were reviewed and approved by Institutional Ethics Committee of the First Affiliated Hospital of Fourth Military Medical University. The patients/participants provided their written informed consent to participate in this study.

AUTHOR CONTRIBUTIONS

JL, XT, and YN contributed equally to this study. HZ, XZ, WS designed the whole study. JL, XT, and YN conducted the statistical analysis. TZ, XL, YW, and ZM performed the qRT-PCR analysis. JL, XT, and YN drafted the manuscript. WW and YH made relevant edits to the manuscript. WS revised the manuscript.

FUNDING

This work was supported by the National Natural Science Foundation of China (81672716, 81900571).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, orclaim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2021.762541/full#supplementary-material

Supplementary Figure S1 | (A) Sankey diagram of correlation between BTBD10 and tumor stages, grades and survival status distribution of HCC patients. Each column represents a characteristic variable, different colors represent different types or stages, and lines represent the distribution of the same sample in different characteristic variables. (B) TCGA data were used to predict the signaling pathways that BTBD10 might affect the progression of HCC, and the representative hallmark term GSEA plot was drawn.

Supplementary Figure S2 | In TCGA data, effect of ferroptosis-related genes on overall survival in patients with HCC: (A) CARS1, (B) FANCD2, (C) SLC1A5, (D) SLC7A11, (E) TFRC, and (F) SAT1.

REFERENCES

Abd El Aziz, M. A., Facciorusso, A., Nayfeh, T., Saadi, S., Elnaggar, M., Cotsoglou, C., et al. (2020). Immune Checkpoint Inhibitors for Unresectable Hepatocellular Carcinoma. Vaccines (Basel) 8 (4), 8040616. doi:10.3390/vaccines8040616

Chen, J., Xu, J., Ying, K., Cao, G., Hu, G., Wang, L., et al. (2004). Molecular Cloning and Characterization of a Novel Human BTB Domain-Containing Gene, BTBD10, Which Is Down-Regulated in Glioma. Gene 340 (1), 61–69. doi:10.1016/j.gene.2004.05.028

Chen, W., Zheng, R., Baade, P. D., Zhang, S., Zeng, H., Bray, F., et al. (2016). Cancer Statistics in China, 2015. CA: A Cancer J. Clinicians 66 (2), 115–132. doi:10.3322/caac.21338

Cheon, J., Yoo, C., Hong, J. Y., Kim, H. S., Lee, D. W., Lee, M. A., et al. (2021). Efficacy and Safety of Atezolizumab Plus Bevacizumab in Korean Patients with Advanced Hepatocellular Carcinoma[J]. Liver Int . doi:10.1111/liv.15102

D'Avola, D., Granito, A., de la Torre-Alaez, M., and Piscaglia, F. (2021). The Importance of Liver Functional reserve in the Non-surgical Treatment of Hepatocellular Carcinoma[J]. J. Hepatol. doi:10.1016/j.jhep.2021.11.013

Du, Y., Zhang, D., Wang, Y., Wu, M., Zhang, C., Zheng, Y., et al. (2021). A Highly Stable Multifunctional Aptamer for Enhancing Antitumor Immunity against Hepatocellular Carcinoma by Blocking Dual Immune Checkpoints. Biomater. Sci. 9 (11), 4159–4168. doi:10.1039/d0bm02210a

Gong, Z.-J., Guo, W., Sun, Y.-F., Zhang, X., Qiu, S.-J., Zhou, J., et al. (2016). Prognostic Value of Fever Grade Combined with Neutrophil Percentage in Hepatocellular Carcinoma Patients Presenting Fever as the Initial Manifestation. Onco Targets Ther 9, 6281–6290. doi:10.2147/ott.s109023

Gunasekaran, G., Bekki, Y., Lourdusamy, V., and Schwartz, M. (2021). Surgical Treatments of Hepatobiliary Cancers. Hepatology 73 (Suppl. 1), 128–136. doi:10.1002/hep.31325

He, Y., Pei, J.-h., Li, X.-q., and Chi, G. (2021). IL-35 Promotes EMT through STAT3 Activation and Induces MET by Promoting M2 Macrophage Polarization in HCC. Biochem. biophysical Res. Commun. 559, 35–41. doi:10.1016/j.bbrc.2021.04.050

Hughes, C. B., and Humar, A. (2021). Liver Transplantation: Current and Future. Abdom. Radiol. 46 (1), 2–8. doi:10.1007/s00261-019-02357-w

Jiang, P., Gu, S., Pan, D., Fu, J., Sahu, A., Hu, X., et al. (2018). Signatures of T Cell Dysfunction and Exclusion Predict Cancer Immunotherapy Response. Nat. Med. 24 (10), 1550–1558. doi:10.1038/s41591-018-0136-1

Jiang, P., Gu, S., Pan, D., Fu, J., Sahu, A., Hu, X., et al. (2018). Signatures of T Cell Dysfunction and Exclusion Predict Cancer Immunotherapy Response. Nat. Med. 24 (10), 1550–1558. doi:10.1038/s41591-018-0136-1

Keller, T. E., Han, P., and Yi, S. V. (2016). Evolutionary Transition of Promoter and Gene Body DNA Methylation across Invertebrate-Vertebrate Boundary. Mol. Biol. Evol. 33 (4), 1019–1028. doi:10.1093/molbev/msv345

Kudo, M., Finn, R. S., Qin, S., Han, K.-H., Ikeda, K., Piscaglia, F., et al. (2018). Lenvatinib versus Sorafenib in First-Line Treatment of Patients with Unresectable Hepatocellular Carcinoma: a Randomised Phase 3 Non-inferiority Trial. The Lancet 391 (10126), 1163–1173. doi:10.1016/s0140-6736(18)30207-1

Li, Y., Xia, J., Shao, F., Zhou, Y., Yu, J., Wu, H., et al. (2021). Sorafenib Induces Mitochondrial Dysfunction and Exhibits Synergistic Effect with Cysteine Depletion by Promoting HCC Cells Ferroptosis. Biochem. biophysical Res. Commun. 534, 877–884. doi:10.1016/j.bbrc.2020.10.083

Lin, Z., Niu, Y., Wan, A., Chen, D., Liang, H., Chen, X., et al. (2020). RNA M6 A Methylation Regulates Sorafenib Resistance in Liver Cancer through FOXO3-Mediated Autophagy. EMBO J. 39 (12), e103181. doi:10.15252/embj.2019103181

Liu, G.-M., Li, X.-G., and Zhang, Y.-M. (2019). Prognostic Role of PD-L1 for HCC Patients after Potentially Curative Resection: a Meta-Analysis. Cancer Cel Int 19, 22. doi:10.1186/s12935-019-0738-9

Lu, X., Jiang, L., Zhang, L., Zhu, Y., Hu, W., Wang, J., et al. (2019). Immune Signature-Based Subtypes of Cervical Squamous Cell Carcinoma Tightly Associated with Human Papillomavirus Type 16 Expression, Molecular Features, and Clinical Outcome. Neoplasia 21 (6), 591–601. doi:10.1016/j.neo.2019.04.003

Nawa, M., Kage-Nakadai, E., Aiso, S., Okamoto, K., Mitani, S., and Matsuoka, M. (2012). Reduced Expression of BTBD10, an Akt Activator, Leads to Motor Neuron Death. Cell Death Differ 19 (8), 1398–1407. doi:10.1038/cdd.2012.19

Nawa, M., Kanekura, K., Hashimoto, Y., Aiso, S., and Matsuoka, M. (2008). A Novel Akt/PKB-Interacting Protein Promotes Cell Adhesion and Inhibits Familial Amyotrophic Lateral Sclerosis-Linked Mutant SOD1-Induced Neuronal Death via Inhibition of PP2A-Mediated Dephosphorylation of Akt/PKB. Cell Signal. 20 (3), 493–505. doi:10.1016/j.cellsig.2007.11.004

Nawa, M., and Matsuoka, M. (2013). KCTD20, a Relative of BTBD10, Is a Positive Regulator of Akt. BMC Biochem. 14, 27. doi:10.1186/1471-2091-14-27

Ou, Y., Wang, S.-J., Li, D., Chu, B., and Gu, W. (2016). Activation of SAT1 Engages Polyamine Metabolism with P53-Mediated Ferroptotic Responses. Proc. Natl. Acad. Sci. USA 113 (44), E6806–E6812. doi:10.1073/pnas.1607152113

Pérez-Romasanta, L. A., González-Del Portillo, E., Rodríguez-Gutiérrez, A., and Matías-Pérez, Á. (2021). Stereotactic Radiotherapy for Hepatocellular Carcinoma, Radiosensitization Strategies and Radiation-Immunotherapy Combination. Cancers (Basel) 13 (2), 192. doi:10.3390/cancers13020192

Pinter, M., Scheiner, B., and Peck-Radosavljevic, M. (2021). Immunotherapy for Advanced Hepatocellular Carcinoma: a Focus on Special Subgroups. Gut 70 (1), 204–214. doi:10.1136/gutjnl-2020-321702

Postow, M. A., Sidlow, R., and Hellmann, M. D. (2018). Immune-Related Adverse Events Associated with Immune Checkpoint Blockade. N. Engl. J. Med. 378 (2), 158–168. doi:10.1056/nejmra1703481

Shen, M., Li, Y., Wang, Y., Shao, J., Zhang, F., Yin, G., et al. (2021). N6-methyladenosine Modification Regulates Ferroptosis through Autophagy Signaling Pathway in Hepatic Stellate Cells. Redox Biol. 47, 102151. doi:10.1016/j.redox.2021.102151

Sturm, G., Finotello, F., Petitprez, F., Zhang, J. D., Baumbach, J., Fridman, W. H., et al. (2019). Comprehensive Evaluation of Transcriptome-Based Cell-type Quantification Methods for Immuno-Oncology. Bioinformatics (Oxford, England) 35 (14), i436–i445. doi:10.1093/bioinformatics/btz363

Sun, W., Bunn, P., Jin, C., Little, P., Zhabotynsky, V., Perou, C. M., et al. (2018). The Association between Copy Number Aberration, DNA Methylation and Gene Expression in Tumor Samples. Nucleic Acids Res. 46 (6), 3009–3018. doi:10.1093/nar/gky131

Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., et al. (2021). Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA A. Cancer J. Clin. 71 (3), 209–249. doi:10.3322/caac.21660

Wan, P. K.-T., Ryan, A. J., and Seymour, L. W. (2021). Beyond Cancer Cells: Targeting the Tumor Microenvironment with Gene Therapy and Armed Oncolytic Virus. Mol. Ther. 29 (5), 1668–1682. doi:10.1016/j.ymthe.2021.04.015

Wang, X., Gong, Y., Zheng, M., Xie, Q., Tang, H., Wang, D., et al. (2013). Early Changes in GMRP1 after Intracerebral Hemorrhage: Involvement in Brain Damage and Cell Apoptosis. Acta Neurochir Suppl. 118, 163–167. doi:10.1007/978-3-7091-1434-6_30

Wang, X., Liu, Y., Yang, Z., Zhang, Z., Zhou, W., Ye, Z., et al. (2011). Glucose Metabolism-Related Protein 1 (GMRP1) Regulates Pancreatic Beta Cell Proliferation and Apoptosis via Activation of Akt Signalling Pathway in Rats and Mice. Diabetologia 54 (4), 852–863. doi:10.1007/s00125-011-2048-1

Wu, J., Gao, W., Tang, Q., Yu, Y., You, W., Wu, Z., et al. (2021). Retracted: M2 Macrophage-Derived Exosomes Facilitate HCC Metastasis by Transferring α M β 2 Integrin to Tumor Cells. Hepatology 73 (4), 1365–1380. doi:10.1002/hep.31432

Xiao, M.-C., Qian, H., Huang, C.-K., Zheng, B.-N., Yan, F.-Z., Liu, F., et al. (2021). Imatinib Inhibits the Malignancy of Hepatocellular Carcinoma by Suppressing Autophagy. Eur. J. Pharmacol. 906, 174217. doi:10.1016/j.ejphar.2021.174217

Xu, X., Ye, L., Zhang, Q., Shen, H., Li, S., Zhang, X., et al. (2021). Group-2 Innate Lymphoid Cells Promote Hepatocellular Carcinoma Progression via CXCL2-Neutrophil Induced Immunosuppression[J]. Hepatology 74 (5), 2526–2543.

Yang, L.-Y., Luo, Q., Lu, L., Zhu, W.-W., Sun, H.-T., Wei, R., et al. (2020). Increased Neutrophil Extracellular Traps Promote Metastasis Potential of Hepatocellular Carcinoma via Provoking Tumorous Inflammatory Response. J. Hematol. Oncol. 13 (1), 3. doi:10.1186/s13045-019-0836-0

Zhang, X., Zhou, H., Cai, L., Fan, C., Liu, Y., Wang, L., et al. (2017). Kctd20 Promotes the Development of Non-small Cell Lung Cancer through Activating Fak/AKT Pathway and Predicts Poor Overall Survival of Patients. Mol. Carcinog 56 (9), 2058–2065. doi:10.1002/mc.22660

Zhang, X., Zhou, H., Cai, L., Fan, C., Liu, Y., Wang, L., et al. (2017). Kctd20 Promotes the Development of Non-small Cell Lung Cancer through Activating Fak/AKT Pathway and Predicts Poor Overall Survival of Patients. Mol. Carcinog 56 (9), 2058–2065. doi:10.1002/mc.22660

Zhou, G., Sprengers, D., Boor, P. P. C., Doukas, M., Schutz, H., Mancham, S., et al. (2017). Antibodies against Immune Checkpoint Molecules Restore Functions of Tumor-Infiltrating T Cells in Hepatocellular Carcinomas. Gastroenterology 153 (4), 1107–1119. e1110. doi:10.1053/j.gastro.2017.06.017

Zhu, Q., Pan, Q.-Z., Zhong, A.-L., Hu, H., Zhao, J.-J., Tang, Y., et al. (2020). Annexin A3 Upregulates the Infiltrated Neutrophil-Lymphocyte Ratio to Remodel the Immune Microenvironment in Hepatocellular Carcinoma. Int. immunopharmacology 89, 107139. doi:10.1016/j.intimp.2020.107139

Zong, Z., Zou, J., Mao, R., Ma, C., Li, N., Wang, J., et al. (2019). M1 Macrophages Induce PD-L1 Expression in Hepatocellular Carcinoma Cells through IL-1β Signaling. Front. Immunol. 10, 1643. doi:10.3389/fimmu.2019.01643

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Li, Tian, Nie, He, Wu, Lei, Zhang, Wang, Mao, Zhang, Zhang and Song. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 04 January 2022
doi: 10.3389/fmolb.2021.774215

Hypermethylated PCDHGB7 as a Biomarker for Early Detection of Endometrial Cancer in Endometrial Brush Samples and Cervical Scrapings

Jiangjing Yuan¹†, Zhanrui Mao²†, Qi Lu³†, Peng Xu⁴†, Chengyang Wang², Xiaona Xu¹, Zhaowei Zhou¹, Tongsheng Zhang⁴, Wenqiang Yu⁴*, Shihua Dong²*, Yudong Wang¹* and Weiwei Cheng¹*

¹Department of Obstetrics and Gynecology, International Peace Maternity and Child Health Hospital, Shanghai Key Laboratory of Embryo Original Diseases, and Institute of Birth Defects and Rare Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China

²Shanghai Epiprobe Biotechnology Co., Ltd, Shanghai, China

³Department of Obstetrics and Gynecology, Jinshan Hospital of Fudan University, Shanghai, China

⁴Shanghai Public Health Clinical Center and Department of General Surgery, Huashan Hospital, Cancer Metastasis Institute and Laboratory of RNA Epigenetics, Institutes of Biomedical Sciences, Shanghai Medical College, Fudan University, Shanghai, China

Edited by:
Xin Wang, The Chinese University of Hong Kong, China

Reviewed by:
Hao Huang, City University of Hong Kong, Hong Kong SAR, China
Wei Wang, Huzhou Maternity and Child Health Care Hospital, China

* Correspondence: Wenqiang Yu, wenqiangyu@fudan.edu.cn; Shihua Dong, dongshihua@epiprobe.com; Yudong Wang, owangyudong@126.com; Weiwei Cheng, wwcheng29@shsmu.edu.cn

†These authors have contributed equally to this work

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 11 September 2021
Accepted: 16 November 2021
Published: 04 January 2022

Citation: Yuan J, Mao Z, Lu Q, Xu P, Wang C, Xu X, Zhou Z, Zhang T, Yu W, Dong S, Wang Y and Cheng W (2022) Hypermethylated PCDHGB7 as a Biomarker for Early Detection of Endometrial Cancer in Endometrial Brush Samples and Cervical Scrapings. Front. Mol. Biosci. 8:774215. doi: 10.3389/fmolb.2021.774215

Endometrial cancer (EC) is one of the most common gynecologic cancers in developed countries. Presently, it is imperative to develop a reliable, noninvasive, or minimally invasive detection method for EC. We explored the possibility of using DNA methylation marker from endometrial brush samples (with a “Tao brush”) and cervical scrapes (with a “Pap brush”) for early detection of EC. We analyzed the methylation data of EC and normal endometrial tissues from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) data sets. An optimized methylation-sensitive restriction enzyme combined with real-time fluorescent quantitative PCR (MSRE-qPCR) was used for methylation detection. Included in the training set were 143 endometrial tissues, 103 Tao, and 109 Pap brush samples. The validation set included 110 Tao and 112 Pap brush samples. PCDHGB7 was significantly hypermethylated in EC compared with normal endometrial tissues in the TCGA and GEO data sets (AUC >0.95), which was verified in clinical samples. In the Pap brush samples, the AUC was 0.86 with 80.65% sensitivity and 82.81% specificity, whereas the Tao brush samples exhibited higher specificity (95.31%). The combination of Tao and Pap brush samples significantly increased the sensitivity to 90.32%. In the validation set, the final model yielded a sensitivity of 98.61%, specificity of 60.53%, positive predictive value of 82.56%, and negative predictive value of 95.83%. These results demonstrate the potential application of the novel methylation marker, hypermethylated PCDHGB7, in cervical scrapings and endometrial brush, which provides a viable, noninvasive, or minimally invasive method for early endometrial cancer detection across different clinical features and histologies to supplement current hysteroscopy diagnosis.

Keywords: endometrial cancer, DNA methylation biomarker, PCDHGB7, cervical scrapings, endometrial brush

HIGHLIGHTS

• This work reveals that PCDHGB7 is hypermethylated in EC by bioinformatic analysis and clinical validation.

• We systematically evaluated the performance of hypermethylated PCDHGB7 as a novel biomarker for EC detection in endometrial tissues and Tao and Pap brush samples.

• PCDHGB7 hypermethylation functions as an early stage biomarker for its emergence at the early stage of EC progression.

• The robustness of hypermethylated PCDHGB7 enables Tao and Pap brush samples to be minimally invasive or even noninvasive samples for early EC detection.

INTRODUCTION

Endometrial cancer (EC) is one of the most common malignant tumors of the female reproductive tract worldwide (Sung et al., 2021). In view of the increasing prevalence of EC risk factors, such as diabetes and obesity in the general population, EC’s incidence is expected to increase to 82,000 and 122,000 new cases per year in 2020 and 2030, respectively (Rahib et al., 2014). EC is highly curable in the early stages with a 5-year overall survival of 95% for stage I disease, which is only 19% in stage IV. Although most EC occurs in postmenopausal women, there has been a recent significant increase of EC occurrence ranging from 2% to14% in women aged 40 years or younger (Duska et al., 2001; Ota et al., 2005; Nagase et al., 2019). For young women of childbearing age, if diagnosed in the early stage of EC without myometrial invasion and extrauterine spread, they still hold the opportunity to retain the uterus and/or ovaries. Therefore, early diagnosis of EC is crucial, which reduces female mortality and strives for treatment opportunities in younger patients to retain fertility or reproductive endocrine function.

Presently, effective early detection or screening methods for EC are urgently needed. The current clinical evaluation is not carried out until the patient has symptoms, such as abnormal uterine bleeding (AUB) or postmenopausal bleeding (PMB) (Clarke et al., 2018). Due to the lack of noninvasive or minimally invasive triage diagnosis, women with AUB or PMB have to undergo invasive procedures to obtain endometrial tissues by curettage and hysteroscopic biopsy to exclude EC. Most ECs appear to develop from endometrial hyperplasia (EH) to atypical hyperplasia (AH) with progress stretching for years. During the long course of disease progression, AUB and PMB patients endure invasive endometrial biopsies repeatedly. Only 5%–10% of them have an underlying EC or an EC precursor (Clarke et al., 2018), resulting in physical, psychological, and economic pressures.

The most common detection method for EC in postmenopausal women without clinical symptoms is transvaginal ultrasound (TVS), which measures endometrium thickness. However, it is not reliable to distinguish between benign and malignant endometrium due to its high false-positive rate (Jacobs et al., 2011). Considering serous ECs (SECs) with a poor prognosis can be present in the atrophic endometrium, which is often diagnosed at an advanced stage (Urick and Bell, 2019), EC can also occur in women without endometrial thickening. Therefore, it is necessary to develop a noninvasive or minimally invasive and effective molecular method for early detection of EC among symptomatic and asymptomatic high-risk populations, such as women with Lynch syndrome or increased BMI and tamoxifen users (Costas et al., 2019) to avoid a large number of unnecessary invasive diagnostic workups for most women with benign endometrium.

It is well-known that genomic and epigenomic alterations participate in carcinogenesis in various human organs (Baylin and Jones, 2016; Jones et al., 2016). DNA methylation changes are among essential epigenomic alterations leading to chromosomal instability and aberrant expression of tumor-related genes (Kanai, 2010; Ahuja et al., 2016). Accumulating evidence reveals DNA methylation as a promising cancer biomarker (Widschwendter et al., 2018). Previously, our group found that PCDHGB7 was hypermethylated in various cancer types compared with their corresponding normal tissues, and hypermethylated PCDHGB7 was identified as a universal cancer only marker (UCOM). (Dong et al., 2019; Dong et al., 2021). However, hypermethylated PCDHGB7 has not been systematically investigated in EC until now. The shedding of EC cells and/or cell-free EC DNA into the lower genital tract provides an opportunity to leverage more sensitive molecular testing and less invasive biospecimen sampling methods comprising endometrial brushes (Wentzensen et al., 2014; Bakkum-Gamez et al., 2015), cervical scrapes (De Strooper et al., 2014; Huang et al., 2017; Liew et al., 2019), vaginal swabs, and vaginal tampons (Fiegl et al., 2004; Bakkum-Gamez et al., 2015; Sangtani et al., 2020).

Optimizing sample collection methods and identifying the molecular markers with the greatest sensitivity to detect EC and its precursors are all critical in the development of early detection among symptomatic and asymptomatic patients. In terms of sampling location, endometrial brush sampling is the closest to the neoplastic foci and can collect the most amount of DNA content in theory, and this has higher absolute methylation percentages compared with the cervical scrape samples also taken by physicians. However, the endometrial brush’s main flaw is that it is to be inserted into the uterine cavity with an unsuccessful insertion rate of 20% in nulliparous and 8% in parous women (Williams et al., 2008). Cervical scrape sampling is a relatively noninvasive and more convenient method, and it may be more accepted even for postmenopausal women.

The present study aimed to identify PCDHGB7 methylation in EC and to further compare the performance of PCDHGB7 hypermethylation in the detection of EC by cervical scrapes (with a “Pap brush”) with that of endometrial brush sampling (with a “Tao brush”) so as to provide evidence for adopting a more suitable, noninvasive or minimally invasive sampling method of DNA methylation markers in clinical settings for women with AUB or PMB and within the asymptomatic detection population.

MATERIALS AND METHODS

The Cancer Genome Atlas and Gene Expression Omnibus DNA Methylation Data Analysis

The Illumina 450 K methylation array data from The Cancer Genome Atlas (TCGA) database was downloaded from the UCSC Xena browser (https://xenabrowser.net/). The absolute methylation values were calculated from the β values of a 450 K methylation array [methylation value = (β value +0.5) ×100%]. The only six probes (cg13933262, cg14011639, cg10435816, cg23563234, cg08938584, cg17011276) within the CpG island in the PCDHGB7 promoter region were used, the final methylation value of each sample was calculated by the average of these six probes. The basic information and methylation levels of all samples from TCGA project are listed in Supplementary Table S1. The methylation array data sets (from the GSE136791, GSE93589, GSE67116, GSE33422, and GSE40032 series) from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) were downloaded. The absolute methylation values were calculated by the same method as above. A total of 11 probes (cg14011639, cg27487435, cg08938584, cg05558169, cg14378860, cg23563234, cg13933262, cg10435816, cg01643675, cg03510378, cg17011276) of EPIC methylation array, six probes (cg13933262, cg14011639, cg10435816, cg23563234, cg08938584, cg17011276) of 450 K methylation array, and two probes (cg14011639, cg23563234) of 27 K methylation array within the CpG island in the PCDHGB7 promoter region were used, respectively, and the final methylation value of each sample was calculated by the average of all used probes. The basic information and methylation level of all samples from GEO database are listed in Supplementary Table S2.

Clinical Samples Collection

A total of 577 samples, including 355 samples in the training set and 222 samples in the validation set (Supplementary Table S3), were collected from the International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China. The study was approved by the Ethics Committee of International Peace Maternity and Child Health Hospital. Informed consent was acquired from all patients and control subjects.

The benign endometrium (BE) group (normal endometrium (NE) and EH patients) was enrolled before hysteroscopy or hysterectomy, including women with an endometrium pathology diagnosis by hysteroscopy because of PMB, AUB, increased endometrial thickness, or by hysterectomy for benign indications. The EC and AH patients newly diagnosed by hysteroscopy or curettage and endometrial biopsy were prospectively enrolled prior to hysterectomy. Women were excluded if they had recurrent EC, other known cancers, or received neoadjuvant chemotherapy or radiotherapy. In the cases of EC and AH, patients underwent cervical scrape sampling with a Cervex-Brush® (Rovers Medical Devices B.V., Oss, Netherlands) and endometrial brush sampling with a Tao brush (Cook Medical, Bloomington IN) by the physician before the day of hysterectomy or hysteroscopy, which were rinsed in SurePathTM Preservative Fluid (TriPath Imaging®, Inc., Burlington, United States), respectively, and used for subsequent testing. Moreover, control group specimens were collected before their hysterectomy or hysteroscopy by the same method. Fresh-frozen tissue specimens were collected immediately after hysterectomy or hysteroscopy.

DNA Extraction

Genomic DNA (gDNA) from endometrial tissue samples was extracted with the TIANamp Genomic DNA Kit (Tiangen Biotech, DP304), and gDNA from Pap brush and Tao brush samples was extracted with the EP Genomic DNA Kit (Epiprobe Biotech, K-21), both following the manufacturer’s protocol. The quantity and quality of gDNA were determined with Nanodrop 2000 (Thermo Scientific, Waltham, Massachusetts, United States).

MSRE-qPCR and DNA Methylation Evaluation

Methylation-sensitive restriction enzyme-combined real-time fluorescent quantitative PCR (MSRE-qPCR) was used to detect PCDHGB7 methylation as described previously (Dong et al., 2021). GAPDH gene-related region absence of any cutting sites was selected for normalization. For each digestion reaction, 100 ng gDNA was taken as input, and every two units of the HhaI (NEB, R0139) and HpaII (NEB, R0171) were added, making the final volume 25 μL, followed by digestion at 37°C for 30 min and heat inactivation at 95°C for 5 min. The subsequent dual real-time PCR was performed on the 7500 Real-Time PCR System (Life Technologies) with a program as follows: initiation at 95°C for 10 min, then 45 cycles of 94°C for 20 s and 60°C for 60 s. The DNA methylation level for each sample was evaluated by ΔCt = Ct_PCDHGB7 – Ct_GAPDH.

Statistical Analysis

p values were calculated using the two-tailed nonparametric Mann–Whitney test by GraphPad Prism 7.0. ROC analysis was conducted using GraphPad Prism 7.0. The sensitivity, specificity, positive predictive values (PPVs), and negative predictive values (NPVs) were calculated by clinical calculator 1 (http://vassarstats.net/clin1.html).

RESULTS

PCDHGB7 Hypermethylation as a Biomarker in EC Detection

In our previous study, PCDHGB7 was found to be hypermethylated in various types of cancer and identified as a universal cancer only marker (UCOM). Dong et al. (2019); Dong et al., 2021). Therefore, it is intriguing to investigate its potential application in alternative carcinoma detection sequentially. Methylation microarray data from the TCGA and GEO databases were analyzed. As expected, the methylation level of PCDHGB7 in EC endometrial tissue samples was higher compared with normal tissue samples in 478 samples from the TCGA database and in 269 samples from the GEO database (Figures 1A,B). In addition, we evaluated the performance of PCDHGB7 methylation as a biomarker for EC by receiver operating characteristic (ROC) analysis, and the results showed that the area under the curve (AUC) was 0.98 and 0.95, respectively (Figure 1C), suggesting that PCDHGB7 hypermethylation holds potential to be a diagnostic marker for EC.

FIGURE 1 | Hypermethylated PCDHGB7 acted as a potential marker in EC in TCGA and GEO data sets. (A) PCDHGB7 was hypermethylated in 432 EC endometrial tissues compared with 46 normal endometrial tissues in the TCGA database. (B) PCDHGB7 was hypermethylated in 246 EC endometrial tissues compared with 23 normal endometrial tissues in GEO database. (C) The ROC curve of hypermethylated PCDHGB7 in endometrial tissues from TCGA database and GEO database. (D) PCDHGB7 methylation level in 468 endometrial tissues with determined stage from TCGA database. (E) The ROC curve of hypermethylated PCDHGB7 in endometrial tissues with different stages from the TCGA database. In both (A, B) and (D), p values were calculated using the two-tailed unpaired parametric test by GraphPad Prism 7.0. ****, p < .0001.

Moreover, to examine its probable application in early stage detection, we sorted 468 primary endometrial tissue samples with stages determined from the TCGA database and conducted the ROC curve analysis. The analysis shows significant differences of PCDHGB7 methylation levels between normal and EC tissue samples of Stages I and II∼IV (Figure 1D). Meanwhile, the AUC of Stage I, II, III, and IV was 0.93, 0.95, 0.98, and 1.00, respectively (Figure 1E), suggesting that hypermethylation of PCDHGB7 is a powerful marker for early stage ECs.

Validation of PCDHGB7 Hypermethylation in Endometrial Tissue Samples From EC Patients

Overall, 355 samples of the training set, including endometrial tissue samples, Pap brush samples, and Tao brush samples from 144 individuals, were included in this study, including 70 patients with NE, 29 patients with EH, 10 patients with AH, and 35 patients with EC, whereas 24 of 35 EC patients were in the early stage (Stage I). The statistics of samples refers to Supplementary Table S3. The age, BMI, histopathologic diagnosis, stage, and other clinics’ pathologic information for the participants are shown in Table 1. To assess whether the differences in age and BMI affect the methylation level of PCDHGB7, we analyzed the correlation between PCDHGB7 methylation and age/BMI in both the TCGA database and clinical samples enrolled in the study. It turned out that the methylation level of PCDHGB7 showed no relevance to age or BMI regardless of pathological groups (Supplementary Figure S1).

TABLE 1 | Clinical characteristics of participants.

The flowchart for the study is shown in Figure 2A. To evaluate the bioinformatics analysis results, we validated the methylation level of PCDHGB7 in 33 endometrial cancer samples from EC patients, and 105 BE or tumor-adjacent endometrial tissue samples using MSRE-qPCR (Supplementary Table S4). As expected, the methylation level of PCDHGB7 was found to be significantly higher in endometrial tissues from EC patients than in endometrial tissue samples of the BE group (Figure 2B). Additionally, we also found that PCDHGB7 methylation was significantly higher in tissue samples from patients with EC/AH than patients with NE or EH (Figure 2C), demonstrating that detection of hypermethylated PCDHGB7 using MSRE-qPCR contributes to early stage detection of EC by distinguishing AH from EH in endometrial tissue samples.

FIGURE 2 | Methylation status of PCDHGB7 had the capacity to make distinctions between BEs and ECs in clinical endometrial tissue samples. (A) The workflow of this study. NE, Normal endometrium; EH, Endometrial hyperplasia without atypia; BE, benign endometrium (NE + EH); AH, Atypical hyperplasia; EC, Endometrial cancer; BC, Breast cancer; OC, Ovarian cancer. (B) PCDHGB7 methylation level in 138 endometrial tissue samples by MSRE-qPCR. (C) PCDHGB7 methylation level in NE, EH, AH, and EC groups. In both (B, C), p values were calculated using the two-tailed unpaired parametric test by GraphPad Prism 7.0. **, p < .01; ****, p < .0001.

Evaluation of PCDHGB7 Hypermethylation in Pap Brush and Tao Brush Samples From Patients With EC and AH

To make our EC detection method more applicable and cost-effective, we applied our assay in Tao brush samples of 44 patients with NE, 20 patients with EH, 8 patients with AH, and 31 patients with EC as well as Pap brush samples of 47 patients with NE, 20 patients with EH, 9 patients with AH, and 33 patients with EC (Supplementary Tables S3, S4). We found that PCDHGB7 methylation was significantly higher in Tao brush samples from patients with EC than in NE and EH, whereas the difference between AH and EH showed no statistical significance (Figure 3A). However, significant differences between PCDHGB7 methylation levels were found between Pap brush samples of AH and EH (Figure 3B). Furthermore, the clinical performance of PCDHGB7 hypermethylation in the detection of EC from Pap brush and Tao brush samples were calculated. In Tao brush samples, PCDHGB7 achieved ROC curves of 0.83 (95% CI = 0.73–0.93), and in Pap brush samples, it resulted in ROC curves of 0.86 (95% CI = 0.77–0.95) (Figure 3C). Meanwhile, the sensitivity and specificity of PCDHGB7 methylation were 80.65% and 82.81% in Pap brush samples, whereas in Tao brush samples, the specificity (95.31%) was higher though with 61.29% sensitivity in the detection of EC (Figure 3D). Considering early stage EC is operable and largely curable, it is crucial to evaluate the robustness of hypermethylated PCDHGB7 in early stage EC detection. As shown in Figure 3C, the performance of PCDHGB7 hypermethylation in detection of Stage I EC (AUC = 0.88 in Pap brush and AUC = 0.83 in Tao brush) was comparable of it in detection of all EC samples, which was also supported by the sensitivity and specificity data in Figure 3D.

FIGURE 3 | Detection of EC by using hypermethylated PCDHGB7 in Pap brush and Tao brush. (A) PCDHGB7 methylation level in 103 Tao brush samples by MSRE-qPCR. (B) PCDHGB7 methylation level in 109 Pap brush samples by MSRE-qPCR. (C) The ROC curves of hypermethylated PCDHGB7 in Pap brush and Tao brush samples for detecting ECs and early stage ECs. (D) The sensitivity and specificity of PCDHGB7 hypermethylation in Pap brush and Tao brush samples. In both (A, B), p values were calculated using the two-tailed unpaired parametric test by GraphPad Prism 7.0. *, p < .1; ***, p < .001; ****, p < .0001.

Moreover, similar to the TCGA database results, significant differences in PCDHGB7 methylation levels can be found between EC and BE groups of early stage and early grade in Tao brush and Pap brush samples (Supplementary Figures S2A–S2D). Moreover, the methylation level of PCDHGB7 was also significantly different in patients with tumor size less than 2 cm compared with BE groups (NEs and EHs) (Supplementary Figures S2E, S2F). Additionally, hypermethylation of PCDHGB7 in Tao brush and Pap brush has also been observed in patients without myometrial invasion, vascular invasion, or AUB/PMB (Figure Supplementary Figures S2G–2L). These results support that detecting PCDHGB7 hypermethylation in Tao brush and Pap brush samples are applicable in the early diagnosis of EC.

To further assess its application value in detecting EC, we compared the performance of hypermethylated PCDHGB7 and the widely implemented EC biomarker, serum CA125 (Farias-Eisner et al., 2010). It demonstrated that serum CA125 of normal and cancer samples were largely overlapped (Supplementary Figure S3A). The inability of discriminating between normal and cancer samples indicates serum CA125 is a terrible marker for EC detection, which is further supported by the low AUC (0.61) in detecting EC (Supplementary Figure S3B). Under the cutoff (CA125 = 35 U/ml), the sensitivity of serum CA125 was 24.78%, which was far from satisfaction, though the specificity was 92.31%. The performance of hypermethylated PCDHGB7 in both Pap and Tao brushes significantly outperformed serum CA125, suggesting that hypermethylated PCDHGB7 was a robust marker for noninvasive or minimally invasive detection of EC.

Construction and Validation of the Diagnostic Model for the Detection of Early Stage EC From Tao and Pap Brush Samples

We assessed whether the combination of hypermethylated PCDHGB7 in Tao brush and Pap brush might provide better performance for EC early stage detection in terms of sensitivity and specificity. As shown in Table 2, the sensitivity and specificity (either positive as positive) of combined assay were 90.32% and 73.44%, respectively (PPV = 62.22%, NPV = 94.00%, accuracy = 78.95%). Furthermore, the sensitivity and specificity of both positive as positive were 61.29% and 100%, respectively (PPV = 100%, NPV = 84.21%, accuracy = 87.37%). Hypermethylated PCDHGB7 was designated to be used for the early detection (screening or auxiliary diagnosis) of EC by using Pap or Tao brushes in the current study. In these application scenarios, sensitivity was the overarching priority; thus, the first model was chosen as the final diagnostic model.

TABLE 2 | Performance of PCDHGB7 methylation model in the detection of EC.

In addition, the results of the training set were further validated in an independent testing set of 112 patients that included 73 EC and 39 BE groups (Supplementary Tables S3, S5). The EC group included 50 patients (68.49%) in the early stage (41 Stage IA, 9 Stage IB). The methylation level of PCDHGB7 in Tao brush (Figure 4A) and Pap brush (Figure 4B) were both higher than those in controls. The clinical performance of PCDHGB7 was calculated in the detection of EC from Tao brush and Pap brush, separately and collectively. The diagnostic model resulted in sensitivity of 98.61% and specificity of 60.53% (PPV = 82.56%, NPV = 95.83%, accuracy = 85.45%) (Figure 4C).

FIGURE 4 | Validation of PCDHGB7 performance for the detection of EC in validation set. (A) PCDHGB7 methylation level in 68 Tao brush samples by MSRE-qPCR. (B), PCDHGB7 methylation level in 69 Pap brush samples by MSRE-qPCR. (C) The performance of PCDHGB7 hypermethylation diagnostic model in training set and validation set. In both (A, B), p values were calculated using the two-tailed unpaired parametric test by GraphPad Prism 7.0. ****, p < .0001.

DISCUSSION

PCDHGB7 is a member of protocadherins, which plays an inhibitory role in tumorigenesis and cancer progression by inducing cell cycle arrest and apoptosis (Hou et al., 2019). Previous studies demonstrate that PCDHGB7 is crucial in the process of self-recognition and mutual recognition between synapses, the movement of synapses, and the establishment of the nervous system network (Sano et al., 1993; Frank and Kemler, 2002; Junghans et al., 2005; Morishita and Yagi, 2007). PCDHGB7 is reported to be significantly hypermethylated in non-Hodgkin’s lymphoma (Shi et al., 2007). Besides, PCDHGB7 hypermethylation gene was detected in approximately 80% of breast cancer, and PCDHGB7 expression was reduced in breast cancer tissue (Shan et al., 2016a; Shan et al., 2016b). Importantly, we raised the concept of universal cancer only marker (UCOM) and identified hypermethylated PCDHGB7 as a UCOM (Dong et al., 2019; Dong et al., 2021). This is the first study that enables PCDHGB7 methylation in endometrial tissue and Pap/Tao brush samples for early EC detection. The great performance of UCOM, hypermethylated PCDHGB7, in noninvasive early detection of EC not only extends it clinical applications, but also proves the robustness of UCOM once again. The potential applications of UCOM in other cancer types deserve further investigation.

In this study, we verify that hypermethylated PCDHGB7 can be used to early detect EC by Pap and Tao brush sampling even in EC patients of stage IA without myometrial invasion, G1, tumor < 2 cm in greatest dimension, lymphovascular space invasion (LVSI) (-) and without AUB/PMB (Additional file 2: Supplementary Figure S2). However, we did not observe different methylation levels of PCDHGB7 in EC among various tumor sizes, histologic subtypes, and stages with or without myometrial invasion and LVSI, which reports similar results in previous studies (Bakkum-Gamez et al., 2015; Liew et al., 2019). It is indicated that ECs may share common epigenetic events, and that DNA methylation changes regardless of genetic heterogeneity and clinicopathology (Liew et al., 2019). However, it was encouraging that significant hypermethylated PCDHGB7 could be detected via Pap or Tao brush in early low-risk EC patients of stage IA without myometrial invasion, G1, tumor < 2 cm in greatest dimension and LVSI (-), which are suitable for a fertility preservation procedure. Thus, DNA methylation marker PCDHGB7 by noninvasive or minimally invasive sampling is promising for early diagnosis of EC in women of childbearing age, so these women can choose conservative alternatives to hysterectomy. Taken together, these data suggest that PCDHGB7 hypermethylation may hold potential to increase the likelihood of women achieving disease-free survival and provide opportunities for the EC women of childbearing age to choose fertility preservation treatment. Moreover, monitoring of PCDHGB7 methylation level from Pap/Tao brush samples may provide a convenient and noninvasive approach as a viable alternative to conventional endometrial surveillance during conservative treatment, which requires repeated endometrial biopsy every 3 months until the histological regression.

Pap brush and Tao brush samples are noninvasive or minimally invasive and conveniently obtained during routine office visits without anesthesia. The quality of the two samplings, which are taken by qualified physicians, is robust compared with cervicovaginal self-sampling methods. The Tao brush allows the sampler to be inserted to the intrauterine cavity, which is the closest to the anatomical sites of the tumors without injury to the myometrium or contamination from the endocervical canal (Liew et al., 2019). However, its main flaw is its unsuccessful insertion rate (Williams et al., 2008). In the present study, we failed to insert to the intrauterine cavity for seven women (2 EC, 1 AH, 4 BE). Therefore, we implemented cervical scrape sampling with Pap brush, which is aimed to sample the exocervical and endocervical canal before Tao brush sampling. Cervical scrapes are usually used for cervical cancer detection in the general population, causing minimal discomfort and hence being widely accepted by women. In our study, all cervical scrape specimens were taken successfully and yielded sufficient DNA for methylation analysis. Despite the results diverging from our expectations, we encouragingly found that the clinical performance of PCDHGB7 hypermethylation in the detection of EC from Pap brush was more excellent than that from Tao brush samples evidenced by ROC curve analysis (AUC = 0.86 vs. AUC = 0.83), the sensitivity of PCDHGB7 methylation in the Pap brush was superior to that of the Tao brush (80.65 vs. 61.29%), whereas the specificity of PCDHGB7 methylation in the Tao brush was higher than that of the Pap brush as expected (95.31 vs. 82.81%). There are two possible explanations for this unexpected but enticing finding. One possible reason is that the sample size of our study is relatively small, and the marker performance could be influenced by clinical characteristics during population selection. The median tumor diameter was 2.6 cm (1.4, 4.1) in the present study, whereas the maximum tumor diameter < 2 cm was in 8 EC whose tumor may be localized to a small area of endometrium and may go undetected with the Tao brush. The other possible explanation is that the Tao brush has the disadvantage of not collecting enough endometrial cells of the uterine horns due to its round configuration (Du et al., 2016). The Pap brush was sampled from the cervix, serving as a conduit for malignant endometrial cells shedding from the whole uterine cavity, including uterine horns then gathering into endocervical canal, which could facilitate the sensitivity of Pap brush sampling method. Considering the individual advantages of Tao brush and Pap brush sampling, we suggest the combined diagnostic model (either positive as positive) if the Tao brush can be inserted into the intrauterine cavity successfully. Meanwhile, just using Pap brush specimens to detect PCDHGB7 hypermethylation is acceptable as a complementary sampling method for those unsuccessful uterine cavity insertions as they are less invasive and more acceptable. In addition, we found significant PCDHGB7 hypermethylation occurring within the Pap brush specimens of both AUB or PMB and asymptomatic EC women. As such, the utilization of the Pap brush for a DNA methylation marker as a biospecimen collection device carries promise as a means of detection in high-risk asymptomatic populations, particularly in postmenopausal women with atrophic uterus.

In spite of these novel findings, this study has a few limitations to be overcome. First, considering it was a single-center study, the inclusion of samples from multicenters will enhance the robustness of the conclusions. Moreover, the relatively small sample size of thevalidation set yielded high sensitivity and low specificity, suggested that the current diagnostic model might need a slight adjustment before direct application to clinical settings. In future studies, we propose to verify the clinical diagnostic accuracy in larger, prospective, and unbiased cohorts, especially for the AH group. Given endometrial surveillance, one should include repeated endometrial biopsy every 3 months until histological regression. Further studies are required to elucidate the role of the PCDHGB7 methylation marker in surveillance of early EC or AH conservative treatment by a noninvasive or minimally invasive sampling method as well as consecutive surveillance of PCDHGB7 methylation level using Pap or Tao brush sampling. Moreover, future multicenter clinical trials of PCDHGB7 methylation via Pap and Tao brush sampling in women with AUB or postmenopausal bleeding and in high-risk asymptomatic populations should be planned.

CONCLUSION

We demonstrate the usefulness of hypermethylated PCDHGB7 as a biomarker for noninvasive or minimally invasive early EC detection. Combining the Tao brush with Pap brush sampling could acquire better detection performance, whereas a Pap brush would be more practical in detection settings in wider asymptomatic populations.

DATA AVAILABILITY STATEMENT

All data generated or analyzed during this study are included in this article (and its Supplementary Material files).

ETHICS STATEMENT

The study was approved by the Ethics Committee of International Peace Maternity and Child Health Hospital. Informed consent was acquired from all patients and control subjects.

AUTHOR CONTRIBUTIONS

SD and ZM had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Conception and design: SD, YW, WC, and WY. Acquisition of data (acquired and managed patients, provided facilities, etc.): JY, ZM, CW, XX, and ZZ. Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): ZM, JY, SD, and PX. Writing, review, and/or revision of the manuscript: PX, ZM, SD, JY, YW, and TZ. Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): JY, QL, and YW. Study supervision: WC. All authors have approved the submitted version and have agreed both to be personally accountable for the author’s own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature. All authors read and approved the final manuscript.

FUNDING

National Key R&D Program of China (2018YFC1005004), the Science and Technology Innovation Action Plan of Shanghai (17411950900), the National Natural Science Foundation of China (NSFC) (31671308, 31872814, 81172477, 81402135), Major Special Projects of Basic Research of Shanghai Science and Technology Commission (18JC1411101), Shanghai Science and Technology Commission (21Y11906600), Xuhui District Health and Family Planning Commission Important Disease Joint Research Project (XHLHGG201805), Traditional Chinese and Western medicine in Shanghai (No. 18411963500), The Interdisciplinary Program of Shanghai Jiao Tong University (ZH2018ZDA31). Shanghai Municipal Key Clinical Specialty (shslczdzk06302), Science and Technology Innovation Action Plan International Science and Technology Cooperation Project (20550760600), Shanghai Shenkang Hospital Development Center, Clinical Technology Innovation Project (SHDC12020130), Science and Technology Commission of Xuhui District and Health Commission of Xuhui District, Shanghai (XHLHGG202111), the Project of the Science and Technology Commission of Shanghai Municipality (20Y11914100, 17441907400), the Project of the Science and Technology Commission of Shanghai Xuhui District (2020-018), Shanghai Jiao Tong University Medicine-Engineering Fund (YG2017MS41), Shanghai Jiao Tong University School of Medicine, multi-center clinical research project (DLY201827).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

ACKNOWLEDGMENTS

We thank Yue Yu for editorial help and comments on the manuscript.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2021.774215/full#supplementary-material

REFERENCES

Ahuja, N., Sharma, A. R., and Baylin, S. B. (2016). Epigenetic Therapeutics: A New Weapon in the War against Cancer. Annu. Rev. Med. 67, 73–89. doi:10.1146/annurev-med-111314-035900

American cancer society [Online] (2019). Available: https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/cancer-facts-figures-2019.html (Accessed.

Bakkum-Gamez, J. N., Wentzensen, N., Maurer, M. J., Hawthorne, K. M., Voss, J. S., Kroneman, T. N., et al. (2015). Detection of Endometrial Cancer via Molecular Analysis of DNA Collected with Vaginal Tampons. Gynecol. Oncol. 137, 14–22. doi:10.1016/j.ygyno.2015.01.552

Baylin, S. B., and Jones, P. A. (2016). Epigenetic Determinants of Cancer. Cold Spring Harb Perspect. Biol. 8, a019505. doi:10.1101/cshperspect.a019505

Clarke, M. A., Long, B. J., Del Mar Morillo, A., Arbyn, M., Bakkum-Gamez, J. N., and Wentzensen, N. (2018). Association of Endometrial Cancer Risk with Postmenopausal Bleeding in Women. JAMA Intern. Med. 178, 1210–1222. doi:10.1001/jamainternmed.2018.2820

Costas, L., Frias‐Gomez, J., Guardiola, M., Benavente, Y., Pineda, M., Pavón, M. Á., et al. (2019). New Perspectives on Screening and Early Detection of Endometrial Cancer. Int. J. Cancer 145, 3194–3206. doi:10.1002/ijc.32514

De Strooper, L. M. A., Van Zummeren, M., Steenbergen, R. D. M., Bleeker, M. C. G., Hesselink, A. T., Wisman, G. B. A., et al. (2014). CADM1, MAL and miR124-2 Methylation Analysis in Cervical Scrapes to Detect Cervical and Endometrial Cancer. J. Clin. Pathol. 67, 1067–1071. doi:10.1136/jclinpath-2014-202616

Dong, S., Lu, Q., Xu, P., Chen, L., Duan, X., Mao, Z., et al. (2021). Hypermethylated PCDHGB7 as a Universal Cancer Only Marker and its Application in Early Cervical Cancer Screening. Clin. Transl Med. 11, e457. doi:10.1002/ctm2.457

Dong, S., Li, W., Wang, L., Hu, J., Song, Y., Zhang, B., et al. (2019). Histone-Related Genes Are Hypermethylated in Lung Cancer and Hypermethylated HIST1H4F Could Serve as a Pan-Cancer Biomarker. Cancer Res. 79, 6101–6112. doi:10.1158/0008-5472.can-19-1019

Du, J., Li, Y., Lv, S., Wang, Q., Sun, C., Dong, X., et al. (2016). Endometrial Sampling Devices for Early Diagnosis of Endometrial Lesions. J. Cancer Res. Clin. Oncol. 142, 2515–2522. doi:10.1007/s00432-016-2215-3

Duska, L. R., Garrett, A., Rueda, B. R., Haas, J., Chang, Y., and Fuller, A. F. (2001). Endometrial Cancer in Women 40 Years Old or Younger. Gynecol. Oncol. 83, 388–393. doi:10.1006/gyno.2001.6434

Farias-Eisner, G., Su, F., Robbins, T., Kotlerman, J., Reddy, S., and Farias-Eisner, R. (2010). Validation of Serum Biomarkers for Detection of Early- and Late-Stage Endometrial Cancer. Am. J. Obstet. Gynecol. 202, 73e71–5. doi:10.1016/j.ajog.2009.07.049

Fiegl, H., Gattringer, C., Widschwendter, A., Schneitter, A., Ramoni, A., Sarlay, D., et al. (2004). Methylated DNA Collected by Tampons-a New Tool to Detect Endometrial Cancer. Cancer Epidemiol. Biomarkers Prev. 13, 882–888.

Frank, M., and Kemler, R. (2002). Protocadherins. Curr. Opin. Cel Biol. 14, 557–562. doi:10.1016/s0955-0674(02)00365-4

Hou, S., Shan, M., Gao, C., Feng, X., Yang, Y., Zhang, R., et al. (2019). PCDHGB7 Increases Chemosensitivity to Carboplatin by Inhibiting HSPA9 via Inducing Apoptosis in Breast Cancer. Dis. Markers 2019, 6131548. doi:10.1155/2019/6131548

Huang, R.-L., Su, P.-H., Liao, Y.-P., Wu, T.-I., Hsu, Y.-T., Lin, W.-Y., et al. (2017). Integrated Epigenomics Analysis Reveals a DNA Methylation Panel for Endometrial Cancer Detection Using Cervical Scrapings. Clin. Cancer Res. 23, 263–272. doi:10.1158/1078-0432.ccr-16-0863

Jacobs, I., Gentry-Maharaj, A., Burnell, M., Manchanda, R., Singh, N., Sharma, A., et al. (2011). Sensitivity of Transvaginal Ultrasound Screening for Endometrial Cancer in Postmenopausal Women: a Case-Control Study within the UKCTOCS Cohort. Lancet Oncol. 12, 38–48. doi:10.1016/s1470-2045(10)70268-0

Jones, P. A., Issa, J.-P. J., and Baylin, S. (2016). Targeting the Cancer Epigenome for Therapy. Nat. Rev. Genet. 17, 630–641. doi:10.1038/nrg.2016.93

Junghans, D., Haas, I. G., and Kemler, R. (2005). Mammalian Cadherins and Protocadherins: about Cell Death, Synapses and Processing. Curr. Opin. Cel Biol. 17, 446–452. doi:10.1016/j.ceb.2005.08.008

Kanai, Y. (2010). Genome-wide DNA Methylation Profiles in Precancerous Conditions and Cancers. Cancer Sci. 101, 36–45. doi:10.1111/j.1349-7006.2009.01383.x

Liew, P.-L., Huang, R.-L., Wu, T.-I., Liao, C.-C., Chen, C.-W., Su, P.-H., et al. (2019). Combined Genetic Mutations and DNA-Methylated Genes as Biomarkers for Endometrial Cancer Detection from Cervical Scrapings. Clin. Epigenet 11, 170. doi:10.1186/s13148-019-0765-3

Morishita, H., and Yagi, T. (2007). Protocadherin Family: Diversity, Structure, and Function. Curr. Opin. Cel Biol. 19, 584–592. doi:10.1016/j.ceb.2007.09.006

Nagase, S., Ohta, T., Takahashi, F., and Enomoto, T. (2019). Annual Report of the Committee on Gynecologic Oncology, the Japan Society of Obstetrics and Gynecology: Annual Patients Report for 2015 and Annual Treatment Report for 2010. J. Obstet. Gynaecol. Res. 45, 289–298. doi:10.1111/jog.13863

Ota, T., Yoshida, M., Kimura, M., and Kinoshita, K. (2005). Clinicopathologic Study of Uterine Endometrial Carcinoma in Young Women Aged 40 Years and Younger. Int. J. Gynecol. Cancer 15, 657–662. doi:10.1111/j.1525-1438.2005.00129.x

Rahib, L., Smith, B. D., Aizenberg, R., Rosenzweig, A. B., Fleshman, J. M., and Matrisian, L. M. (2014). Projecting Cancer Incidence and Deaths to 2030: the Unexpected burden of Thyroid, Liver, and Pancreas Cancers in the United States. Cancer Res. 74, 2913–2921. doi:10.1158/0008-5472.can-14-0155

Sangtani, A., Wang, C., Weaver, A., Hoppman, N. L., Kerr, S. E., Abyzov, A., et al. (2020). Combining Copy Number, Methylation Markers, and Mutations as a Panel for Endometrial Cancer Detection via Intravaginal Tampon Collection. Gynecol. Oncol. 156, 387–392. doi:10.1016/j.ygyno.2019.11.028

Sano, K., Tanihara, H., Heimark, R. L., Obata, S., Davidson, M., St John, T., et al. (1993). Protocadherins: a Large Family of Cadherin-Related Molecules in central Nervous System. EMBO J. 12, 2249–2256. doi:10.1002/j.1460-2075.1993.tb05878.x

Shan, M., Su, Y., Kang, W., Gao, R., Li, X., and Zhang, G. (2016a). Aberrant Expression and Functions of Protocadherins in Human Malignant Tumors. Tumor Biol. 37, 12969–12981. doi:10.1007/s13277-016-5169-9

Shan, M., Yin, H., Li, J., Li, X., Wang, D., Su, Y., et al. (2016b). Detection of Aberrant Methylation of a Six-Gene Panel in Serum DNA for Diagnosis of Breast Cancer. Oncotarget 7, 18485–18494. doi:10.18632/oncotarget.7608

Shi, H., Guo, J., Duff, D. J., Rahmatpanah, F., Chitima-Matsiga, R., Al-Kuhlani, M., et al. (2007). Discovery of Novel Epigenetic Markers in Non-hodgkin's Lymphoma. Carcinogenesis 28, 60–70. doi:10.1093/carcin/bgl092

Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., et al. (2021). Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA A. Cancer J. Clin. 71, 209–249. doi:10.3322/caac.21660

Urick, M. E., and Bell, D. W. (2019). Clinical Actionability of Molecular Targets in Endometrial Cancer. Nat. Rev. Cancer 19, 510–521. doi:10.1038/s41568-019-0177-x

Wentzensen, N., Bakkum-Gamez, J. N., Killian, J. K., Sampson, J., Guido, R., Glass, A., et al. (2014). Discovery and Validation of Methylation Markers for Endometrial Cancer. Int. J. Cancer 135, 1860–1868. doi:10.1002/ijc.28843

Widschwendter, M., Jones, A., Jones, A., Evans, I., Reisel, D., Dillner, J., et al. (2018). Epigenome-based Cancer Risk Prediction: Rationale, Opportunities and Challenges. Nat. Rev. Clin. Oncol. 15, 292–309. doi:10.1038/nrclinonc.2018.30

Williams, A., Brechin, S., Porter, A., Warner, P., and Critchley, H. (2008). Factors Affecting Adequacy of Pipelle and Tao Brush Endometrial Sampling. BJOG 115, 1028–1036. doi:10.1111/j.1471-0528.2008.01773.x

Conflict of Interest: SD is one of the co-founders and the R & D director of Shanghai Epiprobe Biotechnology Co., Ltd. ZM and CW are employed by Epiprobe. WY serves on the scientific advisory boards of Epiprobe. SD has pending patents related to this work.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Yuan, Mao, Lu, Xu, Wang, Xu, Zhou, Zhang, Yu, Dong, Wang and Cheng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 05 January 2022
doi: 10.3389/fmolb.2021.705148

Development and Verification of a Hypoxic Gene Signature for Predicting Prognosis, Immune Microenvironment, and Chemosensitivity for Osteosarcoma

Fengfeng Wu¹†, Juntao Xu²†, Mingchao Jin³†, Xuesheng Jiang³, Jianyou Li³, Xiongfeng Li³, Zhuo Chen³, Jiangbo Nie³, Zhipeng Meng⁴ and Guorong Wang³*

¹Department of Orthopedics and Rehabilitation, Huzhou Central Hospital, Affiliated Central Hospital of Huzhou University, Zhejiang University Huzhou Hospital, Huzhou, China

²Department of Orthopedics, Huzhou Traditional Chinese Medicine Hospital, Affiliated to Zhejiang Chinese Medical University, Huzhou, China

³Department of Orthopedics, Huzhou Central Hospital, Affiliated Central Hospital of Huzhou University, Zhejiang University Huzhou Hospital, Huzhou, China

⁴Department of Anesthesiology, Huzhou Central Hospital, Affiliated Central Hospital of Huzhou University, Zhejiang University Huzhou Hospital, Huzhou, China

Edited by:
Ismail Hosen, University of Dhaka, Bangladesh

Reviewed by:
Pankaj Chaturvedi, University of Illinois at Urbana-Champaign, United States
Wenlong Lin, Zhejiang University, China

* Correspondence: Guorong Wang, sxg2211hu@163.com

†These authors have contributed equally to this work

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 04 May 2021
Accepted: 29 November 2021
Published: 05 January 2022

Citation: Wu F, Xu J, Jin M, Jiang X, Li J, Li X, Chen Z, Nie J, Meng Z and Wang G (2022) Development and Verification of a Hypoxic Gene Signature for Predicting Prognosis, Immune Microenvironment, and Chemosensitivity for Osteosarcoma. Front. Mol. Biosci. 8:705148. doi: 10.3389/fmolb.2021.705148

Objective: Hypoxic tumors contribute to local failure and distant metastases. Nevertheless, the molecular hallmarks of hypoxia remain ill-defined in osteosarcoma. Here, we developed a hypoxic gene signature in osteosarcoma prognoses.

Methods: With the random survival forest algorithm, a prognostic hypoxia-related gene signature was constructed for osteosarcoma in the TARGET cohort. Overall survival (OS) analysis, receiver operating characteristic (ROC) curve, multivariate cox regression analysis, and subgroup analysis were utilized for assessing the predictive efficacy of this signature. Also, external validation was presented in the GSE21257 cohort. GSEA was applied for signaling pathways involved in the high- and low-risk samples. Correlation analyses between risk score and immune cells, stromal/immune score, immune checkpoints, and sensitivity of chemotherapy drugs were performed in osteosarcoma. Then, a nomogram was built by integrating risk score, age, and gender.

Results: A five-hypoxic gene signature was developed for predicting survival outcomes of osteosarcoma patients. ROC curves confirmed that this signature possessed the well predictive performance on osteosarcoma prognosis. Furthermore, it could be independently predictive of prognosis. Metabolism of xenobiotics by cytochrome P450 and nitrogen metabolism were activated in the high-risk samples while cell adhesion molecules cams and intestinal immune network for IgA production were enriched in the low-risk samples. The low-risk samples were characterized by elevated immune cell infiltrations, stromal/immune scores, TNFRSF4 expression, and sensitivity to cisplatin. The nomogram accurately predicted 1-, 3-, and 5-years survival duration.

Conclusion: These findings might offer an insight into the optimization of prognosis risk stratification and individualized therapy for osteosarcoma patients.

Keywords: osteosarcoma, prognosis, hypoxia, gene signature, immune microenvironment, nomogram

INTRODUCTION

Osteosarcoma represents the main primary bone malignancy, occurring in 2.4% of children and adolescents (Yang et al., 2020). This disease typically presents in distal femoral metaphysis, followed by the upper extremities, pelvis, and the like (Kelley et al., 2020). Surgical resection and chemotherapy distinctly prolong survival duration of patients with local osteosarcoma. Non-metastatic patients reach the 60–70% of the 5-years survival rate following diagnosis (Cao et al., 2020). However, osteosarcoma is characterized by high metastatic and recurrent risks. For metastatic or relapsed subjects, the 5-years survival rate is only 20% (Shi et al., 2020). It is urgent to probe biomarkers for assisting clinicians to predict survival outcomes as well as offer a basis upon personalized medicine.

The intratumoral hypoxia is a noticeable feature in solid tumors, and hypoxia is in relation to aggressive phenotypes and metastases (Song et al., 2020). For osteosarcoma, hypoxia is involved in facilitating excessive tumor proliferative and invasive capacities through inducing serial molecular reactions to promote the formation of the neoplastic microenvironment (Chen et al., 2020). Under hypoxic conditions, various genes can be activated in osteosarcoma cells. For instance, hypoxia induces chemoresistance through down-regulation of SKA1 expression in osteosarcoma (Ma et al., 2017). Hypoxia-induced WSB1 accelerates invasion and metastasis of osteosarcoma cells (Cao et al., 2015). Nevertheless, no hypoxic gene signature has been established yet for osteosarcoma patients’ prognosis. Transcriptomic analysis may uncover the entire expression patterns as well as identify cancer-related markers. Here, this study effectively developed a robust prognosis-related hypoxic gene signature for osteosarcoma, which might provide a basis for an in-depth understanding of risk stratification and therapeutic effects of osteosarcoma patients.

MATERIALS AND METHODS

Data Collection

The gene expression profiles of 84 cases of osteosarcoma patients with complete follow-up information were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET; https://ocg.cancer.gov/programs/target) database, which were utilized as the training set. The GSE21257 dataset containing expression profiling of 53 osteosarcoma subjects was retrieved from the Gene Expression Omnibus (GEO) repository (https://www.ncbi.nlm.nih.gov/geo/) (Buddingh et al., 2011), which was used as the external validation set. Genes up-regulated in response to hypoxia were retrieved from the “HALLMARK_HYPOXIA” gene set in the Molecular Signatures database v7.2 with the gene set enrichment analysis (GSEA) software (Subramanian et al., 2005). Figure 1 depicts the workflow of this study.

FIGURE 1 | Schematic diagram of the workflow of this study.

Establishment of a Prognostic Gene Signature

Survival-related hypoxic genes with p < 0.05 were screened for osteosarcoma samples from the TARGET cohort with the univariate cox regression analyses. Then, the importance of these prognostic genes was ranked using the random survival forest algorithm. The number of Monte Carlo iterations was set as 100 and the number of steps forward was set as 5. The genes with relative importance >0.25 were feature factors. Multivariate cox regression analyses were then presented. The risk scoring model was established as follows: risk score = coefficient of gene 1 * expression of gene 1 + coefficient of gene 2 * expression of gene 2 +…+ coefficient of gene n * expression of gene n. The specimens were classified into high and low risk groups on the basis of the mean value of risk scores. Expression patterns of genes in the signature were visualized into a heat map. Survival status was evaluated between the two groups. Kaplan-Meier curves of overall survival (OS) were then depicted between groups and survival differences were compared via log-rank test. The time-dependent receiver operating characteristic (ROC) was analyzed for examining the sensitivity and specificity of the gene signature for predicting OS. The area under the curve (AUC) was determined for assessing the prognostic accuracy. Furthermore, the AUC of this signature was compared with other gene signatures developed by Wen C. et al. (2020), Wu Z.-l. et al. (2020), Xiao et al. (2020) and Zhang et al. (Yiqi et al., 2020). Univariate and multivariate cox regression analyses were carried out based on risk score, age, and gender as variables. p values, hazard ratio (HR) as well as 95% confidence interval (CI) were calculated. Moreover, survival analysis of each gene in this signature was presented for osteosarcoma subjects in the TARGET cohort.

Subgroup Analysis

Osteosarcoma samples were classified into different subgroups according to age (<16 and ≥16) and gender (female and male). In each subgroup, OS differences were compared between high and low risk samples with Kaplan-Meier curves and log-rank tests.

GSEA

Osteosarcoma specimens in the TARGET cohort were classified into high and low risk groups. GSEA was carried out to explore the underlying pathways involved in the two groups. The |normalized enrichment score (NES)| > 1.5 and false discovery rate (FDR) < 0.05 were used to determine statistical significance.

Single-Sample Gene Set Enrichment Analysis

The enrichment scores of activated B cells, activated CD4 T cells, activated CD8 T cells, central memory CD4 T cells, central memory CD8 T cells, effector memory CD4 T cells, effector memory CD8 T cells, gamma delta T cells, immature B cells, memory B cells, regulatory T cells, T follicular helper cells, type 1 T helper cells, type 17 T helper cells, type 2 T helper cells, activated dendritic cells, CD56bright natural killer cells, CD56dim natural killer cells, eosinophils, immature dendritic cells, macrophages, mast cells, MDSCs, monocytes, natural killer cells, natural killer T cells, neutrophils, and plasmacytoid dendritic cells were determined in high and low risk samples from the TARGET cohort with the ssGSEA algorithm.

Estimation of Stromal and Immune Cells in Malignant Tumours Using Expression Data

With the ESTIMATE algorithm, stromal and immune scores were inferred in high and low risk samples from the TARGET cohort based on the gene expression profiling (Yoshihara et al., 2013).

Correlation Between Risk Score and Immune Checkpoint Expression

Pearson’s correlations between risk scores and expression of immune checkpoints including ADORA2A, BTLA, BTNL2, CD160, CD200, CD200R1, CD244, CD27, CD274, CD276, CD28, CD40, CD40LG, CD44, CD48, CD70, CD80 CD86, CTLA4, HAVCR2, HHLA2, ICOS, ICOSLG, IDO1, IDO2, KIR3DL1, LAG3, LAIR1, LGALS9, NRP1, PDCD1, PDCD1LG2, TIGIT, TMIGD2, TNFRSF14, TNFRSF18, TNFRSF25, TNFRSF4, TNFRSF8, TNFRSF9, TNFSF14, TNFSF15, TNFSF18, TNFSF4, TNFSF9, VSIR, and VTCN1 were assessed in the osteosarcoma specimens from the TARGET cohort.

Assessment of Chemotherapy Drug Sensitivity

Through the Genomics of Drug Sensitivity in Cancer (GDSC) database (www.cancerRxgene.org) (Yang et al., 2013), the IC50 values of chemotherapy drugs (cisplatin, doxorubicin, methotrexate, and paclitaxel) were estimated in each osteosarcoma specimen with the pRRophetic package (Geeleher et al., 2014).

Nomogram

A nomogram was established by integrating gender, age, and risk score with the “rms” package for osteosarcoma prognoses. The calibration plots were depicted for verifying the accuracy of prediction of 1-, 3-, and 5-years survival. Also, the predictive efficacy of this nomogram was evaluated by time-dependent ROC analysis.

Quantitative Real-Time Polymerase Chain Reaction

RNA extraction was performed with Trizol and extracted RNA was converted to cDNA utilizing Super Script II cDNA synthesis kit (Beyotime, China). qRT-PCR was carried out utilizing iTaq universal SYBR Green Supermix (BioRad, CA) on the CFX6100 qPCR instrument (BioRad, CA). The primer sequences were as follows: Stearoyl-CoA desaturase (SCD), 5′-TCT​AGC​TCC​TAT​ACC​ACC​ACC​A-3’ (forward), 5′-TCG​TCT​CCA​ACT​TAT​CTC​CTC​C-3’ (reverse); Epidermal Growth Factor Receptor (EGFR), 5′-AGG​CAC​GAG​TAA​CAA​GCT​CAC-3’ (forward), 5′-ATG​AGG​ACA​TAA​CCA​GCC​ACC-3’ (reverse); MAX Interactor 1 (MXI1), 5′-GCG​CCT​TTG​TTT​AGA​ACG​CTT-3’ (forward), 5′-AAT​GCT​GTC​CAT​TCG​TAT​TCG​T-3’ (reverse); Caveolae Associated Protein 1 (CAVIN1), 5′- GAG​GAC​CCC​ACG​CTC​TAT​ATT-3’ (forward), 5′-CCC​CGA​TGA​TTT​TGT​CCA​GGA-3’ (reverse); Testin (TES), 5′-ATG​GGC​TTA​GGT​CAC​GAG​C-3’ (forward), 5′-TCC​CAC​TTT​TCG​ATC​CTC​TTC​A-3’ (reverse); GAPDH, 5′-GGA​GCG​AGA​TCC​CTC​CAA​AAT-3’ (forward), 5′-GGC​TGT​TGT​CAT​ACT​TCT​CAT​GG-3’ (reverse). The relative mRNA expression was calculated with 2−ΔΔCt method.

Western Blotting

After implementing protein extraction from tissues utilizing RIPA, protein was loaded into SDS-PAGE gel and transferred into polyvinylidene difluoride membranes. Thereafter, the membranes were sealed by 5% BSA blocking buffer as well as incubated with primary antibodies against SCD (1:1,000; ab236868; Abcam, United States), EGFR (1:1,000; ab52894; Abcam), MXI1 (1:1,000; 12360-1-AP; Proteintech, Wuhan, China), CAVIN1 (1:1,000; ab76919; Abcam), TES (1:1,000; 10258-1-AP; Proteintech), and GAPDH (1:1,000; 6,004-1-lg; Proteintech) overnight at 4°C. Afterward, HRP-conjugated anti-rabbit IgG (1:2000; SA00001-2; Proteintech) was applied for hatching the membranes for 2 h. The membranes were exposed to enhanced chemiluminescence (ECL) solution and the gray value of protein bands was quantified with ImageJ software (NIH, United States).

Cell Culture and Transfection

Human osteoblast hFOB1.19 and two human osteosarcoma cells Saos-2 and U2OS (ATCC, United States) were maintained in DMEM plus 10% heat-inactivated fetal bovine serum (Sigma, United States) and 2% antibiotic-antimycotic solution (Corning, United States) at 37°C in a humidified atmosphere with 5% CO₂. Specific siRNAs against MXI1 (si-MXI1) and negative control (si-NC) were synthesized by GenePharma (Shanghai, China). In total, 3 × 10⁵ Saos-2 and U2OS cells were seeded onto a 6-well plate, and transiently transfected with 10 nM siRNA utilizing Lipofectamine iMAX kit (Invitrogen, United States). Saos-2 and U2OS cells were collected for RNA extraction following 48 h of incubation with specific siRNAs.

Colony Formation Assay

In total, 500 Saos-2 and U2OS cells were planted onto a 6-well dish. After culturing for 2 weeks, the colonies were washed by PBS and dyed with a dyeing solution containing 0.1% crystal violet and 20% methanol. Thereafter, the colonies were counted.

Transwell Assays

Transwell assays were performed for measuring migration and invasion of Saos-2 and U2OS cells utilizing 24-well inserts (8 μm pore, Corning, United States). For migration, Saos-2 and U2OS cells re-suspended in 100 μl serum-free DMEM medium were added to the upper chamber without coating membrane. For invasion, re-suspended Saos-2 and U2OS cells were added to the upper chamber with Matrigel-coated membrane. DMEM plus 600 μl 10% FBS was added to the lower chamber. Migrated or invaded cells from the bottom surfaces were fixed by 4% paraformaldehyde, followed by being stained by 0.1% crystal violet for 20 min. Finally, the cells were investigated and counted under a microscope (Leica, Germany).

Statistical Analysis

All statistics were carried out by applying the R software (version 3.6.5; https://www.R-project.org) and its packages. Comparisons between two groups were presented via Wilcoxon or Student’s t tests. Multiple comparisons were presented through one- or two-way ANOVA test. p values <0.05 were set as statistical significances.

RESULTS

Establishment of a Prognostic Hypoxic Gene Signature in Osteosarcoma

In total, 197 hypoxic genes were obtained for this study (Supplementary Table S1). The chromosomal locations of these genes are shown in Figure 2A. Through univariate cox regression analysis, 26 hypoxic genes exhibited significant correlations to prognosis of osteosarcoma patients in the TARGET cohort (Table 1). The most important hypoxic genes related to osteosarcoma prognosis were selected using the random survival forest algorithm (Table 2). When the number of classification trees was 5, the error rate was the lowest (Figure 2B). The feature importance ranking of the top five genes is shown in Figure 2C, including TES (chr7: 116210506–116258783), SDC3 (chr1: 30869466–30908758), MXI1 (chr10: 110207605–110287365), CAVIN1 (chr17: 42402449–42423256), and EGFR (chr7: 55019017–55211628). Following multivariate cox regression analysis, a five-hypoxic gene signature was constructed in osteosarcoma (Figure 2D). The scoring formula was as follows: risk score = 0.081751 * EGFR expression + (−0.3013) * CAVIN1 expression +0.812177 * MXI1 expression + (−0.46302) * SDC3 expression + (−0.37064) * TES expression. According to the median value of risk scores, osteosarcoma patients were separated into high- and low-risk subgroups (n = 42). The heat map visualized the expression patterns of TES, SDC3, MXI1, CAVIN1, and EGFR in high- and low-risk specimens (Figure 2E). The proportions of dead patients were separately 50 and 14% in high- and low-risk groups (Figure 2F). Moreover, high-risk scores were indicative of unfavorable survival outcomes of osteosarcoma patients (Figure 2G).

FIGURE 2 | Establishment of a prognostic hypoxic gene signature in osteosarcoma in the TARGET cohort. (A) The chromosomal locations of 197 hypoxic genes. (B) The relationships between the number of trees and error rates. (C) The rank of relative importance of the top five variables. (D) Univariate cox regression analysis of TES, SDC3, MXI1, CAVIN1, and EGFR in osteosarcoma. (E) Heat map visualizing the up- (red) and down-regulated (green) genes in high- and low-risk groups. (F) The distributions and proportions of survival status (alive: green; dead: red). (G) Kaplan-Meier curves of OS between high- and low-risk specimens. The differences were compared by log-rank tests.

TABLE 1 | Univariate cox regression analysis for prognosis-related hypoxic genes in the TARGET cohort.

TABLE 2 | The relative importance of prognosis-related hypoxic genes.

The Hypoxic Gene Signature Robustly and Independently Predicts Osteosarcoma Prognosis

To evaluate the predictive efficacy of this hypoxic gene signature on survival outcomes, time-independent ROC curves were conducted and the AUCs of 1-, 3-, and 5-years OS were 0.789, 0.826, and 0.789, respectively (Figure 3A). In comparison to other prognostic models, this signature had the higher accuracy and sensitivity for predicting prognosis of osteosarcoma patients (Figure 3B). Through univariate cox regression analyses, risk score displayed a significant association with osteosarcoma prognosis (p < 0.001, HR: 1.083, 95%CI: 1.035–1.132; Figure 3C). Multivariate cox regression analyses confirmed that risk score was independently predictive of clinical outcomes (p < 0.001, HR: 1.083, 95%CI: 1.033–1.136) with age, gender, and risk score as variables (Figure 3D).

FIGURE 3 | The hypoxic gene signature robustly and independently predicts osteosarcoma patients’ prognosis in the TARGET cohort. (A) Time-independent ROC curves of 1-, 3-, and 5-years survival based on risk score. (B) Comparisons of AUCs among different gene signatures for osteosarcoma patients. (C, D) Univariate and multivariate cox regression analyses for the associations between prognosis and age, gender, and risk scores.

External Validation of the Predictive Performance of Hypoxic Gene Signature on Osteosarcoma Prognosis

With the same formula, the risk score of each subject in the GSE21257 cohort was determined. Figure 4A depicted the expression of MXI1, SDC3, TES, EGFR, and CAVIN1 in high and low risk osteosarcoma specimens. The percentages of dead patients were 57 and 26% in the high- and low-risk groups, respectively (Figure 4B). Furthermore, unfavorable survival outcomes were found in high-risk subjects (p = 1.757e-02; Figure 4C), which exhibited the consistency with the results in the TARGET cohort. The AUCs of 1-, 3-, and 5-years survival were 0.668, 0.679, and 0.746, respectively (Figure 4D). Under univariate cox regression analyses, the risk score was in relation to desirable clinical outcomes of osteosarcoma (p = 0.005, HR = 2.467, and 95% CI: 1.314–4.631; Figure 4E). Nevertheless, age and gender were not significantly correlated to osteosarcoma prognosis. Multivariate cox regression analyses demonstrated that this signature was capable of independently predicting prognosis of osteosarcoma patients (p = 0.005, HR = 2.485, and 95% CI: 1.317–4.689; Figure 4F).

FIGURE 4 | External validation of the predictive performance of hypoxic gene signature on osteosarcoma prognosis in the GSE21257 cohort. (A) Heat map visualizing the up- (red) and down-regulated (green) genes in high- and low-risk groups. (B) The distributions and proportions of survival status (alive: green; dead: red). (C) Kaplan-Meier curves of OS between high- and low-risk subjects. The differences were compared by log-rank tests. (D) Time-independent ROC curves of 1-, 3-, and 5-years survival based on risk score. (E, F) Univariate and multivariate cox regression analyses for the associations between prognosis and age, gender, and risk scores.

Subgroup Analysis Identifies Prognostic Value of the Gene Signature in Osteosarcoma

Subgroup analysis was applied to evaluate the predictive performance of the gene signature on osteosarcoma prognosis. First, patients from the TARGERT cohort were classified into age <16 and ≥16 subgroups. We found that high risk scores were in relation to worse survival duration both in two subgroups (p = 0.001 and 0.049; Figures 5A,B). Also, both in female (p = 0.032; Figure 5C) and male (p = 0.002; Figure 5D) subgroups, high-risk scores were indicative of shorter survival outcomes of osteosarcoma patients.

FIGURE 5 | Evaluation of prognostic value of the gene signature in osteosarcoma by subgroup analysis. (A, B) Kapan-Meir curves of OS between high- and low-risk patients in (A) age <16 and (B) age ≥16 subgroups. (C, D) Kapan-Meir curves of OS between high- and low-risk patients in (C) female and (D) male subgroups. The survival differences were assessed by log-rank tests.

GSEA Identifies Involved Signaling Pathways

GSEA results revealed that altered genes were distinctly enriched in several common pathways. Metabolism of xenobiotics by cytochrome P450 (NES = 1.56 and NOM p-value = 0.045) and nitrogen metabolism (NES = 1.52 and NOM p-value = 0.039) were mainly activated in the high-risk osteosarcoma samples (Figures 6A,B). In the meanwhile, low-risk samples were distinctly related to cell adhesion molecules cams (NES = -1.74 and NOM p-value = 0.035) and intestinal immune network for IgA production (NES = -1.78 and NOM p-value = 0.041; Figures 6C,D).

FIGURE 6 | Identifying involved signaling pathways. (A) Metabolism of xenobiotics by cytochrome P450; (B) nitrogen metabolism; (C) cell adhesion molecules cams; and (D) intestinal immune network for IgA production.

Prognostic Values of Hypoxic Genes From the Gene Signature

The prognostic values of hypoxic genes from the gene signature were independently assessed in the TARGET cohort. As a result, high expression of CAVIN1 (p = 0.026, HR = 0.43, and 95% CI = 0.18–1.03), EGFR (p = 0.008, HR = 0.38, and 95% CI = 0.16–0.89), SCD3 (p = 0.004, HR = 0.35, and 95% CI = 0.15–0.84), and TES (p < 0.001, HR = 0.23, and 95% CI = 0.1–0.53) exhibited the prolonged survival duration of osteosarcoma patients (Figures 7A–D). Meanwhile, MXI1 up-regulation was in relation to unfavorable survival outcomes of osteosarcoma patients (p < 0.001, HR = 5.91, and 95% CI = 2.48–14.07; Figure 7E). Thus, each factor of the gene signature was associated to osteosarcoma prognosis.

FIGURE 7 | Prognostic values of hypoxic genes from the gene signature in osteosarcoma. Kaplan-Meier curves for overall survival of osteosarcoma patients in the high and low (A) CAVIN1, (B) EGFR, (C) SCD3, (D) TES, and (E) MXI1 expression groups from the TARGET cohort. The survival differences were assessed via log-rank tests.

Differences in Immune Status Between High- and Low-Risk Osteosarcoma

The enrichment scores of immune cells were quantified in osteosarcoma specimens by applying the ssGSEA algorithm. Our data demonstrated that higher infiltrations of activated B cell s, activated CD8 T cells, central memory CD4 T cells, central memory CD8 T cells, regulatory T cells, type 1 T helper cells, CD56bright natural killer cells, macrophages, MDSC, natural killer cells, and natural killer T cells were found in the low-risk osteosarcoma in comparison to the high-risk osteosarcoma in the TARGET cohort (Figure 8A). Also, there were higher stromal scores (p = 0.00024) as well as immune scores (p = 0.045) in the low-risk specimens than the high-risk specimens (Figure 8B). The correlations between risk scores and immune checkpoints were assessed in osteosarcoma subjects. As a result, risk scores were positively correlated to TNFSF4 (Figure 8C).

FIGURE 8 | Assessment of differences in immune status between high- and low-risk osteosarcoma in the TARGET cohort. (A) Relative enrichment scores of immune cell subpopulations in high- and low-risk osteosarcoma samples. (B) Distributions of stromal and immune scores between high- and low-risk osteosarcoma samples. The differences were evaluated through Wilcoxon test. (C) Heatmap for the correlations between risk score and immune cell subpopulations in osteosarcoma. The darker the color, the stronger the correlation. Ns: not significant; *p < 005; **p < 0.01; ***p < 0.001.

Correlations Between Risk Score and Sensitivity to Chemotherapy Drugs

The IC50 values of chemotherapy drugs were determined in osteosarcoma patients from the TARGET cohort. Lowered IC50 values of cisplatin were detected in the high-risk compared to low-risk specimens (p = 0.015; Figure 9A), indicating that high-risk patients might be more sensitive to cisplatin. However, there were no significant differences in IC50 values of doxorubicin, methotrexate, and paclitaxel between groups (Figures 9B–D).

FIGURE 9 | Evaluation of the correlations between risk score and sensitivity to chemotherapy drugs in the TARGET cohort. (A) Cisplatin; (B) doxorubicin; (C) methotrexate; (D) paclitaxel. The differences were assessed by Wilcoxon tests.

Construction and Verification of a Personalized Prognostic Nomogram

To offer an applicable quantitative tool for clinicians, a nomogram combining risk score, age, and gender was built for prediction of the probability of 1-, 3-, and 5-years survival of osteosarcoma patients in the TARGET cohort (Figure 10A). Each patient was determined based on each prognostic index. The higher number of total points was indicative of an undesirable prognosis for osteosarcoma patients. The AUC of OS time was 0.730, demonstrating that this nomogram exhibited high potential upon clinical utility (Figure 10B). Compared to the ideal model, the nomogram displayed a similar performance at 1-, 3-, and 5-years survival (Figures 10C–E). This prognostic nomogram model was externally verified in the GSE21257 osteosarcoma dataset (Figure 10F). The AUC value (0.737) showed that this nomogram possessed the favorable predictive capacity for the OS in osteosarcoma patients (Figure 10G). Our calibration curves showed excellent agreement between the nomogram prediction and actual observation at 1-, 3-, and 5-years survival in the GSE21257 dataset (Figures 10H–J). The above data suggested the appreciable reliability of this nomogram.

FIGURE 10 | A nomogram for prediction of osteosarcoma patients’ prognosis. (A) A nomogram combining gender, age, and risk score for determining the 1-, 3-, and 5-years survival probability of osteosarcoma patients in the TARGET cohort. (B) The time-dependent ROC curves of this nomogram for overall survival of osteosarcoma patients. (C–E) The calibration plots of the nomogram for prediction of survival probability at 1, 3, and 5 years. The x-axis indicates the nomogram-predicted probability, and the y-axis indicates the actual survival. (F) External verification of the prognostic efficacy of this nomogram in predicting the 1-, 3-, and 5-years survival probability of osteosarcoma patients in the GSE21257 cohort. (G) The time-dependent ROC curves of the nomogram for 1-, 3-, and 5-years survival. (H–J) The calibration curves for the nomogram at 1, 3, and 5 years.

Verification of the Expression of Hypoxic Genes From the Gene Signature

The expression of hypoxic genes from the gene signature was verified in osteosarcoma and normal cell lines. Our results confirmed that SCD3, EGFR, and MXI1 were remarkably up-regulated while CAVIN1 and TES were prominently down-regulated in U2OS osteosarcoma cells compared with hFOB1.19 normal cells (Figures 11A–G). We also compared their expression in hFOB1.19 normal cells and Saos-2 osteosarcoma cells. Compared with hFOB1.19 cells, SCD3, EGFR, and MXI1 displayed higher expression while CAVIN1 and TES presented lower expression in Saos-2 cells, indicating that the above genes might participate in osteosarcoma progression (Figures 11H–N).

FIGURE 11 | Verification of the expression of hypoxic genes from the gene signature. (A–E) RT-qPCR for detecting the expression of SCD3, EGFR, MXI1, CAVIN1, and TES in U2OS osteosarcoma cells and hFOB1.19 normal cells. (F, G) Western blotting for the expression of the above genes in U2OS osteosarcoma cells and hFOB1.19 normal cells. (H, I) Western blotting for the expression of the above genes in Saos-2 osteosarcoma cells and hFOB1.19 normal cells. (J–N) RT-qPCR for the expression of the above genes in tumors with Saos-2 osteosarcoma cells and hFOB1.19 normal cells. **p < 0.01; ***p < 0.001; ****p < 0.0001. U2OS osteosarcoma cells compared with hFOB1.19 normal cells.

Silencing MXI1 Attenuates Proliferation, Migration, and Invasion of Osteosarcoma Cells

The biological functions of MXI1 were further investigated through in vitro experiments. First, the expression of MXI1 was remarkably silenced by its specific siRNAs both in U2OS and Saos-2 cells (Figures 12A,B). Compared with the si-NC group, MXI1 knockdown prominently attenuated proliferation (Figures 12C,D), migration (Figures 12E,F), and invasion (Figures 12G,H) of U2OS and Saos-2 cells.

FIGURE 12 | Silencing MXI1 attenuates proliferation, migration, and invasion of osteosarcoma cells. (A, B) RT-qPCR for verifying the expression of MXI1 in U2OS and Saos-2 cells following transfection with its specific siRNAs. (C, D) Colony formation for measuring the proliferative capacity of U2OS and Saos-2 cells after silencing MXI1 expression. (E–H) Transwell assays for detecting the (E, F) migration and (G, H) invasion of U2OS and Saos-2 cells with MXI1 knockdown. **p < 0.01; ****p < 0.0001.

DISCUSSION

Hypoxia, a hallmark of cancer, may be induced by an insufficient oxygen supply, which is correlated to unfavorable survival outcomes, increased tumor heterogeneity, and drug resistance as well as immune escape (Vito et al., 2020). Here, we developed and verified a hypoxic gene signature for robustly predicting osteosarcoma prognoses, which could become a clinical stratified tool.

The hypoxic gene signature was built with the random survival forest algorithm, which consisted of CAVIN1, EGFR, SCD3, TES, and MXI1. High-risk scores were indicative of undesirable survival outcomes. Our ROCs confirmed the well predictive performance of this signature in osteosarcoma prognoses. The multivariate cox regression analyses were indicative of the predictive independency of the signature. Previously, a 28-gene hypoxic signature was proposed, which possessed the robust and independent prognostic efficacy in prostate cancer (Yang L. et al., 2018). Furthermore, a four-hypoxic gene model served as a prognostic factor for lung adenocarcinoma, which might predict the immunotherapeutic responses (Mo et al., 2020). The signaling pathways involved in risk scores were analyzed in osteosarcoma subjects. Metabolism of xenobiotics by cytochrome P450 and nitrogen metabolism were activated in the high-risk samples while cell adhesion molecules cams and intestinal immune network for IgA production were enriched in the low-risk samples. Evidence suggests that intestinal immune network for IgA production pathway is associated with cell proliferation and migration of hepatocellular carcinoma cells (Yang Z. et al., 2018). However, no study has reported the role of this pathway in osteosarcoma progression. Our study indicated that low-risk samples might have enhanced immune activation compared with high-risk samples. In the five-gene signature, we separately evaluated the prognostic potential of CAVIN1, EGFR, SCD3, TES, and MXI1 in osteosarcoma. Among them, up-regulation of CAVIN1, EGFR, SCD3, and TES was related to favorable survival outcomes while MXI1 up-regulation was associated with undesirable prognoses. Previously, CAVIN1 emerges as a novel tumor suppressor in Ewing sarcoma (Huertas-Martínez et al., 2017). Overactivity of EGFR is frequently observed in osteosarcoma and becomes an underlying therapeutic target (Ji and He, 2019). MXI1 is a key transcription factor involved in osteosarcoma progression (Xu et al., 2017). Additionally, our experimental results confirmed that SCD3, EGFR, and MXI1 were remarkably up-regulated while CAVIN1 and TES were prominently down-regulated in osteosarcoma cells compared with normal cells. The above findings highlighted the implications of these genes in osteosarcoma prognoses and progression. Our in vitro experiments demonstrated that silencing MXI1 attenuated proliferation, migration, and invasion of osteosarcoma cells, confirming the important role of MXI1 in osteosarcoma progression. Thus, our study is novel and better suited than previously published prognostic protocols for osteosarcoma (Wu Z.-l. et al., 2020; Qiu et al., 2021).

Cancer progression is highly correlated with the physiological state of osteosarcoma immune microenvironment that consists of innate as well as adaptive immune cells (Roma-Rodrigues et al., 2019). Osteosarcoma possesses a unique tumor microenvironment triggered by distinct molecular characteristics (Dyson et al., 2019). The immune microenvironment profiles are preponderant on prognoses and anti-tumor therapeutic efficacy. Immunotherapy emerges as a promising therapeutic strategy against osteosarcoma (Chen et al., 2021). Nevertheless, only minor subjects experience the desired anti-osteosarcoma immunity as well as impressive clinical response. For maximizing treatment effects of osteosarcoma subjects, it is a priority to alter the osteosarcoma immune microenvironment (Riera-Domingo et al., 2020). Hypoxia participates in anti-tumor immune response through lowering cytolytic and migratory activation of effector cells and the production and release of effector cytokines as well as triggering immunosuppressive cells, production and release of immunosuppressive cytokines, and expressions of immune checkpoint inhibitors (Multhoff and Vaupel, 2020). Herein, we found that there were higher infiltrations of activated B cells, activated CD8 T cells, central memory CD4 T cells, central memory CD8 T cells, regulatory T cells, type 1 T helper cells, CD56bright natural killer cells, macrophages, MDSC, natural killer cells, and natural killer T cells in the low-risk osteosarcoma than the high-risk osteosarcoma, indicating that this signature might reflect the immune microenvironment of osteosarcoma for bench observations. We noted that immunosuppressive cells and immuno-promoting cells were all significantly activated in low-risk groups, indicating the complex interactions between immunosuppressive cells and immuno-promoting cells during osteosarcoma progression. Infiltrating stromal and immune cells constitute the main fractions of the tumor microenvironment. Previous research has demonstrated that high stromal or immune scores indicated favorable survival outcomes of osteosarcoma (Hong et al., 2020). Their scores were determined in osteosarcoma tissues. The increase in stromal and immune scores was detected in the low-risk osteosarcoma specimens. Limited clinical activity of immune checkpoint inhibitors is investigated in osteosarcoma subjects (Wu C.-C. et al., 2020). Hence, it is of significance to gain the immunogenic potential of osteosarcoma. TNFRSF4 possesses potential as a target upon cancer immunotherapy (Buchan et al., 2018). Our data showed that risk score exhibited a positive correlation to immune checkpoint TNFRSF4 in osteosarcoma.

Chemotherapy resistance is the main issue in osteosarcoma therapy, which leads to undesirable prognoses (Prudowsky and Yustein, 2020). Alleviating hypoxia through tumor reoxygenation sensitizes the chemotherapy in osteosarcoma (Fu et al., 2021). Currently, there is a lack of clinically relevant molecular biomarkers that are predictive of the responses to chemotherapies. Cisplatin represents the first-line chemotherapy drug regarding osteosarcoma treatment, but chemoresistance limits the effectiveness of cisplatin (Wen J.-F. et al., 2020). Our data demonstrated that low-risk patients were more sensitive to cisplatin, indicating that this risk score might aid in overcoming cisplatin chemoresistance. The nomogram, as an appropriate clinical tool, has been widely utilized for quantitatively determining a person’s prognosis in the clinical setting through integration of several prognostic factors (Wu and Zhang, 2020). Here, the nomogram model combined risk score, age, and gender was established for predicting the probability of 1-, 3-, and 5-years survival time in osteosarcoma subjects. By calculating all the points, the nomogram yielded the numerical possibility of osteosarcoma patients concerning clinical outcomes. Following verification by ROCs and calibration plots, the nomogram model could be well predictive of 1-, 3-, and 5-years survival possibilities of osteosarcoma subjects. Nevertheless, the predictive efficacy of this nomogram requires to be verified in a larger osteosarcoma cohort.

CONCLUSION

Collectively, we developed a hypoxic gene signature for risk stratification in osteosarcoma. After external verification, this risk score may be independently predictive of patients’ survival outcomes. Also, the risk score was in relation to osteosarcoma immune microenvironment and sensitivity to the chemotherapy drug cisplatin. A nomogram that combined the risk score, age, and gender became a promising prognostic classifier for osteosarcoma. Thus, our findings offered insights into personalized prognostication and therapy as well as follow-up scheduling.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.

AUTHOR CONTRIBUTIONS

GW conceived and designed the study. FW, JX, MJ, and XJ conducted most of the experiments and data analysis and wrote the manuscript. JL, XL, ZC, JN and ZM participated in collecting data and helped to draft the manuscript. All authors reviewed and approved the manuscript.

FUNDING

This research was supported by Research on the Application of Public Welfare Technology in Zhejiang Province (LGF21H060001); Science and Technology Bureau Program of Huzhou (2020GY23); The Traditional Chinese Medicine Scientific Research and Technology Project of Zhejiang Province (2021ZB280); Medical Health Science and Technology Project of Zhejiang Province (2021RC029).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2021.705148/full#supplementary-material

ABBREVIATIONS

AUC, area under the curve; CI, confidence interval; ESTIMATE, Estimation of STromal and Immune cells in MAlignant Tumours using Expression data; FDR, false discovery rate; GDSC, Genomics of Drug Sensitivity in Cancer; GSEA, gene set enrichment analysis; GEO, Gene Expression Omnibus; HR, hazard ratio; NES, normalized enrichment score; OS, overall survival; ROC, receiver operating characteristic; TARGET, Therapeutically Applicable Research to Generate Effective Treatments; ssGSEA, Single-Sample Gene Set Enrichment Analysis.

REFERENCES

Buchan, S. L., Rogel, A., and Al-Shamkhani, A. (2018). The Immunobiology of CD27 and OX40 and Their Potential as Targets for Cancer Immunotherapy. Blood 131 (1), 39–48. doi:10.1182/blood-2017-07-741025

Buddingh, E. P., Kuijjer, M. L., Duim, R. A. J., Bürger, H., Agelopoulos, K., Myklebost, O., et al. (2011). Tumor-infiltrating Macrophages Are Associated with Metastasis Suppression in High-Grade Osteosarcoma: a Rationale for Treatment with Macrophage Activating Agents. Clin. Cancer Res. 17 (8), 2110–2119. doi:10.1158/1078-0432.Ccr-10-2047

Cao, J., Wang, Y., Dong, R., Lin, G., Zhang, N., Wang, J., et al. (2015). Hypoxia-Induced WSB1 Promotes the Metastatic Potential of Osteosarcoma Cells. Cancer Res. 75 (22), 4839–4851. doi:10.1158/0008-5472.Can-15-0711

Cao, M., Zhang, J., Xu, H., Lin, Z., Chang, H., Wang, Y., et al. (2020). Identification and Development of a Novel 4-Gene Immune-Related Signature to Predict Osteosarcoma Prognosis. Front. Mol. Biosci. 7, 608368. doi:10.3389/fmolb.2020.608368

Chen, C., Xie, L., Ren, T., Huang, Y., Xu, J., and Guo, W. (2021). Immunotherapy for Osteosarcoma: Fundamental Mechanism, Rationale, and Recent Breakthroughs. Cancer Lett. 500, 1–10. doi:10.1016/j.canlet.2020.12.024

Chen, G., Gu, H., Fang, T., Zhou, K., Xu, J., and Yin, X. (2020). Hypoxia-induced let-7f-5p/TARBP2 Feedback Loop Regulates Osteosarcoma Cell Proliferation and Invasion by Inhibiting the Wnt Signaling Pathway. Aging 12 (8), 6891–6903. doi:10.18632/aging.103049

Dyson, K. A., Stover, B. D., Grippin, A., Mendez-Gomez, H. R., Lagmay, J., Mitchell, D. A., et al. (2019). Emerging Trends in Immunotherapy for Pediatric Sarcomas. J. Hematol. Oncol. 12 (1), 78. doi:10.1186/s13045-019-0756-z

Fu, J., Li, T., Yang, Y., Jiang, L., Wang, W., Fu, L., et al. (2021). Activatable Nanomedicine for Overcoming Hypoxia-Induced Resistance to Chemotherapy and Inhibiting Tumor Growth by Inducing Collaborative Apoptosis and Ferroptosis in Solid Tumors. Biomaterials 268, 120537. doi:10.1016/j.biomaterials.2020.120537

Geeleher, P., Cox, N., and Huang, R. S. (2014). pRRophetic: an R Package for Prediction of Clinical Chemotherapeutic Response from Tumor Gene Expression Levels. PLoS One 9 (9), e107468. doi:10.1371/journal.pone.0107468

Hong, W., Yuan, H., Gu, Y., Liu, M., Ji, Y., Huang, Z., et al. (2020). Immune-related Prognosis Biomarkers Associated with Osteosarcoma Microenvironment. Cancer Cell Int 20, 83. doi:10.1186/s12935-020-1165-7

Huertas-Martínez, J., Court, F., Rello-Varona, S., Herrero-Martín, D., Almacellas-Rabaiget, O., Sáinz-Jaspeado, M., et al. (2017). DNA Methylation Profiling Identifies PTRF/Cavin-1 as a Novel Tumor Suppressor in Ewing Sarcoma when Co-expressed with Caveolin-1. Cancer Lett. 386, 196–207. doi:10.1016/j.canlet.2016.11.020

Ji, X. L., and He, M. (2019). Sodium Cantharidate Targets STAT3 and Abrogates EGFR Inhibitor Resistance in Osteosarcoma. Aging 11 (15), 5848–5863. doi:10.18632/aging.102193

Kelley, L. M., Schlegel, M., Hecker-Nolting, S., Kevric, M., Haller, B., Rössig, C., et al. (2020). Pathological Fracture and Prognosis of High-Grade Osteosarcoma of the Extremities: An Analysis of 2,847 Consecutive Cooperative Osteosarcoma Study Group (COSS) Patients. Jco 38 (8), 823–833. doi:10.1200/jco.19.00827

Ma, Q., Zhang, Y., Liu, T., Jiang, K., Wen, Y., Fan, Q., et al. (2017). Hypoxia Promotes Chemotherapy Resistance by Down-Regulating SKA1 Gene Expression in Human Osteosarcoma. Cancer Biol. Ther. 18 (3), 177–185. doi:10.1080/15384047.2017.1294285

Mo, Z., Yu, L., Cao, Z., Hu, H., Luo, S., and Zhang, S. (2020). Identification of a Hypoxia-Associated Signature for Lung Adenocarcinoma. Front. Genet. 11, 647. doi:10.3389/fgene.2020.00647

Multhoff, G., and Vaupel, P. (2020). Hypoxia Compromises Anti-cancer Immune Responses. Adv. Exp. Med. Biol. 1232, 131–143. doi:10.1007/978-3-030-34461-0_18

Prudowsky, Z. D., and Yustein, J. T. (2020). Recent Insights into Therapy Resistance in Osteosarcoma. Cancers 13 (1), 83. doi:10.3390/cancers13010083

Qiu, Z., Du, X., Chen, K., Dai, Y., Wang, S., Xiao, J., et al. (2021). Gene Signatures with Predictive and Prognostic Survival Values in Human Osteosarcoma. PeerJ 9, e10633. doi:10.7717/peerj.10633

Riera-Domingo, C., Audigé, A., Granja, S., Cheng, W.-C., Ho, P.-C., Baltazar, F., et al. (2020). Immunity, Hypoxia, and Metabolism-the Ménage à Trois of Cancer: Implications for Immunotherapy. Physiol. Rev. 100 (1), 1–102. doi:10.1152/physrev.00018.2019

Roma-Rodrigues, C., Mendes, R., Baptista, P., and Fernandes, A. (2019). Targeting Tumor Microenvironment for Cancer Therapy. Ijms 20 (4), 840. doi:10.3390/ijms20040840

Shi, Y., He, R., Zhuang, Z., Ren, J., Wang, Z., Liu, Y., et al. (2020). A Risk Signature‐based on Metastasis‐associated Genes to Predict Survival of Patients with Osteosarcoma. J. Cell Biochem 121 (7), 3479–3490. doi:10.1002/jcb.29622

Song, Z., Pearce, M. C., Jiang, Y., Yang, L., Goodall, C., Miranda, C. L., et al. (2020). Delineation of Hypoxia-Induced Proteome Shifts in Osteosarcoma Cells with Different Metastatic Propensities. Sci. Rep. 10 (1), 727. doi:10.1038/s41598-019-56878-x

Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., et al. (2005). Gene Set Enrichment Analysis: a Knowledge-Based Approach for Interpreting Genome-wide Expression Profiles. Proc. Natl. Acad. Sci. 102 (43), 15545–15550. doi:10.1073/pnas.0506580102

Vito, A., El-Sayes, N., and Mossman, K. (2020). Hypoxia-Driven Immune Escape in the Tumor Microenvironment. Cells 9 (4), 992. doi:10.3390/cells9040992

Wen, C., Wang, H., Wang, H., Mo, H., Zhong, W., Tang, J., et al. (2020a). A Three-Gene Signature Based on Tumour Microenvironment Predicts Overall Survival of Osteosarcoma in Adolescents and Young Adults. Aging 13 (1), 619–645. doi:10.18632/aging.202170

Wen, J.-F., Jiang, Y.-Q., Li, C., Dai, X.-K., Wu, T., and Yin, W.-Z. (2020b). LncRNA-SARCC Sensitizes Osteosarcoma to Cisplatin through the miR-143-Mediated Glycolysis Inhibition by Targeting Hexokinase 2. Cbm 28 (2), 231–246. doi:10.3233/cbm-191181

Wu, C.-C., Beird, H. C., Andrew Livingston, J., Advani, S., Mitra, A., Cao, S., et al. (2020a). Immuno-genomic Landscape of Osteosarcoma. Nat. Commun. 11 (1), 1008. doi:10.1038/s41467-020-14646-w

Wu, G., and Zhang, M. (2020). A Novel Risk Score Model Based on Eight Genes and a Nomogram for Predicting Overall Survival of Patients with Osteosarcoma. BMC Cancer 20 (1), 456. doi:10.1186/s12885-020-06741-4

Wu, Z.-l., Deng, Y.-j., Zhang, G.-z., Ren, E.-h., Yuan, W.-h., and Xie, Q.-q. (2020b). Development of a Novel Immune-Related Genes Prognostic Signature for Osteosarcoma. Sci. Rep. 10 (1), 18402. doi:10.1038/s41598-020-75573-w

Xiao, B., Liu, L., Li, A., Xiang, C., Wang, P., Li, H., et al. (2020). Identification and Verification of Immune-Related Gene Prognostic Signature Based on ssGSEA for Osteosarcoma. Front. Oncol. 10, 607622. doi:10.3389/fonc.2020.607622

Xu, J., Li, D., Cai, Z., Zhang, Y., Huang, Y., Su, B., et al. (2017). An Integrative Analysis of DNA Methylation in Osteosarcoma. J. Bone Oncol. 9, 34–40. doi:10.1016/j.jbo.2017.05.001

Yang, C., Tian, Y., Zhao, F., Chen, Z., Su, P., Li, Y., et al. (2020). Bone Microenvironment and Osteosarcoma Metastasis. Ijms 21 (19), 6985. doi:10.3390/ijms21196985

Yang, L., Roberts, D., Takhar, M., Erho, N., Bibby, B. A. S., Thiruthaneeswaran, N., et al. (2018a). Development and Validation of a 28-gene Hypoxia-Related Prognostic Signature for Localized Prostate Cancer. EBioMedicine 31, 182–189. doi:10.1016/j.ebiom.2018.04.019

Yang, W., Soares, J., Greninger, P., Edelman, E. J., Lightfoot, H., Forbes, S., et al. (2013). Genomics of Drug Sensitivity in Cancer (GDSC): a Resource for Therapeutic Biomarker Discovery in Cancer Cells. Nucleic Acids Res. 41, D955–D961. doi:10.1093/nar/gks1111

Yang, Z., Tao, Y., Xu, X., Cai, F., Yu, Y., and Ma, L. (2018b). Bufalin Inhibits Cell Proliferation and Migration of Hepatocellular Carcinoma Cells via APOBEC3F Induced Intestinal Immune Network for IgA Production Signaling Pathway. Biochem. Biophysical Res. Commun. 503 (3), 2124–2131. doi:10.1016/j.bbrc.2018.07.169

Yiqi, Z., Ziyun, L., Qin, F., Xingli, W., and Liyu, Y. (2020). Identification of 9-Gene Epithelial-Mesenchymal Transition Related Signature of Osteosarcoma by Integrating Multi Cohorts. Technol. Cancer Res. Treat. 19, 153303382098076. doi:10.1177/1533033820980769

Yoshihara, K., Shahmoradgoli, M., Martínez, E., Vegesna, R., Kim, H., Torres-Garcia, W., et al. (2013). Inferring Tumour Purity and Stromal and Immune Cell Admixture from Expression Data. Nat. Commun. 4, 2612. doi:10.1038/ncomms3612

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Wu, Xu, Jin, Jiang, Li, Li, Chen, Nie, Meng and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 13 January 2022
doi: 10.3389/fmolb.2021.757644

Prognostic Value of Oral Epstein–Barr Virus DNA Load in Locoregionally Advanced Nasopharyngeal Carcinoma

Yong-Qiao He¹†, Ting Zhou1,²†, Da-Wei Yang³, Yi-Jing Jia³, Lei-Lei Yuan³, Wen-Li Zhang¹, Tong-Min Wang¹, Ying Liao¹, Wen-Qiong Xue¹, Jiang-Bo Zhang¹, Xiao-Hui Zheng1,², Xi-Zhao Li1,², Pei-Fen Zhang1,², Shao-Dan Zhang1,², Ye-Zhu Hu1,², Fang Wang¹, William C. Cho⁴, Jun Ma¹, Ying Sun¹ and Wei-Hua Jia1,2,³*

¹State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, China

²Biobank of Sun Yat-sen University Cancer Center, Guangzhou, China

³School of Public Health, Sun Yat-sen University, Guangzhou, China

⁴Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong SAR, China

Edited by:
Michael Mauk, Drexel University, United States

Reviewed by:
Zhiqiang Wu, Tianjin Medical University Cancer Institute and Hospital, China
Kai Sun, Liuzhou People’s Hospital, China
Zhenyuan Han, Peking University Hospital of Stomatology, China

* Correspondence: Wei-Hua Jia, jiawh@sysucc.org.cn

†These authors have contributed equally to this work

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 12 August 2021
Accepted: 15 December 2021
Published: 13 January 2022

Citation: He Y-Q, Zhou T, Yang D-W, Jia Y-J, Yuan L-L, Zhang W-L, Wang T-M, Liao Y, Xue W-Q, Zhang J-B, Zheng X-H, Li X-Z, Zhang P-F, Zhang S-D, Hu Y-Z, Wang F, Cho WC, Ma J, Sun Y and Jia W-H (2022) Prognostic Value of Oral Epstein–Barr Virus DNA Load in Locoregionally Advanced Nasopharyngeal Carcinoma. Front. Mol. Biosci. 8:757644. doi: 10.3389/fmolb.2021.757644

Background: Plasma Epstein–Barr virus (EBV) DNA load has been widely used for nasopharyngeal carcinoma (NPC) prognostic risk stratification. However, oral EBV DNA load, a non-invasive biomarker that reflects the EBV lytic replication activity, has not been evaluated for its prognostic value in NPC yet.

Methods: A total number of 1,194 locoregionally advanced NPC (LA-NPC) patients from south China were included from a prospective observational cohort (GARTC) with a median follow-up of 107.3 months. Pretreatment or mid-treatment mouthwashes were collected for EBV DNA detection by quantitative polymerase chain reaction (qPCR). The difference of pre- and mid-treatment oral EBV DNA load was tested by the Wilcoxon signed-rank test. The associations of oral EBV DNA load with overall survival (OS), progression-free survival (PFS), distant metastasis–free survival (DMFS), and locoregional relapse-free survival (LRFS) were assessed using the log-rank test and multivariate Cox regression.

Results: The high level of the oral EBV DNA load (>2,100 copies/mL) was independently associated with worse OS (HR = 1.45, 95% CI: 1.20–1.74, p < 0.001), PFS (HR = 1.38, 95% CI: 1.16–1.65, p < 0.001), DMFS (HR = 1.66, 95% CI: 1.25–2.21, p = 0.001), and LRFS (HR = 1.43, 95% CI: 1.05–1.96, p = 0.023). Similar and robust associations between oral EBV DNA load and prognosis were observed for patients in both the pretreatment and mid-treatment stages. The detection rate (71.7 vs. 48.6%, p < 0.001) and the median load of oral EBV DNA (13,368 vs. 382 copies/mL, p < 0.001) for patients in the pretreatment stage were significantly higher than those in the mid-treatment stage. The combination of the oral EBV DNA load and TNM staging provided a more precise risk stratification for the LA-NPC patients.

Conclusion: Oral EBV DNA load was an alternative non-invasive predictor of prognosis and may facilitate risk stratification for the LA-NPC patients.

Keywords: Epstein–Barr virus, oral EBV load, nasopharyngeal carcinoma, survival, locoregionally advanced

INTRODUCTION

Nasopharyngeal carcinoma (NPC) is characterized by its distinguished geographical distribution, with more than 70% of all worldwide cases located in south China and southeast Asia (Tang et al., 2016a; Bray et al., 2018). Early-stage NPC is relatively asymptomatic; thus, nearly 80% of patients present locally advanced disease with poor prognosis (Pan et al., 2016; Lee et al., 2017). Given the advanced disease stage and the peak occurrence at a relatively young age of 40–65 y, NPC contributes prominently to the cancer burden with substantial economic and societal impacts in endemic areas. Since studies have suggested that additional treatment such as induction chemotherapy (IC) could improve the prognosis of patients with advanced NPC (Frikha et al., 2018; Hong et al., 2018; Li et al., 2019), it is crucial to identify patients with poor prognoses so that timely guided therapeutic strategies could be implemented. The clinical stage at diagnosis is one of the most important indicators for NPC prognosis. However, due to tumor heterogeneity, traditional anatomy–based TNM staging is inadequate for accurately predicting the prognosis or therapeutic benefits of patients with locoregionally advanced NPC (Leung et al., 2006). This highlights the importance of identifying feasible biomarkers to help risk stratification and risk-adapted therapeutic strategy modification for the long-term follow-up of NPC survivors.

Various blood biomarkers, such as Epstein–Barr virus (EBV) DNA, EBV antibodies, and microRNAs, have been explored as prognostic indicators for NPC (Liu et al., 2014; Tang et al., 2015; Yao et al., 2018; Cai et al., 2019). Plasma EBV DNA, which is mainly released from the tumor cells, is a robust biomarker for prognosis prediction and disease surveillance of NPC (Lo et al., 2000; Lin et al., 2004; Tang et al., 2016b; Sun et al., 2019). Regarding NPC being located at the nasopharynx of the head, apart from the circulating peripheral blood system, saliva is another important liquid biopsy for detecting the present of EBV. We know that EBV infection in the oral epithelial tissue tends to lead to productive lytic replication (Young et al., 2016); hence, the oral EBV DNA load can reflect the host’s EBV lytic replication activity and may be potentially correlated with the development of EBV-associated diseases. However, the relationship between oral EBV DNA and NPC prognosis was largely unknown.

Although plasma EBV DNA has been used for NPC prognosis, the sampling method by venipuncture is an invasive procedure that might cause complications, for example, potential infection and development of bruises where the procedure took place, which is harmful to the cancer patients. On the contrary, saliva or mouthwash collection is non-invasive and can be easily conducted without a professional phlebotomist and operations. This supports the rationale to investigate whether oral EBV DNA might be an alternative method for NPC prognosis prediction. To the best of our knowledge, this is the first study focusing on the prognostic value of oral EBV DNA in NPC patients. Therefore, based on our present prospective cohort comprising relatively large sample size and long follow-up time, we aimed to explore whether the baseline oral EBV DNA load was able to independently predict the prognosis of LA-NPC patients to guide risk stratification.

MATERIALS AND METHODS

Study Population

The GARTP study (ChiCTRROC-17012658) is a prospective observational clinical trial that aims to identify the risk factors contributing to disease prognosis and radiation-induced complications in NPC patients (Wang et al., 2019). The primary cohort recruited 1,845 NPC patients under 80 y of age who were residing in Guangdong for at least 5 y from Sun Yat-sen University Cancer Center (SYSUCC, Guangzhou, China) during October 2005 and October 2007 (Xu et al., 2012; Xue et al., 2018). Our study included 1,194 eligible patients from the GARTP cohort according to the following criteria: 1) histology-confirmed NPC of WHO type II or III; 2) locoregionally advanced NPC (TxN2-3M0 or T2-4N0-3M0 according to the 6th AJCC/UICC TNM staging systems); 3) no other EBV-associated diseases, such as T-cell lymphoma, natural killer cell leukemia/lymphoma, Hodgkin’s disease, pyothorax-associated B-cell lymphoma, gastric carcinoma, and HIV infection; 4) received radiotherapy with or without chemotherapy; 5) available mouthwashes collected before the completion of radiotherapy; 6) available necessary clinical information; and 7) no loss to follow-up within 2 y. The flowchart is shown in Figure 1. This study was approved by the Ethics Committee of the SYSUCC, and written informed consent was all obtained at the patient enrollment. Study data were deposited at the Research Data Deposit platform (RDDA2020001573).

FIGURE 1 | Flowchart of patient inclusion and exclusion. NPC = nasopharyngeal carcinoma.

Oral EBV DNA Assay

The oral EBV DNA load was measured in the collected mouthwashes before the commencement of any therapy (pretreatment), or between the start of treatment and the end of radiotherapy/chemoradiotherapy (mid-treatment). All mouthwash samples were collected and processed by standard procedures as reported in our previous studies (Xue et al., 2018; He et al., 2019). Briefly, 30 min before sampling, the patients were advised not to eat or drink anything. Then, each patient was instructed to rinse their mouth with 10 mL of physiological saline (0.9% NaCl) for 30 s. EBV DNA was isolated from mouthwash using a phenol–chloroform procedure and measured by quantitative polymerase chain reaction (qPCR), with primers and probe targeting a 76-bp segment in the BamHI-W region of the EBV genome, which were consistent with those commonly used for plasma EBV DNA quantification (Lo et al., 1999). Plasmid DNA containing the target region in serial dilution was used as a standard for absolute quantification. The oral EBV DNA load was expressed as the copies of EBV DNA per mL of mouthwash (Pow et al., 2011; He et al., 2019). More detailed methods are described in Supplementary Method. All testing samples and quantification standard samples were tested in duplicate, and the coefficient of variance (CV) and the intraclass correlation coefficient (ICC) were estimated to evaluate the reproducibility of the EBV DNA test. The median CVs for retesting samples and quantification standards were 0.34 and 0.52%, respectively. The ICCs for retesting samples and quantification standards were 88.84 and 99.63%, respectively.

Clinical Assessment and Treatment

Before treatment, patients underwent a complete baseline evaluation, including physical examination, fiberoptic nasopharyngoscopy, magnetic resonance imaging (MRI) of the nasopharynx and neck, biochemistry and hematology profiles, chest radiography or tomography, abdominal sonography, and bone scintigraphy. The treatment administration was conducted following the routine practice of SYSUCC. All patients underwent radiotherapy, and the prescribed radiation doses were 68–74 Gy for primary tumor and 50–66 Gy for regional lymph nodes. Since the patients were enrolled before 2007 when the role of chemotherapy, such as concurrent chemoradiotherapy (CCRT) and induction chemotherapy (IC), has not been well established, there was no consensus on chemotherapy, and it mainly depended on the experience and the decisions of clinicians.

Outcome and Follow-Up

After completing treatment, the patients attended regular follow-up every 3 months during the first 2 y, every 6 months during 3–5 y, and annually thereafter. Locoregional/distant failure was confirmed by nasopharyngeal biopsy or two radiographic diagnoses (CT\MRI\ECT) not more than 3 months apart. They were also followed up through telephone interviews and death registration at the public security bureau if their recent medical records were not recorded. The last follow-up date was September 2019. The primary endpoint was overall survival (OS, time for the NPC diagnosis to death from any cause or censored at the date of the last follow-up). The secondary endpoints included progression-free survival (PFS, time from NPC diagnosis to the first event or death from any cause or censored at the date of the last follow-up), locoregional relapse-free survival (LRFS, time from NPC diagnosis to the first locoregional recurrence or censored at the date of the last follow-up), and distant metastasis–free survival (DMFS, time from NPC diagnosis to first distant failure or censored at the date of the last follow-up).

Statistical Analyses

All patients were first categorized into either the low EBV (≤2,100 copies/mL) or high (>2,100 copies/mL) EBV groups, according to the optimum cutoff value determined by the receiver operating characteristic (ROC) curve analysis for OS in all subjects, and the cutoff value was set to 2,100 copies/mL rather than the calculated 2,086 copies/mL for its potential acceptance and clinical application. According to the treatment stage at which the mouthwash samples were collected, patients were further grouped into pre- and mid-treatment subsets. Since the distribution of data on the oral EBV load was highly skewed, the oral EBV DNA load was log10 transformed and compared between the pretreatment subset and mid-treatment subset by the Wilcoxon rank-sum test. The univariate and multivariate logistic regressions were performed to assess the associations between clinical characteristics and EBV DNA levels. Survival curves were depicted using the Kaplan–Meier method and compared using the log-rank test. Multivariate Cox regression analyses include age, sex, smoking status, T stage, N stage, IC, CCRT, radiation technology, and oral EBV DNA level. To describe the dose–effect relationship between the oral EBV DNA load and NPC survivals, EBV DNA copy numbers were log10 transformed in the COX regressions as a continuous variable. Statistical significance was defined as two-sided p < 0.05. Analyses were performed using R software, version 4.0.2 (http://www.r-project.org).

RESULTS

Patient Characteristics

The total study population comprised 1,194 patients with locoregionally advanced NPC. There were 864 (72.4%) males and 330 (27.6%) females with a median age of 45 y. Among them, 375 patients received RT alone; 254 received platinum-based CCRT; 349 received 1–2 cycles of TP or PF, IC, and RT; and 216 received IC followed by CCRT. The median follow-up time was 107.3 (range 3.5–164.6) months. The 5-y survival rates for OS, PFS, DMFS, and LRFS were 77.5, 69.7, 83.1, and 88.0%, respectively. According to the treatment stage at which the mouthwash samples were collected, 636 patients were grouped into the pretreatment subset and 558 patients were in the mid-treatment subset (Table 1).

TABLE 1 | Clinical characteristics of 1,194 locoregional advanced NPC patients.

Correlation Between the Oral EBV DNA Level and Clinical Characteristics

EBV DNA could be detected in 456 (71.7%) patients in the pretreatment subgroup and 271 (48.6%) patients in the mid-treatment subgroup (p < 0.001). And the median oral EBV DNA load of mid-treatment samples was significantly lower than that of pretreatment ones (382 vs. 13,368 copies/mL, Wilcoxon p < 0.001, Supplementary Figure 1A). Univariate and multivariate logistic analyses of pretreatment, mid-treatment, and overall patients showed that patients with older age (≥45 y), male gender, and advanced T stage (T3–4) tended to have a higher copy number of oral EBV DNA, albeit with some associations not reaching the significant threshold. In contrast, patients with the advanced N stage were more likely to present a lower level of the oral EBV DNA load (Table 2, Supplementary Table 1).

TABLE 2 | Multivariate associations of clinical characteristics with the oral Epstein–Barr virus DNA load at different treatment time points in nasopharyngeal carcinoma patients.

Prognostic Role of Oral EBV DNA in LA-NPC Patients

We observed that patients with a high level of the oral EBV DNA load (>2,100 copies/mL) had inferior OS (5-y rate, 74.0 vs. 82.0%, log-rank p < 0.001), PFS (64.6 vs. 76.1%, log-rank p < 0.001), DMFS (79.8 vs. 87.1%, log-rank p < 0.001), and LRFS (84.4 vs. 92.4%, log-rank p = 0.005) than patients with a low oral EBV DNA (≤2,100 copies/mL) (Figure 2A). Further stratified according to the treatment stage, similar trends were also observed in both pretreatment and mid-treatment subsets. In the pretreatment subset, high-EBV patient had inferior 5-y OS (74.6 vs. 84.5%, log-rank p < 0.001), PFS (65.2 vs. 78.3%, log-rank p < 0.001), and DMFS (80.9 vs. 88.4%, log-rank p = 0.023) than patients with low oral EBV, while the 5-y LRFS rate was comparable between patients with high oral EBV and those with low oral EBV (85.0 vs. 93.4%, log-rank p = 0.150) (Figure 2B). In the mid-treatment subset, the 5-y OS rate (73.0 vs. 80.3%, log-rank p = 0.001), PFS rate (63.6 vs. 74.5%; log-rank p = 0.003), DMFS rate (78.0 vs. 86.2%, log-rank p = 0.003), and LRFS rate (83.5 vs. 91.6%, log-rank p = 0.010) for high-EBV patients were significantly lower than the corresponding rates for low-EBV patients (Figure 2C).

FIGURE 2 | Kaplan–Meier curves for NPC survival segregated by the oral EBV DNA level at different treatment time points: (A) overall, (B) pretreatment, before any treatment, (C) mid-treatment, between the start of treatment and the end of radiotherapy/chemoradiotherapy. Survivals included overall survival (OS), progression-free survival (PFS), distant metastasis–free survival (DMFS), and locoregional relapse-free survival (LRFS).

Multivariate analyses suggested that the high level of the oral EBV DNA load was independently associated with worse OS (HR = 1.45, 95% CI: 1.20–1.74, p < 0.001), PFS (HR = 1.38, 95% CI: 1.16–1.65, p < 0.001), DMFS (HR = 1.66, 95% CI: 1.25–2.21, p = 0.001), and LRFS (HR = 1.43, 95% CI: 1.05–1.96, p = 0.023). The pretreatment EBV DNA copy numbers were log10 transformed as a continuous variable, and significant dose–effect relationships of the oral EBV DNA load with all four outcomes were observed (p = 0.001∼0.012). Similar associations between the oral EBV DNA load and NPC survivals were observed for patients in both the pretreatment and mid-treatment subgroups, except that no significant association was observed between pretreatment oral EBV DNA and LRFS (Table 3).

TABLE 3 | Multivariate Cox regression analysis of the oral EBV DNA load with NPC survivals.

We have tried to divide the whole population into three subgroups instead of two, namely, pretreatment (sampling before any treatment), post-IC (sampling between the start of IC and the start of radiotherapy/chemoradiotherapy), and mid-RT (sampling during radiotherapy/chemoradiotherapy). We found no significant difference in the oral EBV DNA load between the post-IC and mid-RT subgroups (Wilcoxon p = 0.340, Supplementary Figure 1B); thus, we combined the two groups to form the mid-treatment group instead. To test the robustness of our results, we performed further analyses by setting different cutoffs for the oral EBV DNA load. These results all showed similar trends that high copy number of oral EBV DNA was an independent predictor of poor prognosis for LA-NPC patients (Supplementary Table S2). And we have performed further analyses by breaking the oral EBV DNA load into three and four levels, and the results consistently supported the dose–response relationships between the oral EBV DNA load and NPC survivals (Supplementary Table S3).

Patient Outcomes in Subgroups Defined by Oral EBV DNA Combined With the TNM Stage

To refine the risk stratification for NPC patients, all patients were subdivided into more subgroups based on TNM staging combined with an oral EBV DNA level. Specifically, patients were grouped into five new groups: stage II, low EBV in stage III, high EBV in stage III, low EBV in stage IVa, and high EBV in stage IVa. Survival curves suggested that the new grouping provided more detailed risk discrimination. With the increase of the clinical stage and oral EBV DNA levels, the hazard of death and progression presented a gradually increasing trend (Figure 3). Interestingly, we observed that patients in stage IVa with the low oral EBV DNA load had similar DMFS and LRFS compared to patients in stage III with the high oral EBV DNA load (Figures 3C,D). Compared to TNM staging, the new grouping combining the oral EBV DNA load and the TNM stage showed improved risk discrimination [concordance index (c-index), 0.636 vs. 0.617 for OS, 0.621 vs. 0.603 for PFS]. Multivariate Cox analyses further confirmed these observations (Figure 4). Furthermore, similar trends were observed as the whole set during subgroup analyses in pretreatment and mid-treatment subsets of patients (Supplementary Figure 2 and Supplementary Figure 3).

FIGURE 3 | Kaplan–Meier survival curves for NPC survivals segregated by the oral EBV DNA level and TNM staging in all 1,194 NPC patients: (A) overall survival (OS), (B) progression-free survival (PFS), (C) distant metastasis–free survival (DMFS), (D) locoregional relapse-free survival (LRFS).

FIGURE 4 | Multivariate associations of new groups defined by oral EBV DNA level and TNM staging with survivals in all 1,194 NPC patients. Survivals included overall survival (OS), progression-free survival (PFS), distant metastasis–free survival (DMFS), and locoregional relapse-free survival (LRFS).

Correlation Between the Oral EBV DNA Load and the Plasma EBV DNA Load

In a subset of 229 NPC patients with available data on the plasma EBV load, we found no significant correlation between the oral EBV load and the plasma EBV load (Spearman R² = 0.062, p = 0.349, Supplementary Figure 4).

DISCUSSION

Saliva has become an attractive hotspot in the identification of cancer biomarkers (Bonne and Wong, 2012) because of simple and non-invasive sample collection, which dramatically reduces the discomfort and incompliance of patients with repeated sampling for disease monitoring. The use of saliva as a test specimen is also a cost-efficient method without the need for highly trained professionals, and the processing of saliva is much easier and less demanding than blood, requiring no in-time centrifugation. The convenience and flexibility of the saliva sample collection make “self-sampling” feasible for disease screening and surveillance in the future, which provides more potential application scenarios. Recently, researchers have reported salivary high-risk human papillomavirus (HPV) DNA could serve as a biomarker for the detection and prognostic monitoring of oropharyngeal cancer (Weeramange et al., 2021). Oral cavity is an important place of EBV infection and existence, where oral epithelium infected with EBV routinely secretes viral particles into saliva (Ling et al., 2003a), which is primarily responsible for the human-to-human transmission of EBV. The salivary EBV load has been explored as biomarkers in various diseases including lymphoproliferative disorders (Saito et al., 1991), periodontitis (Dawson et al., 2009), and human immunodeficiency virus (HIV) (Ling et al., 2003b), but rarely in patients with NPC (Pow et al., 2011; Xue et al., 2018) or other EBV-related malignancies.

A previous study with 46 NPC patients reported that patients with T3–4 stages had the higher posttreatment salivary EBV DNA load (median 50,040 copies/mL) than patients with T1–2 stages (54 copies/mL) (Pow et al., 2011). Here, we also observed that NPC patients at the advanced T stage (T3–4) had significantly higher pretreatment (median 19,895 vs. 4,189 copies/mL) and mid-treatment (median 1,141 vs. 0 copies/mL) oral EBV DNA load than patients at the early T stage (T1–2). Additionally, the positive association between the T stage and EBV DNA levels in the oropharynx was consistent with that observed in plasma or nasopharynx brushings (Lo et al., 1999; Tong et al., 2002; Zheng et al., 2015; Zhang et al., 2018). EBV DNA in plasma or nasopharynx brushings of NPC patients are considered to be “naked” DNA fragments mainly released by apoptotic tumor cells, though, which remains to be defined (Chan et al., 2003; Stevens et al., 2005; Ramayanti et al., 2017). Compared to patients with the early T stage, more tumor cells releasing EBV are expected in advanced T stage NPC patients, which may partly explain why a higher EBV DNA level was obtained in blood or nasopharynx. Given the proximity of the nasopharynx and oropharynx, we hypothesize that the respiratory and swallowing movements would result in the transfer of viruses from the tumor site in the nasopharynx to the oropharynx, which affects the number of EBV in the oropharynx. This may partly explain why higher oral EBV DNA levels were obtained in advanced T stage patients and provided a reasonable explanation that patients with advanced NPC tended to have higher oral EBV DNA levels and presented poorer outcomes, albeit more detailed mechanisms remain to be further studied.

In this study, the EBV DNA load in mid-treatment mouthwashes was found significantly lower than that in the pretreatment samples. This finding agreed with the trends observed in plasma that the EBV DNA load decreased with the course of treatment (Huang et al., 2019; Lv et al., 2019). This is easy to understand. After treatment, the tumor burden decreases, and so does the patient’s EBV DNA load, further supporting the association between the oral EBV DNA load and tumor burden. However, our result disagreed with that of Pow et al. (2011) who reported that the median posttreatment salivary EBV DNA load (3,007 copies/mL) was higher than the pretreatment level (99 copies/mL) in 46 NPC patients. This discrepancy could be due to various reasons. First, the sample size of that study was small, and the results were likely to be highly biased. Second, the two studies had inconsistent definitions of pre- and post-treatment, with pre- and post-treatment in that study referring to before and 2 months after radiotherapy, whereas in our study, it was before and after the initiation of any treatment (induction chemotherapy/radiotherapy/chemoradiotherapy), respectively. Third, Lv et al. (2019) reported inter-patient heterogeneity in plasma EBV DNA response to treatment, with the virus in some patients being completely cleared, some persisting, and some rebounding. So, it is possible that patients in our study and Pow’s responded differently to treatment, leading to different directions of change in the oral EBV DNA load before and after treatment. Anyway, the dynamic changes of the oral EBV DNA load throughout treatment need to be studied further by sequential monitoring of individuals with a sufficient sample size. Nevertheless, regardless of how it changes with treatment course, our study showed that the high oral EBV DNA load was a prognostic risk factor for LA-NPC patients, both pre- and mid-treatment. This result was consistent with previous findings regarding another well-proven prognostic biomarker, plasma EBV DNA (Lin et al., 2004; Leung et al., 2014; Liu et al., 2015; Huang et al., 2019).

With the relatively large cohort and long-term follow-ups, here we demonstrated that the oral EBV DNA load was an independent indicator for NPC survivals, mainly OS, PFS, and DMFS, while its associations with LRFS were not that significant. Since the HRs of the associations between the oral EBV DNA load and LRFS have been always greater than 1, we consider the statistical insignificance in some analyses may be due to the small number of locoregional recurrence events followed up (179 events) in our study. Furthermore, we found that the oral EBV DNA load was able to complement TNM staging in the prognosis prediction of NPC, which may be helpful in treatment guidelines for the best benefit of NPC patients. Unlike circulating cell-free EBV DNA that has been studied for decades of years and is considered to be released mainly from the tumor cells (Mutirangura et al., 1998), little is known about the situation of oral EBV in NPC or other EBV-associated cancers. Our preliminary analysis in a limited subset of 229 patients with available pretreatment plasma EBV DNA data demonstrated no significant correlation between the oral and plasma EBV DNA loads, which may partly indicate the different origins of the two biomarkers. In addition to tumor cells, salivary EBV DNA of NPC patients may also originate from oropharyngeal epithelium and B lymphocytes (Hadinoto et al., 2009). Our previous preliminary study investigated the EBV presence in fractionating mouthwash samples of NPC patients by low- and ultra-speed centrifugation and found the EBV DNA was present in the separate component fractions of mouthwash, such as in the pellets from ultra-speed centrifugation, fragmented DNA from ultra-speed centrifugation, and pellets from low-speed centrifugation, which indicated the existence of cell-free EBV, fragmented EBV DNA, and cell-derived EBV, respectively (Xue et al., 2018). The oral cavity is such a complex environment that EBV reproduction in the oropharynx may be altered by various endogenous and exogenous factors, including but not limited to the local tumor, immune microenvironment, miR-200 family members (Lin et al., 2016), cigarette smoking, and other lifestyle factors (He et al., 2019). Further studies are warranted to investigate the origin, half-life, and molecular characteristics of oral EBV DNA, and to evaluate the relationships of EBV DNA load in the oropharynx with blood or nasopharynx. Our preliminary study on the prognostic value of oral EBV in NPC patients may be helpful to expand our understanding of EBV carcinogenesis and provide some clues for further in-depth mechanistic research in the future.

Since only one mouthwash sample was taken per patient in this study, we could not intuitively compare the oral EBV DNA load changes during treatment and progression, and assess the prognostic value of such changes. Hence, it would be of great significance to further clarify these issues in future well-designed dynamic studies, which could provide more insights into the oncogenic and the prognostic role of oral EBV DNA for NPC. It is worth mentioning that we have previously observed a decrease in the oral EBV DNA load among NPC patients compared to healthy controls (Xue et al., 2018). We think that the overall decrease of the oral EBV DNA load in NPC patients was possibly the result of tumorigenesis, during which the lytic replication of EBV in the oral cavity may be suppressed by some mechanisms that have not yet been well elucidated, such as the epigenetic modification of EBV genes or the dysregulation of the host’s regulatory genes. Hence, the associations between the oral EBV DNA load and NPC risk or NPC survival deserve further in-depth exploration.

In the efforts to promote the clinical application of salivary biomarkers, the discovery of effective salivary biomarkers, the understanding of their origin and biological mechanism, the optimization of biodetection methods, and the standardization of saliva sampling procedures based on the type of downstream assays are all critical. We will continue to make more explorations and discoveries in these aspects.

In summary, through this prospective observational clinical study, we first comprehensively evaluated the prognostic value of the oral EBV DNA load in LA-NPC patients and demonstrated that a high level of the oral EBV DNA load was an independent predictor of poor prognosis for LA-NPC patients, which could be a feasible consideration to complement TNM staging for NPC risk stratification.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding author.

ETHICS STATEMENT

The Institutional Review Board of Sun Yat-Sen University Cancer Center approved this study. The patients/participants provided their written informed consents to participate in this study.

AUTHOR CONTRIBUTIONS

W-HJ, Y-QH, JM, and YS contributed to study concept and design; W-QX and J-BZ contributed to study supervision; TZ, D-WY, FW, Y-JJ, L-LY, W-LZ, YL, X-HZ, X-ZL, P-FZ, S-DZ, and Y-ZH contributed to acquisition of data; TZ, D-WY, Y-QH, and T-MW contributed to analysis and interpretation of data; Y-QH and TZ contributed to drafting the article; W-HJ and WC contributed to critical review, edit, and revision of the article. All authors approved the final manuscript.

FUNDING

This work was supported by grants from the Science and Technology Planning Project of Guangzhou, China (201804020094 and 201904010467); The Basic and Applied Basic Research Foundation of Guangdong Province, China (2021B1515420007); the National Key Research and Development Program of China (2016YFC0902001); Sino-Sweden Joint Research Program (81861138006); the National Natural Science Foundation of China (81973131, 81903395, 81803319, and 81802708); the National Science Fund for Distinguished Young Scholars of China (81325018); the Fundamental Research Funds for the Central Universities (19ykpy185); the Key Project for International Cooperation and Exchange of the National Natural Science Foundation of China (81220108022); the Natural Science Foundation of Guangdong Province (2017A030312003); the Science and Technology Planning Project of Guangdong Province, China (2019B030316031); the Key Area Research and Development Program of Guangdong Province, China (2019B110233004).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

ACKNOWLEDGMENTS

We would like to thank all the patients who participated in this study. We thank Huan-Lin Yu, Zi-Yun Qiu, and Jing-Lan Liu at Sun Yat-sen University Cancer Center for their help in patient data collection.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2021.757644/full#supplementary-material

REFERENCES

Bonne, N. J., and Wong, D. T. (2012). Salivary Biomarker Development Using Genomic, Proteomic and Metabolomic Approaches. Genome Med. 4 (10), 82. doi:10.1186/gm383

Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., and Jemal, A. (2018). Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer J. Clinicians 68 (6), 394–424. doi:10.3322/caac.21492

Cai, L., Long, Y., Chong, T., Cai, W., Tsang, C. M., Zhou, X., et al. (2019). BV-miR-BART7-3p Imposes Stemness in Nasopharyngeal Carcinoma Cells by Suppressing SMAD7. Front Genet. 10, 939. doi:10.3389/fgene.2019.00939

Chan, K. C., Zhang, J., Chan, A. T., Lei, K. I., Leung, S. F., Chan, L. Y., et al. (2003). Molecular Characterization of Circulating EBV DNA in the Plasma of Nasopharyngeal Carcinoma and Lymphoma Patients. Cancer Res. 63 (9), 2028–2032.

Dawson III, D., Wang, C., Danaher, R., Lin, Y., Kryscio, R., Jacob, R., et al. (2009). Salivary Levels of Epstein-Barr Virus DNA Correlate with Subgingival Levels, Not Severity of Periodontitis. Oral Dis. 15 (8), 554–559. doi:10.1111/j.1601-0825.2009.01585.x

Frikha, M., Auperin, A., Tao, Y., Elloumi, F., Toumi, N., Blanchard, P., et al. (2018). A Randomized Trial of Induction Docetaxel-Cisplatin-5fu Followed by Concomitant Cisplatin-RT versus Concomitant Cisplatin-RT in Nasopharyngeal Carcinoma (GORTEC 2006-02). Ann. Oncol. 29 (3), 731–736. doi:10.1093/annonc/mdx770

Hadinoto, V., Shapiro, M., Sun, C. C., and Thorley-Lawson, D. A. (2009). The Dynamics of EBV Shedding Implicate a central Role for Epithelial Cells in Amplifying Viral Output. Plos Pathog. 5 (7), e1000496. doi:10.1371/journal.ppat.1000496

He, Y.-Q., Liao, X.-Y., Xue, W.-Q., Xu, Y.-F., Xu, F.-H., Li, F.-F., et al. (2019). Association between Environmental Factors and Oral Epstein-Barr Virus DNA Loads: A Multicenter Cross-Sectional Study in China. J. Infect. Dis. 219 (3), 400–409. doi:10.1093/infdis/jiy542

Hong, R. L., Hsiao, C. F., Ting, L. L., Ko, J. Y., Wang, C. W., Chang, J. T. C., et al. (2018). Final Results of a Randomized Phase III Trial of Induction Chemotherapy Followed by Concurrent Chemoradiotherapy versus Concurrent Chemoradiotherapy Alone in Patients with Stage IVA and IVB Nasopharyngeal Carcinoma-Taiwan Cooperative Oncology Group (TCOG) 1303 Study. Ann. Oncol. 29 (9), 1972–1979. doi:10.1093/annonc/mdy249

Huang, C.-L., Sun, Z.-Q., Guo, R., Liu, X., Mao, Y.-P., Peng, H., et al. (2019). Plasma Epstein-Barr Virus DNA Load after Induction Chemotherapy Predicts Outcome in Locoregionally Advanced Nasopharyngeal Carcinoma. Int. J. Radiat. Oncol. Biol. Phys. 104 (2), 355–361. doi:10.1016/j.ijrobp.2019.01.007

Lee, A. W. M., Tung, S. Y., Ng, W. T., Lee, V., Ngan, R. K. C., Choi, H. C. W., et al. (2017). A Multicenter, Phase 3, Randomized Trial of Concurrent Chemoradiotherapy Plus Adjuvant Chemotherapy versus Radiotherapy Alone in Patients with Regionally Advanced Nasopharyngeal Carcinoma: 10-year Outcomes for Efficacy and Toxicity. Cancer 123 (21), 4147–4157. doi:10.1002/cncr.30850

Leung, S.-f., Zee, B., Ma, B. B., Hui, E. P., Mo, F., Lai, M., et al. (2006). Plasma Epstein-Barr Viral Deoxyribonucleic Acid Quantitation Complements Tumor-Node-Metastasis Staging Prognostication in Nasopharyngeal Carcinoma. Jco 24 (34), 5414–5418. doi:10.1200/jco.2006.07.7982

Leung, S. F., Chan, K. C. A., Ma, B. B., Hui, E. P., Mo, F., Chow, K. C. K., et al. (2014). Plasma Epstein-Barr Viral DNA Load at Midpoint of Radiotherapy Course Predicts Outcome in Advanced-Stage Nasopharyngeal Carcinoma. Ann. Oncol. 25 (6), 1204–1208. doi:10.1093/annonc/mdu117

Li, W. F., Chen, N. Y., Zhang, N., Hu, G. Q., Xie, F. Y., Sun, Y., et al. (2019). Concurrent Chemoradiotherapy With/without Induction Chemotherapy in Locoregionally Advanced Nasopharyngeal Carcinoma: Long‐term Results of Phase 3 Randomized Controlled Trial. Int. J. Cancer 145 (1), 295–305. doi:10.1002/ijc.32099

Lin, J.-C., Wang, W.-Y., Chen, K. Y., Wei, Y.-H., Liang, W.-M., Jan, J.-S., et al. (2004). Quantification of Plasma Epstein-Barr Virus DNA in Patients with Advanced Nasopharyngeal Carcinoma. N. Engl. J. Med. 350 (24), 2461–2470. doi:10.1056/NEJMoa032260

Lin, Z., Swan, K., Zhang, X., Cao, S., Brett, Z., Drury, S., et al. (2016). Secreted Oral Epithelial Cell Membrane Vesicles Induce Epstein-Barr Virus Reactivation in Latently Infected B Cells. J. Virol. 90 (7), 3469–3479. doi:10.1128/jvi.02830-15

Ling, P. D., Lednicky, J. A., Keitel, W. A., Poston, D. G., White, Z. S., Peng, R., et al. (2003a). The Dynamics of Herpesvirus and Polyomavirus Reactivation and Shedding in Healthy Adults: A 14‐Month Longitudinal Study. J. Infect. Dis. 187 (10), 1571–1580. doi:10.1086/374739

Ling, P. D., Vilchez, R. A., Keitel, W. A., Poston, D. G., Peng, R. S., White, Z. S., et al. (2003b). Epstein-Barr Virus DNA Loads in Adult Human Immunodeficiency Virus Type 1-infected Patients Receiving Highly Active Antiretroviral Therapy. Clin. Infect. Dis. 37 (9), 1244–1249. doi:10.1086/378808

Liu, L.-T., Tang, L.-Q., Chen, Q.-Y., Zhang, L., Guo, S.-S., Guo, L., et al. (2015). The Prognostic Value of Plasma Epstein-Barr Viral DNA and Tumor Response to Neoadjuvant Chemotherapy in Advanced-Stage Nasopharyngeal Carcinoma. Int. J. Radiat. Oncol. Biol. Phys. 93 (4), 862–869. doi:10.1016/j.ijrobp.2015.08.003

Liu, N., Cui, R.-X., Sun, Y., Guo, R., Mao, Y.-P., Tang, L.-L., et al. (2014). A Four-miRNA Signature Identified from Genome-wide Serum miRNA Profiling Predicts Survival in Patients with Nasopharyngeal Carcinoma. Int. J. Cancer 134 (6), 1359–1368. doi:10.1002/ijc.28468

Lo, Y. M., Chan, A. T., Chan, L. Y., Leung, S. F., Lam, C. W., Huang, D. P., et al. (2000). Molecular Prognostication of Nasopharyngeal Carcinoma by Quantitative Analysis of Circulating Epstein-Barr Virus DNA. Cancer Res. 60 (24), 6878–6881.

Lo, Y. M., Chan, L. Y., Lo, K. W., Leung, S. F., Zhang, J., Chan, A. T., et al. (1999). Quantitative Analysis of Cell-free Epstein-Barr Virus DNA in Plasma of Patients with Nasopharyngeal Carcinoma. Cancer Res. 59 (6), 1188–1191.

Lv, J., Chen, Y., Zhou, G., Qi, Z., Tan, K. R. L., Wang, H., et al. (2019). Liquid Biopsy Tracking during Sequential Chemo-Radiotherapy Identifies Distinct Prognostic Phenotypes in Nasopharyngeal Carcinoma. Nat. Commun. 10 (1), 3941. doi:10.1038/s41467-019-11853-y

Mutirangura, A., Pornthanakasem, W., Theamboonlers, A., Sriuranpong, V., Lertsanguansinchi, P., Yenrudi, S., et al. (1998). Epstein-Barr Viral DNA in Serum of Patients with Nasopharyngeal Carcinoma. Clin. Cancer Res. 4 (3), 665–669.

Pan, J. J., Ng, W. T., Zong, J. F., Lee, S. W. M., Choi, H. C. W., Chan, L. L. K., et al. (2016). Prognostic Nomogram for Refining the Prognostication of the Proposed 8th Edition of the AJCC/UICC Staging System for Nasopharyngeal Cancer in the Era of Intensity-Modulated Radiotherapy. Cancer 122 (21), 3307–3315. doi:10.1002/cncr.30198

Pow, E. H. N., Law, M. Y. T., Tsang, P. C. S., Perera, R. A. P. M., and Kwong, D. L. W. (2011). Salivary Epstein-Barr Virus DNA Level in Patients with Nasopharyngeal Carcinoma Following Radiotherapy. Oral Oncol. 47 (9), 879–882. doi:10.1016/j.oraloncology.2011.06.507

Ramayanti, O., Juwana, H., Verkuijlen, S. A. M. W., Adham, M., Pegtel, M. D., Greijer, A. E., et al. (2017). Epstein-Barr Virus mRNA Profiles and Viral DNA Methylation Status in Nasopharyngeal Brushings from Nasopharyngeal Carcinoma Patients Reflect Tumor Origin. Int. J. Cancer 140 (1), 149–162. doi:10.1002/ijc.30418

Saito, I., Nishimura, S., Kudo, I., Fox, R. I., and Moro, I. (1991). Detection of Epstein-Barr Virus and Human Herpes Virus Type 6 in Saliva from Patients with Lymphoproliferative Diseases by the Polymerase Chain Reaction. Arch. Oral Biol. 36 (11), 779–784. doi:10.1016/0003-9969(91)90026-q

Stevens, S. J. C., Verkuijlen, S. A. W. M., Hariwiyanto, B., HarijadiFachiroh, J., Fachiroh, J., Paramita, D. K., et al. (2005). Diagnostic Value of Measuring Epstein-Barr Virus (EBV) DNA Load and Carcinoma-specific Viral mRNA in Relation to Anti-EBV Immunoglobulin A (IgA) and IgG Antibody Levels in Blood of Nasopharyngeal Carcinoma Patients from Indonesia. J. Clin. Microbiol. 43 (7), 3066–3073. doi:10.1128/jcm.43.7.3066-3073.2005

Sun, X. S., Chen, W. H., Liu, S. L., Liang, Y. J., Chen, Q. Y., Guo, S. S., et al. (2019). Individualized Concurrent Chemotherapy by Pretreatment Plasma Epstein‐Barr Viral DNA in II‐III Stage Nasopharyngeal Carcinoma: A Propensity Score Matching Analysis Using a Large Cohort. Cancer Med. 8 (9), 4214–4225. doi:10.1002/cam4.2343

Tang, L.-L., Chen, W.-Q., Xue, W.-Q., He, Y.-Q., Zheng, R.-S., Zeng, Y.-X., et al. (2016a). Global Trends in Incidence and Mortality of Nasopharyngeal Carcinoma. Cancer Lett. 374 (1), 22–30. doi:10.1016/j.canlet.2016.01.040

Tang, L.-Q., Li, C.-F., Chen, Q.-Y., Zhang, L., Lai, X.-P., He, Y., et al. (2015). High-sensitivity C-Reactive Protein Complements Plasma Epstein-Barr Virus Deoxyribonucleic Acid Prognostication in Nasopharyngeal Carcinoma: a Large-Scale Retrospective and Prospective Cohort Study. Int. J. Radiat. Oncol. Biol. Phys. 91 (2), 325–336. doi:10.1016/j.ijrobp.2014.10.005

Tang, L.-Q., Li, C.-F., Li, J., Chen, W.-H., Chen, Q.-Y., Yuan, L.-X., et al. (2016b). Establishment and Validation of Prognostic Nomograms for Endemic Nasopharyngeal Carcinoma. JNCI.J 108 (1), djv291. doi:10.1093/jnci/djv291

Tong, J. H., Tsang, R. K., Lo, K. W., Woo, J. K., Kwong, J., Chan, M. W., et al. (2002). Quantitative Epstein-Barr Virus DNA Analysis and Detection of Gene Promoter Hypermethylation in Nasopharyngeal (NP) Brushing Samples from Patients with NP Carcinoma. Clin. Cancer Res. 8 (8), 2612–2619.

Wang, T.-M., Shen, G.-P., Chen, M.-Y., Zhang, J.-B., Sun, Y., He, J., et al. (2019). Genome-Wide Association Study of Susceptibility Loci for Radiation-Induced Brain Injury. J. Natl. Cancer Inst. 111 (6), 620–628. doi:10.1093/jnci/djy150

Weeramange, C. E., Liu, Z., Hartel, G., Li, Y., Vasani, S., Langton-Lockton, J., et al. (2021). Salivary High-Risk Human Papillomavirus (HPV) DNA as a Biomarker for HPV-Driven Head and Neck Cancers. J. Mol. Diagn. 23 (10), 1334–1342. doi:10.1016/j.jmoldx.2021.07.005

Xu, F.-H., Xiong, D., Xu, Y.-F., Cao, S.-M., Xue, W.-Q., Qin, H.-D., et al. (2012). An Epidemiological and Molecular Study of the Relationship between Smoking, Risk of Nasopharyngeal Carcinoma, and Epstein-Barr Virus Activation. J. Natl. Cancer Inst. 104 (18), 1396–1410. doi:10.1093/jnci/djs320

Xue, W. Q., He, Y. Q., Liao, X. Y., Li, F. F., Xu, Y. F., Xu, F. H., et al. (2018). Decreased Oral Epstein‐Barr Virus DNA Loads in Patients with Nasopharyngeal Carcinoma in Southern China: A Case‐control and a Family‐based Study. Cancer Med. 7, 3453–3464. doi:10.1002/cam4.1597

Yao, J.-J., He, X.-J., Lawrence, W. R., Zhang, W.-J., Kou, J., Zhang, F., et al. (2018). Prognostic Value of Circulating Lipoprotein in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma. Cell Physiol Biochem 48 (1), 285–292. doi:10.1159/000491728

Young, L. S., Yap, L. F., and Murray, P. G. (2016). Epstein-Barr Virus: More Than 50 Years Old and Still Providing Surprises. Nat. Rev. Cancer 16 (12), 789–802. doi:10.1038/nrc.2016.92

Zhang, P.-F., Zheng, X.-H., Li, X.-Z., Tian, T., Zhang, S.-D., Hu, Y.-Z., et al. (2018). Nasopharyngeal Brushing: a Convenient and Feasible Sampling Method for Nucleic Acid-Based Nasopharyngeal Carcinoma Research. Cancer Commun. 38 (1), 8. doi:10.1186/s40880-018-0278-z

Zheng, X. H., Lu, L. X., Li, X. Z., and Jia, W. H. (2015). Quantification of Epstein-Barr Virus DNA Load in Nasopharyngeal Brushing Samples in the Diagnosis of Nasopharyngeal Carcinoma in Southern China. Cancer Sci. 106 (9), 1196–1201. doi:10.1111/cas.12718

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 He, Zhou, Yang, Jia, Yuan, Zhang, Wang, Liao, Xue, Zhang, Zheng, Li, Zhang, Zhang, Hu, Wang, Cho, Ma, Sun and Jia. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 18 January 2022
doi: 10.3389/fmolb.2021.735871

Bioinformatic Analyses of the Ferroptosis-Related lncRNAs Signature for Ovarian Cancer

Jianfeng Zheng1,², Jialu Guo², Yahui Wang¹, Yingling Zheng¹, Ke Zhang³ and Jinyi Tong1,²*

¹Department of Obstetrics and Gynecology, Affiliated Hangzhou Hospital, Nanjing Medical University, Hangzhou, China

²Department of Obstetrics and Gynecology, Hangzhou Women’s Hospital, Hangzhou, China

³Department of Radiation Oncology, Hangzhou Cancer Hospital, Hangzhou, China

Edited by:
Florence Le Calvez-Kelm, International Agency For Research On Cancer (IARC), France

Reviewed by:
Jiarong Chen, Jiangmen Central Hospital, China
Kenneth S. Hettie, Stanford University, United States

* Correspondence: Jinyi Tong, tongjinyi252@zju.edu.cn

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 03 July 2021
Accepted: 08 December 2021
Published: 18 January 2022

Citation: Zheng J, Guo J, Wang Y, Zheng Y, Zhang K and Tong J (2022) Bioinformatic Analyses of the Ferroptosis-Related lncRNAs Signature for Ovarian Cancer. Front. Mol. Biosci. 8:735871. doi: 10.3389/fmolb.2021.735871

Both ferroptosis and lncRNAs are significant for ovarian cancer (OC). Whereas, the study of ferroptosis-related lncRNAs (FRLs) still few in ovarian cancer. We first constructed an FRL-signature for patients with OC in the study. A total of 548 FRLs were identified for univariate Cox regression analysis, and 21 FRLs with significant prognosis were identified. The prognostic characteristics of nine FRLs was constructed and validated, showing opposite prognosis in two subgroups based on risk scores. The multivariate Cox regression analysis and nomogram further verified the prognostic value of the risk model. By calculating ferroptosis score through ssGSEA, we found that patients with higher risk scores exhibited higher ferroptosis scores, and high ferroptosis score was a risk factor. There were 40 microenvironment cells with significant differences in the two groups, and the difference of Stromal score between the two groups was statistically significant. Six immune checkpoint genes were expressed at different levels in the two groups. In addition, five m6A regulators (FMR1, HNRNPC, METTL16, METTL3, and METTL5) were higher expressed in the low-risk group. GSEA revealed that the risk model was associated with tumor-related pathways and immune-associated pathway. We compared the sensitivity of chemotherapy drugs between the two risk groups. We also explored the co-expression, ceRNA relation, cis and trans interaction of ferroptosis-related genes and lncRNAs, providing a new idea for the regulatory mechanisms of FRLs. Moreover, the nine FRLs were selected for detecting their expression levels in OC cells and tissues.

Keywords: ovarian cancer, ferroptosis, lncRNA, risk model, immune

INTRODUCTION

Approximately 150,000 women die of ovarian cancer (OC) every year, making it the highest death rate among gynecological tumors (Lheureux et al., 2019). The development of OC is regulated by many cytokines and signaling pathways (Narod, 2016). Although the prognosis for early-stage cancer patients is better, the vast majority of patients are already in the advanced stage when they are first diagnosed (Siegel et al., 2020). Hence, the current urgent problem to be solved is to improve the prognosis of OC patients.

Ferroptosis is a process that regulates cell death and its particularity is lipid peroxide in cell membrane in an iron-dependent manner, which is different from apoptosis and necrosis (Yang and Stockwell, 2008). The key to ferroptosis lies in the production of reactive oxygen species in Fenton reaction and the reduction of glutathione-dependent peroxidase 4 (GPX4) (Xie et al., 2016; Yang and Stockwell, 2016). Ferroptosis is correlated with a variety of biochemical processes and can inhibit the proliferation of tumor tissues by depriving iron in cancer cells or changing the metabolism of iron ions in tumor tissues (Mou et al., 2019; Xu et al., 2019; Wang et al., 2020a; Wu et al., 2020). Studies have shown that continuous iron stimulation is one of the high-risk factors for the occurrence and development of OC(Lattuada et al., 2015) and ferroptosis is considered as a potential therapeutic target for OC (Lin and Chi, 2020).

The length of lncRNAs is more than 200 nucleotides and lncRNAs are involved in carcinogenesis or suppression in a variety of cancers, including OC(Wang et al., 2019a; Braga et al., 2020). Studies have shown that lncRNAs can regulate ferroptosis in tumor cells at the transcriptional or post-transcriptional level, and its mechanisms involve glutamine decomposition, mitochondria associated proteins, iron metabolism, glutathione metabolism, lipid peroxidation, and p53 signaling pathways (Jiang et al., 2021). However, there are few studies on the mechanism of ferroptosis-related lncRNA (FRL) in OC pathology.

In our study, nine optimized FRLs were identified. The prognostic signature of nine FRLs was constructed and validated, showing opposite prognosis in two subgroups based on risk scores. In addition, we identified differences in immune microenvironment, m6A regulatory factors, immune checkpoints, and sensity to several chemotherapeutic agents between risk groups. We also explored the co-expression, ceRNA relation, cis and trans interaction of ferroptosis-related genes and lncRNAs, providing a new idea for the regulatory mechanisms of FRLs. Furthermore, the nine FRLs were selected for detecting their expression levels.

MATERIALS AND METHODS

Data Collection

The processed RNA sequencing profiles (UCSC TOIL RNA-seq) with clinical information were extracted from TCGA(Goldman et al., 2020). There were 416 OC samples and 88 normal samples in our study. After converting Ensemble Gene to Gene Symbol and filtering through genes with low expression, the lncRNAs were identified according to the annotation information in GENCODE database (Harrow et al., 2012). We obtained 288 ferroptosis-related genes (FRGs), of which 108 FRGs were drivers, 69 FRGs were suppressors and 111 FRGs were markers, from FerrDb database (Zhou et al., 2020). In the aggregate, 215 mRNAs associated with ferroptosis were screened via matching it to the filtered mRNAs above. Differential analysis of ferroptosis-related mRNAs and annotated lncRNAs between tumor and normal samples was according to linear regression and Empirical Bayes (p < 0.05, |logFC|>1) (Ritchie et al., 2015). In addition, we used the Benjamini and Hochberg for multiple testing correction to get adjusted p value and assessed the differences at multiple and significance levels (p-adjusted<0.05, |logFC|>1) (Ferreira, 2007; Ghosh, 2019). We calculated the Pearson correlation coefficient between FRGs and lncRNAs to filter the lncRNAs that are significantly related to mRNAs (|r|>0.5, p < 0.001).

Bioinformatic Analysis

According to R’s survival package (Wang et al., 2016), univariate Cox regression was performed on candidate FRLs to screened prognostic lncRNAs. The R-language random function was used to randomly divide the total OC samples (total set, TOS) into two sets, training set (TS) and validation set (VS). Whereafter, using the LASSO Cox regression (Tibshirani, 1997)in the glmnet software package of R (Engebretsen and Bohlin, 2019)and 20 times cross-validation analyses, the optimal combination of FRL markers was screened based on the TS set. We constructed a risk model for OC patients based on formula:

In the formula, ßlncRNA represented the Lasso prognostic coefficient for optimized FRLs and ExplncRNA meant the expression level. In the TS set, the OC samples were classified into high-risk (with a RS higher than or equal to the median value of RSs, n = 104) and low-risk (with a RS lower than the median value of RSs, n = 104) according to the mean value of RSs. In addition, in order to verify the accuracy of the model, RS values of each sample in TOS (high-risk: n = 208; low-risk: n = 208) and VS (high-risk: n = 104; low-risk: n = 104) sets were calculated using the same regression coefficient. The association between the risk groups and the prognosis statuses was evaluated by using the Kaplan-Meier curve method of survival package in R.

A visual nomogram was constructed after univariate and multivariate Cox regression analysis of clinical features and risk groups, and verified by correction curves to determine the accuracy of the risk model.

Based on ssGSEA algorithm, we calculated the enrichment fraction of FRGs in different samples to obtain the ferroptosis score (FS). Based on the median of FSs, the samples were divided into high-score (n = 208) and low-score (n = 208) groups. We compared the differences in survival between patients with high-score and low-score and, in conjunction with the risk scores, we also analyzed survival among patients in different subgroups (low-risk + low-score: n = 104; low-risk + high-score: n = 104; high-risk + low-score: n = 104; high-risk + high-score: n = 104). DAVID was used to analyze the biological functions of potential FRGs (Huang et al., 2009a; Huang et al., 2009b). Differentially expressed genes (DEGs, |logFC| and FDR<0.05) were filtered between the two risk groups on the basis of the limma R package (Clark et al., 2014). Candidate small molecular drugs and mechanisms of action were predicted via uploading DEGs to CMAP database (Lamb et al., 2006; Lamb, 2007).

We used five algorithms (Estimate (Yoshihara et al., 2013), ssGSEA (Jia et al., 2018); (Hänzelmann et al., 2013), CIBERSORT (Newman et al., 2015; Charoentong et al., 2017), MCPcounter (Shi et al., 2020), and xCell (Aran et al., 2017)) to estimate differences in immune microenvironment between risk groups. The expression levels of immune checkpoint genes were extracted, and the expression differences among groups were compared by inter-group T test.

FRG-FRL co-expression (|PCC|>5, p < 0.001) network was constructed and visualized by Cytoscape3.6.1 (Shannon et al., 2003). The miRWalk speculated that the ferroptosis-related mRNAs targeted by the corresponding miRNAs (Dweep and Gretz, 2015). Furthermore, we combined the results of six commonly used databases (Microt4, miRWalk, miRDB, miRanda, Targetscan and RNA22) to obtain the miRNA-FRG relationship pairs. The miRNAs targeted by corresponding FRLs of the risk model were speculated by miranda (Enright et al., 2003). FRLs and FRGs regulated by the same miRNA were defined as ceRNAs mutually. The UCSC data set (Yuan et al., 2019) searched for potential cis-interactions between FRLs and mRNAs transcribed from the same chromosome within 200 kb. Then, further construct a network based on overlapped TFs to detect the trans-regulatory function of FRLs and FRGs (Shu et al., 2019).

Moreover, we assessed the Tumor Immune Dysfunction and Exclusion (TIDE) score through the TIDE database to predict the response to Immune Checkpoint Blockade (Fu et al., 2020). The chemotherapy drugs were extracted from Genomics of Drug Sensitivity in Cancer (GDSC) database (Yang et al., 2013) and the pRRophetic R package is used to calculate the 50% maximum inhibitory concentration (IC50) (Geeleher et al., 2014).

Ethics Statement

With the approval of the Ethics Committee, specimens were taken from 30 patients. Fresh specimens (≤0.5 cm) were rapidly immersed in RNA Stabilization Solution (GenePharma, China) and incubated the samples overnight at 4°C. The tissue was completely permeated and then transferred to a -80 °C refrigerator for subsequent experiments.

Real Time qPCR Analysis

Using ultrasonic crusher, the tissues were cracked by lysate. Total RNA was extracted from OC cells and tissues by using RNA Extraction Kit (TAKARA, Japan). Moreover, cDNA was gained after reverse transcription by using the PrimeScriptTMRT (TAKARA, Japan). PCR was performed by ABI 7500 instrument to evaluate the expression level of the nine FRLs by using TB Green® Premix Ex TaqTM (TAKARA, Japan). Total 1 μL cDNA was used as the template in a final 10 μL PCR reaction volume containing 5 μL TB Green Premix EX Taq, 0.4 μL corresponding primers (GenePharma, China), 0.2ul ROX Reference Dye II and 3.4 μL DEPC water. PCR was run as follows: 95.0 °C for 5s, 40 cycles of 95.0 °C for 5s and 60.0 °C for 34s. The melting curve was obtained at 95.0 °C for 15s, 60.0 °C for 1min and 95.0 °C for 15s. The primer sequences are shown in Supplementary Table S1.

Statistical Analysis

We used R software package, TBtools, and GraphPad Prism for statistical analysis and visualization. Log-rank test was used to evaluate the statistical significance of the difference between the survival curves of two subgroups (Bland and Altman, 2004). Wilcoxon test was used to compare the differences between the two groups. The experiment was made in triplicate, and each experiment was repeated three times. Student’s T test or ANOVA were used for the experimental data. p < 0.05 was considered statistically significant.

RESULTS

In order to make our entire study facilitate to understanding, we made a flow chart, as shown in Figure 1.

FIGURE 1 | Flow diagram of our study.

Differential Analysis

A total of 98 differential FRGs (Figure 2A) and 1,145 exegetical lncRNAs were identified (Figure 2B). Enrichment analysis further exhibited that 131 GO and 24 KEGG pathways were enriched based on aforementioned 98 FRGs. The result showed that the identified FRGs were closely related to several important biological processes or pathways, such as cellular response to tumor necrosis factor (Figure 2C), response to hypoxia (Figure 2C), PI3K-Akt/MAPK/HIF-1/p53 signaling pathway (Figure 2D), etc.

FIGURE 2 | Differential and enrichment analysis (A,B) Volcano map of differentially expressed mRNAs (A) and lncRNAs (B). Red triangle: up regulated; blue square: down regulated (C) GO analysis. The color scale represented p value and the circle size indicated count (D) KEGG analysis. The color scale represented p value and the circle size indicated count.

Construction and Validation of the Predictive Signature

548 FRLs were obtained by Pearson correlation analysis (|PCC|>0.5, p < 0.001) and FRL with significant prognosis was mined (p < 0.05). The FRL-signature was constructed by the LASSO Cox analysis of 70 prognostic FRLs. The λ selection diagram is shown in Figures 3A,B.

FIGURE 3 | Construction and validation of the risk model (A) λ selection diagram. The two dotted lines indicated two particular values of λ. The left side was λmin and the right side was λ1se. The λmin was selected to build the model for accuracy in our study (B) LASSO Cox analysis (C) Forest plot of the prognostic ability of the nine optimal FRLs (D-F) The K-M survival curves of training set (D), total set (E), and validation set (F).

For 70 prognostic FRLs obtained, the LASSO Cox Regression model from glmnet package and 20-fold cross-validation analysis were further used to screen the optimal biomarker combinations of FRLs. Nine optimal FRLs were filtrated and FRL CTC-246B18.8 was a risk factor with HR > 1, while FRLs AC133644.2, RP1-223E5.4, RP1-313I6.12, RP11-872J21.3, RP3-512B11.3, RP5-1120P11.1, SNHG10, and USP30-AS1 were protective factors with HR < 1 (Figure 3C, Supplementary Figure S1). To further validate the expression levels of the nine FRLs in OC, the qPCR analysis was detected in our collected specimens and cells. The results showed that FRL CTC-246B18.8 was significantly upregulated in OC cells and samples (Supplementary Figures S2,3), while FRLs SNHG10 and USP30-AS1 were downregulated compared with normal control (Supplementary Figures S2,3).

According to the mean value of RSs, the patients with OC from TCGA database were divided into two risk subgroups. The K-M survival curves indicated a worse prognosis in the high-risk group (Figures 3D–F). The univariate and multivariate Cox regression analysis of clinical features and the risk model showed that age, “primary therapy outcome success”, and risk model were independent prognostic factors (Figures 4A,B). A nomogram was further constructed based on age, “primary therapy outcome success”, and risk model (Figure 4C). The calibration curve was drawn to prove the accuracy of the model (Figure 4D).

FIGURE 4 | Clinical value of risk score by independent prognostic analysis (A) The univariate Cox regression analysis (B) The Cox regression analysis (C) The Nomogram model based on risk model and clinical features (D) The calibration plots of the nomogram. The closer it was to 45 degrees or the gray lines in the graph, the better the fitting effect.

Ferroptosis Score Analysis

We calculated the ferroptosis enrichment score of each sample. Then, we found that patients in the high-risk group had a higher ferroptosis score (Figure 5A). Patients with OC were divided into two subgroups based on the median of ferroptosis scores (FSs). The Kaplan-Meier survival curve showed that the OS of patients with the high ferroptosis score was significantly lower (Figure 5B), indicating that both high ferroptosis score and the high-risk score are risk factors (Figure 5C). Heatmap of the associations among the expression levels of nine FRLs, risk scores, ferroptosis scores and clinical features (age, stage, grade, therapy outcome, and tumor residual disease) were displayed in Figure 5D. We found that low expression of CTC-246B18.8 was associated with lower risk score, while the opposite was true for the remaining eight FRLs (Supplementary Figure S4). Low expression of CTC-246B18.8 showed lower ferroptosis score, while RP1-313I6.12, RP1-313I6.12, RP3-512B11.3, and RP5-1120P11.1 did the opposite (Supplementary Figure S5).

FIGURE 5 | Ferroptosis score analysis (A) Ferroptosis scores between high and low-risk groups (B) The K–M survival curves of OC patients with high or low ferroptosis score (C) The K–M survival curves of four subgroups based on risk score and ferroptosis score (D) Heatmap of the associations among the expression levels of nine FRLs, risk scores, ferroptosis scores and clinical features.

Functional Pathways and Small Molecule Drugs of the Risk Groups

The enrichment analysis of functional pathways showed that 91 pathways showed significant differences between the two risk subgroups. The KEGG pathways were sorted according to the p value, and the top 10 were selected for display (Figure 6A). GSEA enrichment analysis showed that the risk groups were associated with tumor-related pathways, such as Hedgehog Signaling Pathway, Homologous Recombination, base Excision Repair, and immune-associated pathway, Antigen Processing and Presentation (Figure 6B). The differentially expressed genes (DEGs) were uploaded to CMAP database and compared with the potential drugs. The small molecule drugs were identified according to the p value, and the top ten were selected for display (Figure 6C).

FIGURE 6 | Differences in functional pathway between the risk groups (A) Heat map of KEGG Pathway GSVA enrichment scores (B) GSEA enrichment of KEGG pathway between high-risk and low-risk groups (C) Heatmap of small molecule drugs.

Immunity and m6A Regulators Analyses

The matrix score, immune score, estimated score, and relative infiltrate abundance of immune and stromal cells per sample were estimated based on five algorithms which were combined and shown in the heat map (Supplementary Figure S6). Wilcoxon was further used to compare the significance of each cell between the two groups. The results showed that there were statistically significant differences in interstitial fraction among 40 microenvironment cells (Figure 7A).

FIGURE 7 | Immune and m6A analysis (A) Heatmap of immune microenvironment revealed that a total of 40 immune cells and stromal score had significant differences between the two risk groups (B) Expression of seven immune checkpoint genes between high and low-risk group. The other six genes had significant differences between the two groups (p < 0.05), except HAVCR2 (p = 0.66) (C) TIDE scores in the low-risk group were lower than those in the high-risk group (D) The expression of 20 m6A regulators between high and low-risk groups. Data are shown as means ± S.D. ns: not significant, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

We compared the expression of seven checkpoint genes. The block diagram of the expression distribution of seven immune checkpoint genes (CD47, CD274, LAG3, HAVCR2, TNFRSF4, SIRPA, and VTCN1) between the two risk groups was shown in Figure 7B. The results showed that the other six genes emerged remarkable differences (p < 0.05), except HAVCR2 (p = 0.66). SIRPA had a higher expression, while the other five genes had a lower expression in the high-risk group. In Figure 7C, TIDE score of OC patients in the low-risk group was lower than that in the high-risk group, suggesting that OC patients with low RSs were more sensitive to ICB treatment.

We matched 20 m6A regulators and saw that the expression levels of FMR1, HNRNPC, METTL16, METTL3, and METL5 were significantly lower in the high-risk group. However, the differences of the remaining m6A regulators between the two groups did not reach a significant level (Figure 7D).

Sensitivity of Chemotherapy Drugs

Figure 8 showed the results of nine commonly used OC chemotherapy agents. Our data showed that the IC50 levels of Rucaparib in the low-risk group were significantly higher than that in the high-risk group. Inversely, the IC50 levels of Cisplatin, Paclitaxel, Veliparib, and Vinblastine in low-risk group were significantly lower than that in high-risk group, indicating that the OC patients in the low-risk group were more sensitive to these drugs. However, there were no significant differences in sensitivity between the two risk groups to Bleomycin, Docetaxel, Gemcitabine, and Vinorelbine.

FIGURE 8 | Sensitivity of chemotherapy drugs (A-I) Difference in the estimated IC50 levels of Bleomycin (A), Cisplatin (B), Docetaxel (C), Gemcitabine (D), Paclitaxel (E), Rucaparib (F), Veliparib (G), Vinblastine (H), and Vinorelbine (I). Data are shown as means ± S.D. ns: not significant, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

Network Construction

We selected 21 FRLs with significant prognosis to fabricate the FRG-FRL co-expression network (Figure 9A). A total of 105 FRL-miRNA-FRG relationship pairs were obtained, including nine FRLs, 80 miRNAs, and 12 FRGs (Figure 9B). The 12 FRGs included: CEBPG, CISD2, CYBB, GPT2, HMOX1, NCF2, NRAS, OTUB1, SLC2A1, SRC, TAZ, and ULK1. The networks according to the cis and trans interaction were displayed in Figures 9C,D. And, we found that p53 may regulate FRGs and FRLs (CTC-246B18.8-SRC/ULK1, RP11-872J21.3-OTUB1, RP5-1120P11.1-SLC2A1/CEBPG) through trans interactions. These findings may provide a basis for understanding the regulatory mechanisms of FRLs correlated with FRGs.

FIGURE 9 | Network analyses (A) FRG-FRL co-expression network. Ellipses: FRGs; rhombus: FRLs (B) ceRNA network. Ellipses: FRGs; rhombus: FRLs; hexagon: miRNAs (C) lncRNA-nearby mRNA interaction networks. Rhombus: FRLs; ellipses: nearby mRNAs (D) Trans interaction for lncRNA-TF-mRNA relationship pairs. Rhombus: FRLs; ellipses: mRNAs; octagon: TFs.

DISCUSSION

At present, platinum and paclitaxel are the main chemotherapy drugs for OC, but the prognosis of patients with advanced OC remains poor due to drug resistance (Mikuła-Pietrasik et al., 2019). Ferroptosis, as a newly discovered non-programmed cell death mode, is closely related to a variety of physiological and pathological processes (Li et al., 2020a). The iron dependence of tumor tissue is a potential therapeutic target for tumors (Shen et al., 2018; Liang et al., 2019). The use of iron chelating agents and the action of cytotoxic drugs such as TFR can inhibit the growth of cancer cells to a certain extent and improve their sensitivity to chemotherapy drugs by depriving iron or changing the metabolic ways of iron uptake and transfer in cancer cells (Moussa et al., 2019). Hence, the further studies on the signaling pathway and induction mechanism of ferroptosis are worth doing in clinical, and metal antitumor drugs may replace platinum chemotherapy drugs as a new generation of anticancer drugs to improve the prognosis.

Recently, continuous studies confirm that lncRNA is involved in the regulation of ferroptosis in tumor cells. It is unveiled that LINC00336 accelerated tumor formation and inhibited ferroptosis of lung cancer through the LSH/ELAVL1/LINC00336 axis (Wang et al., 2019b; Wang et al., 2020b). In hepatocellular carcinoma, the chemical stimulation produced by ferroptosis inducer Erastin can up-regulate the expression of lncRNA GABPB1-AS1 and prolong its half-life (Qi et al., 2019). In lung cancer, lncRNA NEAT1 was proved to be a regulator of ferroptosis sensitivity (Wu and Liu, 2021), and lncRNA MT1DP modulated erastin-induced ferroptosis via miR-365a-3p/NRF2 axis (Gai et al., 2020). LINC00618 was confirmed as accelerant of ferroptosis via reducing the expression of SLC7A11 in leukemia (Wang et al., 2021). Regrettably, researches of FRLs are still scarce.

As for OC, lncRNA-based prognostic signatures have been reported in previous investigations (Li et al., 2021; Pan and Bi, 2021). There are also lncRNA-based prognostic signatures for other cancers, such as breast cancer (Li et al., 2020b; Ma et al., 2020), glioma (Lin et al., 2020), bladder urothelial carcinoma (Sun et al., 2020), colorectal cancer (Sun et al., 2020; Zhang et al., 2021), esophageal cancer (Liu et al., 2021). To our knowledge, our study is the initial to identify and comprehensively analyze the ferroptosis-related lncRNAs signature for OC, providing a promising strategy for guiding individual therapy and improving prognosis prediction, and has important clinical implication. We also provide an effective quantitative method of FRL-related nomogram for predicting the OS in OC patients in combination with clinical factor. In addition, all the prognostic models reported are different, and no unified prognostic model has been applied in clinical practice. In this study, a new prognostic risk model was proposed, and its clinical applicability deserves further study.

Additionally, a total of 40 microenvironment cells, six immune checkpoint genes, and five m6A regulators (FMR1, HNRNPC, METTL16, METTL3, and METTL5) showed different levels between the two groups. Research reported that activated CD8⁺ T cells during immunotherapy could heighten the ferroptosis specific lipid peroxidation in tumor cells and, conversely, activation of ferroptosis are conducive the antitumor effect of immunotherapy (Wang et al., 2019c). It has also been found that immunotherapy could sensitize tumor cells to treatment by regulating ferroptosis of tumor cells (Lang et al., 2019). In addition, PD-1 antibodies could produce an immunogenic microenvironment and induce ferroptosis of tumor cells, and tumor antigens released after cell death in turn promote the immunogenicity of the microenvironment, suggesting that the cyclical synergistic effect of ferroptosis and immune regulation achieves effective antitumor activity in vivo (Zhang et al., 2019). A for the relationship between ferroptosis and m6A modification, the m6A reader YTHDC2 serving as ferroptosis inducer was proved to regulate SLC3A2 in lung adenocarcinoma (Ma et al., 2021). It was shown that miR-4443 inhibited FSP1-mediated ferroptosis via m6A writer METLL3 (Song et al., 2021). All these provide a theoretical basis for us to study the specific mechanism of ferroptosis in tumors. The occurrence and development mechanism of malignant tumors is very complicated. We hope to explore the molecular mechanism of iron disease (through m6A modification or immunity) through further research, so as to improve the efficacy of immunotherapy for malignant tumors.

The signature we identified and validated containing nine FRLs with prognostic value, which have never been reported in OC. Among the nine FRLs, USP30-AS1 was identified as autophagy-related lncRNA in bladder urothelial carcinoma (Sun et al., 2020), OC (Meng et al., 2020), and bladder cancer (Wan et al., 2021), immune-related lncRNA in cervical cancer (Chen et al., 2020), and epithelial–mesenchymal transition-related lncRNA in bladder cancer via bioinformatic analysis. In the literature mentioned, USP30-AS1 has been identified as a protective factor in cancers which is consistent with our study. SNHG10 has been confirmed as a malignant gene in hepatocellular carcinoma (Lan et al., 2019; Zhu et al., 2019), gastric cancer (Zhang Y et al., 2021; Yuan et al., 2021), glioma (Jin et al., 2020), osteosarcoma (He et al., 2020; Zhu et al., 2020), and acute myeloid leukemia (Xiao et al., 2021), while it was certified as a suppressor in non-small cell lung cancer (Zhang et al., 2020b) and our study. Hence, SNHG10 may play a dual role in malignancy.

We further explored the differences in respond to ICB therapy and chemotherapy drug between risk groups. Our result revealed that the patients of OC with lower RSs were more sensitive to ICB therapy, Cisplatin, Paclitaxel, Veliparib, and Vinblastine, while the patients were more sensitive to Rucaparib in the high-risk group. Findings of our study uncovered FRLs-based risk models identified potential biomarkers and therapeutic targets.

There are still some limitations. Firstly, there are only 30 OC patients in our cohort did not have OS, hence future studies need more time and samples for follow-up. Secondly, the number of OC samples in TCGA is very limited, so more independent data sets are needed to verify the risk model we identified. However, the tumor risk prediction model plays an important role in the treatment of tumor, and its value is considerable. Clinicians can carry out more accurate treatment according to the model, and also estimate the prognosis of patients according to the risk model, and develop more personalized treatment plans.

CONCLUSION

In conclusion, the risk model identified and validated based on nine ferroptosis-related lncRNAs is an independent prognostic factor. By comprehensive analysis, finding of our study revealed potential biomarkers and therapeutic targets for FRL-based risk models.

DATA AVAILABILITY STATEMENT

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.

ETHICS STATEMENT

The studies involving human participants were reviewed and approved by The Ethics Committee of Hangzhou First People’s Hospital. The patients/participants provided their written informed consent to participate in this study.

AUTHOR CONTRIBUTIONS

JT conceived, designed, and supervised the study. JZ performed data analysis and experiments and drafted the manuscript. YW and YZ collected the data. JG arranged the figures. All authors reviewed and approved the final manuscript.

FUNDING

This work was supported by the Medical Scientific Research Foundation of Zhejiang Province (grant number 2019KY495) and Natural Science Foundation of Zhejiang Province (grant number LGF18H160087).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2021.735871/full#supplementary-material

ABBREVIATIONS

FRG, Ferroptosis-related gene; FRL, Ferroptosis-related lncRNA; FS, Ferroptosis score; GO, Gene ontology; GRCh38, Genome Reference Consortium Human Build 38; GSEA, Gene Set Enrichment Analysis; HR, Hazard ratio; K-M, Kaplan–Meier; LASSO, Least absolute shrinkage and selection operator; m6A, N6-methyladenosine; OC, Ovarian cancer; OS, Overall survival; PCC, Pearson correlation coefficient; RS, Risk score; TCGA, The Cancer Genome Atlas.

REFERENCES

Aran, D., Hu, Z., and Butte, A. J. (2017). xCell: Digitally Portraying the Tissue Cellular Heterogeneity Landscape. Genome Biol. 18 (1), 220. doi:10.1186/s13059-017-1349-1

Bland, J. M., and Altman, D. G. (2004). The Logrank Test. Bmj 328 (7447), 1073. doi:10.1136/bmj.328.7447.1073

Braga, E. A., Fridman, M. V., Moscovtsev, A. A., Filippova, E. A., Dmitriev, A. A., and Kushlinskii, N. E. (2020). LncRNAs in Ovarian Cancer Progression, Metastasis, and Main Pathways: ceRNA and Alternative Mechanisms. Int. J. Mol. Sci. 21 (22). doi:10.3390/ijms21228855

Charoentong, P., Finotello, F., Angelova, M., Mayer, C., Efremova, M., Rieder, D., et al. (2017). Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cel Rep. 18 (1), 248–262. doi:10.1016/j.celrep.2016.12.019

Chen, P., Gao, Y., Ouyang, S., Wei, L., Zhou, M., You, H., et al. (2020). A Prognostic Model Based on Immune-Related Long Non-coding RNAs for Patients with Cervical Cancer. Front. Pharmacol. 11, 585255. doi:10.3389/fphar.2020.585255

Clark, N. R., Hu, K. S., Feldmann, A. S., Kou, Y., Chen, E. Y., Duan, Q., et al. (2014). The Characteristic Direction: a Geometrical Approach to Identify Differentially Expressed Genes. BMC bioinformatics 15, 79. doi:10.1186/1471-2105-15-79

Dweep, H., and Gretz, N. (2015). miRWalk2.0: a Comprehensive Atlas of microRNA-Target Interactions. Nat. Methods 12 (8), 697. doi:10.1038/nmeth.3485

Engebretsen, S., and Bohlin, J. (2019). Statistical Predictions with Glmnet. Clin. Epigenet 11 (1), 123. doi:10.1186/s13148-019-0730-1

Enright, A. J., John, B., Gaul, U., Tuschl, T., Sander, C., and Marks, D. S. (2003). MicroRNA Targets in Drosophila. Genome Biol. 5 (1), R1. doi:10.1186/gb-2003-5-1-r1

Ferreira, J. A. (2007). The Benjamini-Hochberg Method in the Case of Discrete Test Statistics. Int. J. Biostat 3 (1)–11. doi:10.2202/1557-4679.1065

Fu, J., Li, K., Zhang, W., Wan, C., Zhang, J., Jiang, P., et al. (2020). Large-scale Public Data Reuse to Model Immunotherapy Response and Resistance. Genome Med. 12 (1), 21. doi:10.1186/s13073-020-0721-z

Gai, C., Liu, C., Wu, X., Yu, M., Zheng, J., Zhang, W., et al. (2020). MT1DP Loaded by Folate-Modified Liposomes Sensitizes Erastin-Induced Ferroptosis via Regulating miR-365a-3p/NRF2 axis in Non-small Cell Lung Cancer Cells. Cell Death Dis 11 (9), 751. doi:10.1038/s41419-020-02939-3

Geeleher, P., Cox, N., and Huang, R. S. (2014). pRRophetic: an R Package for Prediction of Clinical Chemotherapeutic Response from Tumor Gene Expression Levels. PloS one 9 (9), e107468. doi:10.1371/journal.pone.0107468

Ghosh, D. (2019). Wavelet-based Benjamini-Hochberg Procedures for Multiple Testing under Dependence. Math. Biosci. Eng. 17 (1), 56–72. doi:10.3934/mbe.2020003

Goldman, M. J., Craft, B., Hastie, M., Repečka, K., McDade, F., Kamath, A., et al. (2020). Visualizing and Interpreting Cancer Genomics Data via the Xena Platform. Nat. Biotechnol. 38 (6), 675–678. doi:10.1038/s41587-020-0546-8

Hänzelmann, S., Castelo, R., and Guinney, J. (2013). GSVA: Gene Set Variation Analysis for Microarray and RNA-Seq Data. BMC bioinformatics 14, 7. doi:10.1186/1471-2105-14-7

Harrow, J., Frankish, A., Gonzalez, J. M., Tapanari, E., Diekhans, M., Kokocinski, F., et al. (2012). GENCODE: the Reference Human Genome Annotation for the ENCODE Project. Genome Res. 22 (9), 1760–1774. doi:10.1101/gr.135350.111

He, P., Xu, Y., and Wang, Z. (2020). LncRNA SNHG10 Increases the Methylation of miR-218 Gene to Promote Glucose Uptake and Cell Proliferation in Osteosarcoma. J. Orthop. Surg. Res. 15 (1), 353. doi:10.1186/s13018-020-01865-6

Huang, D. W., Sherman, B. T., and Lempicki, R. A. (2009). Bioinformatics Enrichment Tools: Paths toward the Comprehensive Functional Analysis of Large Gene Lists. Nucleic Acids Res. 37 (1), 1–13. doi:10.1093/nar/gkn923

Huang, D. W., Sherman, B. T., and Lempicki, R. A. (2009). Systematic and Integrative Analysis of Large Gene Lists Using DAVID Bioinformatics Resources. Nat. Protoc. 4 (1), 44–57. doi:10.1038/nprot.2008.211

Jia, Q., Wu, W., Wang, Y., Alexander, P. B., Sun, C., Gong, Z., et al. (2018). Local Mutational Diversity Drives Intratumoral Immune Heterogeneity in Non-small Cell Lung Cancer. Nat. Commun. 9 (1), 5361. doi:10.1038/s41467-018-07767-w

Jiang, N., Zhang, X., Gu, X., Li, X., and Shang, L. (2021). Progress in Understanding the Role of lncRNA in Programmed Cell Death. Cell Death Discov. 7 (1), 30. doi:10.1038/s41420-021-00407-1

Jin, L., Huang, S., Guan, C., and Chang, S. (2020). ETS1-activated SNHG10 Exerts Oncogenic Functions in Glioma via Targeting miR-532-3p/FBXL19 axis. Cancer Cel Int 20 (1), 589. doi:10.1186/s12935-020-01649-2

Lamb, J., Crawford, E. D., Peck, D., Modell, J. W., Blat, I. C., Wrobel, M. J., et al. (2006). The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease. Science 313 (5795), 1929–1935. doi:10.1126/science.1132939

Lamb, J. (2007). The Connectivity Map: a New Tool for Biomedical Research. Nat. Rev. Cancer 7 (1), 54–60. doi:10.1038/nrc2044

Lan, T., Yuan, K., Yan, X., Xu, L., Liao, H., Hao, X., et al. (2019). LncRNA SNHG10 Facilitates Hepatocarcinogenesis and Metastasis by Modulating its Homolog SCARNA13 via a Positive Feedback Loop. Cancer Res. 79 (13), 3220–3234. doi:10.1158/0008-5472.can-18-4044

Lang, X., Green, M. D., Wang, W., Yu, J., Choi, J. E., Jiang, L., et al. (2019). Radiotherapy and Immunotherapy Promote Tumoral Lipid Oxidation and Ferroptosis via Synergistic Repression of SLC7A11. Cancer Discov. 9 (12), 1673–1685. doi:10.1158/2159-8290.cd-19-0338

Lattuada, D., Uberti, F., Colciaghi, B., Morsanuto, V., Maldi, E., Squarzanti, D. F., et al. (2015). Fimbrial Cells Exposure to Catalytic Iron Mimics Carcinogenic Changes. Int. J. Gynecol. Cancer 25 (3), 389–398. doi:10.1097/igc.0000000000000379

Lheureux, S., Gourley, C., Vergote, I., and Oza, A. M. (2019). Epithelial Ovarian Cancer. The Lancet 393 (10177), 1240–1253. doi:10.1016/s0140-6736(18)32552-2

Li, J., Cao, F., Yin, H.-l., Huang, Z.-j., Lin, Z.-t., Mao, N., et al. (2020). Ferroptosis: Past, Present and Future. Cel Death Dis 11 (2), 88. doi:10.1038/s41419-020-2298-2

Li, X., Li, Y., Yu, X., and Jin, F. (2020). Identification and Validation of Stemness-Related lncRNA Prognostic Signature for Breast Cancer. J. Transl Med. 18 (1), 331. doi:10.1186/s12967-020-02497-4

Li, Y., Huo, F. F., Wen, Y. Y., and Jiang, M. (2021). Screening and Identification of an Immune-Associated lncRNA Prognostic Signature in Ovarian Carcinoma: Evidence from Bioinformatic Analysis. Biomed. Res. Int. 20212021, 6680036. doi:10.1155/2021/6680036

Liang, C., Zhang, X., Yang, M., and Dong, X. (2019). Recent Progress in Ferroptosis Inducers for Cancer Therapy. Adv. Mater. 31 (51), e1904197. doi:10.1002/adma.201904197

Lin, C.-C., and Chi, J.-T. (2020). Ferroptosis of Epithelial Ovarian Cancer: Genetic Determinants and Therapeutic Potential. Oncotarget 11 (39), 3562–3570. doi:10.18632/oncotarget.27749

Lin, W., Huang, Z., Xu, Y., Chen, X., Chen, T., Ye, Y., et al. (2020). A Three-lncRNA Signature Predicts Clinical Outcomes in Low-Grade Glioma Patients after Radiotherapy. Aging 12 (10), 9188–9204. doi:10.18632/aging.103189

Liu, X., Shi, X., Guo, W., Ke, Y., Li, Y., Pan, S., et al. (2021). A Promising Esophageal Cancer Prognostic Signature of Ferroptosis-Related LncRNA to Predict Immune Scenery and Immunotherapy Response. Ijgm Vol. 14, 5845–5862. doi:10.2147/ijgm.s327555

Ma, L., Zhang, X., Yu, K., Xu, X., Chen, T., Shi, Y., et al. (2021). Targeting SLC3A2 Subunit of System XC− Is Essential for m6A Reader YTHDC2 to Be an Endogenous Ferroptosis Inducer in Lung Adenocarcinoma. Free Radic. Biol. Med. 168, 25–43. doi:10.1016/j.freeradbiomed.2021.03.023

Ma, W., Zhao, F., Yu, X., Guan, S., Suo, H., Tao, Z., et al. (2020). Immune-related lncRNAs as Predictors of Survival in Breast Cancer: a Prognostic Signature. J. Transl Med. 18 (1), 442. doi:10.1186/s12967-020-02522-6

Meng, C., Zhou, J. Q., and Liao, Y. S. (2020). Autophagy-related Long Non-coding RNA Signature for Ovarian Cancer. J. Int. Med. Res. 48 (11), 300060520970761. doi:10.1177/0300060520970761

Mikuła-Pietrasik, J., Witucka, A., Pakuła, M., Uruski, P., Begier-Krasińska, B., Niklas, A., et al. (2019). Comprehensive Review on How Platinum- and Taxane-Based Chemotherapy of Ovarian Cancer Affects Biology of normal Cells. Cell Mol Life Sci 76 (4), 681–697. doi:10.1007/s00018-018-2954-1

Mou, Y., Wang, J., Wu, J., He, D., Zhang, C., Duan, C., et al. (2019). Ferroptosis, a New Form of Cell Death: Opportunities and Challenges in Cancer. J. Hematol. Oncol. 12 (1), 34. doi:10.1186/s13045-019-0720-y

Moussa, R. S., Park, K. C., Kovacevic, Z., and Richardson, D. R. (2019). Ironing Out the Role of the Cyclin-dependent Kinase Inhibitor, P21 in Cancer: Novel Iron Chelating Agents to Target P21 Expression and Activity. Free Radic. Biol. Med. 133, 276–294. doi:10.1016/j.freeradbiomed.2018.03.027

Narod, S. (2016). Can Advanced-Stage Ovarian Cancer Be Cured?Nat. Rev. Clin. Oncol. 13 (4), 255–261. doi:10.1038/nrclinonc.2015.224

Newman, A. M., Liu, C. L., Green, M. R., Gentles, A. J., Feng, W., Xu, Y., et al. (2015). Robust Enumeration of Cell Subsets from Tissue Expression Profiles. Nat. Methods 12 (5), 453–457. doi:10.1038/nmeth.3337

Pan, X., and Bi, F. (2021). A Potential Immune-Related Long Non-coding RNA Prognostic Signature for Ovarian Cancer. Front. Genet. 12, 694009. doi:10.3389/fgene.2021.694009

Qi, W., Li, Z., Xia, L., Dai, J., Zhang, Q., Wu, C., et al. (2019). LncRNA GABPB1-AS1 and GABPB1 Regulate Oxidative Stress during Erastin-Induced Ferroptosis in HepG2 Hepatocellular Carcinoma Cells. Sci. Rep. 9 (1), 16185. doi:10.1038/s41598-019-52837-8

Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., et al. (2015). Limma powers Differential Expression Analyses for RNA-Sequencing and Microarray Studies. Nucleic Acids Res. 43 (7), e47. doi:10.1093/nar/gkv007

Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., et al. (2003). Cytoscape: a Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Res. 13 (11), 2498–2504. doi:10.1101/gr.1239303

Shen, Z., Song, J., Yung, B. C., Zhou, Z., Wu, A., and Chen, X. (2018). Emerging Strategies of Cancer Therapy Based on Ferroptosis. Adv. Mater. 30 (12), e1704007. doi:10.1002/adma.201704007

Shi, J., Jiang, D., Yang, S., Zhang, X., Wang, J., Liu, Y., et al. (2020). LPAR1, Correlated with Immune Infiltrates, Is a Potential Prognostic Biomarker in Prostate Cancer. Front. Oncol. 10, 846. doi:10.3389/fonc.2020.00846

Shu, X., Shu, S., and Cheng, H. (2019). A Novel lncRNA-Mediated Trans-regulatory Mechanism in the Development of Cleft Palate in Mouse. Mol. Genet. Genomic Med. 7 (2), e00522. doi:10.1002/mgg3.522

Siegel, R. L., Miller, K. D., and Jemal, A. (2020). Cancer Statistics, 2020. CA A. Cancer J. Clin. 70 (1), 7–30. doi:10.3322/caac.21590

Song, Z., Jia, G., Ma, P., and Cang, S. (2021). Exosomal miR-4443 Promotes Cisplatin Resistance in Non-small Cell Lung Carcinoma by Regulating FSP1 m6A Modification-Mediated Ferroptosis. Life Sci. 276, 119399. doi:10.1016/j.lfs.2021.119399

Sun, Z., Jing, C., Xiao, C., and Li, T. (2020). An Autophagy-Related Long Non-coding RNA Prognostic Signature Accurately Predicts Survival Outcomes in Bladder Urothelial Carcinoma Patients. Aging 12 (15), 15624–15637. doi:10.18632/aging.103718

Tibshirani, R. (1997). The Lasso Method for Variable Selection in the Cox Model. Statist. Med. 16 (4), 385–395. doi:10.1002/(sici)1097-0258(19970228)16:4<385:aid-sim380>3.0.co;2-3

Wan, J., Guo, C., Fang, H., Xu, Z., Hu, Y., and Luo, Y. (2021). Autophagy-Related Long Non-coding RNA Is a Prognostic Indicator for Bladder Cancer. Front. Oncol. 11, 647236. doi:10.3389/fonc.2021.647236

Wang, J.-y., Lu, A.-q., and Chen, L.-j. (2019). LncRNAs in Ovarian Cancer. Clinica Chim. Acta 490, 17–27. doi:10.1016/j.cca.2018.12.013

Wang, M., Mao, C., Ouyang, L., Liu, Y., Lai, W., Liu, N., et al. (2020). Correction to: Long Noncoding RNA LINC00336 Inhibits Ferroptosis in Lung Cancer by Functioning as a Competing Endogenous RNA. Cell Death Differ 27 (4), 1447. doi:10.1038/s41418-019-0394-6

Wang, M., Mao, C., Ouyang, L., Liu, Y., Lai, W., Liu, N., et al. (2019). Long Noncoding RNA LINC00336 Inhibits Ferroptosis in Lung Cancer by Functioning as a Competing Endogenous RNA. Cel Death Differ 26 (11), 2329–2343. doi:10.1038/s41418-019-0304-y

Wang, P., Wang, Y., Hang, B., Zou, X., and Mao, J.-H. (2016). A Novel Gene Expression-Based Prognostic Scoring System to Predict Survival in Gastric Cancer. Oncotarget 7 (34), 55343–55351. doi:10.18632/oncotarget.10533

Wang, W., Green, M., Choi, J. E., Gijón, M., Kennedy, P. D., Johnson, J. K., et al. (2019). CD8+ T Cells Regulate Tumour Ferroptosis during Cancer Immunotherapy. Nature 569 (7755), 270–274. doi:10.1038/s41586-019-1170-y

Wang, Y., Wei, Z., Pan, K., Li, J., and Chen, Q. (2020). The Function and Mechanism of Ferroptosis in Cancer. Apoptosis 25 (11-12), 786–798. doi:10.1007/s10495-020-01638-w

Wang, Z., Chen, X., Liu, N., Shi, Y., Liu, Y., Ouyang, L., et al. (2021). A Nuclear Long Non-coding RNA LINC00618 Accelerates Ferroptosis in a Manner Dependent upon Apoptosis. Mol. Ther. 29 (1), 263–274. doi:10.1016/j.ymthe.2020.09.024

Wu, H., and Liu, A. (2021). Long Non-coding RNA NEAT1 Regulates Ferroptosis Sensitivity in Non-small-cell Lung Cancer. J. Int. Med. Res. 49 (3), 300060521996183. doi:10.1177/0300060521996183

Wu, Y., Zhang, S., Gong, X., Tam, S., Xiao, D., Liu, S., et al. (2020). The Epigenetic Regulators and Metabolic Changes in Ferroptosis-Associated Cancer Progression. Mol. Cancer 19 (1), 39. doi:10.1186/s12943-020-01157-x

Xiao, S., Zha, Y., and Zhu, H. (2021). miR-621 May Suppress Cell Proliferation via Targeting lncRNA SNHG10 in Acute Myeloid Leukemia. Cmar Vol. 13, 2117–2123. doi:10.2147/cmar.s269528

Xie, Y., Hou, W., Song, X., Yu, Y., Huang, J., Sun, X., et al. (2016). Ferroptosis: Process and Function. Cel Death Differ 23 (3), 369–379. doi:10.1038/cdd.2015.158

Xu, T., Ding, W., Ji, X., Ao, X., Liu, Y., Yu, W., et al. (2019). Molecular Mechanisms of Ferroptosis and its Role in Cancer Therapy. J. Cel Mol Med 23 (8), 4900–4912. doi:10.1111/jcmm.14511

Yang, W., Soares, J., Greninger, P., Edelman, E. J., Lightfoot, H., Forbes, S., et al. (2013). Genomics of Drug Sensitivity in Cancer (GDSC): a Resource for Therapeutic Biomarker Discovery in Cancer Cells. Nucleic Acids Res. 41 (Database issue), D955–D961. doi:10.1093/nar/gks1111

Yang, W. S., and Stockwell, B. R. (2016). Ferroptosis: Death by Lipid Peroxidation. Trends Cell Biology 26 (3), 165–176. doi:10.1016/j.tcb.2015.10.014

Yang, W. S., and Stockwell, B. R. (2008). Synthetic Lethal Screening Identifies Compounds Activating Iron-dependent, Nonapoptotic Cell Death in Oncogenic-RAS-Harboring Cancer Cells. Chem. Biol. 15 (3), 234–245. doi:10.1016/j.chembiol.2008.02.010

Yoshihara, K., Shahmoradgoli, M., Martínez, E., Vegesna, R., Kim, H., Torres-Garcia, W., et al. (2013). Inferring Tumour Purity and Stromal and Immune Cell Admixture from Expression Data. Nat. Commun. 4, 2612. doi:10.1038/ncomms3612

Yuan, C.-L., Jiang, X.-M., Yi, Y., E, N.-D., Luo, X., Zou, N., et al. (2019). Identification of Differentially Expressed lncRNAs and mRNAs in Luminal-B Breast Cancer by RNA-Sequencing. BMC cancer 19 (1), 1171. doi:10.1186/s12885-019-6395-5

Yuan, X., Yang, T., Xu, Y., Ou, S., Shi, P., Cao, M., et al. (2021). SNHG10 Promotes Cell Proliferation and Migration in Gastric Cancer by Targeting miR-495-3p/CTNNB1 Axis. Dig. Dis. Sci. 66 (8), 2627–2636. doi:10.1007/s10620-020-06576-w

Zhang, F., Li, F., Lu, G.-H., Nie, W., Zhang, L., Lv, Y., et al. (2019). Engineering Magnetosomes for Ferroptosis/Immunomodulation Synergism in Cancer. ACS nano 13 (5), 5662–5673. doi:10.1021/acsnano.9b00892

Zhang, W., Fang, D., Li, S., Bao, X., Jiang, L., and Sun, X. (2021). Construction and Validation of a Novel Ferroptosis-Related lncRNA Signature to Predict Prognosis in Colorectal Cancer Patients. Front. Genet. 12, 709329. doi:10.3389/fgene.2021.709329

Zhang, Y., Guo, H., and Zhang, H. (2021). SNHG10/DDX54/PBX3 Feedback Loop Contributes to Gastric Cancer Cell Growth. Dig. Dis. Sci. 66 (6), 1875–1884. doi:10.1007/s10620-020-06488-9

Zhang, Z., Nong, L., Chen, M.-L., Gu, X.-L., Zhao, W.-W., Liu, M.-H., et al. (2020). Long Noncoding RNA SNHG10 Sponges miR-543 to Upregulate Tumor Suppressive SIRT1 in Nonsmall Cell Lung Cancer. Cancer Biother. Radiopharm. 35 (10), 771–775. doi:10.1089/cbr.2019.3334

Zhou, N., and Bao, J. (2020). FerrDb: a Manually Curated Resource for Regulators and Markers of Ferroptosis and Ferroptosis-Disease Associations. Database (Oxford) 2020, 2020. doi:10.1093/database/baaa021

Zhu, Q., Yang, H., Cheng, P., and Han, Q. (2019). Bioinformatic Analysis of the Prognostic Value of the lncRNAs Encoding snoRNAs in Hepatocellular Carcinoma. BioFactors 45 (2), 244–252. doi:10.1002/biof.1478

Zhu, S., Liu, Y., Wang, X., Wang, J., and Xi, G. (2020). lncRNA SNHG10 Promotes the Proliferation and Invasion of Osteosarcoma via Wnt/β-Catenin Signaling. Mol. Ther. - Nucleic Acids 22, 957–970. doi:10.1016/j.omtn.2020.10.010

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Zheng, Guo, Wang, Zheng, Zhang and Tong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 18 January 2022
doi: 10.3389/fmolb.2021.763697

Identification of Five Ferroptosis-Related LncRNAs as Novel Prognosis and Diagnosis Signatures for Renal Cancer

Xiangjun Shu1,²†, Zaiqi Zhang²†, Zhi-Yong Yao² and Xiao-Liang Xing1,²*

¹School of Public Health and Laboratory Medicine, Hunan University of Medicine, Huaihua, China

²The First Affiliated Hospital, Hunan University of Medicine, Huaihua, China

Edited by:
William C. Cho, QEH, Hong Kong SAR, China

Reviewed by:
Tabish H. Khan, University of Virginia, United States
Siva Koganti, Stony Brook Medicine, United States

* Correspondence: Xiao-Liang Xing, xiaoliangxinghnm@126.com

†These authors have contributed equally to this work

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 24 August 2021
Accepted: 02 December 2021
Published: 18 January 2022

Citation: Shu X, Zhang Z, Yao Z-Y and Xing X-L (2022) Identification of Five Ferroptosis-Related LncRNAs as Novel Prognosis and Diagnosis Signatures for Renal Cancer. Front. Mol. Biosci. 8:763697. doi: 10.3389/fmolb.2021.763697

Background: Ferroptosis is a novel regulated cell death that is characterized by iron-dependent oxidative damage. Renal cancer is the second most common cancer of the urinary system, which is highly correlated with iron metabolism. The aim of our present study was to identify suitable ferroptosis-related prognosis signatures for renal cancer.

Methods: We downloaded the RNA-seq count data of renal cancer from The Cancer Genome Atlas database and used the DESeq2, Survival, and Cox regression analyses to screen the prognosis signatures.

Results: We identified 5 ferroptosis-related differentially expressed lncRNAs (FR-DELs) (DOCK8-AS1, SNHG17, RUSC1-AS1, LINC02609, and LUCAT1) to be independently correlated with the overall survival (OS) of patients with renal cancer. The risk assessment model and diagnosis model constructed by those 5 FR-DELs could well predict the outcome and the diagnosis of renal cancer.

Conclusion: Our present study not only suggested those 5 FR-DELs could be used as prognosis and diagnosis signatures for renal cancer but also provided strategies for other cancers in the screening of ferroptosis-related biomarkers.

Keywords: renal cancer, ferroptosis, differentially expressed lncRNAs, prognosis, diagnosis

INTRODUCTION

Renal cell cancer (kidney cancer) is the second most common cancer of the urinary system. In 2020, there were almost 430,000 new cases (accounting for 2.2% of all new cases) and 180,000 deaths due to renal cancer (accounting for 1.8% of all death cases) (Sung et al., 2021). Renal cell carcinoma (RCC) is a heterogeneous group of cancers originating from the renal epithelium, which comprises the major subtype clear cell RCC (KIRC) and papillary RCC (KIRP). The most common pathologic type is KIRC, which accounts for about 70%–80% of renal cell cancer (Lipworth et al., 2016; Low et al., 2016). A previous study indicated that approximately 40% of patients with advanced KIRC would develop distant metastasis, and the 5-year survival rate is about 10% (Rao et al., 2018). KIRP comprises 15%–20% of renal cell cancer, which stems from the proximal nephron, the same origin with clear cell type (Malouf et al., 2016). Surgical treatment is also the main option for renal cancer. However, conventional therapeutic methods are the main treatment manners even though the results are not very satisfactory for those patients with metastases (Gill et al., 2017).

Iron is an essential element for the human body, and its metabolic disorder is involved in the occurrence and development of many diseases (Huang, 2003; Manz et al., 2016). Previous studies indicated that iron metabolism is no doubt the key role in the process of ferroptosis. Dixon et al. found that iron chelators could block ferroptotic cell death in vitro and in vivo (Dixon et al., 2012). They also found that exogenous iron administration could increase the induction of iron drop in cells (Dixon et al., 2012). Li et al. found that excessive heme and non-heme iron could directly induce ferroptosis (Yang et al., 2020; Zou et al., 2020). Ferroptosis is a newly coined non-apoptotic programmed cell death characterized by iron-dependent lipid peroxidation (Dixon et al., 2012; Chen et al., 2020). It is induced by the accumulation of ferric ions (Hirschhorn and Stockwell, 2019). Several signaling pathways or small molecules, such as MAPK, P53, Nrf2, erastin, and cisplatin, have been demonstrated to be involved in the regulation of ferroptosis (Dixon et al., 2012; Yu et al., 2015; Small et al., 2017; Xie et al., 2017; Guo et al., 2018). Dysregulated ferroptosis is associated with various diseases, including cancers, cardiovascular, neurodegenerative, and hepatic diseases (Han et al., 2020). The kidney is an organ related to iron metabolism, and several studies have shown that renal cancer is highly correlated with iron metabolism (Moon et al., 2019; Zhang et al., 2020).

Long noncoding RNAs (LncRNAs) are over 200 nucleotides in length, which accounts for nearly 70% of human transcriptome (Iyer et al., 2015). LncRNAs are increasingly recognized as the crucial mediators in the regulation of ferroptosis in various manners (Mou et al., 2019). For example, Wang et al. found that LINC00618 could inhibit ferroptosis by attenuating the expressions of lymphoid-specific helicase and solute carrier family 7 member 11 (Wang et al., 2021). Wang et al. found that overexpression of LINC00336 could inhibit ferroptosis by directly decreasing intracellular concentrations of iron and Fe²⁺, lipid reactive oxygen species (ROS), and mitochondrial superoxide (Wang M. et al., 2019). Ni et al. found that lncRNA-ZFAS1 could promote cardiomyocyte ferroptosis by downregulating Cyclin D2 through miR-150-5p (Ni et al., 2021). Accumulative studies have suggested that lncRNAs play pivotal roles in the progression of cancers and could be used as biomarkers to predict the outcome of cancers (Xiao et al., 2017; Zhai et al., 2017; Dong et al., 2019; You et al., 2019; Li et al., 2020). Therefore, the aim of our present study was to screen suitable ferroptosis-related lncRNAs as biomarkers to predict the outcome for renal cancer. The screened suitable biomarkers could be not only prognostic biomarkers but also potential therapeutic targets for renal cancer.

METHODS

Data Acquisition and Processing

RNA-seq count data of renal cancer (KIRC (72 controls vs. 530 cancers) and KIRP (35 controls vs. 291 cancers)) and the corresponding clinical information were downloaded from an open database The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/). Renal cancer samples were randomly divided into the training group and validation group (Table 1) (Cai et al., 2021; Zhang et al., 2021). DESeq2 in R (3.6.2) was used to screen the differentially expressed genes (DEGs) with the specific criterion baseMean ≥ 100, |LogFoldChange| ≥ 1.0, and adj. p < 0.05. The lncRNAs and ferroptosis-related genes were downloaded from GENCODE (https://www.gencodegenes.org/) and FerrDb (http://www.zhounan.org/ferrdb), respectively. Spearman’s correlation analyses were used to screen the ferroptosis-related lncRNAs.

TABLE 1 | Characteristics of renal cancer patients.

Overall Survival Analyses and the Specific Model Construction

We utilized the survival packages in R (3.6.2) to carry out the Kaplan–Meier (K-M) survival analyses and univariate Cox regression analyses for ferroptosis-related differentially expressed lncRNAs (FR-DELs) to determine their overall survival (OS) relationship with renal cancer patients. Least absolute shrinkage and selection operator (LASSO) analyses were introduced to avoid overfitting. The FR-DELs filtered by the K-M analyses and univariate Cox regression analyses could be used as candidate biomarkers. Multivariate Cox regression analyses were performed for the candidate FR-DELs to eliminate the FR-DELs that may not be independent prognosis factors.

The specific prognosis model was constructed after multivariate Cox regression analyses, as follows (Xing et al., 2021): Risk value = βFR-DEL1 * ExpFR-DEL1 + βFR-DEL2 * ExpFR-DEL2 … + βFR-DELn * ExpFR-DELn. The “Exp” means the expression of FR-DELs, and the “β” is the regression coefficient obtained from the multivariate Cox regression analyses.

The diagnostic model was constructed using the candidate signatures, as follows (Tang et al., 2020): Logit score = 0.675 + −0.044 * Exp(DOCK8-AS1) + 0.095 * Exp(SNHG17) + 0.120 * Exp(RUSC1-AS1) + 0.145 * Exp(LINC02609) + 0.109 * Exp(LUCAT1).

Protein Interaction Network and Functional Enrichment Analyses

We utilized the Search Tool for the Retrieval of Interacting Genes 11 (STRING 11) to assess the interactions with the default parameters (https://string-db.org/), and we utilized Cytoscape 3.7.2 to visualize the interactions. DAVID 6.8 was utilized to perform the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses (https://david.ncifcrf.gov/).

Principal Component Analyses and Statistical Analyses

We utilized the principal component analysis (PCA) to reduce the dimension and visualize the renal cancer patients with different risk values. A repeated-measures ANOVA followed by unpaired two-tailed Student’s t-test was used as indicated. All results are expressed as mean ± SEM.

RESULTS

Identification of Ferroptosis-Related Differentially Expressed LncRNAs

There were 3,519 DEGs (Supplementary Figure S1A), including 2,382 upregulated DEGs and 1,137 downregulated DEGs, that had been screened by DESeq2. To determine which of these 3,519 DEGs are FR-DEGs, all of these 3,519 DEGs were overlapped with the recognized IR-DEGs. By overlapping analysis, we obtained 61 FR-DEGs (39 upregulated FR-DEGs and 22 downregulated FR-DEGs) (Supplementary Figure S1B). To determine which of these 3,519 DEGs are DELs, all of these 3,519 DEGs were overlapped with the recognized DELs. By overlapping analysis, we obtained 171 DELs (141 upregulated DELs and 35 downregulated DELs) (Supplementary Figure S1C).

To obtain FR-DELs, we introduced Spearman’s correlation analyses for those 61 FR-DEGs and 176 DELs. We found that 631 pairs of FR-DEGs-DELs had correlation R values greater than 0.5, involving 129 DELs and 42 FR-DEGs (Supplementary Table S1). We named those 129 DELs as 129 FR-DELs. We then performed protein interactions analyses for those 42 FR-DEGs, and the interactions are displayed in Supplementary Figure S2. The expressions of those 129 FR-DELs and 42 FR-DEGs are displayed in Supplementary Figure S3A and Supplementary Figure S3B, respectively.

Development of Prognosis Signatures

To obtain suitable prognosis signatures for renal cancer in the training group, we firstly performed the univariate Cox regression analyses followed with LASSO analyses and identified 14 FR-DELs associated with the OS of renal cancer (Figure 1A, Supplementary Figures S4A,B). Similarly, we performed the K-M survival analyses followed by LASSO analyses and identified 17 FR-DELs associated with the OS of renal cancer (Figure 1B, Supplementary Figures S4C,D). By overlapping analyses, we obtained 7 FR-DEGs (DOCK8-AS1, SNHG17, DUXAP8, RUSC1-AS1, LINC02609, FOXD2-AS1, and LUCAT1) as candidate prognosis signatures. Then, we performed multivariate Cox regression analyses for those 7 FR-DELs and identified 5 FR-DELs (Figure 1C) independently associated with the OS of renal cancer. The expression of DOCK8-AS1 (Figure 1D) was significantly decreased, while the expressions of SNHG17, RUSC1-AS1, LINC02609, and LUCAT1 (Figure 1D) were increased significantly in patients with renal cancer. The patients with renal cancer with lower expression of DOCK8-AS1 displayed worse OS, and the patients with renal cancer with higher expressions of SNHG17, RUSC1-AS1, LINC02609, and LUCAT1 displayed worse OS (Figures 1E–I).

FIGURE 1 | Identification of FR-DELs as prognosis signatures. (A, B), Univariate Cox regression and K-M analyses illustrate that 14 FR-DELs (A) and 17 FR-DELs (B) were associated with OS in the training group, respectively. (C) Multivariate Cox regression illustrates that 5 FR-DELs were independently associated with OS in the training group. (D) The expression of those 5 FR-DELs in normal group and cancer group. (E–I) Kaplan–Meier curves of those 5 FR-DELs (DOCK8-AS1 (E), SNHG17 (F), RUSC1-AS1 (G), LINC02609 (H), and LUCAT1(I)) in renal cancers. *p < 0.05, **p < 0.01, ***p < 0.001. FR-DELs, ferroptosis-related differentially expressed lncRNAs; K-M, Kaplan–Meier; OS, overall survival.

Subsequently, we utilized those 5 FR-DELs (DOCK8-AS1, SNHG17, RUSC1-AS1, LINC02609, and LUCAT1) to construct a risk assessment model in the training group. The risk values and survival status of each renal cancer patient are displayed in Figure 2A. We used Youden’s index as optima cutoff value to regroup the renal patients into the high-risk group and low-risk group (Supplementary Figure S5). The expressions of SNHG17, RUSC1-AS1, LINC02609, and LUCAT1 were significantly higher, while the expression of DOCK8-AS1 was lower in the high-risk group (Figure 2B). Patients with renal cancer with high-risk values displayed worse OS (Figure 2C). We also plot the receiver operating characteristic (ROC) curve and found that the area under the curve (AUC) of the risk model was comparable with that of the pathologic stage was slightly higher than that of the pathologic TNM (Figure 2D).

FIGURE 2 | Development of prognosis signatures based on FR-DEMs and FR-DEGs. (A–D) Distribution of the risk values (up) and survival status (down) (A), expression (B), Kaplan–Meier curves (C), and ROC curve (D) of the risk assessment model in the training group. (E–H) Distribution of the risk values (up) and survival status (down) (E), expression (F), Kaplan–Meier curves (G), and ROC curve (H) of the risk assessment model in the validation group. (I–L) Distribution of the risk values (up) and survival status (down) (I), expression (J), Kaplan–Meier curves (K), and ROC curve (L) of the risk assessment model in the entire group. *p < 0.05, **p < 0.01, ***p < 0.001. FR-DEMs, ferroptosis-related differentially expressed miRNAs; FR-DEGs, ferroptosis-related differentially expressed genes; ROC, receiver operating characteristic.

To determine whether those 5 FR-DELs (DOCK8-AS1, SNHG17, RUSC1-AS1, LINC02609, and LUCAT1) could be prognosis signatures for renal cancer, we used the validation group data and entire group data for verification. The risk model constructed by using those 5 FR-DELs (DOCK8-AS1, SNHG17, RUSC1-AS1, LINC02609, and LUCAT1) yielded similar results in the validation and entire group as in the training group (Figures 2E–L).

Independent Prognosis Factors of Overall Survival

To determine whether this risk model could be independently used for prognosis diagnosis, K-M and multivariate Cox regression analyses for clinicopathological features and the risk model were performed. In the training group, we found that the pathologic TNM, pathologic stage, and risk model were significantly correlated with the OS of renal cancer as measured by K-M analyses (Figure 3A). The pathologic M and the risk model were still correlated with the OS of renal cancers as measured by multivariate Cox regression analyses (Figure 3D). To determine whether our risk assessment was feasible, we used validation group data and entire group data for verification. In the validation group and entire group, we also found that the pathologic TNM, pathologic stage, and risk model were also significantly correlated with the OS of renal cancer (Figures 3B,C). Meanwhile, the pathologic NM and the risk model were independently correlated with the OS of renal cancers in the multivariate Cox regression analyses (Figures 3E,F).

FIGURE 3 | Independent prognosis factors and correlation analyses. (A–C) Univariate Cox regression of prognosis factors in training group (A), validation group (B), and entire group (C). (D–F) Multivariate Cox regression of prognosis factors in training group (D), validation group (E), and entire group (F). (G–J) Correlation of risk values (left) and those 5 FR-DELs (right) with the pathologic T (G), pathologic N (H), pathologic N (I), and pathologic stage (J). *p < 0.05, **p < 0.01, ***p < 0.001. FR-DELs, ferroptosis-related differentially expressed lncRNAs.

By retrospective examination of the ROC curves in the entire group, we found that although both the pathologic NM and the risk model were independently associated with OS of renal cancer, the AUC values of the pathologic NM were lower than those of the risk model (Figure 2L). We then performed ROC curves analyses in the entire group at 1, 3, 5, and 10 years. All AUC values were over 0.7 (Supplementary Figure S6).

Subsequently, we analyzed the risk values in different clinicopathological feature groups. There were significant differences for risk values in different pathologic TNM and pathologic stage (Figures 3G–J, left). We also analyzed the expressions of those 5 FR-DELs (DOCK8-AS1, SNHG17, RUSC1-AS1, LINC02609, and LUCAT1) in different clinicopathological feature groups, and the results are displayed in Figures 3G–J, right.

Principal Component Analysis and Enrichment Analyses

To know whether those DEGs could be used to distinguish the high-risk patients from the low-risk patients, we performed PCAs by using those 61 FR-DEGs (filtered by DEGs crossed with ferroptosis-related genes), 176 FR-DELs (filtered by DEGs crossed with lncRNAs), 42 FR-DEGs (filtered by Spearman’s correlation analyses), 129 FR-DELs (filtered by Spearman’s correlation analyses), 7 FR-DELs (filtered by univariate Cox regression analyses and K-M survival analyses), and 5 FR-DELs (filtered by multivariate Cox regression analyses). As we can see from Figures 4A–E, the renal cancer patients with high-risk values are basically distinguishable from those with low-risk patients by using those DELs. Particularly, we found that the distribution of renal cancer patients with low-risk values was relatively concentrated, which could be well distinguished from renal cancer patients with high-risk values by using those 5 FR-DELs (DOCK8-AS1, SNHG17, RUSC1-AS1, LINC02609, and LUCAT1) (Figure 4F).

FIGURE 4 | PCA for renal cancer patients with different risk values. PCA plots display the distribution of patients with renal cancer with high-risk value and low-risk value based on 61 FR-DEGs (A), 176 DELs (B), 42 FR-DEGs (C), 129 FR-DELs (D), 7 FR-DELs (E), and 5 FR-DELs (F). Blue means low risk. Read means high risk. PCA, principal component analysis; FR-DEGs, ferroptosis-related differentially expressed genes; DELs, differentially expressed lncRNAs; FR-DELs, ferroptosis-related differentially expressed lncRNAs.

We then performed differential expression analyses between high-risk renal cancer patients and low-risk renal cancer patients and found that 460 DEGs were upregulated and 381 DEGs were downregulated (Supplementary Figure S7). We performed the protein interaction and functional enrichment analyses for those 841 DEGs. If a gene is connected with more genes, it is considered generally to have a more important role. In the present study, we obtained 206 central DEGs by using the average node degree (9.25) as the criterion. The interaction of those 206 DEGs is displayed in Supplementary Figure S8.

GO analyses indicated that 171 biological processes (BPs), 39 cellular components (CCs), and 65 molecular functions (MFs) were enriched, of which 120 BPs, 29 CCs, and 45 MFs were enriched significantly (Supplementary Table S2). The significantly enriched BPs, CCs, and MFs with the number of genes ranked in the top 10 are shown in Figure 5A and Figure 6C. KEGG analyses indicated that 27 signaling pathways were enriched, of which 18 signaling pathways were enriched significantly (Supplementary Table S3). The significantly enriched signaling pathways with the number of genes ranked in the top 10 are shown in Figure 5D.

FIGURE 5 | Functional enrichment analyses. (A–C) The significantly enriched GO term (top 10). BP, biological process (B). CC, cellular component (B). MF, molecular function (C). (D) The significantly enriched KEGG pathway (top 10). GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.

FIGURE 6 | Diagnosis model constructed by those 5 FR-DELs. (A) β value of those 5 FR-DELs analyzed by stepwise logistic regression. (B) Diagnostic values between cancer and normal. (C) Correlation analysis for the expression of those 5 FR-DELs and diagnostic values. (D) Construction matrix of the classification results of the diagnostic model. (E) ROC curves for evaluating the predictive performance of the diagnostic model. *p < 0.05, **p < 0.01, ***p < 0.001. FR-DELs, ferroptosis-related differentially expressed lncRNAs; ROC, receiver operating characteristic.

Construction of the Diagnostic Model

A diagnostic model integrating those 5 FR-DELs (DOCK8-AS1, SNHG17, RUSC1-AS1, LINC02609, and LUCAT1) was established to separate patients with renal cancer from the normal group by using a stepwise logistic regression method. Diagnostic scores were identified as follows: Logit score = 0.675 + −0.044 * Exp(DOCK8-AS1) + 0.095 * Exp(SNHG17) + 0.120 * Exp(RUSC1-AS1) + 0.145 * Exp(LINC02609) + 0.109 * Exp(LUCAT1) (Figure 6A). The logit value of patients with renal cancer was significantly higher than that of the normal by t-test analysis (Figure 6B). The expression of DOCK8-AS1 was significantly decreased, while the expressions of those 4 signatures (SNHG17, RUSC1-AS1, LINC02609, and LUCAT1) were significantly increased in the patients with renal cancer (Supplementary Table S4). The correlation analysis indicated that the expressions of those 4 FR-DELs (SNHG17, RUSC1-AS1, LINC02609, and LUCAT1) were significantly correlated with the logit value (Figure 6C). The sensitivity and specificity of the diagnostic model were 88.52% and 93.27%, respectively (Figure 6D). We also plot the ROC curve of the diagnostic model, and the AUC value was 0.9450.

DISCUSSIONS

Renal cancer is the second most common cancer of the urinary system after bladder cancer. About 40% of patients with advanced renal cancer would develop distant metastasis and have a poor prognosis, with the 5-year prognosis being about 10% (Rao et al., 2018). For those patients with metastatic tumors, conventional therapeutic methods are the main treatments. However, these results are not very satisfactory (Gill et al., 2017). The developments of new therapeutic methods and suitable prognosis signatures have important practical clinical significance.

Ferroptosis is a novelty recognized process of regulated cell death, and accumulative evidences indicate that it could be used as a new therapeutic target for cancers (Huang et al., 2005; Liu et al., 2011; Roh et al., 2016). Additionally, previous studies also indicated that lncRNAs are involved in the development of cancers and could regulate ferroptosis (Xiao et al., 2017; Zhai et al., 2017; Dong et al., 2019; Mou et al., 2019). To obtain suitable signatures closely related to survival, we integrated univariate Cox regression analyses, K-M analyses, and LASSO analyses. In order to increase the clinical availability of these candidate signatures, we constructed and assessed the diagnostic model and prognostic model using them. Finally, we identified 5 FR-DELs (DOCK8-AS1, SNHG17, RUSC1-AS1, LINC02609, and LUCAT1) correlated with the OS of renal cancer patients independently through a series of bioinformatics analyses. The ROC curve suggested that the risk assessment model constructed by those 5 FR-DELs (DOCK8-AS1, SNHG17, RUSC1-AS1, LINC02609, and LUCAT1) could be used for prognosis signatures for renal cancer patients.

In the present study, we found that the expressions of SNHG17, RUSC1-AS1, LINC02609, and LUCAT1 were significantly increased while the expression of DOCK8-AS1 was significantly decreased in the patients with renal cancer and in renal cancer patients with high-risk values. Renal cancer patients with low DOCK8-AS expression displayed worse OS. At present, there are no studies on DOCK8-AS and cancers. Therefore, further researches are needed to find out whether DOCK8-AS could be related to renal cancer and be used for the prediction of renal cancer. Previous studies have demonstrated that the expressions of SNHG17, RUSC1-AS1, LINC02609, and LUCAT1 were significantly increased in many cancers. For example, SNHG17 has been found to be significantly higher in breast cancer, colorectal cancer, gastric cancer, and prostate cancer (Zhang et al., 2019; Du et al., 2020; Liu et al., 2020a; Zhao et al., 2021). It could regulate the progression of cancer in various manners. Accumulative studies have demonstrated that SNHG17 could promote the proliferation, migration, and invasion by inhibiting miRNAs (miR-23a-3p, miR-124-3p, and miRNA-375-3p) (Du et al., 2020; Liu et al., 2020a; Cao et al., 2021), by epigenetically silencing p15 and p57 (Zhang et al., 2019) and by β-catenin signaling pathway (Zhao et al., 2021). In the present study, we found that SNHG17 was correlated with the pathologic TNM and pathologic stage and correlated with the OS of renal cancer, which was consistent with previous reports (Zhang et al., 2019; Zhao et al., 2021). For example, the high expression of SNHG17 was associated with the increased invasion depth, lymph node metastasis, and advanced TNM stage of gastric cancer (Zhang et al., 2019). SNHG17 high expression was associated with poor outcomes in patients with prostate cancer (Zhao et al., 2021). RUSC1-AS1 has been found to be significantly higher in cervical cancer, breast cancer, and osteosarcoma (Hu et al., 2019). Guo et al. found that the expression of RUSC1-AS1 was significantly increased in the cervical cancer tissues and cell lines (Guo et al., 2020). High expression of RUSC1-AS1 could promote the proliferation, apoptosis, migration, and invasiveness of cervical cancer cells (Guo et al., 2020). Hu et al. also found that the expression of RUSC1-AS1 was significantly increased in the breast cancer tissues, and the silence of RUSC1-AS1 could inhibit viability, clonality, cell cycle progression, and apoptosis of breast cancer cells (Hu et al., 2019). Additionally, a previous study also demonstrated that the expression of RUSC1-AS1 was correlated with OS. Patients with cervical cancer with high expression of RUSC1-AS1 displayed worse OS (Guo et al., 2020). Our study also found that RUSC1-AS1 was significantly correlated with renal cancer, which reinforced the pivotal role of RUSC1-AS1 in cancers. Su et al. found that the expression of LINC02609 was not only significantly higher in advanced stages and grades than in early stages and grades but also significantly higher in tumor and distant metastatic tissues than in normal and non-distant metastatic controls (Su et al., 2021). In the present study, we also found that the expression of LINC02609 was significantly different in differential clinicopathological features. It was significant in the patients with renal cancer with pathologic T3+4, pathologic M1, and pathologic stage III+IV. Our present study also indicated that LINC02609 was correlated with metastatic. Accumulative evidences have shown that LUCAT1 is involved in the progression of several cancers. It is highly expressed in liver cancer, colorectal cancer, and ovarian cancer. LUCAT1 could promote proliferation, migration, and invasion of cancer cells in various manners, such as inhibiting KISS1 expression (Liu et al., 2019), targeting KLF6 and KLF15 (Liu et al., 2020b), and targeting the L40-MDM2-p53 pathway through binding with UBA52 (Zhou et al., 2019). For the treatment of colorectal, Lin et al. found that chemotherapeutic agents combined with antisense oligonucleotides downregulated LUCAT1 yielded better results than the chemotherapeutic agents alone (Huan et al., 2020). Therefore, it is necessary to determine the expression of LUCAT1 for the treatment of colorectal cancer. Moreover, previous studies indicated that LUCAT1 could be a potential prognostic biomarker for patients with several cancers (Yoon et al., 2018; Yu et al., 2018; Zhou et al., 2019; Huan et al., 2020; Yang and Xia, 2020), including renal cancer (Wang et al., 2018; Zheng et al., 2018; Wang Y et al., 2019). In the present study, we also found that LUCAT1 could use as a prognostic biomarker for renal cancer.

CONCLUSION

In the present study, we identified 5 FR-DELs (DOCK8-AS1, SNHG17, RUSC1-AS1, LINC02609, and LUCAT1) that could be used as biomarkers to predict the outcome of renal cancer. We will carry out the functional and clinical verification research for those 5 FR-DELs to see the feasibility of clinical applications in the next steps. Our study may also provide a new research strategy for exploring the ferroptosis-related signatures for cancers.

DATA AVAILABILITY STATEMENT

Publicly available datasets were analyzed in this study. These data can be found here: https://portal.gdc.cancer.gov/.

AUTHOR CONTRIBUTIONS

X-LX conceived and designed the experiments. XS and ZZ performed the analyses. Z-YY helped to analyze the data. X-LX wrote the paper.

FUNDING

This project is financially supported by Doctor Foundation of Hunan University of Medicine (No. 2020122004), Hunan Provincial Science and Technology Department (No. 2020SK51202).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2021.763697/full#supplementary-material

REFERENCES

Cai, H.-j., Zhuang, Z.-c., Wu, Y., Zhang, Y.-y., Liu, X., Zhuang, J.-f., et al. (2021). Development and Validation of a Ferroptosis-Related lncRNAs Prognosis Signature in colon Cancer. Bosn J. Basic Med. Sci. 21, 569–576. doi:10.17305/bjbms.2020.5617

Cao, S., Li, H., and Li, L. (2021). LncRNA SNHG17 Contributes to the Progression of Cervical Cancer by Targeting microRNA-375-3p. Cmar 13, 4969–4978. doi:10.2147/CMAR.S312469

Chen, X., Yu, C., Kang, R., and Tang, D. (2020). Iron Metabolism in Ferroptosis. Front. Cel Dev. Biol. 8, 590226. doi:10.3389/fcell.2020.590226

Dixon, S. J., Lemberg, K. M., Lamprecht, M. R., Skouta, R., Zaitsev, E. M., Gleason, C. E., et al. (2012). Ferroptosis: an Iron-dependent Form of Nonapoptotic Cell Death. Cell 149, 1060–1072. doi:10.1016/j.cell.2012.03.042

Dong, J. S., Wu, B., and Jiang, B. (2019). LncRNA SNHG7 Promotes the Proliferation and Inhibits Apoptosis of Renal Cell Cancer Cells by Downregulating CDKN1A. Eur. Rev. Med. Pharmacol. Sci. 23, 10241–10247. doi:10.26355/eurrev_201912_19661

Du, Y., Wei, N., Hong, J., and Pan, W. (2020). Long Non-coding RNASNHG17 Promotes the Progression of Breast Cancer by Sponging miR-124-3p. Cancer Cel Int. 20, 40. doi:10.1186/s12935-020-1129-y

Gill, D. M., Agarwal, N., and Vaishampayan, U. (2017). Evolving Treatment Paradigm in Metastatic Renal Cell Carcinoma. Am. Soc. Clin. Oncol. Educ. Book 37, 319–329. doi:10.14694/EDBK_17446910.1200/EDBK_174469

Guo, J., Xu, B., Han, Q., Zhou, H., Xia, Y., Gong, C., et al. (2018). Ferroptosis: A Novel Anti-tumor Action for Cisplatin. Cancer Res. Treat. 50, 445–460. doi:10.4143/crt.2016.572

Guo, Q., Zhang, Q., Lu, L., and Xu, Y. (2020). Long Noncoding RNA RUSC1-AS1 Promotes Tumorigenesis in Cervical Cancer by Acting as a Competing Endogenous RNA of microRNA-744 and Consequently Increasing Bcl-2 Expression. Cell Cycle 19, 1222–1235. doi:10.1080/15384101.2020.1749468

Han, C., Liu, Y., Dai, R., Ismail, N., Su, W., and Li, B. (2020). Ferroptosis and its Potential Role in Human Diseases. Front. Pharmacol. 11, 239. doi:10.3389/fphar.2020.00239

Hirschhorn, T., and Stockwell, B. R. (2019). The Development of the Concept of Ferroptosis. Free Radic. Biol. Med. 133, 130–143. doi:10.1016/j.freeradbiomed.2018.09.043

Hu, C. C., Liang, Y. W., Hu, J. L., Liu, L. F., Liang, J. W., and Wang, R. (2019). LncRNA RUSC1-AS1 Promotes the Proliferation of Breast Cancer Cells by Epigenetic Silence of KLF2 and CDKN1A. Eur. Rev. Med. Pharmacol. Sci. 23, 6602–6611. doi:10.26355/eurrev_201908_18548

Huan, L., Guo, T., Wu, Y., Xu, L., Huang, S., Xu, Y., et al. (2020). Hypoxia Induced LUCAT1/PTBP1 axis Modulates Cancer Cell Viability and Chemotherapy Response. Mol. Cancer 19, 11. doi:10.1186/s12943-019-1122-z

Huang, X. (2003). Iron Overload and its Association with Cancer Risk in Humans: Evidence for Iron as a Carcinogenic Metal. Mutat. Research/Fundamental Mol. Mech. Mutagenesis 533, 153–171. doi:10.1016/j.mrfmmm.2003.08.023

Huang, Y., Dai, Z., Barbacioru, C., and Sadée, W. (2005). Cystine-glutamate Transporter SLC7A11 in Cancer Chemosensitivity and Chemoresistance. Cancer Res. 65, 7446–7454. doi:10.1158/0008-5472.CAN-04-4267

Iyer, M. K., Niknafs, Y. S., Malik, R., Singhal, U., Sahu, A., Hosono, Y., et al. (2015). The Landscape of Long Noncoding RNAs in the Human Transcriptome. Nat. Genet. 47, 199–208. doi:10.1038/ng.3192

Li, Y., Jiang, T., Zhou, W., Li, J., Li, X., Wang, Q., et al. (2020). Pan-cancer Characterization of Immune-Related lncRNAs Identifies Potential Oncogenic Biomarkers. Nat. Commun. 11, 1000. doi:10.1038/s41467-020-14802-2

Lipworth, L., Morgans, A. K., Edwards, T. L., Barocas, D. A., Chang, S. S., Herrell, S. D., et al. (2016). Renal Cell Cancer Histological Subtype Distribution Differs by Race and Sex. BJU Int. 117, 260–265. doi:10.1111/bju.12950

Liu, C., Wang, L., Li, Y. W., Cui, Y. S., Wang, Y. Q., and Liu, S. (2019). Long Noncoding RNA LUCAT1 Promotes Migration and Invasion of Prostate Cancer Cells by Inhibiting KISS1 Expression. Eur. Rev. Med. Pharmacol. Sci. 23, 3277–3283. doi:10.26355/eurrev_201904_17689

Liu, X.-X., Li, X.-J., Zhang, B., Liang, Y.-J., Zhou, C.-X., Cao, D.-X., et al. (2011). MicroRNA-26b Is Underexpressed in Human Breast Cancer and Induces Cell Apoptosis by Targeting SLC7A11. FEBS Lett. 585, 1363–1367. doi:10.1016/j.febslet.2011.04.018

Liu, Y., Li, Q., Tang, D., Li, M., Zhao, P., Yang, W., et al. (2020a). SNHG17 Promotes the Proliferation and Migration of Colorectal Adenocarcinoma Cells by Modulating CXCL12-Mediated Angiogenesis. Cancer Cel Int. 20, 566. doi:10.1186/s12935-020-01621-0

Liu, Y., Cheng, T., Du, Y., Hu, X., and Xia, W. (2020b). LncRNA LUCAT1/miR-181a-5p axis Promotes Proliferation and Invasion of Breast Cancer via Targeting KLF6 and KLF15. BMC Mol. Cel Biol. 21, 69. doi:10.1186/s12860-020-00310-0

Low, G., Huang, G., Fu, W., Moloo, Z., and Girgis, S. (2016). Review of Renal Cell Carcinoma and its Common Subtypes in Radiology. Wjr 8, 484–500. doi:10.4329/wjr.v8.i5.484

Malouf, G. G., Su, X., Zhang, J., Creighton, C. J., Ho, T. H., Lu, Y., et al. (2016). DNA Methylation Signature Reveals Cell Ontogeny of Renal Cell Carcinomas. Clin. Cancer Res. 22, 6236–6246. doi:10.1158/1078-0432.CCR-15-1217

Manz, D. H., Blanchette, N. L., Paul, B. T., Torti, F. M., and Torti, S. V. (2016). Iron and Cancer: Recent Insights. Ann. N.Y. Acad. Sci. 1368, 149–161. doi:10.1111/nyas.13008

Moon, D., Kim, J., and Yoon, S. P. (2019). Yeast Extract Inhibits the Proliferation of Renal Cell Carcinoma Cells via Regulation of Iron Metabolism. Mol. Med. Rep. 20, 3933–3941. doi:10.3892/mmr.2019.10593

Mou, Y., Wang, J., Wu, J., He, D., Zhang, C., Duan, C., et al. (2019). Ferroptosis, a New Form of Cell Death: Opportunities and Challenges in Cancer. J. Hematol. Oncol. 12, 34. doi:10.1186/s13045-019-0720-y

Ni, T., Huang, X., Pan, S., and Lu, Z. (2021). Inhibition of the Long Non‐coding RNA ZFAS1 Attenuates Ferroptosis by Sponging miR‐150‐5p and Activates CCND2 against Diabetic Cardiomyopathy. J. Cel Mol Med. 25, 9995–10007. doi:10.1111/jcmm.16890

Rao, A., Wiggins, C., and Lauer, R. C. (2018). Survival Outcomes for Advanced Kidney Cancer Patients in the Era of Targeted Therapies. Ann. Transl. Med. 6, 165. doi:10.21037/atm.2018.04.44

Roh, J.-L., Kim, E. H., Jang, H. J., Park, J. Y., and Shin, D. (2016). Induction of Ferroptotic Cell Death for Overcoming Cisplatin Resistance of Head and Neck Cancer. Cancer Lett. 381, 96–103. doi:10.1016/j.canlet.2016.07.035

Small, W., Bacon, M. A., Bajaj, A., Chuang, L. T., Fisher, B. J., Harkenrider, M. M., et al. (2017). Cervical Cancer: A Global Health Crisis. Cancer 123, 2404–2412. doi:10.1002/cncr.30667

Su, Y., Zhang, T., Tang, J., Zhang, L., Fan, S., Zhou, J., et al. (2021). Construction of Competitive Endogenous RNA Network and Verification of 3-Key LncRNA Signature Associated with Distant Metastasis and Poor Prognosis in Patients with Clear Cell Renal Cell Carcinoma. Front. Oncol. 11, 640150. doi:10.3389/fonc.2021.640150

Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., et al. (2021). Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA A. Cancer J. Clin. 71, 209–249. doi:10.3322/caac.21660

Tang, B., Zhu, J., Li, J., Fan, K., Gao, Y., Cheng, S., et al. (2020). The Ferroptosis and Iron-Metabolism Signature Robustly Predicts Clinical Diagnosis, Prognosis and Immune Microenvironment for Hepatocellular Carcinoma. Cell Commun Signal 18, 174. doi:10.1186/s12964-020-00663-1

Wang, L. N., Zhu, X. Q., Song, X. S., and Xu, Y. (2018). Long Noncoding RNA Lung Cancer Associated Transcript 1 Promotes Proliferation and Invasion of clear Cell Renal Cell Carcinoma Cells by Negatively Regulating miR‐495‐3p. J. Cel Biochem. 119 (9), 7599–7609. doi:10.1002/jcb.27099

Wang, M., Mao, C., Ouyang, L., Liu, Y., Lai, W., Liu, N., et al. (2019). Long Noncoding RNA LINC00336 Inhibits Ferroptosis in Lung Cancer by Functioning as a Competing Endogenous RNA. Cell Death Differ 26, 2329–2343. doi:10.1038/s41418-019-0304-y

Wang, Y., Li, Z., Li, W., Zhou, L., and Jiang, Y. (2019). Prognostic Significance of Long Non-coding RNAs in clear Cell Renal Cell Carcinoma. Medicine (Baltimore) 98 (40), e17276. doi:10.1097/MD.0000000000017276

Wang, Z., Chen, X., Liu, N., Shi, Y., Liu, Y., Ouyang, L., et al. (2021). A Nuclear Long Non-coding RNA LINC00618 Accelerates Ferroptosis in a Manner Dependent upon Apoptosis. Mol. Ther. 29, 263–274. doi:10.1016/j.ymthe.2020.09.024

Xiao, Z.-D., Han, L., Lee, H., Zhuang, L., Zhang, Y., Baddour, J., et al. (2017). Energy Stress-Induced lncRNA FILNC1 Represses C-Myc-Mediated Energy Metabolism and Inhibits Renal Tumor Development. Nat. Commun. 8, 783. doi:10.1038/s41467-017-00902-z

Xie, Y., Zhu, S., Song, X., Sun, X., Fan, Y., Liu, J., et al. (2017). The Tumor Suppressor P53 Limits Ferroptosis by Blocking DPP4 Activity. Cel Rep. 20, 1692–1704. doi:10.1016/j.celrep.2017.07.055

Xing, X.-L., Xing, C., Huang, Z., Yao, Z.-Y., and Liu, Y.-W. (2021). Immune-Related lncRNAs to Construct Novel Signatures and Predict the Prognosis of Rectal Cancer. Front. Oncol. 11, 661846. doi:10.3389/fonc.2021.661846

Yang, T., and Xia, S. (2020). Study of the Biological Function of LncRNA LUCAT1 on Cervical Cancer Cells by Targeting miR-199b-5p. Biosci. Rep. 40, BSR20200422. doi:10.1042/BSR20200422

Yang, W.-H., Huang, Z., Wu, J., Ding, C.-K. C., Murphy, S. K., and Chi, J.-T. (2020). A TAZ-ANGPTL4-NOX2 Axis Regulates Ferroptotic Cell Death and Chemoresistance in Epithelial Ovarian Cancer. Mol. Cancer Res. 18, 79–90. doi:10.1158/1541-7786.MCR-19-0691

Yoon, J.-H., You, B.-H., Park, C. H., Kim, Y. J., Nam, J.-W., and Lee, S. K. (2018). The Long Noncoding RNA LUCAT1 Promotes Tumorigenesis by Controlling Ubiquitination and Stability of DNA Methyltransferase 1 in Esophageal Squamous Cell Carcinoma. Cancer Lett. 417, 47–57. doi:10.1016/j.canlet.2017.12.016

You, B.-H., Yoon, J.-H., Kang, H., Lee, E. K., Lee, S. K., and Nam, J.-W. (2019). HERES, a lncRNA that Regulates Canonical and Noncanonical Wnt Signaling Pathways via Interaction with EZH2. Proc. Natl. Acad. Sci. USA 116, 24620–24629. doi:10.1073/pnas.1912126116

Yu, H., Xu, Y., Zhang, D., and Liu, G. (2018). Long Noncoding RNA LUCAT1 Promotes Malignancy of Ovarian Cancer Through Regulation of miR-612/HOXA13 Pathway. Biochem. Biophysical Res. Commun. 503, 2095–2100. doi:10.1016/j.bbrc.2018.07.165

Yu, Y., Xie, Y., Cao, L., Yang, L., Yang, M., Lotze, M. T., et al. (2015). The Ferroptosis Inducer Erastin Enhances Sensitivity of Acute Myeloid Leukemia Cells to Chemotherapeutic Agents. Mol. Cell Oncol. 2, e1054549. doi:10.1080/23723556.2015.1054549

Zhai, W., Sun, Y., Guo, C., Hu, G., Wang, M., Zheng, J., et al. (2017). LncRNA-SARCC Suppresses Renal Cell Carcinoma (RCC) Progression via Altering the Androgen Receptor(AR)/miRNA-143-3p Signals. Cel Death Differ. 24, 1502–1517. doi:10.1038/cdd.2017.74

Zhang, A., Yang, J., Ma, C., Li, F., and Luo, H. (2021). Development and Validation of a Robust Ferroptosis-Related Prognostic Signature in Lung Adenocarcinoma. Front. Cel Dev. Biol. 9, 616271. doi:10.3389/fcell.2021.616271

Zhang, G., Xu, Y., Wang, S., Gong, Z., Zou, C., Zhang, H., et al. (2019). LncRNA SNHG17 Promotes Gastric Cancer Progression by Epigenetically Silencing of P15 and P57. J. Cel Physiol. 234, 5163–5174. doi:10.1002/jcp.27320

Zhang, S., Chang, W., Wu, H., Wang, Y. H., Gong, Y. W., Zhao, Y. L., et al. (2020). Pan‐cancer Analysis of Iron Metabolic Landscape across the Cancer Genome Atlas. J. Cel Physiol. 235, 1013–1024. doi:10.1002/jcp.29017

Zhao, H., Dong, H., Wang, P., and Zhu, H. (2021). Long Non-coding RNA SNHG17 E-nhances the A-ggressiveness of C4-2 H-uman P-rostate C-ancer C-ells in A-ssociation with β-catenin S-ignaling. Oncol. Lett. 21, 472. doi:10.3892/ol.2021.12733

Zheng, Z., Zhao, F., Zhu, D., Han, J., Chen, H., Cai, Y., et al. (2018). Long Non-coding RNA LUCAT1 Promotes Proliferation and Invasion in Clear Cell Renal Cell Carcinoma through AKT/GSK-3β Signaling Pathway. Cell Physiol Biochem. 48 (3), 891–904. doi:10.1159/000491957

Zhou, Q., Hou, Z., Zuo, S., Zhou, X., Feng, Y., Sun, Y., et al. (2019). LUCAT1 Promotes Colorectal Cancer Tumorigenesis by Targeting the Ribosomal Protein L40‐ MDM 2‐p53 Pathway through Binding with UBA 52. Cancer Sci. 110 (4), 1194–1207. doi:10.1111/cas.13951

Zou, Y., Li, H., Graham, E. T., Deik, A. A., Eaton, J. K., Wang, W., et al. (2020). Cytochrome P450 Oxidoreductase Contributes to Phospholipid Peroxidation in Ferroptosis. Nat. Chem. Biol. 16, 302–309. doi:10.1038/s41589-020-0472-6

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Shu, Zhang, Yao and Xing. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 20 January 2022
doi: 10.3389/fmolb.2021.783915

Development of Personalized Signature Based on the Immune Landscape to Predict the Prognosis of Osteosarcoma and the Response to Immunotherapy and Targeted Therapy

Xiaofei Feng¹, Zhenrui Zhao¹, Yuhao Zhao¹, Zhengdong Song¹, Yao Ma² and Wenji Wang³*

¹Department of Orthopedics, The First Clinical Medical College of Lanzhou University, Gansu, China

²Clinical Laboratory Center, Gansu Provincial Maternity and Child-Care Hospital, Gansu, China

³Department of Orthopedics, Lanzhou University First Affiliated Hospital, Gansu, China

Edited by:
Hossain Shekhar, University of Dhaka, Bangladesh

Reviewed by:
Wei Meng, Sichuan University, China
Saroj Kumari, Nation Institute of Immunology, India
Xiongwen Lv, Anhui Medical University, China

* Correspondence: Wenji Wang, ldyyjzwwj@163.com

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 27 September 2021
Accepted: 30 December 2021
Published: 20 January 2022

Citation: Feng X, Zhao Z, Zhao Y, Song Z, Ma Y and Wang W (2022) Development of Personalized Signature Based on the Immune Landscape to Predict the Prognosis of Osteosarcoma and the Response to Immunotherapy and Targeted Therapy. Front. Mol. Biosci. 8:783915. doi: 10.3389/fmolb.2021.783915

As a heterogeneous and aggressive disease, osteosarcoma (OS) faces great challenges to prognosis and individualized treatment. Hence, we explore the role of immune-related genes in predicting prognosis and responsiveness to immunotherapy and targeted therapies in patients with OS based on the immunological landscape of osteosarcoma. Based on the database of the Therapeutical Applicable Research to Generate Effective Treatments (TARGET), single-sample gene set enrichment analysis (ssGSEA) was used to obtain the enrichment scores of 29 immune characteristics. A series of bioinformatics methods were performed to construct the immune-related prognostic signature (IRPS). Gene set enrichment analysis and gene set variation analysis were used to explore the biological functions of IRPS. We also analyzed the relationship between IRPS and tumor microenvironment. Lastly, the reactivity of IRPS to immune checkpoint therapy and targeted drugs was explored. The ssGSEA algorithm was used to define two immune subtypes, namely Immunity_High and Immunity_Low. Immunity_High was associated with a good prognosis and was an independent prognostic factor of OS. The IRPS containing 7 genes was constructed by the least absolute shrinkage and selection operator Cox regression. The IRPS can divide patients into low- and high-risk patients. Compared with high-risk patients, low-risk patients had a better prognosis and were positively correlated with immune cell infiltration and immune function. Low-risk patients benefited more from immunotherapy, and the sensitivity of targeted drugs in high- and low-risk groups was determined. IRPS can be used to predict the prognosis of OS patients, and provide therapeutic responsiveness to immunotherapy and targeted therapy.

Keywords: osteosarcoma, prognosis, tumor microenvironment, immune checkpoint, targeted therapy

INTRODUCTION

Osteosarcoma (OS) is the most common primary malignant bone tumor, which mostly occurs in children and young people (Gill and Gorlick, 2021). The standard treatment of OS, involving surgery and chemotherapy, extended survival for more than 60% of patients with localized disease (Bernthal et al., 2012; Isakoff et al., 2015). Although this has made a significant contribution to improving the prognosis of OS patients, clinical outcomes have hardly made significant progress over the past decades (Smeland et al., 2019). For patients with recurrence and metastasis, the overall 5 years survival rate is even less than 25% due to the development of resistance to radiation or chemotherapy (Zhao et al., 2020). To date, the main therapies for OS have remained largely unchanged, so there is an urgent need to understand the molecular mechanisms of OS occurrence and progression to identify more effective therapeutic targets.

The intrinsic genetic heterogeneity and dynamic immunogenicity characteristics significantly would influence the outcome of treatment (Suehara et al., 2019; Wang et al., 2019). In recent years, immunotherapy has made important breakthroughs in a variety of solid tumors (Hellmann et al., 2018; Forschner et al., 2019). Sarcoma is the first tumor model in which immunotherapy has been suggested as a therapeutic strategy (Coley, 1891). The immune microenvironment of OS is mainly composed of tumor-associated macrophages, lymphocytes, dendritic cells, and myeloid cells (Inagaki et al., 2016). Studies have shown that increased TAM infiltration is associated with decreased metastasis and increased survival rate of high-grade OS (Buddingh et al., 2011; Gomez-Brouchet et al., 2017). In addition, the number of CD8⁺ T cell infiltrates into OS was positively correlated with overall survival (Wang et al., 2016). Several clinical studies and trials have demonstrated the potential of immunotherapy to enhance the outcome of patients with OS. The use of the macrophage activator Mifamurtide in combination with standard chemotherapy significantly improved 6 years overall survival in an OS randomized clinical trial (Johal et al., 2013). New immune-based treatments, such as immune checkpoint inhibitors, may considerably improve the outcome of the disease (Kansara et al., 2014). However, the efficacy of anti-PD-L1 therapy for OS is limited (Tawbi et al., 2017; D’Angelo et al., 2018; Le Cesne et al., 2019). The heterogeneity of OS immune microenvironment may be the reason for the poor efficacy of immunotherapy. Therefore, the individualized evaluation of OS immune microenvironment is extremely critical to improve the therapeutic effect. For better prognosis and effective treatment, it is necessary to identify key genes from tumor-specific immunophenotypes and explore the underlying mechanisms involved.

In this study, we aimed to construct a personalized immune-related prognostic signature (IRPS) to predict the prognosis of OS patients. We also explored its relationship between the immune microenvironment and its sensitivity to immune checkpoint therapy and targeted drugs, which provides reliable guidance of clinical precision medicine.

METHODS

Data Sources and Clustering

Download gene expression data and clinical information of OS patients from the Therapeutical Applicable Research to Generate Effective Treatments (TARGET) database. Download the corresponding data of the GSE21257 chip from the Gene Expression Omnibus (GEO) database for an independent external verification set. The single-sample gene set enrichment analysis (ssGSEA) was used to obtain the enrichment fractions of 29 immune characteristics in each osteosarcoma sample (Hänzelmann et al., 2013), and hierarchical clustering was performed using the ConsensusClusterPlus package (Wilkerson and Hayes, 2010).

Assessment of Immune Cell Infiltration Level, Tumor Purity, and Stromal Content in OS

The stromal score, ESTIMATE score, immune cell infiltration level (immune score), and tumor purity level in a single sample were assessed by ESTIMATE (Yoshihara et al., 2013).

Construction of Co-Expression Network and IRPS

The WGCNA package was used to perform the weighted gene co-expression network (WGCNA) of genes with a variation rate greater than 0.5 to obtain modules related to Immunity_H (Langfelder and Horvath, 2008). Univariate Cox regression analysis was performed using genes contained in the module most associated with Immunity_H to obtain prognostic genes. Then, the least absolute shrinkage and selection operator (LASSO) regression was performed using the “glmnet” R package to build the IRPS. Patients in the TARGET and GSE21257 cohorts were divided into high- and low-risk patients using the median risk score in the TARGET cohort as the threshold.

Verification of IRPS

In the TARGET and GSE21257 cohorts, the “survival” R package was used to establish the survival curve of the high- and low-risk patients through the Kaplan-Meier diagram, and the difference in survival curve was analyzed by the log-rank test. Cox regression analysis was used for univariate and multivariate analyses to evaluate the impact of IRPS and other clinical factors on prognosis. The time-dependent receiver operating characteristic curve (ROC) was performed using the “timeROC” R package.

Gene Set Enrichment Analysis and Gene Set Variation Analysis

GSEA and GSVA were used to investigate the biological function and pathway enrichment in different risk patients. With “c5.go.bp.v7.4.symbols.gmt” and “c2.cp.kegg.v7.4.symbols.gmt” as reference gene sets, the analyses were carried out by GSEA software (version 4.1.0). The “gsva” R package was used for GSVA with the “h.all.v7.4.symbols.gmt” gene set as a reference (Hänzelmann et al., 2013).

Explore the Reactivity of IRPS to Immune Checkpoint Therapy and the Sensitivity of Targeted Drugs

We explored the expression and correlation between current common immune checkpoints in high- and low-risk patients in the TARGET and GSE21257 cohorts. In addition, the “pRRophetic” R package were used to analyze the sensitivity of targeted drugs in high- and low-risk patients (Geeleher et al., 2014).

Statistical Analysis

Kaplan-Meier curve and log-rank test were used to analyze overall survival. Independent prognostic factors of OS were determined by univariate and multivariate COX regression. The Wilcoxon rank-sum test was used to compare the distribution of any continuous variables between the two groups. Pearson correlation coefficient was used to assess correlation. The above analyses were carried out in R software (version 4.1.0). For all statistical results, p < 0.05 was considered statistically significant.

RESULTS

Immunogenomic Analysis to Identify Two OS Immune Subtypes

In the TARGET cohort, ssGSEA was used to quantify each sample with 29 immune-related gene sets as a reference and then hierarchical clustering was performed (Figure 1A). We have defined two immune subtypes, namely Immunity_high (Immunity_H) and Immunity_low (Immunity_L). In the heat map, the Immunity_H subtype has higher immunological activity, and the Immunity_L subtype has lower immunological activity (Figure 1B). To ensure the effectiveness of clustering, we compared the stromal score, immune score, ESTIMATE score, and tumor purity between the two subtypes. Compared with the Immunity_L subtype, the Immunity_H subtype had higher stromal score (p < 0.001), immune score (p < 0.001) and ESTIMATE score (p < 0.001), while tumor purity (p < 0.001) is lower (Figures 1C–F). These results showed that this grouping method is reasonable and can be used for follow-up research. Survival analysis showed that the Immunity_H subtype had a better prognostic outcome than the Immunity_L subtype (p < 0.05) (Figure 1G). After adjustment for age, sex, and metastasis, it was determined that the Immunity_H subtype was an independent prognostic factor (p < 0.05, Figure 1H).

FIGURE 1 | Determine the two subtypes and prognostic analysis. (A) The heat map showing samples clustering results, with consensus k identified as 2. (B) The heat map showing the immune landscape of the two subtypes. (C–F) Comparison of (C) stromal score, (D) immune score, (E) ESTIMATE score, and (F) tumor purity between the two subtypes in the TCGA cohort (∗∗∗, p < 0.001). (G) Comparison of survival prognosis between OS subtypes. (H) Univariate and multivariate Cox regression analysis.

WGCNA Identifies Key Modules and Builds IRPS

A co-expression network was constructed for genes with a variation rate greater than 0.5. When the optimal soft threshold is 3, the scale-free R² can reach 0.9 (Figure 2A). WGCNA merged similar modules to generate 11 modules with different colors, among which Immunity_H has the highest correlation with the yellow module (Figure 2B, r = 0.6, p < 0.001). The genes contained in the yellow module were analyzed by univariate COX regression. 16 genes with HR < 1 and p < 0.05 were obtained in the TARGET and GSE21257 cohorts (Figure 2C). LASSO Cox regression analysis was performed to construct personalized IRPS (Figures 2D,E), which included 7 genes. The median risk score, which divided patients into high- or low-risk patients, was calculated as follows: Risk score = (−0.051) * WAS + (−0.482) * IFNGR1 + (−0.150) * PILRA + (−0.110) * TMEM86A+ (−0.051) *CXCL16 +(−0.852) * CTNNBIP1 + (−0.333) * APOL6.

FIGURE 2 | Identification of key modules by WGCNA in the TARGET cohort and establishment of IRPS by LASSO regression analysis. (A) Identification of power value. When the power value is 3, the red line represents R² > 0.9. (B) Heat map of the correlation between gene modules and immune subtypes. The value of each cell represents the correlation coefficient. The value of parentheses is the p value. The figure on the right shows a scatter plot of the relationship between gene significance and module membership in the yellow module. (C) Univariate Cox regression analysis of prognostic genes in TARGET and GSE21257 cohorts. (D,E) Lasso Cox regression analysis identified 7 genes as candidates for IRPS construction.

Verification of IRPS

To determine the prognostic value of IRPS, patients in the TARGET cohort were divided into high- or low-risk patients based on the above risk score. High-risk patients had a lower overall survival rate than low-risk patients (Log-rank p < 0.001, Figure 3A). The ROC curve showed 0.845 at 3 years, 0.855 at 5 years, and 0.864 at 8 years (Figure 3B). In addition, the calibration chart showed that IRPS had good accuracy in predicting the prognosis (Figure 3C). We applied IRPS to the GSE21257 cohort as an independent external verification set. The Kaplan-Meier survival curve showed that the overall survival of high-risk patients was significantly shorter than that of low-risk patients (Log-rank p = 0.023, Figure 3D). The ROC curve showed 0.771 at 3 years, 0.758 at 5 years, and 0.711 at 8 years (Figure 3E). The calibration chart also indicated that IRPS was effective for predicting prognosis (Figure 3F). These results suggest that IRPS can also accurately predict survival in OS patients in other independent cohorts.

FIGURE 3 | Prognostic analysis of IRPS. Kaplan-Meier analysis of IRPS in the (A) TARGET cohort and (D) GSE21257 cohort. Time-dependent ROC curves of IRPS in the (B) TARGET cohort and (E) GSE21257 cohort at 3-, 5-, and 8-years. Calibration chart analysis suggested a high accuracy of 3-, 5-, and 8-years OS prediction in the (C) TARGET cohort and (F) GSE21257 cohort.

Evaluation of IRPS as an Independent Prognostic Factor

According to different clinicopathological characteristics (age, gender, and metastasis), OS patients were stratified, and Kaplan-Meier survival analysis was performed to evaluate the prognostic value of IRPS. We found that low-risk patients had better prognostic outcomes than high-risk patients regardless of age, sex, and metastasis (Figure 4A). In univariate Cox regression analysis, high-risk patients showed poor overall survival in both the TARGET cohort and GSE21257 cohort (HR = 4.412, 95% CI = 2.590–7.513, p < 0.001; HR = 2.040, 95% CI = 1.336–3.115, p = 0.001) (Figures 4B,C). In multivariate Cox regression analysis, high-risk patients also showed poor overall survival in the two cohorts (HR = 4.344, 95% CI = 2.531–7.456, p < 0.001; HR = 2.050, 95% CI = 1.312–3.204, p = 0.002) (Figures 4B,C). Therefore, IRPS is an independent prognostic factor of OS.

FIGURE 4 | Survival analysis of IRPS with different clinical characteristics and evaluation of independent prognostic factors. (A) Stratified analysis to investigate the prognostic value of IRPS in the TARGET cohort. Univariate and multivariate Cox regression analysis in the (B) TARGET cohort and (C) GSE21257 cohort.

GSEA and GSVA

GSEA and GSVA were performed to determine important functional phenotypes between high- and low-risk patients. GSEA results showed that significant immune-related functions are enriched in low-risk patients, such as response to interferon-gamma, positive regulation of phagocytosis, T cell receptor signaling pathway and dendritic cell differentiation, etc. (Figures 5A,B). GSVA results showed that allograft rejection, IL6/JAK/STAT3 signaling, interferon-gamma response, interferon-alpha response, complement, coagulation, and inflammatory response were activated in the low-risk group (Figure 5C). The immune activity is higher in low-risk patients, which may be related to the prolonged survival time. These results are consistent with the good survival of the above-mentioned Immunity_H patients.

FIGURE 5 | Analysis of GSEA and GSVA in high- and low-risk patients. (A) GSEA with “c5.go.bp.v7.4.symbols.gmt” as a reference gene set. (B) GSEA with “c2.cp.kegg.v7.4.symbols.gmt” as a reference gene set. (C) GSVA with “h.all.v7.4.symbols.gmt” as the reference gene set.

The Correlation Between IRPS and Tumor Microenvironment

In order to better characterize the immune function of IRPS in TME, we evaluated the relationship between high-risk and low-risk patients and the level of immune cell infiltration (immune score), ESTIMATE score, stroma score, and tumor purity. The results showed that in the TARGET and GSE21257 cohorts, low-risk patients had the higher immune score, ESTIMATE score, and stromal score (Figures 6A–C), and tumor purity was lower in low-risk patients (Figure 6D). The IRPS was significantly negatively correlated with the immune score, ESTIMATE score, and stromal score (Figures 6E–G), but positively correlated with tumor purity (Figure 6H). Secondly, the correlation between the IRPS with immune cell infiltration and immune cell function was evaluated. Immune cell infiltration analysis showed that in the TARGET cohort, CD8⁺ T cells, DCs, Macrophages, and Neutrophils were higher in low-risk patients than in high-risk patients (Figure 7A). The same results were observed in the GSE21257 cohort (Figure 7B). Immune cell functions analysis showed that in the TARGET cohort, APC_co_inhibition, Check point, HLA, and MHC_class_I were more active in low-risk patients compared with high-risk patients (Figure 7C). Similar results were observed in the GSE21257 cohort (Figure 7D). Finally, in the TARGET and GSE21257 cohorts, we found that the IRPS was negatively correlated with the above-mentioned immune cell infiltration and immune cell function (Figure 7E). Above all, these evidences are also consistent with the above results, which further proves the accuracy and robustness of our results.

FIGURE 6 | Correlation between IRPS and TME in OS. (A–D) In the TARGET and GSE21257 cohorts, box plots of IRPS and (A) immune cell infiltration level, (B) ESTIMATE score, (C) stromal score, and (D) tumor purity. (E–H) Correlation between IRPS and (E) immune cell infiltration level, (F) ESTIMATE score, (G) stromal score, and (H) tumor purity.

FIGURE 7 | The relationship between IRPS and immune cell infiltration and immune function. (A,B) Box plots of IRPS and immune cell infiltration in the TARGET (A) and GSE21257 (B) cohorts. (C,D) Box plots of IRPS and immune function are in the TARGET (C) and GSE21257 (D) cohorts. (E) Heat map of the correlation between IRPS and immune cells and immune function.

Reactivity of Immune Checkpoint Therapy and Sensitivity of Targeted Drugs

In the TARGET and GSE21257 cohorts, immune checkpoint markers (CD48, HAVCR2, LAIR1, LGALS9, TNFRSF14) were expressed higher in low-risk patients, and there was a significant negative correlation between IRPS and these markers (r < −0.5, p < 0.05, Figures 8A–E). We estimated the IC50 of each sample and observed that the IC50 of six drugs had significant differences between the two groups. The results showed that Axitinib, Cyclopamine, and Vorinostat were highly sensitive in high-risk patients (Figures 9A–C). MG.132, Shikonin, and Luminespib were more sensitive in low-risk patients (Figures 9D–F). These results may provide accurate and personalized treatment strategies for OS patients.

FIGURE 8 | The expression and correlation between immune checkpoints in high- and low-risk patients in the TARGET and GSE21257 cohorts. (A) CD48 (B) HAVCR2 (C) LAIR1 (D) LGALS9 (E) TNFRSF14.

FIGURE 9 | In the TARGET and GSE21257 cohorts, high- and low-risk patients and targeted drug sensitivity. (A) Axitinib (B) Cyclopamine (C) Vorinostat (D) MG.132 (E) Shikonin (F) Luminespib.

DISCUSSION

OS is a very aggressive bone malignant tumor with a poor prognosis, and it is commonly characterized by early distant metastasis and recurrence. Therefore, improving the prognosis of OS patients remains a huge challenge. Immunotherapy is accelerating the pace of cancer treatment, and using immunotherapy in a personalized way will help improve the prognosis of cancer patients. A comprehensive analysis of the OS immune landscape determined that immunophenotypes play a key role in immunotherapeutic response and prognosis (Wu et al., 2020). Therefore, we aim to identify OS immune subtypes and construct a model system with the immune capability to predict patient prognosis and response to immunotherapy.

In our study, we used the TARGET cohort to present the immune landscape of OS. Cluster analysis showed that OS could be divided into two subtypes: Immunity_H and Immunity_L. The prognostic outcome of Immunity_H patients was significantly better than that of Immunity_L patients, and Immunity_H was an independent prognostic factor. The genes associated with Immunity_H were identified by WGCNA to ensure the specificity of the prognostic signature. We then used univariate Cox regression and LASSO Cox regression models to determine a robust IRPS. Survival analyses showed that low-risk patients had a better prognosis than high-risk patients. Univariate and multivariate COX regression showed that IRPS is an independent prognostic factor in OS patients. Stratify analysis showed that IRPS remained effective in predicting the prognosis of patients with different clinical characteristics. We also used the GSE21257 cohort as the independent external validation set to verify IRPS, and the results showed that IRPS also has a good predictive ability for other independent cohorts.

There have been some studies as predictive prognosis and biomarkers of OS, which provide certain insights for the prognosis and treatment of OS patients (Liu et al., 2021; Yang et al., 2021; Wang et al., 2021). Therefore, we comprehensively analyzed the OS immune landscape to construct IRPS, including 7 genes (WAS, IFNGR1, PILRA, TMEM86A, CXCL16, CTNNBIP1, and APOL6). Interestingly, all of these genes are associated with immunity. The WAS encodes WISkott-Aldrich syndrome protein (WASp), which belongs to the actin nucleation promoting factor family. WASP is expressed only in hematopoietic cells, including dendritic cells, macrophages, T cells, B cells, macrophages, and natural killer cells (Matalon et al., 2013). WASP deficiency will lead to functional defects of adaptive immunity and innate immunity (Thrasher and Burns, 2010). The deficiency of IFNGR1 causes tumor cells to be unresponsive to IFNγ and promotes tumor growth (Dunn et al., 2005). The degradation of IFNGR1 results in impaired IFNγ signaling, decreased MHC-I expression, and enhanced immune evasion ability, and stabilization of IFNGR1 expression enhances the sensitivity of checkpoint therapy (Du et al., 2021). In contrast, the loss of IFNGR1 counteracts the reactivity of immune checkpoint inhibitors (Gao et al., 2016). PILRA is widely expressed in immune-related cells, including macrophages, dendritic cells, B cells, natural killer cells, and neutrophils (Shiratori et al., 2004). PILRA may play an important role in the regulation of immune cells (Wilson et al., 2006). We found in THE HUMAN PROTEIN ATLAS (www.proteinatlas.org) that transmembrane protein 86A (TMEM86A) is highly expressed in dendritic cells, T cells, and B cells, which may play a role in immune regulation. CXCL16 is the ligand of CXCL6, and CXCR6 upregulation is critical for continuous tumor control mediated by CD8⁺ cytotoxic T cells (Di Pilato et al., 2021). CXCL16 controls the accumulation of natural killer T cells and inhibits tumor growth (Ma et al., 2018). CTNNBIP1 is a β-catenin interacting protein, which is considered to be a tumor suppressor gene (Bi et al., 2018; Chang et al., 2019). APOL6 is a lipid-binding protein with the BH3 domain. It mediates apoptosis through interaction with members of the Bcl-2 family and affects the innate immunity of different microbial pathogens (Liu et al., 2005; Pant et al., 2021).

Given that the role of TME in OS cannot be underestimated, we explored the relationship between IRPS and immune infiltrating cells. The results showed there were significantly higher immune scores, ESTIMATE scores, stroma scores, and lower tumor purity in low-risk patients. We found that compared with high-risk patients, CD8⁺ T cells, DCs, Macrophages, and tumor-infiltrating lymphocytes (TIL) cells in the low-risk patients were abundant in infiltration and functionally active, suggesting that high tumor immunological infiltration has a better prognosis. CD8⁺ T cells can directly kill tumor cells and improve the survival rate of cancer (Galon et al., 2006; Ogura et al., 2018). A multicenter retrospective study has shown that patients with high CD8⁺/FOXP3⁺ rates have improved survival rates (Fritzsching et al., 2015). DCs are key coordinators of the immune response, which can activate specific immune systems to accelerate anti-tumor immunity, and the total number of DCs is associated with a good prognosis (Böttcher and Reis e Sousa, 2018). A recent study showed that dendritic cells can effectively inhibit tumor growth and metastasis in OS mouse models (Zhou et al., 2020). The direct or indirect anti-tumor effect of macrophages in OS is greater than their supportive effect on the tumor (Buddingh et al., 2011), and the total number of macrophages is associated with better overall survival of OS patients (Heymann et al., 2019). TIL cells are consumed in the OS environment and accelerate tumor recurrence, and adjuvant therapy plus TIL cells can prolong survival (Shi et al., 2020). These results are consistent with our findings.

Among the immune checkpoint markers, CD48, HAVCR2, LAIR1, LGALS9, and TNFRSF14 were lower expressed in high-risk patients, suggesting that high-risk patients have limited benefit from immune checkpoint therapy. In addition, we investigated the responsiveness of high- and low-risk patients to targeted drugs. We found that low-risk patients were more sensitive to MG.132, Shikonin, and Luminespib, while high-risk patients were more sensitive to Axitinib, Cyclopamine, and Vorinostat. These findings provide an effective strategy for the stratified treatment of IRPS.

Although our research has important clinical significance for OS, there are inevitably some limitations. First of all, this is a retrospective study and it is necessary to further verify it in prospective trials. Secondly, our prognostic signature is established by several genes, and the biological function of OS should be further examined. Third, the epigenetic and intra-tumor genetic heterogeneity may lead to sampling bias.

Our study provided a comprehensive assessment of the immune landscape of OS and developed a novel IRPS that has been well validated in an independent cohort. We also revealed the biological mechanism of IRPS. IRPS is closely linked to the infiltration of a variety of immune cell types, and the immune checkpoint response and drug sensitivity were explored, which may have guiding significance for the prognosis and treatment of OS patients. In the future, large-scale, multi-center, and prospective studies are required to verify the effectiveness of the IRPS we proposed.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

AUTHOR CONTRIBUTIONS

WW and XF: conceptualization. ZZ and YZ: software and methodology. ZS and YM: writing—original draft preparation. WW: writing—review and editing. All authors have read and agreed to the published version of the manuscript.

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

ACKNOWLEDGMENTS

We would like to acknowledge the TARGET and the GEO for providing data.

REFERENCES

Bernthal, N. M., Federman, N., Eilber, F. R., Nelson, S. D., Eckardt, J. J., Eilber, F. C., et al. (2012). Long-term Results (>25 Years) of a Randomized, Prospective Clinical Trial Evaluating Chemotherapy in Patients with High-Grade, Operable Osteosarcoma. Cancer 118, 5888–5893. doi:10.1002/cncr.27651

Bi, W., Huang, J., Nie, C., Liu, B., He, G., Han, J., et al. (2018). CircRNA circRNA_102171 Promotes Papillary Thyroid Cancer Progression through Modulating CTNNBIP1-dependent Activation of β-catenin Pathway. J. Exp. Clin. Cancer Res. 37, 275. doi:10.1186/s13046-018-0936-7

Böttcher, J. P., and Reis e Sousa, C. (2018). The Role of Type 1 Conventional Dendritic Cells in Cancer Immunity. Trends Cancer 4, 784–792. doi:10.1016/j.trecan.2018.09.001

Buddingh, E. P., Kuijjer, M. L., Duim, R. A. J., Bürger, H., Agelopoulos, K., Myklebost, O., et al. (2011). Tumor-infiltrating Macrophages Are Associated with Metastasis Suppression in High-Grade Osteosarcoma: a Rationale for Treatment with Macrophage Activating Agents. Clin. Cancer Res. 17, 2110–2119. doi:10.1158/1078-0432.Ccr-10-2047

Chang, J.-M., Tsai, A. C.-D., Huang, W.-R., and Tseng, R.-C. (2019). The Alteration of CTNNBIP1 in Lung Cancer. Int. J. Mol. Sci. 20, 5684. doi:10.3390/ijms20225684

Coley, W. B. (1891). Contribution to the Knowledge of Sarcoma. Ann. Surg. 14, 199–220. doi:10.1097/00000658-189112000-00015

D'Angelo, S. P., Mahoney, M. R., Van Tine, B. A., Atkins, J., Milhem, M. M., Jahagirdar, B. N., et al. (2018). Nivolumab with or without Ipilimumab Treatment for Metastatic Sarcoma (Alliance A091401): Two Open-Label, Non-comparative, Randomised, Phase 2 Trials. Lancet Oncol. 19, 416–426. doi:10.1016/s1470-2045(18)30006-8

Di Pilato, M., Kfuri-Rubens, R., Pruessmann, J. N., Ozga, A. J., Messemaker, M., Cadilha, B. L., et al. (2021). CXCR6 Positions Cytotoxic T Cells to Receive Critical Survival Signals in the Tumor Microenvironment. Cell 184, 4512–4530. doi:10.1016/j.cell.2021.07.015

Du, W., Hua, F., Li, X., Zhang, J., Li, S., Wang, W., et al. (2021). Loss of Optineurin Drives Cancer Immune Evasion via Palmitoylation-dependent IFNGR1 Lysosomal Sorting and Degradation. Cancer Discov. 11, 1826–1843. doi:10.1158/2159-8290.Cd-20-1571

Dunn, G. P., Sheehan, K. C. F., Old, L. J., and Schreiber, R. D. (2005). IFN Unresponsiveness in LNCaP Cells Due to the Lack of JAK1 Gene Expression. Cancer Res. 65, 3447–3453. doi:10.1158/0008-5472.Can-04-4316

Forschner, A., Battke, F., Hadaschik, D., Schulze, M., Weißgraeber, S., Han, C.-T., et al. (2019). Tumor Mutation burden and Circulating Tumor DNA in Combined CTLA-4 and PD-1 Antibody Therapy in Metastatic Melanoma - Results of a Prospective Biomarker Study. J. Immunother. Cancer 7, 180. doi:10.1186/s40425-019-0659-0

Fritzsching, B., Fellenberg, J., Moskovszky, L., Sápi, Z., Krenacs, T., Machado, I., et al. (2015). CD8+/FOXP3+-ratio in Osteosarcoma Microenvironment Separates Survivors from Non-survivors: a Multicenter Validated Retrospective Study. Oncoimmunology 4, e990800. doi:10.4161/2162402x.2014.990800

Galon, J., Costes, A., Sanchez-Cabo, F., Kirilovsky, A., Mlecnik, B., Lagorce-Pagès, C., et al. (2006). Type, Density, and Location of Immune Cells within Human Colorectal Tumors Predict Clinical Outcome. Science 313, 1960–1964. doi:10.1126/science.1129139

Gao, J., Shi, L. Z., Zhao, H., Chen, J., Xiong, L., He, Q., et al. (2016). Loss of IFN-γ Pathway Genes in Tumor Cells as a Mechanism of Resistance to Anti-CTLA-4 Therapy. Cell 167, 397–404. e9. doi:10.1016/j.cell.2016.08.069

Geeleher, P., Cox, N., and Huang, R. S. (2014). pRRophetic: an R Package for Prediction of Clinical Chemotherapeutic Response from Tumor Gene Expression Levels. PloS one 9, e107468. doi:10.1371/journal.pone.0107468

Gill, J., and Gorlick, R. (2021). Advancing Therapy for Osteosarcoma. Nat. Rev. Clin. Oncol. 18, 609–624. doi:10.1038/s41571-021-00519-8

Gomez-Brouchet, A., Illac, C., Gilhodes, J., Bouvier, C., Aubert, S., Guinebretiere, J.-M., et al. (2017). CD163-positive Tumor-Associated Macrophages and CD8-Positive Cytotoxic Lymphocytes Are Powerful Diagnostic Markers for the Therapeutic Stratification of Osteosarcoma Patients: An Immunohistochemical Analysis of the Biopsies Fromthe French OS2006 Phase 3 Trial. Oncoimmunology 6, e1331193. doi:10.1080/2162402x.2017.1331193

Hänzelmann, S., Castelo, R., and Guinney, J. (2013). GSVA: Gene Set Variation Analysis for Microarray and RNA-Seq Data. BMC bioinformatics 14, 7. doi:10.1186/1471-2105-14-7

Hellmann, M. D., Ciuleanu, T.-E., Pluzanski, A., Lee, J. S., Otterson, G. A., Audigier-Valette, C., et al. (2018). Nivolumab Plus Ipilimumab in Lung Cancer with a High Tumor Mutational Burden. N. Engl. J. Med. 378, 2093–2104. doi:10.1056/NEJMoa1801946

Heymann, M.-F., Lézot, F., and Heymann, D. (2019). The Contribution of Immune Infiltrates and the Local Microenvironment in the Pathogenesis of Osteosarcoma. Cell Immunol. 343, 103711. doi:10.1016/j.cellimm.2017.10.011

Inagaki, Y., Hookway, E., Williams, K. A., Hassan, A. B., Oppermann, U., Tanaka, Y., et al. (2016). Dendritic and Mast Cell Involvement in the Inflammatory Response to Primary Malignant Bone Tumours. Clin. Sarcoma Res. 6, 13. doi:10.1186/s13569-016-0053-3

Isakoff, M. S., Bielack, S. S., Meltzer, P., and Gorlick, R. (2015). Osteosarcoma: Current Treatment and a Collaborative Pathway to Success. J. Clin. Oncol. 33, 3029–3035. doi:10.1200/jco.2014.59.4895

Johal, S., Ralston, S., and Knight, C. (2013). Mifamurtide for High-Grade, Resectable, Nonmetastatic Osteosarcoma Following Surgical Resection: a Cost-Effectiveness Analysis. Value in Health 16, 1123–1132. doi:10.1016/j.jval.2013.08.2294

Kansara, M., Teng, M. W., Smyth, M. J., and Thomas, D. M. (2014). Translational Biology of Osteosarcoma. Nat. Rev. Cancer 14, 722–735. doi:10.1038/nrc3838

Langfelder, P., and Horvath, S. (2008). WGCNA: an R Package for Weighted Correlation Network Analysis. BMC bioinformatics 9, 559. doi:10.1186/1471-2105-9-559

Le Cesne, A., Marec-Berard, P., Blay, J.-Y., Gaspar, N., Bertucci, F., Penel, N., et al. (2019). Programmed Cell Death 1 (PD-1) Targeting in Patients with Advanced Osteosarcomas: Results from the PEMBROSARC Study. Eur. J. Cancer 119, 151–157. doi:10.1016/j.ejca.2019.07.018

Liu, W., Xie, X., Qi, Y., and Wu, J. (2021). Exploration of Immune-Related Gene Expression in Osteosarcoma and Association with Outcomes. JAMA Netw. Open 4, e2119132. doi:10.1001/jamanetworkopen.2021.19132

Liu, Z., Lu, H., Jiang, Z., Pastuszyn, A., and Hu, C. A. (2005). Apolipoprotein L6, a Novel Proapoptotic Bcl-2 Homology 3-only Protein, Induces Mitochondria-Mediated Apoptosis in Cancer Cells. Mol. Cancer Res. 3, 21–31.

Ma, C., Han, M., Heinrich, B., Fu, Q., Zhang, Q., Sandhu, M., et al. (2018). Gut Microbiome-Mediated Bile Acid Metabolism Regulates Liver Cancer via NKT Cells. Science 360, eaan5931. doi:10.1126/science.aan5931

Matalon, O., Reicher, B., and Barda-Saad, M. (2013). Wiskott-Aldrich Syndrome Protein - Dynamic Regulation of Actin Homeostasis: from Activation through Function and Signal Termination in T Lymphocytes. Immunol. Rev. 256, 10–29. doi:10.1111/imr.12112

Ogura, K., Hosoda, F., Arai, Y., Nakamura, H., Hama, N., Totoki, Y., et al. (2018). Integrated Genetic and Epigenetic Analysis of Myxofibrosarcoma. Nat. Commun. 9, 2765. doi:10.1038/s41467-018-03891-9

Pant, J., Giovinazzo, J. A., Tuka, L. S., Peña, D., Raper, J., and Thomson, R. (2021). Apolipoproteins L1-6 Share Key Cation Channel-Regulating Residues but Have Different Membrane Insertion and Ion Conductance Properties. J. Biol. Chem. 297, 100951. doi:10.1016/j.jbc.2021.100951

Shi, J., Li, M., and Yang, R. (2020). Tumor-infiltrating Lymphocytes as a Feasible Adjuvant Immunotherapy for Osteosarcoma with a Poor Response to Neoadjuvant Chemotherapy. Immunotherapy 12, 641–652. doi:10.2217/imt-2020-0107

Shiratori, I., Ogasawara, K., Saito, T., Lanier, L. L., and Arase, H. (2004). Activation of Natural Killer Cells and Dendritic Cells upon Recognition of a Novel CD99-like Ligand by Paired Immunoglobulin-like Type 2 Receptor. J. Exp. Med. 199, 525–533. doi:10.1084/jem.20031885

Smeland, S., Bielack, S. S., Whelan, J., Bernstein, M., Hogendoorn, P., Krailo, M. D., et al. (2019). Survival and Prognosis with Osteosarcoma: Outcomes in More Than 2000 Patients in the EURAMOS-1 (European and American Osteosarcoma Study) Cohort. Eur. J. Cancer 109, 36–50. doi:10.1016/j.ejca.2018.11.027

Suehara, Y., Alex, D., Bowman, A., Middha, S., Zehir, A., Chakravarty, D., et al. (2019). Clinical Genomic Sequencing of Pediatric and Adult Osteosarcoma Reveals Distinct Molecular Subsets with Potentially Targetable Alterations. Clin. Cancer Res. 25, 6346–6356. doi:10.1158/1078-0432.Ccr-18-4032

Tawbi, H. A., Burgess, M., Bolejack, V., Van Tine, B. A., Schuetze, S. M., Hu, J., et al. (2017). Pembrolizumab in Advanced Soft-Tissue Sarcoma and Bone Sarcoma (SARC028): a Multicentre, Two-Cohort, Single-Arm, Open-Label, Phase 2 Trial. Lancet Oncol. 18, 1493–1501. doi:10.1016/s1470-2045(17)30624-1

Thrasher, A. J., and Burns, S. O. (2010). WASP: a Key Immunological Multitasker. Nat. Rev. Immunol. 10, 182–192. doi:10.1038/nri2724

Wang, D., Niu, X., Wang, Z., Song, C.-L., Huang, Z., Chen, K.-N., et al. (2019). Multiregion Sequencing Reveals the Genetic Heterogeneity and Evolutionary History of Osteosarcoma and Matched Pulmonary Metastases. Cancer Res. 79, 7–20. doi:10.1158/0008-5472.Can-18-1086

Wang, X., Wang, L., Xu, W., Wang, X., Ke, D., Lin, J., et al. (2021). Classification of Osteosarcoma Based on Immunogenomic Profiling. Front. Cel Dev. Biol. 9, 696878. doi:10.3389/fcell.2021.696878

Wang, Z., Li, B., Ren, Y., and Ye, Z. (2016). T-Cell-Based Immunotherapy for Osteosarcoma: Challenges and Opportunities. Front. Immunol. 7, 353. doi:10.3389/fimmu.2016.00353

Wilkerson, M. D., and Hayes, D. N. (2010). ConsensusClusterPlus: a Class Discovery Tool with Confidence Assessments and Item Tracking. Bioinformatics (Oxford, England) 26, 1572–1573. doi:10.1093/bioinformatics/btq170

Wilson, M. D., Cheung, J., Martindale, D. W., Scherer, S. W., and Koop, B. F. (2006). Comparative Analysis of the Paired Immunoglobulin-like Receptor (PILR) Locus in Six Mammalian Genomes: Duplication, Conversion, and the Birth of New Genes. Physiol. genomics 27, 201–218. doi:10.1152/physiolgenomics.00284.2005

Wu, C.-C., Beird, H. C., Andrew Livingston, J., Advani, S., Mitra, A., Cao, S., et al. (2020). Immuno-genomic Landscape of Osteosarcoma. Nat. Commun. 11, 1008. doi:10.1038/s41467-020-14646-w

Yang, M., Cao, Y., Wang, Z., Zhang, T., Hua, Y., and Cai, Z. (2021). Identification of Two Immune Subtypes in Osteosarcoma Based on Immune Gene Sets. Int. immunopharmacology 96, 107799. doi:10.1016/j.intimp.2021.107799

Yoshihara, K., Shahmoradgoli, M., Martínez, E., Vegesna, R., Kim, H., Torres-Garcia, W., et al. (2013). Inferring Tumour Purity and Stromal and Immune Cell Admixture from Expression Data. Nat. Commun. 4, 2612. doi:10.1038/ncomms3612

Zhao, J., Dean, D. C., Hornicek, F. J., Yu, X., and Duan, Z. (2020). Emerging Next-Generation Sequencing-Based Discoveries for Targeted Osteosarcoma Therapy. Cancer Lett. 474, 158–167. doi:10.1016/j.canlet.2020.01.020

Zhou, Y., Slone, N., Chrisikos, T. T., Kyrysyuk, O., Babcock, R. L., Medik, Y. B., et al. (2020). Vaccine Efficacy against Primary and Metastatic Cancer with In Vitro-generated CD103+conventional Dendritic Cells. J. Immunother. Cancer 8, e000474. doi:10.1136/jitc-2019-000474

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Feng, Zhao, Zhao, Song, Ma and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 21 January 2022
doi: 10.3389/fmolb.2021.792779

Integrative Genomic Analyses of 1,145 Patient Samples Reveal New Biomarkers in Esophageal Squamous Cell Carcinoma

Binbin Zou1,², Dinghe Guo1,², Pengzhou Kong1,², Yanqiang Wang1,², Xiaolong Cheng1,²* and Yongping Cui1,2,³*

¹Key Laboratory of Cellular Physiology of the Ministry of Education, Shanxi Medical University, Taiyuan, China

²Department of Pathology, Shanxi Medical University, Taiyuan, China

³Shenzhen Peking University-Hong Kong University of Science and Technology (PKU-HKUST) Medical Center, Peking University Shenzhen Hospital, Shenzhen, China

Edited by:
Xin Wang, The Chinese University of Hong Kong, Hong Kong SAR, China

Reviewed by:
Feng Gao, The Sixth Affiliated Hospital of Sun Yat-sen University, China
Hao Huang, City University of Hong Kong, Hong Kong SAR, China

* Correspondence: Xiaolong Cheng, chengxl@sxmu.edu.cn; Yongping Cui, cuiyp@sxmu.edu.cn

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 11 October 2021
Accepted: 02 December 2021
Published: 21 January 2022

Citation: Zou B, Guo D, Kong P, Wang Y, Cheng X and Cui Y (2022) Integrative Genomic Analyses of 1,145 Patient Samples Reveal New Biomarkers in Esophageal Squamous Cell Carcinoma. Front. Mol. Biosci. 8:792779. doi: 10.3389/fmolb.2021.792779

Due to the lack of effective diagnostic markers and therapeutic targets, esophageal squamous cell carcinoma (ESCC) shows a poor 5 years survival rate of less than 30%. To explore the potential therapeutic targets of ESCC, we integrated and reanalyzed the mutation data of WGS (whole genome sequencing) or WES (whole exome sequencing) from a total of 1,145 samples in 7 large ESCC cohorts, including 270 ESCC gene expression data. Two new mutation signatures and 20 driver genes were identified in our study. Among them, AP3S1, MUC16, and RPS15 were reported for the first time. We also discovered that the KMT2D was associated with the multiple clinical characteristics of ESCC, and KMT2D knockdown cells showed enhanced cell migration and cell invasion. Furthermore, a few neoantigens were shared between ESCC patients. For ESCC, compared to TMB, neoantigen might be treated as a better immunotherapy biomarker. Our research expands the understanding of ESCC mutations and helps the identification of ESCC biomarkers, especially for immunotherapy biomarkers.

Keywords: esophageal squamous cell carcinoma, mutation, neoantigen, immunotherapy, KMT2D, bioinformactics

INTRODUCTION

Esophageal cancer (EC) is the fourth most common cancer in China, with 375,000 annual deaths, 90% of which are esophageal squamous cell carcinoma (ESCC) (Chen et al., 2016). Due to the lack of effective diagnostic markers, most of ESCC patients are diagnosed at the advanced stages and are not suitable for surgical treatment. Due to the limited treatment methods, the 5 years survival rate of ESCC is less than 30% (Younes et al., 2002; Koshy et al., 2004). Therefore, it is imperative to explore new biomarkers and novel targets for ESCC diagnosis, prognosis, and clinical treatment.

In recent years, series of whole genome sequencing (WGS) and whole exome sequencing (WES) studies had been performed in ESCC (Lin et al., 2014; Song et al., 2014; Cao et al., 2015; Zhang et al., 2015; Hu et al., 2016; Qin et al., 2016; Sawada et al., 2016; Chang et al., 2017; Chen et al., 2017; Dai et al., 2017; Du et al., 2017; Liu et al., 2017; Nature, 2017; Dai et al., 2018; Yan et al., 2019; Yu et al., 2019; Cui et al., 2020; Dutta et al., 2020; He et al., 2020; Liu et al., 2020; Mangalaparthi et al., 2020). As a result of that, several ESCC driver genes had been identified. However, limitations in sample size and the algorithms used in driver gene identification still exist. More than half of the driver genes were identified in only one cohort, and few biomarkers were identified as especial immunotherapy-related biomarkers. At the same time, because of the lack of therapeutic targets for ESCC, targeted therapy treatment has not been improved significantly.

Here, we performed an integrative analysis using multiple datasets from 7 published cohorts, 270 ESCC gene expression data to explore potential biomarker especially immunotherapy biomarkers of ESCC. We found that the integration of data with a larger sample size is helpful in not only discovering new mutation signatures and new driver genes, but also identifying more potential biomarkers, especially low-frequency mutation genes.

METHODS

Data Collection and Analysis

ESCC mutation data: The DNA mutation data was downloaded from the results of articles of 7 cohorts (Lin et al., 2014; Song et al., 2014; Zhang et al., 2015; Sawada et al., 2016; Chang et al., 2017; Nature, 2017; Cui et al., 2020), which provided whole mutation data, whole clinical information of sample and the sample number was bigger. All mutation results were uniformly converted into MAF format (https://docs.gdc.cancer.gov/Data/File_Formats/MAF_Format, Supplementary Table S2, Supplementary Table S3 was the clinical information of samples). ESCC gene expression data: 1) TCGA-ESCA gene expression data download from UCSC xena, different expression genes (DEGs) were identified by t test. 2) GSE53625 chip data download from NCBI GEO. Use blast alignment for each probe of the GSE53625 chip data against the hg19 reference genome. 100% alignment was required, and mismatches and indels were not allowed. For multiple probes aligned to the same gene, the expression value of the gene was the average of all probes. There were a total of 270 ESCC gene expression data.

Mutation Signature Analysis

We used sigflow to analyze the mutation signature of the exon regions of all samples in SBS, DBS, ID types, and we also analyzed the whole genome of the 508 samples in SBS, DBS, ID types.

TMB and Neoantigen Analysis

TMB calculation: total TMB (aTMB)=(SNV + indel)/30M, fTMB= (exclude nonsynonymous SNV + indel)/30 M.

HLA allele typing: Use optitype to identify HLA-A, HLA-B, HLA-C alleles on the 90 WES samples.

Neoantigen identified: First used maf2vcf (https://github.com/mskcc/vcf2maf) to convert the MAF to VCF, then used vep (McLaren et al., 2016) to annotate VCF referred to pvactools manual, next used pvactools to perform neoantigen analysis on samples. The BindLevel of the peptide with SB or WB as candidate neoantigen.

Tumor neoantigen burden (TNB): Count the number of all candidate neoantigen in the sample.

Tumor neoantigen score (TNS): The neoantigen score was the sum of the scores of all mutant peptides of the gene (Gscore).

Where Gscore represented the neoantigen score of the gene, W represented the binding score of wild-type peptide with the HLA allele, M represented the binding score of the mutant peptide with the HLA allele, and AAscore represented the ratio of wild-type peptide binding score to mutant peptide binding score, if the mutant peptides did not have a corresponding wild-type peptide, such as mutant peptides produced by frameshift caused by indel, the ratio was 15.

Driver Gene Analysis

MutSigCV, driverml, oncodrivefml, and oncodriveclutl were used to identify driver genes based on coding region mutation of 1145 ESCC patients. For driverml, the p < 0.01 was used as the threshold, and for MutSigCV, oncodrivefml, oncodriveclutl, the FDR<0.05 was used as the threshold. The gene that was discovered by two software and more was identified as driver gene of ESCC. Used maftools (Mayakonda et al., 2018) to draw a mutation profile of the driver gene.

Mutual Exclusion Analysis

We used maftools to analyze gene exclusion and analysis, with a p = 0.05 as a threshold.

Clinical Correlation Analysis

Generally, we divided cancer patients into two groups: young and old by the threshold value of age as 60. To improve the grouping method, we used the kmeans clustering method (https://github.com/dstein64/kmeans1d) to divide the patients into two groups according to age. And the clustering was also based on 60 years old. The samples were divided respectively into two groups by the different clinical characteristics: T stage (T1-T2 VS T3-T4), TNM (S1-S2 VS S3-S3), gender (male VS female), lymph node metastasis (YES VS NO), smoking (YES VS NO), drinking (YES VS NO), tumor grade (G1-G2 VS G3-G4). For the tumor location, the samples were divided into upper, middle, and lower three groups. And because there are only 6 samples of M1, so we do not analyze M stage. Clinical information of all ESCC were in Supplementary Table S3.

Data Visualization

We used ggscatterstats, ggwithinstats, venn, volcano graph, and histogram to draw related graphs on the hiplot platform (https://hiplot.com.cn/), and used R ggplot2 package to plot other graphs.

Statics Analysis

The Fisher test was used to analyze the gene mutation with clinical characteristics, and the t-test was used to analyze the correlation between the gene mutation with numerical variables, such as age, aTMB, fTMB etc.

For survival analysis, we used the survminer of the R package to analyze the survival differences between groups. For numerical variables, we use surv_cutpoint to determine the best cut point.

Cell Culture Conditions

The ESCC cell lines used in this study were from our lab. All ESCC cell lines were cultured in HyClone™ RPMI-1640 medium (GE Healthcare Life Sciences, HyClone Laboratories, Logan, UT, United States) with 10% fetal bovine serum (FBS; Gibco; Thermo Fisher Scientific, Inc., Waltham, MA, USA) at 37°C in a 5% CO₂ incubator. Replace the culture medium according to the cell status and subculture cells when the density of cells increases to about 80–90%.

Knockdown of KMT2D in ESCC Lines

For the knockdown of KMT2D, we used the interfering RNAs (siRNAs) containing the sequence: 5′-GCA​CCA​TCA​TTC​GGA​ACG​A-3’ (KMT2D-si1) and 5′- CCA​GTA​CTT​TCG​CTT​CGA​A -3’ (KMT2D-si2). Cells at the logarithmic growth phase were transfected with siRNA using riboFECTTM CP Reagent (RIBOBIO, Guangzhou, China) according to the company’s recommendation. The efficiency of knockdown was determined by real time-PCR.

RNA Extraction and Real Time PCR

Total RNA of ESCC cells was isolated by using RNAiso plus (Takara, Dalian, China). Complementary DNA (cDNA) was synthesized from 2 μg of total RNA using a PrimeScript® RT reagent Kit with gDNA Eraser (Takara). TB Green® Premix Ex Taq® II kit (Takara) was used in real time PCR. All real time PCR reactions were performed in triplicate with an Applied Biosystems Step One Plus (ABI, Foster City, CA, United States). The relative expression of target genes was determined by normalization to GAPDH expression and calculated using the 2−ΔΔCt formula. The primer sequences of GAPDH and KMT2D used in this study were listed in Supplementary Table S7.

Cell Invasion and Migration Assay

The invasion and migration ability of cells were detected using transwell plates (8 μm, Corning, Inc.). For the migration assays, 50,000 to 80,000 cells/well were inoculated into the upper compartment of the transwell plate with serum-free culture. The lower part of the chamber was filled with culture fluid containing 10% FBS. The upper surface cells are removed after 24 h or 48 h. The cells passing through the membrane are fixed with 4% formaldehyde and stained with 0.1% crystal violet. Random five fields were chosen to count the number of transmigrated cells. For the invasion assays, the upper chambers were precoated with 100 μl Matrigel (1: 8 mixed with FBS-free media; BD Biosciences, Heidelberg, Germany) and proceeded as the same as described above.

Cell Proliferation Assay

In total, 5 × 10³ transfected cells were seeded into each well of a 96-well plate in a final volume of 200 µl. After cultured for 24, 48, 72, 96 h, 20 μl CCK8 solution was added into each well, then the cells were incubated at 37°C for 30 min. The relative number of surviving cells in each group was measured by a spectrophotometer at 490 nm.

Colony Formation Assay

Cells were seeded in a 6-well plate at a density of 1,000 or 1,500 cells/well and incubated for 10 days at 37°C with 5% CO₂. After PBS cleaning, The cells were fixed with 4% polyformaldehyde for 30 min and stained with 1% crystal violet for 30 min. The numbers of colonies containing more than 50 cells were counted under the microscope.

RESULTS

ESCC Cohort and Samples

Dozens of research cohorts have been published since 2014 (Supplementary Table S1) (Liu et al., 2017; Yu et al., 2019; Dutta et al., 2020; He et al., 2020; Mangalaparthi et al., 2020; Song et al., 2014; Cao et al., 2015; Zhang et al., 2015; Hu et al., 2016; Qin et al., 2016; Sawada et al., 2016; Chang et al., 2017; Chen et al., 2017; Dai et al., 2017; Du et al., 2017; Dai et al., 2018; Yan et al., 2019; Cui et al., 2020; Liu et al., 2020; Nature, 2017; Lin et al., 2014). In this study, we integrated and re-analyzed the 7 large cohorts including a total of 1,145 samples (Table 1) (Lin et al., 2014; Song et al., 2014; Zhang et al., 2015; Sawada et al., 2016; Chang et al., 2017; Nature, 2017; Cui et al., 2020). The whole analysis pipeline was shown in Figure 1A. There were 508 WGS samples and 637 WES samples a total of 149,085 mutations in the coding region (Supplementary Table S2). The median of ESCC tumor mutation burden (TMB) of nonsilent mutation in the coding region was located between Uterine Corpus Endometrial Carcinoma (UCES) and Liver hepatocellular carcinoma (LIHC), and was lower than Esophageal adenocarcinoma (EAC) (Supplementary Figure S1).

TABLE 1 | ESCC cohort and samples.

FIGURE 1 | Analysis pipeline and mutation signature of whole genome region in ESCC. (A) Analysis pipeline, (B) SBS signature of WGS, (C) DBS signature of WGS, (D) ID signature of WGS.

Mutational Signatures in ESCC

The previous analyses of ESCC mutation signatures of ESCC were all based on single base substitution (SBS). Here, we re-analyzed the SBS, double-base substitution (DBS), and small insertions and deletion (ID) signatures separately in both the coding regions of 1,145 ESCC samples and the whole genome of 508 ESCC using sigflow (Wang et al., 2020a). Total of 8 SBS, 9 DBS, 9 ID signatures were identified in the whole genome region (Figures 1B–D). 6 SBS and 2 DBS, and 3 ID signatures in the coding regions (Figures 2A–C). Annotated with the COSMIC signatures, we found that the signatures in the coding region (Supplementary Table S4) and those in whole genome region (Supplementary Table S6) are similar in some aspects. A larger number of signatures were identified in our study. Besides that, two new signatures that have not been reported before were discovered in the whole genome region. One was WGS-SBS-S3 related to possibly damage by reactive oxygen species and the other was WGS-DBS-S1 related to prior chemotherapy treatment with platinum drugs. WGS-DBS-S1were correlated with the overall survival (OS) rates (Figure 2D, p = 0.00044). Next, we analyzed the correlation between signatures of coding regions and clinical characteristics (Figure 2E, Supplementary Table S5) or the OS rates. We found that most of them were related to drinking, smoking, gender and lymph metastasis (FDR<0.05). WES-SBS-S6 (Figure 2F, p = 0.00044) and WES-ID-Sig1 (Figure 2G, p = 0.00044) were correlated with the OS rates.

FIGURE 2 | Mutation signature of coding region in ESCC. (A) SBS signatures of WES, (B) DBS signatures of WES, (C) ID signatures of WES, (D) WGS-DBS-S1 survival analysis, (E) WES signature correlation with clinical characteristics, (F) WES-SBS-S6 survival analysis, (G) WES-ID-S1 survival analysis. In the survival analysis, red line represents the ESCC patients enrichment in the signature, the green line represents ESCC patients without enrichment in the signature.

Tumor Mutation Burden in ESCC

TMB is an important cancer immunotherapy biomarker (Boumber, 2018a; Chan et al., 2019; Jardim et al., 2020). Adult and pediatric patients with high TMB (≥10 mutations/megabase) were approval for pembrolizumab by FDA (Subbiah et al., 2020). Currently, there is not a standard way to calculate TMB. Some studies excluded synonymous mutations (Zehir et al., 2017), while some studies include synonymous mutations (Xu et al., 2019). In this study, we defined aTMB which includes synonymous mutations and fTMB which excludes synonymous mutations. The average value of aTMB was 4.30 in the 1145 ESCC patients, and the median value of aTMB was 3.58 in the 1145 ESCC patients. The average value of fTMB was 3.27 in the 1145 ESCC patients, and the median value of aTMB was 2.75 in the 1145 ESCC patients. The consistency between aTMB and fTMB in ESCC was significant (Figure 3A,R = 1, p = 0), but the values calculated by the two methods are significantly different (Figure 3B, p = 4.13e-13). Thus, we speculated that the calculation method needed to be considered when using a specific threshold.

FIGURE 3 | TMB in ESCC. (A) the correlation between aTMB and fTMB, (B) compared the mean value between aTMB and fTMB, (C) Survival analysis of aTMB (high aTMB >10), (D) Survival analysis of fTMB (high fTMB >10), (E) Survival analysis of aTMB (high aTMB >8.0), (F) Survival analysis of fTMB (high fTMB >5.7).

We compared different group OS rates using different TMB values. If grouping patients with the TMB value 10 (Cui et al., 2020), there was no significant difference between the high TMB group and the low TMB group regardless of using aTMB (Figure 3C, p = 0.14) or fTMB (Figure 3D, p = 0.43) method. However, using survminer (Kassambara et al., 2017), the optimal grouping point was automatically selected by the surv_cutpoint method. Using the aTMB value 8.0 as the threshold, we found that there was a significant difference OS rates between the high and low TMB groups (Figure 3E, p = 0.014). There was also significant difference between the high and low fTMB groups (fTMB >5.7, Figure 3F, p = 0.04). Therefore, different analysis methods require the corresponding TMB threshold values. In addition, the commonly defined high-TMB threshold value should not be applied in different cancer types. For ESCC, we thought the aTMB as 8.0 was better than 10. And fTMB as 5.7 was recommended for the similar study. However, the accurate value of the high TMB threshold suitable for clinical grouping requires further validation.

High Heterogeneity of Neoantigen in ESCC

Neoantigens play an important role in tumor immunotherapy. We analyzed the neoantigens of 90 WES samples (Zhang et al., 2015) with pvactools (Hundal et al., 2020) for obtaining fastq data. 2 new neoantigen indicators were defined as follows. One was the tumor neoantigen burden (TNB), which was the number of neoantigen in the sample. The other was the tumor neoantigen score (TNS), which was the score of neoantigen in the sample (See methods).

The correlation between TNS and TMB was very high (Figure 4A, R = 0.85, p = 6.35e-26), and the correlation between TNB and fTMB was also high (Figure 4B, R = 0.81, p = 1.84e-22). Since there were no mutation hotspots in ESCC, the highest mutation frequency was TP53:p.R282W (2.41%), so the neoantigens produced by patients are different. The highest frequency of the neoantigen peptides can only be found in 3 patients. Only 2 of this neoantigen accounted for 0.6% of all neoantigens. 60 neoantigens appeared in 2 patients accounted for 1.8%. The proportion of neoantigens that appeared in one patient was 97.6%. Therefore, we thought that the effective cancer vaccine is hard to be developed for ESCC based on our results.

FIGURE 4 | Neoantigen in ESCC. (A) The correlation bewtten TNB and TNS, (B) Compared the mean value between TNB and fTMB, (C) Survival analysis of TNS (high TNS >2.2), (D) Survival analysis of TNB (high TNB>2.5), (E) Survival analysis of fTMB (high fTMB >1.6), (F) Survival analysis aTMB (high aTMB >1.8).

The 90 patients were divided into 2 groups based on the TNB or the TNS, by the surv_cutpoint method in survminer. There were significant differences between the high and low groups (TNS>2.2, p = 0.027, Figure 4C; TNB>2.5, p = 0.022, Figure 4D). Compared with the TNB high group, the high group of TNS had more patients, the ratio of the high-low group is to 34:35, and the ratio is 15:54 in the TNB.

Compared with neoantigen, 90 patients were divided into 2 groups based on the fTMB (high fTMB>1.6) or aTMB (high aTMB>1.8), by the surv_cutpoint method in survminer. There was no significant difference between the high and low groups (Figure 4E, p = 0.14, Figure 4F, p = 0.09). Compared with the OS, neoantigen was a better biomarker than TMB. And the TNS was a suitable factor for neoantigen calculation compared with the TNB.

Potential Targeted Therapy Drugs in ESCC

At present, limited targeted drugs have been approved for ESCC. In order to find potential targeted drugs for ESCC, we searched the targeted drug databases OncoKB (Chakravarty et al., 2017) and Civic (Griffith et al., 2017). Currently, there were no special targeted drugs for ESCC in the OncoKB database. In the Civic database, the targeted drugs for ECA and ESCC were different, but the highest evidence level was only B for ESCC. Recently, some studies have begun to treat different cancer patients with specific targeted drugs based on the same gene mutation position (Gambacorti‐Passerini et al., 2018). Our study uses the OncoKB and Civic databases for analysis of targeted drugs for particular gene mutation in ESCC. We found that there are only drugs targeting only TP53 and PIK3CA gene mutations (Supplementary Figure S4). Among them, 135 patients, accounting for 11.8%, can benefit from the drug AMGMDS3 targeting TP53 gene (Supplementary Figure S4). From the data of the two databases, patients with ESCC can benefit little from the existing targeted drugs. More research is required to find new targeted drugs in the future.

Significant Mutated Genes in ESCC

We re-analyzed more than 20 sequencing datasets (Liu et al., 2017; Yu et al., 2019; Dutta et al., 2020; He et al., 2020; Mangalaparthi et al., 2020; Song et al., 2014; Cao et al., 2015; Zhang et al., 2015; Hu et al., 2016; Qin et al., 2016; Sawada et al., 2016; Chang et al., 2017; Chen et al., 2017; Dai et al., 2017; Du et al., 2017; Dai et al., 2018; Yan et al., 2019; Cui et al., 2020; Liu et al., 2020; Nature, 2017; Lin et al., 2014) of ESCC and found that only 11 studies included the driver gene analyses (Supplementary Table S1). Seven of them used MutSigCV for analysis (Lawrence et al., 2013), dNdScv was used in one study (Martincorena et al., 2017), and MutSigCV and oncodriveFML were applied together in another study (Mularoni et al., 2016). A total of 47 driver genes were found in 11 published studies. Among them, 7 genes (TP53 (11), NOTCH1 (9), CDKN2A (8), ZNF750 (8), NFE2L2 (7), PIK3CA (7), RB1 (6)) were considered driver genes in more than 5 studies and 26 genes were considered driver genes in only one study (Figure 5A). Previous studies were limited because of using fewer analysis tools or the relatively small sample size. Therefore, the reported driver genes identified by previous studies is limited.

FIGURE 5 | SMGs in ESCC. (A) SMGs of ESCC in previous studies, (B) SMGs of ESCC identified by 4 software, (C) The distribution of SMGs of ESCC identified by 4 software in previous studies, (D) Mutation profile of SMGs in ESCC, (E) MUC16 mutation in ESCC, (F) AP3S1 mutation in ESCC, (G) RPS15 mutation in ESCC, (H) The correlation between SMGs of ESCC and clinical characteristics, (I) The correlation between NOTCH1 mutation and lymph node metastasis of ESCC, (J) The correlation between NOTCH1 mutation and different age group of ESCC patients (young and old), (K) The correlation between NOTCH1 mutation and lymph node metastasis of young ESCC patients (age<60), (L) The correlation between NOTCH1 and lymph node metastasis of old ESCC patients (age >= 60).

To identify the driver genes of ESCC in a more accurate and comprehensive way, four software were applied in our study: MutSigCV, driverml (Han et al., 2019), OncodriveFML, and OncodriveCLUSTL (Arnedo-Pac et al., 2019). A total of 20 driver candidate genes were identified (Figure 5B, Figure 5C). Among them, TP53 was a driver gene recognized by all software. EP300, FAT1, FBXW7, PIK3CA, ZNF750, and AP3S1 were identified as driver genes in three software. Among the 20 driver genes, 14 genes were reported in three or more studies, FAT2 and PTEN were reported as driver genes in only 2 studies, and CUL3 was reported as driver gene in only 1 study. AP3S1, MUC16, RPS15 had not been reported as a driver gene (Figure 5C). AP3S1, MUC16, and RPS15 were three newly discovered driver genes. The MUC16 had been considered as a driver gene in the pan-cancer driver gene research (Martínez-Jiménez et al., 2020), the mutation frequency of MUC16 in ESCC was high (17%, Figure 5D), and there was not hotspot mutation in the MUC16 (Figure 5E). while AP3S1 and RPS15 had not been reported as driver genes before, and there were hotspot mutations in the AP3S1 (Figure 5F) and RPS15 (Figure 5G) in the ESCC, the mutation frequency of AP3S1 and RPS15 were low in the TCGA, and there was not hotspot mutation in the TCGA (Supplementary Figures S2, S3).

Next, we analyzed the correlation between driver gene and clinical characteristics (Figure 5H). The results showed that some genes were related to multiple clinical characteristics, such as ZNF750 related to lymph node metastasis and TNM stage. NOTCH1 mutated were related lymph node metastasis (p = 0.005, Figure 5I) and related to age (p = 0.002, Figure 5J). Moreover, we found that NOTCH1 mutated was related to young (p = 0.005, Figure 5K), not to old (p = 0.190, Figure 5L).

We analyzed the mutual exclusion and co-mutation of all mutant genes (Supplementary Figure S5), and found that ZNF750 and CDKN2A was mutually exclusive (p = 0.004809937). A total of 52 pairs of genes have been found to have co-mutations, the most significant of which are TTN and lFSIP2 (p = 2.94E-05).

Correlation Between Gene Mutation and Lymph Node Metastasis

Most cancer death was originally caused by metastasis. Tumor metastasis was mainly included lymph node metastasis and blood metastasis. In our study, there were 52% ESCC patients with lymph node metastasis (Figure 6A). And the survival time of ESCC patients with lymph node metastasis was lower than that of ESCC patients without lymph node metastasis (p < 0.0001, Figure 6B). We found that the drinking history, smoking history, T stage, tumor location and tumor grade were related to lymph node metastasis (p < 0.05). It was worth noting that among these clinical characteristics, tobacco consumption largely promoted the increase in the frequency of lymph node metastasis. Compared with non-smoking patients, the frequency of lymph node metastasis was 43%, rising to 63% (p = 9.58E-11, Figure 6C).

FIGURE 6 | Gene mutation with lymph node metastasis. (A) the frequency of lymph node metastasis in ESCC, (B) Survival analysis of lymph node metastasis in ESCC, (C) smoking related with lymph node metastasis in ESCC, (D) volcano plot of the correlation gene mutation with lymph node metastasis, x-axis was the log₂ (the ratio of mutated rate of lymph node metastasis group and the mutated rate of without lymph node metastasis group), and y-axis was P (the correlation gene mutation with lymph node metastasis), (E) KEGG enrichment pathway of the higher mutation gene of lymph node metastasis compared with no lymph node metastasis, (F) KEGG enrichment pathway of the lower mutation gene of lymph node metastasis compared with no lymph node metastasis.

There were 237 mutated genes related with lymph node metastasis (p < 0.05). Among them, 188 genes showed higher mutation rate of lymph node metastasis than no lymph node metastasis group, called “higher mutated genes (HMG)”. These gene mutations may promote lymph node metastasis. While 49 genes showed lower mutation rate in lymph node metastasis group than that in no lymph node metastasis group, called “lower mutated gene (LMG)” (Figure 6D). And these gene mutations may prevent lymph node metastasis. We used kobas (Xie et al., 2011) respectively to do KEGG pathway enrichment analysis of these two types of genes. We found that the Pathways in cancer, Herpes simplex virus 1 infection, Human papillomavirus infection were the top 3 pathways in enrichment of those LHG (Figure 6E). And pathways in cancer, MicroRNAs in cancer, Breast cancer were the top 3 pathways of those LWG (Figure 6F).

KMT2D Plays a Tumor Suppressor Role in ESCC

The frequency of KMT2D mutation was 15.02% (top 3 of all mutated gene), and there was no hotspot mutation in this gene mutation in ESCC (Figure 7A) and other cancer (Figure 7B). The KMT2D mutation frequency was highest in BLCA closed to 30% (Figure 7C). The expression of KMT2D in tumor was lower than that in normal tissues in the GSE53625 (Figure 7D). The different expression pattern between normal and tumor tissues existed in many cancer types in the TCGA data (Figure 7E). We analyzed the correlation between gene mutation and clinical characteristics (Figure 7F). KMT2D mutation was related to multiple clinical characteristics (drinking, location, lymph, gender, and smoking, top1 of the number with gene-related clinical characteristics). To verify the biological role of KMT2D in ESCC, we compared the expression levels of KMT2D in several cell lines including normal esophageal epithelial cells such as NE3, HET-1A and ESCC cell lines such as KYSE150, KYSE180, KYSE450, TE-5, and TE-9. The expression levels of KMD2T in different cell lines were shown in Figure 8A. We selected KYSE150 cell line with a relatively high endogenous level expression for perform knockdown experiments. Knockdown of KMD2T in KYSE150 cell line showed a significant decrease (Figure 8B). The results of CCK8 and the colony formation assays showed that the cell proliferation, colony formation ability was significantly improved in KMT2D knockdown group compared with the control group (Figures 8C,D). Besides that, the knockdown of KMT2D can markedly promote cell invasion and migration of KYSE150 (Figures 8E,F).

FIGURE 7 | KMT2D mutation, KMT2D expression and the correlation between KMT2D mutation and clinical characteristics. (A) KMT2D mutation in ESCC, (B) KMT2D mutation in TCGA, (C) mutation and CNV of KMT2D in TCGA cancer, (D) the expression of KMT2D in tumor and normal of ESCC in the GSE53625, (E) expression of KMT2D in TCGA cancer, (F) the correlation of gene mutation with clinical characteristics.

FIGURE 8 | The effect of KMT2D in KYSE150 cell line. (A) The mRNA expression of KMT2D in normal esophageal epithelial cell lines and ESCC cell lines detected by q-RTPCR, (B) The knockdown efficiency of KMT2D in KYSE150 cell line was verified by q-RTPCR, (C) KMT2D knockdown promoted the ability of colony formation in KYSE150 cell line compared to the control, (D) KMT2D knockdown promoted the ability of proliferation in KYSE150 cell line compared to the control, (E) KMT2D knockdown promoted the ability of migration in KYSE150 cell line compared to the control, (F) KMT2D knockdown promoted the ability of migration and invasion in KYSE150 cell line compared to the control. All data are presented as the mean ± standard deviation of the three in-dependent experiments.

DISCUSSION

Mutation signature has always been an important part of cancer research. The analysis of mutation signature has also evolved from SBS to DBS, ID and other complex mutations (Alexandrov et al., 2020). In this study, for the first time, the SBS and ID of ESCC were analyzed. We identified 8 SBS, 9 DBS and 9 ID signatures in the whole genome region. And We also found 6 SBS,2 DBS, and 3 ID signatures the coding region. Most signatures in WGS and WES were similar in ESCC, for example WES-SBS-S1, WGS-SBS-S1 and WGS-SBS-S5 were correlation with APOBEC. However, in the whole genome region, there were more signatures and new signatures identified. For example, WGS-DBS-S1 correlation with prior chemotherapy treatment with platinum drugs, which cannot be identified in the coding region. The mutation signatures in whole genome region can provide more valuable information compared with coding region. This not only deepens our understanding of the mutational signature of ESCC, but also enriches the number of mutational signatures of ESCC. And we also found two new mutation signatures. These mutation signatures can not only be used for the identification of ESCC, but also can be used as new factors for related machine learning or deep learning.

Using a larger size of samples and various driver gene identify software will be helpful in determining the accurate driver genes of ESCC. In our study, we identified 20 drivers using the mutation data of 1,145 samples by using 4 software and 3 (AP3S1, MUC16, RPS15) of them were discovered for the first time. The mutation frequency of MUC16 in ESCC was high. MUC16was recognized as a driver gene in the pan-cancer driver gene research (Martínez-Jiménez et al., 2020). MUC16 was reported as an important biomarker for cancer (Felder et al., 2014a; Aithal et al., 2018). According to studies in other cancers (Felder et al., 2014b; Aithal et al., 2018; Yeku et al., 2020; Yu et al., 2020), MUC16 may also be an important immunotherapy target for ESCC. The mutation frequency of AP3S1and RPS15 were low both in ESCC and TCGA, but hotspot mutation was found in the ESCC. Even with hotspot mutations in the two gene but too low mutation frequency limits their value in clinical applications.

We tried to find better immunotherapy and targeted therapy targets for ESCC according to the results obtained from mutations of multiple cohorts. From the OncoKB and Civic databases, there was no targeted therapy drug for ESCC. Both TMB and neoantigen were new immunotherapy markers (Schumacher and Schreiber, 2015; Lu and Robbins, 2016; Łuksza et al., 2017; Boumber, 2018b; Samstein et al., 2019; Zhou et al., 2020; Yin et al., 2021). In the 7 cohorts, the TMB was analysis in only one cohort (508 ESCC patients, published in 2020 years on Cell Research). In this cohort more than 10 mutations per Mb was as TMB-H. TMB was attracted more and more attention by researchers. In recent years, researchers have carried out some systematic research on related immunotherapy on TMB. As a continuous variable, TMB has no recognized cutoff suitable for all cancers. TMB cutoff values is supposed to be different in various cancer types. For ESCC, there is no relevant research to determine the appropriate TMB cutoff. For ESCC, the conventional value for TMB was slightly higher. It may be more appropriate to use the aTMB as 8, and it was more appropriate to use the fTMB as 5.7. The threshold of fTMB and aTMB were caculated by the surv_cutpoint method of “survminer” R package (Kassambara et al., 2017). This method was widely used in various cancers survival analysis for continuous variable (Zhou et al., 2019; Wang et al., 2020b; Lai et al., 2020; Wu et al., 2020). But we need more research to determine the true suitable threshold of TMB. Only a few somatic mutations will produce peptides that have been properly processed and loaded onto the MHC complex. Generally, the more somatic mutations a tumor has, the more likely it is to form a neoantigen (Coulie et al., 2014; Snyder and Chan, 2015). In the 90 WES ESCC cohort, we found that compared with TMB, neoantigen was related to the OS. In ESCC, compared with TMB, neoantigen may be a better marker, however, due to the limitation of sample size, further research is needed. In the relationship between TMB and prognosis, we observed the opposite phenomenon. High TMB had a poor prognosis in 1,145 samples, but in 90 WES samples, high TMB had a good prognosis. This may be due to the number of samples. Similar phenomena have also been found in lung cancer research (Greillier et al., 2018; Alborelli et al., 2020; Nie et al., 2020). Neoantigen therapy is a highly individualized tumor immunotherapy, different patients often have different neoantigens for immunotherapy (Türeci et al., 2018; Blass and Ott, 2021; Jou et al., 2021). For ESCC, neoantigen presents high heterogeneity. Thus, personalized immunotherapy targeting to neoantigens may be a promising treatment for ESCC patients.

Lymph node metastasis is an essential cause of death in cancer (Chaffer and Weinberg, 2011). We analyzed the gene mutation with different clinical characteristics and focused on analyzing the gene mutations with or without lymph node metastasis. Therefore, the identification of biomarkers in ESCC lymph node metastasis will greatly help ESCC treatment and prognosis. Some gene mutations promoted lymph node metastasis, while other gene mutations inhibited lymph node metastasis. We have identified these two types of genes and performed an enrichment pathway analysis, which could help us to understand the mechanism of lymph node metastasis in ESCC. We found that NOTCH1 mutations were only associated with lymph node metastasis in young ESCC patients, which may be used for the diagnosis of early lymph node metastasis in ESCC.

We found that KMT2D (Lysine methyltransferase 2D) was related to multiple clinical characteristics and this gene had a relatively high mutation frequency. KMT2D was a gene of histone methyltransferase that methylates the Lys-4 position of histone H3, and there some research in other cancers (Guo et al., 2013; Toska et al., 2017; Xiong et al., 2018; Li et al., 2019; Sun et al., 2019; Alam et al., 2020; Zheng et al., 2021), but there was still a lack of reports in ESCC (Abudureheman et al., 2018). Knockdown of this gene in ESCC cell lines increased invasion and migration ability. The results indicated that KMT2D may plays an important role in ESCC. This gene can be used as a potential target for ESCC treatment.

Our results can deepen the understanding of the mutations of ESCC. Based on the mutation data of large samples, we can identify potential biomarkers of ESCC, especially markers for immunotherapy, and provide new ideas for the diagnosis and treatment of ESCC.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.

AUTHOR CONTRIBUTIONS

BZ performed the study, analyzed the omics and experimental data and wrote the manuscript. DG performed the experiment. QW and PK revised the manuscript. XC and YC designed the study and revised the manuscript.

FUNDING

This work was supported by the National Natural Science Foundation of China (81972613, 81672768), the Fund for Shanxi “1331 Project”, and the funding for “Sanjin Scholars”.

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2021.792779/full#supplementary-material

REFERENCES

Abudureheman, A., Ainiwaer, J., Hou, Z., Niyaz, M., Turghun, A., Hasim, A., et al. (2018). High MLL2 Expression Predicts Poor Prognosis and Promotes Tumor Progression by Inducing EMT in Esophageal Squamous Cell Carcinoma. J. Cancer Res. Clin. Oncol. 144, 1025–1035. doi:10.1007/s00432-018-2625-5

Aithal, A., Rauth, S., Kshirsagar, P., Shah, A., Lakshmanan, I., Junker, W. M., et al. (2018). MUC16 as a Novel Target for Cancer Therapy. Expert Opin. Ther. Targets 22, 675–686. doi:10.1080/14728222.2018.1498845

Alam, H., Tang, M., Maitituoheti, M., Dhar, S. S., Kumar, M., Han, C. Y., et al. (2020). KMT2D Deficiency Impairs Super-enhancers to Confer a Glycolytic Vulnerability in Lung Cancer. Cancer Cell 37, 599–617. doi:10.1016/j.ccell.2020.03.005

Alborelli, I., Leonards, K., Rothschild, S. I., Leuenberger, L. P., and Jermann, P. (2020). Tumor Mutational burden Assessed by Targeted NGS Predicts Clinical Benefit from Immune Checkpoint Inhibitors in Non‐small Cell Lung Cancer. J. Pathol. , 25019–25029. doi:10.1002/path.5344

Alexandrov, L. B., Kim, J., Kim, J., Haradhvala, N. J., Huang, M. N., Tian Ng, A. W., et al. (2020). The Repertoire of Mutational Signatures in Human Cancer. Nature 578, 94–101. doi:10.1038/s41586-020-1943-3

Arnedo-Pac, C., Mularoni, L., Muiños, F., Gonzalez-Perez, A., and Lopez-Bigas, N. (2019). OncodriveCLUSTL: a Sequence-Based Clustering Method to Identify Cancer Drivers. Bioinformatics 35, 4788–4790. doi:10.1093/bioinformatics/btz501

Blass, E., and Ott, P. A. (2021). Advances in the Development of Personalized Neoantigen-Based Therapeutic Cancer Vaccines. Nat. Rev. Clin. Oncol. 18, 215–229. doi:10.1038/s41571-020-00460-2

Boumber, Y. (2018). Tumor Mutational burden (TMB) as a Biomarker of Response to Immunotherapy in Small Cell Lung Cancer. J. Thorac. Dis. 10, 4689–4693. doi:10.21037/jtd.2018.07.120

Boumber, Y. (2018). Tumor Mutational burden (TMB) as a Biomarker of Response to Immunotherapy in Small Cell Lung Cancer. J. Thorac. Dis. 10, 4689. doi:10.21037/jtd.2018.07.120

Cao, W., Wu, W., Yan, M., Tian, F., Ma, C., Zhang, Q., et al. (2015). Multiple Region Whole-Exome Sequencing Reveals Dramatically Evolving Intratumor Genomic Heterogeneity in Esophageal Squamous Cell Carcinoma. Oncogenesis 4, e175. doi:10.1038/oncsis.2015.34

Chaffer, C. L., and Weinberg, R. A. (2011). A Perspective on Cancer Cell Metastasis. Science 331, 1559–1564. doi:10.1126/science.1203543

Chakravarty, D., Gao, J., Phillips, S., Kundra, R., Zhang, H., Wang, J., et al. (2017). OncoKB: A Precision Oncology Knowledge Base. JCO Precision Oncol. 1, 1–16. doi:10.1200/po.17.00011

Chan, T. A., Yarchoan, M., Jaffee, E., Swanton, C., Quezada, S. A., Stenzinger, A., et al. (2019). Development of Tumor Mutation burden as an Immunotherapy Biomarker: Utility for the Oncology Clinic. Ann. Oncol. 30, 44–56. doi:10.1093/annonc/mdy495

Chang, J., Tan, W., Ling, Z., Xi, R., Shao, M., Chen, M., et al. (2017). Genomic Analysis of Oesophageal Squamous-Cell Carcinoma Identifies Alcohol Drinking-Related Mutation Signature and Genomic Alterations. Nat. Commun. 8, 15290. doi:10.1038/ncomms15290

Chen, W., Zheng, R., Baade, P. D., Zhang, S., Zeng, H., Bray, F., et al. (2016). Cancer Statistics in China, 2015. CA: A Cancer J. Clinicians 66, 115–132. doi:10.3322/caac.21338

Chen, X.-X., Zhong, Q., Liu, Y., Yan, S.-M., Chen, Z.-H., Jin, S.-Z., et al. (2017). Genomic Comparison of Esophageal Squamous Cell Carcinoma and its Precursor Lesions by Multi-Region Whole-Exome Sequencing. Nat. Commun. 8, 524. doi:10.1038/s41467-017-00650-0

Coulie, P. G., Van den Eynde, B. J., Van Der Bruggen, P., and Boon, T. (2014). Tumour Antigens Recognized by T Lymphocytes: at the Core of Cancer Immunotherapy. Nat. Rev. Cancer 14, 135–146. doi:10.1038/nrc3670

Cui, Y., Chen, H., Xi, R., Cui, H., Zhao, Y., Xu, E., et al. (2020). Whole-genome Sequencing of 508 Patients Identifies Key Molecular Features Associated with Poor Prognosis in Esophageal Squamous Cell Carcinoma. Cell. Res. 30, 902–913. doi:10.1038/s41422-020-0333-6

Dai, J. Y., Wang, X., Buas, M. F., Zhang, C., Ma, J., Wei, B., et al. (2018). Whole-genome Sequencing of Esophageal Adenocarcinoma in Chinese Patients Reveals Distinct Mutational Signatures and Genomic Alterations. Commun. Biol. 1, 174. doi:10.1038/s42003-018-0182-8

Dai, W., Ko, J. M. Y., Choi, S. S. A., Yu, Z., Ning, L., Zheng, H., et al. (2017). Whole-exome Sequencing Reveals Critical Genes Underlying Metastasis in Oesophageal Squamous Cell Carcinoma. J. Pathol. 242, 500–510. doi:10.1002/path.4925

Du, P., Huang, P., Huang, X., Li, X., Feng, Z., Li, F., et al. (2017). Comprehensive Genomic Analysis of Oesophageal Squamous Cell Carcinoma Reveals Clinical Relevance. Sci. Rep. 7, 15324. doi:10.1038/s41598-017-14909-5

Dutta, M., Nakagawa, H., Kato, H., Maejima, K., Sasagawa, S., Nakano, K., et al. (2020). Whole Genome Sequencing Analysis Identifies Recurrent Structural Alterations in Esophageal Squamous Cell Carcinoma. PeerJ 8, e9294. doi:10.7717/peerj.9294

Felder, M., Kapur, A., Gonzalez-Bosquet, J., Horibata, S., Heintz, J., Albrecht, R., et al. (2014). MUC16 (CA125): Tumor Biomarker to Cancer Therapy, a Work in Progress. Mol. Cancer 13, 1–15. doi:10.1186/1476-4598-13-129

Felder, M., Kapur, A., Gonzalez-Bosquet, J., Horibata, S., and Patankar, M. S. (2014). MUC16 (CA125): Tumor Biomarker to Cancer Therapy, a Work in Progress. Mol. Cancer 13, 129. doi:10.1186/1476-4598-13-129

Gambacorti‐Passerini, C., Orlov, S., Zhang, L., Braiteh, F., Huang, H., Esaki, T., et al. (2018). Long‐term Effects of Crizotinib in ALK‐positive Tumors (Excluding NSCLC). A phase 1b open‐label study 93, 607–614.

Greillier, L., Tomasini, P., and Barlesi, F. (2018). The Clinical Utility of Tumor Mutational burden in Non-small Cell Lung Cancer. Transl. Lung Cancer Res. 7, 639–646. doi:10.21037/tlcr.2018.10.08

Griffith, M., Spies, N. C., Krysiak, K., McMichael, J. F., Coffman, A. C., Danos, A. M., et al. (2017). CIViC Is a Community Knowledgebase for Expert Crowdsourcing the Clinical Interpretation of Variants in Cancer. Nat. Genet. 49, 170–174. doi:10.1038/ng.3774

Guo, C., Chen, L. H., Huang, Y., Chang, C.-C., Wang, P., Pirozzi, C. J., et al. (2013). KMT2D Maintains Neoplastic Cell Proliferation and Global Histone H3 Lysine 4 Monomethylation. Oncotarget 4, 2144. doi:10.18632/oncotarget.1555

Han, Y., Yang, J., Qian, X., Cheng, W.-C., Liu, S.-H., Hua, X., et al. (2019). DriverML: a Machine Learning Algorithm for Identifying Driver Genes in Cancer Sequencing Studies. Nucleic Acids Res. 47, e45. doi:10.1093/nar/gkz096

He, W., Leng, X., Yang, Y., Peng, L., Shao, Y., Li, X., et al. (2020). Genetic Heterogeneity of Esophageal Squamous Cell Carcinoma with Inherited Family History. Ott Vol. 13, 8795–8802. doi:10.2147/ott.s262512

Hu, N., Kadota, M., Liu, H., Abnet, C. C., Su, H., Wu, H., et al. (2016). Genomic Landscape of Somatic Alterations in Esophageal Squamous Cell Carcinoma and Gastric Cancer. Cancer Res. 76, 1714–1723. doi:10.1158/0008-5472.can-15-0338

Hundal, J., Kiwala, S., McMichael, J., Miller, C. A., Xia, H., Wollam, A. T., et al. (2020). pVACtools: a Computational Toolkit to Identify and Visualize Cancer Neoantigens. Cancer Immunol. Res. 8, 409–420. doi:10.1158/2326-6066.CIR-19-0401

Jardim, D. L., Goodman, A., Gagliato, D., and Kurzrock, R. (2020). The Challenges of Tumor Mutational Burden as an Immunotherapy Biomarker. Cancer Cell 39, 154–173. doi:10.1016/j.ccell.2020.10.001

Jou, J., Harrington, K. J., Zocca, M.-B., Ehrnrooth, E., and Cohen, E. (2021). The Changing Landscape of Therapeutic Cancer Vaccines—Novel Platforms and Neoantigen Identification. Clin. Cancer Res. 27, 689–703. doi:10.1158/1078-0432.ccr-20-0245

Kassambara, A., Kosinski, M., Biecek, P., and Fabian, S. (2017). Survminer: Drawing Survival Curves Usin gggplot2', R package version 0.3, 1.

Koshy, M., Esiashvilli, N., Landry, J. C., Thomas, C. R., and Matthews, R. H. (2004). Multiple Management Modalities in Esophageal Cancer: Combined Modality Management Approaches. The Oncologist 9, 147–159. doi:10.1634/theoncologist.9-2-147

Lai, R., Zhang, W., He, X., Liao, X., Liu, X., Fu, W., et al. (2020). Prognostic Role of ACTL10 in Cytogenetic Normal Acute Myeloid Leukemia. J. Cancer 11, 5150. doi:10.7150/jca.39467

Lawrence, M. S., Stojanov, P., Polak, P., Kryukov, G. V., Cibulskis, K., Sivachenko, A., et al. (2013). Mutational Heterogeneity in Cancer and the Search for New Cancer-Associated Genes. Nature 499, 214–218. doi:10.1038/nature12213

Li, S.-S., Jiang, W.-L., Xiao, W.-Q., Li, K., Zhang, Y.-F., Guo, X.-Y., et al. (2019). KMT2D Deficiency Enhances the Anti-cancer Activity of L48H37 in Pancreatic Ductal Adenocarcinoma. World J. Gastrointest. Oncol. 11, 599. doi:10.4251/wjgo.v11.i8.599

Lin, D.-C., Hao, J.-J., Nagata, Y., Xu, L., Shang, L., Meng, X., et al. (2014). Genomic and Molecular Characterization of Esophageal Squamous Cell Carcinoma. Nat. Genet. 46, 467–473. doi:10.1038/ng.2935

Liu, M., An, H., Zhang, Y., Sun, W., Cheng, S., Wang, R., et al. (2020). Molecular Analysis of Chinese Oesophageal Squamous Cell Carcinoma Identifies Novel Subtypes Associated with Distinct Clinical Outcomes. EBioMedicine 57, 102831. doi:10.1016/j.ebiom.2020.102831

Liu, X., Zhang, M., Ying, S., Zhang, C., Lin, R., Zheng, J., et al. (2017). Genetic Alterations in Esophageal Tissues from Squamous Dysplasia to Carcinoma. Gastroenterology 153, 166–177. doi:10.1053/j.gastro.2017.03.033

Lu, Y.-C., and Robbins, P. F. (2016). Cancer Immunotherapy Targeting Neoantigens, Seminars in Immunology. Amsterdam, Netherlands: Elsevier, 22–27. doi:10.1016/j.smim.2015.11.002

Łuksza, M., Riaz, N., Makarov, V., Balachandran, V. P., Hellmann, M. D., Solovyov, A., et al. (2017). A Neoantigen Fitness Model Predicts Tumour Response to Checkpoint Blockade Immunotherapy. Nature 551, 517–520.

Mangalaparthi, K. K., Patel, K., Khan, A. A., Manoharan, M., Karunakaran, C., Murugan, S., et al. (2020). Mutational Landscape of Esophageal Squamous Cell Carcinoma in an Indian Cohort. Front. Oncol. 10, 1457. doi:10.3389/fonc.2020.01457

Martincorena, I., Raine, K. M., Gerstung, M., Dawson, K. J., Haase, K., Van Loo, P., et al. (2017). Universal Patterns of Selection in Cancer and Somatic Tissues. Cell 171, 1029–1041. doi:10.1016/j.cell.2017.09.042

Martínez-Jiménez, F., Muiños, F., Sentís, I., Deu-Pons, J., Reyes-Salazar, I., Arnedo-Pac, C., et al. (2020). A Compendium of Mutational Cancer Driver Genes. Nat. Rev. Cancer 20, 555–572. doi:10.1038/s41568-020-0290-x

Mayakonda, A., Lin, D.-C., Assenov, Y., Plass, C., and Koeffler, H. P. (2018). Maftools: Efficient and Comprehensive Analysis of Somatic Variants in Cancer. Genome Res. 28, 1747–1756. doi:10.1101/gr.239244.118

McLaren, W., Gil, L., Hunt, S. E., Riat, H. S., Ritchie, G. R., Thormann, A., et al. (2016). The ensembl variant effect predictor17, 1–14. doi:10.1186/s13059-016-0974-4

Mularoni, L., Sabarinathan, R., Deu-Pons, J., Gonzalez-Perez, A., and López-Bigas, N. (2016). OncodriveFML: a General Framework to Identify Coding and Non-Coding Regions with Cancer Driver Mutations. Genome Biol. 17, 128. doi:10.1186/s13059-016-0994-0

Nature, C. (2017). Integrated Genomic Characterization of Oesophageal Carcinoma. Nature 541, 169–175. doi:10.1038/nature20805

Nie, W., Xu, M., Gan, L., Zhang, Y., and Han, B. H. (2020). Advanced Non-small Cell Lung Cancer Patients with Low Tumor Mutation Burden Might Derive Benefit from Immunotherapy. J. Immunother. 43, 189–195. doi:10.1097/CJI.0000000000000318

Qin, H.-D., Liao, X.-Y., Chen, Y.-B., Huang, S.-Y., Xue, W.-Q., Li, F.-F., et al. (2016). Genomic Characterization of Esophageal Squamous Cell Carcinoma Reveals Critical Genes Underlying Tumorigenesis and Poor Prognosis. Am. J. Hum. Genet. 98, 709–727. doi:10.1016/j.ajhg.2016.02.021

Samstein, R. M., Lee, C.-H., Shoushtari, A. N., Hellmann, M. D., Shen, R., Janjigian, Y. Y., et al. (2019). Tumor Mutational Load Predicts Survival after Immunotherapy across Multiple Cancer Types. Nat. Genet. 51, 202–206. doi:10.1038/s41588-018-0312-8

Sawada, G., Niida, A., Uchi, R., Hirata, H., Shimamura, T., Suzuki, Y., et al. (2016). Genomic Landscape of Esophageal Squamous Cell Carcinoma in a Japanese Population. Gastroenterology 150, 1171–1182. doi:10.1053/j.gastro.2016.01.035

Schumacher, T. N., and Schreiber, R. (2015). Neoantigens in Cancer Immunotherapy. Science 348, 69–74. doi:10.1126/science.aaa4971

Snyder, A., and Chan, T. A. (2015). Immunogenic Peptide Discovery in Cancer Genomes. Curr. Opin. Genet. Dev. 30, 7–16. doi:10.1016/j.gde.2014.12.003

Song, Y., Li, L., Ou, Y., Gao, Z., Li, E., Li, X., et al. (2014). Identification of Genomic Alterations in Oesophageal Squamous Cell Cancer. Nature 509, 91–95. doi:10.1038/nature13176

Subbiah, V., Solit, D. B., Chan, T. A., and Kurzrock, R. (2020). The FDA Approval of Pembrolizumab for Adult and Pediatric Patients with Tumor Mutational burden (TMB) ≥10: a Decision Centered on Empowering Patients and Their Physicians. Ann. Oncol. 31, 1115–1118. doi:10.1016/j.annonc.2020.07.002

Sun, P., Wu, T., Sun, X., Cui, Z., Zhang, H., Xia, Q., et al. (2019). KMT2D Inhibits the Growth and Metastasis of Bladder Cancer Cells by Maintaining the Tumor Suppressor Genes. Biomed. Pharmacother. 115, 108924. doi:10.1016/j.biopha.2019.108924

Toska, E., Osmanbeyoglu, H. U., Castel, P., Chan, C., Hendrickson, R. C., Elkabets, M., et al. (2017). PI3K Pathway Regulates ER-dependent Transcription in Breast Cancer through the Epigenetic Regulator KMT2D. Science 355, 1324–1330. doi:10.1126/science.aah6893

Türeci, Ö., Löwer, M., Schrörs, B., Lang, M., Tadmor, A., and Sahin, U. (2018). Challenges towards the Realization of Individualized Cancer Vaccines. Nat. Biomed. Eng. 2, 566–569. doi:10.1038/s41551-018-0266-2

Wang, S., Tao, Z., Wu, T., and Liu, X.-S. (2020). Sigflow: an Automated and Comprehensive Pipeline for Cancer Genome Mutational Signature Analysis. Bioinformatics . doi:10.1093/bioinformatics/btaa895

Wang, Z., Pan, L., Guo, D., Luo, X., Tang, J., Yang, W., et al. (2020). A Novel Five-Gene Signature Predicts Overall Survival in Hepatocellular Carcinoma. Front. Oncol. doi:10.3389/fonc.2021.642563

Wu, Z., Long, X., Tsang, S. Y., Hu, T., Yang, J.-F., Mat, W. K., et al. (2020). Genomic Subtyping of Liver Cancers with Prognostic Application. BMC Cancer 20, 1–13. doi:10.1186/s12885-020-6546-8

Xie, C., Mao, X., Huang, J., Ding, Y., Wu, J., Dong, S., et al. (2011). KOBAS 2.0: a Web Server for Annotation and Identification of Enriched Pathways and Diseases. Nucleic Acids Res. 39, W316–W322. doi:10.1093/nar/gkr483

Xiong, W., Deng, Z., Tang, Y., Deng, Z., Li, M. J. B., and communications, b. r. (2018). Downregulation of KMT2D Suppresses Proliferation and Induces Apoptosis of Gastric Cancer. Biochem. Biophys. Res. Commun. 504, 129–136. doi:10.1016/j.bbrc.2018.08.143

Xu, Z., Dai, J., Wang, D., Lu, H., Dai, H., Ye, H., et al. (2019). Assessment of Tumor Mutation burden Calculation from Gene Panel Sequencing Data. Ott Vol. 12, 3401–3409. doi:10.2147/ott.s196638

Yan, T., Cui, H., Zhou, Y., Yang, B., Kong, P., Zhang, Y., et al. (2019). Multi-region Sequencing Unveils Novel Actionable Targets and Spatial Heterogeneity in Esophageal Squamous Cell Carcinoma. Nat. Commun. 10, 1670. doi:10.1038/s41467-019-09255-1

Yeku, O. O., Rafiq, S., Koneru, M., Purdon, T. J., Song, M., Wang, P., et al. (2020). MUC16-directed Immunotherapy for Ovarian Cancer. Philadelphia, PA: Clinical Cancer Research, Amer Assoc Cancer Research 615 Chestnut ST, 17th Floor, 26.

Yin, L., Zhou, L., and Xu, R. (2021). Identification of Tumor Mutation burden and Immune Infiltrates in Hepatocellular Carcinoma Based on Multi-Omics Analysis. Front. Mol. Biosci. 7, 496. doi:10.3389/fmolb.2020.599142

Younes, M., Henson, D. E., Ertan, A., and Miller, C. C. (2002). Incidence and Survival Trends of Esophageal Carcinoma in the United States: Racial and Gender Differences by Histological Type. Scand. J. Gastroenterol. 37, 1359–1365. doi:10.1080/003655202762671215

Yu, Y., Cao, J., Wu, W., Zhu, Q., Tang, Y., Zhu, C., et al. (2019). Genome-wide Copy Number Variation Analysis Identified ANO1 as a Novel Oncogene and Prognostic Biomarker in Esophageal Squamous Cell Cancer. Carcinogenesis 40, 1198–1208. doi:10.1093/carcin/bgz077

Yu, Y., Lin, D., Li, A., Chen, Y., and Yao, H. (2020). Association of Immune Checkpoint Inhibitor Therapy with Survival in Patients with Cancers with MUC16 Variants. JAMA Netw. Open 3, e205837. doi:10.1001/jamanetworkopen.2020.5837

Zehir, A., Benayed, R., Shah, R. H., Syed, A., Middha, S., Kim, H. R., et al. (2017). Mutational Landscape of Metastatic Cancer Revealed from Prospective Clinical Sequencing of 10,000 Patients. Nat. Med. 23, 703–713. doi:10.1038/nm.4333

Zhang, L., Zhou, Y., Cheng, C., Cui, H., Cheng, L., Kong, P., et al. (2015). Genomic Analyses Reveal Mutational Signatures and Frequently Altered Genes in Esophageal Squamous Cell Carcinoma. Am. J. Hum. Genet. 96, 597–611. doi:10.1016/j.ajhg.2015.02.017

Zheng, B., Song, Z., Chen, Y., and Yan, W. (2021). Genomic Analyses of Metaplastic or Sarcomatoid Carcinomas from Different Organs Revealed Frequent Mutations in KMT2D. Front. Mol. Biosci. 8, 587. doi:10.3389/fmolb.2021.688692

Zhou, C., Li, C., Peng, S., Zhou, L., and Li, H. (2020). C, 7. Omprehensive Analysis of the Relationships between Tumor Mutation burden with Immune Infiltrates in Cervical Cell Carcinoma. Front. Mol. Biosci. doi:10.3389/fmolb.2020.582911

Zhou, R., Zeng, D., Zhang, J., Sun, H., Wu, J., Li, N., et al. (2019). A Robust Panel Based on Tumour Microenvironment Genes for Prognostic Prediction and Tailoring Therapies in Stage I–III colon Cancer. EBioMedicine 42, 420–430. doi:10.1016/j.ebiom.2019.03.043

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Zou, Guo, Kong, Wang, Cheng and Cui. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 27 January 2022
doi: 10.3389/fmolb.2021.770624

Over-Expression and Prognostic Significance of FATP5, as a New Biomarker, in Colorectal Carcinoma

Qi-Shun Geng¹, Mei-Jia Yang², Li-Feng Li², Zhi-Bo Shen², Long-Hao Wang², Yuan-Yuan Zheng², Wen-Hua Xue¹ and Jie Zhao1,²*

¹Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China

²Internet Medical and System Applications of National Engineering Laboratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China

Edited by:
Tianhui Chen, University of Chinese Academy of Sciences, China

Reviewed by:
Metin Çalışkan, Uşak University, Turkey
Xiaolong Liu, Mengchao Hepatobiliary Hospital of Fujian Medical University, China

* Correspondence: Jie Zhao, zhaojie@zzu.edu.cn

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 04 September 2021
Accepted: 13 December 2021
Published: 27 January 2022

Citation: Geng Q-S, Yang M-J, Li L-F, Shen Z-B, Wang L-H, Zheng Y-Y, Xue W-H and Zhao J (2022) Over-Expression and Prognostic Significance of FATP5, as a New Biomarker, in Colorectal Carcinoma. Front. Mol. Biosci. 8:770624. doi: 10.3389/fmolb.2021.770624

Background: Fatty acid transporters (FATPs) family play an important role in the uptake and metabolism regulation of long-chain fatty acids, which influence the occurrence and developing of multiple tumors. Fatty acid transporter 5(FATP5), a member of FATPs family, participates in fatty acid transport and lipid metabolism and is related to tumor development, whose mechanism in colorectal cancer (CRC) remains unclear.

Methods: In this study, we comprehensively utilized a range of relevant bioinformatic tools along with multiple databases to analyze the expression of FATPs family and investigate the biological function and prognostic value of FATP5 in CRC. Besides, cell proliferation and cell cycle distribution analysis, western blotting and immunohistochemistry (IHC) further validated the conclusion of bioinformatics analysis.

Results: FATP5 is the only member of FATPs family which was overexpressed in CRC. In the survival analysis based on the GSE39582 databases, the low expression of FATP5 predicts poor prognosis in CRC. Similar results were also observed in GSE17536, GSE28814 and TCGA colon cohorts. The potential function of DNA methylation regulated the abnormal expression of FATP5 in CRC. In addition, enrichment analysis indicated that FATP5 also participates in the regulation of cell cycle. Furthermore, Gene Set Enrichment Analysis (GSEA) showed a strong negative correlation between FATP5 and cell growth, implying that it may participate in regulating cancer cell proliferation by the regulation of cell cycle G2/M transition. At last, we identified that FATP5 was overexpressed in colorectal carcinoma tissues through immunohistochemistry staining, and played an important role in cell cycle by cell proliferation and cell cycle distribution analysis.

Conclusion: This study suggested that FATP5 was overexpression in colorectal carcinoma and predicted favorable prognosis, indicating it as a novel appealing prognostic marker for CRC.

Keywords: colorectal carcinoma, biomarker, FATP5, cell cycle, survival

INTRODUCTION

Due to factors such as aging population, rapid population growth, and environmental pollution, cancer incidence and mortality continued to increase worldwide. Among all cancers, the morbidity and mortality of CRC were at the forefront of global cancer statistics, which indicate it ranks the third among the most frequently diagnosed cancers and that it’s the fourth leading cause of cancer death worldwide (Favoriti et al., 2016; Bray et al., 2018). With the application of diagnostic technology and equipment, screening can effectively reduce the incidence and mortality of CRC. However, due to economic conditions and social status, CRC screening was only available to a small part of the global population, which led to wide variations in the morbidity and mortality of CRC (Schreuders et al., 2015). Due to changes in multiple system such as blood and immunity, the pathogenesis of colorectal cancer was very complicated (Mlecnik et al., 2016). Studies focusing on the molecular mechanism of CRC occurrence and development can be beneficial to the diagnose and treatment of this malignant tumor. However, the overall 5-years survival rate for patients with CRC is still very low (Overman et al., 2017). Therefore, finding reliable biomarkers for early diagnosis, accurate prognosis and replacement therapy still wait for further discovery.

Long-chain fatty acids (LCFA) are one of the main energy sources for cells, whose uptake and activation can affect many cellular processes, including membrane synthesis, intracellular signal transduction, and energy metabolism. The uptake of LCFA involves a variety of membrane proteins, such as FATPs (Stahl, 2004). The FATPs family consists of six members, FATP1-6, encoded by SLC27A1-6. FATPs are complete membrane proteins with at least one transmembrane domain, ranging in size from 63 to 80 KDa. The FATPs are also an indispensable transmembrane protein, which can enhance the absorption of long-chain and ultra-long-chain fatty acids to cells. All FATP members are located at the C-terminus, and have highly conserved 311 amino acid natural sequence, called FATP sequence, and AMP binding domain, which are responsible for LCFA binding and uptake. Despite the sequence similarity of FATP members, the proteins are differentially expressed in tissues and cell types (involving adipose tissue, liver, intestine, and endothelial cells, etc.) (Anderson and Stahl, 2013). FATP5 is a liver-specific member of the FATP family. The molecule of FATP5 is bile BAL, which catalyzes the binding of bile acids to amino acids and plays an unexpected part in bile acid metabolism. Previous studies indicated that FATP5 plays an important role in fatty acid transport, lipid metabolism, and weight regulation (Hubbard et al., 2006). Nevertheless, what role FATP5 plays in the progression of CRC is still ambiguous and remains to be elucidated.

In this study, we utilized a wide range of comprehensive bioinformatics methods and molecular biology experiments to analyze FATP5 expression, potential function, and impacts on prognosis of human CRC, which provided a theoretical basis for future clinical research. The study identified the overexpression of FATP5 in colorectal cancer tissues by immunohistochemical staining and played an important role in cell cycle by cell proliferation and cell cycle distribution analysis (Figure 1).

FIGURE 1 | Flow diagram of the study.

MATERIALS AND METHODS

Cell Culture and Cell Transfection

CRC cells including CACO-2 and HCT116 cells were acquired from Chinese Academy of Sciences (Shanghai, China). The cells were maintained in DMEM with 10% FBS (Thermo, United States) and cultured at 37°C, 5% CO2 and saturated humidity.

FATP5-siRNAs (siFATP5) and control-siRNA (siNC) were chemically synthesized by Tsingke Biotechnology Co., Ltd (Beiing, China), and used for transfection into CRC cell lines. To silence the expression of FATP5, siRNAs specifically targeting to FATP5 was obtained. The target sequences of FATP5 is shown below: si-FATP5: 5-CAG​AAG​GCA​ACA​UGG​GCU​UAG​UCA​A-3. JetPRIME® Transfection Reagent was used for cell transfection (Polyplus Transfection, United States).

The Cancer Genome Atlas (TCGA) Database and Gene Expression Omnibus (GEO) Datasets

Gene expression array in CRC tumor and related clinical information were downloaded from the GEO2. 566 cancer samples and 19 normal samples were obtained from GSE39582 (557 samples had prognostic information and 525 samples had clinical parameters). In addition, the expression profiling of CRC patients and survival information were extracted from GEO: GSE17536 (N = 177) and GSE28814 (N = 125) datasets. The acquisition of FATP5 transcriptome data from colorectal tumors in the TCGA dataset is available from the Cancer Genome Browser website of the University of California, Santa Cruz (http://xena.ucsc.edu/) (Goldman et al., 2019), which was also used to elevate the correlation of FATP5 and cell cycle regulation. We retrieved CRC tumor sample (n = 285) data from TCGA.

The Oncomine Platform and GEPIA Dataset

The Oncomine platform (https://www.oncomine.org/resource/login.html) providing open access to cancer microarray database online was used to facilitate the analysis of genome-wide expression in various cancers (Rhodes et al., 2004). We used the Oncomine database to compare the mRNA expressions of FATPs between clinical colorectal cancer specimens and normal controls, whose purpose was to pick out genes that are significantly differently expressed in FATPs. In order to obtain the important gene probe, we set the following thresholds: p-value < 1e-4, fold change >2 and the gene ranks in the top 10%. The online database Gene Expression Profiling Interactive Analysis (GEPIA) (http://gepia.cancer-pku.cn/index.html), as a newly developed interactive web server, enables RNA sequencing expression data of 9,736 tumors as well as 8,587 normal samples from the TCGA and GTEx projects (Tang et al., 2017). The gene expression of FATPs was verified in the GEPIA database, which was also used to gauge the expression and overall survival of genes.

cBioPortal Database and Catalogue of Somatic Mutations in Cancer (COSMIC) Dataset

The cBioPortal (http://cbioportal.org) Cancer Genomics portal serves as a resource offering open data to interactively explore multidimensional genomic datasets on cancer and currently has access to over 5,000 tumor samples from 20 cancer studies, which can greatly assist researchers to access molecular profiles and clinical attributes in extensive cancer genome projects, and enable scientific research personnel to transform abundant data sets into relevant biological information and clinical applications (Cerami et al., 2012; Gao et al., 2013). We applied this database to determine the genes that are positively associated with FATP5 expression in CRC and obtained related information of copy number alterations (CNAs). COSMIC(http://cancer.sanger.ac.uk)—the Catalogue of Somatic Mutations in Cancer—is the world’s largest source of professional manually-compiled somatic mutation information relating to human cancers (Tate et al., 2019). This database compiles data, which covers a wide range of the field of cancer genomics in terms of somatic mutations. Therefore, we used the database to analyze mutations of FATP5 in CRC.

DNA Methylation Analysis

MEXPRESS(https://mexpress.be/) is a data visualization tool meant to simplify the visualization of TCGA expression, DNA methylation and clinical data, along with the relationships between them (Koch et al., 2019), which was used to complete data analysis and get relevant data. Pearson’s correlation between FATP5 gene expression level and methylation of its CpG islands was evaluated by using R software.

The PPI (Protein-Protein Interact) Network Diagram Construction and Function Enrichment Analysis of Co-Expressed Genes

The String (http://string-db.org/) (Szklarczyk et al., 2019) database is a system that searches for interactions between proteins. We select multiple protein tools and define the species as human, obtain a PPI network of co-expressed genes, which was visualized by Cytoscape (version 3.7.2) software (Smoot et al., 2011). ClueGO (version 3.0.3) (Maere et al., 2005) is a plug-in playing the character to visualize the nonredundant biological terms for large clusters of genes in a functionally-grouped network, which executed the biologic process of functional annotation analysis of co-expressed genes as well. DAVID Bioinformatics Resources (http://david.abcc.ncifcrf.gov/),a comprehensive biological biology and analysis tool that systematically extracts biological significance from gene or protein lists, was used for function enrichment analysis (Huang et al., 2009a; Huang et al., 2009b). Gene Set Enrichment Analysis is a computational method employed to determine whether a priori defined set of genes shows statistically significant, consistent difference between two biological states. Each sample was defined as either “H” or “L”, depending on whether it was greater than the median mRNA expression value of FATP5. GSEA was performed using clusterProfiler package with gene set c5 (c5. go.v7.4. symbols.gmt).

Cell Proliferation and Cell Cycle Distribution Analysis

CCK-8 assays were performed by a CCK-8 kit from Beyotime (Beijing, China). In brief, cells in log phase growth were planted in 96-well dishes at a concentration of 5 × 10³ cells/well and cultivated in DMEM containing 10% FBS for cell attachment. Cell viability was detected with CCK-8 reagent followed by the protocol of manufacturer at the indicated time point (24, 48 and 72 h).

Propidium iodide (PI; Sigma, P4170) staining was used to analyze DNA content and cell cycle profile. Assays were performed 48 h after the transfection of siNC and siFATP5. Cells were washed twice with ice-cold PBS, harvested, and fixed with 70% ethanol at 4°C overnight. Cells were resuspended in PBS, permeabilized with 0.125% Triton X-100 (Sigma-Aldrich, T9284) and stained with 40 μg/ml PI for 30 min on ice. Cell cycle distributions were analyzed by flow cytometry (ACEA NovoCyteF, Agilent).

qRT-PCR, Western Blotting and Immunohistochemistry(IHC)

TRIzol reagent (Invitrogen, Carlsbad, CA, United States) was used to extracted RNA from cells. Complementary DNA (cDNA) was synthesized from total RNA using PrimeScript RT reagent Kit (Takara, Dalian, China), and PCR was performed using SYBR Green RT-PCR Kit (AG11701, ACCURATE BIOTECHNOLOGY, HUNAN,Co.,Ltd.). GAPDH served as an internal control. PCR was run on the StepOne Plus Real-Time PCR System (Applied Biosystems, Foster City, CA, United States), and data were analyzed using the 2-ΔΔCT method. The primer sequences used in the experiment is as follow. FN1, 5′- GCT​CCC​TGC​CTA​TGC​CAC​ACC-3′ (forward), 5′- CAC​ACA​CAG​CCT​GGT​ACA​CAT-3′ (reverse).

Cells were lysed using RIPA buffer (Beyotime) supplemented with protease inhibitor cocktail (Thermo Scientific, Waltham, MA, United States) and PMSF. A BCA method was used to measure the protein concentration. 40 μg cell lysates were subjected to sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), transferred to polyvinyldene fluoride (PVDF) membrane (Sigma, St Louis, MO, United StatesA). The membrane was incubated with specific antibody for FATP5 (1:1,000 dilution;Affinity, DF3845), p27 (1:1,000 dilution; Affinity, AF6324) and CCNB1 (1:2000 dilution; proteintech, 67686-1-Ig), followed by incubation with horseradish peroxidase-conjugated anti-IgG. Autoradiograms were densitometrically quantified (Quantity One software; Bio-Rad), with Β-actin serving as internal control.

Immunohistochemistry of colorectal cancer arrays (HCol-Can060 PT-01, SHANGHAI OUTDO BIOTECH CO,LTD.; National Engineering Center for Biochip at Shanghai; National Human Genetic Resources Sharing Service Platform, 2005DKA21300, China) was performed by using FATP5 antibody (1:200 dilution; Affinity). Briefly, after xylol deparaffinization and rehydration in graded alcohols, colorectal cancer arrays were boiled in citrate buffer, pre-incubated with H₂O₂, and blocked with rabbit or goat serum (DAKO, Glostrup, Denmark). Arrays were then incubated with a primary antibody and then with an HRP-conjugated secondary antibody. Before counterstaining with hematoxylin, diaminobenzidine was used to develop the target proteins. The IHC staining was evaluated by applying a scoring system from 0 to 3 (0 = negative or no staining, 1 = weak or low staining; 2 = moderate or intermediate staining; and 3 = strong or high staining), which determine the score for each tissue based on the percentage of positive cells and intensity of staining. The arrays were read by a pathologist using Olympus BX41 microscope.

Statistical Analysis

By default, all statistical analysis is performed as described in the web resources. In short, the Student’s t-test is used to compare the expression of mRNA in the Oncomine database. GEPIA difference analysis uses one-way ANOVA by defining disease status (tumor or normal) as a variable. Spearman correlation analysis was used to evaluate the correlation of gene expression in cBioPortal and UCSC databases. Fisher’s exact test is used to measure gene richness in annotated terms in David’s annotation system. Besides, the χ² test was utilized for analysis on correlation between FATP5 expression and clinical parameters. Univariate and multivariate analyses were performed using Cox proportional hazard model. p < 0.05 indicates statistical significance (*, p < 0.05; **, p < 0.01).

RESULTS

Overexpression of FATP5 in Colorectal Cancer

The Oncomine database was used to analyze the expression profile of FATPs family. We found that the expression of FATP1 and FATP5 was significantly elevated in colorectal cancer (Figure 2A). Then, we determined the expression of FATP1 and FATP5 using GEPIA database, which further validated FATP5 was the only member of FATPs family that upregulated in colorectal cancer tissues compared to normal (Figures 2B,C). What’s more, Oncomine analysis of cancer vs normal samples in different patient datasets showed that the expression of FATP5 was dramatically higher in colon adenoma, colon carcinoma epithelia, colon mucinous adenocarcinoma and rectal adenocarcinoma (Figure 2D; Table 1).

FIGURE 2 | FATP5 mRNA expression was elevated in human colorectal cancer (CRC). (A) Using the Oncomine database, this graph shows the numbers of datasets with statistically significant mRNA overexpression (red) or downexpression (blue) of FATPs (cancer tissues vs corresponding normal tissues). The threshold was defined with the following parameters: p-value of 1E-4, fold change of 2 and gene ranking of 10%. (B–C) The GEPIA database verified that FATP5 gene expression was significantly upregulated in colon cancerFATP1 tissues (n = 275) compared with normal colon tissues (n = 349), *p < 0.05. (D) The Oncomine database analysis of FATP5 gene expressions in different subtypes of colorectal cancer, ***p < 0.001.

TABLE 1 | The Oncomine database analysis indicated that FATP5 expressions are upregulated in different subtypes of colorectal carcinoma.

Low FATP5 Expression Predicts Poor Prognosis in Colorectal Cancer

χ-tile 3.6.1 software was used to identify the optimum cut-off value for distinguishing high-expression FATP5 patients from low-expression FATP5 patients. Low-expression FATP5 patients had shorter overall survival (OS) (HR = 1.65; 95% CI: 0.216–2.35; p < 0.01) and shorter relapse free survival (RES) (HR = 1.57; 95% CI: 1.17–2.1; p < 0.01) than patients with high-expression FATP5 patients (Figures 3A,B). To determine whether the FATP5 expression for predicting prognostic was robust, the performance of the FATP5 expression was assessed in three independent colon cohorts, which totally consisted of 587 colon patients. The optimum cut-off value for colon cohorts was identified with χ-tile 3.6.1 software, based on the expression of FATP5. Similar results were observed in GSE17536, GSE28814 and TCGA colon cohorts, which suggested that low-expression FATP5 group had significantly worse OS than those who were assigned to the low-expression FATP5 group according to the expression of FATP5 (Figure 3C). We further compared the clinical characteristics (including age, sex, location, clinical stage, and tumor grade) between FATP5 expression-high and low groups and managed to observe obvious statistics differences in lymph node metastasis and distant metastasis, while no observation for other clinical features indicated significant differences (Table 2). In addition, the univariate and multivariate Cox regression model using GSE39582 accession demonstrated that distant metastasis and FATP5 expression were irrelevant prognostic factors for OS of patients with CRC (Table 3). In order to further illustrate the importance of FATP5 for prognosis, we construct a nomogram based on clinical parameters and the expression of FATP5 (Figure 3D). The ROC and the calibration curves indicated decent accuracy (Area Under Curve (AUC) of 1-year survival: 0.828; AUC of 5-years survival: 0.738) and good discrimination (C-index = 0.763) (Figures 3E,F).

FIGURE 3 | The survival analysis of FATP5 in human colorectal cancer. The overall survival (A) and relapse free survival (B) of FATP5 in colorectal cancer based on GSE39582 accession. (C) The survival analysis of FATP5 in GSE17536, GSE28814 and TCGA colon cohorts. (D) A nomogram consisting of risk score and other clinical indicators for predicting 1-, and 5-years OS of CRC. (E) Calibration plot of nomogram for predicting probabilities of 1, and 5-years overall survival of patients. (F) Time-dependent ROC for 1-, and 5-years OS predictions for the nomogram compared with actual observations.

TABLE 2 | Comparison of clinical characteristics between low-expression FATP5 group and high-expression FATP5 group in GSE39582 cohort.

TABLE 3 | Univariate and multivariate regression analyses for predicting overall survival in GSE39582 cohort.

Analysis of the Potential Genetic and Epigenetic Alterations Associated With FATP5 Dysregulation

Considering rare mutation and significant prognosis of FATP5, we next explored the potential mechanism of FATP5 abnormal expression in CRC. Copy number alterations (CNAs) has been confirmed as a fundamental mechanism of oncogene activation in tumor (Chang et al., 2020). Aimed to ascertain whether there is firm evidence to establish the casual association between CNAs and the abnormal expression of FATP5 in CRC, we analyzed data with CNAs available in TCGA dataset by using CbioPortal tool. The patterns of CNAs were no significant differences in the FATP5-high and low groups (Figures 4A,B). Analogous to CNAs’ activating oncogene function, studies integrating DNA-methylation profiles and gene expression data have testified that methylation in different genomic regions is related to gene expression levels (Moore et al., 2013). To investigate whether DNA methylation results in FATP5 dysfunction, MEXPRESS was used to examine the status of CpG sites in colorectal carcinoma (Figure 4C). There are 15 CpG sites with a significant correlation with the expression of FATP5 (p < 0.05); among them, 14 CpG sites, including cg23478354, cg22309518, cg07061962, cg05983698, cg09364328 etc., were negatively correlated with FATP5 expression. Then, we employed the Pearson correlation coefficient to the five CpG sites with the greatest correlation and found that FATP5 expression was negatively correlated with the five CpG sites (Figure 4D, p < 0.001). Collectively, these data demonstrated the potential function of DNA methylation in regulating the abnormal expression of FATP5 in CRC, showing the potential mechanism is worth further exploring.

FIGURE 4 | Analysis of the potential genetic and epigenetic alterations associated with FATP5 dysregulation. (A,B) The CNAs in FATP5-high and low expression groups. (C) Analysis of CpG island methylation and abnormal FATP5 expression using TCGA dataset. (D) Correlation between FATP5 expression and CpG island methylation was performed. ***p < 0.001.

PPI Network Construction and Analysis of Co-expression Genes

The cBioPortal dataset was applied to obtain 29 genes in protein expression that are correlated with FATP5 in mRNA expression (p-value <0.05) for colorectal adenocarcinoma (TCGA, Firehose Legacy). We employed the STRING platform and cytoscape software to construct a protein-protein interact diagram of 29 co-expression genes (Figure 5A). In this PPI network, number of nodes:29, number of edges:89, average node degree:6.14, avg. local clustering coefficient:0.716, expected number of edges:25, PPI enrichment p-value:< 1.0e-16, these shown that co-expressed genes were closely related and highly reliable. Then, we made use of ClueGO and CluePedia to further performed the biological process analysis of hub genes. Cyclin-dependent protein serine/threonine kinase regulator activity, DNA damage response, signal transduction by p53 class mediator resulting in cell cycle and mitotic cell cycle checkpoint were largely altered (Figure 5B).

FIGURE 5 | PPI network construction and enrichment analysis of FATP5 correlation genes. (A) Clustering analysis of FATP5 co-expressed genes by STRING tools. (B) The biological process analysis of hub genes was performed using the BiNGO plug-in, *p < 0.05. (C) KEGG pathway enrichment analysis of hub genes. GSEA enrichment analysis of FATP5 based TCGA dataset (D) and GSE39582 accession (E).

GO and KEGG Pathway Enrichment Analysis Revealing Related Biological Functions of FATP5

Subsequently, we used the DAVID database for GO and KEGG pathway enrichment analysis to analysis biological functions of co-expression genes. A total of 46 enrichment results were obtained, including 21 biological processes, 5 molecular functions, and 5 cellular components, the top 5 go enrichment results of which were shown in Table 4. The biological processes mainly contained G1/S transition of mitotic cell cycle, positive regulation of cell proliferation, negative regulation of apoptotic process, cell proliferation, and positive regulation of mitotic cell cycle, which are consistent with the enrichment results in molecular functions and cellular components. Besides, KEGG pathway enrichment analysis shown that more genes were enriched in PI3K-AKT signaling pathway and cell cycle (Figure 5C). Based on TCGA dataset and GSE39582 accession, the enrichment results of GSEA demonstrate that FATP5 is closely related to the cell growth and cell cycle. In short, the enrichment analysis demonstrates that FATP5 plays an important role in regulating cell cycle in CRC.

TABLE 4 | The top 5 terms of GO enrichment analysis results of co-expressed genes with FATP5.

siFATP5 Facilitates Cell Growth and Cell Cycle G2/M Phase Transition

To explore the effects of FATP5 on CRC cells, siRNA transfection technology was applied on CACO-2 and HCT116 cells, respectively. Transfection results were confirmed by qRT-PCR and western blotting, which indicate that siRNA had a good knockdown efficiency (Figures 6A,B). Then, we studied the effect of FATP5 on CRC cell proliferation in vitro. The cytotoxicity CCK-8 assay revealed that downregulation of FATP5 in both cell types significantly enhanced cell proliferation compared to that in the control cells (p < 0.05, Figure 6C). In addition, the results of flow cytometry also showed that the low expression of FATP5 had an obvious effect on cell cycle G2/M transition. Compared with the control group, the proportion of G2/M phase in siFATP5 group was significantly reduced (p < 0.05, Figures 6D,E). We also determined cycle-related proteins, showing that siFATP5 augmented CCNB1 expression while reduced p27 expression (p < 0.05, Figure 6F).

FIGURE 6 | siFATP5 facilitates cell growth and cell cycle G2/M phase transition. FATP5 expression was detected after transfection in CRC cells by qRT-PCR (A) and western blotting (B,C) CCK-8 assays were used to detect cell viability of CRC cells after transfection. (D,E) Flow cytometric analysis was used to detect cell cycle distribution of CRC cells after transfection. (F) The expression of cycle-related protein. *p < 0.05, **p < 0.01.

Colorectal Tumors From Patients Have Higher FATP5 Expression Than Paracancerous Tissues

Through mining the database, we found that the expression of FATP5 protein in colorectal cancer is significantly higher than that in normal tissues, and FATP5 is closely related to the prognosis of colorectal cancer patients. In order to confirm aforementioned findings, we analyzed the FATP5 expression in primary tissues of CRC patients and paracancerous tissues by IHC staining, including four pathological types (Figure 7A). FATP5 were located in cytoplasm and distributed diffusely. The results showed that, except in signet ring cell carcinoma (n = 6), the expression of FATP5 in adenocarcinoma (n = 8), mucinous adenocarcinoma (n = 8) and adenosquamous carcinoma (n = 5) was significantly higher than paracancerous tissues, which verified that FATP5 plays an important role in colorectal cancer (Figures 7B–E).

FIGURE 7 | Representative IHC staining and quantitative analysis showing the expression of FATP5 in colorectal tumors and adjacent tumor tissues. (A) IHC staining of tumour tissues and paracancerous tissues. The protein expression levels of FATP5 in adenocarcinoma (B), Mucinous. adenocarcinoma (C), Signet ring cell carcinoma (D) and Adenosquamous carcinoma (E). **p < 0.01.

DISCUSSION

CRC is the fourth most common cancer and the second leading cause of cancer death in the United States (Benson et al., 2018). Besides, the challenges in treatment of this disease have not been fully addressed. Due to a paradigm shift in CRC risk factors, cancer prevention and early diagnosis, as well as treatment methods, CRC morbidity and mortality have partially improved. The pathogenesis and diagnosis of CRC remain an area of complexity, which needs further investigation (Auyeung and Ko, 2017). Studies have shown that early screening of diseases based on biomarkers can greatly reduce the mortality of diseases and improve the prognosis, which indicates that biomarkers play a vital role in the diagnosis and treatment of diseases (Wolf et al., 2018).

Changes in lipid metabolism contribute to different aspects of tumorigenesis and fatty acid abnormality is a risk factor of many tumors, including CRC (May-Wilson et al., 2017). FATPs play a role in the uptake and metabolism regulation of long-chain fatty acids, whose dysregulation has been reported in many cancers. Even though the role of FATPs activators in the tumorigenesis and prognosis of several cancers has been partially confirmed, further bioinformatics analysis of FATPs still remains to be conducted. FATPs also play a key character in many diseases, such as obesity, diabetes, cardiopathies, skin syndromes and carcinoma (Dourlen et al., 2015). During the progress of these diseases, FATP5 is involved in fatty acid transport and bile acid metabolism, which may be a crucial regulator in tumors. To our knowledge, our study explored the mRNA expression and prognostic values of FATP5 factors in CRC for the first time. We hope that our findings will complement previous research and contribute to existing knowledge, treatment designs improvement, and enhancing prognosis accuracy for CRC patients.

Molecular biomarkers that can be used as prognostic indicators many help determine the patient’s personalized treatment plan. In this study, we applied TCGA and GEO database to investigating the expression pattern of FATP5 in certain cancer types. The results revealed that FATP5 was the only overexpressed member of FATPs family in CRC tissues, implying its predominant role in regulating FA transport and activation in colorectal carcinoma. Besides, we found that FATP5 expression was higher and closely related to prognosis of CRC patients. Four independent data sets showed that the expression of FATP5 was closely related to OS in CRC patients. In addition, high-expression FATP5 and low-expression groups shown an obvious statistics differences in lymph node metastasis and distant metastasis, which indicated that FATP5 can be used to predict whether lymph node metastasis and lymph node metastasis occur in colorectal patients. Interestingly, although FATP5 was high expression in the tumor, the high expression level predicted a good prognosis, indicating that the expression and distribution of FATP5 and its receptors may interfere with tumor microenvironment, further affecting the prognosis. The research on the mechanism of FATP5 in the occurrence and development of CRC can help us to have a clear understanding of CRC, which can be used for clinical drug guidance and the research and development of targeted drugs. A similar phenomenon was also observed in CLDN4 expression and its prognostic significance in human pancreatic ductal adenocarcinoma. Higher CLDN4 expression represents better outcomes in human pancreatic ductal adenocarcinoma (Tsutsumi et al., 2012). These results highlighted that the prognostic value and clinical significance of FATP5 in CRC. Further research focusing on the prognosis relevance of FATP5 in larger prospective studies might also prove beneficial.

It has been revealed that FATP5 participated in PI3K/AKT signaling pathway (Liu et al., 2019), Cyclin-dependent protein serine/threonine kinase regulator activity and p53 signaling pathway (Liu et al., 2020), indicating FATP5 acted as a significant part to cell cycle in CRC. Previous study has shown that the PI3K/Akt signaling pathway and p53 signaling pathway are involved in cell proliferation and apoptosis in breast cancer (Chen et al., 2018), indicating the significant role of FATP5 in cell cycle. In addition, FA are the building blocks for all lipids and hence are essential components of all biological membrane structures. As a result, proliferating cells need to gain sufficient fatty acids to support membrane growth and integrity. Previous study has shown that de novo fatty acid synthesis serves as a vital factor in the intracellular fatty acid pool of tumor cells (Menendez and Lupu, 2007), implying the crucial role of FATP5 in cell cycle. Mechanistically, FATP5 is high expression in tumor cells, which can participate in the regulation of cell cycle by regulating lipid synthesis (Zhu and Thompson, 2019). Based on TCGA dataset and GSE39582 accession, the enrichment results of GSEA revealed that the high expression of FATP5 was associated with the negative regulation of cell cycle G2/M transition, which may explain the suggestion that FATP5 high expression represents a good prognosis in CRC. The results of flow cytometry also proved that the high expression of FATP5 had an obvious negative effect on cell cycle G2/M transition.

Previous study has also suggested that hepatic FATP5 expression is related to histological progression and loss of hepatic fat in Nonalcoholic Fatty Liver Disease (NAFLD) patients (Enooku et al., 2020), indicating that down-regulated expression of hepatic FATP5 in NAFLD is related to histological progression, and may be associated with hepatic fat loss during NASH progression to cirrhosis. Besides, FATP5 deficiency activates nrf2/txnrd1 pathway by increasing lipid peroxidation in HCC (Gao et al., 2020), suggesting that FATP5 acts as a novel tumor suppressor in the development of liver cancer and is positive correlation with the prognosis of HCC patients. These findings keep consistent with our results in CRC, further highlighting the prognostic relevance of FATP5 in a wide variety of human cancers.

CONCLUSION

In summary, we have shown the up-regulation of FATP5 mRNA expression in CRC and verified its significance as prognostic factor. We supposed that up-regulation of FATP5 would predict prognosis in CRC more accurately, suggesting the crucial role of FATP5 as a novel diagnostic biomarker in this aggressive carcinoma, by regulating the cell cycle G2/M transition.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

ETHICS STATEMENT

The studies involving human participants were reviewed and approved by the Ethics Committee of the First Affiliated Hospital of Zhengzhou, Henan, China (Data August 11, 2020). The experiments were carried out following the Declaration of Helsinki. The patients/participants provided their written informed consent to participate in this study.

AUTHOR CONTRIBUTIONS

Q-SG is responsible for the acquisition of the data, analysis and interpretation of the data. M-JY and L-FL are responsible for the drafting of the manuscript, and statistical analysis. Others contributed to the critical revision of the manuscript. All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

FUNDING

This work was supported by the Collaborative Innovation Major Project of Zhengzhou (Grant No. 20XTZX08017). The National Natural Science Foundation of China (Grant No. 82002433), and Science and Technology Project of Henan Provincial Department of Education (Grant No. 18A320044, 21A320036), Henan Province Medical Science and Technology Research Project Joint Construction Project (Grant No. LHGJ20190003, LHGJ20190055) also support this work.

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2021.770624/full#supplementary-material

REFERENCES

Anderson, C. M., and Stahl, A. (2013). SLC27 Fatty Acid Transport Proteins. Mol. Aspects Med. 34 (2-3), 516–528. doi:10.1016/j.mam.2012.07.010

Auyeung, K. K., and Ko, J. K. (2017). Angiogenesis and Oxidative Stress in Metastatic Tumor Progression: Pathogenesis and Novel Therapeutic Approach of Colon Cancer. Curr. Pharm. Des. 23 (27), 3952–3961. doi:10.2174/1381612823666170228124105

Benson, A. B., Venook, A. P., Al-Hawary, M. M., Cederquist, L., Chen, Y.-J., Ciombor, K. K., et al. (2018). NCCN Guidelines Insights: Colon Cancer, Version 2.2018. J. Natl. Compr. Canc Netw. 16 (4), 359–369. doi:10.6004/jnccn.2018.0021

Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., and Jemal, A. (2018). Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer J. Clinicians 68 (6), 394–424. doi:10.3322/caac.21492

Cerami, E., Gao, J., Dogrusoz, U., Gross, B. E., Sumer, S. O., Aksoy, B. A., et al. (2012). The cBio Cancer Genomics Portal: An Open Platform for Exploring Multidimensional Cancer Genomics Data: Figure 1. Cancer Discov. 2 (5), 401–404. doi:10.1158/2159-8290.cd-12-0095

Chang, L. C., Chiu, H. M., Ho, B. C., Chen, M. H., Hsu, Y. C., Chiu, W. T., et al. (2020). Copy Number Alterations of Depressed Colorectal Neoplasm Predict the Survival and Response to Oxaliplatin in Proximal Colon Cancer. Cancers (Basel) 12 (6). doi:10.3390/cancers12061527

Chen, M., Zhang, H., Zhang, G., Zhong, A., Ma, Q., Kai, J., et al. (2018). Targeting TPX2 Suppresses Proliferation and Promotes Apoptosis via Repression of the PI3k/AKT/P21 Signaling Pathway and Activation of P53 Pathway in Breast Cancer. Biochem. Biophys. Res. Commun. 507 (1-4), 74–82. doi:10.1016/j.bbrc.2018.10.164

Dourlen, P., Sujkowski, A., Wessells, R., and Mollereau, B. (2015). Fatty Acid Transport Proteins in Disease: New Insights from Invertebrate Models. Prog. lipid Res. 60, 30–40. doi:10.1016/j.plipres.2015.08.001

Enooku, K., Tsutsumi, T., Kondo, M., Fujiwara, N., Sasako, T., Shibahara, J., et al. (2020). Hepatic FATP5 Expression Is Associated with Histological Progression and Loss of Hepatic Fat in NAFLD Patients. J. Gastroenterol. 55 (2), 227–243. doi:10.1007/s00535-019-01633-2

Favoriti, P., Carbone, G., Greco, M., Pirozzi, F., Pirozzi, R. E. M., and Corcione, F. (2016). Worldwide burden of Colorectal Cancer: a Review. Updates Surg. 68 (1), 7–11. doi:10.1007/s13304-016-0359-y

Gao, J., Aksoy, B. A., Dogrusoz, U., Dresdner, G., Gross, B., Sumer, S. O., et al. (2013). Integrative Analysis of Complex Cancer Genomics and Clinical Profiles Using the cBioPortal. Sci. Signal. 6 (269), pl1. doi:10.1126/scisignal.2004088

Gao, Q., Zhang, G., Zheng, Y., Yang, Y., Chen, C., Xia, J., et al. (2020). SLC27A5 Deficiency Activates NRF2/TXNRD1 Pathway by Increased Lipid Peroxidation in HCC. Cell Death Differ 27 (3), 1086–1104. doi:10.1038/s41418-019-0399-1

Goldman, M., Craft, B., Hastie, M., Repečka, K., McDade, F., Kamath, A., et al. (2019). The UCSC Xena Platform for Public and Private Cancer Genomics Data Visualization and Interpretation. bioRxiv , 326470.

Huang, D. W., Sherman, B. T., and Lempicki, R. A. (2009). Bioinformatics Enrichment Tools: Paths toward the Comprehensive Functional Analysis of Large Gene Lists. Nucleic Acids Res. 37 (1), 1–13. doi:10.1093/nar/gkn923

Huang, D. W., Sherman, B. T., and Lempicki, R. A. (2009). Systematic and Integrative Analysis of Large Gene Lists Using DAVID Bioinformatics Resources. Nat. Protoc. 4 (1), 44–57. doi:10.1038/nprot.2008.211

Hubbard, B., Doege, H., Punreddy, S., Wu, H., Huang, X., Kaushik, V. K., et al. (2006). Mice Deleted for Fatty Acid Transport Protein 5 Have Defective Bile Acid Conjugation and Are Protected from Obesity. Gastroenterology 130 (4), 1259–1269. doi:10.1053/j.gastro.2006.02.012

Koch, A., Jeschke, J., Van Criekinge, W., van Engeland, M., and De Meyer, T. (2019). MEXPRESS Update 2019. Nucleic Acids Res. 47 (W1), W561–w565. doi:10.1093/nar/gkz445

Liu, H., Weng, W., Guo, R., Zhou, J., Xue, J., Zhong, S., et al. (2020). Olig2 SUMOylation Protects against Genotoxic Damage Response by Antagonizing P53 Gene Targeting. Cell Death Differ 27, 3146–3161. doi:10.1038/s41418-020-0569-1

Liu, J.-S., Huo, C.-Y., Cao, H.-H., Fan, C.-L., Hu, J.-Y., Deng, L.-J., et al. (2019). Aloperine Induces Apoptosis and G2/M Cell Cycle Arrest in Hepatocellular Carcinoma Cells through the PI3K/Akt Signaling Pathway. Phytomedicine 61, 152843. doi:10.1016/j.phymed.2019.152843

Maere, S., Heymans, K., and Kuiper, M. (2005). BiNGO: a Cytoscape Plugin to Assess Overrepresentation of Gene Ontology Categories in Biological Networks. Bioinformatics 21 (16), 3448–3449. doi:10.1093/bioinformatics/bti551

May-Wilson, S., Sud, A., Law, P. J., Palin, K., Tuupanen, S., Gylfe, A., et al. (2017). Pro-inflammatory Fatty Acid Profile and Colorectal Cancer Risk: A Mendelian Randomisation Analysis. Eur. J. Cancer 84, 228–238. doi:10.1016/j.ejca.2017.07.034

Menendez, J. A., and Lupu, R. (2007). Fatty Acid Synthase and the Lipogenic Phenotype in Cancer Pathogenesis. Nat. Rev. Cancer 7 (10), 763–777. doi:10.1038/nrc2222

Mlecnik, B., Bindea, G., Angell, H. K., Maby, P., Angelova, M., Tougeron, D., et al. (2016). Integrative Analyses of Colorectal Cancer Show Immunoscore Is a Stronger Predictor of Patient Survival Than Microsatellite Instability. Immunity 44 (3), 698–711. doi:10.1016/j.immuni.2016.02.025

Moore, L. D., Le, T., and Fan, G. (2013). DNA Methylation and its Basic Function. Neuropsychopharmacol 38 (1), 23–38. doi:10.1038/npp.2012.112

Overman, M. J., McDermott, R., Leach, J. L., Lonardi, S., Lenz, H.-J., Morse, M. A., et al. (2017). Nivolumab in Patients with Metastatic DNA Mismatch Repair-Deficient or Microsatellite Instability-High Colorectal Cancer (CheckMate 142): an Open-Label, Multicentre, Phase 2 Study. Lancet Oncol. 18 (9), 1182–1191. doi:10.1016/s1470-2045(17)30422-9

Rhodes, D. R., Yu, J., Shanker, K., Deshpande, N., Varambally, R., Ghosh, D., et al. (2004). ONCOMINE: A Cancer Microarray Database and Integrated Data-Mining Platform. Neoplasia 6 (1), 1–6. doi:10.1016/s1476-5586(04)80047-2

Schreuders, E. H., Ruco, A., Rabeneck, L., Schoen, R. E., Sung, J. J. Y., Young, G. P., et al. (2015). Colorectal Cancer Screening: a Global Overview of Existing Programmes. Gut 64 (10), 1637–1649. doi:10.1136/gutjnl-2014-309086

Smoot, M. E., Ono, K., Ruscheinski, J., Wang, P.-L., and Ideker, T. (2011). Cytoscape 2.8: New Features for Data Integration and Network Visualization. Bioinformatics 27 (3), 431–432. doi:10.1093/bioinformatics/btq675

Stahl, A. (2004). A Current Review of Fatty Acid Transport Proteins (SLC27). Pflugers Archiv Eur. J. Physiol. 447 (5), 722–727. doi:10.1007/s00424-003-1106-z

Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., et al. (2019). STRING V11: Protein-Protein Association Networks with Increased Coverage, Supporting Functional Discovery in Genome-wide Experimental Datasets. Nucleic Acids Res. 47 (D1), D607–d613. doi:10.1093/nar/gky1131

Tang, Z., Li, C., Kang, B., Gao, G., Li, C., and Zhang, Z. (2017). GEPIA: a Web Server for Cancer and normal Gene Expression Profiling and Interactive Analyses. Nucleic Acids Res. 45 (W1), W98–w102. doi:10.1093/nar/gkx247

Tate, J. G., Bamford, S., Jubb, H. C., Sondka, Z., Beare, D. M., Bindal, N., et al. (2019). COSMIC: the Catalogue of Somatic Mutations in Cancer. Nucleic Acids Res. 47 (D1), D941–d947. doi:10.1093/nar/gky1015

Tsutsumi, K., Sato, N., Tanabe, R., Mizumoto, K., Morimatsu, K., Kayashima, T., et al. (2012). Claudin-4 Expression Predicts Survival in Pancreatic Ductal Adenocarcinoma. Ann. Surg. Oncol. 19 (Suppl. 3), S491–S499. doi:10.1245/s10434-011-1970-2

Wolf, A. M. D., Fontham, E. T. H., Church, T. R., Flowers, C. R., Guerra, C. E., LaMonte, S. J., et al. (2018). Colorectal Cancer Screening for Average-Risk Adults: 2018 Guideline Update from the American Cancer Society. CA: A Cancer J. Clinicians 68 (4), 250–281. doi:10.3322/caac.21457

Zhu, J., and Thompson, C. B. (2019). Metabolic Regulation of Cell Growth and Proliferation. Nat. Rev. Mol. Cell Biol 20 (7), 436–450. doi:10.1038/s41580-019-0123-5

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Geng, Yang, Li, Shen, Wang, Zheng, Xue and Zhao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 04 February 2022
doi: 10.3389/fmolb.2021.766887

Down-Regulated miR-130a/b Attenuates Rhabdomyosarcoma Proliferation via PPARG

Yi Pan¹†, Junyang Li1,²†, Susu Lou¹, Wanbiao Chen1,³, Yihang Lin1,², Nan Shen1,⁴* and Youjin Li²*

¹Shanghai Children’s Medical Center, School of Medicine, Pediatric Translational Medicine Institute, Shanghai Jiao Tong University, Shanghai, China

²Department of Otolaryngology, Shanghai Children’s Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

³Hefei National Laboratory for Physical Sciences at Microscale, The First Affiliated Hospital of USTC, MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, CAS Center for Excellence in Biomacromolecules, and School of Life Sciences, University of Science and Technology of China, Hefei, China

⁴Department of Infectious Diseases, Shanghai Children’s Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

Edited by:
Tianhui Chen, University of Chinese Academy of Sciences, China

Reviewed by:
Xing Hu, Central South University, China
Jie He, Guangzhou First People’s Hospital, China Peijie He, Fudan University, China

* Correspondence: Nan Shen, shennan@scmc.com.cn; Youjin Li, kevinliyoujin@outlook.com

†These authors have contributed equally to this work

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 30 August 2021
Accepted: 02 December 2021
Published: 04 February 2022

Citation: Pan Y, Li J, Lou S, Chen W, Lin Y, Shen N and Li Y (2022) Down-Regulated miR-130a/b Attenuates Rhabdomyosarcoma Proliferation via PPARG. Front. Mol. Biosci. 8:766887. doi: 10.3389/fmolb.2021.766887

Background: Rhabdomyosarcoma (RMS) is one of the most common types of soft-tissue sarcomas in children, and it exhibits a low 5-years survival rate. The survival outcome has shown no significant improvements in the past 30 years miRNA profiling of RMS might therefore provide a novel insight into uncovering new molecular targets for therapy.

Methods: We analyzed miRNA and RNA sequencing data from patients and the TARGET database to reveal the potential miRNA-mRNA axes and validated them in patients’ samples. After the miRNA antagomirs were used to silence the target miRNAs in the cell model, qRT-PCR, western immunoblotting analysis, and proliferation assays were performed to explore the interaction between miR-130a/b and peroxisome proliferator-activated receptor gamma (PPARG) and their effects.

Results: In RMS patients, the expression of miR-130a/b was augmented, and its related PPARG gene was suppressed. Bioinformatics analysis showed that miR-130a/b targeted the PPARG gene and inhibited the proliferation of human RMS cell lines. In addition, rosiglitazone maleate activated the expression of PPARG in human RMS cell lines to suppress proliferation.

Conclusion: miR-130a/b regulates the malignant process in RMS by targeting PPARG. Furthermore, the PPARG agonist rosiglitazone maleate attenuated the proliferation of RD cells and might therefore be of benefit to RMS patients.

Keywords: rhabdomyosarcoma, PPARG, microRNA, miR-130a/b, proliferation

INTRODUCTION

Soft-tissue sarcoma (STS) is a rare malignancy that originates in connective tissue and accounts for approximately 1% of adult cancers and 7% of childhood cancers (Hawkins et al., 2013). Rhabdomyosarcoma (RMS) is one of the most common types of STS in children and typically occurs in the head and neck and in the genitourinary system (Toro et al., 2006). The latest World Health Organization (WHO) classification (Jo and Doyle., 2016) categorizes RMS into four main subtypes: embryonal (ERMS), alveolar (ARMS), pleomorphic (PRMS), and spindle cell/sclerosing rhabdomyosarcoma (Chen et al., 2017; Smolle et al., 2017). ERMS is the primary histological subtype of RMS and accounts for ∼60% of pediatric RMS (Weihkopf et al., 2008). In addition, despite the development of treatment strategies, RMS is still a highly malignant disease with apparent metastatic tendencies, and its prognosis remains poor (Ferrari et al., 2018), with a 5-years survival rate of 25–65% (Punyko et al., 2005). Therefore, there is an urgent need to uncover novel methods to develop effective RMS treatments for children.

MicroRNAs (miRNAs/miRs) are small, non-coding regulatory RNAs that can extensively modulate target genes by combining with complementary sequences (Engels and Hutvagner, 2006). miRNAs play an essential role in the onset and development of tumors by regulating the expression of various oncogenes and tumor-suppressor genes, including proliferation, metastasis, and invasion (Lv et al., 2015; Hron and Asakura, 2017). For example, the miR-130 family members miR-130a and miR-130b promote cancer cell proliferation, invasion, and metastasis in gastric cancer, ovarian carcinoma, and osteosarcoma (Chen et al., 2016; Wang et al., 2017; Zhou et al., 2017). In addition, miR-130a acts as a tumor suppressor in prostate cancer and triple-negative breast cancer, inhibiting androgen receptor (AR) and mitogen-activated protein kinase (MAPK) pathways and targeting foS-like antigen 1 (FOSL1) (Boll et al., 2013; Chen et al., 2018). miR-130b may also inhibit the proliferation and invasion of pancreatic cancer cells by inhibiting the expression of activator of transcription 3 (STAT3) (Zhao et al., 2013). Therefore, miR-130a/b plays different roles in various types of tumors. However, its governing mechanism and role in the development and progression of RMS remain unclear.

In the present study, we detected increased miR-130a/b expression levels in RMS tissues compared with normal controls and showed that down-regulated expression of miR-130a/b suppressed cellular proliferation in RMS. We also found miR-130a/b-mediated peroxisome proliferator-activated receptor gamma (PPARG) expression to be essential for RMS progression. Exploring the molecular mechanism subserving the action of PPARG may thus provide a novel anti-tumor treatment strategy for RMS.

MATERIALS AND METHODS

Patients and Clinical Samples

Rhabdomyosarcoma (RMS) tissues of the head and neck and adjacent non-cancerous muscle tissues were obtained from two children with RMS who underwent surgery at the Shanghai Children’s Medical Center (SCMC) between 2020 and 2021. The two patients were male and female, and both were first diagnosed with RMS and high-risk stage III tumors at about 8 months of age. No preoperative adjuvant therapy was performed in these patients prior to their surgeries. After collection, all tissue samples were immediately snap-frozen in liquid nitrogen and then stored at −80°C until use. This study was approved by the Institutional Review Board and the Ethics Committee of SCMC (SCMCIRB-K2018057), and informed consent was obtained from each participant’s parents.

RNA-Seq and Data Processing for mRNA and miRNA

According to the manufacturer’s instructions, total RNA was extracted from 10 mg of tissue after grinding with a homogenizer (Scientz, China) and using TRIzol® Reagent (Invitrogen). The integrity of the total RNA was determined with a 2100 Bioanalyser (Agilent) and quantified using a NanoDrop spectrophotometer (Thermo Scientific, USA). The rRNA-depleted sequencing libraries from total RNA were prepared using an Illumina TruSeq Stranded Total RNA Gold preparation Kit for mRNA. About 1 μg of total RNA was used as input material, and then a Ribo-Zero Gold kit was used to remove both cytoplasmic and mitochondrial rRNA. After purifying the remaining RNA without rRNA, we fragmented the RNA into small pieces using divalent cations under elevated temperatures. The cleaved RNA fragments were copied into the first-strand cDNA using reverse transcriptase and random primers, followed by second-strand cDNA synthesis. According to Illumina’s library construction protocol, these cDNA fragments were then subjected to end-repair, phosphorylation, and “A” base addition. Finally, the products were purified and enriched with PCR, and the AMPure XP Beads (Beckmen) were used to clean up the amplified target fragments to create the final cDNA library. Sequencing was performed using an Illumina system following Illumina-provided protocols for 2 × 150 paired-end sequencing by Mingma Technologies in Shanghai, China. RNA-seq data were quantified using salmon, an alignment-free tool, and the DESeq2 package was used to test for differential expression (Love et al., 2014), (Sahraeian et al., 2017). A fold-change >2 and adjusted p-value <0.05 were used to determine the significantly differentially expressed genes between the groups. We implemented gene-set enrichment analysis (GSEA) (Subramanian et al., 2005) using gene sets specific to the PPAR-signaling pathway (Martens et al., 2021).

The small RNA-sequencing libraries from total RNA were prepared using a QIAseq miRNA Library Kit (Qiagen) for miRNA. First, approximately 5 µL of eluted total RNA was applied to ligate 3′ adapters and this was then followed by 5′ adapter ligation. Then, taking these adapter-ligated fragments as templates, the first-strand cDNA was synthesized using reverse transcriptase and RT primers containing 12-bp unique molecular identifiers (UMIs), followed by second-strand cDNA synthesis. Next, a universal forward primer paired with reverse primers assigning a sample index was used to amplify the library. After library amplification, a cleanup of the miRNA library was performed using a streamlined magnetic bead-based method. After sequencing, the raw data were uploaded directly to the GeneGlobe Data Analysis Center of QIAGEN for primary mapping and molecular-tag counting.

Bioinformatics Analysis of miRNA130a/b and PPARG Interactions

The miRNA130a/b targeting PPARG was identified using RNAhybrid by detecting the most energetically favorable hybridization sites (Krüger and Rehmsmeier, 2006). The publicly available Rhabdoid Tumor data set acquired from the TARGET (Therapeutically Applicable Research to Generate Effective Treatments) program contained data from 29 rhabdomyosarcoma patients, 66 primary tumor tissues, and six normal solid tissues. The clinicopathological characteristics of all the patients are listed in Supplementary Table S2.

Cell Culture

Human RMS cell lines, RD (Procell CL-0193) were purchased from Procell (Wuhan, China) and cultured in medium composed of Dulbecco’s modified Eagle’s medium (DMEM) or Dulbecco’s modified Eagle’s Medium:Nutrient Mixture F-12 (DMEM/F12) supplemented with 10% fetal bovine serum (FBS) and incubated at 37°C in a humidified incubator with 5% CO₂. In addition, rosiglitazone maleate was purchased from MedChemExpress (MCE, China) and dissolved in dimethyl sulfoxide (DMSO) (Sigma-Aldrich). RD cells were treated with rosiglitazone maleate at different concentrations to screen dosage.

Cell-Proliferation Assay

RD cells were seeded in a 96-well plate (5,000 cells per well) and cultured at 37°C. At the indicated time, we used the CellTiter-Glo® assay (Promega, Madison, WI, United States) to detect cellular proliferation, and the luminescence signal was determined with a microplate reader (Bio-Rad, Laboratories, United States).

Cell Transfection

The specific antagomirs for miR-130a/b and negative control were designed and synthesized by Sangon Biotech (Sangon, China). The sequence for the miR-130a antagomir was sense 5′-CAG​UGC​AAU​GUU​AAA​AGG​GCA​U-3′ and antisense 5′-AUG​CCC​UUU​UAA​CAU​UGC​ACU​G-3′. The sequence for the miR-130b antagomir was sense 5′-CAG​UGC​AAU​GAU​GAA​AGG​GCA​U-3′ and antisense 5′-AUG​CCC​UUU​CAU​CAU​UGC​ACU​G-3′. The miR-130a/b antagomirs were transfected into RD cells using Lipofectamine RNAiMAX (Invitrogen) following the manufacturer’s instructions.

Verification of miRNA and mRNA Expression with Quantitative Real-Time PCR

Total RNA was extracted using Trizol reagent (Thermo Fisher Scientific), and miRNA reverse-transcription was performed with the Mir-X miRNA First-Strand Synthesis Kit (Takara). Subsequent qPCR procedures were implemented using the Hieff qPCR SYBR Green Master Mix (Yeasen). qRT-PCR was performed using a CFX Connect Real-Time System (Bio-Rad) under the following cycle conditions: 95°C for 2 min, 40 amplification cycles of 95°C for 5 s, and 60°C for 30 s, followed by a final cycle of 95°C for 5 s and 65°C for 5 s. We used GAPDH or the small RNA U6 as our internal reference standard and calculated relative expression using the 2−ΔΔCT method. The levels of miRNA and target gene in the control RD cells were set at 1 for normalization. qRT-PCR for each gene was performed in technical triplicates in three independent experiments. Primer sequences are listed in Supplementary Table S1.

Western Blot Analysis

Western blot analysis was implemented as described previously (Ma et al., 2019). In brief, aliquots of total protein extract (20 μg) from cells were loaded and resolved by 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). After electrophoresis, proteins were transferred onto polyvinylidene difluoride membranes (PVDF; EMD Millipore, Billerica, MA, United States). Blots were blocked with 5% non-fat milk in PBST (PBS containing 0.1% TWEEN-20) for 1 h at room temperature, and the membrane was incubated with primary antibody against PPARG (Abcam, ab178860) at 4°C overnight. The membrane was then washed three times with PBST and exposed to horseradish peroxidase (HRP)-conjugated anti-rabbit IgG (1:2000 dilution, Cell Signaling Technology, United States) for 1 h at room temperature. After three washes with PBST, antibody binding was detected with enhanced chemiluminescence substrates (Millipore, United States) and visualized with an ImageQuant LAS 4000 mini-densitometer (GE Healthcare Life Sciences, United States).

Statistical Analysis

We executed all statistical analyses using IBM SPSS 24.0 statistical software (SPSS Inc., United States) and generated graphs with GraphPad PRISM 6.0 (GraphPad Software, Inc., United States). The differential expression levels of miRNAs between different groups were compared using Wilcoxon rank-sum test, and continuous variables were analyzed using the Student’s t-test. p < 0.05 was considered statistically significant.

RESULTS

Aberrant Expression of miR-130a/b-PPARG in RMS Patients

We executed RNA-seq for mRNA and miRNA to compare human RMS of the head and neck and pair-matched muscle tissues to examine the RNA expression profile in RMS patients (Supplementary Figure S1). According to the mRNA sequencing data, the differentially expressed genes were enriched in the PPAR-signaling pathway (Figure 1A and Figure 1B), and the critical gene PPARG was downregulated in RMS tissues compared to the normal tissues (Figure 1C). We also validated PPARG gene expression in RMS patients through the TARGET database (Figure 1D). To find the PPARG-related miRNA, we combined our miRNA sequencing results with miRTarBase, an experimentally validated microRNA–target interaction database. miR-130a/b was found to be highly expressed in RMS tissues and showed an interaction with the PPARG gene, and the expression of miR-130a/b was upregulated in RMS tissues compared to normal tissues according to the TARGET database (Figure 1E).

FIGURE 1 | RNA-seq of miRNA and mRNA in RMS Patients. (A) KEGG-pathway enrichment of differential mRNA expression between RMS patients of the head and neck and adjacent non-cancerous muscle tissues. (B) GSEA for differential expression levels of mRNA was exploited with respect to the PPAR-signaling pathway using the WikiPathways database. (C, D) The differential expression of the PPARG gene in tumor tissues and normal tissues from our data and those of the TARGET database. (E) Differential expression of miR-130a/b in tumor and normal tissues from the TARGET database.

To determine the expression of miR-130a/b in RMS patients, we measured miR-130a/b levels in human RMS of the head and neck and pair-matched muscle tissues by qRT-PCR, and our results revealed that miR-130a/b expression was enhanced in RMS tissues relative to their matched control samples (Figure 2A). Western blotting analysis was then employed to assess PPARG expression in human RMS and pair-matched muscle tissues, and we demonstrated that the expression of PPARG protein was significantly diminished in RMS tissues compared with normal muscle tissues (Figures 2B,C). These results were also confirmed by qRT-PCR of PPARG mRNA expression (Figure 2D).

FIGURE 2 | MiR-130a/b-PPARG expression in RMS Patients. (A) Evaluation of miR-130a/b expression by qRT-PCR in RMS tissues of the head and neck and adjacent non-cancerous muscle tissues. (B, C) Statistical analysis of western blots showed that the expression of PPARG was decreased in RMS tissues compared to their matched control samples. (D) qRT-PCR was used to evaluate the expression of PPARG in RMS tissues of the head and neck and adjacent non-cancerous muscle tissue. ****p < 0.0001.

MiR-130a/b Inhibits PPARG Expression in RD Cells

To explore whether miR-130a/b manifests a potential interaction with the PPARG gene, we applied the RNAhybrid tool. Based on the minimum free energy (mfe) scores, we found two potential miR-130a/b targets (Figure 3A). According to the TARGET database, the expression of miR-130a/b and that of the PPARG gene were negatively correlated (Figure 3B).

FIGURE 3 | MiR-130a/b downregulates PPARG expression in RD cells. (A) miR-130a/b-PPARG interactions were predicted using RNAhybrid tools. (B) The expression of miR-130a/b and the PPARG gene showed a negative correlation according to the TARGET database. (C, D) Expression of miR130a/b and PPARG mRNA levels after transfection with miR130a/b antagomirs, as determined by qRT-PCR. The data showed that the expression of miR-130a/b was negatively correlated with PPARG expression levels. (E) Statistical analysis of western blots showed that the expression of PPARG was upregulated in RD cells after transfection with miR130a/b antagomirs. (F) Statistical analysis of western blots of PPARG protein. ***p < 0.001; ****p < 0.0001.

To confirm the interaction between miR-130a/b and PPARG, we transfected the miR-130a/b antagomirs into RD cells. As determined by qRT-PCR, the expression of PPARG was negatively associated with miR-130a/b (Figures 3C,D), suggesting that PPARG may act as a target of the antagonist for miR-130a/b in RD cells. In addition, western blotting analysis showed that the expression of PPARG was upregulated in RD cells after transfection with miR-130a/b antagomirs (Figures 3E,F). These results indicated that miR-130a/b may inhibit PPARG expression in RD cells.

Down-Regulated miR-130a/b Suppresses Proliferation of RD Cells

To further evaluate the functions of miR-130a/b in RMS treatment, miR-130a/b antagomirs were synthesized and transfected into RD cells. A CTG cell-proliferation assay showed that miR-130a/b inhibitors significantly decreased proliferation of RD cells at 72 h (p < 0.0001) after transfection (Figure 4A). Collectively, these data indicated that downregulation of miR-130a/b significantly reduced the proliferation of RD cells.

FIGURE 4 | The upregulation of PPARG attenuates proliferation in RD cell lines. (A) CTG cell proliferation assay demonstrated that miR-130a/b antagomirs significantly decreased the proliferation of RD cells after transfection. (B) Evaluation of proliferation by CTG assay in RD cell lines following treatment with different concentrations of rosiglitazone maleate (125 and 250 μM). (C) Cell-proliferation assay showed that the combination of miR-130a/b inhibitors and rosiglitazone maleate significantly suppressed the proliferation of RD cells compared with the non-transfected RD cells treated with rosiglitazone maleate alone. **p < 0.01, ***p < 0.001, and ****p < 0.0001.

Rosiglitazone Maleate Suppresses Proliferation Via PPARG in RD Cells

To further verify whether the effects of miR-130a/b on the proliferation in RD cells were mediated by targeting PPARG, we applied rosiglitazone maleate (a PPARG agonist) in functional testing. RD cells were treated with different concentrations of rosiglitazone maleate (0, 7.8125, 15.625, 31.25, 62.5, 125, 250, 500 μM, or 1 mM), and we found that cellular proliferation was markedly decreased after treatment with 125 μM rosiglitazone maleate (Figure 4B and Supplementary Figure S2). In order to further evaluate functions after targeting PPARG in RMS treatment, miR-130a/b antagomirs were transfected into RD cells, and then 125 μM rosiglitazone maleate was added to RD cells after transfection for 72 h. The CTG cell-proliferation assay showed that the combination of miR-130a/b inhibitors and rosiglitazone maleate significantly suppressed proliferation of RD cells after 72 h compared with the non-transfected RD cells treated with rosiglitazone maleate alone (Figure 4C). Taken together, our data indicated that miR-130a/b inhibited RD cell proliferation by regulating PPARG expression.

DISCUSSION

The standard treatment for newly diagnosed childhood rhabdomyosarcoma (RMS) is a multi-modal approach that comprises surgery, radiation therapy, and chemotherapy. The order in which these treatments are administered depends on where the tumor started, the size of the tumor, the type of tumor, and whether the tumor had metastasized to lymph nodes or other parts of the body. Unfortunately, the survival outcomes of metastatic and recurrent RMS have not seen significant improvements in the last 30 years (Malempati et al., 2012). However, novel treatments are currently being tested in clinical trials to halt cancer recurrence or reduce the side effects of cancer treatment. In addition, patients with RMS may benefit from molecularly targeted and immunotherapeutic approaches, thus reducing the treatment-associated toxicities caused by current chemotherapy and radiation therapy.

Targeted therapy is a type of treatment that exploits drugs or other substances to identify and attack specific cancer cells. There are several targets researchers are focusing on in the treatment of RMS, including directly targeting PAX-FOXO1 fusion protein, coregulators of PAX-FOXO1, modulators of PAX-FOXO1 activity and stability, and the receptor tyrosine kinase/RAS/PI3K axis (Chen et al., 2019).

PPARG plays an essential role in cancer-cell proliferation. Traditionally, PPARG has been hypothesized to be a regulator of lipid metabolism (Galbraith et al., 2021). In fact, recent studies have reported on the use of PPARG as a promising molecular target for specific cancer types, including breast cancer (Xu et al., 2019). In our study, the results of the TARGET dataset analysis revealed significant differences in PPARG expression between normal and tumor tissues, and western blot analysis and qRT-PCR results in RD cell lines confirmed these findings. Upregulation of PPARG was used to explore the function of PPARG in RMS, and it was found to significantly inhibit the biological function (i.e., proliferation) of the RD cells.

Studies have also shown that miRNA is a critical player in cellular migration, proliferation, autophagy, transformation, and other biological functions (Bayoumi et al., 2016). For example, miR-130a/b was recently reported to be important in several types of cancers (Arabpour et al., 2021; Fan et al., 2021; Vieira et al., 2021). In epithelial ovarian cancer, miR-130b inhibited cancer cell migration and invasion via TGF-β signaling (Zhou et al., 2017), and in nasopharyngeal carcinoma, miR-130a promoted apoptosis by targeting BACH2 (Chen et al., 2017). Further, miR-130a was found to downregulate TBL1XR1-mediated cell migration and invasion in gastric carcinoma (Wang et al., 2018). We herein demonstrated that miR-130a/b targets the PPARG gene is expressed at relatively high levels in RMS. Our subsequent experiments showed that miR-130a/b suppresses PPARG expression in RD cell lines, indicating that it can promote RMS development as a tumor promoter.

In addition, as a PPARG agonist, rosiglitazone maleate significantly inhibits biological functions in RD cells. Rosiglitazone maleate has traditionally been widely used to reduce sugar in the blood of patients with diabetes mellitus (Zhao et al., 2021). However, investigators have recently demonstrated that rosiglitazone maleate also has a therapeutic effect on various cancers, including lung cancer, breast cancer, and pancreatic cancer (Dang et al., 2018; Wang et al., 2020; Wu et al., 2020; Wang et al., 2021; Zhou et al., 2021). We found that rosiglitazone maleate had a significant therapeutic effect on RD cells and inhibited RD cell proliferation by mediating the PPARG pathway. Therefore, it should be possible to target the PPARG pathway by treatment with rosiglitazone maleate as a conventional drug, but with a novel use in improving the prognosis of RMS patients. Our study thus provides new insights into the role of rosiglitazone maleate in RMS and expands the application of PPARG agonists as promising interventions in the treatment of RMS. However, further in vivo experiments need to be conducted to verify these findings.

CONCLUSION

Our findings revealed that miR-130a/b can regulate the malignant process underlying RMS by suppressing PPARG and confirmed that PPARG might be used as a novel therapeutic target in RMS.

DATA AVAILABILITY STATEMENT

Raw sequence data have been deposited in the China National GeneBank (CNGB) Nucleotide Sequence Archive (CNSA) database under accession identification CNP0002601 (http://db.cngb.org/cnsa/project/CNP0002601/reviewlink/). The source data underlying all figures except for those not including statistics are provided as a Source Data file.

ETHICS STATEMENT

The studies involving human participants were reviewed and approved by Institutional Review Board and the Ethics Committee of Shanghai Children’s Medical Center. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

AUTHOR CONTRIBUTIONS

YP and NS designed the study. YP wrote the manuscript, analyzed the published data, and performed bioinformatics analysis. JL performed the experiments and wrote the manuscript. SL, WC, and YL, analyzed the published data. YL edited the paper and revised the manuscript. NS and YL supervised the study. All authors read and approved the final manuscript.

FUNDING

This study was supported by a grant from the Chinese National Natural Science Foundation, Grant No. 82071015.

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

ACKNOWLEDGMENTS

We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2021.766887/full#supplementary-material

Supplementary Figure S3 | Cluster analysis and heat map of the top 100 genes showing highest or lowest gene expression in RMS patients’ tissues compared to controls.

REFERENCES

Arabpour, M., Layeghi, S. M., Bazzaz, J. T., Naghizadeh, M. M., Majidzadeh-A, K., and Shakoori, A. (2021). The Potential Roles of Lncrnas Duxap8, Linc00963, and Foxd2-As1 in Luminal Breast Cancer Based on Expression Analysis and Bioinformatic Approaches. Hum. Cel 34 (4), 1227–1243. doi:10.1007/s13577-021-00539-7

Bayoumi, A., Sayed, A., Broskova, Z., Teoh, J.-P., Wilson, J., Su, H., et al. (2016). Crosstalk between Long Noncoding RNAs and MicroRNAs in Health and Disease. Ijms 17 (3), 356. doi:10.3390/ijms17030356

Boll, K., Reiche, K., Kasack, K., Mörbt, N., Kretzschmar, A. K., Tomm, J. M., et al. (2013). Mir-130a, Mir-203 and Mir-205 Jointly Repress Key Oncogenic Pathways and Are Downregulated in Prostate Carcinoma. Oncogene 32 (3), 277–285. doi:10.1038/onc.2012.55

Chen, C., Dorado Garcia, H., Scheer, M., and Henssen, A. G. (2019). Current and Future Treatment Strategies for Rhabdomyosarcoma. Front. Oncol. 9, 1458. doi:10.3389/fonc.2019.01458

Chen, E., Ricciotti, R., Futran, N., and Oda, D. (2017). Head and Neck Rhabdomyosarcoma: Clinical and Pathologic Characterization of Seven Cases. Head Neck Pathol. 11 (3), 321–326. doi:10.1007/s12105-016-0771-0

Chen, J., Yan, D., Wu, W., Zhu, J., Ye, W., and Shu, Q. (2016). Microrna-130a Promotes the Metastasis and Epithelial-Mesenchymal Transition of Osteosarcoma by Targeting Pten. Oncol. Rep. 35 (6), 3285–3292. doi:10.3892/or.2016.4719

Chen, X., Yue, B., Zhang, C., Qi, M., Qiu, J., Wang, Y., et al. (2017). Mir-130a-3p Inhibits the Viability, Proliferation, Invasion, and Cell Cycle, and Promotes Apoptosis of Nasopharyngeal Carcinoma Cells by Suppressing Bach2 Expression. Biosci. Rep. 37 (3). doi:10.1042/bsr20160576

Chen, X., Zhao, M., Huang, J., Li, Y., Wang, S., Harrington, C. A., et al. (2018). microRNA‐130a Suppresses Breast Cancer Cell Migration and Invasion by Targeting FOSL1 and Upregulating ZO‐1. J. Cel. Biochem. 119 (6), 4945–4956. doi:10.1002/jcb.26739

Dang, Y.-F., Jiang, X.-N., Gong, F.-L., and Guo, X.-L. (2018). New Insights into Molecular Mechanisms of Rosiglitazone in Monotherapy or Combination Therapy against Cancers. Chemico-Biological Interactions 296, 162–170. doi:10.1016/j.cbi.2018.09.019

Engels, B. M., and Hutvagner, G. (2006). Principles and Effects of Microrna-Mediated post-transcriptional Gene Regulation. Oncogene 25 (46), 6163–6169. doi:10.1038/sj.onc.1209909

Fan, Q., Huang, T., Sun, X., Yang, X., Wang, J., Liu, Y., et al. (2021). miR130a3p Promotes Cell Proliferation and Invasion by Targeting Estrogen Receptor α and Androgen Receptor in Cervical Cancer. Exp. Ther. Med. 21 (5), 414. doi:10.3892/etm.2021.9858

Ferrari, A., Bleyer, A., Patel, S., Chiaravalli, S., Gasparini, P., and Casanova, M. (2018). The challenge of the Management of Adolescents and Young Adults with Soft Tissue Sarcomas. Pediatr. Blood Cancer 65 (7), e27013. doi:10.1002/pbc.27013

Galbraith, L. C. A., Mui, E., Nixon, C., Hedley, A., Strachan, D., MacKay, G., et al. (2021). Ppar-gamma Induced Akt3 Expression Increases Levels of Mitochondrial Biogenesis Driving Prostate Cancer. Oncogene 40 (13), 2355–2366. doi:10.1038/s41388-021-01707-7

Hawkins, D. S., Spunt, S. L., and Skapek, S. X. (2013). Children's Oncology Group's 2013 Blueprint for Research: Soft Tissue Sarcomas. Pediatr. Blood Cancer 60 (6), 1001–1008. doi:10.1002/pbc.24435

Hron, A. J., and Asakura, A. (2017). An Examination of the Role of Transcriptional and Posttranscriptional Regulation in Rhabdomyosarcoma. Stem Cell Int. 2017, 1–10. doi:10.1155/2017/2480375

Jo, V. Y., and Doyle, L. A. (2016). Refinements in Sarcoma Classification in the Current 2013 World Health Organization Classification of Tumours of Soft Tissue and Bone. Surg. Oncol. Clin. North America 25 (4), 621–643. doi:10.1016/j.soc.2016.05.001

Krüger, J., and Rehmsmeier, M. (2006). Rnahybrid: Microrna Target Prediction Easy, Fast and Flexible. Nucleic Acids Res. 34, W451–W454. doi:10.1093/nar/gkl243

Love, M. I., Huber, W., and Anders, S. (2014). Moderated Estimation of Fold Change and Dispersion for RNA-Seq Data with DESeq2. Genome Biol. 15, 550. doi:10.1186/s13059-014-0550-8

Lv, C., Zhou, Y.-h., Wu, C., Shao, Y., Lu, C.-l., and Wang, Q.-y. (2015). The Changes in Mir-130b Levels in Human Serum and the Correlation with the Severity of Diabetic Nephropathy. Diabetes Metab. Res. Rev. 31 (7), 717–724. doi:10.1002/dmrr.2659

Ma, J., Xu, M., Yin, M., Hong, J., Chen, H., Gao, Y., et al. (2019). Exosomal Hsa-Mir199a-3p Promotes Proliferation and Migration in Neuroblastoma. Front. Oncol. 9, 459. doi:10.3389/fonc.2019.00459

Malempati, S., and HawkinsRhabdomyosarcoma, D. S. (2012). Rhabdomyosarcoma: Review of the Children's Oncology Group (COG) Soft-Tissue Sarcoma Committee Experience and Rationale for Current COG Studies. Pediatr. Blood Cancer 59 (1), 5–10. doi:10.1002/pbc.24118

Martens, M., Ammar, A., Riutta, A., Waagmeester, A., Slenter, D. N., Hanspers, K., et al. (2021). Wikipathways: Connecting Communities. Nucleic Acids Res. 49 (D1), D613–D621. doi:10.1093/nar/gkaa1024

Punyko, J. A., Mertens, A. C., Baker, K. S., Ness, K. K., Robison, L. L., and Gurney, J. G. (2005). Long-term Survival Probabilities for Childhood Rhabdomyosarcoma. Cancer 103 (7), 1475–1483. doi:10.1002/cncr.20929

Sahraeian, S. M. E., Mohiyuddin, M., Sebra, R., Tilgner, H., Afshar, P. T., Au, K. F., et al. (2017). Gaining Comprehensive Biological Insight into the Transcriptome by Performing a Broad-Spectrum RNA-Seq Analysis. Nat. Commun. 8, 59. doi:10.1038/s41467-017-00050-4

Smolle, M. A., Leithner, A., Posch, F., Szkandera, J., Liegl-Atzwanger, B., and Pichler, M. (2017). Micrornas in Different Histologies of Soft Tissue Sarcoma: A Comprehensive Review. Int. J. Mol. Sci. 18 (9), 1960. doi:10.3390/ijms18091960

Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., et al. (2005). Gene Set Enrichment Analysis: A Knowledge-Based Approach for Interpreting Genome-wide Expression Profiles. Proc. Natl. Acad. Sci. 102 (43), 15545–15550. doi:10.1073/pnas.0506580102

Toro, J. R., Travis, L. B., Wu, H. J., Zhu, K., Fletcher, C. D. M., and Devesa, S. S. (2006). Incidence Patterns of Soft Tissue Sarcomas, Regardless of Primary Site, in the Surveillance, Epidemiology and End Results Program, 1978-2001: An Analysis of 26,758 Cases. Int. J. Cancer 119 (12), 2922–2930. doi:10.1002/ijc.22239

Vieira, L. M., Jorge, N. A. N., de Sousa, J. B., Setubal, J. C., Stadler, P. F., and Walter, M. E. M. T. (2021). Competing Endogenous Rna in Colorectal Cancer: An Analysis for colon, Rectum, and Rectosigmoid junction. Front. Oncol. 11, 681579. doi:10.3389/fonc.2021.681579

Wang, H., Zhang, Y., Zeng, X., Pei, W., Fan, R., Wang, Y., et al. (2021). A Combined Self-Assembled Drug Delivery for Effective Anti-breast Cancer Therapy. Ijn . 16, 2373–2388. doi:10.2147/ijn.S299681

Wang, S., Han, H., Hu, Y., Yang, W., Lv, Y., Wang, L., et al. (2018). Microrna-130a-3p Suppresses Cell Migration and Invasion by Inhibition of Tbl1xr1-Mediated Emt in Human Gastric Carcinoma. Mol. Carcinog 57 (3), 383–392. doi:10.1002/mc.22762

Wang, Y., Zhang, X., Tang, W., Lin, Z., Xu, L., Dong, R., et al. (2017). miR-130a Upregulates mTOR Pathway by Targeting TSC1 and Is Transactivated by NF-Κb in High-Grade Serous Ovarian Carcinoma. Cell Death Differ 24 (12), 2089–2100. doi:10.1038/cdd.2017.129

Wang, Z., Shen, W., Li, X., Feng, Y., Qian, K., Wang, G., et al. (2020). The PPARγ Agonist Rosiglitazone Enhances the Radiosensitivity of Human Pancreatic Cancer Cells. Dddt . 14, 3099–3110. doi:10.2147/dddt.S242557

Weihkopf, T., Blettner, M., Dantonello, T., Jung, I., Klingebiel, T., Koscielniak, E., et al. (2008). Incidence and Time Trends of Soft Tissue Sarcomas in German Children 1985-2004 - a Report from the Population-Based German Childhood Cancer Registry. Eur. J. Cancer 44 (3), 432–440. doi:10.1016/j.ejca.2007.11.013

Wu, Y., Sreeharsha, N., Sharma, S., Mishra, A., Singh, A. K., and Gubbiyappa, S. K. (2020). Anticancer Effect of Rosiglitazone, a PPAR-γ Agonist against Diethylnitrosamine-Induced Lung Carcinogenesis. ACS Omega 5 (10), 5334–5339. doi:10.1021/acsomega.9b04357

Xu, Y. Y., Liu, H., Su, L., Xu, N., Xu, D. H., Liu, H. Y., et al. (2019). PPARγ Inhibits Breast Cancer Progression by Upregulating PTPRF Expression. Eur. Rev. Med. Pharmacol. Sci. 23 (22), 9965–9977. doi:10.26355/eurrev_201911_19563

Zhao, D., Guo, J., Liu, L., and Huang, Y. (2021). Rosiglitazone Attenuates High Glucose-Induced Proliferation, Inflammation, Oxidative Stress and Extracellular Matrix Accumulation in Mouse Mesangial Cells through the Gm26917/mir-185-5p Pathway. Endocr. J. 68 (7), 751–762. doi:10.1507/endocrj.EJ20-0783

Zhao, G., Zhang, J.-g., Shi, Y., Qin, Q., Liu, Y., Wang, B., et al. (2013). Mir-130b Is a Prognostic Marker and Inhibits Cell Proliferation and Invasion in Pancreatic Cancer through Targeting Stat3. PLoS One 8 (9), e73803. doi:10.1371/journal.pone.0073803

Zhou, D., Zhang, L., Sun, W., Guan, W., Lin, Q., Ren, W., et al. (2017). Cytidine Monophosphate Kinase Is Inhibited by the TGF-β Signalling Pathway through the Upregulation of miR-130b-3p in Human Epithelial Ovarian Cancer. Cell Signal. 35, 197–207. doi:10.1016/j.cellsig.2017.04.009

Zhou, T., Liu, J., Xie, Y., Yuan, S., Guo, Y., Bai, W., et al. (2021). Ese3/ehf, a Promising Target of Rosiglitazone, Suppresses Pancreatic Cancer Stemness by Downregulating Cxcr4. Gut , gutjnl–2020. doi:10.1136/gutjnl-2020-321952

Zhou, Y., Li, R., Yu, H., Wang, R., and Shen, Z. (2017). Microrna-130a Is an Oncomir Suppressing the Expression of Crmp4 in Gastric Cancer. Ott 10, 3893–3905. doi:10.2147/ott.S139443

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Pan, Li, Lou, Chen, Lin, Shen and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 08 February 2022
doi: 10.3389/fmolb.2021.785767

FOXM1/lncRNA TYMSOS/miR-214-3p–Mediated High Expression of NCAPG Correlates With Poor Prognosis and Cell Proliferation in Non–Small Cell Lung Carcinoma

Yixiao Yuan¹†, Xiulin Jiang1,²†, Lin Tang¹†, Juan Wang¹, Dahang Zhang¹, William C. Cho³* and Lincan Duan¹*

¹The Department of Thoracic Surgery, The Third Affiliated Hospital of Kunming Medical University, Kunming, China

²Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences/ Kunming Institute of Zoology, Kunming, China

³Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, Hong Kong SAR, China

Edited by:
Ismail Hosen, University of Dhaka, Bangladesh

Reviewed by:
José Díaz-Chávez, Instituto Nacional de Cancerología (INCAN), Mexico
Chen Li, Free University of Berlin, Germany

* Correspondence: Lincan Duan, duanmumuhuosan@163.com; William C. Cho, chocs@ha.org.hk

†These authors have contributed equally to this work

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 29 September 2021
Accepted: 31 December 2021
Published: 08 February 2022

Citation: Yuan Y, Jiang X, Tang L, Wang J, Zhang D, Cho WC and Duan L (2022) FOXM1/lncRNA TYMSOS/miR-214-3p–Mediated High Expression of NCAPG Correlates With Poor Prognosis and Cell Proliferation in Non–Small Cell Lung Carcinoma. Front. Mol. Biosci. 8:785767. doi: 10.3389/fmolb.2021.785767

Lung cancer is the most common cancer with high mortality. Increasing evidence has demonstrated that nonstructural maintenance of chromosomes condensin I complex subunit G (NCAPG) plays a crucial role in the progression of human cancers. However, the biological function and underlying mechanism of NCAPG in non–small cell lung cancer (NSCLC) are still unclear. Here, we utilized diverse public databases to analyze the expression of NCAPG in pan-cancer. We found that NCAPG was highly expressed in various human cancers, especially in NSCLC. NCAPG expression was significantly positively correlated with poor clinical-pathological features, poor prognosis, tumor mutational burden, DNA microsatellite instability, and immune cell infiltration in NSCLC. In addition, our results showed that depletion of NCAPG significantly inhibited NSCLC cell proliferation, migration, and self-renewal abilities, yet these could be reversed by adding microRNA (miRNA)-214-3p. Knockdown of long noncoding RNA (lncRNA) thymidylate synthetase opposite strand (TYMSOS) also inhibits the NSCLC cell proliferation, migration, and self-renewal abilities. In summary, our findings demonstrated that the crucial roles of the FOXM1/lncRNA-TYMSOS/miRNA-214-3p/NCAPG axis in NSCLC may shed light on how NCAPG may act as a therapeutic target for NSCLC.

Keywords: NSCLC, NCAPG, TYMSOS, miRNA-214-3p, tumor immune infiltration, cancer progression

INTRODUCTION

Lung cancer is the major cause of cancer-related death worldwide, according to cancer statistics in 2020, the morbidity rate of lung cancer ranks second, whereas the death rate of lung cancer ranks the top (Siegel et al., 2020). Lung cancer is composed of non–small cell lung carcinoma (NSCLC) and small cell lung carcinoma, NSCLC includes lung adenocarcinoma (LUAD), lung squamous cell carcinoma, and large-cell lung carcinoma, the NSCLC accounts for approximately 85% of all cases (Molina et al., 2008; Fan et al., 2021). The high lung cancer mortality is owing to cancer metastatic with the time of diagnosis, indicating that ameliorating patient’s survival will require more effective systemic therapies (Morgensztern et al., 2010). However, there is much progress achieved in cancer treatment methods, ranging from targeted therapies to immunotherapy (Herbst et al., 2018); the survival period of lung cancer patients was very short (Xu et al., 2020a; Liang et al., 2021). Therefore, finding a new therapeutic target or biomarker is essential for effectively preventing the development of lung cancer.

Recently, studies have started to characterize the regulatory effects that condensin complexes may have on diverse cancers, including colon adenocarcinoma and liver hepatocellular carcinoma. These condensin complexes play a different role in the progression of cancer, as well as drug resistance (Wu et al., 2019). Studies have indicated that nonstructural maintenance of chromosomes condensin I complex subunit G (NCAPG) was essential for chromatin separation (Eberlein et al., 2009). Its elevated expression has been reported in several cancers, including hepatocellular carcinoma (HCC) (Gong et al., 2019), gastric cancer (Sun et al., 2020), ovarian cancer (Xu et al., 2020b), and cardia adenocarcinoma (Zhang et al., 2021). It has been shown that elevated NCAPG level promotes cancer cell migration by activating Wnt/β-catenin signaling (Zhang et al., 2021). A recent study found that NCAPG through activating the PI3K/AKT pathway promotes HCC cell proliferation and antiapoptosis (Gong et al., 2019). Furthermore, up-regulation of NCAPG expression significantly facilitated the progression of BRCA. It has been shown that NCAPG regulated the stability of STAT3 confers trastuzumab resistance in HER2-positive breast cancer (Jiang et al., 2020). In gastric cancer, Song et al. reported that miR-193b-3p via targets the NCAPG and reduces the expression of NCAPG, resulting in inhibition of the cell proliferation of gastric cancer (Song et al., 2018). Moreover, up-regulated NCAPG expression is also linked to the cancer pathological stage of castration-resistant prostate cancer; knockdown of NCAPG inhibited cancer cell aggressiveness (Arai et al., 2018). However, a comprehensive analysis of the function and regulation mechanism of NCAPG in LUAD is still insufficient. Thus, our study utilized various bioinformatics approaches and functional studies to examine whether NCAPG is involved in NSCLC progression, immune infiltration, and its plausible molecular regulation.

In this study, we compared the expression of NCAPG between NSCLC tissues and normal samples and investigated the correlation between NCAPG expression and clinical parameters of NSCLC. In addition, we explored the prognostic value and clinical significance of NCAPG in NSCLC. Meanwhile, the correlation between NCAPG expression and immune infiltration was analyzed to explore the potential mechanisms involved in NCAPG modulation in the progression of NSCLC. Finally, the biological role of NCAPG was identified in NSCLC. In summary, we demonstrated the potential role of NCAPG in regulating tumor progression and its potential application in the diagnosis and prognostic evaluation in NSCLC.

MATERIALS AND METHODS

Data Collection

The Cancer Genome Atlas (TCGA)–LUAD cohort data and corresponding clinical information of 535 LUAD patients were downloaded from the TCGA website (https://portal.gdc.cancer.gov/repository). LUAD patients were classified into low- and high-NCAPG expression groups according to the median NCAPG expression value. NCAPG expression data and clinical information from datasets GSE10072 and GSE18842 were downloaded from the GEO database and validated for survival analyses. The gene expression profiles were normalized using the scale method provided in the “limma” R package. Data analysis was performed with the R (version 3.6.3) and ggplot2 (3.3.3) packages. The expression data were normalized to transcripts per kilobase million values before further analysis. Besides, the ROC curve was used to evaluate the diagnostic value of NCAPG L using the pROC R package and ggplot2 R package.

Construction and Evaluation of the Nomogram

The RMS R package was performed to generate a nomogram. C-index and calibration curve were performed by the Hmisc R package. In our study, C-index was performed to determine the discrimination of nomogram and used a bootstrap method with 1,000 resamples to calculate C-index.

Gene Expression Profiling Interactive Analysis

Gene Expression Profiling Interactive Analysis (GEPIA) (http://gepia.cancer-pku.cn/index.html) is a user-friendly web portal for gene expression analysis based on TCGA and GTEx data (Tang et al., 2017). In the current study, GEPIA was used to analyze the expression and prognosis values of NCAPG in pan-cancer. Furthermore, we used GEPIA analysis of the correlation between NCAPG expression and pathological stage.

Kaplan–Meier Plotter Database Analysis

We used KM Plotter (http://kmplot.com), an online database that contains gene expression data and survival information of 3,452 clinical lung cancer patients, to analyze the prognostic value of NCAPG in lung cancer (Győrffy, 2021). The patient samples were separated into two groups by median expression (high expression and low expression) to analyze the overall survival (OS) with hazard ratios (HRs) with 95% confidence intervals and log-rank p values.

Immune Infiltration Analysis

TIMER (https://cistrome.shinyapps.io/timer/) web server is a comprehensive resource for systematical analysis of immune infiltrates across diverse cancer types. In this study, TIMER was used to examine the correlation between somatic copy number alterations of NCAPG and the infiltration level of B cells, CD4⁺ T cells, CD8⁺ T cells, neutrophils, macrophages, and dendritic cells.

Function and Pathway Analysis by Gene Set Enrichment Analysis

In the present research, we utilized the linkedomics database (http://www.linkedomics.org/login.php) to obtain the coexpression genes of NCAPG in LGG. The gene set KEGG v6.2. symbols.gmt,” which served as a reference gene set, was downloaded from the Molecular Signatures Database (MSigDB) (http://software.broadinstitute.org/gsea/msigdb). We used the Gene Set Enrichment Analysis (GSEA) software and clusterProfiler package to perform the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis signaling pathway of NCAPG in lung cancer (Subramanian et al., 2005; Yu et al., 2012; Vasaikar et al., 2018).

The NCAPG-Interacting Genes and Protein Analysis

The GeneMANIA database (http://www.genemania.org) was applied to construct the NCAPG interaction network (Zuberi et al., 2013). The STRING online database (https://string-db.org/) was used to construct a protein–protein interaction (PPI) network of NCAPG (Szklarczyk et al., 2019).

Prediction of Long Noncoding RNA and c8eRNA Network Construction

We used the starBase (http://starbase.sysu.edu.cn/) to predict the potential upstream microRNAs (miRNAs) of NCAPG and examine the expression, prognosis, and correlation between the miRNA-214-3p and long noncoding RNA (lncRNA); we used the starBase to predict the binding sites among the miRNA, mRNA, and lncRNA (Li et al., 2014). The lncLocator (www.csbio.sjtu.edu.cn/bioinf/lncLocator.), a subcellular localization predictor for lncRNAs based on a stacked ensemble classifier, and CPC2 (http://cpc2.cbi.pku.edu.cn), a fast and accurate coding potential calculator based on sequence intrinsic features (Kang et al., 2017; Cao et al., 2018). In this study, lncLocator and CPC2 were used to explore the subcellular localization and the protein-coding ability of thymidylate synthetase opposite strand (TYMSOS).

Cell Culture, RNA Isolation, and Real-Time Polymerase Chain Reaction

Cell culture, RNA isolation, and real-time polymerase chain reaction (PCR) assay were performed as published (Shi et al., 2019). The BEAS-2B cell line was purchased from the cell bank of Kunming Institute of Zoology and cultured in BEGM media (Lonza, CC-3170). HEK-293T was obtained from ATCC. Lung cancer cell lines, including A549, H1299, and H1975, were purchased from Cobioer (China) with STR documents; A549, H1299, and H1975 cells were all cultured in RPMI 1640 medium (Corning) supplemented with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin. HEK-293T cells were cultured in a Dulbecco modified eagle medium (Corning). The detail information of primer used in this study are as follows: NCAPG-F: GAG​GCT​GCT​GTC​GAT​TAA​GGA, NCAPG-R: AAC​TGT​CTT​ATC​ATC​CAT​CGT​GC, TYMSOS-F: ATG​ACG​CCC​GCC​TCG​GGG​GCC, TYMSOS-R: TCA​GGA​AGG​ACG​ACC​GCA​CGG​GCA​CC, FOXM1-F: CGT​CGG​CCA​CTG​ATT​CTC​AAA, FOXM1-R: GGC​AGG​GGA​TCT​CTT​AGG​TTC, miRNA-214-3p-F TTT​TTA​CTA​CTA​TGG​CGG​GTG​ATA​AAA​CGT​GTA, miRNA-214-3p-R: GCA​AGC​TGT​AAT​CGA​CGG​GAA​GAG​CAT​GCC​CAT​CC, ACTIN-F: CTTCGCGGGCGACGAT, and ACTIN-R: CCA​TAG​GAA​TCC​TTC​TGA​CC. The expression quantification was obtained with the 2−ΔΔCt method.

Constructs, Transfection, and Infection

The FOXM1 and NCAPG shRNAs and control scrambled shRNA were transfected into HEK-293 T cells with the psPAX2/pMD2 G plasmids (Addgene) using calcium phosphate. After transfection, the cell supernatants were harvested and used to infect the A549 cell, and the stably lentiviral infected cells were selected with puromycin. Two shRNAs for knockdown of TYMSOS are as follows (sh-TYMSOS#1: 5ʹ-TAAGAAGATCTAATGCATCCT-3ʹand sh-TYMSOS#2: 5ʹ-AAC​ACT​TTA​TTA​TCA​CAT​CAG-3ʹ) were used to silence TYMSOS. Two shRNAs for knockdown of FOXM1 are as follows: sh-FOXM1 #1: 5ʹ-TCC​TGG​AGG​CTC​ACG​CCC​CCA-3ʹ and sh-FOXM1#2:5ʹ-GCCACAGGTTTCTGGCCTTGC-3ʹ, used to silence FOXM1.

Cell Proliferation, Colony Formation, and Tumorsphere Formation Assays

Cell proliferation assay was performed as previously described (Xu et al., 2020a). Indicated tumor cells were plated onto 12-well plates; the cell numbers were subsequently counted each day using an automatic cell analyzer countstar (Shanghai Ruiyu Biotech Co.). For colony formation assay, a total of 500 indicated cells/well were plated onto 6-well plates and cultured for 2 weeks at 37°C. The medium was changed every 3 days. Two weeks later, indicated cells were fixed with 4% paraformaldehyde for 30 min at room temperature and subsequently stained with 0.1% crystal violet for 30 min at room temperature.

Cell Migration Assay

The cell migration assay was performed as documented (Shi et al., 2019). For wound healing assay, to produce a wound, the monolayer cells in 6-well plate were scraped in a straight line with pipette tips. Plate was then washed with warm PBS to remove detached cells. Photographs of the scratch were taken at indicated time points using Nikon inverted microscope. For Transwell assay, 2.5 × 10⁴ cells in 100 µL serum-free medium were plated in 24-well plate chamber insert (Corning Life Sciences, catalog no. 3422), with medium containing 10% FBS at the bottom of the insert. Cells were incubated for 24 h and then fixed with 4% paraformaldehyde for 20 min. After washing, cells were stained with 0.5% crystal violet blue. The positively stained cells were examined under the light microscope.

Nuclear and Cytoplasmic Extraction

The nuclear and cytoplasmic fractions of RNA were extracted with a PARIS™ kit (Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA). The cell slide was placed at the bottom of the 6-well plate, and an appropriate number of cells (3 × 10⁵/well) were cultivated. Before the experiment, the cell confluence reached 60%–70%.

Luciferase Activity Assays

The sequences of TYMSOS, NCAPG–3′ UTR, and their miR-214-3p binding sites mutant versions were synthesized and added to luciferase reporter vector pGL3 (Promega, Shanghai, China), named TYMSOS-WT, TYMSOS-Mut, NCAPG-WT, and NCAPG-Mut, respectively. The experimental steps follow the manufacturer’s protocols of Dual Luciferase Assay Kit (Promega).

Immunohistochemistry Staining

Immunohistochemistry (IHC) staining was performed as documented (Kang et al., 2017). Briefly, cancer tissues were collected; primary antibody overnight incubation and second antibody incubation were performed. Finally, develop using the instrument. The detailed information of primers used in this study is as follows: (NCAPG, catalog no. 24563-1-AP, 1:200; Proteinch, Shanghai, China). Lung cancer tissue and normal lung tissues were obtained from advanced-stage lung cancer patients from the Third Affiliated Hospital of Kunming Medical University (Yunnan Tumor Hospital), as well as ethics approval and consent to participate. This study was approved by the ethics committee of the Third Affiliated Hospital of Kunming Medical University (Yunnan Tumor Hospital), and informed consent was obtained from all patients.

Statistical Analysis

The results of Kaplan–Meier plots, PrognoScan, and GEPIA are displayed with HR and P or Cox p values from a log-rank test. The Wilcoxon signed-rank test and Wilcoxon rank-sum test were performed to investigate the expression of NCAPG in paired and nonpaired samples, respectively. The Wilcoxon signed-rank test was used to analyze the relations between NCAPG expression and clinical features. The univariate and multivariate analyses using the Cox regression model were carried out to evaluate death risk, including gender, age, number of pack-years smoked, T stage, N stage, M stage, pathological stage, and NCAPG expression. All the tests were two-sided, and p < 0.05 was considered statistically significant. p < 0.05 (*), p < 0.01 (**), and p < 0.001 (***) were considered significant.

RESULTS

Expression Level, Clinical-Pathological Features, and Prognosis of NCAPG in Pan-Cancer

To examine the expression of NCAPG in various cancers, we applies the TIMER database to analyze the expression of NCAPG in human cancer. The results revealed that NCAPG was significantly up-regulated in 21 cancer types than normal samples, including bladder urothelial carcinoma, breast invasive carcinoma, cervical squamous cell carcinoma and endocervical adenocarcinoma, cholangiocarcinoma, colon adenocarcinoma, esophageal carcinoma, glioblastoma multiforme, head and neck squamous cell carcinoma, kidney chromophobe, kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, liver hepatocellular carcinoma, LUAD, lung squamous cell carcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectum adenocarcinoma, skin cutaneous melanoma, stomach adenocarcinoma, thyroid carcinoma, and uterine corpus endometrial carcinoma were prominently ascended when matched to normal tissues (Figure 1A). However, no significant difference of NCAPG in lymphoid neoplasm diffuse large B-cell lymphoma, brain lower-grade glioma, ovarian serous cystadenocarcinoma, sarcoma, uterine carcinosarcoma, and uveal melanoma was observed. Next, we utilized the Oncomine database to examine the differences in expression of NCAPG in various cancers. The analysis result has shown that the expression of NCAPG elevated in 17 tumors compared with the normal tissues (Figure 1B). Besides, we showed that the expression of NCAPG was decreased in acute myeloid leukemia (LAML). Furthermore, we check the level of NCAPG in TCGA pan-cancers using the GEPIA tool. The result showed that NCAPG expression in ACC, BLCA, BRCA, CESC, COAD, DLBC, esophageal carcinoma, glioblastoma multiforme, HNSC, LIHC, LUAD, LUSC, OV, pancreatic adenocarcinoma, rectum adenocarcinoma, SKCM, STAD, THYM, UCEC, and UCS was significantly elevated in cancer than corresponding normal controls. And in LAML, NCAPG was decreased (Figure 1C). Finally, we used the CCLE database to analyze the expression of NCAPG in various tumor cells; the results indicated that NCAPG was elevated in LUAD cells (Figure 1D). Taken together, these results confirm that NCAPG plays a crucial role in carcinogenesis of the human cancer.

FIGURE 1 | Expression analysis for NCAPG in human cancers (A) NCAPG expression of different tumor types in pan-cancer examined by the TIMER database. (B) Increased expression of NCAPG in pan-cancer examined by the Oncomine database. (C) NCAPG expression in pan-cancer examined by TCGA and GTEx data. (D) The expression of NCAPG in different tumor cells lines was examined by the CCLE database. *p < 0.05, **p < 0.01, ***p < 0.001.

Given that NCAPG was highly expressed in diverse cancers, therefore, we next examined the expression of NCAPG whether correlated with the clinical-pathological features in NSCLC; we applied the GEPIA approach to analyze NCAPG expression and the pathological stages across various cancer types of TCGA. The results showed that high expression of NCAPG was significantly correlated with the pathological stages of human cancers, including ACC, BRCA, KIRC, KIRP, LIHC, LUAD, LUSC, OV, SKCM, TGCT, and THCA (Supplementary Figure S1).

Finally, survival time analysis of NCAPG in pan-cancer was conducted. Four prognostic indices, consisting of OS, disease-free survival (DFS), progression-free survival (PFS), and disease-specific survival (DSS) were contained. Regarding OS, up-regulation of NCAPG expression correlated with unfavorable prognosis in ACC, KIRC, KIRP, LGG, LIHC, LUAD, MESO, PAAD, PCPG, and SARC, but THYM patients elevated NCAPG expression exhibit good prognosis (Supplementary Figure S2). For DFS, about these varies cancer, up-regulation of NCAPG shown poor prognosis in ACC, KICH, KIRC, KIRP, LGG, LIHC, PAAD, PCPG, PRAD, SARC, THCA, and UVM (Supplementary Figure S3). Concerning PFS, in human cancers, up-regulation of NCAPG expression shows a bad prognosis in ACC, KICH, KIRC, KIRP, LGG, LIHC, MESO, PAAD, PCPG, PRAD, and UVM (Supplementary Figure S4A). Concerning DSS, elevated expression of NCAPG implies poor prognosis in CESC, KIRP, LIHC, MESO, SARC, and THCA (Supplementary Figure S4B). Through analysis of the OS, RFS, PFS, and DSS of NCAPG in human cancer, NCAPG may be used for an unfavorable prognostic biomarker in patients with ACC, KIRC, KIRP, LGG, LIHC, LUAD, MESO, PAAD, PCPG, and SARC. The above results demonstrated that NCAPG plays an important role in diverse cancer progression.

Correlation Between NCAPG Expression and Tumor Mutational Burden, DNA Microsatellite Instability

Studies have demonstrated that tumor mutational burden (TMB), which represents the number of somatic mutations that occur on the coding region of the tumor cell genome, may be a biomarker of sensitivity to immune checkpoint inhibitors (Campbell et al., 2017; Chalmers et al., 2017; Hu et al., 2021). Microsatellite instability (MSI) refers to the random changes in microsatellites in tumor cells. These MSI changes have been linked to increased sensitivity to checkpoint inhibitor drugs (Cortes-Ciriano et al., 2017). We figured out the TMB and MSI of every sample in human cancer and then examined the relevance between NCAPG expression and TMB, and MSI in various human cancers. The analysis result indicated that the elevated expression of NCAPG correlated positively with TMB in ACC, UCEC, TGCT, STAD, SKCM, SARC, READ, PRAD, PAAD, OV, MESO, LUSC, LUAD, LGG, KIRC, KICH, HNSC, COAD, BRCA, and BLCA but notable negative within THYM (Supplementary Figure S5A). Furthermore, up-regulated expressions of NCAPG positively correlated with MSI in COAD, ESCA, LIHC, LUSC, SARC, STAD, UCEC, and UCS but negative in DLBC, LAML, and READ (Supplementary Figure S5B).

The Expression of NCAPG in Immune and Molecular Subtypes of Human Cancers

Next, we use the TISIDB website to examine the expression of NCAPG in different immune and molecular subtypes in various tumors (Qiu et al., 2021). Immune subtype was including wound healing, IFN-gamma dominant, inflammatory, lymphocyte depleted, immunologically quit, TGF-b dominant. The results indicated that NCAPG expression was related to diverse immune subtypes in BLCA, BRCA, CESC, KIRC, KIRP, LIHC, LUAD, LUSC, MESO, OV, PRAD, SARC, SKCM, STAD, UVM, and UCEC (Supplementary Figure S6). For example, in LUAD, NCAPG had high expression in the wound healing group, IFN-gamma dominant group, and low expression in lymphocyte depleted types. For molecular subtypes of cancers, a positive connection with NCAPG expression existed in ACC, BRCA, COAD, ESCA, HNSC, KIRP, LGG, LIHC, LUSC, OV, SKCM, and UCEC (Supplementary Figure S7). Above all, these findings confirm that NCAPG has distinct patterns of expression in different cancer tissues.

NCAPG Was Highly Expressed in NSCLC

To elucidate the biological functions of NCAPG in NSCLC, we first examine the expression of NCAPG in NSCLC by using TCGA and GEO database, the results showed that the expression of NCAPG, but not of NCAPG2, was significantly up-regulated in NSCLC (Figures 2A, B), Notably, NCAPG expression was positively associated with poor clinical-pathological features, including the pathologic stage, TNM stage, primary therapy outcome, gender, and smoking status (Figures 2C, D).

FIGURE 2 | NCAPG was a high expression in NSCLC. (A–D) The expression of NCAPG in NSCLC was examined by TCGA and GEO databases. (C,D) NCAPG expression is associated with clinicopathological characteristics in NSCLC. (E) Receiver operating characteristic analysis (ROC) of NCAPG in NSCLC. (*p < 0.05, **p < 0.01, ***p < 0.001).

We used logistics analysis to examine the correlation between NCAPG expression and clinicopathological characteristics. We found that NCAPG expression was positively correlated with the clinical stage including the TNM stage, pathologic stage, primary therapy outcome, gender, and smoker (Table 1). ROC curve analysis showed that the AUC for NCAPG in LUAD and LUSC patients was 0.963 and 0.995, respectively (Figure 2E). These findings indicated that the elevated expression level of NCAPG was correlated with poor pathobiology features of NSCLC patients. Given that NCAPG was significantly associated with poor pathobiology features in NSCLC patients, therefore, we further analyzed the prognosis value of NCAPG in NSCLC, the results showed that high expression of NCAPG was correlated with poor prognosis in NSCLC, this result was verified by GEO data (Figures 3A–D). The prognosis analysis for different subgroups showed that high expression of NCAPG was associated with poor prognosis (Figure 3E).

TABLE 1 | Logistic regression analysis of correlation between clinical–pathological characteristics and NCAPG expression in NSCLC patients.

FIGURE 3 | The prognosis of NCAPG in NSCLC. (A–C) The prognosis of NCAPG in NSCLC is examined by the TCGA database. (D) The prognosis of NCAPG in NSCLC is examined by the GEO database. (E) Distinct clinical outcomes based on NCAPG expression in NSCLC patient subgroups. *p < 0.05, **p < 0.01, ***p < 0.001.

All the statistically significant prognostic factors in each multivariate Cox regression analysis were utilized to construct a prognostic nomogram, the calibration curve was drawn to examine the efficiency of the nomogram. The clinical pathologic stage, TNM stage, and NCAPG expression, were included in the nomogram to predict OS, DSS, and progression-free interval (PFI); the c-index was OS: C-index: 0.709 (0.684–0.733), DSS: C-index: 0.710 (0.679–0.741), PFI: C-index: 0.645 (0.620–0.669) (Figures 4A–F). The calibration curves all presented desirable predictions of the three nomograms for the 1-, 3-, and 5-year clinical outcomes.

FIGURE 4 | Nomogram for prediction of prognosis in NSCLC patients. Construction and validation of nomograms based on NCAPG expression. Shown are the nomograms constructed to establish NCAPG expression-based risk scoring models for 1-, 3-, and 5-year overall survival (A), disease-specific survival (B), and progression-free interval (C). Calibration plots validate the efficiency of nomograms for overall survival (D), disease-specific survival (E), and progression-free interval (F). OS, overall survival; FI, progression-free interval; DSS, disease-specific survival. *p < 0.05, **p < 0.01, ***p < 0.001.

Analysis of the Function of NCAPG in LUAD

After determining the prognostic value of NCAPG in LUAD, we next explored the functions of NCAPG in NSCLC progression. The function module of linked-omics was used to examine the NCAPG coexpression gene in TCGA-NSCLC cohort (Figure 5A); the genes that were most positively related to NCAPG are exhibited in the heat map (Figures 5B). KEGG pathway enrichment analysis showed that NCAPG-coexpressed genes mainly were involved in primary immunodeficiency, hematopoietic cell lineage, T-cell receptor signaling pathway, cell adhesion molecules, cell differentiation of Th1 and Th2, cell differentiation of Th17, natural killer cell–mediated cytotoxicity, and Fc epsilon RI signaling pathway (Figure 5C). These conclusions intensively indicated that NCAPG is involved in the regulation of the immune response in NSCLC.

FIGURE 5 | GO and KEGG enrichment analysis for NCAPG. (A) Volcano plot of genes differentially expressed in correlation with NCAPG. (B) Heat maps of genes positively correlated with NCAPG (top 50) (C) Analysis of the KEGG pathway of NCAPG in NSCLC by linkedomics. (D–F) GO analysis for NCAPG in NSCLC. (G) The gene interaction meshwork of NCAPG was constructed by using GeneMania. (H) Utilized the STRING to construction the protein interaction meshwork of NCAPG. *p < 0.05, **p < 0.01, ***p < 0.001.

For the GO term, NCAPG-coexpressed genes are mainly involved in the molecular function of microtubule-binding, tubulin binding, catalytic activity, acting on DNA, microtubule motor activity ATPase activity, motor activity, histone deacetylase binding, DNA helicase activity, exonuclease activity, and kinase regulator activity (Figure 5D). NCAPG-coexpressed genes are mainly involved in the biology process of mitotic nuclear division, chromosome segregation, nuclear division, microtubule cytoskeleton organization involved in mitosis, DNA replication, organelle fission, mitotic sister chromatid segregation, sister chromatid segregation, mitotic spindle organization, and spindle organization (Figure 5E). NCAPG-coexpressed genes are mainly involved in the cellular component of the chromosomal region, spindle, chromosome, centromeric region, kinetochore, microtubule, condensed chromosome kinetochore, mitotic spindle, condensed chromosome, centromeric region, kinesin complex, and intercellular bridge (Figure 5F). Furthermore, we used the Gene Mania database to analyze the gene interaction meshwork of NCAPG and the altered adjacent to genes. The results suggest the 20 constantly changing genes were closely correlated with NCAPG, including MKI87, NCAPH, NCAPD2, CDK1, HSF2, CCNB1, and AURKB (Figure 5G). Finally, we also used the STRING database to construct the PPI network of NCAPG (Figure 5H). We further analyzed the functions of this interaction gene with NCAPG; the result was suggestive of these genes mainly linked to the cell cycle and cell proliferation. Collectively, these findings emphasize the crucial role of NCAPG in regulating the progression of NSCLC.

Analysis NCAPG-Related Signaling Pathways in LUAD by GSEA

To examine the potential mechanisms influenced by NCAPG in LUAD, GSEA was conducted. The analysis result showed that NCAPG mainly participates in apoptosis, epithelial–mesenchymal transition, G2/M checkpoint, IL-2 STAT5 signal pathway, IL-6–JAK–STAT3 signal pathway, and MTORC1signaling pathway in LUAD (Figures 6A–H). Similarly, relating to the KEGG pathways, the enriched result proves that the NCAPG is mainly involved in B-cell receptor signal pathway and natural killer cell–mediated cytotoxicity signaling pathway (Figures 6I–P). The aforementioned findings intensively were indicative of NCAPG participation in lung cancer progression and immune response.

FIGURE 6 | The signaling pathway for NCAPG enrichment by GSEA software. (A–P) The signaling pathway for NCAPG enrichment by GSEA software.

NCAPG Expression Is Correlated With Immune Infiltration in NSCLC

Tumor-infiltrating lymphocyte plays crucial roles in the prognosis of NSCLC (Fan et al., 2021; Zeng et al., 2021). First, we used TIMER online analysis tool to examine the relationship between the immune infiltration and the somatic copy number alterations of NCAPG in LUAD. Particularly, NCAPG CNV has memorable correlations with infiltrating levels of B cells, CD4⁺ T cells, macrophages, neutrophils, and dendritic cells (Figure 7A). Next, we further explored the correlation between NCAPG expression and the infiltration levels of different immune cells in TCGA-LUAD patients. NCAPG expression was weakly correlated with macrophages (r = 0.22, p = 2.91e-07), moderately with CD8⁺ T cells (r = 0.38, p = 3.43e-19) and B cells (r = 0.40, p = 1.1e-20), and intensively with neutrophils cells (r = 0.61, p = 3.65e-13), myeloid dendritic cells (r = 0.66, p = 1.12e-64), and CD4⁺ T cells (r = 0.67, p = 5.22e-69) (Figures 7B–G).

FIGURE 7 | The correlation between NCAPG expression and immune infiltration in NSCLC. (A) The NCAPG gene copy numbers affect the infiltration level of different immune cells in LUAD. (B–G) NCAPG positively linked to the infiltration of different immune cells examined by TIMER databases. *p < 0.05, **p < 0.01, ***p < 0.001.

To further examine the functions of NCAPG in tumor immune, we used the TIMER database to explore the correlation between the NCAPG expression and immune cell–related markers in LUAD. The result indicated that NCAPG was markedly positively linked to different immune cells, containing B cells, CD8⁺ T cells, CD8⁺ T cells, M1 macrophage biomarkers, M2 macrophage biomarkers, neutrophil and dendritic cell–related markers in LUAD (Table 2).

TABLE 2 | The correlation between NCAPG and gene markers of immune cells examined by TIMER database.

Immune checkpoints play a significant role in cancer immune evasion (Qu et al., 2021). In consideration of the crucial roles of NCAPG in LUAD, the correlation of NCAPG with immune checkpoints related genes was assessed. NCAPG expression was significantly positively linked to CD274 (r = 0.566, p = 4.79e-45), CTLA4 (r = 0.716, p = 4.9e-82), PDCD1 (r = 0.659, p = 1.84e-65), TIGIT (r = 0.738, p = 1.06e-89), PDCD1LG2 (r = 0.596, p = 7.58e-51), HAVCR2 (r = 0.578, p = 3.21e-47), LAG3 (r = 0.551, p = 3.39e-42), and SIGLEC15 (r = 0.327, p = 2.74e-14) in LUAD (Supplementary Figures S8A–S8H). In addition, we also found significant correlations of NCAPG with immune infiltration and immune inhibitors across human heterogeneous cancers (Supplementary Figures S8I–S8J). These results indicated that NCAPG might play significant roles in the regulation of immune infiltration of LUAD.

Analysis of the Prognosis of the Immune Infiltration and NCAPG Expression in LUAD

NCAPG expression is markedly related to immune infiltration and unfavorable prognosis in LUAD (Fan et al., 2021). Next, we examined whether NCAPG expression affects the prognosis of LUAD through immune infiltrate. We used the Kaplan–Meier plotter analysis of the prognosis of NCAPG in LUAD in different related subgroups of immune cells. The analysis results suggest elevated NCAPG expression and reduced level of B cells, CD4⁺ memory T cells, CD8⁺ memory T cells, macrophages, and natural killer T cells, which had an unfavorable prognosis observed in the LUAD patient (Figures 8A–L). The prognosis of LUAD was of no relevance in other immune cell groups. These findings suggest that NCAPG may affect the survival state of LUAD patients, partially owing to immune infiltrate.

FIGURE 8 | The prognosis analysis based on the expression of NCAPG in different immune cell subgroups in NSCLC. (A–L) Correlation between NCAPG expression and OS in different immune cell infiltration groups in LUAD patients. *p < 0.05, **p < 0.01, ***p < 0.001.

Depletion of NCAPG Inhibits NSCLC Progression

To examine the protein expression of NCAPG in NSCLC, we utilized an IHC assay to examine the expression of NCAPG in lung cancer. The results verify that the elevated expression of NCAPG was observed in lung cancer than the normal tissues (Figures 9A, B). Next, we examined the mRNA expression of NCAPG in BEAS2B and NSCLC cell lines. The result showed that NCAPG expressions are significantly elevated in all NSCLC cells, highest expression NCAPG observe in A549 (Figure 9C). Owing to NCAPG being highly expressed in NSCLC cells lines, we speculated that NCAPG might play a crucial role in the progression of NSCLC. We then selected the A549 cell for further studies. We inhibited the NCAPG mRNA expression using two independent lentiviral shRNAs targeting different regions of NCAPG. The efficiency and specificity of NCAPG knockdown in A549 cells were verified by quantitative reverse transcriptase (qRT)–PCR (Figure 9D). Growth curve and colony formation assays were carried out to evaluate cell proliferation, and the results showed that NCAPG silencing notably inhibited the proliferation of A549 cells (Figures 9E, F). Transwell and wound healing experiments showed that NCAPG knockdown reduced the migration of A549 cells (Figures 9G, H). Tumorsphere assay showed that NCAPG knockdown reduced the self-renewal of GSCs (Figures 9I). Collectively, these findings indicate NCAPG exerts an oncogenic role in NSCLC cells.

FIGURE 9 | NCAPG silencing on cell on proliferation, migration, and self-renewal ability of NSCLC Cell. (A) IHC analysis of NCAPG in lung cancer tissues and normal tissues. (B) IHC scores statistical analysis of NCAPG in lung cancer tissues and normal tissues. (C) The expression of NCAPG in NSCLC cell lines was examined by qRT-PCR assay. (D) The establishment of NCAPG knockdown in A549 cell lines and verified by real-time qPCR (E,F) Growth curve and clone formation assays were utilized to detect silencing of NCAPG on the growth of A549 cell. (G,H) Transwell and wound healing assays were used to detect silencing of NCAPG on the migration of A549 cells. (I) A tumorsphere experiment was used to detect silencing of NCAPG on the self-renewal of A549 cells. *p < 0.05, **p < 0.01, ***p < 0.001.

Identification of miR-214-3p as the Direct Binding miRNA of NCAPG in LUAD

To uncover whether NCAPG was regulated by miRNAs, we predicted the potential miRNAs that bind with NCAPG by using the RNA22 (Loher and Rigoutsos, 2012), miRmap (Goossens et al., 2019), DIANA-microT web server v5.0 (Paraskevopoulou et al., 2013), miRanda (Rost and McGregor, 2012), and TargetScan (Agarwal et al., 2015). Eventually, only four miRNAs, including miRNA-15a, miRNA-147a, miRNA-375, and miRNA-214-3p, were commonly predicted. Next, we assessed their expression levels and prognostic values in NSCLC using TCGA-LUAD data (Figures 10A, B). According to the ceRNA theory, the miRNA could bind with 3′ UTR of mRNA and inhibit the expression of mRNA; thus, the expression of miRNA and mRNA should have a negative correlation pattern. We performed the expression correlation between NCAPG and miRNA-214-3p, the result demonstrated that NCAPG was significantly negatively correlated with miRNA-214-3p in LUAD (Figure 10C). In addition, Among the four miRNAs, only miR-214-3p was significantly decreased in LUAD than a control group. Survival analysis demonstrated that the elevated miR-214-3p expression has a favorable prognosis in LUAD. By integration of the expression analysis, correlation analysis, and survival analysis, we supposed that miR-214-3p was the most potential miRNA that may be potentially bound to NCAPG, the potential binding site between the miRNA-214-3p and NCAPG 3′ UTR were predicted base-pairing with miRNA-214-3p by starBase (Figure 10D). Next, we analyzed the expressions of miRNA-214-3p in whole blood of human lung cancer and lung cancer by using the GEO database (Patnaik et al., 2017); these data also revealed miRNA-214-3p expressions are down-regulated in the whole blood of human lung cancer and lung cancer (Figures 10E,F). Finally, we also analyzed the expressions of miRNA-214-3p in normal human bronchial epithelial cells and NSCLC cell lines. Consistent with our findings in TCGA, we found that miRNA-214-3p expressions are down-regulated in all NSCLC cell lines (Figure 10G). To investigate the effects of miRNA-214-3p on the expression of NCAPG, we conducted overexpression analysis. Overexpressing miRNA-214-3p significantly reduced the mRNA expressions of NCAPG in A549 cells (Figure 10H). The luciferase assay showed that transfection of miRNA-214-3p mimics significantly the reduced relative luciferase activity of NCAPG-3UTR-WT–treated lung cancer cells, but did not affect that of NCAPG-3UTR-MUT–treated lung cancer cells (Figure 10I). Collectively, these data imply that miRNA-214-3p might target the NCAPG and plays pivotal roles in the regulation of NCAPG expression in NSCLC.

FIGURE 10 | Identification of upstream miRNAs of NCAPG in LUAD. (A) The expression of miRNA-15a, miRNA-147a, miRNA-375, and miRNA-214-3p in TCGA-LUAD. (B) The prognostic values of miRNA-15a, miRNA-147a, miRNA-375, and miRNA-214-3p in TCGA-LUAD. (C) The correlation between expression of miRNA-214-3p and NCAPG in LUAD. (D) The potential binding sites of miR-214-3p and NCAPG. (E) The expression of miRNA-214-3p in NSCLC was examined by the GEO database. (F) The expression of miRNA-214-3p in NSCLC determined by GEO database (G) The expression of miRNA-214-3p in NSCLC cell lines. (H) The expression of NCAPG in A549 cell after overexpression of miRNA-214-3p examined by the qRT-PCR assay. (I) Relative luciferase activities of wild type (WT) and mutated (MUT) NCAPG–3′ UTR reporter plasmid in A549 cells cotransfected with miRNA-214-3p mimics. *p < 0.05, **p < 0.01, ***p < 0.001.

Identification of TYMSOS as Upstream lncRNA of miRNA-214-3p in LUAD

Diverse regulatory mechanisms of gene expression have been well established in the NSCLC progression. Emerging evidence has demonstrated that lncRNAs play an indispensable role in regulating gene expression. Next, the upstream lncRNAs of miRNA-214-3p were predicted using the starBase database and DIANA-LncBase v3 (Karagkouni et al., 2020). There are four lncRNAs were predicted, including LINC01228, RUSC1-AS1, SNHG3, and TYMSOS. Identically, the expression levels of and prognostic values of lncRNAs in LUAD were determined by using TCGA-LUAD data. The analysis results confirmed that LINC01228, RUSC1-AS1, and SNHG3 were down-regulated in NSCLC, on the contrary, TYMSOS was significantly up-regulation in NSCLC and high expression of TYMSOS was correlated with unfavorable prognosis in NSCLC (Figures 11A,B). In the light of the competitive endogenous RNAs theory, lncRNAs able to up-regulation the mRNA level via reducing the expression of miRNAs. Therefore, the elevated expression of lncRNA should decrease the level of miRNA, and positive correlation between mRNA. Among all the four lncRNAs, only TYMSOS was negatively correlated with miRNA-214-3p in LUAD (Figure 11C), and TYMSOS was positively correlated with NCAPG in LUAD (Figure 11D). The target sites in the miRNA-214-3p and TYMSOS were predicted to pair with miRNA-214-3p by TarBase (Figure 11E). Moreover, the TYMSOS was markedly elevated in LUAD matched to normal controls by analysis of the GEO databases (Mezheyeuski et al., 2018) (Figure 11F). Finally, to further confirm the interaction between TYMSOS and miRNA-214-3p, the luciferase assay was performed, the luciferase assay indicated that transfection of miRNA-214-3p mimics significantly decreased the relative luciferase activity of TYMSOS-WT–treated lung cancer cells, but did not affect that of TYMSOS-MUT–treated lung cancer cells (Figure 11G). Taken together, the data suggest that TYMSOS acted as a sponge for miRNA-214-3p in NSCLC cells.

FIGURE 11 | Identification of upstream lncRNAs of miRNA-214-3p in LUAD. (A) The expression of LINC01228, RUSC1-AS1, SNHG3, and TYMSOS in TCGA-LUAD. (B) The prognostic values of LINC01228, RUSC1-AS1, SNHG3, and TYMSOS in TCGA-LUAD. (C) The correlation between expression of miRNA-214-3p and TYMSOS in LUAD. (D) The correlation between expression of NCAPG and TYMSOS in LUAD. (E) The potential binding sites of miR-214-3p and TYMSOS. (F) The expression of TYMSOS in GSE81089 and control normal samples determined by GEO database. (G) Relative luciferase activities of wild-type (WT) and mutated (MUT) THTMSOS reporter plasmid in A549 cells cotransfected with miRNA-214-3p mimics. (H) The expression of FOXM1 in LUAD was examined by using the GEPIA database. (I) The pathological stage analysis for FOXM1 in LUAD was determined by the GEPIA database. (J) The overall survival analysis for FOXM1 in LUAD was examined by using the GEPIA database. (K) The correlation between expression of FOXM1 and TYMSOS in TCGA-LUAD. (L) The correlation between expression of FOXM1 and NCAPG in TCGA-LUAD. (M) TYMSOS expression in A549 cell after knockdown FOXM1 was examined by real-time PCR. *p < 0.05, **p < 0.01, ***p < 0.001.

To explore the up-regulation mechanism of TYMSOS in LUAD by using the JASPAR, PROMO, ConTra v3, and UCSC databases. We identified transcription factors that may regulate the expression of TYMSOS. Data indicated that FOXM1 was the most qualified one. We examined the mRNA expression of FOXM1 by using the GEPIA database. FOXM1expression was substantially elevated in LUAD (Figure 11H), the expression of FOXM1 was significantly elevated in LUAD patients than normal controls based on tumor grade (Figure 11I). Up-regulation of FOXM1expression is associated with the poor prognosis in lung cancer patients based on the GEPIA database (Figure 11J). We examined the expression relationship between FOXM1 and TYMSOS in TCGA-LUAD. The expression of FOXM1 was markedly linked to the expression of TYMSOS and NCAPG in LUAD (Figures 11K, L). Next, the knockdown plasmids of FOXM1 were constructed and respectively transfected into the A549 cell. Then, qPCR assays revealed that knockdown of FOXM1 can markedly reduce the expression of TYMSOS levels (Figure 11M). Collectively, these findings indicated that FOXM1 may be a potential transcriptional activator of TYMSOS.

Depletion of TYMSOS Inhibits Cell Proliferation, Migration, and Self-Renewal Abilities of NSCLC Cells

The underlying mechanisms of TYMSOS were determined by cellular localization. The subcellular localization of TYMSOS used the lncLocator and by performing a nuclear/cytoplasmic separate experiment. The results indicated that TYMSOS was mainly located in the cytoplasm of A549 cells (Figures 12A,B). We also analyzed the coding potential of TYMSOS by performing the coding potential calculator, the results showed that TYMSOS does not possess the protein-coding ability (Figure 12C). We first examined the expression of TYMSOS in NSCLC cells lines; the analysis results indicated that TYMSOS was significantly up-regulated in NSCLC cells (Figure 12D). To explore the biological function of TYMSOS in lung cancer cells, the TYMSOS knockdown plasmid was transfected into the A549 cell. The results indicated that the expression of NCAPG and miRNA-214-3p were decreased and increased, respectively, after the knockdown of TYMSOS in A549 cells (Figure 12E). The growth curve and colony formation experiment showed that TYMSOS knockdown significantly inhibits cell proliferation (Figures 12F,G). To estimate the significance of TYMSOS in NSCLC progression, migration and self-renewal of A549 cells were investigated after the knockdown of TYMSOS. The tumorsphere assays indicated that the self-renewal ability of A549 cells was significantly suppressed by down-regulation of TYMSOS (Figure 12H). The Transwell assays indicated that the migratory ability of A549 cells was significantly suppressed by the down-regulation of TYMSOS (Figure 12I). These findings indicated that TYMSOS functioned as an oncogenic lncRNA in promoting cell growth, metastasis, and self-renewal of NSCLC cells. These data suggest that the TYMSOS/miRNA-214-3p/NCAPG axis plays a significant role in the progression of lung cancer.

FIGURE 12 | TYMSOS silencing on cell on proliferation, migration, and self-renewal ability of NSCLC cell. (A) The cellular localization for TYMSOS was predicted by using lncLocator. (B) The cellular localization for TYMSOS was examined by nuclear and cytoplasmic extraction experiments. (C) The coding ability for TYMSOS was predicted by using Coding Potential Calculator. (D) The expression of TYMSOS in NSCLC cells lines was examined by qRT-PCR assay. (E) The expression of miRNA-214-3p and NCAPG in A549 cells after knockdown of TYMSOS was examined by qRT-PCR assay. (F,G) The growth curve and clone formation assays were used to detect the silencing of TYMSOS on the growth of A549 cells. (H) The tumorsphere assay was utilized to detect silencing of TYMSOS on the self-renewal of the A549 cell. (I) The Transwell assay was utilized to detect silencing of TYMSOS on the migration of A549 cells. *p < 0.05, **p < 0.01, ***p < 0.001.

DISCUSSION

According to the global cancer statistics in 2020, lung cancer results in the highest morbidity rates and brought huge economic burden worldwide (Sung et al., 2021). Despite there being many different treatments, including immunotherapy, more than half of the patients are diagnosed at an advanced stage, and the survival rate of patients is very low (Hirsch et al., 2017). The initiation and progression of lung cancer are an exceedingly complicated process that involves genetic mutations, tumor microenvironment, and the dysregulation of epigenetic pathways (Govindan et al., 2012; Duruisseaux and Esteller, 2018; Shi et al., 2019). Concurrently, epigenetic changes in lung cancer such as histone modifications (Chen et al., 2018), DNA methylation (Hulbert et al., 2017) and noncoding RNAs (ncRNAs) (Li et al., 2019) have been extensively studied. The RNAs mainly include snRNAs, snoRNAs, piRNAs, tRNAs, and ribosomal RNAs, miRNAs, lncRNAs, circular RNAs (Esteller, 2011; Hombach and Kretz, 2016; Li et al., 2018). Accumulating evidence has revealed that numerous ncRNAs are dysregulated and involved in lung cancer initiation and progression (Chen et al., 2016; Wang et al., 2018; Iqbal et al., 2019). Many studies demonstrate that miRNAs and lncRNAs are involved in the pathogenesis of lung cancer. For example, lncRNA H19 the elevated cell growth and metastasis ability by reducing the level of miR-200 (Zhao et al., 2019). LncRNA TUC338 promotes invasion of lung cancer by activating the MAPK pathway (Zhang et al., 2018).

NCAPG plays key roles in mitotic chromosome condensation and cancer progression (Sun et al., 2020). It has also been reported that NCAPG serves as a prognostic biomarker in HCC (Xiao et al., 2020). Combining bioinformatics analyses and biological function validation, our study provides evidence that NCAPG was elevated in lung cancer, and its high expression was correlated with poor clinical-pathological features, including the pathologic stage, TNM stage, primary therapy outcome, gender, and smoking status. Finally, OS analyses showed that the up-regulated expression of NCAPG was associated with a poorer survival state in NSCLC patients. Aberrant expression of NCAPG has been elucidated in various human cancers. For example, NCAPG exerted oncogenic functions in hepatocellular carcinoma via regulating PI3K/AKT signaling (Gong et al., 2019). Furthermore, NCAPG has been reported to inhibit cardia adenocarcinoma apoptosis and promote epithelial–mesenchymal transition through the Wnt/β-catenin signaling pathway (Zhang et al., 2021). All these studies indicated that NCAPG might exert a pivotal function in human cancers. In our study, we observed increased NCAPG in NSCLC, which was also associated with a poor prognosis. Similarly, high expression of NCAPG is associated with poor prognosis in ovarian cancer (Xu et al., 2020b).

For the function of NCAPG in NSCLC, we performed the GSEA and KEGG pathway enrichment analyses in TCGA-LUAD; the results indicated that NCAPG participates in the immune system in LUAD. It has been well recognized that the tumor microenvironment is able to affect the productiveness of immunotherapy and involved in the process of cancer (Fridman et al., 2017; Jarosz-Biej et al., 2019; McLaughlin et al., 2020). It has been demonstrated that immune modulators play crucial roles in tumor cell evasion of immune surveillance (Xue et al., 2017). Studies have confirmed that the increased immune checkpoints such as PD-L1, CTLA-4, TIM-3, and LAG3 in glioma help tumor immune evasion, leading to T-cell dysfunction (Xue et al., 2017; Woroniecka et al., 2018). Here, we take the lead to review that up-regulation of NCAPG was positively correlated with the immune cell infiltration in lung cancer. Moreover, we also found that the NCAPG expression was not only significantly positively associated with various immune cell markers but also positively correlated with the expression of immune checkpoints–related genes. More significantly, we showed that NCAPG expression could affect the OS state of LUAD patients through immune infiltration. These results suggest that NCAPG could as an effective immunotherapy target in LUAD. However, the crucial functions of NCAPG in the tumor-immune regulation still need further research.

A growing number of studies have reported that lncRNA plays an important role in gene expression regulation (Zhang et al., 2020). To figure out the upstream regulatory microRNA about NCAPG, we used different databases to predict potential microRNAs that may be linked to NCAPG. After performing comprehensive analysis and functional experiments, miRNA-214-3p was chosen as miRNA that may be binding to the NCAPG. Furthermore, previous findings as well indicated that miRNA-214-3p played inhibitory roles in tumor cell proliferation (Li et al., 2020). These results indicate that miRNA-214-3p could act as an upstream regulatory miRNA of NCAPG in LUAD. Through conducting lncRNA molecular characteristics, expression, survival, and correlation analysis, one of the most potential elevated lncRNA, TYMSOS, was identified. It has been reported to function as oncogenes in gastric cancer (Gu et al., 2021). For instance, TYMSOS through binding the miR-4739 and resulting in the elevated expression of ZNF703 gives rise to the progression of gastric cancer (Gu et al., 2021). In this study, we found that the TYMSOS and NCAPG expression was markedly elevated in LUAD matched to the control group, and the elevated expression of TYMSOS and NCAPG was associated with the poor outcome by survival analysis. On the contrary, miR-214-3p expression was reduced in LUAD, and the elevated miR-214-3p expression indicated better OS prognosis in LUAD. In addition, we also identified FOXM1 as a potential transcriptional activator of TYMSOS. We also performed functional assay to verify the function of TYMSOS and NCAPG in NSCLC; the results verified that knockdown of TYMSOS and NCAPG significantly inhibited the cell proliferation, migration, and self-renewal abilities of NSCLC cells. We also confirm the interaction between TYMSOS, miRNA-214-3p, and NCAPG. Although various molecular mechanisms can lead to increased gene expression, such as the DNA methylation, regulation of transcription factors, and gene copy number amplification, lncRNA/miRNA axis is one of the main regulatory mechanisms of gene expression.

We revealed that NCAPG was up-regulated in different cancers and positively correlated to poor prognosis in NSCLC. The upstream complex molecular regulation mechanism of NCAPG was first revealed by us, that is, FOXM1/TYMSOS/miRNA-214-3p (Figure 13). Furthermore, our work demonstrates that NCAPG was further implicated in the alteration of the tumor microenvironment. TYMSOS and NCAPG knockdown inhibited cell proliferation, cell migration, and cancer stem cell self-renewal in A549 cells. These findings suggest that NCAPG may play an oncogene role in the progression of lung cancer and represent an effective target of immunotherapy in the NSCLC.

FIGURE 13 | LncRNA-TYMSOS/miRNA-214-3p/NCAPG axis facilitated NSCLC progression.

To the best of our knowledge, this is the first study to explore the correlation between NCAPG and NSCLC. However, there are some limitations to our research. First, our study was based on expression data extracted from TCGA but may be more convincing if supported by a prospective clinical study. Furthermore, the biological functions of NCAPG need to be further explored in in vivo experiments. In the future, we will pay more attention to the function of NCAPG in tumor progression and tumor microenvironment regulation of NSCLC. Furthermore, we will perform more in vivo and in vitro experiments to explore the function and the potential molecular mechanisms of NCAPG in tumor progression and tumor microenvironment regulation of NSCLC.

CONCLUSION

Our study found that NCAPG expression was increased in NSCLC, which was also associated with a poor prognosis. Furthermore, NCAPG might be involved in the progression of NSCLC by regulating the function of immune-infiltrating cells and immune response–related signaling pathways. Herein, we revealed the biological functions of NCAPG in NSCLC and offered a potential strategy for the diagnosis and treatment of NSCLC.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.

AUTHOR CONTRIBUTIONS

YY, XJ, and LT designed this work and performed related function assay. JW, and DZ analyzed data. WC and LD supervised and wrote the manuscript. All authors have read and approved the final version of the manuscript.

FUNDING

This work was supported by National Nature Science Foundation of China (No. 82160508) and Yunnan Applied Basic Research Projects (YNWRMY-2019-067, 2019FE001) and Yunnan Province Specialized Training Grant for High-Level Healthcare Professionals (D-201614).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

ACKNOWLEDGMENTS

We would like to thank the Core Technology Facility, Kunming Institute of Zoology, Chinese Academy of Sciences for providing us with bioinformatics analysis. And we are grateful to Guolan Ma for her technical support.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2021.785767/full#supplementary-material

ABBREVIATIONS

ACC, adrenocortical carcinoma; BLCA, bladder urothelial carcinoma; BRCA, breast invasive carcinoma; CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma; COAD, colon adenocarcinoma; CSC, cancer stem cell; ESCA, esophageal carcinoma; GBM, glioblastoma multiforme; HNSC, head and neck squamous cell carcinoma; KICH, kidney chromophobe; KIRC, kidney renal clear cell carcinoma; KIRP, kidney renal papillary cell carcinoma; LAML, acute myeloid leukemia; LGG, brain lower grade glioma; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; MESO, mesothelioma; MSI, microsatellite instability; OV, ovarian serous cystadenocarcinoma; PAAD, pancreatic adenocarcinoma; PCPG, pheochromocytoma and paraganglioma; PRAD, prostate adenocarcinoma; READ, colon adenocarcinoma; READ, rectum adenocarcinoma; STAD, stomach adenocarcinoma; TGCT, testicular germ cell tumors; THCA, thyroid carcinoma; TMB, tumor mutational burden; UCEC, uterine corpus endometrial carcinoma; UCS, uterine carcinosarcoma.

REFERENCES

Agarwal, V., Bell, G. W., Nam, J. W., and Bartel, D. P. (2015). Predicting Effective microRNA Target Sites in Mammalian mRNAs. Elife 4, e05005. doi:10.7554/elife.05005

Arai, T., Okato, A., Yamada, Y., Sugawara, S., Kurozumi, A., Kojima, S., et al. (2018). Regulation ofNCAPGbymiR-99a-3p(passenger Strand) Inhibits Cancer Cell Aggressiveness and Is Involved in CRPC. Cancer Med. 7 (5), 1988–2002. doi:10.1002/cam4.1455

Campbell, B. B., Light, N., Fabrizio, D., Zatzman, M., Fuligni, F., de Borja, R., et al. (2017). Comprehensive Analysis of Hypermutation in Human Cancer. Cell 171 (5), 1042–1056. e10. doi:10.1016/j.cell.2017.09.048

Cao, Z., Pan, X., Yang, Y., Huang, Y., and Shen, H.-B. (2018). The lncLocator: a Subcellular Localization Predictor for Long Non-coding RNAs Based on a Stacked Ensemble Classifier. Bioinformatics 34 (13), 2185–2194. doi:10.1093/bioinformatics/bty085

Chalmers, Z. R., Connelly, C. F., Fabrizio, D., Gay, L., Ali, S. M., Ennis, R., et al. (2017). Analysis of 100,000 Human Cancer Genomes Reveals the Landscape of Tumor Mutational burden. Genome Med. 9 (1), 34. doi:10.1186/s13073-017-0424-2

Chen, Y., Liu, X., Li, Y., Quan, C., Zheng, L., and Huang, K. (2018). Lung Cancer Therapy Targeting Histone Methylation: Opportunities and Challenges. Comput. Struct. Biotechnol. J. 16, 211–223. doi:10.1016/j.csbj.2018.06.001

Chen, Z., Li, J.-L., Lin, S., Cao, C., Gimbrone, N. T., Yang, R., et al. (2016). cAMP/CREB-Regulated LINC00473 marks LKB1-Inactivated Lung Cancer and Mediates Tumor Growth. J. Clin. Invest. 126 (6), 2267–2279. doi:10.1172/jci85250

Cortes-Ciriano, I., Lee, S., Park, W.-Y., Kim, T.-M., and Park, P. J. (2017). A Molecular Portrait of Microsatellite Instability across Multiple Cancers. Nat. Commun. 8, 15180. doi:10.1038/ncomms15180

Duruisseaux, M., and Esteller, M. (2018). Lung Cancer Epigenetics: From Knowledge to Applications. Semin. Cancer Biol. 51, 116–128. doi:10.1016/j.semcancer.2017.09.005

Eberlein, A., Takasuga, A., Setoguchi, K., Pfuhl, R., Flisikowski, K., Fries, R., et al. (2009). Dissection of Genetic Factors Modulating Fetal Growth in Cattle Indicates a Substantial Role of the Non-SMC Condensin I Complex, Subunit G (NCAPG) Gene. Genetics 183 (3), 951–964. doi:10.1534/genetics.109.106476

Esteller, M. (2011). Non-coding RNAs in Human Disease. Nat. Rev. Genet. 12 (12), 861–874. doi:10.1038/nrg3074

Fan, Y., Liu, B., Chen, F., Song, Z., Han, B., Meng, Y., et al. (2021). Hepcidin Upregulation in Lung Cancer: A Potential Therapeutic Target Associated with Immune Infiltration. Front. Immunol. 12, 612144. doi:10.3389/fimmu.2021.612144

Fridman, W. H., Zitvogel, L., Sautès–Fridman, C., and Kroemer, G. (2017). The Immune Contexture in Cancer Prognosis and Treatment. Nat. Rev. Clin. Oncol. 14 (12), 717–734. doi:10.1038/nrclinonc.2017.101

Gong, C., Ai, J., Fan, Y., Gao, J., Liu, W., Feng, Q., et al. (2019). NCAPG Promotes the Proliferation of Hepatocellular Carcinoma through PI3K/AKT Signaling. Onco Targets Ther. 12, 8537–8552. doi:10.2147/ott.s217916

Goossens, E. A. C., de Vries, M. R., Simons, K. H., Putter, H., Quax, P. H. A., and Nossent, A. Y. (2019). miRMap: Profiling 14q32 microRNA Expression and DNA Methylation throughout the Human Vasculature. Front. Cardiovasc. Med. 6, 113. doi:10.3389/fcvm.2019.00113

Govindan, R., Ding, L., Griffith, M., Subramanian, J., Dees, N. D., Kanchi, K. L., et al. (2012). Genomic Landscape of Non-small Cell Lung Cancer in Smokers and Never-Smokers. Cell 150 (6), 1121–1134. doi:10.1016/j.cell.2012.08.024

Gu, Y., Wan, C., Zhou, G., Zhu, J., Shi, Z., and Zhuang, Z. (2021). TYMSOS Drives the Proliferation, Migration, and Invasion of Gastric Cancer Cells by Regulating ZNF703 via Sponging miR-4739. Cell Biol Int 45 (8), 1710–1719. doi:10.1002/cbin.11610

Győrffy, B. (2021). Survival Analysis across the Entire Transcriptome Identifies Biomarkers with the Highest Prognostic Power in Breast Cancer. Comput. Struct. Biotechnol. J. 19, 4101–4109. doi:10.1016/j.csbj.2021.07.014

Herbst, R. S., Morgensztern, D., and Boshoff, C. (2018). The Biology and Management of Non-small Cell Lung Cancer. Nature 553 (7689), 446–454. doi:10.1038/nature25183

Hirsch, F. R., Scagliotti, G. V., Mulshine, J. L., Kwon, R., Curran, W. J., Wu, Y.-L., et al. (2017). Lung Cancer: Current Therapies and New Targeted Treatments. The Lancet 389 (10066), 299–311. doi:10.1016/s0140-6736(16)30958-8

Hombach, S., and Kretz, M. (2016). Non-coding RNAs: Classification, Biology and Functioning. Adv. Exp. Med. Biol. 937, 3–17. doi:10.1007/978-3-319-42059-2_1

Hu, J., Qiu, D., Yu, A., Hu, J., Deng, H., Li, H., et al. (2021). YTHDF1 Is a Potential Pan-Cancer Biomarker for Prognosis and Immunotherapy. Front. Oncol. 11, 607224. doi:10.3389/fonc.2021.607224

Hulbert, A., Jusue-Torres, I., Stark, A., Chen, C., Rodgers, K., Lee, B., et al. (2017). Early Detection of Lung Cancer Using DNA Promoter Hypermethylation in Plasma and Sputum. Clin. Cancer Res. 23 (8), 1998–2005. doi:10.1158/1078-0432.ccr-16-1371

Iqbal, M. A., Arora, S., Prakasam, G., Calin, G. A., and Syed, M. A. (2019). MicroRNA in Lung Cancer: Role, Mechanisms, Pathways and Therapeutic Relevance. Mol. Aspects Med. 70, 3–20. doi:10.1016/j.mam.2018.07.003

Jarosz-Biej, M., Smolarczyk, R., Cichoń, T., and Kułach, N. (2019). Tumor Microenvironment as A "Game Changer" in Cancer Radiotherapy. Int. J. Mol. Sci. 20 (13), 3212. doi:10.3390/ijms20133212

Jiang, L., Ren, L., Chen, H., Pan, J., Zhang, Z., Kuang, X., et al. (2020). NCAPG Confers Trastuzumab Resistance via Activating SRC/STAT3 Signaling Pathway in HER2-Positive Breast Cancer. Cell Death Dis 11 (7), 547. doi:10.1038/s41419-020-02753-x

Kang, Y.-J., Yang, D.-C., Kong, L., Hou, M., Meng, Y.-Q., Wei, L., et al. (2017). CPC2: a Fast and Accurate Coding Potential Calculator Based on Sequence Intrinsic Features. Nucleic Acids Res. 45 (W1), W12–w16. doi:10.1093/nar/gkx428

Karagkouni, D., Paraskevopoulou, M. D., Tastsoglou, S., Skoufos, G., Karavangeli, A., Pierros, V., et al. (2020). DIANA-LncBase V3: Indexing Experimentally Supported miRNA Targets on Non-coding Transcripts. Nucleic Acids Res. 48 (D1), D101–d110. doi:10.1093/nar/gkz1036

Li, J. H., Liu, S., Zhou, H., Qu, L. H., and Yang, J. H. (2014). starBase v2.0: Decoding miRNA-ceRNA, miRNA-ncRNA and Protein-RNA Interaction Networks from Large-Scale CLIP-Seq Data. Nucleic Acids Res. 42, D92–D97. Database issue. doi:10.1093/nar/gkt1248

Li, S., Sun, Y., Tian, T., Qin, X., Lin, S., Zhang, T., et al. (2020). MicroRNA-214-3p Modified Tetrahedral Framework Nucleic Acids Target Survivin to Induce Tumour Cell Apoptosis. Cell Prolif 53 (1), e12708. doi:10.1111/cpr.12708

Li, X., Yang, L., and Chen, L.-L. (2018). The Biogenesis, Functions, and Challenges of Circular RNAs. Mol. Cel 71 (3), 428–442. doi:10.1016/j.molcel.2018.06.034

Li, Y., Yin, Z., Fan, J., Zhang, S., and Yang, W. (2019). The Roles of Exosomal miRNAs and lncRNAs in Lung Diseases. Sig Transduct Target. Ther. 4, 47. doi:10.1038/s41392-019-0080-7

Liang, R., Li, X., Li, W., Zhu, X., and Li, C. (2021). DNA Methylation in Lung Cancer Patients: Opening a "window of Life" under Precision Medicine. Biomed. Pharmacother. 144, 112202. doi:10.1016/j.biopha.2021.112202

Loher, P., and Rigoutsos, I. (2012). Interactive Exploration of RNA22 microRNA Target Predictions. Bioinformatics 28 (24), 3322–3323. doi:10.1093/bioinformatics/bts615

McLaughlin, M., Patin, E. C., Pedersen, M., Wilkins, A., Dillon, M. T., Melcher, A. A., et al. (2020). Inflammatory Microenvironment Remodelling by Tumour Cells after Radiotherapy. Nat. Rev. Cancer 20 (4), 203–217. doi:10.1038/s41568-020-0246-1

Mezheyeuski, A., Bergsland, C. H., Backman, M., Djureinovic, D., Sjöblom, T., Bruun, J., et al. (2018). Multispectral Imaging for Quantitative and Compartment-specific Immune Infiltrates Reveals Distinct Immune Profiles that Classify Lung Cancer Patients. J. Pathol. 244 (4), 421–431. doi:10.1002/path.5026

Molina, J. R., Yang, P., Cassivi, S. D., Schild, S. E., and Adjei, A. A. (2008). Non-small Cell Lung Cancer: Epidemiology, Risk Factors, Treatment, and Survivorship. Mayo Clinic Proc. 83 (5), 584–594. doi:10.4065/83.5.584

Morgensztern, D., Ng, S. H., Gao, F., and Govindan, R. (2010). Trends in Stage Distribution for Patients with Non-small Cell Lung Cancer: a National Cancer Database Survey. J. Thorac. Oncol. : official Publ. Int. Assoc. Study Lung Cancer 5 (1), 29–33. doi:10.1097/JTO.0b013e3181c5920c

Paraskevopoulou, M. D., Georgakilas, G., Kostoulas, N., Vlachos, I. S., Vergoulis, T., Reczko, M., et al. (2013). DIANA-microT Web Server v5.0: Service Integration into miRNA Functional Analysis Workflows. Nucleic Acids Res. 41, W169–W173. Web Server issue. doi:10.1093/nar/gkt393

Patnaik, S. K., Kannisto, E. D., Mallick, R., Vachani, A., and Yendamuri, S. (2017). Whole Blood microRNA Expression May Not Be Useful for Screening Non-small Cell Lung Cancer. PLoS One 12 (7), e0181926. doi:10.1371/journal.pone.0181926

Qiu, M.-j., Wang, Q.-s., Li, Q.-t., Zhu, L.-s., Li, Y.-n., Yang, S.-l., et al. (2021). KIF18B Is a Prognostic Biomarker and Correlates with Immune Infiltrates in Pan-Cancer. Front. Mol. Biosci. 8, 559800. doi:10.3389/fmolb.2021.559800

Qu, S., Liu, J., and Wang, H. (2021). EVA1B to Evaluate the Tumor Immune Microenvironment and Clinical Prognosis in Glioma. Front. Immunol. 12, 648416. doi:10.3389/fimmu.2021.648416

Rost, G. C., and McGregor, K. K. (2012). Miranda Rights Comprehension in Young Adults with Specific Language Impairment. Am. J. Speech Lang. Pathol. 21 (2), 101–108. doi:10.1044/1058-0360(2011/10-0094)

Shi, Y., Fan, S., Wu, M., Zuo, Z., Li, X., Jiang, L., et al. (2019). YTHDF1 Links Hypoxia Adaptation and Non-small Cell Lung Cancer Progression. Nat. Commun. 10 (1), 4892. doi:10.1038/s41467-019-12801-6

Siegel, R. L., Miller, K. D., and Jemal, A. (2020). Cancer Statistics, 2020. CA A. Cancer J. Clin. 70 (1), 7–30. doi:10.3322/caac.21590

Song, B., Du, J., Song, D.-f., Ren, J.-c., and Feng, Y. (2018). Dysregulation of NCAPG, KNL1, miR-148a-3p, miR-193b-3p, and miR-1179 May Contribute to the Progression of Gastric Cancer. Biol. Res. 51 (1), 44. doi:10.1186/s40659-018-0192-5

Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., et al. (2005). Gene Set Enrichment Analysis: a Knowledge-Based Approach for Interpreting Genome-wide Expression Profiles. Proc. Natl. Acad. Sci. 102 (43), 15545–15550. doi:10.1073/pnas.0506580102

Sun, D.-P., Lin, C.-C., Hung, S.-T., Kuang, Y.-Y., Hseu, Y.-C., Fang, C.-L., et al. (2020). Aberrant Expression of NCAPG Is Associated with Prognosis and Progression of Gastric Cancer. Cancer Manag. Res. 12, 7837–7846. doi:10.2147/cmar.s248318

Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., et al. (2021). Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA A. Cancer J. Clin. 71 (3), 209–249. doi:10.3322/caac.21660

Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., et al. (2019). STRING V11: Protein-Protein Association Networks with Increased Coverage, Supporting Functional Discovery in Genome-wide Experimental Datasets. Nucleic Acids Res. 47 (D1), D607–d613. doi:10.1093/nar/gky1131

Tang, Z., Li, C., Kang, B., Gao, G., Li, C., and Zhang, Z. (2017). GEPIA: a Web Server for Cancer and normal Gene Expression Profiling and Interactive Analyses. Nucleic Acids Res. 45 (W1), W98–w102. doi:10.1093/nar/gkx247

Vasaikar, S. V., Straub, P., Wang, J., and Zhang, B. (2018). LinkedOmics: Analyzing Multi-Omics Data within and across 32 Cancer Types. Nucleic Acids Res. 46 (D1), D956–d963. doi:10.1093/nar/gkx1090

Wang, L., Tong, X., Zhou, Z., Wang, S., Lei, Z., Zhang, T., et al. (2018). Circular RNA Hsa_circ_0008305 (circPTK2) Inhibits TGF-β-Induced Epithelial-Mesenchymal Transition and Metastasis by Controlling TIF1γ in Non-small Cell Lung Cancer. Mol. Cancer 17 (1), 140. doi:10.1186/s12943-018-0889-7

Woroniecka, K., Chongsathidkiet, P., Rhodin, K., Kemeny, H., Dechant, C., Farber, S. H., et al. (2018). T-cell Exhaustion Signatures Vary with Tumor Type and Are Severe in Glioblastoma. Clin. Cancer Res. 24 (17), 4175–4186. doi:10.1158/1078-0432.ccr-17-1846

Wu, F., Huang, W., Yang, L., and Xu, F. (2019). MicroRNA-101-3p Regulates Gastric Cancer Cell Proliferation, Invasion and Apoptosis by Targeting PIM 1 Expression. Cel Mol Biol (Noisy-le-grand) 65 (7), 118–122. doi:10.14715/cmb/2019.65.7.20

Xiao, C., Gong, J., Jie, Y., Cao, J., Chen, Z., Li, R., et al. (2020). NCAPG Is a Promising Therapeutic Target across Different Tumor Types. Front. Pharmacol. 11, 387. doi:10.3389/fphar.2020.00387

Xu, P., Jiang, L., Yang, Y., Wu, M., Liu, B., Shi, Y., et al. (2020). PAQR4 Promotes Chemoresistance in Non-small Cell Lung Cancer through Inhibiting Nrf2 Protein Degradation. Theranostics 10 (8), 3767–3778. doi:10.7150/thno.43142

Xu, T., Dong, M., Wang, Z., Li, H., and Li, X. (2020). Elevated mRNA Expression Levels of NCAPG Are Associated with Poor Prognosis in Ovarian Cancer. Cmar 12, 5773–5786. doi:10.2147/cmar.s253349

Xue, S., Hu, M., Iyer, V., and Yu, J. (2017). Blocking the PD-1/pd-L1 Pathway in Glioma: a Potential New Treatment Strategy. J. Hematol. Oncol. 10 (1), 81. doi:10.1186/s13045-017-0455-6

Yu, G., Wang, L.-G., Han, Y., and He, Q.-Y. (2012). clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters. OMICS: A J. Integr. Biol. 16 (5), 284–287. doi:10.1089/omi.2011.0118

Zeng, Y., Shi, Y., Xu, L., Zeng, Y., Cui, X., Wang, Y., et al. (2021). Prognostic Value and Related Regulatory Networks of MRPL15 in Non-small-cell Lung Cancer. Front. Oncol. 11, 656172. doi:10.3389/fonc.2021.656172

Zhang, X., Xie, K., Zhou, H., Wu, Y., Li, C., Liu, Y., et al. (2020). Role of Non-coding RNAs and RNA Modifiers in Cancer Therapy Resistance. Mol. Cancer 19 (1), 47. doi:10.1186/s12943-020-01171-z

Zhang, X., Zhu, M., Wang, H., Song, Z., Zhan, D., Cao, W., et al. (2021). Overexpression of NCAPG Inhibits Cardia Adenocarcinoma Apoptosis and Promotes Epithelial-Mesenchymal Transition through the Wnt/β-Catenin Signaling Pathway. Gene 766, 145163. doi:10.1016/j.gene.2020.145163

Zhang, Y. X., Yuan, J., Gao, Z. M., and Zhang, Z. G. (2018). LncRNA TUC338 Promotes Invasion of Lung Cancer by Activating MAPK Pathway. Eur. Rev. Med. Pharmacol. Sci. 22 (2), 443–449. doi:10.26355/eurrev_201801_14193

Zhao, Y., Feng, C., Li, Y., Ma, Y., and Cai, R. (2019). LncRNA H19 Promotes Lung Cancer Proliferation and Metastasis by Inhibiting miR-200a Function. Mol. Cel Biochem 460 (1-2), 1–8. doi:10.1007/s11010-019-03564-1

Zuberi, K., Franz, M., Rodriguez, H., Montojo, J., Lopes, C. T., Bader, G. D., et al. (2013). GeneMANIA Prediction Server 2013 Update. Nucleic Acids Res. 41, W115–W122. Web Server issue. doi:10.1093/nar/gkt533

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The handling editor IH is currently organizing a Research Topic with one of the authors WC.

Copyright © 2022 Yuan, Jiang, Tang, Wang, Zhang, Cho and Duan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 08 February 2022
doi: 10.3389/fmolb.2021.690826

Decreased Expression of circ_0000160 in Breast Cancer With Axillary Lymph Node Metastasis

Ya-Wen Wang¹†, Xu Chen²†, Yaru Tian³, Long Liu² and Peng Su²*

¹Department of Breast Surgery, General Surgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China

²Department of Pathology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China

³Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China

Edited by:
William C. Cho, QEH, China

Reviewed by:
Didik Setyo Heriyanto, Gadjah Mada University, Indonesia
Sandra Romero-CÃrdoba, Universidad Nacional Autónoma de México, Mexico
Kafil Akhtar, Aligarh Muslim University, India

* Correspondence: Peng Su, supeng_sdql@163.com

†These authors have contributed equally to this work and share first authorship

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 04 April 2021
Accepted: 30 December 2021
Published: 08 February 2022

Citation: Wang Y-, Chen X, Tian Y, Liu L and Su P (2022) Decreased Expression of circ_0000160 in Breast Cancer With Axillary Lymph Node Metastasis. Front. Mol. Biosci. 8:690826. doi: 10.3389/fmolb.2021.690826

Background: Circular RNAs (circRNAs) have been shown to play important roles in the development and progression of human cancers. Emerging evidence shows that circRNAs have the potential to be promising biomarkers for cancer diagnosis and prognosis. However, the roles of circRNAs in breast cancer axillary lymph node metastasis (ALNM) remain to be determined.

Methods: Transcriptome sequencing was utilized to screen the differentially expressed circRNAs in three breast cancer tissues with ALNM and three without ALNM. Differentially expressed circRNAs were further verified by quantitative real-time PCR. Moreover, receiver operating characteristic (ROC) curve analysis was performed to calculate the value of circRNAs to distinguish breast cancer tissues with ALNM and those without ALNM. To explore the potential mechanism of the circRNAs, a circRNA–miRNA–mRNA network was constructed based on the CircInteractome, circBank, and mirDIP online software.

Results: In total, 31 differentially expressed circRNAs were identified by transcriptome sequencing; among them, 21 were upregulated and 10 were downregulated in breast cancer with ALNM compared to those without ALNM. Circ_0000160 was validated to be downregulated in breast cancer tissues with ALNM compared with those without ALNM. The ROC curve showed the ability of circ_0000160 to distinguish breast cancer tissues with ALNM and those without ALNM, with an area under the curve of 0.7435. Furthermore, bioinformatics analysis revealed that the predicted mRNAs for circ_0000160 may be related to lymph node metastasis. The predicted mRNAs for circ_0000160 may be involved in many cancer-related pathways.

Conclusion: A decreased expression of circ_0000160 was found in breast cancer with axillary lymph node metastasis. Circ_0000160 may have the potential to distinguish breast cancer with axillary lymph node metastasis from those without axillary lymph node metastasis.

Keywords: breast cancer, circRNA, lymph node metastasis, circ_0000160, transcriptome sequencing

INTRODUCTION

Breast cancer is one of the most common malignant tumors and the leading cause of cancer-related death in women. For breast cancer, deaths are usually caused not by the primary tumor but by metastases (Wang et al., 2020; Kim, 2021). Axillary lymph node metastasis (ALNM) is one of the most important prognostic indicators and a crucial component in the staging system (Sawaki et al., 2019) Metastasis to lymph nodes is a high-risk factor for relapse and poor survival (Si et al., 2019). Additionally, identifying metastasis is essential for treatment regimens (Choi et al., 2017). Currently, ideal biomarkers are needed for ALNM prediction (Lee et al., 2018; Chang et al., 2020).

Circular RNAs (circRNAs) are single-stranded, covalently closed RNA molecules and are the downstream products of precursor mRNA back-splicing (Vo et al., 2019). Their high abundance, stability, evolutionary conservation among species, and high cell and tissue specificity endow circRNAs with the potential as molecular biomarkers for cancer diagnosis (Wang S. et al., 2021; Wei et al., 2021). For example, circEHD2, circENGLN3, and circNETO2 were found to be upregulated in clear cell renal carcinoma and showed diagnostic and prognostic values (Frey et al., 2021). Recently, Mani et al. identified hsa_circ_0006743 and hsa_circ_0002496 to be significantly upregulated in early-stage breast cancer tissues compared to normal samples (Rao et al., 2021). We have previously found that circ_0000745, circ_0001531, and circ_0001640 were upregulated in breast cancer, compared with benign tumor and healthy control (Wang Y.-W. et al., 2021). However, the profile of ALNM-related circRNAs in breast cancer has not been well established.

In this study, we investigated the profile of circRNAs using transcriptome sequencing and quantitative real-time PCR (qRT–PCR) in breast cancer tissues with and without ALNM.

MATERIALS AND METHODS

Tissue Samples

For transcriptome sequencing, breast cancer tissues were collected from six patients, including three with ALNM and three without ALNM, at the Qilu Hospital of Shandong University (Shandong, China). The inclusion criteria were breast cancer patients who had undergone mastectomy, were pathologically diagnosed with ALNM or without ALNM, and signed informed consent. Patients who had preoperative chemotherapy or radiotherapy were excluded. Patients with ALNM were matched with patients without ALNM with respect to the timing of the sampling and duration of specimen storage. In the validation stage, another 56 breast cancer tissues (24 with ALNM and 32 without ALNM) were collected from the Qilu Hospital of Shandong University. Typical figures of hematoxylin and eosin (H&E)-stained sections of breast cancer with ALNM and without ALNM are provided in Supplementary Figure S1. All cases of breast cancer were evaluated histologically according to the WHO Classification of Tumors of the Breast (4th edition, 2012) and staged using the Tumor–Node–Metastasis (TNM) staging of the American Joint Committee on Cancer (AJCC, 7th edition, 2010). All fresh specimens were frozen in liquid nitrogen until further use. Written informed consent was obtained from all of the enrolled participants. The present study was approved by the Ethics Committee of the Qilu Hospital of Shandong University.

Transcriptome Sequencing for circRNA Profiling

Total RNA was isolated from breast cancer tissues using TRIzol reagent (Invitrogen, Carlsbad, CA, United States) according to the manufacturer’s protocol. RNA quality and quantity were measured on a Nanodrop spectrophotometer (ND-1000, Nanodrop Technologies), and RNA integrity was determined by gel electrophoresis. Total RNA was used to remove the rRNAs using Ribo-Zero rRNA Removal Kits (Illumina, United States) following the manufacturer’s instructions. RNA libraries were constructed by using rRNA-depleted RNAs with the TruSeq Stranded Total RNA Library Prep Kit (Illumina, United States) according to the manufacturer’s instructions. Libraries were controlled for quality and quantified using the BioAnalyzer 2,100 system (Agilent Technologies, United States). Ten pM libraries were denatured as single-stranded DNA molecules, captured on Illumina flow cells, amplified in situ as clusters, and finally sequenced for 150 cycles on an Illumina HiSeq Sequencer according to the manufacturer’s instructions. Paired-end reads were harvested from an Illumina HiSeq Sequencer after quality filtering. The reads were aligned to the reference genome/transcriptome with Bowtie2 software, and circRNAs were detected and identified with find_circ software. Raw junction reads for all samples were normalized by a total read number and log2 transformed. Differentially expressed circRNAs were identified by the t test between two groups. The transcriptome sequencing data were deposited in the Gene Expression Omnibus (GEO) database under the accession number GSE173766.

Quantitative Real-Time PCR

Briefly, total RNA was extracted from breast cancer tissues using TRIzol reagent (Invitrogen, Carlsbad, CA, United States) according to the manufacturer’s protocol. Reverse transcription was performed using the Invitrogen SuperScript III Reverse Transcriptase kit (Invitrogen). qRT–PCR was performed on a ViiA 7 Real-time PCR system (Applied Biosystems) using the AceQ qPCR SYBR Green Master Mix (Vazyme, Nanjing, China). ACTB levels were used to normalize the expression of circRNAs. The data were analyzed by the 2−ΔΔCT method. Related primers are shown in Supplementary Table S1.

CircRNA–miRNA–mRNA ceRNA (Competing Endogenous RNA) Network Analysis

The circRNA–miRNA interaction was predicted using CircInteractome (Dudekula et al., 2016) and circBank (Liu et al., 2019). miRNAs shared by the two software programs were selected for further mRNA predictions. The mRNAs targeted by miRNAs were predicted using mirDIP v4.1 (Tokar et al., 2018). The top 10 mRNA targets of miRNAs were first screened based on the integrated score provided by mirDIP. The circRNA–miRNA–mRNA ceRNA network was visualized by Cytoscape software (Shannon et al., 2003). KEGG pathway analysis for mRNAs was performed by the WebGestalt online tool (Liao et al., 2019), and the top 10 pathways based on the p value are presented.

Bioinformatics Analysis

For analysis of the predicted miRNAs, mRNAs and host gene for circ_0000160, miRNA, and mRNA expression profiles and the corresponding clinicopathological information [lymph node metastasis (LNM) status] of the breast cancer patients were obtained from The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/). For further analysis, the RNA-sequencing data were normalized as TPM (transcripts per kilobase of exon model per million mapped reads) for mRNA or reads of exon model per million mapped reads for miRNA. Log2-transformed normalized values were used for identification of differentially expressed genes between two groups (with LNM and without LNM) using the Wilcoxon rank sum test.

Statistical Analysis

Statistical analyses were performed using GraphPad Prism 5.0 (GraphPad Software, Inc., San Diego, CA, United States) and SPSS 20.0 (SPSS, Chicago, IL, United States). The significance of the differences was determined by a nonparametric test between two groups and one-way analysis of variance between multiple groups. The chi-square test or Fisher’s exact test was used, as appropriate, to analyze the relationship between the circRNA expression and clinicopathological variables. p < 0.05 was considered statistically significant.

RESULTS

Expression Profiling of circRNAs in Breast Cancer With Axillary Lymph Node Metastasis and Without ALNM

To identify dysregulated circRNAs associated with breast cancer ALNM, transcriptome sequencing was performed on three breast cancer tissues with ALNM and three without ALNM (Figure 1A). In total, 31 circRNAs were found to be differentially expressed in breast cancer with ALNM vs. without ALNM (fold change >2, p < 0.05) (Figure 1B). Among the 31 circRNAs, 21 were upregulated and 10 were downregulated in breast cancer with ALNM compared with those without ALNM.

FIGURE 1 | Screening axillary lymph node metastasis (ALNM)-related circRNAs in breast cancer using transcriptome sequencing. (A) The flow diagrams of the current study. (B) Human transcriptome sequencing was used to screen dysregulated circRNAs associated with breast cancer axillary ALNM in three breast cancer tissues with ALNM and three without ALNM. In total, 21 circRNAs were upregulated and 10 were downregulated in breast cancer with ALNM compared with those without ALNM. (C) Four circRNAs were selected for further investigation due to their high raw signal (junction reads) in transcriptome sequencing and potential association with breast cancer determined by the Circ2Traits database. (D) qRT–PCR was used to detect the expression of the four circRNAs in three breast cancer tissues with ALNM and three without ALNM. Circ_0000160 and circ_0001798 were downregulated, and circ_0001750 was upregulated in three breast cancer tissues with ALNM compared with three breast cancer tissues without ALNM (*p < 0.05, **p < 0.01). Circ_0000002 did not show significant differences between the two groups.

Next, we utilized Circ2Traits (Ghosal et al., 2013), a database collecting disease-associated circRNAs based on bioinformatics analysis and publications, to screen potential disease-associated circRNAs. Nine circRNAs were included in the Circ2Traits database, which were supposed to be associated with human cancers, cardiac hypertrophy, nonalcoholic fatty liver disease, Duchenne muscular dystrophy, and other human diseases. Finally, four of the nine circRNAs were selected for further investigation. These four circRNAs (upregulated: circ_0000002 and circ_0001750; downregulated: circ_0000160, circ_0001798) showed relatively high raw signals (junction reads) in transcriptome sequencing and were thought to be associated with breast cancer, as determined by the Circ2Traits database (Figure 1C).

Validation of Differentially Expressed circRNAs Using qRT–PCR

First, qRT–PCR was used to detect the expression of circ_0000002 and circ_0001750, circ_0000160, and circ_0001798 in the testing set (the six patients used in the transcriptome sequencing). The data showed that circ_0000160 and circ_0001798 were downregulated and that circ_0001750 was upregulated in three breast cancer tissues with ALNM compared with three breast cancer tissues without ALNM (Figure 1D, p < 0.05). The expression of circ_0000002 did not show significant differences between the two groups (Figure 1D).

Then, the differential expression of circ_0000160, circ_0001798, and circ_0001750 was further validated in a larger cohort containing 56 patients. Only circ_0000160 was downregulated in 24 breast cancer tissues with ALNM compared with 32 breast cancer tissues without ALNM (Figures 2A–C). The receiver operating characteristic (ROC) curve reflected strong separation between tissues with ALNM and those without ALNM, with an area under the curve (AUC) of 0.7435 (p = 0.0020) (Figure 2D). A decreasing trend of circ_0000160 was found in breast cancer tissues with more advanced N stages (increasing ALNM number, Figure 3A, one-way analysis of variance, p = 0.0157); however, the comparison for every two stages did not reach statistical significance (N1 vs. N2, p = 0.8520; N1 vs. N3, p = 0.4128; N2 vs. N3, p = 0.5176).

FIGURE 2 | Decreased expression of circ_0000160 was found in breast cancer with ALNM in the validation cohort containing 56 patients. (A) Circ_0000160 was downregulated in 24 breast cancer tissues with ALNM compared to 32 tissues without ALNM. (B,C) Circ_0001798 and circ_0001750 did not show significant differences between the two groups. (D) ROC analysis showed that circ_0000160 had an area under the curve of 0.7435 (p = 0.0020) to discriminate tissues with ALNM from those without ALNM.

FIGURE 3 | Association between the circ_0000160 expression and clinicopathological parameters in the validation cohort containing 56 patients. (A) A decreasing trend of circ_0000160 was found in breast cancer tissues with more advanced N stages (increasing ALNM number, one-way analysis of variance, p = 0.0157); however, the comparison for each two stages did not reach statistical significance (N1 vs. N2, p = 0.8520; N1 vs. N3, p = 0.4128; N2 vs. N3, p = 0.5176). (B) Circ_0000160 was even decreased in breast cancer with a more advanced tumor stage (one-way analysis of variance, p = 0.0484; stage I vs. stage II, p = 0.3152; II vs. III, p = 0.0509; I vs. III, p = 0.0221). (C)-(I) Circ_0000160 expression showed no relationship with the tumor size, tumor grade, ER/PR/HER2/Ki-67 expression, or molecular type (ER, estrogen receptor; PR, progesterone receptor).

Association Between the circRNA Expression and Clinicopathological Parameters of Breast Cancer Patients

To further determine the clinical significance of circRNAs, we analyzed the association between the circRNA expression and the clinicopathological parameters of breast cancer patients (Table 1). Here, the patients were divided into a high-expression group and a low-expression group based on the median expression value of circRNA. Our results showed that the low expression of circ_0000160 was correlated with axillary lymph node metastasis (p = 0.031). Additionally, circ_0000160 was even decreased in breast cancer with a more advanced tumor stage (Figure 3B, one-way analysis of variance, p = 0.0484; Stage I vs. Stage II, p = 0.3152; II vs. III, p = 0.0509; I vs. III, p = 0.0221). For circ_0001750, a higher expression was found in breast cancer tissues with a higher tumor grade, negative progesterone receptor (PR), or higher Ki-67 expression (Supplementary Figure S2; Table 1). No association was found between the circ_0001798 expression and any clinicopathological parameters (Supplementary Figure S3).

TABLE 1 | Association between the circRNA expression and clinicopathologic features in breast cancer.

CircRNA–miRNA–mRNA ceRNA Network Construction

Circ_0000160 was subjected to further bioinformatics analysis to explore the potential mechanisms in breast cancer. Based on the prediction of CircInteractome and circBank, eight miRNAs (miR-607, miR-127-5p, miR-1272, miR-1299, miR-1183, miR-217, miR-767-3p, and miR-1256) could potentially interact with circ_0000160. Then, the mRNA targets of these miRNAs were predicted using mirDIP v4.1, and the top 10 mRNAs of each miRNA were included in the circRNA–miRNA–mRNA network construction (Figure 4). The potential targets of miRNAs were enriched in cancer-related pathways, such as pathways in cancer, the cell cycle, cellular senescence, and the JAK-STAT signaling pathway (Table 2).

FIGURE 4 | circRNA–miRNA–mRNA ceRNA network for circ_0000160. miRNAs were predicted by CircInteractome and circBank. mRNAs were predicted using mirDIP v4.1. The top 10 mRNA targets of each miRNA were selected based on the integrated score provided by mirDIP. Cytoscape software was used to visualize the network.

TABLE 2 | KEGG analysis of circ_0000160 based on the ceRNA network.

TCGA data were used to test whether the predicted miRNAs and mRNAs (and the host gene SUCO) for circ_0000160 are deregulated in breast cancers with lymph node metastasis. Among the predicted miRNAs and mRNAs, ADAM9, CAND1, CNOT8, DACH1, EIF3J, RAP2C, RB1, and VCAN were found to be deregulated in breast cancer with lymph node metastasis compared with those without lymph node metastasis (Figures 5A-H). Of note, DACH1 and VCAN showed obvious upregulation in breast cancer with lymph node metastasis. However, the host gene SUCO (Figure 5I) and the analyzed miRNAs (data not shown) showed no obvious difference in breast cancer with lymph node metastasis and those without lymph node metastasis.

FIGURE 5 | Differentially expressed mRNAs between breast cancer with lymph node metastasis and those without lymph node metastasis based on the TGCA dataset (BRCA). ADAM9, CAND1, CNOT8, DACH1, EIF3J, RAP2C, RB1, and VCAN were upregulated in breast cancer with lymph node metastasis (n = 551) compared with those without lymph node metastasis (A-H, n = 505). The SUCO expression showed no obvious difference in breast cancer with lymph node metastasis and those without lymph node metastasis (I).

DISCUSSION

Breast cancer is one of the most common cancers. Recently, circRNAs have shown emerging promise as potential disease biomarkers (Verduci et al., 2021). Based on transcriptome sequencing followed by qRT–PCR confirmation, we identified circ_0000160 as significantly associated with ALNM in breast cancer. Circ_0000160 showed a relatively high predictive value to distinguish breast cancer patients with and without ALNM. It is noted that three pairs were evaluated by transcriptome sequencing as a discovery set, which may result in bias, which means that a small number of samples in high-throughput screening may produce “false positive” and “false negative” results. First, potentially differentially expressed circRNAs revealed by transcriptome sequencing may not be truly differential and should be further validated in a larger cohort. Second, we cannot exclude the possibility that there are other ALNM-related circRNAs other than the 31 circRNAs revealed by our transcriptome sequencing data. More ALNM-related circRNAs should be explored with a larger cohort using high-throughput technology.

Circ_0000160 is a novel circRNA that has not ever been investigated in human diseases, including cancers. The host gene SUCO (the SUN domain containing the ossification factor) has rarely been investigated in human cancers. Harland et al. found that mice lacking SUCO showed impaired bone formation and spontaneous fractures, and their results suggested SUCO as a candidate gene for brittle bone disorders (Sohaskey et al., 2010). Recently, exome sequencing identified SUCO mutations in mesial temporal lobe epilepsy, and a lack of SUCO led to abnormal development of neurons (Sha et al., 2015). SUCO was found to be upregulated in hepatocellular carcinoma (Yue et al., 2019) and may be targeted by miR-497 (Chen et al., 2021). Our data showed that circ_0000160 was downregulated, while the host gene SUCO showed no significant change in breast cancer with lymph node metastasis compared with those without lymph node metastasis. Recently, Kristensen et al. found that the expression of circRNAs does not often correlate well with the expression of host genes linearly (Kristensen et al., 2018). This argues that circRNAs are not merely steady-state byproducts of mRNA splicing but rather the product of a new type of regulated alternative splicing. We then analyzed the isoform expression data of the SUCO gene in breast cancer based on the GEPIA2 database (Tang et al., 2017). SUCO has 10 main isoforms (transcripts), and they showed distinct expression distributions in breast cancer (Supplementary Figure S4A). Overall, SUCO was upregulated in breast cancer tissues compared with normal breast tissues (Supplementary Figure S4B). However, some transcripts (e.g., ENST00000263688.3, circ_0000160 was derived from this transcript) were upregulated, some (e.g., ENST00000608566.1) showed no obvious change, and some (e.g., ENST00000609685.1) even showed a downregulation trend in breast cancer (Supplementary Figures S4C–L). These results suggested that different isoforms may show distinct expression patterns. Further investigation on the function and mechanism of circ_0000160, as well as its relationship with SUCO, is needed in the future.

There were a few limitations in our study. First, the sample size was small. Circ_0000160 and the other two circRNAs circ_0001798 and circ_0001750, which did not show a relationship with ALNM, need further confirmation, ideally, with varied geographic and ethnic populations, with different stages, subtypes, and treatment regimens. Second, the present study mainly focused on clinically significant circRNAs in breast cancer. The function and mechanism of circRNAs in breast cancer need further investigation. Third, a single marker is not sufficiently accurate to predict ALNM. In future studies, the integration of multiple biomarkers to predict lymph node metastasis may supersede their individual predictive value.

In conclusion, we revealed the circRNA profile related to breast cancer ALNM using transcriptome sequencing. A decreased expression of circ_0000160 was found in breast cancer with axillary lymph node metastasis. Bioinformatics analysis showed that circ_0000160 may interact with numerous miRNAs and mRNAs and participate in many cancer-related pathways. The predicted mRNAs for circ_0000160 may be related to lymph node metastasis. This study provides a framework for understanding the mechanisms of breast cancer ALNM from the perspective of circRNAs. Circ_0000160 may have the potential to distinguish breast cancer with axillary lymph node metastasis from those without axillary lymph node metastasis.

DATA AVAILABILITY STATEMENT

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE173766.

ETHICS STATEMENT

The studies involving human participants were reviewed and approved by the Ethics Committee of the Qilu Hospital of Shandong University. The patients/participants provided their written informed consent to participate in this study.

AUTHOR CONTRIBUTIONS

Y-WW and PS conceptualized the study. XC, YT, and LL helped with data collection. Y-WW and XC carried out data analysis and figure preparation. Y-WW and PS contributed to data interpretation and manuscript preparation. PS assisted with supervision. All authors contributed to the manuscript and approved the submitted version.

FUNDING

This work was supported by the National Natural Science Foundation of China (Nos. 81902698 and 81802406), the Shandong Provincial Natural Science Foundation (Nos. ZR2019BH061, ZR2019BH034, and ZR2020LZL009) and Special Funds for Scientific Research on Breast Diseases of Shandong Medical Association (No. YXH2021ZX058).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

ACKNOWLEDGMENTS

The authors thank Cloud-Seq Company (Shanghai, China) for the transcriptome sequencing service.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2021.690826/full#supplementary-material

Supplementary Figure S1 | Typical figures of hematoxylin and eosin (H&E)-stained sections of breast cancer with ALNM (axillary lymph node metastasis) and without ALNM. Two cases were used as examples, and their sections of primary breast cancer and the corresponding axillary lymph node were provided (A,B,E,F, primary breast invasive ductal carcinoma; C,D, without ALNM; G,H, with ALNM) (A,C,E,G, magnification × 40; B,D,F,H, magnification × 200).

Supplementary Figure S2 | Association between the circ_0001750 expression and clinicopathological parameters in the validation cohort containing 56 patients. Circ_0001750 displayed a higher expression in tissues with a higher grade (grade 3 vs. grade 2), negative PR status, or higher Ki-67 expression. The circ_0001750 expression showed no relationship with other parameters (ER, estrogen receptor; PR, progesterone receptor).

Supplementary Figure S3 | Circ_0001798 expression showed no relationship with clinicopathological parameters in the validation cohort containing 56 patients (ER, estrogen receptor; PR, progesterone receptor).

Supplementary Figure S4 | Isoform expression data of the SUCO gene in breast cancer based on the GEPIA2 database. (A) Isoform expression distribution of SUCO in breast cancer. (B–L) Comparison of the SUCO isoform expression in breast cancer and normal tissues (*p < 0.01).

REFERENCES

Chang, J. M., Leung, J. W. T., Moy, L., Ha, S. M., and Moon, W. K. (2020). Axillary Nodal Evaluation in Breast Cancer: State of the Art. Radiology 295 (3), 500–515. doi:10.1148/radiol.2020192534

Chen, S., Fu, Z., Wen, S., Yang, X., Yu, C., Zhou, W., et al. (2021). Expression and Diagnostic Value of miR-497 and miR-1246 in Hepatocellular Carcinoma. Front. Genet. 12, 666306. doi:10.3389/fgene.2021.666306

Choi, H. Y., Park, M., Seo, M., Song, E., Shin, S. Y., and Sohn, Y.-M. (2017). Preoperative Axillary Lymph Node Evaluation in Breast Cancer. Ultrasound Q. 33 (1), 6–14. doi:10.1097/ruq.0000000000000277

Dudekula, D. B., Panda, A. C., Grammatikakis, I., De, S., Abdelmohsen, K., and Gorospe, M. (2016). CircInteractome: A Web Tool for Exploring Circular RNAs and Their Interacting Proteins and microRNAs. RNA Biol. 13 (1), 34–42. doi:10.1080/15476286.2015.1128065

Frey, L., Klümper, N., Schmidt, D., Kristiansen, G., Toma, M., Ritter, M., et al. (2021). CircEHD2, CircNETO2 and CircEGLN3 as Diagnostic and Prognostic Biomarkers for Patients with Renal Cell Carcinoma. Cancers 13 (9), 2177. doi:10.3390/cancers13092177

Ghosal, S., Das, S., Sen, R., Basak, P., and Chakrabarti, J. (2013). Circ2Traits: a Comprehensive Database for Circular RNA Potentially Associated with Disease and Traits. Front. Genet. 4, 283. doi:10.3389/fgene.2013.00283

Kim, M. Y. (2021). Breast Cancer Metastasis. Adv. Exp. Med. Biol. 1187, 183–204. doi:10.1007/978-981-32-9620-6_9

Kristensen, L. S., Hansen, T. B., Venø, M. T., and Kjems, J. (2018). Circular RNAs in Cancer: Opportunities and Challenges in the Field. Oncogene 37 (5), 555–565. doi:10.1038/onc.2017.361

Lee, J.-H., Jeong, H., Choi, J.-W., Oh, H. E., and Kim, Y.-S. (2018). Liquid Biopsy Prediction of Axillary Lymph Node Metastasis, Cancer Recurrence, and Patient Survival in Breast Cancer. Medicine (Baltimore) 97 (42), e12862. doi:10.1097/md.0000000000012862

Liao, Y., Wang, J., Jaehnig, E. J., Shi, Z., and Zhang, B. (2019). WebGestalt 2019: Gene Set Analysis Toolkit with Revamped UIs and APIs. Nucleic Acids Res. 47 (W1), W199–w205. doi:10.1093/nar/gkz401

Liu, M., Wang, Q., Shen, J., Yang, B. B., and Ding, X. (2019). Circbank: a Comprehensive Database for circRNA with Standard Nomenclature. RNA Biol. 16 (7), 899–905. doi:10.1080/15476286.2019.1600395

Rao, A. K. D. M., Arvinden, V. R., Ramasamy, D., Patel, K., Meenakumari, B., Ramanathan, P., et al. (2021). Identification of Novel Dysregulated Circular RNAs in Early-Stage Breast Cancer. J. Cel. Mol. Med. 25 (8), 3912–3921. doi:10.1111/jcmm.16324

Sawaki, M., Shien, T., and Iwata, H. (2019). TNM Classification of Malignant Tumors (Breast Cancer Study Group). Jpn. J. Clin. Oncol. 49 (3), 228–231. doi:10.1093/jjco/hyy182

Sha, Z., Sha, L., Li, W., Dou, W., Shen, Y., Wu, L., et al. (2015). Exome Sequencing Identifies SUCO Mutations in Mesial Temporal Lobe Epilepsy. Neurosci. Lett. 591, 149–154. doi:10.1016/j.neulet.2015.02.009

Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., et al. (2003). Cytoscape: a Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Res. 13 (11), 2498–2504. doi:10.1101/gr.1239303

Si, P., Zhang, P., Chen, T., Liu, G., Lu, H., Chen, H., et al. (2019). Positive Nonsentinel Lymph Nodes Are Associated with Poor Survival in Breast Cancer: Results from a Retrospective Study. Clin. Transl. Oncol. 21 (8), 1085–1092. doi:10.1007/s12094-018-02031-5

Sohaskey, M. L., Jiang, Y., Zhao, J. J., Mohr, A., Roemer, F., and Harland, R. M. (2010). Osteopotentia Regulates Osteoblast Maturation, Bone Formation, and Skeletal Integrity in Mice. J. Cel. Biol. 189 (3), 511–525. doi:10.1083/jcb.201003006

Tang, Z., Li, C., Kang, B., Gao, G., Li, C., and Zhang, Z. (2017). GEPIA: a Web Server for Cancer and normal Gene Expression Profiling and Interactive Analyses. Nucleic Acids Res. 45 (W1), W98–w102. doi:10.1093/nar/gkx247

Tokar, T., Pastrello, C., Rossos, A. E. M., Abovsky, M., Hauschild, A.-C., Tsay, M., et al. (2018). mirDIP 4.1-integrative Database of Human microRNA Target Predictions. Nucleic Acids Res. 46 (D1), D360–d370. doi:10.1093/nar/gkx1144

Verduci, L., Tarcitano, E., Strano, S., Yarden, Y., and Blandino, G. (2021). CircRNAs: Role in Human Diseases and Potential Use as Biomarkers. Cell Death Dis. 12 (5), 468. doi:10.1038/s41419-021-03743-3

Vo, J. N., Cieslik, M., Zhang, Y., Shukla, S., Xiao, L., Zhang, Y., et al. (2019). The Landscape of Circular RNA in Cancer. Cell 176 (4), 869–881. e813. doi:10.1016/j.cell.2018.12.021

Wang, L., Zhang, S., and Wang, X. (2020). The Metabolic Mechanisms of Breast Cancer Metastasis. Front. Oncol. 10, 602416. doi:10.3389/fonc.2020.602416

Wang, S., Zhang, K., Tan, S., Xin, J., Yuan, Q., Xu, H., et al. (2021a). Circular RNAs in Body Fluids as Cancer Biomarkers: the New Frontier of Liquid Biopsies. Mol. Cancer 20 (1), 13. doi:10.1186/s12943-020-01298-z

Wang, Y.-W., Xu, Y., Wang, Y.-Y., Zhu, J., Gao, H.-D., Ma, R., et al. (2021b). Elevated circRNAs Circ_0000745, Circ_0001531 and Circ_0001640 in Human Whole Blood: Potential Novel Diagnostic Biomarkers for Breast Cancer. Exp. Mol. Pathol. 121, 104661. doi:10.1016/j.yexmp.2021.104661

Wei, G., Zhu, J., Hu, H.-B., and Liu, J.-Q. (2021). Circular RNAs: Promising Biomarkers for Cancer Diagnosis and Prognosis. Gene 771, 145365. doi:10.1016/j.gene.2020.145365

Yue, C., Liang, C., Ge, H., Yan, L., Xu, Y., Li, G., et al. (2019). SUCO as a Promising Diagnostic Biomarker of Hepatocellular Carcinoma: Integrated Analysis and Experimental Validation. Med. Sci. Monit. 25, 6292–6303. doi:10.12659/msm.915262

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Wang, Chen, Tian, Liu and Su. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 21 February 2022
doi: 10.3389/fmolb.2021.770729

Clinical Prognostic Value of the PLOD Gene Family in Lung Adenocarcinoma

Yiming Meng¹, Jing Sun², Guirong Zhang¹, Tao Yu³* and Haozhe Piao1,⁴*

¹Department of Central Laboratory, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China

²Department of Biobank, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China

³Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China

⁴Department of Neurosurgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China

Edited by:
Hossain Shekhar, University of Dhaka, Bangladesh

Reviewed by:
Rajesh Sinha, University of Alabama at Birmingham, United States
Jie Wang, Nanjing Medical University, China

* Correspondence: Tao Yu, taoyulnszlyy@sina.com; Haozhe Piao, haozhepiaolnzlyy@sina.com

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 04 September 2021
Accepted: 30 December 2021
Published: 21 February 2022

Citation: Meng Y, Sun J, Zhang G, Yu T and Piao H (2022) Clinical Prognostic Value of the PLOD Gene Family in Lung Adenocarcinoma. Front. Mol. Biosci. 8:770729. doi: 10.3389/fmolb.2021.770729

Accumulating evidence has implicated members of the procollagen-lysine, 2-oxoglutarate 5-dioxygenase (PLOD) gene family, PLOD1, PLOD2, and PLOD3, in cancer progression and metastasis. However, their expression, prognostic value, and mechanisms underlying their roles in lung adenocarcinoma (LUAD) have not yet been reported. We downloaded PLOD data for LUAD and normal tissues from The Cancer Genome Atlas (TCGA). PLOD1-3 protein expression was evaluated using the Clinical Proteomics Tumor Analysis Consortium and Human Protein Atlas. Survival analysis was performed using the Kaplan–Meier method. A protein–protein interaction network was constructed using STRING software. The “ClusterProfiler” package was used for functional-enrichment analysis. The relationship between PLOD mRNA expression and immune infiltration was analyzed using the Tumor Immunity Assessment Resource and Tumor Immune System Interaction Database. The expression of PLODs in LUAD tissues was significantly upregulated compared with that in adjacent normal tissues. PLOD mRNA overexpression is associated with lymph node metastasis and high TNM staging. Receiver operating characteristic curve analysis showed that when the cut-off level was 6.073, the accuracy, sensitivity, and specificity of PLOD1 in distinguishing LUAD from adjacent controls were 84.4, 79.7, and 82.6%, respectively. The accuracy, sensitivity, and specificity of PLOD2 in distinguishing LUAD from adjacent controls were 81.0, 98.3, and 68.0%, respectively, at a cut-off value of 4.360. The accuracy, sensitivity, and specificity of PLOD3 in distinguishing LUAD from adjacent controls were 69.0, 86.4, and 52.0%, respectively, with a cut-off value of 5.499. Kaplan–Meier survival analysis demonstrated that LUAD patients with high PLODs had a worse prognosis than those with low PLODs. Correlation analysis showed that PLOD mRNA expression was related to immune infiltration and tumor purity. Upregulation of PLOD expression was significantly associated with poor survival and immune cell infiltration in LUAD. Our research shows that PLOD family members have potential as novel biomarkers for poor prognosis and as potential immunotherapy targets for LUAD.

Keywords: PLOD family member, LUAD, diagnosis, prognosis, immune infiltration

INTRODUCTION

Lung cancer is one of the most malignant tumors that threaten human lives and health. Statistics on the pathological classification of lung cancer show that the most important type is non-small cell lung cancer (NSCLC), accounting for approximately 80% of all lung cancers. Lung adenocarcinoma (LUAD) is a common clinical type of NSCLC (Shergalis et al., 2020; Li et al., 2021). Although some progress has been made in surgical resection, chemotherapy, and radiotherapy, the 5-year survival rate of patients with LUAD remains low. The main reasons for this may be immunosuppression, cell proliferation, distant metastases, and drug resistance (Chen et al., 2021). Therefore, there is an urgent need to identify new, early diagnostic biomarkers and therapeutic targets for LUAD.

The procollagen-lysine, 2-ketoglutarate 5-dioxygenase (PLOD) gene family includes three members, PLOD1, PLOD2, and PLOD3, located on chromosomes 1p, 3q, and 7q, respectively. Multiple studies have shown that PLOD genes play essential roles in the development and progression of cancer (Guo et al., 2021a). Other studies have shown that, compared with normal kidney tissue, the expression of PLOD genes is increased in hepatocellular carcinoma (HCC) and positively correlated with the prognosis of HCC patients (Yang et al., 2020). In addition, bioinformatics analysis has shown that compared with adjacent normal tissues, PLOD gene family members are expressed at higher levels in LUAD tissues. However, the expression and prognostic effects of PLOD family members in LUAD require further elucidation.

To the best of our knowledge, the prognostic value of PLODs in LUAD and their correlation with immune infiltration are still not fully understood. To test this hypothesis, we evaluated TCGA database data and performed other bioinformatics analyses of the prognostic role of the PLOD family in LUAD. We further investigated the correlation between PLOD and immune cell infiltration.

MATERIALS AND METHODS

Datasets Obtained From the Cancer Genome Atlas (TGCA) Datasets

TCGA lung adenocarcinoma (TCGA disease code: LUAD) cohorts were downloaded from TCGA official website (https://portal.gdc.cancer.gov/) (Tomczak et al., 2015). The 33 enrolled cancer types contained at least five samples in the normal group. At last, the RNA-Seq gene expression data with workflow type of fragments per kilobase per million (FPKM) were subsequently transformed to transcripts per million (TPM) and log2 conversion for further study. Since all the data are available from TCGA, no approval by an ethics committee is required.

RNA Sequencing (RNA-Seq) Data of PLODs in LUAD

Expression of PLODs in the RNA-Seq expression data in LUAD were also obtained from TCGA. This included 59 normal lung samples (normal-adjacent tissue to LUAD) and 535 LUAD tissue data. Specimen characteristics and associated clinical information including age, gender, smoker condition, tumor stage, and location are described. The mRNA expression data were summarized with means ± SD.

Clinical Proteomic Tumor Analysis Consortium (CPTAC)

The protein expression profile was downloaded from the CPTAC resource (https://cptac-data-portal.georgetown.edu) and the proteome of each tumor sample characterized (Edwards et al., 2015). UALCAN (Chandrashekar et al., 2017) is a user-friendly online web resource. The R package “Seurat” was applied for data standardization. Then, the plots were drawn with the given cell classification using R package “ggplot2”. In this study, a throughput analysis of PLOD protein expression was presented.

Biopsy Immunohistochemical Micrographs From the Human Protein Atlas (HPA)

The HPA (https://www.proteinatlas.org/) provides a map showing the distribution and relative abundance of human genes on protein level in normal human tissues and LUAD tissues (Uhlen et al., 2015; Uhlen et al., 2017). The HPA was used to assess tissue protein expression profiles.

Definition of Protein–Protein Interaction (PPI) Modules

Genes form PPI networks at any level that fulfill biological roles, and perturbing protein networks is an ordinary characteristic of disease-associated mutations. Construction of a PPI network was studied by STRING tool (https://www.string-db.org/) (an online database for the retrieval of interacting genes) (Szklarczyk et al., 2011). Gene ontology (GO) enrichment was performed for the categories gene ontology biological process, molecular function, cellular compartment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) (https://www.kegg.jp/) (Yu et al., 2012).

Quantification of Immune Infiltration Based on Key Genes

Immune infiltrate estimation was explored to comprehensively analyze the correlation between PLOD genes and tumor-infiltrating immune components by the Tumor Immune Estimation Resource (TIMER) platform (http://timer.cistrome.org/) (Li et al., 2017). The infiltrating immune cells including B cells, CD4⁺ T cells, CD8⁺ T cells, neutrophils, macrophages, and dendritic cells.

Tumor-Immune System Interaction Database (TISIDB) Analysis

The integrated repository portal for tumor-immune system interactions with PLODs genes was assessed in TISIDB (Ru et al., 2019). Based on the gene expression profile, the relative abundance of tumor-infiltrating lymphocytes (TILs) was inferred by Gene Set Variation Analysis (GSVA). Spearman’s correlation test was conducted to explore the relation between numerical variables.

PrognoScan Database

PrognoScan database (http://dna00.bio.kyutech.ac.jp/PrognoScan/index.html) (Mizuno et al., 2009) provides a powerful online platform for evaluating potential tumor markers and therapeutic targets, as well as a tool for assessing the biological association between gene expression and prognosis. The correlation between PLOD gene expression and survival in LUAD was analyzed by the PrognoScan database (jacob-00182-CANDF, jacob-00182-MSK, jacob-00182-UM GSE31210, HARVARD-LC, MICHIGAN-LC, and jacob-00182-CANDF).

Prognostic Value of PLODs

The Gene Expression Profiling Interactive Analysis (GEPIA) contains high-throughput RNA sequencing data (TCGA and GTEx databases). Prognostic value, including OS and RFS, based on PLOD expression was generated by GEPIA database (http://gepia2.cancer-pku.cn/) (Tang et al., 2017).

Statistical Analyses

Data were performed with R (V 3.5.3) and graphs were generated using the ggplot2 package in R. Statistical significance was measured by paired t-test and Mann–Whitney U test. The “pROC” package (Robin et al., 2011) was performed to calculate the cut-off value. Kaplan–Meier curves were determined by using log-rank tests.

RESULTS

Pan-Cancer Analyses

The mRNA expression levels of PLODs were analyzed using TCGA database. Our analysis revealed that 14 of 33 cancer types exhibited the highest mRNA expression of PLOD1 among the TCGA cohorts. Compared with normal tissues, PLOD2 was significantly upregulated in 11 cancer types. In addition, PLOD3 expression was markedly increased in 17 cancer types. These data imply that PLODs are often overexpressed across different cancer types, especially LUAD (Figure 1). The detailed data are presented in Supplementary Tables 1–3.

FIGURE 1 | Pan-cancer analyses. (A) mRNA expression of PLOD1 upregulated in 14 of 33 cancer types; (B) mRNA expression of PLOD2 upregulated in 11 of 33 cancer types; (C) mRNA expression of PLOD3 upregulated in 17 of 33 cancer types. *p < 0.05, *p < 0.01, ***p < 0.001. ns, no significance; ACC, adrenocortical carcinoma; BLCA, bladder urothelial carcinoma; BRCA, breast invasive carcinoma; CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL, cholangiocarcinoma; COAD, colon adenocarcinoma; DLBC, lymphoid neoplasm diffuse large B-cell lymphoma; ESCA, esophageal carcinoma; GBM, glioblastoma multiforme; HNSC, head and neck squamous cell carcinoma; KICH, kidney chromophobe; KIRC, kidney renal clear cell carcinoma; KIRP, kidney renal papillary cell carcinoma; LAML, acute myeloid leukemia; LGG, brain lower grade glioma; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; MESO, mesothelioma; OV, ovarian serous cystadenocarcinoma; PRAD, prostate adenocarcinoma; PCPG, pheochromocytoma and paraganglioma; READ, rectum adenocarcinoma; SARC, sarcoma; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; THCA, thyroid carcinoma; THYM, thymoma; UCEC, uterine corpus endometrial carcinoma; UCS, uterine carcinosarcoma; UVM, uveal melanoma.

mRNA Expression

Expression of PLODs in normal lung (59 cases) and LUAD (535 cases) tissues from TCGA database were analyzed. Expression levels of PLOD mRNAs in LUAD tumor tissues were higher than those in adjacent normal tissues (Figure 2A). In subsequent analyses, the Mann–Whitney U test for unpaired data showed a significant difference between the expression levels of PLOD mRNAs in LUAD tissues and adjacent normal tissues (Figure 2B).

FIGURE 2 | PLOD mRNA expression. Through TCGA database data screening and analysis, the expression of PLOD family genes in normal tissues and LUAD tissue samples was determined. (A) mRNA expression levels of PLOD1, PLOD2, and PLOD3 in LUAD tissues and matched adjacent normal samples. (B) mRNA expression levels of PLOD family genes in 535 lung adenocarcinoma samples and 59 normal samples. The results showed that, compared with normal lung tissue, the expression of PLOD1, PLOD2, and PLOD3 mRNA in LUAD increased significantly.

Protein Pathological Expression in Lung Tissue

To determine PLOD protein expression, we mined these data using CPTAC with UALCAN. We found that the protein expression of PLODs in LUAD was significantly higher (Figure 3A). Transcript data from the HPA and the corresponding staining patterns of PLODs were further supported by TCGA datasets. As shown in Figure 3B, PLOD1 is located in human cell vesicles and the nucleoplasm, PLOD2 is located in the nucleoli and cytosol, and PLOD3 is secreted by cells. In line with previous data, both mRNA and protein were overexpressed in LUAD tissues.

FIGURE 3 | Protein pathological expression in lung tissue. To facilitate access to the protein expression of PLOD1, PLOD2, and PLOD3 in LUAD, CPTAC data were integrated with the UALCAN data portal (A), and the differential expression and location of PLOD1-3 were observed in the HPA (***p < 0.001) (B). PLOD1: normal tissue, https://www.proteinatlas.org/ENSG00000083444-PLOD1/tissue/lung; tumor tissue, https://www.proteinatlas.org/ENSG00000083444-PLOD1/pathology/lung + cancer. PLOD2: normal tissue, https://www.proteinatlas.org/ENSG00000152952-PLOD2/tissue/lung; tumor tissue, https://www.proteinatlas.org/ENSG00000152952-PLOD2/pathology. PLOD3: normal tissue, https://www.proteinatlas.org/ENSG00000106397-PLOD3/tissue/lung; tumor tissue, https://www.proteinatlas.org/ENSG00000106397-PLOD3/pathology/lung + cancer.

Relationships Between PLOD Expression Data and LUAD Clinicopathological Characteristics

We applied the Mann–Whitney U test and logistic regression analysis. As shown in Table 1, Table 2, Table 3, and Figure 4A-L, PLOD expression was correlated with clinical stage, T stage, OS, histopathological grade, and smoking history. However, PLOD expression and other clinicopathological characteristics, including age, smoking status, M stage, sex, and anatomical location (right and left; periphery and center), were not statistically significant. In summary, these results indicate that PLOD1 and PLOD2 may serve as biomarkers for poor prognosis of LUAD.

TABLE 1 | Clinical Characteristics of the LUAD patients (TCGA)

TABLE 2 | Clinical characteristics of the LUAD patients (TCGA)

TABLE 3 | Clinical characteristics of the LUAD patients (TCGA)

FIGURE 4 | Evaluate relationship between mRNA expression level and various clinicopathological characteristics. The association of mRNA with clinical pathological characteristics including T stage (A), lymph node metastases (B), primary therapy outcome (C), M stage (D), sex (E), anatomic neoplasm subdivision (F), age (G), pathologic stage (H), anatomic neoplasm subdivision 2 (I), residual tumor (J), smoker (K), and smoking status (L) are demonstrated in this plot. ns, no significance, *p < 0.05, **p < 0.01, ***p < 0.001.

Potential Biomarkers for Distinguishing LUAD Samples From Normal Samples

ROC curve analysis showed (Figure 5A) significant predictive potential for PLOD1 (AUC = 0.844, CI = 0.806–0.881) and PLOD2 (AUC = 0.810, CI = 0.776–0.844). However, the predictive potential for PLOD3 was lower (AUC = 0.690, CI = 0.641–0.740). DeLong’s test showed that PLOD1 was superior to PLOD2 in predicting tumor and normal outcomes, but the test was not statistically significant (p = 0.148); PLOD1 was significantly superior to PLOD3 (p ≤ 0.01). The diagnostic efficiency of PLOD2 was significantly better than that of PLOD3 (p ≤ 0.01). Kaplan–Meier curves for the three PLOD family members showed that the OS of LUAD patients with high PLOD2 mRNA expression was shorter than that of patients with low PLOD2, and the difference was statistically significant.

FIGURE 5 | Prospective biomarkers for distinguishing LUAD from normal samples. The survival ROC package in the R language was used to generate the ROC curve. (A) ROC curve showing that PLOD1 and PLOD2 can distinguish lung adenocarcinoma tissue from healthy controls. (B–D) The Kaplan–Meier curve of the PLOD gene family showed that the OS of LUAD patients with high PLOD2 mRNA expression was shorter than that of patients with low PLOD2, and the difference was statistically significant.

PPI Network Analysis and Biological Function Annotation

Using the STRING database, Figure 6A, Figure 7A, and Figure 8A show networks of 10 co-expressed genes related to PLODs in LUAD patients. For PLOD1, the network included COL1A1, COL1A2, COL2A1, COL3A1, COL4A1, COL4A2, COL5A1, COL5A2, COLGALT1, and PLOD2. For PLOD2, the network included COL12A1, COL1A1, COL1A2, COL3A1, COL4A1, COL4A2, COL5A1, COL5A2, COLGALT1, and PLOD1. For PLOD3, the network included COL1A1, COL1A2, COL3A1, COL4A1, COL4A2, COL4A5, COL5A3, COL7A1, COLGALT1, and COLGALT2). As shown in Figure 6, Figure 7, and Figure 8B, the biological processes for PLODs were all related to collagen fibril, extracellular matrix, and extracellular structure organization. The functional annotations for PLOD1 were protein digestion and absorption, ECM–receptor interaction, and the AGE-RAGE signaling pathway in diabetic complications. The functional annotations of PLOD2 and PLOD3 were protein digestion and absorption, amoebiasis, and AGE-RAGE signaling pathways in diabetic complications. Correlation analyses between the expression of PLODs and TCGA LUAD co-expressed genes are shown in Figures 6C–L, Figures 7C–L, and Figures 8C–L.

FIGURE 6 | PLOD1 PPI network and biological function annotation. (A) Network of PLOD1 and its co-expressed genes. (B) Functional enrichment analysis of 10 network genes. PLOD1 is associated with collagen fibril organization, extracellular matrix organization, and extracellular structural organization. These genes are involved in protein digestion and absorption, ECM–receptor interactions, and the AGE-RAGE signaling pathway in diabetic complications. (C–L) Correlations between the expression of PLOD1 and co-expressed genes in LUAD. COL1A1, collagen alpha-1(I) chain; COL1A2, collagen alpha-2(I) chain; COL2A1, collagen alpha-1(II) chain; COL3A1, collagen alpha-1(III) chain; COL4A1, collagen alpha-1(IV) chain; COL4A2, collagen alpha-2(IV) chain; COL5A1, collagen alpha-1(V) chain; COL5A2, collagen alpha-2(V) chain; COLGALT1, procollagen galactosyltransferase 1; PLOD2: procollagen-lysine,2-oxoglutarate 5-dioxygenase 2.

FIGURE 7 | PLOD2 PPI network and biological function annotation. (A) Network of PLOD2 and its co-expressed genes. (B) Functional enrichment analysis of 10 network genes. PLOD2 was associated with collagen fibril organization, extracellular matrix organization, and extracellular structure organization. These genes are involved in protein digestion and absorption, amoebiasis, and AGE-RAGE signaling in diabetes. (C–L) Correlations between the expression of PLOD1 and co-expressed genes in lung adenocarcinoma. COL12A1, collagen alpha-1 (XII) chain; COL1A1, collagen alpha-1(I) chain; COL1A2, collagen alpha-2(I) chain; COL3A1, collagen alpha-1(III) chain; COL4A1, collagen alpha-1(IV) chain; COL4A2, collagen alpha-2(IV) chain; COL5A1, collagen alpha-1(V) chain; COL5A2, collagen alpha-2(V) chain; COLGALT1, procollagen galactosyltransferase 1; PLOD1: procollagen-lysine,2-oxoglutarate 5-dioxygenase 1.

FIGURE 8 | PLOD3 PPI network and biological function annotation. (A) Network of PLOD3 and its co-expressed genes. (B) Functional enrichment analysis of 10 network genes. PLOD3 was associated with collagen fibril organization, extracellular matrix organization, and extracellular structure organization. These genes were involved in protein digestion and absorption, amoebiasis, and AGE-RAGE signaling in diabetes. (C–L) Correlations between the expression of PLOD3 and co-expressed genes in lung adenocarcinoma. COL1A1, collagen alpha-1(I) chain; COL1A2, collagen alpha-2(I) chain; COL3A1, collagen alpha-1(III) chain; COL4A1, collagen alpha-1(IV) chain; COL4A2, collagen alpha-2(IV) chain; COL4A5, collagen alpha-5(IV) chain; COL5A3, collagen alpha-3(V) chain; COL7A1, collagen alpha-1(VII) chain; COLGALT1, procollagen galactosyltransferase 1; COLGALT2, procollagen galactosyltransferase 2.

Associations With Target Genes and Immune Infiltration

We used TCGA to analyze PLODs and overall immune components in tumor immunosuppressive networks (Figures 9A, 10, and 11A). We next expanded our analysis by interrogating TIMER (Figure 9B–L, Figure 10B–L, and Figure 11B–L). We found that in the LUAD patient cohort, high PLOD expression levels were negatively correlated with TFH cells, CD8 T cells, Tcm cells, and B-cell immune infiltration. In contrast, high PLOD1-3 expression was positively correlated with Tregs, macrophages, and cancer-associated fibroblasts.

FIGURE 9 | Association between PLOD1 and immune infiltration. (A) Summary plot of the relationship between PLOD1 expression and immune infiltration. (B–U) PLOD1 expression is negatively related to tumor purity and has correlations with TFH, CD8⁺ T, TH, cytotoxic, mast, and B cells; on the contrary, it is positively correlated with Tregs, macrophages, and cancer-associated fibroblasts.

FIGURE 10 | Association between PLOD2 and immune infiltration. (A) Summary plot of the relationship between PLOD2 expression and immune infiltration. (B–U) PLOD2 expression is negatively related to tumor purity and has correlations with TFH, CD8⁺ T, dendritic, CD56⁺ NK, mast, and B cells; on the contrary, it is positively correlated with Tregs, macrophages, and cancer-associated fibroblasts.

FIGURE 11 | Association between PLOD3 and immune infiltration. (A) Summary plot of the relationship between PLOD2 expression and immune infiltration. (B–U) PLOD3 expression is negatively related to tumor purity and has correlations with TFH, CD8⁺ T, TH, dendritic, cytotoxic, and mast and B cells; on the contrary, it is positively correlated with Tregs, macrophages, and cancer-associated fibroblast cells.

Correlation of PLOD Expression With TILs

Using the TISIDB database, we found that the ratios of TIL subpopulations were highly correlated with PLOD expression in pan-cancer (Figure 12A, Figure 13A, and Figure 14A); representative pictures are shown in Figures 12B–L, Figures 13B–L, and Figures 14B–L. In general, the TISIDB results were similar to those obtained using TIMER.

FIGURE 12 | Correlation of PLOD1 expression with TILs. (A) Heatmap of PLOD1 expression in pan-cancer from the TISIDB database. (B–L) PLOD1 expression level is significantly correlated with infiltrating levels of activated CD8⁺ T cells, activated CD4⁺ T cells, activated B cells, Treg cells, NKT cells, and macrophages.

FIGURE 13 | Correlation of PLOD2 expression with TILs. (A) Heatmap of PLOD2 expression in pan-cancer from the TISIDB database. (B–L) PLOD2 expression level is significantly correlated with infiltrating levels of activated CD8⁺ T cells, activated CD4⁺ T cells, activated DCs, activated CD8⁺ T cells, MDSCs, Treg cells, and NKT cells.

FIGURE 14 | Correlation of PLOD3 expression with TILs. (A) Heatmap of PLOD3 expression in pan-cancer from the TISIDB database. (B–L) PLOD3 expression level is significantly correlated with infiltrating levels of activated CD8⁺ T cells, activated CD4⁺ T cells, activated B cells, Treg cells, and NKT cells.

DISCUSSION

Lung adenocarcinoma has high mortality, ranking first and second in cancer-type incidence in men and women in China, respectively. It is also the leading cause of cancer-related deaths worldwide. Owing to the combined effects of tobacco, aging, and air pollution, the incidence of lung cancer is rapidly increasing. The most common subtype of lung cancer is LUAD. Since it is typically diagnosed late in its progression, the 5-year OS is only 15%. Therefore, it is important to identify differentially expressed molecules to use as markers for its diagnosis (Li et al., 2016; Tang et al., 2020).

In the present study, PLODs were found to be overexpressed in LUAD tissues; therefore, they may be independent prognostic biomarkers. The level of PLOD mRNA expression was positively correlated with tumor progression. ROC analysis demonstrated that PLODs may be a promising diagnostic indicator for distinguishing LUAD from normal tissues. From Kaplan–Meier curves, we confirmed that high expression of PLODs was associated with a shorter survival. Thus, PLODs represent attractive potential prognostic biomarkers for LUAD. Furthermore, PLODs may play specific roles in immune evasion.

Lysine hydroxylase (LH) is encoded by three lysine hydroxylase genes (procollagen-lysine, 2-oxoglutarate 5-dioxygenase PLOD1, PLOD2, and PLOD3) and is a key enzyme that mediates collagen association. PLOD family genes belong to the 2-ketoglutarate-dependent dioxygenase family (Zhao et al., 2021). They form homodimers in the presence of Fe²⁺ and 2-ketoglutaric acid (2-OG) to hydroxylate single-chain procollagen lysines. Collagen that undergoes lysine hydroxylation is secreted by the cell. It can be crosslinked to form stable hydroxylysine pyridine chains (Eisinger-Mathason et al., 2013). When PLOD1 and PLOD2 are significantly overexpressed, hydroxylysine pyridine chains are generated (Heikkinen et al., 1994). This leads to excessive deposition of collagen fibers, which destroys the ECM structure. Eventually, overexpression of PLODs promotes tumor progression and metastasis.

PLOD1 catalyzes hydroxylation of lysyl residues on collagen type V. It was originally used as a biochemical and histochemical marker for the assessment of bone mineral density (Shin et al., 2020). With the development of genome and transcriptome sequencing, PLOD1 mutations have been found to be associated with a variety of cancers (Xu et al., 2019). Wang et al. reported that PLOD1 is a potential therapeutic target for the treatment of mesenchymal gliomas (GBM). PLOD1 also enhances tumor vitality, activity, and migration, and promotes the transformation of GBM into malignant mesenchymal subtypes. They also found that PLOD1 expression is closely related to NF-κB signaling (Wang Z. et al., 2021). Furthermore, high expression level of PLOD1 was significantly correlated with the degree of response in hepatocellular carcinoma (HCC) patients and was positively correlated with immune infiltration. Further study of PLOD1 may provide new insights into diagnostic and prognostic biomarkers in HCC patients (Yang et al., 2020).

PLOD2 encodes lysine hydroxylase 2 (LH2). It is primarily located in the rough endoplasmic reticulum in multiple human tissues. PLOD2 specifically hydroxylates the lysine of the procollagen terminal peptide, which is important for the covalent crosslinking of collagen and tissue stiffness. Therefore, PLOD2 is a hydroxylase that mainly affects covalent crosslinking of collagen. After being secreted from the cell, the lysine residues of the PLOD2-modified collagen terminal peptide can be crosslinked to form a stable hydroxyl lysine pyridine chain (Noda et al., 2012; Cheriyamundath et al., 2021). In contrast, the crosslinks formed by collagen that has not undergone hydroxylation are less stable and prone to degradation. Therefore, the extent of lysine hydroxylation in the single-chain terminal peptide region of procollagen—that is, the expression and activity of LH2—determines the strength and arrangement of collagen fibers in the ECM (Gilkes et al., 2013; Du et al., 2017). In a variety of tumors, fibrous collagen is considered to provide a channel for cancer cell migration and is mainly modified by PLOD2. For example, PLOD2 can increase the invasiveness and migration of gastric cancer (GC) cells and promote their resistance to 5-fluorouracil by upregulating BCRP and inhibiting apoptosis (Wang et al., 2020).

PLOD3, also known as LH3, has a molecular weight of 85 kDa and catalyzes the hydroxylation of lysine residues in collagen. Studies have shown that PLOD3 has galactose hydroxylysine glucosyltransferase activity, which adds glucose groups to the galactose hydroxylysine residues of collagen (Shao et al., 2020). As previously reported, PLOD3 has a remarkable effect on the synthesis and modification of collagen. As modification of collagen and the extracellular matrix is related to tumor invasion and angiogenesis (Taler et al., 2020), PLOD3 may be involved in tumor development. Guo et al. found that PLOD3 is associated with ovarian cancer tumor progression and demonstrated that the interaction of COLGALT2 and PLOD3 can enhance the invasiveness of ovarian cancer (Guo et al., 2021b).

At present, PLODs, as novel oncogenes, have not been studied in LUAD in detail. Through data exploration, we discovered that PLODs were overexpressed in the tumor tissues of LUAD patients. The key findings were confirmed using the PrognoScan, GEPIA, HPA, and CPTAC databases. These results demonstrate that high PLOD expression may contribute to advanced disease, poor differentiation, or poor prognosis. Despite the fact that there was little statistical difference between groups, PLOD overexpression may be associated with tumor progression.

The more advanced T and N classifications were associated with higher PLOD expression, even though there was no statistically significant intergroup difference, possibly due to insufficient sample content. By analyzing GO, KEGG, and PPI network enrichment, we found that PLOD1 expression was positively associated with protein digestion and absorption, ECM–receptor interaction, and the AGE-RAGE signaling pathway in diabetic complications. The ECM–receptor interaction pathway mainly destroys the extracellular matrix, one of the necessary conditions for tumor invasion and metastasis. These data provide another explanation for the cancer-promoting mechanism of PLOD1. Functional annotations of PLOD2 and PLOD3 indicated that they are involved in protein digestion and absorption, amoebiasis, and AGE-RAGE signaling pathways in diabetic complications (Ruiz et al., 2021). Advanced glycation end products (AGEs) result from reducing sugars and substances, ingredients, or non-enzymatic reactions. AGEs interact with their receptor RAGE to activate multiple signal transduction pathways, as well as to disturb redox balance and regulate multiple cell death pathways (Waghela et al., 2021). Thus, PLOD genes play a special role in the progression of LUAD.

The interactions between the immune system and cancer are complex (Symmans, 2021). Several recent studies have revealed that systematic evaluation of immune infiltration is important for predicting clinical outcomes and developing immunotherapies (Nebot-Bral et al., 2017). Immune regulatory cells inhibit the antitumor response; they recognize and eliminate tumor cells that have undergone mutations, thereby inhibiting tumor growth. In addition, the immune system–mediated inflammatory response can promote tumor growth (Wang H. et al., 2021; Hoteit et al., 2021). The alteration of T cell and NK cell compartments found in stage I LUAD lesions suggests that the differential distribution of immune cells is important in the development of immunotherapy strategies for lung cancer (Lavin et al., 2017). The multiple roles of cancer-related T cells in lung cancer have been extensively studied. The characteristic genes of CD8⁺ depletion and pre-depletion T cells, as well as activated tumor Tregs, can be used as clinical biomarkers for LUAD patients (Guo et al., 2018). Studies have pointed out that the expression levels of PLOD1-3 in hepatocellular carcinoma are positively correlated with the activity of infiltrating immune cells, including macrophages, neutrophils, CD4⁺ T cells, and dendritic cells (Yang et al., 2020). We investigated the relationship between PLODs and infiltrating immune cells in LUAD. We found that PLOD1 was negatively correlated with the abundance of CD8⁺ T cells, cytotoxic cells, and Th cells. It was concluded that the expression of PLOD1 mainly reduces the infiltration of CD8⁺ T cells, cytotoxic cells, and Th cells. PLOD2 expression was negatively associated with the abundance of CD8⁺ T cells, B cells, and DCs. PLOD3 expression was negatively correlated with the abundance of CD8⁺ T cells, B cells, DCs, and cytotoxic cells. In contrast, our research has found that the expression of PLODs is positively correlated with the expression of immunosuppressive cells, such as Tregs and MDSCs. Based on our immunological analysis, we hypothesize that PLODs play an important role in immune escape in patients with LUAD.

In summary, we first proposed that PLOD expression is upregulated in LUAD. Our findings underscore the importance of PLODs in relation to disease stage, degree of differentiation, tumor size, and lymph-node metastasis. Therefore, PLODs can be considered an early diagnosis and independent prognostic biomarker for patients with LUAD. Compared with traditional experimental analysis, analyses using tumor databases have the advantages of larger sample sizes and higher reliability, which also provide a preliminary foundation for further research. Our data provide new potential indicators for the clinical prediction of patient survival and fundamental support for the progress of immunotherapy targets. This study provides a new strategy for individualized immunotherapy of LUAD, which will benefit more patients. In future studies, we will further verify the value of PLOD in LUAD.

DATA AVAILABILITY STATEMENT

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.

ETHICS STATEMENT

Written informed consent was not obtained from the individual(s), nor the minor(s)’ legal guardian/next of kin, for the publication of any potentially identifiable images or data included in this article.

AUTHOR CONTRIBUTIONS

HP, TY, and YM conducted research studies. JS and GZ performed data analysis using the tool. YM wrote the article. YM wrote the article. All authors helped with the review of the article.

FUNDING

This research work supported by “the Fundamental Research Funds for the Central Universities”, Natural Science Foundation (20180550488 and 2020-ZLLH-38 to YM) of Liaoning Province, Young and middle-aged technological innovation talents in Shenyang of YM (RC200491), and Excellent Talent Fund of Liaoning Province Cancer Hospital of YM.

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2021.770729/full#supplementary-material

REFERENCES

Chandrashekar, D. S., Bashel, B., Balasubramanya, S. A. H., Creighton, C. J., Ponce-Rodriguez, I., Chakravarthi, B. V. S. K., et al. (2017). UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia 19 (8), 649–658. doi:10.1016/j.neo.2017.05.002

Chen, Z., Huang, Y., Hu, Z., Zhao, M., Li, M., Bi, G., et al. (2021). Landscape and Dynamics of Single Tumor and Immune Cells in Early and Advanced‐stage Lung Adenocarcinoma. Clin. Translational Med. 11 (3), e350. doi:10.1002/ctm2.350

Cheriyamundath, S., Kumar, A., Gavert, N., Brabletz, T., and Ben-Ze’ev, A. (2021). The Collagen-Modifying Enzyme PLOD2 Is Induced and Required during L1-Mediated Colon Cancer Progression. Ijms 22 (7), 3552. doi:10.3390/ijms22073552

Du, H., Pang, M., Hou, X., Yuan, S., and Sun, L. (2017). PLOD2 in Cancer Research. Biomed. Pharmacother. 90, 670–676. doi:10.1016/j.biopha.2017.04.023

Edwards, N. J., Oberti, M., Thangudu, R. R., Cai, S., McGarvey, P. B., Jacob, S., et al. (2015). The CPTAC Data Portal: A Resource for Cancer Proteomics Research. J. Proteome Res. 14 (6), 2707–2713. doi:10.1021/pr501254j

Eisinger-Mathason, T. S. K., Zhang, M., Qiu, Q., Skuli, N., Nakazawa, M. S., Karakasheva, T., et al. (2013). Hypoxia-dependent Modification of Collagen Networks Promotes Sarcoma Metastasis. Cancer Discov. 3 (10), 1190–1205. doi:10.1158/2159-8290.CD-13-0118

Gilkes, D. M., Bajpai, S., Wong, C. C., Chaturvedi, P., Hubbi, M. E., Wirtz, D., et al. (2013). Procollagen Lysyl Hydroxylase 2 Is Essential for Hypoxia-Induced Breast Cancer Metastasis. Mol. Cancer Res. 11 (5), 456–466. doi:10.1158/1541-7786.MCR-12-0629

Guo, T., Gu, C., Li, B., and Xu, C. (2021a). PLODs Are Overexpressed in Ovarian Cancer and Are Associated with gap Junctions via Connexin 43. Lab. Invest. 101 (5), 564–569. doi:10.1038/s41374-021-00533-5

Guo, T., Li, B., Kang, Y., Gu, C., Fang, F., Chen, X., et al. (2021b). COLGALT2 Is Overexpressed in Ovarian Cancer and Interacts with PLOD3. Clin. Translational Med. 11 (3), e370. doi:10.1002/ctm2.370

Guo, X., Zhang, Y., Zheng, L., Zheng, C., Song, J., Zhang, Q., et al. (2018). Global Characterization of T Cells in Non-small-cell Lung Cancer by Single-Cell Sequencing. Nat. Med. 24 (7), 978–985. doi:10.1038/s41591-018-0045-3

Heikkinen, J., Hautala, T., Kivirikko, K. I., and Myllylä, R. (1994). Structure and Expression of the Human Lysyl Hydroxylase Gene (PLOD): Introns 9 and 16 Contain Alu Sequences at the Sites of Recombination in Ehlers-Danlos Syndrome Type VI Patients. Genomics 24 (3), 464–471. doi:10.1006/geno.1994.1654

Hoteit, M., Oneissi, Z., Reda, R., Wakim, F., Zaidan, A., Farran, M., et al. (2021). Cancer Immunotherapy: A Comprehensive Appraisal of its Modes of Application (Review). Oncol. Lett. 22 (3), 655. doi:10.3892/ol.2021.12916

Lavin, Y., Kobayashi, S., Leader, A., Amir, E.-a. D., Elefant, N., Bigenwald, C., et al. (2017). Innate Immune Landscape in Early Lung Adenocarcinoma by Paired Single-Cell Analyses. Cell 169 (4), 750–765. e717. doi:10.1016/j.cell.2017.04.014

Li, S., Choi, Y.-L., Gong, Z., Liu, X., Lira, M., Kan, Z., et al. (2016). Comprehensive Characterization of Oncogenic Drivers in Asian Lung Adenocarcinoma. J. Thorac. Oncol. 11 (12), 2129–2140. doi:10.1016/j.jtho.2016.08.142

Li, T., Fan, J., Wang, B., Traugh, N., Chen, Q., Liu, J. S., et al. (2017). TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res. 77 (21), e108–e110. doi:10.1158/0008-5472.CAN-17-0307

Li, Y., Gu, J., Xu, F., Zhu, Q., Chen, Y., Ge, D., et al. (2021). Molecular Characterization, Biological Function, Tumor Microenvironment Association and Clinical Significance of m6A Regulators in Lung Adenocarcinoma. Brief Bioinform 22 (4). doi:10.1093/bib/bbaa225

Mizuno, H., Kitada, K., Nakai, K., and Sarai, A. (2009). PrognoScan: a New Database for Meta-Analysis of the Prognostic Value of Genes. BMC Med. Genomics 2, 18. doi:10.1186/1755-8794-2-18

Nebot-Bral, L., Brandao, D., Verlingue, L., Rouleau, E., Caron, O., Despras, E., et al. (2017). Hypermutated Tumours in the Era of Immunotherapy: The Paradigm of Personalised Medicine. Eur. J. Cancer 84, 290–303. doi:10.1016/j.ejca.2017.07.026

Noda, T., Yamamoto, H., Takemasa, I., Yamada, D., Uemura, M., Wada, H., et al. (2012). PLOD2 Induced under Hypoxia Is a Novel Prognostic Factor for Hepatocellular Carcinoma after Curative Resection. Liver Int. 32 (1), 110–118. doi:10.1111/j.1478-3231.2011.02619.x

Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., et al. (2011). pROC: an Open-Source Package for R and S+ to Analyze and Compare ROC Curves. BMC Bioinformatics 12, 77. doi:10.1186/1471-2105-12-77

Ru, B., Wong, C. N., Tong, Y., Zhong, J. Y., Zhong, S. S. W., Wu, W. C., et al. (2019). TISIDB: an Integrated Repository portal for Tumor-Immune System Interactions. Bioinformatics 35 (20), 4200–4202. doi:10.1093/bioinformatics/btz210

Ruiz, H. H., Nguyen, A., Wang, C., He, L., Li, H., Hallowell, P., et al. (2021). AGE/RAGE/DIAPH1 axis Is Associated with Immunometabolic Markers and Risk of Insulin Resistance in Subcutaneous but Not Omental Adipose Tissue in Human Obesity. Int. J. Obes. 45, 2083–2094. doi:10.1038/s41366-021-00878-3

Shao, S., Fang, H., Duan, L., Ye, X., Rao, S., Han, J., et al. (2020). Lysyl Hydroxylase 3 Increases Collagen Deposition and Promotes Pulmonary Fibrosis by Activating TGFβ1/Smad3 and Wnt/β-Catenin Pathways. aoms 16 (2), 436–445. doi:10.5114/aoms.2018.81357

Shergalis, A. G., Hu, S., Bankhead, A., and Neamati, N. (2020). Role of the ERO1-PDI Interaction in Oxidative Protein Folding and Disease. Pharmacol. Ther. 210, 107525. doi:10.1016/j.pharmthera.2020.107525

Shin, Y.-L., Park, Y. N., and Jang, M.-A. (2020). Rare Cases of PLOD1-Related Kyphoscoliotic Ehlers-Danlos Syndrome in a Korean Family Identified by Next Generation Sequencing. J. Korean Med. Sci. 35 (10), e96. doi:10.3346/jkms.2020.35.e96

Symmans, W. F. (2021). Interpreting the Complex Landscape of Immune-Tumor Interface. Clin. Cancer Res. 27, 5446–5448. doi:10.1158/1078-0432.CCR-21-2208

Szklarczyk, D., Franceschini, A., Kuhn, M., Simonovic, M., Roth, A., Minguez, P., et al. (2011). The STRING Database in 2011: Functional Interaction Networks of Proteins, Globally Integrated and Scored. Nucleic Acids Res. 39 (Database issue), D561–D568. doi:10.1093/nar/gkq973

Taler, K., Weiss, O., Rotem-Bamberger, S., Rubinstein, A. M., Seritrakul, P., Gross, J. M., et al. (2020). Lysyl Hydroxylase 3 Is Required for normal Lens Capsule Formation and Maintenance of Lens Epithelium Integrity and Fate. Developmental Biol. 458 (2), 177–188. doi:10.1016/j.ydbio.2019.10.020

Tang, Q., Li, W., Zheng, X., Ren, L., Liu, J., Li, S., et al. (2020). MELK Is an Oncogenic Kinase Essential for Metastasis, Mitotic Progression, and Programmed Death in Lung Carcinoma. Sig Transduct Target. Ther. 5 (1), 279. doi:10.1038/s41392-020-00288-3

Tang, Z., Li, C., Kang, B., Gao, G., Li, C., and Zhang, Z. (2017). GEPIA: a Web Server for Cancer and normal Gene Expression Profiling and Interactive Analyses. Nucleic Acids Res. 45 (W1), W98–W102. doi:10.1093/nar/gkx247

Tomczak, K., Czerwińska, P., and Wiznerowicz, M. (2015). Review the Cancer Genome Atlas (TCGA): an Immeasurable Source of Knowledge. wo 1A (1A), 68–77. doi:10.5114/wo.2014.47136

Uhlén, M., Fagerberg, L., Hallström, B. M., Lindskog, C., Oksvold, P., Mardinoglu, A., et al. (2015). Proteomics. Tissue-Based Map of the Human Proteome. Science 347 (6220), 1260419. doi:10.1126/science.1260419

Uhlen, M., Zhang, C., Lee, S., Sjöstedt, E., Fagerberg, L., Bidkhori, G., et al. (2017). A Pathology Atlas of the Human Cancer Transcriptome. Science 357 (6352), eaan2507. doi:10.1126/science.aan2507

Waghela, B. N., Vaidya, F. U., Ranjan, K., Chhipa, A. S., Tiwari, B. S., and Pathak, C. (2021). AGE-RAGE Synergy Influences Programmed Cell Death Signaling to Promote Cancer. Mol. Cel Biochem 476 (2), 585–598. doi:10.1007/s11010-020-03928-y

Wang, H., Liu, Y., Zhu, X., Chen, C., Fu, Z., Wang, M., et al. (2021a). Multistage Cooperative Nanodrug Combined with PD‐L1 for Enhancing Antitumor Chemoimmunotherapy. Adv. Healthc. Mater. 10, 2101199. doi:10.1002/adhm.202101199

Wang, X., Guo, J., Dai, M., Wang, T., Yang, T., Xiao, X., et al. (2020). PLOD2 Increases Resistance of Gastric Cancer Cells to 5-fluorouracil by Upregulating BCRP and Inhibiting Apoptosis. J. Cancer 11 (12), 3467–3475. doi:10.7150/jca.41828

Wang, Z., Shi, Y., Ying, C., Jiang, Y., and Hu, J. (2021b). Hypoxia-induced PLOD1 Overexpression Contributes to the Malignant Phenotype of Glioblastoma via NF-κB Signaling. Oncogene 40 (8), 1458–1475. doi:10.1038/s41388-020-01635-y

Xu, W.-H., Xu, Y., Wang, J., Tian, X., Wu, J., Wan, F.-N., et al. (2019). Procollagen-lysine, 2-oxoglutarate 5-dioxygenases 1, 2, and 3 Are Potential Prognostic Indicators in Patients with clear Cell Renal Cell Carcinoma. Aging 11 (16), 6503–6521. doi:10.18632/aging.102206

Yang, B., Zhao, Y., Wang, L., Zhao, Y., Wei, L., Chen, D., et al. (2020). Identification of PLOD Family Genes as Novel Prognostic Biomarkers for Hepatocellular Carcinoma. Front. Oncol. 10, 1695. doi:10.3389/fonc.2020.01695

Yu, G., Wang, L.-G., Han, Y., and He, Q.-Y. (2012). clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters. OMICS: A J. Integr. Biol. 16 (5), 284–287. doi:10.1089/omi.2011.0118

Zhao, Y., Zhang, X., and Yao, J. (2021). Comprehensive Analysis of PLOD Family Members in Low-Grade Gliomas Using Bioinformatics Methods. PLoS One 16 (1), e0246097. doi:10.1371/journal.pone.0246097

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Meng, Sun, Zhang, Yu and Piao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 22 February 2022
doi: 10.3389/fmolb.2022.790706

Pan-Cancer Analysis and Validation Reveals that D-Dimer-Related Genes are Prognostic and Downregulate CD8⁺ T Cells via TGF-Beta Signaling in Gastric Cancer

Yiming Guan¹, Bing Xu², Yi Sui², Zhezhou Chen³, Yu Luan³, Yan Jiang³, Lijuan Wei³, Wenjing Long³, Sansan Zhao³, Lei Han⁴, Dakang Xu¹, Lin Lin¹* and Qi Guan³*

¹Department of Laboratory Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

²Department of Neurology, Shenyang First People's Hospital (Shenyang Brain Hospital), Shenyang Medical College, Shenyang, China

³Department of Laboratory Medicine, Shenyang First People's Hospital (Shenyang Brain Hospital), Shenyang Medical College, Shenyang, China

⁴Centre for Cancer Molecular Diagnosis, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China

Edited by:
Ismail Hosen, University of Dhaka, Bangladesh

Reviewed by:
Ravi Kumar Alluri, Cleveland Clinic, United States
Giuliana Cavalloni, IRCCS Candiolo Cancer Institute, Italy

* Correspondence: Qi Guan, guanqi1231@sina.com; Lin Lin, ll11737@rjh.com.cn

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 07 October 2021
Accepted: 25 January 2022
Published: 22 February 2022

Citation: Guan Y, Xu B, Sui Y, Chen Z, Luan Y, Jiang Y, Wei L, Long W, Zhao S, Han L, Xu D, Lin L and Guan Q (2022) Pan-Cancer Analysis and Validation Reveals that D-Dimer-Related Genes are Prognostic and Downregulate CD8⁺ T Cells via TGF-Beta Signaling in Gastric Cancer. Front. Mol. Biosci. 9:790706. doi: 10.3389/fmolb.2022.790706

Background: Cancer is considered one of the most lethal diseases worldwide. Venous thromboembolism (VTE) is the second leading cause of death in cancer patients. As one of the most reproducible predictors of thromboembolism, the D-dimer level is commonly considered by oncologists. Previous studies have demonstrated that the most correlated genes at the D-dimer level are F3, F5 and FGA.

Methods: Using data from TCGA and multiple webtools, including GEPIA2, UALCAN, TIMER2.0, Kaplan-Meier Plotter and CIBERSORTx, we analyzed the tumor mutation burden (TMB), microsatellite instability (MSI) and functions of D-dimer-related genes in cancer. Validation was conducted via quantitative real-time polymerase chain reaction (qRT-PCR) and independent GEO + GTEx cohort. All statistical analyses were performed in R software and GraphPad Prism 9.

Results: F3, F5 and FGA were expressed differently in multiple cancer types. TMB, MSI and anti-PD1/PDL1 therapy responses were correlated with D-dimer-related gene expression. D-Dimer-related genes expression affect the survival of cancer patients. F3 and F5 functioned in TGF-beta signaling. F3 and F5 were related to immunity and affected the fraction of CD8⁺ T cells by upregulating the TGF-beta signaling pathway, forming an F3, F5/TGF-beta signaling/CD8+ T cell axis.

Conclusion: F3, F5 and FGA serve as satisfactory GC multibiomarkers and potentially influence the immune microenvironment and survival of cancer patients by influencing TGF-beta signaling.

Keywords: d-dimer, gastric cancer, prognosis, pan-cancer analysis, TGF-beta signaling, CD8⁺ T cells, coagulation factor, fibrinogen alpha chain (FGA)

INTRODUCTION

Cancer is considered a fatal disease worldwide. In 2020, approximately 1,806,590 new cancer cases and 606,520 cancer deaths were expected in the United States (Siegel et al., 2020). Although millions have died because of cancer, only a few of the cancer types are curable. Traditional cancer therapies rely on surgery, chemotherapy and radiotherapy. However, these treatment methods have not provided a satisfactory prognosis for cancer patients despite their tremendous side effects. Recently, new cancer treatment methods have emerged under the guidance of precision medicine. Antitumor-targeted drugs and molecular diagnosis are typical examples of these attempts at novel treatment. Drugs that target immune checkpoints, such as CTLA-4 and PD1/PDL1, have been used to treat multiple cancer types (Yi et al., 2018; Han et al., 2020). Previously discovered biomarkers have helped medical oncologists. Further innovations towards molecular biomarkers are expected to improve the prognosis of cancer patients.

D-Dimer is a fibrin degradation product comprising a small protein fragment in blood after blood clots are dissolved and degraded by plasmin (Weitz et al., 2017). It contains two D fragments of fibrin, which are linked by crosslinking. D-Dimer elevation in plasma is commonly used in hospitals worldwide to suggest multiple diseases, including venous thromboembolism (VTE), disseminated intravascular coagulation (DIC), pulmonary diseases, diabetes, infection and tumors (Halaby et al., 2015). Its special correlation with venous thromboembolism indicates that D-dimer levels may suggest the survival of cancer patients. Because of its unique features, D-dimer monitoring has been used in patients with geriatric diseases. Previous studies have concluded that particular genes are predictors of fibrin D-dimer levels. A study in 2011 pointed out that three genes—F3 (tissue factor), F5 (coagulation factor V) and FGA (fibrinogen alpha chain)—are associated with fibrin D-dimer levels, in which the association of F3 is the strongest (Smith et al., 2011). As few studies have been conducted to investigate the value of the D-dimer marker at the genetic level; further studies on this topic will help identify new applications of D-dimer.

F3, or tissue factor, is a cell surface glycoprotein (Hisada and Mackman, 2019). This factor enables cells to initiate the coagulation cascade and acts as a high affinity receptor for coagulation factor VII. The resulting complex provides a catalytic event responsible for initiating the coagulation protease cascade through specific limited protein hydrolysis. Unlike other cofactors of these protease cascades, this factor is an effective initiator with complete function when expressed on the cell surface. Tissue factor is considered a link between inflammation and coagulation and has regulatory functions in cancers such as pancreatic cancer and ovarian cancer (Khorana et al., 2007; Cohen et al., 2017; Date et al., 2017).

F5, or coagulation factor V, is an essential cofactor in the blood coagulation cascade (Duga et al., 2004). This factor circulates in the plasma and is converted to the active form by thrombin, releasing the activating peptide during coagulation. This activity creates a heavy chain and a light chain, which are linked together by calcium ions. Activated protein is a cofactor that activates prothrombin to thrombin together with activated coagulation factor X (Segers et al., 2007). Coagulation factor V has been identified as a marker for cancers such as breast cancer (Tinholt et al., 2018).

FGA, or fibrinogen alpha chain, is the alpha component of fibrinogen (Mosesson, 2005). Fibrinogen is a blood-derived glycoprotein comprising three different polypeptide chains. After vascular injury, fibrinogen is decomposed by thrombin to form fibrin, which is the most abundant component of the blood clot. Additionally, fibrinogen and its various lysates regulate cell adhesion and diffusion, show vasoconstriction and chemotaxis and are mitogens of various cell types. Fibrinogen alpha chain has also been identified as a diagnostic biomarker for cancers such as gastric cancer (Duan et al., 2018).

In this study, we aimed to explore the potential of the D-dimer-related genes F3, F5 and FGA as biomarkers for various cancer types. After using several bioinformatic tools to reveal D-dimer-related gene survival relationships, we focused on the immune functions of F3 and F5. The novel CIBERSORTx helped us link D-dimer-related genes with immune cell types. Our results demonstrated that D-dimer-related genes influence CD8⁺ T cells via TGF-beta signaling and have the potential to become cancer biomarkers, particularly for gastric cancer.

MATERIALS AND METHODS

Data Acquisition

The RNA-seq data of 33 tumors were downloaded from The Cancer Genome Atlas (TCGA). TMB data, MSI data and clinical data, including age, sex, tumor grade, clinical stage, TNM stage, and survival time, were also downloaded. Gene Expression Omnibus (GEO) datasets of GSE84437, GSE78220, GSE67501 and IMvigor210 were also downloaded (Ascierto et al., 2016; Hugo et al., 2016; Yoon et al., 2020). Data extraction was performed using R software 4.1.0 (R Foundation for Statistical Computing, Vienna, Austria).

TIMER2.0

TIMER2.0 is a comprehensive resource for systematic analysis of immune infiltrates across diverse cancer types (http://timer.cistrome.org). We used TIMER2.0 to analyze the differential expression (tumor vs. normal) in a pan-cancer prospective study (Li et al., 2017; Li et al., 2020).

Gene Expression Profiling Interactive Analysis 2

Gene Expression Profiling Interactive Analysis 2 (GEPIA2) is an enhanced web server that conducts large-scale expression profiling and interactive analysis (http://gepia2.cancer-pku.cn) (Tang et al., 2017; Tang et al., 2019). Here, we used the webtool to generate Kaplan-Meier plots for D-dimer-related gene signatures.

UALCAN

UALCAN is a web resource to analyze cancer OMICS data that is user-friendly, comprehensive and interactive (http://ualcan.path.uab.edu) (Chandrashekar et al., 2017). In this study, we used UALCAN to generate tumor vs. normal boxplots of D-dimer-related genes using TCGA STAD data.

Kaplan-Meier Plotter

Kaplan-Meier Plotter is a webtool to assess the effect of 54k genes, as well as the mRNA, miRNA and protein, on survival in 21 cancer types, including gastric, lung, ovarian and breast cancer (www.kmplot.com) (Nagy et al., 2021). In the present study, we used Kaplan-Meier Plotter to generate Kaplan-Meier plots of D-dimer-related genes using gene chip data.

Long-Term Outcome and Gene Expression Profiling Database of Pan-Cancers

Long-term Outcome and Gene Expression Profiling database of pan-cancers (LOGpc) included 209 expression datasets and 13 survival analyses of 27 different malignancies involving 31,310 patients (http://bioinfo.henu.edu.cn/DatabaseList.jsp) (Xie et al., 2020). We utilized LOGpc to generate survival plots using the gastric cancer data of GEO-GSE84437.

Patients Specimens

Gastric cancer cDNA microarray from 30 GC patients (cDNA-HStmA060CS01), including 30 paired cancer and non-cancerous gastric tissues, were purchased from Shanghai Outdo Biotech Co., Ltd. (Shanghai, China). The access date of the microarray is 3.21.2017. This study was completed under the guidance of the International Ethical Guidelines for Biomedical Research Involving Human Subjects (CIOMS). The Clinical Research Ethics Committee of Shenyang First People's Hospital approved the research protocols. The characteristics of the GC patients’ data can be found in Table 1.

TABLE 1 | Characteristics of GC patients used for qRT-PCR.

Quantitative Reverse Transcriptase PCR

The cDNA used in this study was synthesized by RNA reverse transcription using Thermo Fisher’s SuperScript IV Reverse transcriptase (18090010). It was then used to detect the mRNA levels of F3, F5, and B-actin using Takara’s SYBR® Premix Ex Taq™ II (Tli RNaseH Plus) (RR820Q). The relative mRNA expression was determined using the cycle threshold (CT) formula 2-△△CT, where △CT = [CT (target gene)—CT (B-actin)]. The expression level was normalized against endogenous B-actin. The specific primers were as follows: human F3 forward 5′-GGA​ACC​CAA​ACC​CGT​CAA​TC-3′ and reverse 5′-AGG​AGA​AGA​CCC​GTG​CCA​AG-3′; human F5, forward 5′-TGA​CCT​TCT​CGC​CTT​ATG​A-3′ and reverse 5′-CTC​TGT​ATT​CCT​CGC​CTG​T-3'; human B-actin, forward 5′-GAA​GAG​CTA​CGA​GCT​GCC​TGA-3′ and reverse 5′- CAG​ACA​GCA​CTG​TGT​TGG​CG-3'.

Venn Plots

Venn plots of this study were generated using an online tool (http://bioinformatics.psb.ugent.be/webtools/Venn/).

Gene Ontology Enrichment Analysis

Gene Ontology (GO) enrichment analyses using 82 upregulated and 3 downregulated DEGs were performed using the R packages “clusterProfiler”3.16.1, “enrichplot”1.8.1, and “ggplot2”3.3.2. Terms with p < 0.05 were considered significant (Ashburner et al., 2000; Gene Ontology, 2021).

Gene Set Enrichment Analysis

Gene set enrichment analysis (GSEA) was performed to reveal correlated pathways of D-dimer-related genes (Subramanian et al., 2005). Hallmark gene sets v7.2 were downloaded as the target sets, and GSEA was performed using GSEA_4.1.0 software. Gene sets with a nominal (NOM) p < 0.05 and a false discovery rate (FDR) q < 0.06 were considered significant.

TICs Profile

The novel CIBERSORTx webtool was used to estimate the tumor-infiltrating immune cell (TIC) profile in tumor samples, and samples with p > 0.05 were eliminated from further study (Newman et al., 2015).

Statistical Analyses

All statistical analyses were conducted using R software (version 4.1.0) and GraphPad Prism 9. The statistical significance threshold of all statistical tests was p < 0.05. The log rank test was used to evaluate the significance of Kaplan-Meier survival analysis.

RESULTS

Identification of the Prognostic Value of D-Dimer-Related Genes in Gastric Cancer via a Pan-Cancer Study

To explore the value of the D-dimer-related genes F3, F5 and FGA, we conducted a pan-cancer study of these three genes and found a correlation among them and TMB, MSI and immune-related genes. According to the survival Kaplan-Meier plots grouped by the expression level of the three genes combined, we focused on a particular cancer type, gastric cancer, and explored the expression pattern within tumor and normal gastric samples along with their survival. F3 and F5, which showed higher expression in gastric cancer in qRT-PCR, were selected for further study. We conducted clinicopathology, gene set enrichment analysis and Gene Ontology-based studies and found that F3 and F5 affect TGF-beta signaling. Next, we performed immune cell analysis using CIBERSORTx and found a correlation between F3 and F5 with CD8⁺ T cells. The analytical workflow for the study is shown in Supplementary Figure S1.

D-Dimer Related Genes Upregulated in Multiple Cancers

We first explored the differential expression of D-dimer-related genes between tumor and normal samples using TIMER2.0. The expression differentiation within the 33 cancer types was demonstrated via boxplots (Supplementary Figure S2). The names and abbreviations of these 33 cancer types can be found in the legend. The TIMER2.0 data suggested that F3 expression was significantly upregulated in CHOL and ESCA, F5 expression was significantly upregulated in BRCA, COAD, LIHC, LUAD, PAAD, PCPG, PRAD and STAD, and the expression of FGA was significantly upregulated in BLCA, COAD, HNSC and READ. Taken together, the results suggested that D-dimer-related genes are upregulated in multiple cancer types.

D-Dimer-Related Gene Expression Is Correlated With TMB and MSI Levels

Studies were conducted on the relationship between the gene expression levels and TMB and the MSI levels. TMB, or tumor mutation burden, represents the number of somatic mutations in the coding region of the tumor cell genome at an average size of 1 MB (Campbell et al., 2017). MSI, or microsatellite instability, refers to the random change in the microsatellite length and emergence of new microsatellite alleles in tumor cells because of the insertion or deletion of repeat units compared with normal cells (Cortes-Ciriano et al., 2017). Radar plots were drawn demonstrating the correlation between gene expression and TMB and MSI based on Spearman’s correlation (Figure 1). Significant correlations were found in multiple cancer types. F3 was most positively correlated with TMB in THYM and most negatively correlated with TMB in LIHC. F5 was most positively correlated with TMB in THYM, ESCA and PRAD and most negatively correlated with TMB in LGG. FGA was most positively correlated with TMB in SARC and most negatively correlated with TMB in STAD. F3 was most positively correlated with MSI in TGCTs and COAD and most negatively correlated with MSI in GBM. F5 was most positively correlated with MSI in UVM and most negatively correlated with MSI in DLBC. FGA was most positively correlated with MSI in SARC and most negatively correlated with MSI in PAAD.

FIGURE 1 | Correlation between the expression of D-dimer-related genes and the tumor mutation burden (TMB) and microsatellite instability (MSI) in TCGA cancers. (A–C) Correlation between the expression of D-dimer-related genes and TMB in 33 types of cancers. (D–F) Correlation between the expression of D-dimer-related genes and MSI in 33 types of cancers. * indicates p < 0.05, ** indicates p < 0.01, and *** indicates p < 0.001, every tumors with a significant p value was highlighted with a bold character.

D-Dimer-Related Genes Display Correlations With Multiple Immune-Related Genes

To explore the relationship between D-dimer-related genes and genes correlated with immunity, we drew heatmaps that displayed the correlation factor and P value of D-dimer genes with each immune-related gene (Figure 2). The heatmaps demonstrated that D-dimer-related genes had strong correlations with immune-related genes among multiple cancer types. F3 had correlations with 29 immune-related genes in LIHC, F5 had correlations with 28 immune-related genes in HNSC, and FGA had correlations with only 16 immune-related genes in LUAD. Comparing these three plots revealed that F3 and F5 are more immune correlated than FGA.

FIGURE 2 | Correlation between the expression of D-dimer-related genes and immune-related genes in TCGA cancers. (A–C) Correlation of F3, F5 and FGA with 47 immune-related genes.

D-Dimer-Related Genes Affect Patients’ Responsiveness to anti-PD1/PDL1 Therapy

In order to explore whether D-dimer-related genes are capable of influencing the efficacy of anti-PD1/PDL1 therapy, we downloaded the responsiveness of anti-PD1/PDL1 therapy in three datasets, GSE78220 and GSE67501 of GEO, and also IMvigor210 dataset. The three datasets represent three types of cancer, namely melanoma, renal cell carcinoma and urothelial carcinoma. The transcriptome data included within the three datasets derived from pre-treatment tumors. We generated bar plots of the three genes, and we saw positive results in F3 (melanoma and urothelial carcinoma) and FGA (urothelial carcinoma) (Figure 3). The results are that D-dimer-related genes are potential influencers of anti-PD1/PDL1 therapy and suggest a possible link between D-dimer-related genes and CD8⁺ T cells.

FIGURE 3 | Responses of anti-PD1/PDL1 therapy are affect by D-dimer related genes expression. (A–C) The relationship between F3 expression and responsiveness of anti-PD1/PDL1 therapy in melanoma, renal cell carcinoma and urothelial carcinoma. (D–F) The relationship between F5 expression and responsiveness of anti-PD1/PDL1 therapy, similar with F3. (G–I) The relationship between FGA expression and responsiveness of anti-PD1/PDL1 therapy, similar with F3.

D-Dimer-Related Genes Are Correlated With Survival in Several Cancer Types

We next examined the survival correlations of D-dimer-related genes via Kaplan-Meier plots (Figure 4). We used GEPIA2 and explored the survival correlations by inputting the three genes as a gene set. High and low expression groups were determined by the medium of the expression of the three signature groups. The plots demonstrated that D-dimer-related gene signatures had survival correlations with six cancer types, GBM, LIHC, LUSC, MESO, SKCM and STAD. Among them, two had a hazard ratio smaller than 1, while four had a hazard ratio larger than 1. Among the four cancer types with a hazard ratio larger than 1, STAD had the smallest P value but the largest hazard ratio. The results suggested that the survival correlation of D-dimer-related genes was most significant within STAD or gastric cancer (GC) and that the combination of F3, F5 and FGA might be a potential multibiomarker for GC.

FIGURE 4 | D-Dimer-related genes affect the survival of several types of tumors. (A–F) Kaplan-Meier plots of the three D-dimer-related genes in different tumor types using GEPIA2. p < 0.05 in GBM, LIHC, LUSC, MESO, SKCM AND STAD.

D-Dimer-Related Genes Are Potential Clinicopathology-Related Biomarkers for Gastric Cancer

After focusing on STAD, we conducted studies on the differential expression and survival correlation using UALCAN and KM Plotter. A differential expression boxplot showed that F3 and F5 were overexpressed in tumor samples compared with normal samples, while FGA expression differentiation was not significant (Figures 5A–C). We then utilized KM Plotter to construct Kaplan-Meier plots with mRNA sequences. All three genes were correlated with survival in gastric cancer patients; the high D-dimer-related gene expression group had poorer survival than their low counterparts (Figures 5D–F). We also explored the relationship between D-dimer-related genes and the clinicopathological characteristics of GC patients. The four plots showed that F5, the most well-performed gene, had clear correlations with tumor grade, tumor stage, T and N (Figures 5G–J). The scatterplots of F3 and FGA were in Supplementary Figure S3. Taken together, the results suggested that D-dimer-related genes are potential clinicopathology-related GC biomarkers.

FIGURE 5 | Survival correlation, differential expression and clinicopathological relations in D-dimer-related genes. (A–C) Expression levels of F3, F5 and FGA between GC and normal samples using UALCAN. (D–F) Kaplan-Meier plots of the three D-dimer-related genes in GC using KM plotter. (G–J) Relationship between F5 expression and clinicopathological parameters.

Verification of D-Dimer-Related Genes’ Biomarker Potential in GC by qRT-PCR and GEO Cohort

In hope of verifying D-dimer-related genes F3 and F5’s upregulation in GC and their survival correlation, we first conducted quantitative real-time polymerase chain reaction on the two genes. Patients’ data, including clinicopathological data and 2-△△CT data, can be found in Table 1, while raw data of 2-△△CT can be found in Supplementary Table S1. Demonstrated in Figures 6A,B, the relative mRNA expression of both F3 and F5 were discovered to upregulate in tumor tissues comparing with adjacent normal tissues. The result of the validation experiment was in coordinate with our in-silico exploration result, the expression fold change of each sample can be found in Supplementary Figure S4. We then utilized a GEO gastric cancer cohort, namely GSE84437. The expression difference test was conducted, and the result also proves our former speculation (Figures 6C,D). We also generated Kaplan-Meier plots using GSE84437 data. The results of which was similar with our previous KM test, suggesting higher F3 and F5 expression to link with worse prognosis (Figures 6E,F). The results of our validation tests were supportive to our previous results and improved the reliability of F3 and F5’s biomarker potential.

FIGURE 6 | Validation of F3 and F5’s biomarker potential via qRT-PCR and GEO cohort. (A–B) F3 and F5 expression compared within normal and tumor tissues via qRT-PCR. (C–D) Comparison of F3 and F5 expression via GEO-GSE84437 and GTEx data. (E–F) Kaplan-Meier plots of F3 and F5 using GSE84437 data, generated via LOGpc.

Gene Set Enrichment Analysis Reveals the Functions of D-Dimer Related Genes

To explore the functions of D-dimer-related genes, we performed gene set enrichment analysis (GSEA) of both F3 and F5. The multi-GSEA plots are shown in Figures 7A,B, gene set data for GSEA can be found in Table 2; Supplementary Table S2. Pathways such as coagulation, which is consistent with the apparent functions of coagulation factors, were upregulated in both the F3 and F5 high expression groups. A Venn plot was drawn to show the overlapping pathways of F3 and F5 (Figure 7C). In total, five pathways overlapped—angiogenesis, coagulation, protein secretion, TGF-beta signaling and UV response down. Although the upregulation of angiogenesis and coagulation suggested the regular functions of coagulation factors, the upregulation of TGF-beta signaling might suggest that our two genes have potential functions within the immune system. In summary, the upregulated pathways suggested that F3 and F5 might be related to tumor growth and metastasis.

FIGURE 7 | Gene set enrichment analysis (GSEA) reveals the correlation of the D-dimer-related gene expression with several HALLMARK pathways. (A) Multi-GSEA constructed within F3 (high vs. low). (B) Multi-GSEA constructed within F5 (high vs. low). (C) Venn plot displaying the overlapping pathways of F3 and F5—namely, angiogenesis, coagulation, protein secretion, TGF beta signaling and UV response down.

TABLE 2 | GSEA HALLMARK gene set data for F3.

Differential Expression Analysis Identifies Functions of D-Dimer Related Genes in Metabolic Processes

To identify genes that are expressed correlatively with D-dimer-related genes, we conducted differential expression analysis between the high and low F3 and F5 expression groups. Volcano plots and heatmaps were constructed (Figures 8A–D), and Venn plots demonstrated the overlapping genes of F3 and F5 (Figures 8E,F). In total, 82 genes were upregulated in both the F3 and F5 comparison groups, while only 3 genes were downregulated consistently. Gene Ontology was conducted on these 85 genes, and we observed upregulated functions in both biological processes (BP), cellular components (CC) and molecular functions (MF), including glycoprotein metabolic process and apical part of cell (Figure 8G). Thus, genes related to F3 and F5 had functions in the tumor metabolic process.

FIGURE 8 | GO enrichment of overlapping DEGs. (A) Volcano plot of the DEGs of F3 (high and low). p < 0.05 was the cutoff criterion for DEG significance, and an absolute value of log₂-fold change>0.5 was considered significant. The red dots represent genes that are upregulated, the green dots represent genes that are downregulated, and the black dots resemble genes that are nonsignificant. (B) Heatmap depicting the top 20 upregulated and downregulated genes with F3 high and low expression. A higher fold change is depicted with a redder color. (C) Volcano plot of the DEGs of F3 (high and low expression), similar to (A). (D) Heatmap depicting the top 20 upregulated and downregulated genes with F5 high and low expression, similar to (B). (E–F) Venn plot displaying the overlapping genes of F3 and F5. (G) GO plot illustrating different enriched pathways of overlapping DEGs.

D-Dimer Related Genes Are Potential Influencers of TICs

After we explored the immune relationship of D-dimer-related genes with immunity via immune gene correlation, we analyzed the proportion of tumor-infiltrating immune cells using the novel CIBERSORTx webtool. The STAD immune cell profiles are shown in Supplementary Figure S5. Next, we independently constructed the difference test and correlation test of immune cells using both F3 and F5 (Figures 9A–D). After an overlap demonstration using a Venn plot, we found that CD8⁺ T cells overlapped in all four tests, including the difference test and correlation test of F3 and the difference test and correlation test of F5 (Figure 9E). Additional verification using xCell method suggests similar results (Supplementary Figure S3). These results suggest that CD8-positive T cells can be influenced by the expression of D-dimer-related genes.

FIGURE 9 | Correlation of the TIC proportion with the expression of D-dimer-related genes. (A–B) Violin plot displaying the ratio differentiation of 22 immune cell types between STAD tumor samples with low or high F3 and F5 expression relative to the median gene expression level. Wilcoxon rank sum for the significance test. (C–D) Scatter plots showing the correlation of the top 3 TIC proportions with F3 and F5 expression (p < 0.05). Pearson’s coefficient for the correlation test. (E) Venn plot displaying the overlapping F3 and F5 cells—namely, CD8 T cells.

DISCUSSION

D-Dimer is a soluble fibrin degradation product produced by the orderly decomposition of thrombi by fibrinolytic systems. Many studies have shown that D-dimer is a vital marker of coagulation and fibrinolytic activity. Therefore, D-dimer has been widely used to diagnose venous thromboembolism (VTE). Because cancer patients are at a higher risk of developing VTE, VTE has become a crucial influencer of the prognosis of cancer patients. Previous studies have concluded that VTE is the second leading cause of death in cancer patients (Blom et al., 2006). Thus, surveillance of VTE for cancer patients will most likely benefit these patients. Previous studies have found that thrombocythemia is common in gastrointestinal cancer, ovarian cancer, lung cancer and breast cancer, possibly reducing the threshold required for VTE (Hisada and Mackman, 2017). Thus, the D-dimer level, one of the most reproducible predictors of thromboembolism, should be monitored in patients with cancers such as gastric cancer. The biomarker function of D-dimer inspired us to examine its capabilities in more depth, thus encouraging us to conduct related genetic studies.

In the present study, we first evaluated D-dimer-related genes using a pan-cancer method. We found that the D-dimer-related genes F3, F5 and FGA were differentially expressed in cancer tissues and normal tissues in multiple cancer types, such as STAD and PAAD. The expression differentiation between cancer and normal tissues enabled the three D-dimer-related genes to be used as cancer biomarkers. Tissue factor and factor V, the proteins encoded by F3 and F5, were proved by previous studies to be linked with cancer-associated thrombosis and survival of cancer patients (Thaler et al., 2012; Crous-Bou et al., 2016; Rondon et al., 2019). Our findings further proved F3 and F5’s potential of influencing VTE of cancer patients in a molecular level.

The tumor mutation burden (TMB) and microsatellite instability (MSI) are important factors to classify cancer phenotypes and predict the survival of cancer patients. F3 had the most positive correlation with TMB in THYM and the most negative correlation with TMB in LIHC. F5 had the most positive correlation with TMB in THYM, ESCA and PRAD and the most negative correlation with TMB in LGG. FGA had the most positive correlation with TMB in SARC and the most negative correlation with TMB in STAD. F3 had the most positive correlation with MSI in TGCT and COAD and the most negative correlation with MSI in GBM. F5 had the most positive correlation with MSI in UVM and the most negative correlation with MSI in DLBC. FGA had the most positive correlation with MSI in SARC and the most negative correlation with MSI in PAAD. Our results suggest that D-dimer-related genes are related to TMB and MSI.

Anti-PD1/PDL1 therapy is by far one of the most well-known immunotherapies for cancer treatment (Han et al., 2020). By blocking the PD1/PDL1 checkpoint, cancer cell loses its capability of evading from the attacks of T cells (Jin et al., 2011; Tumeh et al., 2014). Nonetheless, it was discovered that some patients failed to respond to anti-PD1/PDL1 therapy (Nowicki et al., 2018). In our study, we utilized three datasets that provided data on the responses of anti-PD1/PDL1 therapy. We discovered that non-response patients tend to express higher D-dimer related genes. In hope of providing an explanation of this phenomenon, we looked through previous studies and found reports on the potential mechanism that expression of D-dimer related gene to be influenced by T infiltrating lymphocytes and, in turn, influences VTE (Gi et al., 2021). In which case, D-dimer related gene could potentially reflect T infiltration level, therefore being capable of suggesting the responsiveness of anti-PD1/PDL1 therapy.

Prognostic biomarkers have been used to predict the survival of cancer patients in recent years. Although biomarkers such as CA125 are successful for survival prediction, most single biomarkers have failed to provide optimal testing results for cancer patients (Dochez et al., 2019). Previous studies have claimed that the concept of a “single marker” in cancer is incorrect because several pathways and processes in tumor cells have changed (Rodríguez-Enríquez et al., 2011). Thus, identifying novel multibiomarkers is necessary. In our study, we tested the prognostic value of the three D-dimer-related genes F3, F5 and FGA as multibiomarkers in 33 cancer types. Kaplan-Meier plots suggested that elevations in D-dimer-related gene expression in GBM, LUSC, MESO and STAD were prognostic, with STAD being the most satisfactory in both hazard ratios and statistical significance. Our study demonstrated that the combination uses of F3, F5 and FGA as multibiomarkers is prognostic in several cancer types, most ideally in gastric cancer.

Considering that the prognostic value of D-dimer-related genes is most satisfactory in gastric cancer, we assessed the independent prognostic value of F3, F5 and FGA. Although all three genes exhibited survival correlation via Kaplan-Meier plots, only F3 and F5 showed expression differentiation between tumor and normal tissues in STAD. The results ruled out the possibility of FGA being a satisfactory independent GC biomarker. Independent GEO cohort and qRT-PCR results verified F3 and F5’s biomarker potential. Further exploration into the correlation between D-dimer-related gene expression and GC clinicopathological characteristics showed that D-dimer-related gene F3 and F5 expression is correlated with GC clinicopathological characteristics such as N (nodal metastasis).

Previously developed bioinformatic methods such as Gene Set Enrichment Analysis (GSEA) and Gene Ontology (GO) have allowed scientists to explore the functions of selected gene sets. These analytical methods advanced the studies of multiple diseases, including cancer. Our team first used GSEA to examine the related functions of F3 and F5. Consistent with our common knowledge, the coagulation pathway is upregulated in both the F3 and F5 gene sets. Additionally, other pathways that were found to be upregulated enabled the identification of these D-dimer-related genes. Pathways that are commonly regarded as cancer-related were found among the upregulated pathways, such as hypoxia (Jing et al., 2019). TGF-beta signaling, which is also well recognized as a crucial signaling pathway for cancer development, was influenced in both the F3 and F5 gene sets (Katz et al., 2013). After conducting differential expression analysis between the high and low F3 and F5 expression groups, we obtained the overlapping genes of F3 and F5. Gene Ontology was conducted on these 85 genes, and upregulated functions were observed in BP, CC and MF, including glycoprotein metabolic process and apical part of cell, a finding that is consistent with our upregulated pathway of protein secretion in GSEA. The functions of genes related to F3 and F5 potentially influence the tumor metabolic process.

CIBERSORT, or cell-type identification by estimating relative subsets of RNA transcripts, is an analytical tool that uses gene expression data to estimate the abundance of member cell types in a mixed cell population. This tool allowed exploration of the linkage of selected gene expression with immune cell fractions. Because GSEA revealed potential upregulation of immune-related pathways of D-dimer-related genes, we focused on immune functions. First, we conducted a correlation test between the three D-dimer-related genes and immune-related genes using a pan-cancer method. The results imply that F3 and F5 are more immune correlated than FGA. Analysis of previously found immune checkpoints, such as PD-L1 and CTLA4, revealed that both F3 and F5 were positively correlated with CD274 (PD-L1) in LIHC and MESO; both F3 and F5 were positively correlated with CTLA4 in COAD, GBM, LGG, LIHC, READ, SARC, TGCT and THCA. These findings suggest that F3 and F5 are well immunocorrelated with cancers such as LIHC. Inspired by previous reports of the potential mechanism that expression of F3 to be influenced by T infiltrating lymphocytes and, in turn, influences VTE, we propose that genes related to T cell infiltration (CD274, CTLA4, etc.) might be risk factors of VTE. Additional correlation tests were conducted between F3 and F5 and immune cell markers of CD8⁺ T cells and macrophages within STAD, which are immune cell types that we previously studied (Wu et al., 2020; Guan et al., 2021). We found that F5 was negatively correlated with CD8A, which is a marker of CD8⁺ T cells. By facilitating CIBERSORTx, a novel version of CIBERSORT, we linked the F3 and F5 expression levels with the immune cell fraction. Overlap of the correlation test and difference test of F3 and F5 revealed that both F3 and F5 are negatively correlated with the cell fraction of CD8⁺ T cells in STAD, a finding that is consistent with our result with the CD8⁺ T cell marker CD8A. Our study demonstrated that D-dimer-related genes are associated with CD8⁺ T cells. Combined with our discovery of TGF-beta signaling upregulation via GSEA, we could link the decrease in the CD8⁺ T cell fraction with the upregulation of the TGF-beta signaling pathway. Consistent with previous studies showing that neutralizing TGF-beta enhances the antitumor immune response mediated by CD8⁺ T cells, we concluded that F3 and F5 expression levels influence the TGF-beta signaling pathway and influence the enrichment of CD8⁺ T cells, demonstrating the immune function of F3 and F5 in an F3,F5/TGF-beta signaling/CD8+ T cell axis (Yang et al., 2010). Our study successfully identified a new function for D-dimer-related genes.

CONCLUSION

Our group conducted a pan-cancer study of three D-dimer-related genes—F3, F5 and FGA—and revealed their potential as multibiomarkers as well as single markers for cancer, particularly gastric cancer. Additional studies were conducted on the functions of D-dimer-related genes, and upregulated pathways, such as coagulation and protein secretion, were identified. Immune-related characteristics were explored, and F3 and F5 were identified as potential inhibitors of CD8⁺ T cells. Our results demonstrated that the three D-dimer-related genes are potential GC multibiomarkers that correlate with TMB and MSI and have immune-related functions, regulating CD8⁺ T cells. Further study of D-dimer-related genes will help improve the survival of cancers such as GC.

DATA AVAILABILITY STATEMENT

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.

ETHICS STATEMENT

The studies involving human participants were reviewed and approved by Shenyang First People's Hospital. The patients/participants provided their written informed consent to participate in this study.

AUTHOR CONTRIBUTIONS

QG contributed to the conception and design of the study. YG, YS, ZC, YL, YJ, LW, WL, SZ, and LH performed data acquisition, analysis, and interpretation. YG and QG drafted the manuscript. DX, BX, and LL provided critical reagents and supervised the study. All the authors have read and agreed to the published version of the manuscript.

FUNDING

This work was supported by the grants from the Sub-Project of National Key Research and Development Program of China Ministry of Science and Technology (2019YFF0216502).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2022.790706/full#supplementary-material

REFERENCES

Ascierto, M. L., McMiller, T. L., Berger, A. E., Danilova, L., Anders, R. A., Netto, G. J., et al. (2016). The Intratumoral Balance between Metabolic and Immunologic Gene Expression Is Associated with Anti-PD-1 Response in Patients with Renal Cell Carcinoma. Cancer Immunol. Res. 4 (9), 726–733. doi:10.1158/2326-6066.cir-16-0072

Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., et al. (2000). Gene Ontology: Tool for the Unification of Biology. Nat. Genet. 25 (1), 25–29. doi:10.1038/75556

Blom, J. W., Vanderschoot, J. P. M., Oostindier, M. J., Osanto, S., van der Meer, F. J. M., and Rosendaal, F. R. (2006). Incidence of Venous Thrombosis in a Large Cohort of 66 329 Cancer Patients: Results of a Record Linkage Study. J. Thromb. Haemost. 4 (3), 529–535. doi:10.1111/j.1538-7836.2006.01804.x

Campbell, B. B., Light, N., Fabrizio, D., Zatzman, M., Fuligni, F., de Borja, R., et al. (2017). Comprehensive Analysis of Hypermutation in Human Cancer. Cell 171 (5), 1042–1056. doi:10.1016/j.cell.2017.09.048

Chandrashekar, D. S., Bashel, B., Balasubramanya, S. A. H., Creighton, C. J., Ponce-Rodriguez, I., Chakravarthi, B. V. S. K., et al. (2017). UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia 19 (8), 649–658. doi:10.1016/j.neo.2017.05.002

Cohen, J. G., Prendergast, E., Geddings, J. E., Walts, A. E., Agadjanian, H., Hisada, Y., et al. (2017). Evaluation of Venous Thrombosis and Tissue Factor in Epithelial Ovarian Cancer. Gynecol. Oncol. 146 (1), 146–152. doi:10.1016/j.ygyno.2017.04.021

Cortes-Ciriano, I., Lee, S., Park, W.-Y., Kim, T.-M., and Park, P. J. (2017). A Molecular Portrait of Microsatellite Instability across Multiple Cancers. Nat. Commun. 8, 15180. doi:10.1038/ncomms15180

Crous-Bou, M., Harrington, L. B., and Kabrhel, C. (2016). Environmental and Genetic Risk Factors Associated with Venous Thromboembolism. Semin. Thromb. Hemost. 42 (8), 808–820. doi:10.1055/s-0036-1592333

Date, K., Ettelaie, C., and Maraveyas, A. (2017). Tissue Factor-Bearing Microparticles and Inflammation: a Potential Mechanism for the Development of Venous Thromboembolism in Cancer. J. Thromb. Haemost. 15 (12), 2289–2299. doi:10.1111/jth.13871

Dochez, V., Caillon, H., Vaucel, E., Dimet, J., Winer, N., and Ducarme, G. (2019). Biomarkers and Algorithms for Diagnosis of Ovarian Cancer: CA125, HE4, RMI and ROMA, a Review. J. Ovarian Res. 12 (1), 28. doi:10.1186/s13048-019-0503-7

Duan, S., Gong, B., Wang, P., Huang, H., Luo, L., and Liu, F. (2018). Novel Prognostic Biomarkers of Gastric Cancer Based on Gene Expression Microarray: COL12A1, GSTA3, FGA and FGG. Mol. Med. Rep. 18 (4), 3727–3736. doi:10.3892/mmr.2018.9368

Duga, S., Asselta, R., and Tenchini, M. L. (2004). Coagulation Factor V. Int. J. Biochem. Cel Biol. 36 (8), 1393–1399. doi:10.1016/j.biocel.2003.08.002

Gene Ontology, C. (2021). The Gene Ontology Resource: Enriching a GOld Mine. Nucleic Acids Res. 49 (D1), D325–D34.

Gi, T., Yamashita, A., Aman, M., Kuwahara, A., Asada, Y., Kawagoe, Y., et al. (2021). Tissue Factor Expression and Tumor‐infiltrating T Lymphocytes in Ovarian Carcinomas and Their Association with Venous Thromboembolism. Pathol. Int. 71 (4), 261–266. doi:10.1111/pin.13074

Guan, Y., Du, Y., Wang, G., Gou, H., Xue, Y., Xu, J., et al. (2021). Overexpression of PLXDC2 in Stromal Cell-Associated M2 Macrophages Is Related to EMT and the Progression of Gastric Cancer. Front. Cel Dev. Biol. 9, 673295. doi:10.3389/fcell.2021.673295

Halaby, R., Popma, C. J., Cohen, A., Chi, G., Zacarkim, M. R., Romero, G., et al. (2015). D-dimer Elevation and Adverse Outcomes. J. Thromb. Thrombolysis 39 (1), 55–59. doi:10.1007/s11239-014-1101-6

Han, Y., Liu, D., and Li, L. (2020). PD-1/PD-L1 Pathway: Current Researches in Cancer. Am. J. Cancer Res. 10 (3), 727–742.

Hisada, Y., and Mackman, N. (2019). Tissue Factor and Cancer: Regulation, Tumor Growth, and Metastasis. Semin. Thromb. Hemost. 45 (4), 385–395. doi:10.1055/s-0039-1687894

Hisada, Y., and Mackman, N. (2017). Cancer-associated Pathways and Biomarkers of Venous Thrombosis. Blood 130 (13), 1499–1506. doi:10.1182/blood-2017-03-743211

Hugo, W., Zaretsky, J. M., Sun, L., Song, C., Moreno, B. H., Hu-Lieskovan, S., et al. (2016). Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma. Cell 165 (1), 35–44. doi:10.1016/j.cell.2016.02.065

Jin, H. T., Ahmed, R., and Okazaki, T. (2011). Role of PD-1 in Regulating T-Cell Immunity. Curr. Top. Microbiol. Immunol. 350, 17–37. doi:10.1007/82_2010_116

Jing, X., Yang, F., Shao, C., Wei, K., Xie, M., Shen, H., et al. (2019). Role of Hypoxia in Cancer Therapy by Regulating the Tumor Microenvironment. Mol. Cancer 18 (1), 157. doi:10.1186/s12943-019-1089-9

Katz, L. H., Li, Y., Chen, J.-S., Muñoz, N. M., Majumdar, A., Chen, J., et al. (2013). Targeting TGF-β Signaling in Cancer. Expert Opin. Ther. Targets 17 (7), 743–760. doi:10.1517/14728222.2013.782287

Khorana, A. A., Ahrendt, S. A., Ryan, C. K., Francis, C. W., Hruban, R. H., Hu, Y. C., et al. (2007). Tissue Factor Expression, Angiogenesis, and Thrombosis in Pancreatic Cancer. Clin. Cancer Res. 13 (10), 2870–2875. doi:10.1158/1078-0432.ccr-06-2351

Li, T., Fan, J., Wang, B., Traugh, N., Chen, Q., Liu, J. S., et al. (2017). TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res. 77 (21), e108–e110. doi:10.1158/0008-5472.can-17-0307

Li, T., Fu, J., Zeng, Z., Cohen, D., Li, J., Chen, Q., et al. (2020). TIMER2.0 for Analysis of Tumor-Infiltrating Immune Cells. Nucleic Acids Res. 48 (W1), W509–W514. doi:10.1093/nar/gkaa407

Mosesson, M. W. (2005). Fibrinogen and Fibrin Structure and Functions. J. Thromb. Haemost. 3 (8), 1894–1904. doi:10.1111/j.1538-7836.2005.01365.x

Nagy, Á., Munkácsy, G., and Győrffy, B. (2021). Pancancer Survival Analysis of Cancer Hallmark Genes. Sci. Rep. 11 (1), 6047. doi:10.1038/s41598-021-84787-5

Newman, A. M., Liu, C. L., Green, M. R., Gentles, A. J., Feng, W., Xu, Y., et al. (2015). Robust Enumeration of Cell Subsets from Tissue Expression Profiles. Nat. Methods 12 (5), 453–457. doi:10.1038/nmeth.3337

Nowicki, T. S., Hu-Lieskovan, S., and Ribas, A. (2018). Mechanisms of Resistance to PD-1 and PD-L1 Blockade. Cancer J. 24 (1), 47–53. doi:10.1097/ppo.0000000000000303

Rodríguez-Enríquez, S., Pacheco-Velázquez, S. C., Gallardo-Pérez, J. C., Marín-Hernández, A., Aguilar-Ponce, J. L., Ruiz-García, E., et al. (2011). Multi-biomarker Pattern for Tumor Identification and Prognosis. J. Cel. Biochem. 112 (10), 2703–2715. doi:10.1002/jcb.23224

Rondon, A. M. R., Kroone, C., Kapteijn, M. Y., Versteeg, H. H., and Buijs, J. T. (2019). Role of Tissue Factor in Tumor Progression and Cancer-Associated Thrombosis. Semin. Thromb. Hemost. 45 (4), 396–412. doi:10.1055/s-0039-1687895

Segers, K., Dahlbäck, B., and Nicolaes, G. A. (2007). Coagulation Factor V and Thrombophilia: Background and Mechanisms. Thromb. Haemost. 98 (3), 530–542. doi:10.1160/th07-02-0150

Siegel, R. L., Miller, K. D., and Jemal, A. (2020). Cancer Statistics, 2020. CA A. Cancer J. Clin. 70 (1), 7–30. doi:10.3322/caac.21590

Smith, N. L., Huffman, J. E., Strachan, D. P., Huang, J., Dehghan, A., Trompet, S., et al. (2011). Genetic Predictors of Fibrin D-Dimer Levels in Healthy Adults. Circulation 123 (17), 1864–1872. doi:10.1161/circulationaha.110.009480

Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., et al. (2005). Gene Set Enrichment Analysis: a Knowledge-Based Approach for Interpreting Genome-wide Expression Profiles. Proc. Natl. Acad. Sci. 102 (43), 15545–15550. doi:10.1073/pnas.0506580102

Tang, Z., Kang, B., Li, C., Chen, T., and Zhang, Z. (2019). GEPIA2: an Enhanced Web Server for Large-Scale Expression Profiling and Interactive Analysis. Nucleic Acids Res. 47 (W1), W556–W560. doi:10.1093/nar/gkz430

Tang, Z., Li, C., Kang, B., Gao, G., Li, C., and Zhang, Z. (2017). GEPIA: a Web Server for Cancer and normal Gene Expression Profiling and Interactive Analyses. Nucleic Acids Res. 45 (W1), W98–W102. doi:10.1093/nar/gkx247

Thaler, J., Ay, C., Mackman, N., Bertina, R. M., Kaider, A., Marosi, C., et al. (2012). Microparticle-associated Tissue Factor Activity, Venous Thromboembolism and Mortality in Pancreatic, Gastric, Colorectal and Brain Cancer Patients. J. Thromb. Haemost. 10 (7), 1363–1370. doi:10.1111/j.1538-7836.2012.04754.x

Tinholt, M., Garred, Ø., Borgen, E., Beraki, E., Schlichting, E., Kristensen, V., et al. (2018). Subtype-specific Clinical and Prognostic Relevance of Tumor-expressedF5and regulatoryF5variants in Breast Cancer: the CoCaV Study. J. Thromb. Haemost. 16 (7), 1347–1356. doi:10.1111/jth.14151

Tumeh, P. C., Harview, C. L., Yearley, J. H., Shintaku, I. P., Taylor, E. J. M., Robert, L., et al. (2014). PD-1 Blockade Induces Responses by Inhibiting Adaptive Immune Resistance. Nature 515 (7528), 568–571. doi:10.1038/nature13954

Weitz, J. I., Fredenburgh, J. C., and Eikelboom, J. W. (2017). A Test in Context: D-Dimer. J. Am. Coll. Cardiol. 70 (19), 2411–2420. doi:10.1016/j.jacc.2017.09.024

Wu, K., Lin, K., Li, X., Yuan, X., Xu, P., Ni, P., et al. (2020). Redefining Tumor-Associated Macrophage Subpopulations and Functions in the Tumor Microenvironment. Front. Immunol. 11, 1731. doi:10.3389/fimmu.2020.01731

Xie, L., Cai, L., Wang, F., Zhang, L., Wang, Q., and Guo, X. (2020). Systematic Review of Prognostic Gene Signature in Gastric Cancer Patients. Front. Bioeng. Biotechnol. 8, 805. doi:10.3389/fbioe.2020.00805

Yang, L., Pang, Y., and Moses, H. L. (2010). TGF-β and Immune Cells: an Important Regulatory axis in the Tumor Microenvironment and Progression. Trends Immunol. 31 (6), 220–227. doi:10.1016/j.it.2010.04.002

Yi, M., Jiao, D., Xu, H., Liu, Q., Zhao, W., Han, X., et al. (2018). Biomarkers for Predicting Efficacy of PD-1/pd-L1 Inhibitors. Mol. Cancer 17 (1), 129. doi:10.1186/s12943-018-0864-3

Yoon, S.-J., Park, J., Shin, Y., Choi, Y., Park, S. W., Kang, S.-G., et al. (2020). Deconvolution of Diffuse Gastric Cancer and the Suppression of CD34 on the BALB/c Nude Mice Model. BMC Cancer 20 (1), 314. doi:10.1186/s12885-020-06814-4

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Guan, Xu, Sui, Chen, Luan, Jiang, Wei, Long, Zhao, Han, Xu, Lin and Guan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 22 February 2022
doi: 10.3389/fmolb.2022.763248

Comprehensive Analyses of the Immunological and Prognostic Roles of an IQGAP3AR/let-7c-5p/IQGAP3 Axis in Different Types of Human Cancer

Yixiao Yuan¹†, Xiulin Jiang²†, Lin Tang¹†, Hong Yang³†, Juan Wang¹, Dahang Zhang¹ and Lincan Duan¹*

¹Department of Thoracic Surgery, The Third Affiliated Hospital of Kunming Medical University, Kunming, China

²Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Kunming, China

³Department of Urology, The Third Affiliated Hospital of Kunming Medical University, Kunming, China

Edited by:
Xin Wang, The Chinese University of Hong Kong, China

Reviewed by:
Ashok Kumar, All India Institute of Medical Sciences Bhopal, India
Mohammad Kaleem Ahmad, King George’s Medical University, India

* Correspondence: Lincan Duan, duanmumuhuosan@163.com

†These authors have contributed equally to this work

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 23 August 2021
Accepted: 13 January 2022
Published: 22 February 2022

Citation: Yuan Y, Jiang X, Tang L, Yang H, Wang J, Zhang D and Duan L (2022) Comprehensive Analyses of the Immunological and Prognostic Roles of an IQGAP3AR/let-7c-5p/IQGAP3 Axis in Different Types of Human Cancer. Front. Mol. Biosci. 9:763248. doi: 10.3389/fmolb.2022.763248

IQ motif containing GTPase-activating protein 3 (IQGAP3) is a member of the Rho family of guanosine-5′-triphosphatases (GTPases). IQGAP3 plays a crucial part in the development and progression of several types of cancer. However, the prognostic, upstream-regulatory, and immunological roles of IQGAP3 in human cancer types are not known. We found that IQGAP3 expression was increased in different types of human cancer. The high expression of IQGAP3 was correlated with tumor stage, lymph node metastasis, and a poor prognosis in diverse types of human cancer. The DNA methylation of IQGAP3 was highly and negatively correlated with IQGAP3 expression in diverse cancer types. High DNA methylation in IQGAP3 was correlated with better overall survival in human cancer types. High mRNA expression of IQGAP3 was associated with tumor mutational burden, microsatellite instability, immune cell infiltration, and immune modulators. Analyses of signaling pathway enrichment showed that IQGAP3 was involved in the cell cycle. IQGAP3 expression was associated with sensitivity to a wide array of drugs in cancer cells lines. We revealed that polypyrimidine tract–binding protein 1 (PTBP1) and an IQGAP3-associated lncRNA (IQGAP3AR)/let-7c-5p axis were potential regulations for IQGAP3 expression. We provided the first evidence to show that an IQGAP3AR/let-7c-5p/IQGAP3 axis has indispensable roles in the progression and immune response in different types of human cancer.

Keywords: IQGAP3, let-7c-5p, human cancer, prognosis, immunotherapy

INTRODUCTION

Cancer is a major cause of death worldwide and results in considerable social and economic burdens. Despite improvements in the diagnosis and treatment of cancer, the prevalence of cure is low (Siegel et al., 2019). Therefore, the identification of specific and sensitive biomarkers for the diagnosis and treatment of cancer is very important.

IQ motifs containing GTPase-activating protein 3 (IQGAP3) is a member of the Rho family of guanosine-5′-triphosphatase (GTPase). Of the proteins IQGAP1, IQGAP2, and IQGAP3 (Swart-Mataraza et al., 2002), IQGAP1 has been shown to participate mainly in the regulation of cellular motility (Schmidt et al., 2008). Studies have revealed that IQGAP1 promotes the intrinsic GTPase activity of Cdc42, thereby resulting in altered cellular morphology (Swart-Mataraza et al., 2002). IQGAP2 has been reported to be a tumor-suppressor gene in different types of cancer (Swart-Mataraza et al., 2002). IQGAP3 was identified as a member of the IQGAP family in 2007 (Wang et al., 2007) and has indispensable roles in neuronal morphogenesis (Wang et al., 2007). Studies have indicated that IQGAP3 is located mainly in chromosome 1 at 1q21.3 and has been reported to act as an oncogene in different types of human cancer (Xu et al., 2016; Dongol et al., 2020; Zeng et al., 2020). For instance, IQGAP3 expression has been shown to be upregulated in high-grade serous ovarian cancer, and IQGAP3 depletion inhibits the proliferation, migration, and invasion of ovarian cancer cell lines markedly (Dongol et al., 2020). IQGAP3 silencing has been demonstrated to significantly reduce the proliferation, migration, and invasion ability, and induce apoptosis in pancreatic cancer cell lines (Xu et al., 2016). Zhang et al. found that IQGAP3 had high expression in breast cancer tissues and cell lines, and its high expression was correlated with the clinical stage, tumor node metastasis stage, and a poor prognosis (Hua et al., 2020). High expression of IQGAP3 has been observed in colorectal cancer (Wu et al., 2019), hepatocellular carcinoma (Shi et al., 2017), bladder cancer (Xu et al., 2019), and gastric cancer (Huang et al., 2021). Hence, IQGAP3 appears to have important roles in cancer progression and could be a promising biomarker. However, the prognostic and immunological roles of IQGAP3 in human cancer are not known.

In the present study, we first employed public databases to analyze the expression and prognosis in different types of human cancer. Our results indicated that IQGAP3 expression was upregulated significantly in bladder urothelial carcinoma (BLCA), breast-invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), pancreatic adenocarcinoma (PAAD), pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma (PRAD), rectal adenocarcinoma (READ), stomach adenocarcinoma (STAD), thyroid carcinoma (THCA), and uterine corpus endometrial carcinoma (UCEC). High expression of IQGAP3 was associated with poor overall survival (OS) in adrenocortical carcinoma (ACC), KIRC, KIRP, acute myeloid leukemia (LAML), brain lower-grade glioma (LGG), liver hepatocellular carcinoma (LIHC), LUAD, mesothelioma (MESO), and uveal melanoma (UVM). High expression of IQGAP3 was also associated with short disease-free survival (DFS) in ACC, chromophobe kidney cancer (KICH), KIRP, LGG, LIHC, MESO, PRAD, skin cutaneous melanoma (SKCM), and UVM. IQGAP3 expression was not only related to the tumor stage of ACC, BRCA, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, ovarian serous cystadenocarcinoma (OV), PAAD, and THCA, but also correlated with lymph node metastasis in BLCA, BRCA, CESC, CHOL, COAD, ESCA, HNSC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, MESC, PAAD, PRAD, READ, STAD, and THCA. In addition, the low level of DNA methylation and high copy-number variation (CNV) of IQGAP3 significantly affected its expression in different types of cancer. IQGAP3 expression was closely associated with tumor mutational burden (TMB), microsatellite instability (MSI), infiltration of immune cells, and immune modulators.

In addition, we identified a 3060-base pair (bp) long non-coding (lnc) RNA, termed “IQGAP3AR” (IQGAP3-associated lncRNA: ENSG00000234072), which showed high expression in human cancer that predicted a poor prognosis. We also found that the transcription factor PTBP1 and IQGAP3AR/let-7c-5p axis were potential regulators of IQGAP3 expression. High expression of IQGAP3AR correlated with the tumor stage and poor prognosis in different types of cancer. Let-7c-5p expression was decreased in CHOL, BRCA, BLCA, UCEC, THCA, SATD, LUSC, LUAD, LIHC, KICH, HNSC, and COAD. High expression of let-7c-5p was correlated with a good prognosis in BRCA, CECS, ESCA, HNSC, KIRP, LIHC, LUAD, and LUSC. We also showed that IQGAP3 expression was positively correlated with sensitivity to different types of drugs in the Genomics of Drug Sensitivity in Cancer (GDSC) database. Finally, we undertook real-time reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC) assays to show that IQGAP3 had high expression in non–small-cell lung cancer (NSCLC) cell lines and cancer tissue. We provided, for the first time, evidence that the IQGAP3AR/let-7c-5p/IQGAP3 axis has indispensable roles in the progression and immune response in different types of human cancer.

MATERIALS AND METHODS

Analysis of the Expression of IQGAP3 in Pan-Cancer

We employed the TIMER (https://cistrome.shinyapps.io/timer/) (Li et al., 2017), Oncomine (https://www.oncomine.org), and GEPIA databases (http://gepia.cancer-pku.cn/) (Tang et al., 2017) to analyze the expression of IQGAP3 in pan-cancer, and the CCLE tools (https://portals.broadinstitute.org/ccle/) (Ghandi et al., 2019) were employed to examine the expression of IQGAP3 in diverse cancer cells lines. UALCAN tools (http://ualcan.path.uab.edu/) (Chandrashekar et al., 2017) were used to analyze the protein of IQGAP3 in different cancers. The expression of let-7c-5p was analyzed by using starBase (Li et al., 2014). The Kaplan–Meier plotter (http://kmplot.com/analysis/) (Hou et al., 2017) was employed to examine the prognosis of let-7c-5p in pan-cancer.

Analysis of the Prognosis and Clinical Information of IQGAP3 in Pan-Cancer

We employed the GEPIA (http://gepia.cancer-pku.cn/) and prognostic databases (http://dna00.bio.kyutech.ac.jp/PrognoScan/index.html) (Tang et al., 2017; Mizuno et al., 2009) to analyze the OS and RFS of IQGAP3 in pan-cancer; additionally, the correlation between the tumor stage and IQGAP3 expression was analyzed by using GEPIA. Tumor stage, lymph node metastasis, and expression of let-7c-5p were analyzed by using UALCAN tools (http://ualcan.path.uab.edu/) (Chandrashekar et al., 2017). We also employed the prognosis tools to verify the prognosis of IQGAP3 in pan-cancer.

Analysis of the DNA Methylation and Gene Mutation of IQGAP3 in Pan-Cancer

The DNA methylation of IQGAP3 was analyzed by Ualcan tools (http://ualcan.path.uab.edu/) (Chandrashekar et al., 2017), the correlation between the OS and DNA methylation level was analyzed by the Methsurv database (https://biit.cs.ut.ee/methsurv/) (Modhukur et al., 2018). The mutation information of IQGAP3 in pan-cancer was analyzed by the cbioportal database (https://www.cbioportal.org/) (Cerami et al., 2012).

Starbase Database

We employed the starBase database (http://starbase.sysu.edu.cn/) to forecast the potential miRNAs of IQGAP3 (Li et al., 2014), and examine the expression, prognosis, and correlation between let-7c-5p and IQGAP3, we also used the starbase to predict the binding with between the miRNA, mRNA, and lncRNA.

Analysis of the Function of IQGAP3 in Pan-Cancer

We employed the CancerSEA database (http://biocc.hrbmu.edu.cn/CancerSEA/) analysis the function of IQGAP3 in pan-cancer (Yuan et al., 2019), the LinkedOmics (http://www.linkedomics.org/admin.php) was employed to analyze the KEGG pathway of IQGAP3 in LUAD (Vasaikar et al., 2018).

Analysis of the Gene and Protein That Interact With IQGAP3 in Pan-Cancer

We employed the STRING database (https://string-db.org/cgi) to construct the protein interaction networks of IQGAP3 in cancer (Szklarczyk et al., 2017), and the GeneMANIA (http://genemania.org/) was employed to analyze the interaction gene with the IQGAP3 (Franz et al., 2018).

Analysis of the Immunological Roles of IQGAP3 in Pan-Cancer

We employed the TIMER (https://cistrome.shinyapps.io/timer/) and XCELL tools (https://xcell.ucsf.edu/) to analyze the immunological roles of IQGAP3 (Li et al., 2017; Aran et al., 2017), including the correlation between diverse immune cells and the immune regulator. The TISIDB (http://cis.hku.hk/TISIDB/) was adopted to analyze the relationship between IQGAP3 expression and 28 tumor-infiltrating lymphocytes, 45 immune stimulators, 24 immune inhibitors, 41 chemokines, 18 receptors, and 21 MHC molecules in pan-cancer (Ru et al., 2019). The TMB and MSI scores were obtained from TCGA. Correlation analysis between the IQGAP3 expression and TMB or MSI was performed using Spearman’s method.

Analysis of the Correlation Between IQGAP3 Expression and Drug Sensitivity

We employed the Genomics of Drug Sensitivity in Cancer (GDSC) (www.cancerRxgene.org) and CTRP databases to analyze the correlation between IQGAP3 expression and drug sensitivity (Basu et al., 2013; Yang et al., 2013).

Analysis of the Molecular Characteristics of IQGAP3

We employed the lncLocator (www.csbio.sjtu.edu.cn/bioinf/lncLocator) and CPC2 (http://cpc2.cbi.pku.edu.cn) to examine the subcellular localization and the protein-coding ability of IQGAP3 (Kang et al., 2017; Cao et al., 2018).

Cells and Cell Culture Conditions

The BEAS-2B cell line was purchased from the cell bank of Kunming Institute of Zoology and cultured in BEGM media (Lonza, CC-3170). HEK-293T was obtained from ATCC. Lung cancer cell lines, including A549, H1299, and H1975 were purchased from Cobioer, China, with the STR document, and the A549, H1299, and H1975 cells were all cultured in an RPMI1640 medium (Corning) supplemented with 10% fetal bovine serum (Cat# 10099141C, Gibco, United States) and 1% penicillin/streptomycin.

Quantitative Real-Time PCR

The qRT-PCR assay was performed as documented (Yang et al., 2013). The primer sequences are as follows: IQGAP3-F: GCA​GCC​TAT​GAA​CGC​CTC​A, IQGAP3-R: GGA​GGG​TGC​AAA​ACA​GTG​G, β-actin-F: CTTCGCGGGCGACGAT, and β-actin-R: CCA​TAG​GAA​TCC​TTC​TGA​CC. The expression quantification was obtained with the 2−ΔΔCt method.

Immunohistochemistry Staining

Immunohistochemistry staining assay was performed as documented (Basu et al., 2013). Briefly, lung cancer tissue and normal lung tissues were obtained from advanced-stage lung cancer patients from The Third Affiliated Hospital of Kunming Medical University (Yunnan Tumor Hospital), Kunming, China. These tissues were used to perform immunohistochemistry (IHC), primary antibody overnight incubation, and second antibody incubation, and finally, developed using the instrument. The detailed information of antibodies employed in our study is as follows: IQGAP3 antibody (Rabbit polyclonal to IQGAP3, ab219354, 1:500).

Statistical Analysis

Analysis of the IQGAP3 expression pan-cancer was estimated using t-tests. For survival analysis, the HR and p-value were calculated employing univariate Cox regression analysis. Kaplan–Meier analysis was employed to examine the survival time of patients stratified according to high or low levels of the IQGAP3 expression. p-values less than 0.05 were considered statistically significant. For all figures, ∗, ∗∗, and ∗∗∗ indicate p < 0.05, p < 0.01, and p < 0.001, respectively.

RESULTS

IQGAP3 has High Expression in Human Cancer

To examine IQGAP3 expression in different types of human cancer, we first employed the TIMER database (http://timer.comp-genomics.org/). We discovered that IQGAP3 had high expression in BLCA, BRCA, CESC, CHOL, COAD, ESCA, GBM, HNSC, KIRC, KIRP, LUAD, LUSC, PAAD, PCPG, PRAD, READ, STAD, THCA, and UCEC (Figure 1A). We also analyzed IQGAP3 expression in human cancer based on Gene Expression Omnibus (GEO) datasets by employing the Oncomine database (www.oncomine.com/). IQGAP3 expression was increased in the cancer of the bladder, breast, colorectum, stomach, kidney, liver, and lung (Figure 1B). Owing to a lack of data for normal tissue in The Cancer Genome Atlas (TCGA) database (www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga/), we used the GEPIA database (http://gepia.cancer-pku.cn/) to explore IQGAP3 expression in human cancer types. A high expression of IQGAP3 was observed in BLCA, BRCA, CESC, COAD, ESCA, GBM, HNSC, LIHC, LUAD, LUSC, OV, PAAD, READ, SKCM, STAD, Thymoma (THYM), UCEC, and uterine carcinosarcoma (UCS) (Figure 1C). Finally, to determine IQGAP3 expression in different cancer cells lines, we used the Cancer Cell Line Encyclopedia (CCLE) database (https://sites.broadinstitute.org/ccle/). We found that IQGAP3 expression was upregulated in different cancer cells lines (Figure 1D). Overall, these results showed that IQGAP3 expression was upregulated in different types of human cancer.

FIGURE 1 | Expression analysis for IQGAP3 in human cancers. (A) The expression of IQGAP3 in pan-cancer analysis by the TIMER database. (B) The expression of IQGAP3 in pan-cancer analysis by using the oncomine database. (C) The expression of IQGAP3 in pan-cancer analysis by using the GEPIA database. (D) The expression of IQGAP3 in pan-cancer cells lines analysis by using the CCLE database.

Correlation Between IQGAP3 Expression and the Pathological Stage

IQGAP3 expression was associated significantly with the pathological stage of ACC, BRCA, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, OV, PAAD, and THCA (Figure 2 and Supplementary Figure S1). These findings demonstrated that IQGAP3 expression was correlated significantly with the pathological stage of different types of cancer.

FIGURE 2 | Analysis of the tumor stage for IQGAP3 in human cancers. (A) Analysis of the tumor stage for IQGAP3 in adrenocortical carcinoma, thyroid carcinoma, and kidney chromophobe, and (B) analysis of the tumor stage for IQGAP3 kidney renal clear cell carcinoma, and kidney renal papillary cell carcinoma by using the GEPIA database.

Correlation Between IQGAP3 Expression and Lymph Node Metastasis

Lymph node metastasis plays a crucial part in cancer progression. We examined IQGAP3 expression in lymph node metastasis of different types of cancer. We discovered that IQGAP3 expression was positively correlated with lymph node metastasis of BLCA, BRCA, CESC, CHOL, COAD, ESCA, HNSC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, MESC, PAAD, PRAD, READ, STAD, and THCA (Supplementary Figures S2A–C). These findings showed that IQGAP3 expression was significantly related to lymph node metastasis in different types of cancer.

Prognostic Role of IQGAP3 in Human Cancers

To ascertain the prognostic role of IQGAP3 in different types of cancer, we ascertained the OS, DFS, progression-free survival (PFS), disease-specific survival (DSS), and relapse-free survival (RFS) in human cancer types. A high expression of IQGAP3 was not only related to poor OS in ACC, KIRC, KIRP, LAML, LGG, LIHC, LUAD, MESO, and UVM (Figure 3 and Supplementary Figure S3) but also associated with poor DFS in ACC, KICH, KIRP, LGG, LIHC, MESO, PRAD, SKCM, and UVM (Figure 4). Cox regression analysis showed that a high expression of IQGAP3 was related to poor PFS in ACC, KICH, KIRC, KIRP, LGG, LIHC, MESO, PAAD, PCPG, PRAD, THCA, UCEC, and UVM (Supplementary Figure S4). A high expression of IQGAP3 was associated with poor DSS in KIRP, LIHC, PAAD, PRAD, THCA, and UCEC (Supplementary Figure S4). To verify the results shown above, we employed a prognostic database (http://dna00.bio.kyutech.ac.jp/PrognoScan/index.html) to analyze the OS, RFS, and DFS in diverse GEO cohorts: identical results were obtained (Table 1). Collectively, these data indicated that IQGAP3 expression was closely related to the prognosis of patients with different cancer types.

FIGURE 3 | Analysis of the Overall survival for IQGAP3 in human cancers. (A) The overall survival for IQGAP3 in adrenocortical carcinoma, brain lower grade glioma, and kidney renal clear cell carcinoma analysis by using the GEPIA database, and (B) the overall survival for IQGAP3 in mesothelioma and liver hepatocellular carcinoma analysis by using the GEPIA database.

FIGURE 4 | Analysis of the disease-free survival for IQGAP3 in human cancers. (A) The disease-free survival for IQGAP3 in adrenocortical carcinoma, kidney chromophobe, and kidney renal papillary cell carcinoma analysis by using the GEPIA database, and (B) the disease-free survival for IQGAP3 in brain lower grade glioma, prostate adenocarcinoma, and uveal melanoma analysis by using the GEPIA database.

TABLE 1 | The prognosis of IQGAP3 in pan-cancer analysis by using the prognostic database.

IQGAP3 May Act as a Potential Biomarker in Human Cancers

We showed above that a high expression of IQGAP3 was correlated with the prognosis of patients with different cancer types. Next, we investigated if IQGAP3 could act as a biomarker for different cancer types. We undertook an analysis of receiver operating characteristic (ROC) curves of IQGAP3 expression to obtain the area under the ROC curve (AUC) values. The AUC values for BRCA, PRAD, LUSC, LUAD, KIRP, KIRC, COAD, READ, THCA, GBM, LGG, PAAD, SKCM, LIHC, STAD, and ESCA are given in Supplementary Figure S5. The latter showed that IQGAP3 could be used as a biomarker to diagnose different types of cancer with high sensitivity and specificity.

Relationship Between IQGAP3 Expression and Immune Subtypes and Molecular Subtypes in Human Cancers

According to different features, human cancers can be divided into immune subtypes and molecular subtypes. Immune subtypes can be classified further into six types: C1 (wound healing), C2 (interferon-gamma dominant), C3 (inflammatory), C4 (lymphocyte depleted), C5 (immunologically quiet), and C6 (transforming growth factor-β dominant) (Hu et al., 2021). IQGAP3 showed high expression in C1 and C2 and low expression in C3 in LUAD (Supplementary Figure S6). For molecular subtypes, IQGAP3 displayed different expressions in different cancer types (Supplementary Figure S7). These results suggested that IQGAP3 had different expression patterns in human cancer types.

Correlation Between IQGAP3 Expression and TMB and MSI

TMB is the number of noninherited mutations per million bases of an investigated genomic sequence. TMB has emerged as a specific and sensitive biomarker of the response to immune checkpoint inhibitors (Addeo et al., 2021). We examined the correlation between IQGAP3 expression and TMB of human cancers. IQGAP3 expression was markedly positively correlated with TMB of ACC, PAAD, STAD, KICH, LUAD, CHOL, and PRAD and negatively correlated with TMB of DLBC and THYM (Figure 5A).

FIGURE 5 | Analysis of the correlation between the IQGAP3 and TMS, MSI. (A) Analysis of the correlation between IQGAP3 and TMS. (B) Analysis of the correlation between IQGAP3 and MSI.

MSI represents a hyper-mutable state of DNA sequences caused by a lack of activity of DNA repair (Boland and Goel, 2010). We explored the correlation between IQGAP3 expression and MSI in human cancers. IQGAP3 expression was markedly positively correlated with MSI in LUSC, SRAC, and UCEC and negatively correlated with MSI in DLBC (Figure 5B). Collectively, these data implied that IQGAP3 may influence antitumor immunity by regulating the composition and immune mechanism in the tumor microenvironment.

Genetic Alteration of IQGAP3 in Human Cancers

We wished to explore the gene-mutation information of IQGAP3 in human cancer types. We used the cBioPortal (www.cbioportal.org/) database, and the main information is shown in Figure 6A. The frequency of IQGAP3 alterations (>12%) was the highest in hepatocellular carcinoma, with amplification being the main type of alteration (Figure 6B). Amplification was the main reason why the mRNA of IQGAP3 was upregulated in different cancer types (Figure 6C). IQGAP3 mutation was significantly correlated with genes such as FLG, SPTA1, NES, GON4L, INSPP, ASH1L, BCAN, ARHGEFLL, MEF2D, and TTC24 (Figure 6D).

FIGURE 6 | Analysis of the gene mutation of IQGAP3 in pan-cancer. (A) Representation of IQGAP3 mutations (TCGA) in diverse human cancers by using the cBioPortal database. (B) The mutation frequency of IQGAP3 in pan-cancer was examined by employing the cBioPortal database; red: amplification, green: mutation, blue: deep deletion, and purple: structural variant. (C) The correlation between the gene mutation of IQGAP3 and its expression in pan-cancer was examined by employing the cBioPortal database. (D) The correlation between the gene mutation of IQGAP3 and the closest gene in pan-cancer was examined by employing the CIO portal database. (E) The percentages of mutation types of IQGAP3 in pan-cancer were indicated in a pie chart examined by employing the Catalogue of Somatic Mutations. (F) The mutation of IQGAP3 affects the OS of HCC patients examined by employing the cBioPortal database. (G) The mutation of IQGAP3 affects the DFS of HCC patients examined by employing the cBioPortal database. (H) The CNV of IQGAP3 in diverse human cancer by using the GSCA database. (I) The correlation between the CNV of IQGAP3 and its expression in pan-cancer was examined by using the GSCA database.

We also examined the mutation type and base mutation in cancer types. A missense substitution and base mutation C > T were the most common in different cancer types (Figure 6E). The mutation of IQGAP3 affected the prognosis of patients with hepatocellular carcinoma, with a poor OS and DFS being noted (Figures 6F, G). Finally, we showed that the CNV of IQGAP3 in different cancer types was positively correlated with its expression (Figures 6H, I and Supplementary Table S1). Overall, these results suggested that the CNV of IQGAP3 was positively correlated with IQGAP3 expression in different types of human cancer.

Level of DNA Methylation and IQGAP3 Expression in Human Cancers

DNA methylation has a significant role in the regulation of gene expression (Klutstein et al., 2016). Next, we explored if a high expression of IQGAP3 was attributed to low DNA methylation in the promoter region of IQGAP3. We discovered that DNA hypo-methylation in IQGAP3 was negatively correlated with IQGAP3 expression in ACC, BLCA, BRCA, CHOL, COAD, KIRC, LGG, LIHC, LUAD, LUSC, PAAD, READ, SARC, SKCM, STAD, testicular germ cell tumors (TGCT), THCA, UCEC, UCS, and UVM (Supplementary Table S2). We also examined if the methylation level of IQGAP3 affected the prognosis of patients with different types of cancer. A low DNA-methylation level in IQGAP3 was correlated with a better poor prognosis in different cancer types (Table 2). These findings demonstrated that IQGAP3 expression was significantly correlated with DNA methylation in different types of human cancer.

TABLE 2 | Analysis of the correlation between the DNA methylation of IQGAP3 and prognosis of cancer patients.

IQGAP3 Functions in Human Cancers

After discovering that IQGAP3 was correlated markedly to the prognosis, tumor stage, and lymph node metastasis, we explored IQGAP3 functions in human cancer types using CancerSEA (http://biocc.hrbmu.edu.cn/). IQGAP3 was involved mainly in angiogenesis, apoptosis, cell cycle, cell differentiation, DNA damage, epithelial–mesenchymal transition (EMT), hypoxia, inflammation, invasion, metastasis, and proliferation in human cancers (Figure 7A). A high expression of IQGAP3 was positively correlated with the cell cycle (r = 0.74), cell proliferation (r = 0.62), DNA damage (r = 0.53), DNA repair (r = 0.39), EMT (r = 0.30) and inflammation (r = –0.30) in different cancer types (Figure 7B). These findings suggested that IQGAP3 has a pivotal role in the initiation and prognosis of diverse types of human cancer.

FIGURE 7 | Analysis of the function for IQGAP3 in human cancers. (A) The function of IQGAP3 in pan-cancer analysis by using the CancerSEA database. (B) The correlation between the IQGAP3 and diverse function analysis by using the CancerSEA database. (C) The gene interaction meshwork of IQGAP3 was constructed using GeneMania. (D) The STRING database was employed to construct the protein interaction meshwork of IQGAP3.

We employed the GeneMANIA (https://genemania.org/) and Search Tool for the Retrieval of Interacting Genes/Proteins (STRING; https://string-db.org/) databases to construct gene-interaction and protein-interaction networks with IQGAP3. The genes most closely associated with IQGAP3 were those of CDC42, MYL6B, MYH2, RAC1, PPP1R16A, MEF2A, IST1, NDC80, HIF1A, ITPRIPL2, CALM1, RAC3, RAC2, SRGAP3, KIFAP3, COG2, NPHP4, IQGAP1, IQGAP2, and CLUAP1 (Figure 7C). The proteins most closely associated with IQGAP3 were those for CDC42, IQGAP2, KIF20, CDH1, MEN1, CTNNB1, CTNNA1, IQGAP1, RAC1, and CLIP1 (Figure 7D). These proteins have been reported (Modhukur et al., 2018) to have crucial roles in the cell proliferation and cell cycles of different cancer types.

CNV and DNA Methylation of Proteins That Interact With IQGAP3 in Human Cancer

We wished to examine the expression pattern of genes that interact with IQGAP3. We employed the Gene Set Cancer Analysis (GSCA) (http://bioinfo.life.hust.edu.cn/GSCA/#/) database to analyze the CNV and DNA methylation of genes that interact with IQGAP3 in different cancer types.

CTNNB1 had the highest mutation rate (29%) (Figure 8A), and the CNV was markedly positively correlated with CTNNB1 expression in human cancers (Figure 8B). The gene CNV of IQGAP3 interaction genes significantly affected the prognosis of diverse patients (Figure 8C). Next, we examined the DNA-methylation level of genes in different cancer types. DNA methylation of these genes was significantly negatively correlated with mRNA expression in different cancer types (Figure 8D).

FIGURE 8 | Analysis of the mutation for IQGAP3 interaction with the gene in human cancers. (A) The mutation of IQGAP3 interaction with the gene in human cancers was analyzed by using the GSCA tools. (B) The correlation between CNV and expression of IQGAP3 interaction with the gene in human cancers was analyzed by using the GSCA tools. (C) The correlation between the prognosis and CNV of IQGAP3 interaction with the gene in human cancers was analyzed by using the GSCA tools. (D) The correlation between DNA methylation and expression of IQGAP3 interaction with the gene in human cancers was analyzed by using the GSCA tools.

IQGAP3 and Enrichment of Signaling Pathways in Human Cancer

We wished to explore if an “IQGAP3 axis” plays an important part in cancer progression. We used the LinkedOmics (www.linkedomics.org/) database and Kyoto Encyclopedia of Genes and Genomes (www.genome.jp/kegg/) database to analyze which signaling pathways were enriched in different cancer types.

High expression of IQGAP3 was mainly involved in “cell cycle,” “ECM-receptor interaction,” and “miRNA in cancer” in BRCA (Figure 9A); “spliceosome” and “human T cell virus” in COAD (Figure 9B); “cell cycle” and “lysosomes” in KIRP (Figure 9C); “PI3K-AKT signaling pathway” and “rap1 signaling pathway” in LGG (Figure 9D); “cell cycle” and “RNA transport” in LIHC (Figure 9E) “cell cycle,” “spliceosome,” and “RNA transport” in LUAD (Figure 9F); “cell adhesion” and “cell cycle” in LUSC (Figure 9G); “Nod-like receptor signaling pathway” and “tight junctions” in PAAD (Figure 9H). These results demonstrated that IQGAP3 has a crucial role in the development of different cancer types.

FIGURE 9 | Analysis of the signaling pathway for IQGAP3 in human cancers. (A–D) The KEGG pathway of IQGAP3 in BRCA, COAD, KIRP, and LGG was analyzed by using LinkedOmics. (E–H) The KEGG pathway of IQGAP3 in LIHC, LUAD, LUSC, and PAAD analysis by LinkedOmics.

Transcription Factors of IQGAP3 in Human Cancers

Transcription factors have indispensable roles in controlling gene expression (Bradner et al., 2017). We explored the transcription factors that could regulate IQGAP3 transcription. We employed the JASPAR (https://jaspar.genereg.net/), PROMO (http://alggen.lsi.upc.es/cgi-bin/promo_v3/), ConTrav3 (http://bioit2.irc.ugent.be/contra/v3/#/step/1/), and UCSC (https://genome.ucsc.edu/) databases. PTBP1 was positively correlated with IQGAP3 expression in diverse cancer types (Supplementary Figures S8A–8D and Table 3). We used the KNOCK-TF (www.licpathway.net/KnockTF/) database to verify the above result. The depletion of PTBP1 reduced IQGAP3 expression in LIHC (Supplementary Figure S8E). A high expression of PTBP1 was correlated with a poor prognosis in patients with ESCA, KIRP, LIHC, LUAD, PAAD, or SARC, and related to better prognosis in patients with BLCA, ESCC, OV, READ, STAD, or THYM (Supplementary Figures S9A–9D). We examined GEO datasets and obtained identical results (Table 4). Overall, these results demonstrated that PTBP1 may be a transcription factor for IQGAP3 in different cancer types.

TABLE 3 | The correlation between the PTBP1 and IQGAP3 expressions.

TABLE 4 | The prognosis of IQGAP3 in pan-cancer analysis by using the prognostic database.

Upstream microRNAs of IQGAP3 in Human Cancers

miRs have crucial roles in regulating the expression of messenger (m)RNA. According to the competing endogenous RNA (ceRNA) hypothesis, there is a negative correlation between miR expression and mRNA expression. If IQGAP3 shows high expression in cancer, the miR expression in cancer should be low. We used public databases to analyze the upstream miRs of IQGAP3. Using different datasets to obtain intersections, we identified four miRs (miR-196b-5p, miR-422a, miR-18b-5p, let-7c-5p). Among these miRs, only the expression of let-7c-5p was decreased significantly in human cancers.

Next, we analyzed the expression of let-7c-5p in human cancers and investigated its prognostic value. Let-7c-5p had low expression in CHOL, BRCA, BLCA, UCEC, THCA, SATD, LUSC, LUAD, LIHC, KICH, HNSC, and COAD (Supplementary Figure S10 and Supplementary Table S3). A high expression of let-7c-5p was correlated with a good prognosis in patients with BRCA, CECS, ESCA, HNSC, KIRP, LIHC, LUAD, or LUSC, but correlated with a poor prognosis in patients with BLCA, PAAD, or STAD (Supplementary Figure S11). A low expression of let-7c-5p was correlated with the tumor stage in diverse cancer types (Supplementary Figure S12). These results showed that let-7c-5p expression was decreased in most cancer types and that increased expression of let-7c-5p was correlated with a better prognosis in many cancer types. We also analyzed the correlation between let-7c-5p and IQGAP3 in human cancers. Let-7c-5p expression was negatively correlated with IQGAP3 expression in human cancers (Supplementary Table S4). These results suggested that let-7c-5p was the most potential binding IQGAP3 in cancer patients.

Upstream lncRNAs of Let-7c-5p in Human Cancers

We wished to identify the upstream lncRNAs of let-7c-5p in human cancers. We employed the starBase (https://starbase.sysu.edu.cn/) and LncRNAbase databases (http://carolina.imis.athena-innovation.gr/diana_tools/web/index.php?r=lncbasev2%2Findex-predicted) to predict the lncRNAs that may bind with let-7c-5p. We discovered five possible lncRNAs: AC234582.1, AL590666.2, AL590666.2, MIR29B2CHG, and IQGAP3AR. Among these lncRNAs, only the expression of IQGAP3AR was upregulated significantly in BRCA, BLCA, UCEC, STAD, PRAD, LUSC, LUAD, LIHC, KIRP, CHOL, KICH, HNSC, ESCA, and COAD (Supplementary Figures S13A–13D and Supplementary Table S5). We also identified a drug that was negatively correlated with IQGAP3AR expression (Supplementary Figure S13E). Further study revealed that the high expression of IQGAP3AR was not only associated with a poor prognosis in patients with ACC, BRCA, CESC, COAD, KIRC, or LIHC but also correlated with the tumor stage in COAD, LIHC, and OV. The expression of the other lncRNAs was not significantly different in different cancer types (Figures 10A–C).

FIGURE 10 | Analysis upstream lncRNA of let-7c-5p in pan-cancer. (A) The prognosis of IQGAP3AR in ACC, BRCA, and CESC was analyzed by using starBase. (B) The prognosis of IQGAP3AR in COAD, KIRC, and LIHC was analyzed by using starBase. (C) The correlation between the IQGAP3AR and tumor stage in COAD, LIHC, and OV was analyzed by using starBase. (D) The target sites between the IQGAP3, let-7c-5p, and IQGAP3 were predicted by using starBase. (E) The subcellular localization of IQGAP3 was analyzed by using the lncLocator tools. (F) The coding potential of IQGAP3 was analyzed by using the coding potential calculator.

According to the ceRNA hypothesis, lncRNA expression should have a negative correlation with let-7c-5p expression and positive correlation with IQGAP3 expression. The Spearman correlation analysis revealed IQGAP3AR expression to be significantly negatively correlated with let-7c-5p expression (Supplementary Figures S14A–14C and Supplementary Table S6) and positively correlated with the expression of IQGAP3 in pan-cancer (Supplementary Figures S15A–15C and Supplementary Table S7). The target sites between IQGAP3AR, let-7c-5p, and IQGAP3 were predicted by using TarBase (https://carolina.imis.athena-innovation.gr/) (Figure 10D).

The subcellular localization of IQGAP3AR was identified using the lncLocator (www.csbio.sjtu.edu.cn/bioinf/lncLocator/) database. IQGAP3AR was located mainly in the cytoplasm (Figure 10E). We also analyzed the protein-coding potential of IQGAP3AR by performing the coding potential calculator: IQGAP3AR did not possess the protein-coding ability (Figure 10F). Our results suggested that IQGAP3AR may be upstream of the lncRNA let-7c-5p, which regulates IQGAP3 expression in different cancer types.

Correlation Between IQGAP3 Expression and Infiltration of Immune Cells

The infiltration of immune cells has an indispensable role in cancer progression. Next, we explored the relationship between IQGAP3 expression and immune cell infiltration in different cancer types. The TIMER database showed that IQGAP3 expression was correlated significantly with the abundance of cluster of differentiation (CD)8+ T cells in 25 types of cancer, CD4⁺ T cells in 27 cancer types, neutrophils in 29 cancer types, dendritic cells in 30 cancer types, macrophages in 29 cancer types, and B cells in 29 cancer types (Supplementary Figure S16A).

To verify these results, we employed the xCell (https://xcell.ucsf.edu/) database to assess the correlation between IQGAP3 expression and immune cell infiltration in diverse cancer types. IQGAP3 expression was positively correlated with 38 types of immune cells in 27 cancer types and negatively correlated with 38 types of immune cells in one cancer type (Supplementary Figure S16B). These findings indicated that IQGAP3 expression was significantly correlated with the infiltration of immune cells in human cancer.

Correlation Between IQGAP3 Expression and Immune Modulators

We wished to further understand the relationship between IQGAP3 and the tumor microenvironment. We examined the correlation between IQGAP3 expression and immune checkpoint–related genes using the TCGA database. IQGAP3 expression was positively correlated with immune checkpoint–related genes in 31 cancer types. These immune checkpoint–related genes were CD274, CTLA4, HAVCR2, LAG3, PDCD1, PDCD1LG2, SIGLEC15, and TIGIT (Supplementary Figure S17). The TISIDB (http://cis.hku.hk/TISIDB/) database showed that IQGAP3 expression was positively correlated with 28 tumor-infiltrating lymphocytes, 45 immune stimulators, 24 immune inhibitors, 41 chemokines, 18 receptors, and 21 major histocompatibility complex (MHC) molecules in different cancer types (Figures 11A–F). These findings indicated that IQGAP3 had an indispensable role in the regulation of the immune response in human cancers.

FIGURE 11 | Analysis of the correlation between the IQGAP3 expression and diverse immune regulators. (A) The correlation between the IQGAP3 expression and 28 tumor-infiltrating lymphocytes was analyzed in pan-cancer by using the TISIDB database. (B) The correlation between the IQGAP3 expression and 45 immune stimulators in pan-cancer was examined by using the TISIDB database. (C) The correlation between the IQGAP3 expression and 24 immune inhibitors in pan-cancer was examined by using the TISIDB database. (D) The correlation between the IQGAP3 expression and 41 chemokines in pan-cancer was examined by using the TISIDB database. (E) The correlation between the IQGAP3 expression and 18 receptors in pan-cancer was examined by using the TISIDB database. (F) The correlation between the IQGAP3 expression and 21 MHCs in pan-cancer analysis was performed by using the TISIDB database.

Correlation Between IQGAP3 Expression and Drug Sensitivity

The results detailed above suggested that IQGAP3 may have roles in cancer progression. Next, we explored the correlation between IQGAP3 expression and sensitivity to different drugs in different cancer cell lines from the GDSC database and Cancer Therapeutics Response Portal (CTRP) (https://portals.broadinstitute.org/ctrp/).

IQGAP3 expression was positively correlated with sensitivity to the drugs TPCA-1, vorinostat, methotrexate, PHA-793887, PIK-93, XMD13-2, BHG712, AR-42, CUDC-101, ispinesib mesylate, SNX-2112, OSI-027, and vinblastine in the GDSC database (Figure 12A and Supplementary Table S8). In the CTRP database, we observed IQGAP3 expression to be positively correlated with sensitivity to the 53 drugs shown in Figure 12B and Supplementary Table S9. In summary, these results demonstrated that IQGAP3 expression was significantly correlated with sensitivity to many drugs in different cancer cell lines.

FIGURE 12 | Analysis of the correlation between the IQGAP3 expression and drug sensitivity in diverse human cancers. (A) The correlation between the IQGAP3 expression and drug sensitivity in diverse human cancer analyses was performed by employing the GDSC database. (B) The correlation between the IQGAP3 expression and drug sensitivity in diverse human cancer analyses was performed by employing the CTRP database.

IQGAP3 Shows High Expression in NSCLC

We showed that IQGAP3 expression was upregulated significantly in NSCLC using TGCG/LUAD/LUSC data. To verify this finding, we measured the mRNA and protein expressions of IQGAP3 in NSCLC. Real-time RT-qPCR and IHC assays showed that IQGAP3 expression was increased in NSCLC cell lines and lung cancer tissues compared with normal lung cells and normal lung tissues, respectively. These findings demonstrated that IQGAP3 expression was upregulated in NSCLC and indicated that IQGAP3 may have a crucial regulatory role in NSCLC progression (Figure 13).

FIGURE 13 | Analysis of the expression of IQGAP3 expression in NSCLC. (A) The expression of IQGAP3 in LUAD was examined by using the TCGA LUAD database. (B) The expression of IQGAP3 in LUAD was examined by using the TCGA LUAD database. (C) The expression of IQGAP3 in NSCLC cell lines was examined by using the qRT-PCR assay. (D) The expression of IQGAP3 in lung cancer was examined by using an IHC assay.

DISCUSSION

Resection, radiotherapy, and adjuvant chemotherapy can be used to treat cancer, but their efficacy is limited (Siegel et al., 2021). Integrative genomics, transcriptomics, proteomics, metabolomics, and single-cell “omics” have been developed to unearth the biomarkers related to the occurrence and development of cancer.

Emerging evidence has shown that IQGAP3 participates in different cancer-related signaling pathways. IQGAP3, via interaction with protein kinase C (PKC)δ and the competitive inhibition of the interaction between PKCδ and PKCα, leads to PKCα phosphorylation, as well as activation and promotion of cell proliferation (Lin et al., 2019). Studies have suggested that E2F1 can activate IQGAP3 expression at the transcription level (Lin et al., 2019). However, whether lncRNAs and miRs regulate IQGAP3 expression at the post-transcriptional level is not known.

Using various public databases, we found that IQGAP3 expression was upregulated in different types of human cancer. High expression of IQGAP3 was correlated significantly with the tumor stage and lymph-node metastasis of different cancer types. High expression of IQGAP3 was also associated with a poor prognosis in some types of human cancer. These results suggested IQGAP3 to have a crucial role in the oncogenesis and tumor progression in humans. Analyses of DNA methylation in the promoter region of IQGAP3 revealed that a low level of DNA methylation of IQGAP3 was significantly negatively correlated with IQGAP3 expression in ACC, BLCA, BRCA, CHOL, COAD, KIRC, LGG, LIHC, LUAD, LUSC, PAAD, READ, SARC, SKCM, STAD, TGCT, THCA, UCEC, UCS, and UVM. Survival analyses showed that a low DNA-methylation level of IQGAP3 was correlated with the better poor prognosis in diverse cancer. Amplification was the main reason for the mRNA of IQGAP3 to be upregulated in human cancer.

Usually, transcription factors bind to the promoter region of a gene and regulate gene transcription. We found that PTBP1 (an RNA-binding protein) could be a transcription factor of IQGAP3 in human cancer. PTBP1 expression was significantly positively corelated with IQGAP3 expression in human cancer. In LIHC, using short hairpin-RNA knockdown of PTBP1, reduced IQGAP3 expression markedly. These results indicated that PTBP1 may be a potential transcription factor of IQGAP3.

IQGAP3 was involved mainly in angiogenesis, apoptosis, cell cycle, cell differentiation, DNA damage, EMT, hypoxia, inflammation, invasion, metastasis, and proliferation in human cancer. High expression of IQGAP3 was positively correlated with the cell cycle, cell proliferation, DNA damage, DNA repair, EMT, and inflammation in different cancer types. These findings suggested that IQGAP3 has a pivotal role in the initiation and prognosis of human cancer. The genes most closely associated with IQGAP3 were those for CDC42, MYL6B, MYH2, RAC1, PPP1R16A, MEF2A, IST1, NDC80, HIF1A, ITPRIPL2, CALM1, RAC3, RAC2, SRGAP3, KIFAP3, COG2, NPHP4, IQGAP1, IQGAP2, and CLUAP1. The proteins most closely associated with IQGAP3 were CDC42, IQGAP2, KIF20, CDH1, MEN1, CTNNB1, CTNNA1, IQGAP1, RAC1, and CLIP1. These proteins have been reported to have crucial roles in the cell proliferation and cell cycle of diverse cancer types (Maldonado and Dharmawardhane, 2018).

TMB and MSI have emerged as specific and sensitive biomarkers of the response to immune-checkpoint inhibitors (Ghandi et al., 2019). We found that IQGAP3 expression was significantly associated with TMB and MSI in diverse cancer types. Our findings on the link between IQGAP3 expression and the abundance of immune cells, expression of immune checkpoint-related genes, and expression of proinflammatory moieties indicated that IQGAP3 has an indispensable role in regulation of the immune response in human cancer.

Let-7c-5p showed low expression in CHOL, BRCA, BLCA, UCEC, THCA, STAD, LUSC, LUAD, LIHC, KICH, HNSC, and COAD, and low expression of let-7c-5p correlated with the tumor stage in different cancer types. High expression of let-7c-5p not only correlated with a good prognosis in BRCA, CECS, ESCA, HNSC, KIRP, LIHC, LUAD, and LUSC, but also correlated with a poor prognosis in BLCA, PAAD, and STAD. IQGAP3AR expression was significantly negatively correlated with let-7c-5p expression. IQGAP3 expression was positively correlated with sensitivity to different drugs in different cancer cell lines. In total, we provided the first evidence that a IQGAP3AR/let-7c-5p/IQGAP3 axis has indispensable roles in the progression and immune response to different types of human cancer.

CONCLUSIONS

This is the first study to characterize the expression, prognosis, DNA methylation, and gene mutation of IQGAP3 in different types of human cancer. We showed that IQGAP3 expression was positively correlated with TMB, MSI, immune cell infiltration, and immune modulators in diverse human cancers. Collectively, our findings revealed that the IQGAP3AR/let-7c-5p axis–mediated upregulation of IQGAP3 expression promoted cancer progression and immune cell infiltration in different types of human cancer. The IQGAP3AR/let-7c-5p axis could be a diagnostic and therapeutic biomarker for cancers.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

ETHICS STATEMENT

This study was approved by the ethics committee of the Third Affiliated Hospital of Kunming Medical University (Yunnan Tumor Hospital), and informed consent was obtained from all patients.

AUTHOR CONTRIBUTIONS

YY, XJ, LT, and HY designed this work, performed related assays, and analyzed data. JW and DZ contributed to study materials. LD supervised and wrote the manuscript. All authors have read and approved the final version of the manuscript.

FUNDING

This work was supported by Yunnan Applied Basic Research Projects (YNWRMY-2019-067, 2019FE001) and Yunnan Province Specialized Training Grant for High-Level Healthcare Professionals (D-201614). The authors would like to thank support from the Department of Thoracic Surgery II, The Third Affiliated Hospital of Kunming Medical University (Yunnan Tumor Hospital), Kunming, China.

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2022.763248/full#supplementary-material

REFERENCES

Addeo, A., Friedlaender, A., Banna, G. L., and Weiss, G. J. (2021). TMB or Not TMB as a Biomarker: That Is the Question. Crit. Rev. oncology/hematology 163, 103374. doi:10.1016/j.critrevonc.2021.103374

Aran, D., Hu, Z., and Butte, A. J. (2017). xCell: Digitally Portraying the Tissue Cellular Heterogeneity Landscape. Genome Biol. 18, 220. doi:10.1186/s13059-017-1349-1

Basu, A., Bodycombe, N. E., Cheah, J. H., Price, E. V., Liu, K., Schaefer, G. I., et al. (2013). An Interactive Resource to Identify Cancer Genetic and Lineage Dependencies Targeted by Small Molecules. Cell 154, 1151–1161. doi:10.1016/j.cell.2013.08.003

Boland, C. R., and Goel, A. (2010). Microsatellite Instability in Colorectal Cancer. Gastroenterology 138, 2073e2073–2087. doi:10.1053/j.gastro.2009.12.064

Bradner, J. E., Hnisz, D., and Young, R. A. (2017). Transcriptional Addiction in Cancer. Cell 168, 629–643. doi:10.1016/j.cell.2016.12.013

Cao, Z., Pan, X., Yang, Y., Huang, Y., and Shen, H.-B. (2018). The lncLocator: a Subcellular Localization Predictor for Long Non-coding RNAs Based on a Stacked Ensemble Classifier. Bioinformatics (Oxford, England) 34, 2185–2194. doi:10.1093/bioinformatics/bty085

Cerami, E., Gao, J., Dogrusoz, U., Gross, B. E., Sumer, S. O., Aksoy, B. A., et al. (2012). The cBio Cancer Genomics Portal: An Open Platform for Exploring Multidimensional Cancer Genomics Data: Figure 1. Cancer Discov. 2, 401–404. doi:10.1158/2159-8290.cd-12-0095

Chandrashekar, D. S., Bashel, B., Balasubramanya, S. A. H., Creighton, C. J., Ponce-Rodriguez, I., Chakravarthi, B. V. S. K., et al. (2017). UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia 19, 649–658. doi:10.1016/j.neo.2017.05.002

Dongol, S., Zhang, Q., Qiu, C., Sun, C., Zhang, Z., Wu, H., et al. (2020). IQGAP3 Promotes Cancer Proliferation and Metastasis in High-grade S-erous O-varian C-ancer. Oncol. Lett. 20, 1179–1192. doi:10.3892/ol.2020.11664

Franz, M., Rodriguez, H., Lopes, C., Zuberi, K., Montojo, J., Bader, G. D., et al. (2018). GeneMANIA Update 2018. Nucleic Acids Res. 46, W60–w64. doi:10.1093/nar/gky311

Ghandi, M., Huang, F. W., Jané-Valbuena, J., Kryukov, G. V., Lo, C. C., McDonald, E. R., et al. (2019). Next-generation Characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503–508. doi:10.1038/s41586-019-1186-3

Hou, G.-X., Liu, P., Yang, J., and Wen, S. (2017). Mining Expression and Prognosis of Topoisomerase Isoforms in Non-small-cell Lung Cancer by Using Oncomine and Kaplan-Meier Plotter. PloS one 12, e0174515. doi:10.1371/journal.pone.0174515

Hu, J., Qiu, D., Yu, A., Hu, J., Deng, H., Li, H., et al. (2021). YTHDF1 Is a Potential Pan-Cancer Biomarker for Prognosis and Immunotherapy. Front. Oncol. 11, 607224. doi:10.3389/fonc.2021.607224

Hua, X., Long, Z. Q., Guo, L., Wen, W., Huang, X., and Zhang, W. W. (2020). IQGAP3 Overexpression Correlates with Poor Prognosis and Radiation Therapy Resistance in Breast Cancer. Front. Pharmacol. 11, 584450. doi:10.3389/fphar.2020.584450

Huang, Y., Jiang, X., Wang, Q., Gao, Z., and Wu, Y. (2021). The Expression and Clinical Significance of IQGAP3 in Gastric Cancer. Minerva Gastroenterol . doi:10.23736/S2724-5985.21.02931-4

Kang, Y.-J., Yang, D.-C., Kong, L., Hou, M., Meng, Y.-Q., Wei, L., et al. (2017). CPC2: a Fast and Accurate Coding Potential Calculator Based on Sequence Intrinsic Features. Nucleic Acids Res. 45, W12–w16. doi:10.1093/nar/gkx428

Klutstein, M., Nejman, D., Greenfield, R., and Cedar, H. (2016). DNA Methylation in Cancer and Aging. Cancer Res. 76, 3446–3450. doi:10.1158/0008-5472.can-15-3278

Li, J.-H., Liu, S., Zhou, H., Qu, L.-H., and Yang, J.-H. (2014). starBase v2.0: Decoding miRNA-ceRNA, miRNA-ncRNA and Protein-RNA Interaction Networks from Large-Scale CLIP-Seq Data. Nucl. Acids Res. 42, D92–D97. doi:10.1093/nar/gkt1248

Li, T., Fan, J., Wang, B., Traugh, N., Chen, Q., Liu, J. S., et al. (2017). TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res. 77, e108–e110. doi:10.1158/0008-5472.can-17-0307

Lin, M., Liu, Y., Ding, X., Ke, Q., Shi, J., Ma, Z., et al. (2019). E2F1 Transactivates IQGAP3 and Promotes Proliferation of Hepatocellular Carcinoma Cells through IQGAP3-Mediated PKC-Alpha Activation. Am. J. Cancer Res. 9, 285–299.

Maldonado, M. d. M., and Dharmawardhane, S. (2018). Targeting Rac and Cdc42 GTPases in Cancer. Cancer Res. 78, 3101–3111. doi:10.1158/0008-5472.can-18-0619

Mizuno, H., Kitada, K., Nakai, K., and Sarai, A. (2009). PrognoScan: a New Database for Meta-Analysis of the Prognostic Value of Genes. BMC Med. Genomics 2, 18. doi:10.1186/1755-8794-2-18

Modhukur, V., Iljasenko, T., Metsalu, T., Lokk, K., Laisk-Podar, T., and Vilo, J. (2018). MethSurv: a Web Tool to Perform Multivariable Survival Analysis Using DNA Methylation Data. Epigenomics 10, 277–288. doi:10.2217/epi-2017-0118

Ru, B., Wong, C. N., Tong, Y., Zhong, J. Y., Zhong, S. S. W., Wu, W. C., et al. (2019). TISIDB: an Integrated Repository portal for Tumor-Immune System Interactions. Bioinformatics (Oxford, England) 35, 4200–4202. doi:10.1093/bioinformatics/btz210

Schmidt, V. A., Chiariello, C. S., Capilla, E., Miller, F., and Bahou, W. F. (2008). Development of Hepatocellular Carcinoma in Iqgap2 -Deficient Mice Is IQGAP1 Dependent. Mol. Cel Biol 28, 1489–1502. doi:10.1128/mcb.01090-07

Shi, Y., Qin, N., Zhou, Q., Chen, Y., Huang, S., Chen, B., et al. (2017). Role of IQGAP3 in Metastasis and Epithelial-Mesenchymal Transition in Human Hepatocellular Carcinoma. J. Transl Med. 15, 176. doi:10.1186/s12967-017-1275-8

Siegel, R. L., Miller, K. D., Fuchs, H. E., and Jemal, A. (2021). Cancer Statistics, 2021. CA A. Cancer J. Clin. 71, 7–33. doi:10.3322/caac.21654

Siegel, R. L., Miller, K. D., and Jemal, A. (2019). Cancer Statistics, 2019. CA A. Cancer J. Clin. 69, 7–34. doi:10.3322/caac.21551

Swart-Mataraza, J. M., Li, Z., and Sacks, D. B. (2002). IQGAP1 Is a Component of Cdc42 Signaling to the Cytoskeleton. J. Biol. Chem. 277, 24753–24763. doi:10.1074/jbc.m111165200

Szklarczyk, D., Morris, J. H., Cook, H., Kuhn, M., Wyder, S., Simonovic, M., et al. (2017). The STRING Database in 2017: Quality-Controlled Protein-Protein Association Networks, Made Broadly Accessible. Nucleic Acids Res. 45, D362–d368. doi:10.1093/nar/gkw937

Tang, Z., Li, C., Kang, B., Gao, G., Li, C., and Zhang, Z. (2017). GEPIA: a Web Server for Cancer and normal Gene Expression Profiling and Interactive Analyses. Nucleic Acids Res. 45, W98–w102. doi:10.1093/nar/gkx247

Vasaikar, S. V., Straub, P., Wang, J., and Zhang, B. (2018). LinkedOmics: Analyzing Multi-Omics Data within and across 32 Cancer Types. Nucleic Acids Res. 46, D956–d963. doi:10.1093/nar/gkx1090

Wang, S., Watanabe, T., Noritake, J., Fukata, M., Yoshimura, T., Itoh, N., et al. (2007). IQGAP3, a Novel Effector of Rac1 and Cdc42, Regulates Neurite Outgrowth. J. Cel. Sci. 120, 567–577. doi:10.1242/jcs.03356

Wu, J., Chen, Z., Cao, H., Yu, Z., Feng, J., Wang, K., et al. (2019). High Expression of IQGAP3 Indicates Poor Prognosis in Colorectal Cancer Patients. Int. J. Biol. Markers 34, 348–355. doi:10.1177/1724600819876951

Xu, W., Xu, B., Yao, Y., Yu, X., Cao, H., Zhang, J., et al. (2016). Overexpression and Biological Function of IQGAP3 in Human Pancreatic Cancer. Am. J. Transl Res. 8, 5421–5432.

Xu, Y., Kim, Y.-H., Jeong, P., Piao, X.-M., Byun, Y. J., Seo, S. P., et al. (2019). Urinary Cell-free DNA IQGAP3/BMP4 Ratio as a Prognostic Marker for Non-muscle-invasive Bladder Cancer. Clin. Genitourinary Cancer 17, e704–e711. doi:10.1016/j.clgc.2019.04.001

Yang, W., Soares, J., Greninger, P., Edelman, E. J., Lightfoot, H., Forbes, S., et al. (2013). Genomics of Drug Sensitivity in Cancer (GDSC): a Resource for Therapeutic Biomarker Discovery in Cancer Cells. Nucleic Acids Res. 41, D955–D961. doi:10.1093/nar/gks1111

Yuan, H., Yan, M., Zhang, G., Liu, W., Deng, C., Liao, G., et al. (2019). CancerSEA: a Cancer Single-Cell State Atlas. Nucleic Acids Res. 47, D900–d908. doi:10.1093/nar/gky939

Zeng, Y., Jie, X., Wu, B., Wu, G., Liu, L., and Xu, S. (2020). IQGAP3 Interacts with Rad17 to Recruit the Mre11-Rad50-Nbs1 Complex and Contributes to Radioresistance in Lung Cancer. Cancer Lett. 493, 254–265. doi:10.1016/j.canlet.2020.08.042

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Yuan, Jiang, Tang, Yang, Wang, Zhang and Duan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 24 February 2022
doi: 10.3389/fmolb.2022.759792

Determining the Prognostic Value of Spliceosome-Related Genes in Hepatocellular Carcinoma Patients

Jun Liu1,2,³†, Liming Gu2,⁴†, Dangui Zhang⁵ and Wenli Li¹*

¹Reproductive Medicine Center, Yue Bei People’s Hospital, Shantou University Medical College, Shaoguan, China

²Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Shantou University Medical College, Shantou, China

³Medical Research Center, Yue Bei People’s Hospital, Shantou University Medical College, Shaoguan, China

⁴Department of Microbiology and Immunology, Center of Pathogen Biology and Immunology, Shantou University Medical College, Shantou, China

⁵Research Center of Translational Medicine, Second Affiliated Hospital of Shantou University Medical College, Shantou, China

Edited by:
Ismail Hosen, University of Dhaka, Bangladesh

Reviewed by:
Sayan Chatterjee, Guru Gobind Singh Indraprastha University, India
Qinglin Li, University of Chinese Academy of Sciences, China

* Correspondence: Wenli Li, wangyiliwenli@163.com

†These authors have contributed equally to this work

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 17 August 2021
Accepted: 08 February 2022
Published: 24 February 2022

Citation: Liu J, Gu L, Zhang D and Li W (2022) Determining the Prognostic Value of Spliceosome-Related Genes in Hepatocellular Carcinoma Patients. Front. Mol. Biosci. 9:759792. doi: 10.3389/fmolb.2022.759792

Background: The spliceosome plays an important role in mRNA alternative splicing and is aberrantly expressed in several tumors. However, the potential roles of spliceosome-related genes in the progression of hepatocellular carcinoma (HCC) remain poorly understood.

Materials and Methods: Patient data were acquired from public databases. Expression differences and survival analyses were used to assess the importance of spliceosome-related genes in HCC prognosis. To explore the potential regulatory mechanisms of these genes, a protein-protein interaction network was constructed and screened using univariate and multivariate Cox regression and random forest analyses. This was used to create a five-gene prognostic model. The prognostic value and predictive power of the five-gene signature were assessed using the Kaplan-Meier and time-dependent receiver operating characteristic analyses in the training set. These results were further validated in an independent external set. To facilitate clinical application, a nomogram was prepared to predict the overall survival of HCC patients. The relative expression of five genes was detected using real-time quantitative polymerase chain reaction.

Results: The analysis revealed that LSM1-7, SNRPB, SNRPD1-3, SNRPE, SNRPF, SNRPG, and SNRPN could be used as prognostic biomarkers in HCC patients. Moreover, the five-gene risk model could clearly distinguish between the high-and low-risk groups. Furthermore, the risk model was associated with the tumor mutation burden, immune cell infiltration of CD8⁺ T cells, natural killer T cells, M2 macrophages, and immune checkpoint inhibitors, which also demonstrated the predictive efficacy of this risk model in HCC immunotherapy.

Conclusion: Spliceosome-related genes and the five-gene signature could serve as novel prognostic biomarkers for HCC patients, aiding clinical patient monitoring and follow-up.

Keywords: snRNP, prognostic genes, HCC, TMB, immune cell

INTRODUCTION

Hepatocellular carcinoma (HCC) is a malignant tumor that is associated with high morbidity and low advanced survival rates worldwide (Bray et al., 2018). The lack of biomarkers for early diagnosis and prognosis presents a challenge for medical practitioners (Nault and Villanueva, 2015). Investigators have exhaustively researched reliable prognostic indicators that may help in determining the risk of adverse outcomes in patients using tissue or blood samples (O'Brien et al., 2019; Ayoub et al., 2019). Unfortunately, universally acceptable biomarkers or predictive models have not been found yet.

The spliceosome is an organelle-like complex that plays a significant role in regulating gene expression and producing protein diversity (Nilsen and Graveley, 2010). The spliceosome consists of five small nuclear ribonucleoproteins (snRNP; U1, U2, U4, U5, and U6), and the Lsm/sm proteins form the core scaffold of snRNP (Roth et al., 2018). These molecules, including the snRNPs and Lsm proteins, are involved in the splicing of pre-mRNA. SnRNP genes have been regarded as oncogenic in glioblastoma (Correa et al., 2016), and their high expression is also correlated with poor prognosis in non-small cell lung cancer (Valles et al., 2012). However, contradictory to these findings, snRNPs were identified as metastasis suppressor genes in prostate cancer (Yi et al., 2009), highlighting their diverse roles. To date, there have been few reports on the involvement of snRNPs in the progression of HCC and its clinical significance (Peng et al., 2020). In addition, the precise role of Lsm proteins in HCC has not yet been established.

In the current study, we determined the expression of snRNPs and Lsm proteins in HCC and its implication in the prognosis of HCC patients. We measured the association between the expression of snRNPs and Lsm proteins and various clinical characteristics of HCC patients. Further we determined the prognostic value of these markers by using survival curves and Cox regression analysis. We also analyzed the genes with expression patterns similar to those of snRNPs and Lsm proteins to find potential regulatory networks. We successfully extracted a five-gene signature from the co-expressed genes, which will aid clinicians in prognosis assessment.

MATERIALS AND METHODS

Acquisition of Patient Data

The RNA-seq of HCC, single nucleotide mutation, and clinical data of patients were downloaded from The Cancer Genome Atlas (TCGA) (https://cancergenome.nih.gov). Normal tissues adjacent to the tumor were available for 50 patients, which were used as controls. Samples with insufficient information were excluded from the analysis. Expression and clinical data (ICGC-LIRI-JP) acquired from the International Cancer Genome Consortium (ICGC) (https://www.icgc.org) were used for validation.

Analysis of Expression Difference

We used data from TCGA database to analyze the differences in expression of proteins between tumors and normal tissue. We verified these results using data from the ICGC database. Protein expression was compared using the Wilcoxon signed-rank test.

Prognosis and Biological Function Analysis and Protein-Protein Interaction Network Construction

Comparison of overall survival (OS) of patients with high or low snRNPs and Lsm expression was conducted using the Kaplan-Meier (K-M) survival analysis. The best separation was used to define the high-and low-risk groups. The relationship between clinicopathologic features and snRNPs and Lsm was analyzed using the Kruskal test. Gene ontology (GO) was used to identify potential molecular functions of snRNPs and Lsm involved in HCC development. The GeneMANIA database (http://genemania.org) (Warde-Farley et al., 2010) was used to construct a protein-protein interaction network (PPI) and render significant pathways.

Generation and Validation of the Gene Signature

Univariate Cox regression was used to screen the genes associated with OS from the PPI network in the TCGA cohort. Next, the relative importance of OS-related genes was identified and ranked based on the random forest algorithm. Subsequently, multivariate Cox regression analysis was conducted to construct a risk model: risk score = β1×1 + β2×2 + β3×3 +,…,+ βnxn. The HCC patients with survival data were separated into low- and high-risk groups based on the median risk score. The K-M survival and receiver operator characteristic (ROC) curves for the cases were obtained. The ICGC cohort was used as an external validation cohort in the analysis. Univariate and multivariate Cox regression analyses were used to compare the influence of the signature on survival along with other clinical characteristics.

Correlation Analysis Between the Gene Signature and Tumor Mutation Burden and Immune Cell Infiltration

The tumor mutation burden (TMB) was calculated from single nucleotide mutations, and the distribution of TMB and somatic mutations between high- and low-risk groups was analyzed in the TCGA HCC cohort using the “maftools” R package. Simultaneously, single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT algorithms were used to assess the extent of immune cell infiltration.

Construction and Evaluation of a Predictive Nomogram

A combined model comprising all independent prognostic factors was constructed using the rms package to assess the probability of OS in HCC patients. Discrimination and calibration were performed to evaluate the nomograms. Discrimination of the nomogram was performed using decision curve analysis (DCA). The calibration curve of the nomogram was visualized by plotting the nomogram prediction probabilities against the observed rates.

Cell Lines and Cell Culture

The normal human cell line LO2 and HCC cells, HCCLM3, Hep-G2, and Huh7 were purchased from the American Type Culture Collection (ATCC, Manassas, VA, United States). All the cells were cultured at 37°C under 5% CO2.

Quantitative Reverse-Transcription Polymerase Chain Reaction

The PCR primers for SNRPB, LSM10, ATXN2, PRPF3, EDC3, and β action were designed with NCBI primer Blast tool. Detailed primer sequences of these genes were listed in Supplement Table S1. The total RNA was extracted from 4 cell lines, subsequently, RNAs were reverse transcribed into cDNA through reverse transcription Kit (Beyotime, https://www.beyotime.com/). Then the relative expression of these genes was detected using Real-Time Quantitative polymerase Chain Reaction (RT-qPCR).

RESULTS

Patient Characteristics

We used data mining to analyze the prognostic significance of hepatic SNRPB in HCC patients. Data for 370 primary tumors including clinical and gene expression data were downloaded from the TCGA databank. Similarly, data on 232 patients were downloaded from the ICGC databank. The patients with TCGA cohort were draw from United States, and were predominantly Caucasian and African-American. While, the patients with ICGC cohort come from Japan, and were predominantly Asian. The clinical data for patients, including survival status, age, sex, histological grade, stage, and prior malignancy history, have been described in Table 1.

TABLE 1 | The clinical baseline characteristics of HCC patients.

Transcriptional Expression Landscape of Spliceosome Related Genes

The expression levels of LSM1-7, SNRPD1-3, SNRPB, SNRPF, SNRPG, SNRPE, and SNRPN were compared between cancer and non-cancer liver tissues in HCC patients from the TCGA and ICGC cohorts. The mRNA expression of all 15 genes in the cancer tissues was significantly higher than that in the non-cancer tissues in both the TCGA and ICGC cohorts (Figures 1A,B). We then compared the expression of all genes at different tumor stages and grade classifications. The results indicated that the expression levels of LSM2, LSM5, SNRPB, SNRPD1, SNRPD2, SNRPD3, and SNRPG were higher in tumors at more advanced clinical stages in the TCGA cohort (Figure 1C). Further, increased expression of LSM1, LSM2, LSM4, LSM5, LSM7, SNRPB, SNRPD1, SNRPD2, SNRPE, SNRPF, and SNRPG significantly correlated with stage classification in the ICGC cohort (Figure 1E). We also found that high expression of LSM1, LSM2, LSM4, LSM5, LSM7, SNRPB, SNRPD1, SNRPD2, SNRPE, SNRPF, and SNRPG was prevalent in tumors with higher histologic grades (Figure 1D). Thus, we concluded that snRNPs and LSM were overexpressed in HCC tumors, and their expression was significantly correlated with clinical stage and grade classification.

FIGURE 1 | Comparison of spliceosome-related genes in the TCGA and ICGC cohort. Expression difference of LSM1-7, SNRPB, SNRPD1-3, SNRPE, SNRPF, SNRPG, and SNRPN between HCC tumor and non-tumor tissues in TCGA (A) and ICGC (B) cohort. tumor and paired tumor-adjacent tissues from the TCGA cohort. Comparison of spliceosome-related genes expression of patients with different clinical stages (C) and histologic grade (D) from the TCGA cohort. (E) Comparison of spliceosome-related genes expression of patients with different clinical stages from the ICGC cohort. (G) means histological grade, and stage means pathologic TNM staging. *p < 0.05, **p < 0.01, ***p < 0.001.

Prognostic Value of Spliceosome Related Genes

The K-M survival curves suggested that HCC patients with higher expression of LSM1-5, LSM7, SNRPB, SNRPD1, SNRPD2, SNRPE, SNRPF, and SNRPG showed shorter survival rates in the TCGA cohort (Figures 2A-L). Similar results were confirmed in the ICGC cohort: patients with increased LSM2-5, LSM7, SNRPB, SNRPD1, SNRPD2, SNRPE, SNRPF, and SNRPG expression had shorter OS (Figures 3A-L). In addition, high SNRPD3 expression was associated with poor prognosis in the ICGC cohort (Figure 3I). These results further validated the prognostic value of LSM2-5, LSM7, SNRPB, SNRPD1, SNRPD2, SNRPE, SNRPF, and SNRPG in HCC.

FIGURE 2 | Survival analysis of spliceosome-related genes for HCC patients in TCGA cohort. Overall survival of LSM1 (A), LSM2 (B), LSM3 (C), LSM4 (D), LSM5 (E), LSM7 (F), SNRPB (G), SNRPD1 (H), SNRPD2 (I), SNRPE (J), SNRPF (K), and SNRPG (L) were performed by Kaplan-Meier plotter.

FIGURE 3 | Survival analysis of spliceosome-related genes for HCC patients in ICGC cohort. Overall survival of LSM2 (A), LSM3 (B), LSM4 (C), LSM5 (D), LSM7 (E), SNRPB (F), SNRPD1 (G), SNRPD2 (H), SNRPD3 (I) SNRPE (J), SNRPF (K), and SNRPG (L) were performed by Kaplan-Meier plotter.

Co-Expression Correlation and Functional Enrichment Analysis

Co-expression analysis was performed to identify associations among these genes, and the results indicated that there was a very strong correlation among these subunit genes in both the TCGA and ICGC cohorts (Figures 4A,B). The locations of the 16 subunit genes on chromosomes are shown in Figure 4C. To further understand the role of these genes in the development of liver cancer, a protein-protein interaction network was constructed using the GeneMANIA database (Figure 4D). Functional enrichment analysis revealed that these molecules were mainly involved in mRNA splicing, the spliceosomal complex, and Sm-like protein family complex, as expected. GO analyses also showed that these genes were mainly associated with RNA splicing and spliceosomal snRNP assembly (Figure 4E). Meanwhile, KEGG analysis also demonstrated that these genes were involved in Spliceosome, RNA degradation, and Systemic lupus erythematosus (Figure 4F).

FIGURE 4 | | Expression correlation among spliceosome-related genes and construction of protein-protein interaction (PPI) network. Expression correlation among spliceosome-related genes in the TCGA (A) and (B) cohort. (C) The location of spliceosome-related genes on chromosomes. (D) The PPI network of spliceosome-related genes was built based on the GeneMANIA database. (E) Gene ontology enrichment analysis of spliceosome-related genes. (F) KEGG enrichment analysis of spliceosome-related genes.

Generation and Validation of a Prognostic Signature

First, the genes from the PPI network that were significantly associated with the OS of HCC patients from the TCGA cohort were selected as candidate prognostic genes via univariate Cox regression (Figure 5A). Subsequently, a random forest was used to estimate the variable importance values of these prognostic genes and rank them. In this way, the top ten important prognostic genes were identified (Figure 5B). From these ten genes we were able to generate 1,023 risk models. Finally, we compared the -log10 P log-rank value of every possible risk model based on the K-M analysis, and the five-gene signature was identified as the best model (Figure 5C). Subsequently, based on the estimated Cox regression weights, we established a prognostic model, whose formula was computed as Risk score = 0.563 ×LSM10 + 0.527 × ATXN2 + 0.025× SNRPB + 0.167× PRPF3 + 0.253× EDC3. In this risk model, all the coefficients of the five genes were positive. We then calculated the risk score for each patient in the training set and ranked them. The K-M curves indicated that patients in the high-risk group had significantly worse OS than those in the low-risk group (p < 0.001, hazard ratio, HR = 2.34) (Figure 5D). The green dots represent survival, the red dots represent dead patients, and the death risk increased from left to right on the X-axis. The results showed that the dead patients were predisposed to be included in the high-risk score group (Figure 5E). Furthermore, principal component analysis (PCA) was applied to examine the power of the five-gene signature in discriminating high- and low-risk patients. The results indicated that the risk model made an excellent distinction between high- and low-risk patients in the TCGA cohort (Figure 5F). The Chi-squared test revealed that the high-risk group showed poorer survival status, clinical stage, pathologic grade, and T stage compared to the low-risk group, suggesting that highly malignant HCC is associated with high risk (Figure 5G). Pathological stages in the prognostic model showed the prognostic value of the gene signature in the TCGA cohort via univariate and multivariate Cox regression analyses (Figure 5H). Further, the prognostic value of the five-gene signature was assessed by calculating the area under the curve (AUC) of a time-dependent ROC curve (Figure 5I). The higher the AUC, the better the model’s performance; in the training cohort, the five-gene signature exhibited better predictive efficacy than clinical stage.

FIGURE 5 | Generation of the prognostic signature. (A) The univariate Cox regression was applied to filter spliceosome-related genes related to overall survival. (B) The random forest was used to screen and rank the relative important gene for overall survival. (C) The Kaplan-Meier (K–M) plotter was employed to evaluate the prognostic value of 1,023 combinations, the top 20 was identified and sorted according to the p value of (K-M). (D) Kaplan-Meier curves stratified by the five-gene prognostic signature in the TCGA cohort (E) Risk score distribution and survival overview in the TCGA cohort. (F) Principal component analysis (PCA) indicated the risk model has high discriminatory accuracy in distinguishing low-risk group from high-risk groups. (G) Pie charts showing the Chi-squared test of clinicopathologic factors for risk model in HCC. (H) Univariate and multivariate association of the prognostic model and clinicopathological characteristics with overall survival in the TCGA cohort. (I) Time‐dependent ROC curves of the signature and clinical stage in the TCGA cohort.

To confirm the robustness of the five-gene signature, we verified the risk signature in the ICGC cohort. The K-M plots demonstrated that HCC patients with high risk had poorer OS compared to low-risk patients in the validation set (Figure 6A). As seen in the training set, high-risk patients had a higher risk score distribution (Figure 6B). Further, most of the deaths in the validation group were clustered in the high-risk group (Figure 6B). Moreover, PCA showed good discrimination between the high- and low-risk groups (Figure 6C). In addition, the increased value of the risk model significantly correlated with poorer survival status, but not stage classification (Figure 6D). We then performed univariate and multivariate Cox analyses to identify the factors that might affect prognosis. Analysis from the ICGC cohort showed that tumor stage classification, sex, and risk signature were independent factors associated with prognosis (Figure 6E). The time-dependent AUC suggested that the five-gene signature showed favorable performance and stability in predicting the OS of HCC patients (Figure 6F).

FIGURE 6 | Validation of the gene signature in ICGC cohort. (A) The Kaplan-Meier analysis of the signature in ICGC cohort. (B) Risk score distribution and survival overview in the ICGC cohort. (C) Principal component analysis showed that the high- and low-risk groups exhibited distinct layout modes. (D) Pie charts showing the Chi-squared test of clinicopathologic factors for risk model in HCC. (E) Univariate and multivariate association of the prognostic model and clinicopathological characteristics with overall survival in the ICGC cohort. (F) Time‐dependent ROC curves of the signature and clinical stage in the ICGC cohort.

Functional Characteristics of the Five-Gene Signature

Multiple studies have shown that TMB is an emerging biomarker for predicting the efficacy of immunotherapy (Shrestha et al., 2018; Wong et al., 2021). By determining the mutation landscape of the TCGA-LIHC cohort, we found that the high-risk group had a higher TMB than the low-risk group (Figure 7A). Subsequently, we analyzed the distribution of variation of somatic mutations in the high- and low-risk groups in the TCGA cohort. In the analysis, 90.4% of the 177 samples in the high-risk group had a missense mutation, while 79.78% of the 178 samples in the low-risk group had a missense mutation (Figures 7B,C). Further, there was a significant difference in the abundance of TP53 mutations between the high-risk and the low-risk groups: 44% of patients had TP53 mutation in the high-risk group, and only 12% had the mutation in the low-risk group. Moreover, the distribution of risk was significantly different between the TP53 wild-type and TP53-mutation groups (Figure 7D). A total of 43 immune checkpoint inhibitors were obtained from published studies, and we compared their expression levels between the high-and low-risk groups, which indicated that most of them were more highly expressed in the high-risk group than in the low-risk group (Figure 7E). Furthermore, the ssGSEA and CIBERSORT methods were applied to delineate the landscape of immune cell infiltrates in HCC patients from the TCGA cohort (Figures 7F,G). The results demonstrated that CD8⁺ T cells, mast cells, monocytes, natural killer cells, and T helper cells had high infiltration in the low-risk group whereas M2 macrophages had high infiltration in the high-risk group (Figures 7F,G). These results explain why the low-risk group had better OS compared to the high-risk group, which might be associated with the relatively low TMB and high immune cell infiltration.

FIGURE 7 | Potential therapeutic value of the gene signature. (A) The distribution of tumor mutation burden in the high- and low-risk groups. Waterfall plot of tumor somatic mutations in HCC patients with high-risk scores (B) and low-risk scores (C). (D) The distribution of high- and low-risk group in mutation and wild TP53. (E) The expression landscape of immune checkpoint inhibitor in high- and low-risk group. The distribution of immune cells calculated by ssGSEA (F) and CIBERSORT (G) in the TCGA cohort. *p < 0.05, **p < 0.01, ***p < 0.001.

Construction and Validation of a Predictive Nomogram

To facilitate the application of the five-gene signature in clinical practice and to predict the probability of 1-, 3-, and 5- year OS, we generated a nomogram composed of two independent prognostic factors (pathologic stage and the five-gene signature) (Figure 8A), based on the multiple regression analyses. The calibration plots were visualized to evaluate the performance of the nomogram in predicting OS. As shown in Figure 8B, the calibration curves were close to the standard curves, indicating that the nomogram performed well in predicting OS. Finally, DCA was used to compare the different prediction models. Graphically, the DCA shows the clinical usefulness of each model based on a continuum of potential thresholds for death (X-axis) and the net benefit of using the model to risk-stratify patients (Y-axis), while assuming that no patient will die (Figure 8C). In this analysis, the combined model provided a larger net benefit for 1-, 3-, and 5-years OS compared with both the signature and pathological stage models.

FIGURE 8 | Construction and validation of the predictive nomogram. (A) Nomogram predicting 1‐, 3‐ and 5‐years OS for HCC patients. The nomogram is applied by adding up the points identified on the points scale for each variable. The total points projected on the bottom scales indicate the probability of 1‐, 3‐ and 5‐years OS. (B) The calibration curve for predicting 1‐, 3‐ and 5‐years OS for patients with HCC. (C) Relations between net benefit and threshold probability at 1-year, 3-years, and 5-years survival predictions.

The RNA Expression of ATXN2, EDC3, LSM10, PRPF3, and SNRPB

To validate the RNA-seq data, RT-qPCR was used to detect the RNA expression of five genes in vitro. These results suggested that ATXN2, EDC3, LSM10, PRPF3, and SNRPB were upregulated in HCC cell lines, including HCCLM3, Hep-G2, and Huh7, compared to human normal live cell LO2 (Figures 9A–E).

FIGURE 9 | The mRNA expression of five genes. The relative expression of ATXN2 (A), EDC3 (B), LSM10 (C), PRPF3 (D), and SNRPB (E) was detected in HCC cell lines and human normal liver cell. ***p < 0.001.

DISCUSSION

HCC is one of the most fatal solid tumors worldwide (Bray et al., 2018). Although surgery is the first option for HCC patients who can undergo resections, it is still a challenge to deal with recurrence or metastasis, which may shorten the OS by decades (Forner et al., 2018). However, new technologies such as high-throughput genetic sequencing and gene chips give us a more microscopic perspective of complex molecular networks underlying HCC. Further, bioinformatics, which is an emerging approach based on genomics and statistics, can be used to find new predictive information (Berhane et al., 2016; Tai et al., 2019). Wei et al. recently reported a nine-HCV related gene signature for predicting the overall survival of HCC, based on Gene Expression Omnibus (GEO) and TCGA databases (Wei et al., 2021). Liu et al. explored the prognosis value of the mRNA expression-based stemness index (mRNAsi) in HCC, and constructed a new three-gene signature with mRNAsi, based on TCGA and ICGC databases (Liu et al., 2021). In this study, we developed a novel five-gene signature model to predict the outcome of HCC patients based on the spliceosome-related gene expression. Here, the precent study provides a new dimension to understand the malignant process of HCC.

The spliceosome is mainly involved in removing pre-mRNA introns and generating multiple mature mRNA isoforms (Shi, 2017; Wan et al., 2019). Most spliceosome snRNPs contain a common set of core Sm proteins such as SNRPD1, SNRPD2, SNRPD3, SNRPB, SNRPE, SNRPF, and SNRPG (Papasaikas and Valcárcel, 2016). The Lsm1-7/Pat1 RNA-binding protein complex is an Lsm/Sm protein involved in mRNA decay and acts as a chaperone for spliceosomal RNA (Montemayor et al., 2020). SNRPB is an oncogenic splicing factor that participates in the formation of the spliceosome complex SmB/B (Li et al., 2019). Among BRCA1 carriers, the minor allele of rs6138178 in SNRPB was significantly associated with lower risk of breast cancer risk (Wang et al., 2010). In prostate cancer, SNRPB was initially regarded as a metastasis suppressor gene, as its mRNA expression significantly decreased in metastasizing tumors compared to that in non-tumor tissue (Yi et al., 2009). SNRPB was also found to be one of the hubs splicing factors that participate in splicing regulation and are detrimental to the prognosis of glioblastoma, as indicated by a significant correlation between SNRPB expression and the alternative terminator of KIF4A exon32 (coefficient = 0.70) (Li et al., 2019). Recent studies suggest that LSM10 may be implicated in tumor-associated modifications in molecular pathways that control histone gene expression during the cell cycle (Ghule et al., 2009). ATXN2 has been reported to be overexpressed in pancreatic adenocarcinoma (PAAD) tumor tissues, and overexpression of ATXN2 promotes PADD cell proliferation, migration, and invasion (Fang et al., 2021). EDC3 plays an important role in regulating RNA decapping and destruction in cancer progression, and it can also promote tumor growth and invasion (Bearss et al., 2021). However, the expression level and clinical value of LSM10, ATXN2, and EDC3 in HCC have not been explored. Our results showed that these genes were highly expressed in HCC tumor tissue, and high expression was associated with poor OS. Therefore, we infer that LSM10, ATXN2, and EDC3 may act as upstream molecules to promote HCC progression. It has been found that PRPF3 expression was increased in HCC tissues, and could regulate HNF4alpha expression and reinforce the proliferative response to epidermal growth factors (Niehof and Borlak, 2008; Liu et al., 2020a).

Although immunotherapy has been recently used as cancer treatment in clinical practice, studies have found that only approximately 20% of patients with solid tumors may benefit from this treatment (Zongyi and Xiaowu, 2020). TMB, immune cell infiltration, and immune checkpoint inhibitors play critical roles in anti-tumor immune responses and immunotherapy (Zheng et al., 2017; Wu et al., 2019; Liu et al., 2020b; Zhang et al., 2020). Several studies have shown evaluation of TMB, CD8⁺ T cells, CD4⁺ T cells, M2 macrophages, CD276, CD274, and CTLA4 may help in predicting immunotherapy efficacy (Seaman et al., 2017; Ma et al., 2019; Pai et al., 2019; Wu et al., 2021). Recent reports suggest that immune-related gene signatures correlate with the immunophenotype, which can predict the effectiveness of immunotherapy (Cancer Genome Atlas Research Network, 2017; Chen et al., 2020). In this study, we constructed a five spliceosome-related gene risk model, and the high-risk group showed higher TMB and immune checkpoint inhibitor expression and lower immune cell infiltration. This also explains why HCC patients in the high-risk group had worse OS.

In conclusion, we delineated the expression landscape of spliceosome-related genes and revealed the prognostic value of these genes in HCC. We then constructed a spliceosome-related gene signature and developed a nomogram to predict the OS of patients with HCC. We also explored the relationship between TMB, immune checkpoint inhibitors, immune cells, and risk score. Then the five genes were overexpressed in HCC cells than normal liver cell.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

AUTHOR CONTRIBUTIONS

JL, LG, and WL were both involved in analysis and interpretation of the study, drafting the manuscript equally. WL, LG, DZ, and JL took part in the design and data collection process of the study and is responsible for the content of the manuscript. All authors read and approved the final manuscript.

FUNDING

This work was supported by the Undergraduate Research Training Program (UGRTP) (Grant no. LE0003).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2022.759792/full#supplementary-material

REFERENCES

Ayoub, W. S., Steggerda, J., Yang, J. D., Kuo, A., Sundaram, V., and Lu, S. C. (2019). Current Status of Hepatocellular Carcinoma Detection: Screening Strategies and Novel Biomarkers. Ther. Adv. Med. Oncol. 11, 1758835919869120. doi:10.1177/1758835919869120

Bearss, J. J., Padi, S. K., Singh, N., Cardo-Vila, M., Song, J. H., Mouneimne, G., et al. (2021). EDC3 Phosphorylation Regulates Growth and Invasion through Controlling P-Body Formation and Dynamics. EMBO Rep. 22 (4), e50835. doi:10.15252/embr.202050835

Berhane, S., Toyoda, H., Tada, T., Kumada, T., Kagebayashi, C., Satomura, S., et al. (2016). Role of the GALAD and BALAD-2 Serologic Models in Diagnosis of Hepatocellular Carcinoma and Prediction of Survival in Patients. Clin. Gastroenterol. Hepatol. 14 (6), 875–886. doi:10.1016/j.cgh.2015.12.042

Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., and Jemal, A. (2018). Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer J. Clinicians 68 (6), 394–424. doi:10.3322/caac.21492

Chen, W., Ou, M., Tang, D., Dai, Y., and Du, W. (2020). Identification and Validation of Immune-Related Gene Prognostic Signature for Hepatocellular Carcinoma. J. Immunol. Res. 2020, 5494858. doi:10.1155/2020/5494858

Cancer Genome Atlas Research Network (2017), Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma, Cell 169(7), 1327-e23. doi:10.1016/j.cell.2017.05.046

Correa, B. R., de Araujo, P. R., Qiao, M., Burns, S. C., Chen, C., Schlegel, R., et al. (2016). Functional Genomics Analyses of RNA-Binding Proteins Reveal the Splicing Regulator SNRPB as an Oncogenic Candidate in Glioblastoma. Genome Biol. 17 (1), 125. doi:10.1186/s13059-016-0990-4

Fang, L., Wang, S. H., Cui, Y. G., and Huang, L. (2021). LINC00941 Promotes Proliferation and Metastasis of Pancreatic Adenocarcinoma by Competitively Binding miR-873-3p and Thus Upregulates ATXN2. Eur. Rev. Med. Pharmacol. Sci. 25 (4), 1861–1868. doi:10.26355/eurrev_202102_25081

Forner, A., Reig, M., and Bruix, J. (2018). Hepatocellular Carcinoma. The Lancet 391 (10127), 1301–1314. doi:10.1016/s0140-6736(18)30010-2

Ghule, P. N., Dominski, Z., Lian, J. B., Stein, J. L., van Wijnen, A. J., and Stein, G. S. (2009). The Subnuclear Organization of Histone Gene Regulatory Proteins and 3′ End Processing Factors of normal Somatic and Embryonic Stem Cells Is Compromised in Selected Human Cancer Cell Types. J. Cel. Physiol. 220 (1), 129–135. doi:10.1002/jcp.21740

Li, Y., Ren, Z., Peng, Y., Li, K., Wang, X., Huang, G., et al. (2019). Classification of Glioma Based on Prognostic Alternative Splicing. BMC Med. Genomics 12 (1), 165. doi:10.1186/s12920-019-0603-7

Liu, J., Lu, J., and Li, W. (2021). A Comprehensive Prognostic and Immunological Analysis of a New Three-Gene Signature in Hepatocellular Carcinoma. Stem Cell Int 2021, 5546032. doi:10.1155/2021/5546032

Liu, T., Tan, J., Wu, M., Fan, W., Wei, J., Zhu, B., et al. (2020). High-affinity Neoantigens Correlate with Better Prognosis and Trigger Potent Antihepatocellular Carcinoma (HCC) Activity by Activating CD39CD8 T Cells. Gut . doi:10.1136/gutjnl-2020-322196

Liu, Y., Yang, Y., Luo, Y., Wang, J., Lu, X., Yang, Z., et al. (2020). Prognostic Potential of PRPF3 in Hepatocellular Carcinoma. Aging 12 (1), 912–930. doi:10.18632/aging.102665

Ma, J., Zheng, B., Goswami, S., Meng, L., Zhang, D., Cao, C., et al. (2019). PD1Hi CD8+ T Cells Correlate with Exhausted Signature and Poor Clinical Outcome in Hepatocellular Carcinoma. J. Immunotherapy Cancer 7 (1), 331. doi:10.1186/s40425-019-0814-7

Montemayor, E. J., Virta, J. M., Hayes, S. M., Nomura, Y., Brow, D. A., and Butcher, S. E. (2020). Molecular Basis for the Distinct Cellular Functions of the Lsm1-7 and Lsm2-8 Complexes. RNA 26 (10), 1400–1413. doi:10.1261/rna.075879.120

Nault, J.-C., and Villanueva, A. (2015). Intratumor Molecular and Phenotypic Diversity in Hepatocellular Carcinoma. Clin. Cancer Res. 21 (8), 1786–1788. doi:10.1158/1078-0432.ccr-14-2602

Niehof, M., and Borlak, J. (2008). EPS15R, EPS15R, TASP1, and PRPF3 Are Novel Disease Candidate Genes Targeted by HNF4α Splice Variants in Hepatocellular Carcinomas. Gastroenterology 134 (4), 1191–1202. doi:10.1053/j.gastro.2008.01.027

Nilsen, T. W., and Graveley, B. R. (2010). Expansion of the Eukaryotic Proteome by Alternative Splicing. Nature 463 (7280), 457–463. doi:10.1038/nature08909

O'Brien, A., Zhou, T., Tan, C., Alpini, G., and Glaser, S. (2019). Role of Non-coding RNAs in the Progression of Liver Cancer: Evidence from Experimental Models. Cancers (Basel) 11 (11). doi:10.3390/cancers11111652

Pai, C. S., Simons, D. M., Lu, X., Evans, M., Wei, J., Wang, Y. H., et al. (2019). Tumor-conditional Anti-CTLA4 Uncouples Antitumor Efficacy from Immunotherapy-Related Toxicity. J. Clin. Invest. 129 (1), 349–363. doi:10.1172/JCI123391

Papasaikas, P., and Valcárcel, J. (2016). The Spliceosome: The Ultimate RNA Chaperone and Sculptor. Trends Biochem. Sci. 41 (1), 33–45. doi:10.1016/j.tibs.2015.11.003

Peng, N., Li, J., He, J., Shi, X., Huang, H., Mo, Y., et al. (2020). c-Myc-mediated SNRPB Upregulation Functions as an Oncogene in Hepatocellular Carcinoma. Cell Biol Int .

Roth, A. J., Shuman, S., and Schwer, B. (2018). Defining Essential Elements and Genetic Interactions of the Yeast Lsm2-8 Ring and Demonstration that Essentiality of Lsm2-8 Is Bypassed via Overexpression of U6 snRNA or the U6 snRNP Subunit Prp24. RNA 24 (6), 853–864. doi:10.1261/rna.066175.118

Seaman, S., Zhu, Z., Saha, S., Zhang, X. M., Yang, M. Y., Hilton, M. B., et al. (2017). Eradication of Tumors through Simultaneous Ablation of CD276/B7-H3-Positive Tumor Cells and Tumor Vasculature. Cancer Cell 31 (4), 501–e8. doi:10.1016/j.ccell.2017.03.005

Shi, Y. (2017). The Spliceosome: A Protein-Directed Metalloribozyme. J. Mol. Biol. 429 (17), 2640–2653. doi:10.1016/j.jmb.2017.07.010

Shrestha, R., Prithviraj, P., Anaka, M., Bridle, K. R., Crawford, D. H. G., Dhungel, B., et al. (2018). Monitoring Immune Checkpoint Regulators as Predictive Biomarkers in Hepatocellular Carcinoma. Front. Oncol. 8, 269. doi:10.3389/fonc.2018.00269

Tai, D., Choo, S. P., and Chew, V. (2019). Rationale of Immunotherapy in Hepatocellular Carcinoma and its Potential Biomarkers. Cancers (Basel) 11 (12). doi:10.3390/cancers11121926

Valles, I., Pajares, M. J., Segura, V., Guruceaga, E., Gomez-Roman, J., Blanco, D., et al. (2012). Identification of Novel Deregulated RNA Metabolism-Related Genes in Non-small Cell Lung Cancer. PLoS One 7 (8), e42086. doi:10.1371/journal.pone.0042086

Wan, R., Bai, R., and Shi, Y. (2019). Molecular Choreography of Pre-mRNA Splicing by the Spliceosome. Curr. Opin. Struct. Biol. 59, 124–133. doi:10.1016/j.sbi.2019.07.010

Wang, X., Pankratz, V. S., Fredericksen, Z., Tarrell, R., Karaus, M., McGuffog, L., et al. (2010). Common Variants Associated with Breast Cancer in Genome-wide Association Studies Are Modifiers of Breast Cancer Risk in BRCA1 and BRCA2 Mutation Carriers. Hum. Mol. Genet. 19 (14), 2886–2897. doi:10.1093/hmg/ddq174

Warde-Farley, D., Donaldson, S. L., Comes, O., Zuberi, K., Badrawi, R., Chao, P., et al. (2010). The GeneMANIA Prediction Server: Biological Network Integration for Gene Prioritization and Predicting Gene Function. Nucleic Acids Res. 38 (Web Server issue), W214–W220. doi:10.1093/nar/gkq537

Wei, J., Wang, B., Gao, X., and Sun, D. (2021). Prognostic Value of a Novel Signature with Nine Hepatitis C Virus-Induced Genes in Hepatic Cancer by Mining GEO and TCGA Databases. Front. Cel Dev. Biol. 9, 648279. doi:10.3389/fcell.2021.648279

Wong, C. N., Fessas, P., Dominy, K., Mauri, F. A., Kaneko, T., Parcq, P. D., et al. (2021). Qualification of Tumour Mutational burden by Targeted Next‐generation Sequencing as a Biomarker in Hepatocellular Carcinoma. Liver Int. 41 (1), 192–203. doi:10.1111/liv.14706

Wu, J., Gao, W., Tang, Q., Yu, Y., You, W., Wu, Z., et al. (2021). Retracted: M2 Macrophage-Derived Exosomes Facilitate HCC Metastasis by Transferring α M β 2 Integrin to Tumor Cells. Hepatology 73 (4), 1365–1380. doi:10.1002/hep.31432

Wu, X., Gu, Z., Chen, Y., Chen, B., Chen, W., Weng, L., et al. (2019). Application of PD-1 Blockade in Cancer Immunotherapy. Comput. Struct. Biotechnol. J. 17, 661–674. doi:10.1016/j.csbj.2019.03.006

Yi, Y., Nandana, S., Case, T., Nelson, C., Radmilovic, T., Matusik, R. J., et al. (2009). Candidate Metastasis Suppressor Genes Uncovered by Array Comparative Genomic Hybridization in a Mouse Allograft Model of Prostate Cancer. Mol. Cytogenet. 2, 18. doi:10.1186/1755-8166-2-18

Zhang, S., Liu, Z., Wu, D., Chen, L., and Xie, L. (2020). Single-Cell RNA-Seq Analysis Reveals Microenvironmental Infiltration of Plasma Cells and Hepatocytic Prognostic Markers in HCC with Cirrhosis. Front. Oncol. 10, 596318. doi:10.3389/fonc.2020.596318

Zheng, C., Zheng, L., Yoo, J. K., Guo, H., Zhang, Y., Guo, X., et al. (2017). Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing. Cell 169 (7), 1342–e16. doi:10.1016/j.cell.2017.05.035

Zongyi, Y., and Xiaowu, L. (2020). Immunotherapy for Hepatocellular Carcinoma. Cancer Lett. 470, 8–17. doi:10.1016/j.canlet.2019.12.002

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Liu, Gu, Zhang and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 01 March 2022
doi: 10.3389/fmolb.2022.791249

Identification of Down-Regulated ADH1C is Associated With Poor Prognosis in Colorectal Cancer Using Bioinformatics Analysis

Ming Li¹, Ziming Liu², Jia Song¹, Tian Wang², Hongjie Wang1,³, Yanan Wang⁴* and Jiguang Guo¹*

¹School of Basic Medical Sciences, Hebei University, Baoding, China

²College of Clinical Medicine, Hebei University, Baoding, China

³Affiliated Hospital of Hebei University, Baoding, China

⁴Department of Pathology, Affiliated Hospital of Hebei University, Baoding, China

Edited by:
Ismail Hosen, University of Dhaka, Bangladesh

Reviewed by:
Sachin Kumar Deshmukh, University of South Alabama, United States
Shilpita Karmakar, Jackson Laboratory, United States
Xiongwen Lv, Anhui Medical University, China
Saroj Kumari, Nation Institute of Immunology, India

* Correspondence: Yanan Wang, wyn781202@163.com; Jiguang Guo, guojiguang@hbu.edu.cn

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 08 October 2021
Accepted: 08 February 2022
Published: 01 March 2022

Citation: Li M, Liu Z, Song J, Wang T, Wang H, Wang Y and Guo J (2022) Identification of Down-Regulated ADH1C is Associated With Poor Prognosis in Colorectal Cancer Using Bioinformatics Analysis. Front. Mol. Biosci. 9:791249. doi: 10.3389/fmolb.2022.791249

Colorectal cancer (CRC) is the second most deadly cancer in the whole world, with the underlying mechanisms largely indistinct. Therefore, we aimed to identify significant pathways and genes involved in the initiation, formation and poor prognosis of CRC using bioinformatics methods. In this study, we compared gene expression profiles of CRC cases with those from normal colorectal tissues from three chip datasets (GSE33113, GSE23878 and GSE41328) to identify 105 differentially expressed genes (DEGs) that were common to the three datasets. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses showed that the highest proportion of up-regulated DEGs was involved in extracellular region and cytokine-cytokine receptor interaction pathways. Integral components of membrane and bile secretion pathways were identified as containing down-regulated DEGs. 13 hub DEGs were chosen and their expression were further validated by GEPIA. Only four DEGs (ADH1C, CLCA4, CXCL8 and GUCA2A) were associated with a significantly lower overall survival after the prognosis analysis. Lower ADH1C protein level and higher CXCL8 protein level were verified by immunohistochemical staining and western blot in clinical CRC and normal colorectal tissues. In conclusion, our study indicated that the extracellular tumor microenvironment and bile metabolism pathways play critical roles in the formation and progression of CRC. Furthermore, we confirmed ADH1C being down-regulated in CRC and reported ADH1C as a prognostic predictor for the first time.

Keywords: ADH1C, bioinformatics analysis, gene expression Omnibus database, colorectal cancer, differentially expressed gene

INTRODUCTION

Colorectal cancer (CRC) causes approximately 10% of cancer-related deaths each year and is the second most common lethal cancers globally (Dekker et al., 2019; Sung et al., 2021). During the past few decades, the incidence of CRC has increased between two- and four-fold in many Asian countries, including China (Nielsen et al., 2005). Early CRCs are highly treatable and screening for the disease can greatly reduce cancer mortality (Schoen et al., 2012). However, CRC is often diagnosed at the advanced stage due to the limitations of the current screening methods and the high metastatic potential of CRC (Li et al., 2018a). Therefore, it is extremely important to identify more reliable biomarkers for the early diagnosis of CRC and to reveal the underlying pathogenic mechanism of CRC.

In recent years, high-throughput sequencing technology is particularly powerful for screening differentially expressed genes (DEGs) in biological samples (Vogelstein et al., 2013). This has led to an increase in the number of gene expression profiles researches and a huge amount of data have been accumulated in public databases, of which bioinformatic analysis is necessary to obtain valuable insights into disease mechanisms. This is also the case with CRC (Isella et al., 2015) and a lot of data on CRC-related DEGs have been accumulating in the database. Bioinformatics studies on CRC, based on public databases (Guo et al., 2017), have shown that using these methods will help to explain the formation of the disease and reveal the underlying molecular mechanisms.

In this study, we downloaded three original expression microarray datasets: GSE33113, GSE23878, and GSE41328, from the Gene Expression Omnibus (GEO) database (Available online: https://www.ncbi.nlm.nih.gov/geo). These datasets provided 127 CRC cases and 32 normal colorectal tissue samples. To obtain the common DEGs from the three datasets, we used the GEO2R online tool, which is linked with the GEO database and the Draw Venn diagram online software. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) software was then applied to the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and gene ontology (GO) enrichment analysis of these common DEGs. The GO analysis included biological process (BP), cellular component (CC), and molecular function (MF) categories. We also built the protein-protein interaction (PPI) network for the common DEGs and determined the hub genes using Cytoscape plugin cytoHubba. The expression of these hub genes, compared between CRC and normal colorectal tissues, was validated using the Gene Expression Profiling Interactive Analysis (GEPIA) (P < 0.05). We imported the validated hub genes into the UALCAN and OncoLnc online resource to perform the prognostic analysis (P < 0.05). Four DEGs (ADH1C, CLCA4, CXCL8, and GUCA2A) were found to be associated with a significantly worse prognosis and lower overall survival. The down-regulated protein level of ADH1C (Class I Alcohol dehydrogenase 1C, gamma polypeptide) was verified in the CRC tissues and normal colorectal tissues by immunohistochemical staining and western blot. Therefore, we reported ADH1C as a prognostic predictor for CRC for the first time. In conclusion, our bioinformatics study provides some significant pathways and potential biomarkers that may be helpful in the interpretation of the molecular mechanism of CRC and could provide potential markers for CRC diagnosis.

MATERIAL AND METHODS

Information From Three Microarray Datasets

We downloaded three original expression microarray datasets: GSE33113, GSE23878, and GSE41328, from the GEO database (Available online: https://www.ncbi.nlm.nih.gov/geo). These datasets provided 127 CRC cases and 32 normal colorectal tissue samples. The microarray data were all based on GPL570 Platforms (HG-U133_Plus_2 Affymetrix Human Genome U133 Plus 2.0 Array). The GSE33113 dataset included 90 CRC tissues and six normal colorectal tissues. The GSE23878 dataset included 36 CRC tissues and 24 normal colorectal tissues. The GSE41328 dataset included two CRC tissues and two normal colorectal tissues.

Identification of Common DEGs

The DEGs between CRC tissues and normal colorectal tissues were first processed through the GEO2R online tools (Davis and Meltzer, 2007), with an adjusted P-value < 0.05 and logFC < −2 or logFC >2 as the cut-off criterion. The integrated raw data stored in TXT files were analyzed by Draw Venn diagram online software (http://bioinformatics.psb.ugent.be/webtools/Venn/) to obtain the common DEGs from the three databases. The DEGs with a log FC < 0 were deemed to be down-regulated genes, while the DEGs with a log FC > 0 were deemed to be up-regulated genes.

GO and KEGG Pathway Enrichment Analysis of the Common DEGs

GO analysis is a widely used approach for the identification of unique biological characteristics of genes and proteins from data obtained by high-throughput sequencing (Ashburner et al., 2000). The KEGG is a group of databases that are designed to systematically analyze gene functions and link genomic information with higher-order biological function pathways (Kanehisa and Goto, 2000). The DAVID software is an integrative online bioinformatics program that aims to analyze and interpret the biological properties of genes or proteins (Huang da et al., 2009). Therefore, the GO and KEGG pathways enrichment analyses of common DEGs were carried out using DAVID 6.8 (https://david.ncifcrf.gov/). A P value <0.05 was used as the cut-off criterion.

Protein-Protein Interaction Network Analysis

The PPI network of common DEGs was evaluated using STRING (Search Tool for the Retrieval of Interacting Genes), which is an open online tool (Szklarczyk et al., 2015). The PPI network complex of the common DEGs was then imported into Cytoscape, which is a free software for visualization of PPI networks to detect the hub DEGs (confidence score ≥0.4, maximum number of interactors = 0) (Shannon et al., 2003). The Cytoscape application, cytoHubba, was applied to determine the hub genes of the PPI network and the top 15 hub genes was ranking by the Maximal Clique Centrality (MCC) algorithm (Wang et al., 2021).

Validation and Survival Analysis of Hub Genes

The GEPIA website was chosen for validation of the expression level of hub genes that were compared between the CRC tissues and normal colorectal tissues. The GEPIA software was used to collect high-throughput sequencing data from many biological samples of The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) projects (Tang et al., 2017). The UALCAN website provided easy access to available cancer OMICS data (TCGA and MET500) (Chandrashekar et al., 2017) and OncoLnc was used as an online tool for the exploration of survival correlations based on TCGA (Anaya, 2016). We could find the P-value on the plot. A P value of <0.05 was used as the cut-off criterion.

Patients and Specimens

We collected 18 pairs of formalin-fixed paraffin-embedding CRC tissues and matched normal colorectal tissues (13 men and 5 women; age range 43–84 years; mean age ±standard deviation (SD), 67 ± 14 years) from the Affiliated Hospital of Hebei University, among which 8 patients (3 men and 5 women; age range 53–84 years; mean age ±SD, 70 ± 12 years) had fresh CRC and matched normal colorectal tissues frozen in liquid nitrogen within 30 min after resection. All the CRC patients involved in the study were diagnosed with pathological proof and has not been received chemotherapy or radiotherapy before the surgery. The study was authorized by the Ethics Committee of Affiliated Hospital of Hebei University (HDFY-LL-2021-013). Informed consent was obtained from the patients and their families who participated in the study.

Immunohistochemical Staining

The formalin-fixed paraffin-embedding CRC tissues were fixed in 10% formalin, embedded in paraffin, and sliced into 5 μm thick sections. The slides were deparaffinized by xylene and discontinuous concentration of alcohol. After incubating the slices at 95∼100°C for 10 min to retrieve antigens, the samples were cooled to room temperature treated with 3% hydrogen peroxide to block the endogenous peroxidase. Then, the primary antibody of ADH1C (A8081, Abclonal, Wuhan, China, 1:100) and CXCL8 (A2541, Abclonal, Wuhan, China, 1:100) were added to the slices and incubated in a humidified chamber at 4 °C overnight. For the next, the slices were incubated with the HRP*Polyclonal Goat Anti-Rabbit IgG (H + L) (PV-9001, Origene, Beijing, China) at room temperature for 20 min, followed by treatment with DAB substrate solution (ZLI-9019, Origene, Beijing, China). Finally, the slices were incubated with hematoxylin to visualize cell nuclei, dehydrated by alcohol, cleared in xylene, and sealed by using a mounting solution (neutral resin). The assay ensured no signal in the negative control. The result was observed by microscopy and analyzed by ImageJ 1.8.0 and IHC Toolbox plugin (Shu et al., 2016). Briefly, a proper threshold was stetted according to the positive DAB-staining specimen, which will be applied to all the pictures for comparison. The pixels of the area above the threshold, which is considered the positive area were measured automatically, and the % relative area which is the percentage of positive area of the whole picture was used to represent the immunoreactivity of the stained antibody. For each sample at least ten fields were measured.

Western Blot

After the evaporation of the liquid nitrogen, the frozen tissues were homogenized by the homogenizer (02642, PRO Scientific, CT, United States) in the lysis buffer (P0013b, Beyotime, Shanghai, China) with phenylmethanesulfonyl fluoride on ice. Then the suspensions were centrifuged at 4°C 14,000×g for 15 min. The concentration of the total protein was measured by the BCA protein quantification kit (PA101-01, Biomed, Beijing, China). SDS-PAGE electrophoresis was used to separate the equal amounts of protein samples, which were then transferred to a nitrocellulose membrane (HATF00010, Millipore, MA, United States). The nitrocellulose membrane was blocked by the 5% nonfat milk (LP0031B, Solarbio, Beijing, China) diluting in tris-buffered saline with Tween-20 (TBST) at room temperature for 1 h, and followed by incubating with the primary antibodies of ADH1C (A8081, Abclonal, Wuhan, China, 1:1000), CXCL8 (A2541, Abclonal, Wuhan, China, 1:1000), and β-actin (20536-1-AP, Proteintech, Wuhan, China, 1:3000) as the internal reference at 4°C overnight, which was also diluted in TBST. At last, the nitrocellulose membrane was incubated with the secondary antibody (SA00001-2, Proteintech, Wuhan, China, 1:5000) for 1 h at room temperature and visualized by the UItraECL Substrate chemiluminescence detection Kit (Biomed, Beijing, China). The absolute intensity of the blot signals was quantified using the ImageJ 1.8.0 software (National Institutes of Health, Boston, MA, United States).

Quantitative Real-Time PCR

As soon as the liquid nitrogen evaporated, the frozen tissue samples were homogenized by the homogenizer (02642, PRO Scientific, CT, United States) and lysed by the Trizol (CW0580, CWbio, Beijing, China) according to the protocol of the manufacturer. 1 μg of total RNA was used for the reverse transcription using the RT reagent kit with gDNA Eraser (RR047A, Takara, Dalian, China). After that, UltraSYBR Mixture (CW0957M, CWbio, Beijing, China) was used to carried out real-time PCR on CFX96 Optics Module (C1000, Bio-Rad, Singapore). The results were analyzed by the Bio-Rad CFX Manager 3.1 software and calculated by 2-ΔΔCt equation. The internal gene is β-actin. The sequence of the primers that used in the real-time PCR were as following: (ADH1C primers: forward 5′-GGA​CGC​ACG​TGG​AAA​GGA​G-3′ and reverse 5′-GAG​CGA​AGC​AGG​TCA​AAT​CC-3′; CXCL8 primers: forward 5′-CCA​AAC​CTT​TCC​ACC​CCA​AA-3′ and reverse 5′-TTC​TGT​GTT​GGC​GCA​GTG​T-3′; β-actin primers: forward 5′-CAT​GTA​CGT​TGC​TAT​CCA​GGC-3′ and reverse 5′-CTC​CTT​AAT​GTC​ACG​CAC​GAT-3′, and synthesized by Sangon (Shanghai, China).

Statistical Analysis

Paired t-test was used to compare the quantitative data of the IHC, real-time PCR and western blot that conformed to normal distribution by the Graphpad 6.0. The results were represented by mean ± SD. A P value of <0.05 was used as the cut-off criterion.

RESULTS

Identification of Common DEGs in CRC

There were 127 CRC tissues and 32 normal colorectal tissue samples used in this study. We obtained 990, 720, and 280 DEGs from GSE33113, GSE23878, and GSE41328, respectively, using the GEO2R online tool. After integrated analysis, a total of 105 DEGs were identified as common to the three databases. These included 22 up-regulated DEGs (logFC> 0) and 83 down-regulated DEGs (logFC< 0) in the CRC tissues when compared with the normal colorectal tissues (Table 1 and Figure 1).

TABLE 1 | The 105 common DEGs were identified from the three gene expression profile datasets.

FIGURE 1 | Identification of DEGs in the three datasets using the Draw Venn diagram software. Three datasets (GSE33113, GSE23878, and GSE41328) were used and indicated by different colors. The overlapping area indicated common DEGs. (A) Twenty-two DEGs were up-regulated in all three datasets (logFC> 0). (B) Eighty-three DEGs were down-regulated in three datasets (logFC <0).

GO and KEGG Pathway Enrichment Analyses of Common DEGs in CRC

All 22 up-regulated DEGs were analyzed using DAVID online resources. The GO analysis showed that the highest proportion of up-regulated DEGs were involved in 1) collagen catabolic process, cell-cell signaling, extracellular matrix disassembly, negative regulation of protein kinase activity, and positive regulation of protein oligomerization, found in the BP category; 2) proteinaceous extracellular matrix, extracellular region, extracellular space and extracellular matrix, found in the CC category; 3) metalloendopeptidase activity, protein kinase inhibitor activity, serine-type endopeptidase activity and transforming growth factor-beta receptor binding, found in the MF category (Table 2).

TABLE 2 | Gene ontology analysis results of up-regulated DEGs in CRC.

All 83 down-regulated DEGs were also analyzed using the DAVID online database. The GO analysis indicated that the highest proportion of down-regulated DEGs are enriched in 1) bicarbonate transport, one-carbon metabolic process, regulation of intracellular pH, chloride transmembrane transport, and cellular glucuronidation, found in the BP category; 2) an integral component of the plasma membrane, apical plasma membrane, anchored component of the membrane and integral component of membrane, found in the CC category; 3) carbonate dehydratase activity, chloride channel activity, hormone activity, transporter activity and glucuronosyltransferase activity, found in the MF category (Table 3).

TABLE 3 | Gene ontology analysis results of down-regulated DEGs in CRC.

We also performed a KEGG pathway analysis using the DAVID online database. This indicated that up-regulated DEGs were particularly enriched in rheumatoid arthritis, cytokine-cytokine receptor interaction, and bladder cancer pathways, whilst the nitrogen metabolism, bile secretion, retinol metabolism, proximal tubule bicarbonate reclamation, and drug metabolism pathways were identified as the most represented pathways for the down-regulated DEGs (Table 4, P < 0.05).

TABLE 4 | The KEGG pathways involved with the common DEGs in CRC.

PPI Network Complex and Hub Gene Analysis

Among the 105 common DEGs, 73 were used to construct a PPI complex of 73 nodes and 122 edges using STRING (http://string-db.org) and Cytoscape software. The 73 genes included 16 up-regulated and 57 down-regulated genes, whilst the remaining 32 genes were not found in a PPI network complex (Figure 2A). The top 15 ranking hub genes were selected (ADH1B, ADH1C, CLCA4, CLCX5, CXCL8, GCG, GUCA2A, GUCA2B, MS4A12, MMP1, PYY, SLC26A3, SST, UGT2B15 and ZG16) by the MCC algorithm of the cytoHubba application for further research (Table 5; Figure 2B).

FIGURE 2 | PPI network of 105 common DEGs built by STRING and Cytoscape module analysis. (A) 73 out of the 105 DEGs were contained in the PPI network complex. The 73 nodes and 122 edges represent the interaction of proteins. Red circles indicate the up-regulated DEGs and green circles indicate the down-regulated DEGs; (B) The rankings of the top 15 hub genes identified by the Cytoscape plugin cytoHubba MCC. 9 different colors from black to white was chosen to show the rank of the hub genes.

TABLE 5 | The hub genes with top15 ranking of the PPI network.

Validation of Hub Genes by GEPIA

We used GEPIA to compare the expression levels of the 15 hub genes between samples from CRC patients and normal individuals. The results showed that 13 out of the 15 hub genes had significantly different expression levels in CRC samples when compared to normal colorectal mucosa tissues (P < 0.05, Table 6 and Figure 3). SLC26A3 and UGT2B15 did not show differential expression (Table 6).

TABLE 6 | Validation of 13 hub genes using GEPIA.

FIGURE 3 | The expression levels of the hub genes were validated using the GEPIA website. Thirteen of the 15 hub genes showed significant differential expression in CRC samples, when compared with the normal samples (*P < 0.05). Red represents tumor tissues and grey represents normal tissues.

Prognostic Analysis of the Validated Hub Genes by UALCAN and OncoLnc

We performed a prognostic analysis of the 13 validated hub genes using the UALCAN (http://ualcan.path.uab.edu/) online resource database. The results showed that low expression of ADH1C, CLCA4, and GUCA2A was associated with significantly lower survival rates. There was no available data for CXCL8 and the other nine genes showed no significant changes (P < 0.05, Table 7 and Figure 4A). However, down-regulated CXCL8 was also significantly related to a lower survival rate (P < 0.05, Figure 4A), as shown by an analysis on the OncoLnc (http://www.oncolnc.org/) open online database. Furthermore, we collected CRC tissues and matched normal colorectal tissues from the Affiliated Hospital of Hebei University. The immunohistochemical analyses, real-time PCR and western blot confirmed that both the mRNA and protein level of ADH1C was down-regulated, well CXCL8 was up-regulated in the CRC tissues compared with normal colorectal tissues (P < 0.05, Figures 4B–E).

TABLE 7 | The prognostic analysis results of the 13 hub validated genes.

FIGURE 4 | The prognostic analysis and the protein level of the hub genes. (A) UALCAN and OncoLnc online resources were applied to obtain the prognostic results of the 13 hub genes. Only four of the 13 hub genes had significantly lower overall survival rates (P < 0.05). (B,C) Immunohistochemistry (IHC) analysis of CRC tissue and adjacent normal tissue. (B) showed the representative DAB IHC staining with ADH1C, CXCL8 antibody, and cell nuclei stained by hematoxylin of cancer and normal tissue pathology slides. (C) showed the quantitative analysis results by ImageJ and IHC Toolbox plugin. The magnification of the field is ×100, and the scale bar is 75 μm. (D) Real-time PCR results confirmed the dysregulation of the mRNA expression of ADH1C and CXCL8. (E) The protein level of ADH1C and CXCL8 were detected by Western blot. T, tumor tissue; N, normal tissue. n = 8. **P < 0.01 or *P < 0.05 by paired t-test.

DISCUSSION

Numerous studies have focused on the pathology and mechanism of CRC, but the exact mechanism remains largely unknown. To investigate the genes related to pathogenesis and to address the urgent need for early-stage diagnostic biomarkers for CRC, we integrated three gene profile datasets: GSE33113, GSE23878, and GSE41328, from GEO. 22 up-regulated and 83 down-regulated genes were identified when CRC samples were compared with colorectal tissues.

We performed GO and KEGG pathway enrichment analyses using DAVID online resources. Most up-regulated genes had functions that are integral to the extracellular region and extracellular space, which suggested that the formation of CRC has a close relationship with the tumor microenvironment. The largest proportion of down-regulated genes was mainly involved in the integral component of the membrane, an integral component of the plasma membrane, and bicarbonate transport. This is consistent with the fact that the integral cell membrane is essential for the avoidance of pathogens and to maintain the acid-base balance (Than et al., 2016; Westman et al., 2019). In the KEGG pathway analysis, up-regulated genes were particularly enriched in rheumatoid arthritis and the cytokine-cytokine receptor interaction. This is consistent with the GO analysis result of the up-regulated genes because the extracellular matrix and cytokine signal transduction play an important role in CRC invasion and metastasis (Cabrero-de Las Heras and Martinez-Balibrea, 2018; Yuzhalin et al., 2018). The highest proportion of down-regulated genes was significantly associated with nitrogen metabolism and bile secretion. Reactive nitrogen may function as a pro-inflammatory factor that promotes CRC (Janakiram and Rao, 2014). We also noted that many studies have shown that the gut microbiota that participates in the bile acid metabolism takes part in the formation of CRC (Feng et al., 2015; Wong et al., 2017), which is consistent with bile acid being associated with the occurrence of CRC (Liu et al., 2019).

We found that 13 out of the 15 hub genes showed differential expression in CRC samples when compared to normal samples. This was validated using GEPIA (P < 0.05). However, only four (ADH1C, CLCA4, CXCL8, GUCA2A) out of the 13 genes were significantly associated with a lower survival rate. The lower protein level of ADH1C and higher protein level of CXCL8 were further verified in our CRC and matched normal colorectal samples.

ADH1C, also known as ADH3, is an alcohol dehydrogenase that can catalyze ethanol oxidation to metabolite acetaldehyde (Shen et al., 2019). According to THE HUMAN PROTEIN ATLAS database, ADH1C locates mainly to the cytosol and plasma membrane, which is consistent with our IHC result. Genetic polymorphism of the ADH1C gene (ADH1C*1 allele) is associated with various human cancers, such as gastric cancer (Hidaka et al., 2015), oral cancer (Anantharaman et al., 2014) and CRC (Offermans et al., 2018), for it encodes an alcohol dehydrogenase producing 2.5 times more acetaldehyde (Seitz and Stickel, 2010). Chiang et al. showed that ADH1C was the mainly expressed isozyme in colorectal tissues (Chiang et al., 2012). Mashkova et al. observed that lower mRNA level of ADH1C in the CRC tissues compared with the normal or only hyperplasia colorectal tissues (Kropotova et al., 2014). In addition, the latest study showed that ADH1C is down-regulated in familial adenomatous polyposis case adenomas (Thiruvengadam et al., 2019), but up-regulated in patients with ulcerative colitis (Low et al., 2019). Kumamoto et al. (2019) revealed that ADH1C could predict the recurrence of stage III CRC in patients who accept chemotherapy treatment. Our study is the first to show a correlation between the lower expression level of ADH1C and worse prognostic of CRC patients. What’s more, we conducted immunohistochemical analyses and western blot to testify that ADH1C was down-regulated in CRC tissues in comparison with the normal tissues consisting with our bioinformatics analysis. However, there are few studies supporting our conclusion, further experiments are needed to verify our results.

Chloride channel accessory 4 (CLCA4) is well known as a tumor inhibitor and has been shown to suppress the development of various malignant tumors. In bladder cancer and hepatocellular carcinoma, tumor cell proliferation and migration are inhibited by CLCA4 through the PI3K/AKT signalling pathway (Hou et al., 2017; Liu et al., 2018). Low expression of CLCA4 has been reported in CRC patients (Zhao et al., 2019). Chen et al. (2019) reported that the epithelial-mesenchymal transition can also be suppressed by CLCA4 via the PI3K/AKT pathway in CRC, and also indicated that low CLCA4 is correlated with poor survival of CRC patients. This is consistent with our results.

The C-X-C motif chemokine ligand 8 (CXCL8) gene belongs to the CXC chemokine family whose main function is to recruit and activate neutrophils and granulocytes to sites of inflammation (Waugh and Wilson, 2008). Several previous studies have shown that CXCL8 which is a secreted protein functions with its receptors, CXCR1 and CXCR2, to promote the progression of some cancers, such as breast cancer (Yi et al., 2019), prostate cancer (Baci et al., 2019), lung cancer (He et al., 2019), and CRC (Liu et al., 2016). The CXCL8 gene is up-regulated in CRC tissue and correlated with the development of CRC, which is physiologically hard to detect (Rubie et al., 2007). Xia et al. (2015) proved that high levels of expression of CXCL8 are significantly associated with poor overall survival, tumor stage, lymphatic and liver metastasis. This suggests that CXCL8 could be a potential indicator for both detection and prognosis by meta-analysis. Fisher et al. (2019) demonstrated that blocking the CXCL8-CXCR1 pathway can inhibit the tumorigenicity that originates in the CRC stem cells. However, we did observe elevated CXCL8 levels in CRC patients in our study, the bioinformatics analysis indicated that a high level of CXCL8 correlated with poor prognosis at the beginning and in related with longer survival time as the time lasted in the opposite. Therefore, more studies are needed to investigate the exact relationship between the expression of CXCL8 and the CRC.

Guanylate cyclase activator 2A (GUCA2A) is a peptide hormone that is secreted by gut epithelial cells to regulate the function of guanylate cyclase 2C (GUCY2C) signalling in the autocrine and paracrine systems (Pattison et al., 2016). Silencing of the GUCY2C receptor induces genomic instability, hyperproliferation, and transformation of tumor cells (Li et al., 2007; Lin et al., 2016). Bashir et al. (2019) showed that a low level of GUCA2A silences the tumor inhibitory receptor, GUCY2C, in pathophysiological conditions, and leads to microsatellite instability in tumors. Loss of GUCA2A has been observed in CRC and inflammatory bowel disease, in which it may be associated with the disruption of intestinal homeostasis (Brenna et al., 2015; Zhang et al., 2019). Zhang et al. (2019) used analysis of the TCGA database to show that GUCA2A is associated with poor overall survival, which is consistent with our results.

CONCLUSION

In conclusion, our bioinformatics study identified significantly enriched KEGG pathways and four common DEGs (ADH1C, CLCA4, CXCL8, and GUCA2A) that correlate with poor overall survival of CRC patients. Our GO and KEGG pathways analyses indicated that the tumor microenvironment and gut microbiota are involved in the progression of CRC. We confirmed the decreased level of ADH1C in our CRC tissues from our patients and we are also the first to reveal the relationship between ADH1C and the lower overall survival rate of CRC patients. This study provides information on the pathogenesis of CRC and indicates that ADH1C may be a candidate prognostic molecular. More empirical and clinical verifications are needed to add to our analyses and further studies on the mechanism of the disease are also necessary. In short, our results have identified potential prognostic markers for CRC and are also shed light on the mechanism of CRC.

DATA AVAILABILITY STATEMENT

Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/gds/?term=GSE33113; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE23878; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE41328.

ETHICS STATEMENT

The studies involving human participants were reviewed and approved by Ethics Committee of Affiliated Hospital of Hebei University. The patients/participants provided their written informed consent to participate in this study.

AUTHOR CONTRIBUTIONS

ML conducted the Venn diagram, GO and KEGG analysis and also drafted the manuscript. YW and JG performed the immunohistochemical analyses. JS helped to build the PPI network, validate the expression of the hub genes by GEPIA and perform the prognostic analysis by UALCAN and OncoLnc. HW revised of the work and participated in interpreting the data. ZL and TW helped to finish the real-time PCR and western blot. All authors have read and approved the final manuscript.

FUNDING

This research was supported by the President Foundation of Hebei University (XZJJ201919), the Youth Science and Technology Project of Department of Health of Hebei (20190924 and 20210766), the Science and Technology Supporting Program of Baoding (15ZF075), the Advanced Talents Incubation Program of the Hebei University (521000981003), the Giant Plan of Hebei Province (702800116049), the Medical Science Foundation of Hebei University (2020B15 and 2021B04) and the Interdisciplinary Research Program of Natural Science of Hebei University (DXK202005).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

REFERENCES

Anantharaman, D., Chabrier, A., Gaborieau, V., Franceschi, S., Herrero, R., Rajkumar, T., et al. (2014). Genetic Variants in Nicotine Addiction and Alcohol Metabolism Genes, Oral Cancer Risk and the Propensity to Smoke and Drink Alcohol: a Replication Study in India. PLoS One 9 (2), e88240. doi:10.1371/journal.pone.0088240

Anaya, J. (2016). OncoLnc: Linking TCGA Survival Data to mRNAs, miRNAs, and lncRNAs. PeerJ Comput. Sci. 2, e67. doi:10.7717/peerj-cs.67

Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., et al. (2000). Gene Ontology: Tool for the Unification of Biology. Nat. Genet. 25 (1), 25–29. doi:10.1038/75556

Baci, D., Bruno, A., Cascini, C., Gallazzi, M., Mortara, L., Sessa, F., et al. (2019). Acetyl-L-Carnitine Downregulates Invasion (CXCR4/CXCL12, MMP-9) and Angiogenesis (VEGF, CXCL8) Pathways in Prostate Cancer Cells: Rationale for Prevention and Interception Strategies. J. Exp. Clin. Cancer Res. 38 (1), 464. doi:10.1186/s13046-019-1461-z

Bashir, B., Merlino, D. J., Rappaport, J. A., Gnass, E., Palazzo, J. P., Feng, Y., et al. (2019). Silencing the GUCA2A-Gucy2c Tumor Suppressor axis in CIN, Serrated, and MSI Colorectal Neoplasia. Hum. Pathol. 87, 103–114. doi:10.1016/j.humpath.2018.11.032

Brenna, Ø., Bruland, T., Furnes, M. W., Granlund, A. v. B., Drozdov, I., Emgård, J., et al. (2015). The Guanylate Cyclase-C Signaling Pathway Is Down-Regulated in Inflammatory Bowel Disease. Scand. J. Gastroenterol. 50 (10), 1241–1252. doi:10.3109/00365521.2015.1038849

Chandrashekar, D. S., Bashel, B., Balasubramanya, S. A. H., Creighton, C. J., Ponce-Rodriguez, I., Chakravarthi, B. V. S. K., et al. (2017). UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia 19 (8), 649–658. doi:10.1016/j.neo.2017.05.002

Chen, H., Liu, Y., Jiang, C.-J., Chen, Y.-M., Li, H., and Liu, Q.-A. (2019). Calcium-Activated Chloride Channel A4 (CLCA4) Plays Inhibitory Roles in Invasion and Migration through Suppressing Epithelial-Mesenchymal Transition via PI3K/AKT Signaling in Colorectal Cancer. Med. Sci. Monit. 25, 4176–4185. doi:10.12659/MSM.914195

Chiang, C.-P., Jao, S.-W., Lee, S.-P., Chen, P.-C., Chung, C.-C., Lee, S.-L., et al. (2012). Expression Pattern, Ethanol-Metabolizing Activities, and Cellular Localization of Alcohol and Aldehyde Dehydrogenases in Human Large Bowel: Association of the Functional Polymorphisms of ADH and ALDH Genes with Hemorrhoids and Colorectal Cancer. Alcohol 46 (1), 37–49. doi:10.1016/j.alcohol.2011.08.004

Davis, S., and Meltzer, P. S. (2007). GEOquery: a Bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics 23 (14), 1846–1847. doi:10.1093/bioinformatics/btm254

Dekker, E., Tanis, P. J., Vleugels, J. L. A., Kasi, P. M., and Wallace, M. B. (2019). Colorectal Cancer. The Lancet 394 (10207), 1467–1480. doi:10.1016/S0140-6736(19)32319-0

Feng, Q., Liang, S., Jia, H., Stadlmayr, A., Tang, L., Lan, Z., et al. (2015). Gut Microbiome Development along the Colorectal Adenoma-Carcinoma Sequence. Nat. Commun. 6, 6528. doi:10.1038/ncomms7528

Fisher, R. C., Bellamkonda, K., Alex Molina, L., Xiang, S., Liska, D., Sarvestani, S. K., et al. (2019). Disrupting Inflammation-Associated CXCL8-CXCR1 Signaling Inhibits Tumorigenicity Initiated by Sporadic- and Colitis-Colon Cancer Stem Cells. Neoplasia 21 (3), 269–281. doi:10.1016/j.neo.2018.12.007

Guo, Y., Bao, Y., Ma, M., and Yang, W. (2017). Identification of Key Candidate Genes and Pathways in Colorectal Cancer by Integrated Bioinformatical Analysis. Int. J. Mol. Sci. 18 (4), 722. doi:10.3390/ijms18040722

He, C., Liu, X., Jiang, Z., Geng, S., Ma, H., and Liu, B. (2019). Interaction Mechanism of Flavonoids and α-Glucosidase: Experimental and Molecular Modelling Studies. Foods 8 (9), 355. doi:10.3390/foods8090355

Heras, S. C.-d. l., and Martínez-Balibrea, E. (2018). CXC Family of Chemokines as Prognostic or Predictive Biomarkers and Possible Drug Targets in Colorectal Cancer. World J. Gastroenterol. 24 (42), 4738–4749. doi:10.3748/wjg.v24.i42.4738

Hidaka, A., Sasazuki, S., Matsuo, K., Ito, H., Sawada, N., Shimazu, T., et al. (2015). Genetic Polymorphisms of ADH1B, ADH1C and ALDH2, Alcohol Consumption, and the Risk of Gastric Cancer: the Japan Public Health Center-based Prospective Study. Carcinogenesis 36 (2), 223–231. doi:10.1093/carcin/bgu244

Hou, T., Zhou, L., Wang, L., Kazobinka, G., Zhang, X., and Chen, Z. (2017). CLCA4 Inhibits Bladder Cancer Cell Proliferation, Migration, and Invasion by Suppressing the PI3K/AKT Pathway. Oncotarget 8 (54), 93001–93013. doi:10.18632/oncotarget.21724

Huang, D. W., Sherman, B. T., and Lempicki, R. A. (2009). Systematic and Integrative Analysis of Large Gene Lists Using DAVID Bioinformatics Resources. Nat. Protoc. 4 (1), 44–57. doi:10.1038/nprot.2008.211

Isella, C., Terrasi, A., Bellomo, S. E., Petti, C., Galatola, G., Muratore, A., et al. (2015). Stromal Contribution to the Colorectal Cancer Transcriptome. Nat. Genet. 47 (4), 312–319. doi:10.1038/ng.3224

Janakiram, N. B., and Rao, C. V. (2014). The Role of Inflammation in colon Cancer. Adv. Exp. Med. Biol. 816, 25–52. doi:10.1007/978-3-0348-0837-8_2

Kanehisa, M., and Goto, S. (2000). KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28 (1), 27–30. doi:10.1093/nar/28.1.27

Kropotova, E. S., Zinovieva, O. L., Zyryanova, A. F., Dybovaya, V. I., Prasolov, V. S., Beresten, S. F., et al. (2014). Altered Expression of Multiple Genes Involved in Retinoic Acid Biosynthesis in Human Colorectal Cancer. Pathol. Oncol. Res. 20 (3), 707–717. doi:10.1007/s12253-014-9751-4

Kumamoto, K., Nakachi, Y., Mizuno, Y., Yokoyama, M., Ishibashi, K., Kosugi, C., et al. (2019). Expressions of 10 Genes as Candidate Predictors of Recurrence in Stage III colon Cancer Patients Receiving Adjuvant Oxaliplatin-based C-hemotherapy. Oncol. Lett. 18 (2), 1388–1394. doi:10.3892/ol.2019.10437

Li, B., Wang, S., and Wang, S. (2018a). MiR-195 Suppresses colon Cancer Proliferation and Metastasis by Targeting WNT3A. Mol. Genet. Genomics 293 (5), 1245–1253. doi:10.1007/s00438-018-1457-y

Li, P., Schulz, S., Bombonati, A., Palazzo, J. P., Hyslop, T. M., Xu, Y., et al. (2007). Guanylyl Cyclase C Suppresses Intestinal Tumorigenesis by Restricting Proliferation and Maintaining Genomic Integrity. Gastroenterology 133 (2), 599–607. doi:10.1053/j.gastro.2007.05.052

Lin, J. E., Colon-Gonzalez, F., Blomain, E., Kim, G. W., Aing, A., Stoecker, B., et al. (2016). Obesity-Induced Colorectal Cancer Is Driven by Caloric Silencing of the Guanylin-Gucy2c Paracrine Signaling Axis. Cancer Res. 76 (2), 339–346. doi:10.1158/0008-5472.CAN-15-1467-T

Liu, Q., Li, A., Tian, Y., Wu, J. D., Liu, Y., Li, T., et al. (2016). The CXCL8-Cxcr1/2 Pathways in Cancer. Cytokine Growth Factor. Rev. 31, 61–71. doi:10.1016/j.cytogfr.2016.08.002

Liu, T., Song, X., Khan, S., Li, Y., Guo, Z., Li, C., et al. (2019). The Gut Microbiota at the Intersection of Bile Acids and Intestinal Carcinogenesis: An Old story, yet Mesmerizing. Int. J. Cancer 146, 1780–1790. doi:10.1002/ijc.32563

Liu, Z., Chen, M., Xie, L.-K., Liu, T., Zou, Z.-W., Li, Y., et al. (2018). CLCA4 Inhibits Cell Proliferation and Invasion of Hepatocellular Carcinoma by Suppressing Epithelial-Mesenchymal Transition via PI3K/AKT Signaling. Aging 10 (10), 2570–2584. doi:10.18632/aging.101571

Low, E. N. D., Mokhtar, N. M., Wong, Z., and Raja Ali, R. A. (2019). Colonic Mucosal Transcriptomic Changes in Patients with Long-Duration Ulcerative Colitis Revealed Colitis-Associated Cancer Pathways. J. Crohns Colitis 13 (6), 755–763. doi:10.1093/ecco-jcc/jjz002

Nielsen, R., Bustamante, C., Clark, A. G., Glanowski, S., Sackton, T. B., Hubisz, M. J., et al. (2005). A Scan for Positively Selected Genes in the Genomes of Humans and Chimpanzees. Plos Biol. 3 (6), e170. doi:10.1371/journal.pbio.0030170

Offermans, N. S. M., Ketcham, S. M., van den Brandt, P. A., Weijenberg, M. P., and Simons, C. C. J. M. (2018). Alcohol Intake, ADH1B and ADH1C Genotypes, and the Risk of Colorectal Cancer by Sex and Subsite in the Netherlands Cohort Study. Carcinogenesis 39 (3), 375–388. doi:10.1093/carcin/bgy011

Pattison, A. M., Merlino, D. J., Blomain, E. S., and Waldman, S. A. (2016). Guanylyl Cyclase C Signaling axis and colon Cancer Prevention. World J. Gastroenterol. 22 (36), 8070–8077. doi:10.3748/wjg.v22.i36.8070

Rubie, C., Frick, V. O., Pfeil, S., Wagner, M., Kollmar, O., Kopp, B., et al. (2007). Correlation of IL-8 with Induction, Progression and Metastatic Potential of Colorectal Cancer. World J. Gastroenterol. 13 (37), 4996–5002. doi:10.3748/wjg.v13.i37.4996

Schoen, R. E., Pinsky, P. F., Weissfeld, J. L., Yokochi, L. A., Church, T., Laiyemo, A. O., et al. (2012). Colorectal-cancer Incidence and Mortality with Screening Flexible Sigmoidoscopy. N. Engl. J. Med. 366 (25), 2345–2357. doi:10.1056/NEJMoa1114635

Seitz, H. K., and Stickel, F. (2010). Acetaldehyde as an Underestimated Risk Factor for Cancer Development: Role of Genetics in Ethanol Metabolism. Genes Nutr. 5 (2), 121–128. doi:10.1007/s12263-009-0154-1

Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., et al. (2003). Cytoscape: a Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Res. 13 (11), 2498–2504. doi:10.1101/gr.1239303

Shen, X. Y., Liu, X. P., Song, C. K., Wang, Y. J., Li, S., and Hu, W. D. (2019). Genome‐wide Analysis Reveals Alcohol Dehydrogenase 1C and Secreted Phosphoprotein 1 for Prognostic Biomarkers in Lung Adenocarcinoma. J. Cel Physiol 234 (12), 22311–22320. doi:10.1002/jcp.28797

Shu, J., Dolman, G. E., Duan, J., Qiu, G., and Ilyas, M. (2016). Statistical Colour Models: an Automated Digital Image Analysis Method for Quantification of Histological Biomarkers. Biomed. Eng. Online 15, 46. doi:10.1186/s12938-016-0161-6

Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., et al. (2021). Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA A. Cancer J. Clin. 71 (3), 209–249. doi:10.3322/caac.21660

Szklarczyk, D., Franceschini, A., Wyder, S., Forslund, K., Heller, D., Huerta-Cepas, J., et al. (2015). STRING V10: Protein-Protein Interaction Networks, Integrated over the Tree of Life. Nucleic Acids Res. 43 (Database issue), D447–D452. doi:10.1093/nar/gku1003

Tang, Z., Li, C., Kang, B., Gao, G., Li, C., and Zhang, Z. (2017). GEPIA: a Web Server for Cancer and normal Gene Expression Profiling and Interactive Analyses. Nucleic Acids Res. 45 (W1), W98–W102. doi:10.1093/nar/gkx247

Than, B. L. N., Linnekamp, J. F., Starr, T. K., Largaespada, D. A., Rod, A., Zhang, Y., et al. (2016). CFTR Is a Tumor Suppressor Gene in Murine and Human Intestinal Cancer. Oncogene 35 (32), 4191–4199. doi:10.1038/onc.2015.483

Thiruvengadam, S. S., O'Malley, M., LaGuardia, L., Lopez, R., Wang, Z., Shadrach, B. L., et al. (2019). Gene Expression Changes Accompanying the Duodenal Adenoma-Carcinoma Sequence in Familial Adenomatous Polyposis. Clin. Translational Gastroenterol. 10 (6), e00053. doi:10.14309/ctg.0000000000000053

Vogelstein, B., Papadopoulos, N., Velculescu, V. E., Zhou, S., Diaz, L. A., and Kinzler, K. W. (2013). Cancer Genome Landscapes. Science 339 (6127), 1546–1558. doi:10.1126/science.1235122

Wang, X.-J., Gao, J., Wang, Z., and Yu, Q. (2021). Identification of a Potentially Functional microRNA-mRNA Regulatory Network in Lung Adenocarcinoma Using a Bioinformatics Analysis. Front. Cel Dev. Biol. 9, 641840. doi:10.3389/fcell.2021.641840

Waugh, D. J. J., and Wilson, C. (2008). The Interleukin-8 Pathway in Cancer. Clin. Cancer Res. 14 (21), 6735–6741. doi:10.1158/1078-0432.CCR-07-4843

Westman, J., Hube, B., and Fairn, G. D. (2019). Integrity under Stress: Host Membrane Remodelling and Damage by Fungal Pathogens. Cell Microbiol. 21 (4), e13016. doi:10.1111/cmi.13016

Wong, S. H., Zhao, L., Zhang, X., Nakatsu, G., Han, J., Xu, W., et al. (2017). Gavage of Fecal Samples from Patients with Colorectal Cancer Promotes Intestinal Carcinogenesis in Germ-free and Conventional Mice. Gastroenterology 153 (6), 1621–1633. e1626. doi:10.1053/j.gastro.2017.08.022

Xia, W., Chen, W., Zhang, Z., Wu, D., Wu, P., Chen, Z., et al. (2015). Prognostic Value, Clinicopathologic Features and Diagnostic Accuracy of Interleukin-8 in Colorectal Cancer: a Meta-Analysis. PLoS One 10 (4), e0123484. doi:10.1371/journal.pone.0123484

Yi, T., Zhou, X., Sang, K., Huang, X., Zhou, J., and Ge, L. (2019). Activation of lncRNA Lnc-Slc4a1-1 Induced by H3K27 Acetylation Promotes the Development of Breast Cancer via Activating CXCL8 and NF-kB Pathway. Artif. Cell Nanomedicine, Biotechnol. 47 (1), 3765–3773. doi:10.1080/21691401.2019.1664559

Yuzhalin, A. E., Gordon-Weeks, A. N., Tognoli, M. L., Jones, K., Markelc, B., Konietzny, R., et al. (2018). Colorectal Cancer Liver Metastatic Growth Depends on PAD4-Driven Citrullination of the Extracellular Matrix. Nat. Commun. 9 (1), 4783. doi:10.1038/s41467-018-07306-7

Zhang, H., Du, Y., Wang, Z., Lou, R., Wu, J., and Feng, J. (2019). Integrated Analysis of Oncogenic Networks in Colorectal Cancer Identifies GUCA2A as a Molecular Marker. Biochem. Res. Int. 2019, 1–13. doi:10.1155/2019/6469420

Zhao, Z., Fan, X., Yang, L., Song, J. J., Song, J., Fang, S., et al. (2019). The Identification of a Common Different Gene Expression Signature in Patients with Colorectal Cancer. Math. Biosci. Eng. 16 (4), 2942–2958. doi:10.3934/mbe.2019145

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Li, Liu, Song, Wang, Wang, Wang and Guo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 03 March 2022
doi: 10.3389/fmolb.2022.773662

The N6-Methylandenosine-Related Gene BIRC5 as a Prognostic Biomarker Correlated With Cell Migration and Immune Cell Infiltrates in Low Grade Glioma

Xiulin Jiang1,2,³†, Yulin Shi1,⁴†, Xi Chen1,⁴†, Haitao Xu1,⁴, Xiaobin Huang¹, Lihua Li⁴* and Jun Pu¹*

¹Department of Neurosurgery, the Second Affiliated Hospital of Kunming Medical University, Kunming, China

²Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Kunming, China

³Kunming College of Life Science, University of Chinese Academy of Sciences, Beijing, China

⁴Kunming Medical University, Kunming, China

Edited by:
Ismail Hosen, University of Dhaka, Bangladesh

Reviewed by:
Udhaya Kumar S., Vellore Institute of Technology, India
Shilpita Karmakar, JAX Cancer Center, United States

* Correspondence: Lihua Li, lilihua1229@126.com; Jun Pu, pujun303@aliyun.com

†These authors have contributed equally to this work

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 10 September 2021
Accepted: 27 January 2022
Published: 03 March 2022

Citation: Jiang X, Shi Y, Chen X, Xu H, Huang X, Li L and Pu J (2022) The N6-Methylandenosine-Related Gene BIRC5 as a Prognostic Biomarker Correlated With Cell Migration and Immune Cell Infiltrates in Low Grade Glioma. Front. Mol. Biosci. 9:773662. doi: 10.3389/fmolb.2022.773662

Gliomas account for 75% of all primary malignant brain tumors in adults and are associated with high mortality. Emerging evidence has demonstrated that baculoviral inhibitor of apoptosis repeat containing 5 (BIRC5) plays a critical role in cell apoptosis and the progression of diverse cancers. However, no studies have yet focused on the immunological function and mechanisms of upstream BIRC5 regulation in the progression of low-grade gliomas (LGG). Here, we evaluated BIRC5 expression and clinical characteristics in people with LGG using the Chinese Glioma Genome Atlas, The Cancer Genome Atlas, Gene Expression Omnibus, Rembrandt, and Gravendeel databases. We used Kaplan–Meier statistics and receiver operating characteristic (ROC) curves to analyze the prognostic value of BIRC5 in LGG. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment terms were also explored to identify functional roles of BIRC5. The Tumor Immune Estimation Resource (TIMER) and Tumor Immune System Interaction (TISIDB) databases were used to examine the correlation between BIRC5 expression and immune cell infiltration in LGG. The Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Therapeutics Response Portal (CTRP) databases were used to examine the potential drugs targeting BIRC5. We used transwell and wound healing assays to determine the biological functions of BIRC5 in glioma cell migration. Our results demonstrated that BIRC5 was highly expressed in LGG and the expression level correlated with tumor grade, prognosis, histological subtype, isocitrate dehydrogenase 1 (IDH1) mutation, 1p/19q chromosomal co-deletion, chemotherapy status, and O[6]-methylguanine-DNA methyltransferase (MGMT) promoter methylation status. GO and KEGG analysis showed that BIRC5 is primarily involved in cell proliferation and immune response-related signaling pathways. We also found that BIRC5 was significantly correlated with m6A modification and diverse drug sensitivity. TIMER and TISIDB database analysis showed that BIRC5 expression is associated with infiltration of diverse immune cells and immune modulation in LGG. BIRC5 knockdown inhibited LGG cell migration. Collectively, our results demonstrate that BIRC5 is correlated with cell migration and immune infiltration in LGG and may be a useful prognostic biomarker.

Keywords: LGG, BIRC5, M6A RNA methylation, cell migration, immune infiltration, drug sensitivity

1 INTRODUCTION

Low-Grade Glioma (LGG) is a relatively common tumor in the central nervous system that mainly includes World Health Organization (WHO) grade 2 and 3 gliomas (Ostrom et al., 2014). Mounting evidence has demonstrated that the molecular characteristics of gliomas include mutated isocitrate dehydrogenase 1 and 2 genes (IDH1/2) and co-deletion of 1p/19q (Aldape et al., 2019). Despite surgical advances and progress in adjuvant therapeutic strategies, clinical outcomes in people with LGG are still poor. Therefore, uncovering the molecular mechanisms underlying the initiation and progression of LGG and identifying highly reliable biomarkers is crucial to improving the diagnosis and treatment of people with this cancer type.

Members of the BIRC gene family (BIRC1–8) mainly encode inhibitors of apoptosis (LaCasse et al., 1998; Makuch-Kocka et al., 2021). These genes are reported to play important roles in cell cycle regulation, immune system activities, and signal transduction (Liang et al., 2020). Aberrant expression of these genes also contributes to the progression of diverse cancers: for instance, OCT4 elevates the promoter activity of BIRC5, contributing to the progression of hepatocellular carcinoma (Cao et al., 2013). It has been shown that BIRC5 is highly expressed in breast cancer, and its expression is correlated with relapse-free survival and overall survival; it, therefore, may have potential as a useful predictive marker and therapeutic target in this context (Dai et al., 2020). Furthermore, a recent study indicated that microRNA-203 inhibits the expression of BIRC5 and suppresses the proliferation and migration of human triple-negative breast cancer cells (Wang et al., 2012). Multiple studies have found that BIRC genes have a pivotal role in multiple physiological processes including cell growth, cell cycle regulation, immune responses, and cancer progression (Pu et al., 2020). However, no studies have yet focused on the immunological function and upstream regulatory mechanism of BIRC genes in the progression of LGG: in this study, we therefore aimed to investigate the role of BIRC genes in these processes.

By integrated analysis of the genomic, transcriptomic, and clinical data from the Chinese Glioma Genome Atlas (CGGA), The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Rembrandt and Gravendeel datasets, we found that the RNA and protein of BIRC5 were significantly up-regulation in LGG tissue, high level of BIRC5 was correlated with tumor grade and poor prognosis in LGG. Additionally, BIRC5 expression is also associated with histology subtypes, IDH1 mutation, 1p/19q chromosomal co-deletion, chemotherapy status, and MGMT promoter methylation status, well-established molecular characteristics of gliomas. The potential signaling pathway participated by BIRC5 in LGG was examined by GSEA. Furthermore, our results demonstrated that BIRC5 expression in LGG was regulated by DNA hypo-methylation, the correlation between BIRC5 and immune cell infiltration was evaluated. Finally, qRT-PCR was used to validate the expression of BIRC5 in normal human astrocytes cells and glioma cell lines. Knock-down of BIRC5 significantly inhibits the cell migration of gliomas cells in vitro. The findings in our study demonstrated that the crucial role of BIRC5 in LGG patients, and discovered an underlying mechanism between BIRC5 and immune response.

2 MATERIALS AND METHODS

2.1 Gene Expression and Survival Analysis

We download the RNA expression and clinical data of glioma cohort projects from The Cancer Genome Atlas (TCGA) research program (https://www.cancer.gov/tcga), including 505 cases of lower-grade glioma (LGG) and 155 cases of GBM RNA sequencing counts data with clinical information. The various normal tissues expression data obtained from the Genotype-Tissue Expression Project (GTEx) RNA sequencing data as resources (https://www.genome.gov/Funded-Programs-Projects/Genotype-Tissue-Expression-Project). This dataset was utilized analysis the expression of BIRC5 in glioma and used to examine the correlation between BIRC5 and various clinical features. We also download the glioma RNA-seq dataset “mRNAseq_693” recruited in the Chinese Glioma Genome Atlas (CGGA) (HTTP: //www.cgga.org.cn/analyse/RNA-data.) contains 693 glioma samples were utilized as verification of the expression and prognosis of BIRC5 in glioma. The GSE4290, GSE16011 andGSE50161 dataset was downloaded from The Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) database, used to analyze the expression of BIRC5 in low-grade glioma.

2.2 GEPIA Analysis

The GEPIA website (http://gepia.cancer-pku.cn/) was used to explore prognosis of BIRC family gene in LGG. Furthermore, we used GEPIA database examine the correlation between BIRC5 expression and pathological stage of various human cancers.

2.3 Univariate and Multivariate Cox Analyses

Univariate and multivariate Cox analyses were conducted by using the R package “survival.” The WHO grade, IDH status, 1p/19q codeletion, primary therapy outcome, gender, age, and BIRC5 expression were included in these analyses. The receiver operating characteristic (ROC) analysis was performed with the R package “pROC.”

2.4 Gene Set Enrichment Analysis and Kyoto Encyclopedia of Genes and Genome Analyses

Gene expression data of LGG in HTSeq-Counts were downloaded from TCGA website for further analysis. The correlated genes of BIRC5 were screened with Pearson’s correlation coefficients (|r| >0.6 and p < 0.001) with R package “deseq2.” According to the default statistical methods, an adjusted p-value < 0.05 was considered significant. GO and KEGG analysis were conducted on BIRC5 correlated genes with R package “clusterProfiler” to identify the possible biological functions and signaling pathways affected by BIRC5. In the present research, the gene set kegg.v6.2.symbols.gmt, which served as a reference gene set, was downloaded from the Molecular Signatures Database (MSigDB) (http://software.broadinstitute.org/gsea/msigdb). We performed GSEA to examine the potential signaling pathway participated by BIRC5 in low-grade glioma.

2.5 Tumor Immune Estimation Resource

TIDIDB (http://cis.hku.hk/TISIDB/), an integrated repository portal for tumor-immune system interactions, utilized to analysis of the expression of BIRC5 in diverse immune subtype of low-grade glioma (Ru et al., 2019). TIMER (https://cistrome.shinyapps.io/timer/) (Li et al., 2017), an interactive web portal, could perform a comprehensive analysis on the infiltration levels of different immune cells. In this study, we used the TIMER database to explore the correlation between BIRC5 and diverse immune cell infiltration, and different immune-related gene marker in low-grdae glioma.

2.6 DNA Methylation Analysis for BIRC5

We utilized MethSurv (https://biit.cs.ut.ee/methsurv/), a web tool to perform multivariable survival analysis using DNA methylation data, to analyze the diverse DNA methylation sites in the promoter of BIRC5 in glioma (Modhukur et al., 2018). Furthermore, the SMART (http://www.bioinfo-zs.com/smartapp/), an interactive web application for comprehensive DNA methylation analysis and visualization, was used to examine the correlation between BIRC5 expression and DNA methylation in glioma (Li et al., 2019).

2.7 Analysis of the Correlation Between the BIRC5 Expression and Drug Sensitivity

We utilized the GDSC (https://www.cancerrxgene.org/) and CTRP (http://portals.broadinstitute.org/ctrp.v2.1/) databases to analyze the correlation between BIRC5 expression and drug sensitivity (Basu et al., 2013; Yang et al., 2013).

2.8 Cell Culture, RNA Isolation, and Real-Time PCR

The gliomas cell lines were purchased from the cell bank of Kunming Institute of Zoology, and cultured in DMEM medium (Corning) supplemented with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin. HEK-293T cells were cultured in DMEM medium (Corning). Total RNA was extracted according to the manufacturer’s protocol, and then reverse-transcribed using RT reagent Kit (Takara Bio, Beijing, China, Cat# RR047A; TIANGEN Biotech, Beijing, China, Cat# KR211-02). Real-time PCR was performed by FastStart Universal SYBR Green Master Mix (Roche, Cat# 04194194001; TIANGEN Biotech, Beijing, China, Cat# FP411-02) using an Applied Biosystems 7500 machine. The primers used in this study are shown in the following, BIRC5-F: AGG​ACC​ACC​GCA​TCT​CTA​CAT, BIRC5-R: AAG​TCT​GGC​TCG​TTC​TCA​GTG; β-actin-F: CTTCGCGGGCGACGAT, β-actin-R: CCA​TAG​GAA​TCC​TTC​TGA​CC. The expression quantification was obtained with the 2−ΔΔCt method.

2.9 Plasmid Construction and siRNA Interference

The BIRC5 siRNA, control siRNA were synthesized by GenePharma (Shanghai, China), and a scrambled siRNA was synthesized as a negative control. Transfection was performed using Lipofectamine 3000 (Invitrogen, Carlsbad, CA, United States) according to the manufacturer’s instructions. Total RNA was collected 48 h after transfection. All the sequences used in the present study are the following: BIRC5 siRNA: GAA​ACT​GCG​GAG​AAA​GTG​CGC. Scrambled siRNAs were used as negative control.

2.10 Cell Migration Assays

Cell migration assay was performed as previously described (Xiong et al., 2021). In this study, GBM cells, including U251 and A172 cells were utilized to determine the function of BIRC5 on the LGG cell migration. Briefly, indicated cells were seeded into 6-well plates (2 × 10⁶/cell) and incubated for 1 day, and then a straight line was scraped with pipette tips. Detached cells were removed. Photographs were taken at the indicated time, and the relative traveled distance was measured. For the trans-well migration assay, 2 × 10⁴ cells/well in 100 μl serum-free medium were plated in a 24-well plate chamber insert, and the lower chamber was filled with 10 % FBS. After incubation for 24 h, cells were fixed with 4% PFA, washed, and then stained with 0.5% crystal violet for further pictures captured.

2.11 Immunohistochemistry Assay

Immunohistochemistry was performed as described previously (Xu et al., 2020). Briefly, paraffin sections were deparaffinized by xylene, rehydrated with gradient ethanol, and subjected to antigen retrieval. After H₂O₂ treatment and blockage with 10% normal goat serum, the slides were incubated with indicated antibodies (Proteintech, BIRC5 Polyclonal Antibody, Rabbit Polyclonal,Catalog number: 10508-1-AP, 1:200) followed by an incubation with biotinylated secondary antibody and streptavidin-HRP (Dako, K5007).

2.12 Statistical Analysis

The survival data from the CGGA database were acquired by KM. Correlation analysis was performed using the Pearson correlation test. Kaplan-Meier survival curves were plotted to exhibit the overall survival for low-grade glioma patients. Univariate and multivariate Cox regression analyses were used to examine the independent prognostic significance of each variable enrolled in this finding. The significance of the data between the two experimental groups was determined by Student’s t-test, and multiple group comparisons were analyzed by one-way ANOVA. p < 0.05 (*), p < 0.01 (**) and p < 0.001 (***), were considered significant.

3 RESULTS

3.1 Expression and Prognostic Value of BIRC Family Genes in Low-Grade Gliomas

To explore the functions of the BIRC gene family in LGG, we first examined the expression of BIRC1–8 using the TCGA and GTEx datasets. BIRC1-BIRC7 was found to be significantly up-regulated in LGG; BIRC8 genes showed stable expression levels, with no significant difference in expression between LGG and controls (Supplementary Figure S1A). We also used the GEPIA database to determine the prognostic value of BIRC family genes in LGG; among these genes, increased BIRC5 expression was correlated with worse clinical outcomes in this cancer type (Supplementary Figure S1B). Overall, these results indicated that BIRC5 was highly expressed in LGG with high specificity.

3.2 BIRC5 is Highly Expressed in Human Cancer and is Related to Patient Prognosis

We used the TGCA database to examine the expression of BIRC5 in all cancers. Our results demonstrated that BIRC5 was significantly overexpressed in BLCA, BRCA, CHOL, COAD, ESCA, HNSC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, PRAD, READ, STAD, THCA, and UCEC (Figure 1A). Given that BIRC5 was found to be highly expressed in diverse cancer types, we used the kmplot database to examine the prognostic value of BIRC5 across human cancers. The analysis revealed that high levels of BIRC5 expression closely correlated with worse clinical prognosis in ACC, KIRC, KIRP, LGG, LIHC, LUAD, and MESO. In contrast, high levels of BIRC5 expression were associated with improved prognosis in OV (Figure 1B). Together, these findings indicate that BIRC5 expression is closely associated with prognosis in diverse human cancers.

FIGURE 1 | BIRC5 expression and prognosis in patients with other cancer types. (A) Differences in expression of BIRC5in normal and tumor tissues of TCGA. (B) The pathological stage of BIRC5 in diverse cancer examine by UALCAN database. (C) Kaplan-Meier curves were drawn to evaluate the overall survival of BIRC5 in various human cancers.

3.3 BIRC5 is Highly Expressed in Glioma and is Related to Patient Prognosis

To examine the expression of BIRC5 in LGG tissue compared with a healthy control group, we used the public bioinformatics databases, including TCGA, GEO, Rembrandt, and Gravendeel. The results showed that BIRC5 was significantly elevated in LGG tissue compared with controls (Figures 2A,B).

FIGURE 2 | BIRC5 was highly expressed in low grade glioma. (A,B) BIRC5 was upregulated in glioma examined by the TCGA, Rembrandt, Gravendeel, and GEO datasets. (C,D) BIRC5 expression levels among glioma patients with different histological subtype (C) and tumor grade (D) in TCGA-LGG.

We further analyzed the correlation between BIRC5 expression levels and clinical characteristics of LGG, focusing primarily on tumor grade, histological subtype, chemotherapy status, IDH1 mutation, 1p/19q chromosomal co-deletion, and O[6]-methylguanine-DNA methyltransferase (MGMT) promoter methylation. Our results showed that BIRC5 was differentially expressed in different histological subtypes of LGG (Figure 2C). BIRC5 expression levels increased at higher tumor grades (Figure 2D). Interestingly, we found that BIRC5 was markedly decreased in the group with IDH mutations and the group with 1p/19q chromosome co-deletion (Figure 3A and Table 1). Additionally, BIRC5 expression was markedly increased in those receiving chemotherapy and those with unmethylated MGMT promoters. These results indicate that BIRC5 plays a major role in LGG development.

FIGURE 3 | Correlation between the BIRC5 expression and clinical information in LGG. (A) The expression of BIRC5 in diverse clinical information of glioma based on CGGA databases. (B) The overall survival between the high- BIRC5and low- BIRC5 in TCGA, CGGA, Rembrandt, and Gravendeel databases. (C) Diagnostic efficacy of the ROC curve of BIRC5 expression in TCGA-LGG, CGGA, and TCGA-GBM.

TABLE 1 | Correlation between BIRC5 expression and clinicopathological factors based on the TCGA database.

As showed in the Table 2, patients having integral clinical data were included in further Cox regression analysis. In the Cox univariate regression analysis, we found that high expression of BIRC5, WHO grade, IDH status, 1p/19q codeletion, and age were associated with overall survival in LGG patients. Multivariate Cox analysis further indicated that BIRC5 (p = 0.004) was an independent prognostic factor for overall survival in LGG patients, along with WHO grade, IDH status, 1p/19q codeletion, and age.

TABLE 2 | Univariate and multivariate regression analyses of low-grade glioma.

3.4 Prognostic Value of BIRC5 in Low-Grade Gliomas

Given that BIRC5 is highly expressed in LGG, we examined the prognostic value of BIRC5 expression in this cancer type. People with LGG were divided into “high” or “low” expression groups based on the median expression value. High BIRC5 expression was significantly associated with unfavorable prognosis in people with LGG in different datasets (Figure 3B). Receiver operator characteristic (ROC) curve analysis showed that the area under the curve (AUC) of BIRC5 was 0.970 in the CGGA dataset, 0.981 in the TCGA-LGG dataset, 0.9933 in the Rembrandt dataset, and 0.998 in the Gravendeel dataset (Figure 3C). These results indicate that BIRC5 is a highly sensitive and specific marker with the potential to be used in LGG diagnosis.

3.5 Correlation Between DNA Methylation and BIRC5 Expression in Low-Grade Gliomas

DNA methylation is crucial for the epigenetic regulation of gene expression. To elucidate the mechanisms of abnormal upregulation of BIRC5 in LGG tissues, we explored the correlation between DNA methylation and BIRC5 expression in LGG using diverse public databases. Firstly, we found that BIRC5 methylation was lower in LGG than in healthy brain tissue and was negatively correlated with tumor grade in LGG (Figures 4A,B). Treatment with 5-azacytidine, an inhibitor of DNA methyltransferase (Xiong et al., 2021), resulted in increased BIRC5 RNA levels in U251 human brain cells (Figure 4C). Moreover, we found that cg24107848 methylation was significantly negatively correlated with the BIRC5 expression in LGG (r = −0.340, p < 0.001) (Figure 4D). We also showed that decreased methylation levels at cg28734000 correlated with worse prognosis in the CGGA-LGG dataset using the MethSurv statistical tool (Figures 4E,F). These results suggested that hypomethylation in the BIRC5 promoter region results in increased expression of this gene in LGG.

FIGURE 4 | Analysis of the BIRC5 methylation level. (A) The DNA methylation level of BIRC5 in LGG. (B) The DNA methylation level of BIRC5 in diverse tumor grades of LGG based on the CGGA dataset. (C) The expression of BIRC5 in U251 cells after using 5-AZA treatment was examined by qRT-PCR assay. (D) The correlation between DNA methylation and BIRC5 expression in low grade glioma by Pearson’s correlation. (E,F) The prognosis for methylation level of BIRC5 in glioma based on the CGGA dataset.

3.6 BIRC5 Expression is Correlates with Regulators of N6-methyladenosine (m6A) RNA Methylation in Low-Grade Gliomas

Given that N6-methyladenosine (m6A) RNA methylation plays a major role in the initiation and progression of LGG, we examined the correlation between BIRC5 expression and regulators of m6A RNA methylation in this cancer type (Figure 5A). BIRC5 expression was significantly positively correlated with 16 m6A-related genes, including HNRNPA2B1 (r = 0.52, p = 0), HNRNPC (r = 0.0.32, p < 0.001), IGF2BP2 (r = 0.32, p <0.001), IGF2BP3 (r = 0.39, p = 0), METTL3 (r = 0.20, p < 0.001), RBM15 (r = 0.46, p = 0), RBM15B (r = 0.48, p = 0), RBMX (r = 0.41, p = 0), WTAP (r = 0.25, p < 0.001), YTHDC2 (r = 0.26, p < 0.001), ALKBH5 (r = 0.21, p = 0.001), YTHDF1 (r = 0.25, p = 0.001), YTHDF2 (r = 0.47, p = 0) and YTHDF3 (r = 0.24, p = 0.001) (Figures 5B–E). Collectively, these results demonstrate that BIRC5 plays indispensable roles in LGG progression via interaction with regulators of m6A RNA methylation.

FIGURE 5 | Analysis of the correlation between BIRC5 expression and m6A regulator in LGG. (A–E) Analysis of the correlation between BIRC5 expression and m6A regulator in low grade glioma by Pearson’s correlation.

3.7 Functional Analysis of BIRC5 in Low-Grade Gliomas

To explore the specific functions of BIRC5 in LGG progression, we first analyzed genes whose expression was positively correlated with that of BIRC5 in LGG (Figures 6A,B). We then performed Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses based on these co-expressed genes. The results showed that BIRC5 is primarily involved in tubulin, microtubule, glycosaminoglycan, heparin, and growth factor binding, as well as GABA receptor activity and histone activity in the GO analysis (Figure 6C). BIRC5 may participate in cell cycle, human papillomavirus infection, p53 signaling, extracellular matrix (ECM) receptor interaction, DNA replication, small cell lung cancer, and morphine addiction pathways (Figure 6D). These results demonstrate functionally-relevant roles for BIRC5 in LGG progression.

FIGURE 6 | Analysis of the function of BIRC5 expression in LGG. (A,B) Analysis of the co-expression gene of BIRC5 in LGG examines by linkomics database. (C) Analysis of the molecular function of BIRC5 in LGG. (D) Analysis of the KEGG signaling pathway of BIRC5 in LGG.

3.8 BIRC5-Related Signaling Pathways Identified Using Gene Set Enrichment Analysis

To identify signaling pathways that are affected by BIRC5 overexpression in glioma, we performed a gene set enrichment analysis (GSEA). High BIRC5 expression was mainly associated with the regulation of stem cell differentiation, cell cycle growth 1/synthesis (G1/S) phase transition, and chromosome segregation (Figure 7A). Moreover, high BIRC5 expression was associated with apoptosis, cell cycle, ubiquitin-mediated proteolysis, and focal adhesion signaling pathways (Figure 7B). Collectively, these results demonstrate that BIRC5 plays an indispensable role in cell proliferation and immune regulation.

FIGURE 7 | The KEGG signal pathway explored by GSEA software. (A) The biological process involved by BIRC5 in LGG was examined by GSEA software. (B) The signaling pathway involved by BIRC5 in LGG was examined by GSEA software.

3.9 Association Between BIRC5 Expression Levels and Immune Cell Infiltration in Low-Grade Gliomas

The level of immune infiltration affects clinical outcomes from tumors. We, therefore, analyzed the expression of BIRC5 in diverse immune subtypes, which showed that BRIC5 expression was highest in the C4 subtype (Figure 8A). Furthermore, we found that different somatic BIRC5 copy number alterations affected infiltration levels of diverse immune cells, including B cells, CD4⁺ and CD8⁺ T cells, neutrophils, and dendritic cells (Figure 8B). Finally, we using the TIMER database to examine the correlation between BIRC5 expression and immune infiltration levels for diverse immune cells in LGG; results indicated that BIRC5 was significantly correlated with the level of B cells (cor = 0.271 p < 0.001), CD8⁺ T cells (cor = 0.158, p < 0.001), CD4⁺ T cells (cor = 0.208, p < 0.001), macrophages (cor = 0.224, p < 0.001), neutrophils (cor = 0.207, p < 0.001), and dendritic cells (cor = 0.279, p < 0.001), and positively correlated with tumor purity (cor = 0.165, p < 0.001) (Figure 8C). Our Cox proportional hazard model analysis demonstrated that B cells, CD8⁺ and CD4⁺ T cells, macrophages, neutrophils, dendritic cells, and BIRC5 expression were significantly associated with worse overall survival in LGG patients (Figure 8D).

FIGURE 8 | Analysis of the correlation between BIRC5 expression and diverse immune cell infiltration. (A) The expression of BIRC5 in diverse immune subtypes of LGG was examined by TISIDB. (B) The correlation between BIRC5 expression and somatic copy number alterations examine by TIMER database. (C) The correlation between BIRC5 expression and diverse immune cell infiltration was examined by TIMER database. (D) The B cells, CD4⁺ T cells, CD8⁺ T cells, dendritic cells, Macrophages, and Neutrophils are correlated with the cumulative survival rate in LGG examine by TIMER database. (E) The correlations between BIRC5 expression and diverse immune checkpoints related gene examined by TIMER database.

Given that immune checkpoints play a crucial role in tumor immunosuppression, we analyzed the correlation between BIRC5 expression and that of the immune checkpoint-related genes CD274, CTLA4, HAVCR2, LAG3, PDCD1, PDCD1LG2, TIGIT, and SIGLEC15 in LGG using Pearson correlation analysis. BIRC5 expression was significantly positively correlated with the expression of all eight genes in this analysis (Figure 8E), supporting a role for BIRC5 in immune regulation.

3.10 Correlation Between BIRC5 Expression and Drug Sensitivity

Given that BIRC5 has potentially promoted LGG progression, it is therapeutically useful to identify anti-LGG drugs targeting BIRC5. Therefore, we used GSCA tools to analyze the relationship between BIRC5 expression and sensitivity to anti-LGG drugs. BIRC5 expression was positively correlated with drug sensitivity for trametinib, selumetinib, RDEA119, AZ628, PD-0325901, and VX-11e (all r > 0.14, p < 0.001), and negatively associated with drug sensitivity for NSC-207895, navitoclax, vorinostat, NPK76-II-72-1, and KIN001-270 (all r < −0.10, p < 0.001) in the Genomics of Drug Sensitivity to Cancer (GDSC) database (Figure 9A and Table 3). Similarly, CTRP database analysis demonstrated that BIRC5 expression was positively correlated with drug sensitivity for trametinib, selumetinib, PD318088, MLN2480, BYL-719, PD 153035 and GANT-61 (all r > 0.10, p < 0.001), and negatively associated with the drug sensitivity of BRD-K30748066, docetaxel, GSK-J4, niclosamide, STF-31, GSK461364, SID 26681509, tivantinib, BI-2536, ceramic-2, neopeltolide, CAY10618, fluvastatin, 3-Cl-AHPC and methotrexate (r < −0.14, p < 0.001) (Figure 9B and Table 4). Taken together, the results demonstrate that BIRC5 was correlated with sensitivity to diverse drugs from the Cancer Therapeutic Response Portal database.

FIGURE 9 | Correlation between drug sensitivity and BIRC5 expression. (A,B) The correlation between BIRC5 expression and drug sensitivity in pan-RCC was studied by Spearman correlation analysis. Red represents positive correlation between drugs sensitivity and BIRC5 expression, and blue represents negative correlation between drugs sensitivity and BIRC5 expression. (A): GDSC, (B): CTRP.

TABLE 3 | Analysis of the correlation between BIRC5 expression and diverse drug sensitivity by using GDSC database.

TABLE 4 | Analysis of the correlation between BIRC5 expression and diverse drug sensitivity by CTRP database.

3.11 Knockdown of BIRC5 Inhibits Glioma Cell Migration In Vitro

Our KEGG enrichment results showed that BIRC5 may be involved in the focal adhesion signaling pathway, therefore, we used a functional assay to verify this result. We used a quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot assay to detect BIRC5 expression in normal human astrocyte cells and glioma cell lines. BIRC5 was highly expressed in glioma cell lines, especially in U251 cells (Figures 10A,B). IHC assay results also indicated that BIRC5 was highly expressed in glioma tissues than paracancerous tissues (Figure 10C). Based on this result, we constructed BIRC5 knockdown cells using qRT-PCR to verify knockdown efficiency (Figures 10D,E). The resultant depletion of BIRC5 was found to significantly inhibit glioma cell migration by transwell and wound healing assays (Figures 10F–I). Collectively, these results demonstrate that BIRC5 is highly expressed in glioma cells, likely resulting in increased cell migration.

FIGURE 10 | Depletion of BIRC5 inhibits glioma cells migration. (A,B) The expression of BIRC5 in normal human astrocytes cells (NHA) and glioma cell lines (A172 and U251) examined by qRT-PCR and Western blot assay. (C) IHC analysis of BIRC5 expression in glioma cancer tissues. Scale bar, 50 μm. (D,E) The expression of BIRC5 in U251 and A172 cells treated with siRNAs (siRNA -BIRC5#NC, siRNA-BIRC5#) measured by qRT–PCR. *p < 0.05, one-way ANOVA test for the comparison of the two groups. (F–I) Transwell and Wound healing assay of the glioma cancer cell lines U251 and A172 treated with shRNAs. *p < 0.05, one-way ANOVA test for the comparison of the two groups. Scale bar, 100 μm.

4 DISCUSSION

Glioma is a highly fatal primary tumor of the central nervous system. Despite diverse available therapeutic strategies, including surgery, chemotherapy, and immunotherapy, the median survival of people with glioma—which has barely improved since its recognition as a separate cancer type—remains only approximately 14  months (Huang et al., 2021). Therefore, more sensitive and specific diagnostic biomarkers and potential therapeutic targets for glioma patients are of no delay.

BIRC5, also called survivin, is an immune-related gene and member of the apoptotic (IAP) protein family (Li et al., 1998). BIRC5 has been shown to play vital roles in carcinogenesis by affect the cell proliferation and by inhibits cell apoptosis (Yamamoto et al., 2008). Since BIRC5 is frequently up-regulated in diverse cancers and treatment that targets BIRC5 has been increasingly noticed as a novel strategy for various malignant tumors (Jaiswal et al., 2015). For instance, BIRC5 was increased in triple-negative and BIRC5 knockdown significantly inhibits the proliferation of breast cancer cells, implying that BIRC5 acts like a tumor driver (Wang et al., 2012). Furthermore, BIRC5 expression has been found to confer resistance to chemotherapy and radiation, targeting BIRC5 in experimental models improves survival (Jha et al., 2012). Together, these findings indicate that BIRC5 may not only function as an oncogene, but also as a promising predictive biomarker and potential therapeutic target in cancer. A recent study confirmed that BIRC5 could act as a reliable prognostic indicator in breast cancer patients (Dai et al., 2020), but the expression, clinical significance, and its impact on the immune microenvironment remain largely unclear in low-grade glioma.

Brunicardi et al. found that BIRC5 was highly expressed in pancreatic cancer and maybe as a precision diagnostic biomarker for the diagnosis for pancreatic cancer. In this study, we uncover that BIRC5 expression was upregulated in diverse human cancer and correlated with poor prognosis, especially in glioma (Figure 1). Which further validated by using the GEO and CGGA database (Figure 2), the IHC and Western blotting assay in glioma cell lines and glioma tissues. Furthermore, higher expression of BIRC5 was positively correlates with adverse clinicopathologic features of glioma, including the tumor grade, histological subtype, isocitrate dehydrogenase 1 (IDH1) mutation, 1p/19q chromosomal co-deletion, chemotherapy status, and O[6]-methylguanine-DNA methyltransferase (MGMT) promoter methylation status (Figure 3). Accumulating studies have confirmed that IDH1 mutations are associated with the occurrence and development of glioma, and IDH1 mutations are more common in low grade glioma than in LGG (Miroshnikova et al., 2016). Therefore, we speculated that the higher expression of BIRC5 in low grade glioma patients might due to more low grade glioma patients are IDH wild-type tumors, which expressed higher BIRC5 levels. Moreover, the KM survival curve analyses showed that upregulated BIRC5 expression associated with adverse clinical outcomes in glioma from both TCGA and CGGA datasets. ROC curve analysis showed that the BIRC5 expression level has diagnostic value for gliomas (Figure 3). Similarly, high expression of BIRC5 was associated with overall survival in breast cancer, clear-cell renal cell carcinoma, gastric cancer, and lung cancer (Ni et al., 2019; Wan et al., 2019; Zou et al., 2019; Dai et al., 2020). We also performed a univariate analysis including the prognostic factors for overall survival. We found that high expression of BIRC5 was associated with higher tumor grades, histological type, IDH mutation status, 1p/19q chromosome co-deletion, and primary therapy outcome. Furthermore, we utilized the Cox regression model for multivariate analysis; the results demonstrated that BIRC5 expression, WHO grade, IDH status, 1p/19q codeletion, and age were an independent prognostic factor for overall survival in LGG patients. Similarly, high expression of BIRC5 was an independent prognostic factor for overall survival in clear-cell renal cell carcinoma and endometrial cancer (Chuwa et al., 2016; Wan et al., 2019).

A growing number of studies have reported that DNA methylation plays an important role in gene expression regulation (Yamashita et al., 2018). In this study, we found that the DNA methylation level of BIRC5 was down-regulated in glioma tissues than normal tissues. Furthermore, we showed that the methylation sites (cg24107848) were significantly negatively correlated with the expression of BIRC5 in LGG. More importantly, our results showed that lower methylation levels in cg24107848 were correlated with a worse prognosis in LGG patients. We also used a qRT-PCR assay confirm that hypomethylation in the BIRC5 promoter region resulted in the increased expression of this gene in LGG (Figure 4). Although various molecular mechanisms can lead to increased gene expression, such as the lncRNA/miRNA axis, regulation of transcription factors, and gene copy number amplification, DNA hypomethylation is one of the main regulatory mechanisms of gene expression.

M6A RNA methylation is also known to affect glioma malignancy (Chai et al., 2019). For example, it has been shown that YTHDF2 promotes the CSC liver phenotype and cancer metastasis by modulating the m6A methylation of OCT4 mRNA (Zhang et al., 2020). In hepatocellular carcinoma, YTHDF2 silenced in human HCC cells or ablated in mouse hepatocytes provoked inflammation, vascular reconstruction, and metastatic progression (Hou et al., 2019). A recent finding found that YTHDF2 sequesters m6A-circRNA and is essential for suppression of innate immunity (Chen et al., 2019). In this study, we found that BIRC5 expression is significantly associated with regulators of m6A methylation (Figure 5). Crosstalk between m6A RNA modification and BIRC5 expression in low-grade glioma, therefore, requires further characterization in future studies.

Sun et al. found that BIRC5 was highly expressed in HCC tissues and involved in cell cycle regulation (Wang et al., 2011). To explore the possible mechanism of BIRC5 affecting the survival of patients, KEGG and GSEA enrichment analyses were performed based on the differentially expressed genes between BIRC5 groups, and the results suggested that higher BIRC5 expression was mainly associated with the regulation of stem cell differentiation, cell cycle growth 1/synthesis (G1/S) phase transition, and chromosome segregation (Figure 6), which is consistent with previous studies that BIRC5 regulated the stem cell differentiation (Lin et al., 2020). Moreover, high BIRC5 expression was associated with apoptosis, cell cycle, ubiquitin-mediated proteolysis, and focal adhesion signaling pathways. Collectively, these results demonstrate that BIRC5 plays an indispensable role in cell proliferation and immune regulation (Figure 7).

Mounting evidence demonstrates that the tumor microenvironment has a key role in the development of tumors, and can be used as a biomarker for diagnosis and prognosis (Wu and Dai, 2017). CD8 + tumor infiltrating lymphocytes have also been shown to be associated with glioma prognosis (Yu-Ju Wu et al., 2020). In this research, we revealed that BIRC5 somatic copy number alterations affected infiltration levels of various immune cells, including B cells, CD4⁺ and CD8⁺ T cells, neutrophils, and dendritic cells. Furthermore, we found that BIRC5 was significantly correlated with the level of B cells, CD8⁺ T cells, CD4⁺ T cells, macrophages, neutrophils and dendritic cells, and was negatively correlated with tumor purity. We showed that BIRC5 influences the survival time of LGG patients, partially through immune cell infiltration (Figure 8). These findings indicate that BIRC5 could be a novel immune-related therapeutic target in LGG.

It has been demonstrated that immune modulators play crucial roles in tumor cell evasion of immune surveillance (Xue et al., 2017). Studies have confirmed that the increased of immune checkpoints such as PD-L1, CTLA-4, TIM-3, and LAG3 in glioma helps tumor immune evasion, leading to T cell dysfunction (Xue et al., 2017; Woroniecka et al., 2018). Our results confirmed that BIRC5 expression was significantly positively correlated with the expression of CD274, CTLA4, HAVCR2, LAG3, PDCD1, PDCD1LG2, TIGIT, and SIGLEC15, suggesting a crucial role of BIRC5 in regulating the expression of immune checkpoints and immunotherapy (Figure 8). Given the effects of BIRC5 on LGG immune cell infiltration, we can infer that increased expression of BIRC5 may promote mast cell infiltration and contribute to a poor prognosis. Therefore, our results demonstrated that BIRC5 might affect immune cell infiltration, making them a predictive biomarker for immunotherapy in LGG patients. Finally, we found that BIRC5 was correlated with sensitivity to diverse drugs in the cancer therapeutic response portal database, with some positive and some negative correlations (Figure 9). For the biological function on the glioma cells, we found that BIRC5 was highly expressed in glioma cell lines, depletion of BIRC5 significantly inhibiting the migration of glioma cells (Figure 10).

In summary, we found that BIRC5 was significantly aberrantly overexpressed in glioma tissues and cells. Through a series of comprehensive approaches, we demonstrated that the upregulated BIRC5 expression is strongly associated with clinicopathologic features, adverse clinical outcomes, and immune cell infiltration in low-grdae glioma. Altogether, our results suggest that BIRC5 might serve as a valuable prognostic factor and a promising novel immunotherapy target for glioma.

This study improves our understanding of the correlation between BIRC5 and low-grade glioma, but some limitations still exist. First, although we explored the correlation between BIRC5 and immune infiltration in LGG patients, there is a lack of experiments to validation the function of BIRC5 in the tumor microenvironment regulation of LGG. Second, we uncover that depletion of BIRC5 was inhibits cell migration of LGG cells. However, the potential molecular mechanisms of BIRC5 in tumor metastasis need to be explored in further studies. Third, we did not conduct the in vivo experiments to validation the function of BIRC5 in the tumor metastasis and tumor microenvironment regulation of LGG. In the future, we will pay more attention to the function of BIRC5 in tumor metastasis and tumor microenvironment regulation of LGG. Furthermore, we will perform more in vivo and vitro experiments to explore the function and the potential molecular mechanisms of BIRC5 in tumor metastasis and tumor microenvironment regulation of LGG.

Overall, our results confirmed that BIRC5 could serve as a potential novel prognostic biomarker for LGG. Moreover, we explored the underlying evidence indicating that BIRC5 regulates immune cell infiltration in the tumor microenvironment in LGG patients. Therefore, these findings are potentially valuable in advancing our current understanding of not only the role of BIRC5 but also its translational use in LGG prognosis and immunotherapy.

CONCLUSION

Our study found that BIRC5 expression was increased in low-grade glioma, which was also associated with poor prognosis. Furthermore, low-grade glioma might be involved in the initiation and progression of LGG by regulating the function of immune infiltrating cells and cell migration. Herein, we revealed the biological functions of BIRC5 in LGG and offered a potential strategy for the diagnosis and treatment of LGG.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.

AUTHOR CONTRIBUTIONS

XJ, YS, and XC designed this work, performed related assay. HX analyzed data. LL and JP supervised and wrote the manuscript. All authors have read and approved the final version of the manuscript.

FUNDING

This work was supported by National Nature Science Foundation of China (82160512) and Yunnan Applied Basic Research Projects (2017FE467 and 2018FE001).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

ACKNOWLEDGMENTS

The authors would like to thank the support from the department of Neurosurgery, The Second Affiliated Hospital of Kunming Medical University, Kunming 650223, China.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2022.773662/full#supplementary-material

REFERENCES

Aldape, K., Brindle, K. M., Chesler, L., Chopra, R., Gajjar, A., Gilbert, M. R., et al. (2019). Challenges to Curing Primary Brain Tumours. Nat. Rev. Clin. Oncol. 16, 509–520. doi:10.1038/s41571-019-0177-5

Basu, A., Bodycombe, N. E., Cheah, J. H., Price, E. V., Liu, K., Schaefer, G. I., et al. (2013). An Interactive Resource to Identify Cancer Genetic and Lineage Dependencies Targeted by Small Molecules. Cell 154, 1151–1161. doi:10.1016/j.cell.2013.08.003

Cao, L., Li, C., Shen, S., Yan, Y., Ji, W., Wang, J., et al. (2013). OCT4 Increases BIRC5 and CCND1 Expression and Promotes Cancer Progression in Hepatocellular Carcinoma. BMC cancer 13, 82. doi:10.1186/1471-2407-13-82

Chai, R.-C., Wu, F., Wang, Q.-X., Zhang, S., Zhang, K.-N., Liu, Y.-Q., et al. (2019). m6A RNA Methylation Regulators Contribute to Malignant Progression and Have Clinical Prognostic Impact in Gliomas. Aging 11, 1204–1225. doi:10.18632/aging.101829

Chen, Y. G., Chen, R., Ahmad, S., Verma, R., Kasturi, S. P., Amaya, L., et al. (2019). N6-Methyladenosine Modification Controls Circular RNA Immunity. Mol. Cel 76, 96–109. doi:10.1016/j.molcel.2019.07.016

Chuwa, A. H., Sone, K., Oda, K., Ikeda, Y., Fukuda, T., Wada-Hiraike, O., et al. (2016). Significance of Survivin as a Prognostic Factor and a Therapeutic Target in Endometrial Cancer. Gynecol. Oncol. 141, 564–569. doi:10.1016/j.ygyno.2016.04.003

Dai, J. B., Zhu, B., Lin, W. J., Gao, H. Y., Dai, H., Zheng, L., et al. (2020). Identification of Prognostic Significance of BIRC5 in Breast Cancer Using Integrative Bioinformatics Analysis. Biosci. Rep. 40. doi:10.1042/BSR20193678

Hou, J., Zhang, H., Liu, J., Zhao, Z., Wang, J., Lu, Z., et al. (2019). YTHDF2 Reduction Fuels Inflammation and Vascular Abnormalization in Hepatocellular Carcinoma. Mol. Cancer 18, 163. doi:10.1186/s12943-019-1082-3

Huang, X., Zhang, F., He, D., Ji, X., Gao, J., Liu, W., et al. (2021). Immune-Related Gene SERPINE1 Is a Novel Biomarker for Diffuse Lower-Grade Gliomas via Large-Scale Analysis. Front. Oncol. 11, 646060. doi:10.3389/fonc.2021.646060

Jaiswal, P. K., Goel, A., and Mittal, R. D. (2015). Survivin: A Molecular Biomarker in Cancer. Indian J. Med. Res. 141, 389–397. doi:10.4103/0971-5916.159250

Jha, K., Shukla, M., and Pandey, M. (2012). Survivin Expression and Targeting in Breast Cancer. Surg. Oncol. 21, 125–131. doi:10.1016/j.suronc.2011.01.001

LaCasse, E. C., Baird, S., Korneluk, R. G., and MacKenzie, A. E. (1998). The Inhibitors of Apoptosis (IAPs) and Their Emerging Role in Cancer. Oncogene 17, 3247–3259. doi:10.1038/sj.onc.1202569

Li, F., Ambrosini, G., Chu, E. Y., Plescia, J., Tognin, S., Marchisio, P. C., et al. (1998). Control of Apoptosis and Mitotic Spindle Checkpoint by Survivin. Nature 396, 580–584. doi:10.1038/25141

Li, T., Fan, J., Wang, B., Traugh, N., Chen, Q., Liu, J. S., et al. (2017). TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res. 77, e108–e110. doi:10.1158/0008-5472.can-17-0307

Li, Y., Ge, D., and Lu, C. (2019). The SMART App: an Interactive Web Application for Comprehensive DNA Methylation Analysis and Visualization. Epigenetics & Chromatin 12, 71. doi:10.1186/s13072-019-0316-3

Liang, J., Zhao, W., Tong, P., Li, P., Zhao, Y., Li, H., et al. (2020). Comprehensive Molecular Characterization of Inhibitors of Apoptosis Proteins (IAPs) for Therapeutic Targeting in Cancer. BMC Med. Genomics 13, 7. doi:10.1186/s12920-020-0661-x

Lin, T.-Y., Chan, H.-H., Chen, S.-H., Sarvagalla, S., Chen, P.-S., Coumar, M. S., et al. (2020). BIRC5/Survivin Is a Novel ATG12-ATG5 Conjugate Interactor and an Autophagy-Induced DNA Damage Suppressor in Human Cancer and Mouse Embryonic Fibroblast Cells. Autophagy 16, 1296–1313. doi:10.1080/15548627.2019.1671643

Makuch-Kocka, A., Kocki, J., Brzozowska, A., Bogucki, J., Kołodziej, P., Płachno, B. J., et al. (2021). The BIRC Family Genes Expression in Patients with Triple Negative Breast Cancer. Int. J. Mol. Sci. 22. doi:10.3390/ijms22041820

Miroshnikova, Y. A., Mouw, J. K., Barnes, J. M., Pickup, M. W., Lakins, J. N., Kim, Y., et al. (2016). Tissue Mechanics Promote IDH1-dependent HIF1α-Tenascin C Feedback to Regulate Glioblastoma Aggression. Nat. Cel Biol 18, 1336–1345. doi:10.1038/ncb3429

Modhukur, V., Iljasenko, T., Metsalu, T., Lokk, K., Laisk-Podar, T., and Vilo, J. (2018). MethSurv: a Web Tool to Perform Multivariable Survival Analysis Using DNA Methylation Data. Epigenomics 10, 277–288. doi:10.2217/epi-2017-0118

Ni, K., Sun, G. Z., and Sun, G. (2019). The Identification of Key Biomarkers in Patients with Lung Adenocarcinoma Based on Bioinformatics. Math. biosciences Eng. : MBE 16, 7671–7687. doi:10.3934/mbe.2019384

Ostrom, Q. T., Bauchet, L., Davis, F. G., Deltour, I., Fisher, J. L., Langer, C. E., et al. (2014). The Epidemiology of Glioma in Adults: a "state of the Science" Review. Neuro-oncology 16, 896–913. doi:10.1093/neuonc/nou087

Pu, W., Zheng, Y., and Peng, Y. (2020). Prolyl Isomerase Pin1 in Human Cancer: Function, Mechanism, and Significance. Front. Cel Dev. Biol. 8, 168. doi:10.3389/fcell.2020.00168

Ru, B., Wong, C. N., Tong, Y., Zhong, J. Y., Zhong, S. S. W., Wu, W. C., et al. (2019). TISIDB: an Integrated Repository portal for Tumor-Immune System Interactions. Bioinformatics (Oxford, England) 35, 4200–4202. doi:10.1093/bioinformatics/btz210

Wan, B., Liu, B., Yu, G., Huang, Y., and Lv, C. (2019). Differentially Expressed Autophagy-Related Genes Are Potential Prognostic and Diagnostic Biomarkers in clear-cell Renal Cell Carcinoma. Aging 11, 9025–9042. doi:10.18632/aging.102368

Wang, C., Zheng, X., Shen, C., and Shi, Y. (2012). MicroRNA-203 Suppresses Cell Proliferation and Migration by Targeting BIRC5 and LASP1 in Human Triple-Negative Breast Cancer Cells. J. Exp. Clin. Cancer Res. 31, 58. doi:10.1186/1756-9966-31-58

Wang, L., Huang, J., Jiang, M., and Sun, L. (2011). Survivin (BIRC5) Cell Cycle Computational Network in Human No-Tumor Hepatitis/cirrhosis and Hepatocellular Carcinoma Transformation. J. Cel. Biochem. 112, 1286–1294. doi:10.1002/jcb.23030

Woroniecka, K., Chongsathidkiet, P., Rhodin, K., Kemeny, H., Dechant, C., Farber, S. H., et al. (2018). T-cell Exhaustion Signatures Vary with Tumor Type and Are Severe in Glioblastoma. Clin. Cancer Res. 24, 4175–4186. doi:10.1158/1078-0432.ccr-17-1846

Wu, T., and Dai, Y. (2017). Tumor Microenvironment and Therapeutic Response. Cancer Lett. 387, 61–68. doi:10.1016/j.canlet.2016.01.043

Xiong, Q., Jiang, L., Liu, K., Jiang, X., Liu, B., Shi, Y., et al. (2021). miR-133b Targets NCAPH to Promote β-catenin Degradation and Reduce Cancer Stem Cell Maintenance in Non-small Cell Lung Cancer. Sig Transduct Target. Ther. 6, 252. doi:10.1038/s41392-021-00555-x

Xu, P., Jiang, L., Yang, Y., Wu, M., Liu, B., Shi, Y., et al. (2020). PAQR4 Promotes Chemoresistance in Non-small Cell Lung Cancer through Inhibiting Nrf2 Protein Degradation. Theranostics 10, 3767–3778. doi:10.7150/thno.43142

Xue, S., Hu, M., Iyer, V., and Yu, J. (2017). Blocking the PD-1/pd-L1 Pathway in Glioma: a Potential New Treatment Strategy. J. Hematol. Oncol. 10, 81. doi:10.1186/s13045-017-0455-6

Yamamoto, H., Ngan, C. Y., and Monden, M. (2008). Cancer Cells Survive with Survivin. Cancer Sci. 99, 1709–1714. doi:10.1111/j.1349-7006.2008.00870.x

Yamashita, K., Hosoda, K., Nishizawa, N., Katoh, H., and Watanabe, M. (2018). Epigenetic Biomarkers of Promoter DNA Methylation in the new era of Cancer Treatment. Cancer Sci. 109, 3695–3706. doi:10.1111/cas.13812

Yang, W., Soares, J., Greninger, P., Edelman, E. J., Lightfoot, H., Forbes, S., et al. (2013). Genomics of Drug Sensitivity in Cancer (GDSC): a Resource for Therapeutic Biomarker Discovery in Cancer Cells. Nucleic Acids Res. 41, D955–D961. doi:10.1093/nar/gks1111

Yu-Ju Wu, C., Chen, C.-H., Lin, C.-Y., Feng, L.-Y., Lin, Y.-C., Wei, K.-C., et al. (2020). CCL5 of Glioma-Associated Microglia/macrophages Regulates Glioma Migration and Invasion via Calcium-dependent Matrix Metalloproteinase 2. Neuro-oncology 22, 253–266. doi:10.1093/neuonc/noz189

Zhang, C., Huang, S., Zhuang, H., Ruan, S., Zhou, Z., Huang, K., et al. (2020). YTHDF2 Promotes the Liver Cancer Stem Cell Phenotype and Cancer Metastasis by Regulating OCT4 Expression via m6A RNA Methylation. Oncogene 39, 4507–4518. doi:10.1038/s41388-020-1303-7

Zou, J., Liao, X., Zhang, J., and Wang, L. (2019). Dysregulation of miR-195-5p/-218-5p/BIRC5 axis Predicts a Poor Prognosis in Patients with Gastric Cancer. J. Biol. Regul. Homeost Agents 33, 1377–1385. doi:10.23812/19-146-A

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Jiang, Shi, Chen, Xu, Huang, Li and Pu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 04 March 2022
doi: 10.3389/fmolb.2022.774738

Integrated Analysis of Transcriptomic and Genomic Data Reveals Blood Biomarkers With Diagnostic and Prognostic Potential in Non-small Cell Lung Cancer

Ibrahim H. Kaya1,²†, Olfat Al-Harazi²†, Mustafa T. Kaya2,³ and Dilek Colak²*

¹College of Medicine, Alfaisal University, Riyadh, Saudi Arabia

²Department of Molecular Oncology, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia

³King Faisal School, Riyadh, Saudi Arabia

Edited by:
Ismail Hosen, University of Dhaka, Bangladesh

Reviewed by:
Rupkatha Mukhopadhyay, Johns Hopkins Medicine, United States
Jyothi Padiadpu, Oregon State University, United States

* Correspondence: Dilek Colak, dkcolak@gmail.com

†These authors have contributed equally to this work and share first authorship

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 12 September 2021
Accepted: 27 January 2022
Published: 04 March 2022

Citation: Kaya IH, Al-Harazi O, Kaya MT and Colak D (2022) Integrated Analysis of Transcriptomic and Genomic Data Reveals Blood Biomarkers With Diagnostic and Prognostic Potential in Non-small Cell Lung Cancer. Front. Mol. Biosci. 9:774738. doi: 10.3389/fmolb.2022.774738

Background: Lung cancer is the second most common cancer and the main leading cause of cancer-associated death worldwide. Non-small cell lung cancer (NSCLC) accounts for about 85% of lung cancer diagnoses and more than 50% of all lung cancer cases are diagnosed at an advanced stage; hence have poor prognosis. Therefore, it is important to diagnose NSCLC patients reliably and as early as possible in order to reduce the risk of mortality.

Methods: We identified blood-based gene markers for early NSCLC by performing a multi-omics approach utilizing integrated analysis of global gene expression and copy number alterations of NSCLC patients using array-based techniques. We also validated the diagnostic and the prognostic potential of the gene signature using independent datasets with detailed clinical information.

Results: We identified 12 genes that are significantly expressed in NSCLC patients’ blood, at the earliest stages of the disease, and associated with a poor disease outcome. We then validated 12-gene signature’s diagnostic and prognostic value using independent datasets of gene expression profiling of over 1000 NSCLC patients. Indeed, 12-gene signature predicted disease outcome independently of other clinical factors in multivariate regression analysis (HR = 2.64, 95% CI = 1.72–4.07; p = 1.3 × 10⁻⁸). Significantly altered functions, pathways, and gene networks revealed alterations in several key genes and cancer-related pathways that may have importance for NSCLC transformation, including FAM83A, ZNF696, UBE2C, RECK, TIMM50, GEMIN7, and XPO5.

Conclusion: Our findings suggest that integrated genomic and network analyses may provide a reliable approach to identify genes that are associated with NSCLC, and lead to improved diagnosis detecting the disease in early stages in patients’ blood instead of using invasive techniques and also have prognostic potential for discriminating high-risk patients from the low-risk ones.

Keywords: lung cancer, NSCLC, early diagnosis, gene signature, blood, prognosis, biomarker, omics

INTRODUCTION

Despite the advances in cancer therapies and raising awareness, lung cancer continues to be one of the most malignant tumors. It is the second most common cancer and the leading cause of cancer-related death worldwide (Bray et al., 2018). Non-small-cell lung carcinoma (NSCLC) is responsible for about 85% of lung cancers (Santarpia et al., 2015). The poor outcome of many NSCLC patients stems from the fact that many are diagnosed after their cancer has developed into advanced stages (Xie and Xie, 2019; Chen et al., 2020), further indicating the necessity of identifying NSCLC at an early stage for maximizing patient survival.

Recent genomic studies have shown that changes in gene expression and copy number variants (CNVs) have been associated with human diseases, including cancer (Colak et al., 2010; Colak et al., 2013), and identified potential biomarkers for the disease using RNA- or DNA-based approaches (Jabs et al., 2017; Chakraborty et al., 2018). Previous studies also indicated that integrated genomic and network-based analysis may lead to reliable biomarkers for human diseases (Jinhua Sheng et al., 2011; Colak et al., 2013; Al-Harazi et al., 2016; Chakraborty et al., 2018). However, most of the identified biomarkers requires invasive procedures or not able to diagnose the early NSCLC.

The aim of this study is to identify a blood-based gene signature potentially be involved in development of early stage of the disease and have a prognostic value. We performed integrated analysis of transcriptomic and genomic data to identify blood markers with diagnostic and prognostic potential in early NSCLC and validated its significance using over 1000 NSCLC patients from multiple independent genomic datasets with clinical data. The identified gene markers may improve the detection of diseases and help to develop therapeutic strategies.

MATERIALS AND METHODS

Data Collection and the Integrated Analysis

Whole-genome gene expression and copy number alterations (CNAs) datasets for 190 NSCLC patients were obtained from publicly available databases within NCBI GEO (www.ncbi.nlm.nih.gov/geo) (GSE37745 and GSE76730). These datasets were then analyzed as previously described (Jabs et al., 2017). Moreover, data for blood samples for lung cancer patients (n = 3) and controls (n = 3) were gathered from a publicly available database (GSE69732). Furthermore, we downloaded RNAseq dataset for NSCLC patients from The Cancer Genome Atlas (TCGA) that contains 576 samples (n = 517 tumor, 279 of which are with Stage 1 and 59 normal samples). We compared the transcriptome of early stage NSCLC (n = 279) with normal (n = 59) samples and identified the differentially expressed genes (DEGs). The DEGs were identified using Analysis of Variance (ANOVA) with adjusted p-value of <0.05 and absolute fold change (FC) ≥ 1.5. The p values were adjusted for multiple comparisons by false discovery rate (FDR) according to Benjamini–Hochberg step-up procedure (Benjamini and Hochberg, 1995). The integrated analysis was performed using the Venn diagram approach to identify the common DEGs among mRNA, CNA, early-stage NSCLC and blood gene expression datasets. We then identified genes that are significantly associated with patients’ survival by performing overall survival analysis for each gene separately on a dataset containing 1,144 lung cancer samples collected from 14 datasets (GSE4573 (Raponi et al., 2006), GSE14814 (Zhu et al., 2010), GSE8894 (Lee et al., 2008), GSE19188 (Hou et al., 2010), GSE3141 (Bild et al., 2006), GSE31210 (Yamauchi et al., 2012), GSE29013 (Xie et al., 2011), GSE37745 (Botling et al., 2013), caArray (Director’s Challenge Consortium for the Molecular Classification of Lung Adenocarcinoma et al., 2008), and TCGA (Cancer Genome Atlas Research Network, 2012)) (Győrffy et al., 2013). Figure 1 illustrates our methodology.

FIGURE 1 | Schematic flowchart illustrating the methodology.

Validation of the Diagnostic Value of the Gene Signature

For validating the diagnostic value of our gene signature, we used a TCGA dataset (n = 576) and an independent dataset from ArrayExpress (E-MTAB-5231). The independent dataset consists of 22 NSCLC samples and 17 normal adjacent controls. We performed unsupervised principal component analysis (PCA) and two-dimensional hierarchical clustering using PARTEK Genomics Suite (Partek Inc., St. Louis, MO, United States) for each dataset separately. Functional, pathway, and gene interaction network analyses of the gene signature were performed using QIAGEN’s Ingenuity Pathway Analysis (IPA®, QIAGEN Redwood City).

Gene Ontology Enrichment, Pathway, and Gene Network Analyses

Gene ontology (GO) enrichment, pathway, and gene interaction network analyses were performed using (QIAGEN Inc., https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis) and Database for Annotation, Visualization and Integrated Discovery (DAVID) (Dennis et al., 2003). We mapped the NSCLC-associated gene signature to its corresponding gene object in the Ingenuity pathway knowledge base and constructed the gene interaction networks. A right-tailed Fisher’s exact test was used to calculate a p-value determining the probability that the biological function (or pathway) assigned to the data set is explained by chance alone (Colak et al., 2020).

Survival, Multivariate Analyses and NSCLC 12-Gene Classifier

Univariate and multivariate Cox regression analyses were used to assess our gene signature’s prognostic significance together with other clinical variables. We performed overall (OS) and progression free (PFS) survival on 1144 and 596 tumor samples, respectively. We calculated a 12-gene signature expression score for each patient that is average expression of up-regulated genes-average expression of down-regulated genes. We then used the median as the cutoff value for classifying patients into high and low risk groups. Survival curves were then plotted using the Kaplan-Meier method, and significance between survival curves was calculated by the log-rank test. In addition, multivariate analysis was performed using our 12-geneset taking histology (adenocarcinoma and squamous cell carcinoma), gender, and smoking history as covariates. A p-value < 0.05 was considered statistically significant.

Furthermore, we designed an NSCLC classifier using our 12-gene signature using several machine learning algorithms such as K-Nearest Neighbor, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Nearest Centroid, and Support Vector Machine (SVM). We estimated the classification performance on TCGA with 10-fold cross validation. We utilized standardized gene expression levels of the 12-gene signature as feature values. Accuracy, specificity, sensitivity, and area under curve (AUC) were used statistics measures, as described previously (Al-Harazi et al., 2021a; Al-Harazi et al., 2021b). The Nearest Centroid algorithm with proportional prior probability has outperformed other algorithms. The analyses were performed using PARTEK Genomics Suite (Partek Inc., St. Lois, MO, United States).

RESULTS

Identification of a Blood-Based Gene Signature for Early Stage Lung Cancer

We performed an integrated genomic analysis using four different transcriptomic and genomic datasets for human NSCLC. The analysis of transcriptomic and copy number alterations (CNAs) datasets (GSE37745 and GSE76730; 190 NSCLC) revealed 2,280 significantly expressed genes with copy number alterations (Jabs et al., 2017) (Figure 1). The analysis of whole-genome gene expression profiling of early stage NSCLC (n = 279) with normal (n = 59) samples revealed 7,337 genes (adjusted p-value <0.05 and fold change (FC) ≥ 1.5). Moreover, comparison of tumor transcriptome from patients’ blood with that of from normal controls resulted in 728 genes. We used Venn diagram approach to identify the common DEGs among mRNA, CNA, early-stage NSCLC and blood gene expression datasets that revealed 21 genes that are in common among all datasets (Figure 1). We then identified 12 genes (Table 1), defined as “12-gene signature,” that are significantly associated with patients’ survival by performing survival analysis of over 1,000 lung cancer samples (Figure 1).

TABLE 1 | List of 12-gene signature that is identified in this study.

Diagnostic and Prognostic Significance of the 12-Gene Signature

To test the diagnostic value of the 12-gene list, we performed unsupervised two-dimensional hierarchical clustering and principal component analyses (PCA) on two datasets (TCGA, n = 576 and E-MTAB-5231, n = 39 samples). The unsupervised PCA and the two-dimensional hierarchical clustering clearly distinguished patients from normal control samples in both datasets (Figure 2).

FIGURE 2 | Two-dimensional hierarchical clustering using our gene signature clearly separated patients from normal controls in (A) TCGA (n = 576) and (C) E-MTAB-5231 (n = 39), respectively. The hierarchical clustering revealed two main clusters, one mainly composed of tumors and another composed of normal controls. Samples are denoted in columns and genes are denoted in rows. Unsupervised PCA for (B) TCGA (n = 576) and (D) E-MTAB-5231 (n = 39). Red indicates tumor and blue denotes normal samples.

We confirmed the prognostic significance of our blood-based gene signature for overall as well as recurrence-free survival using a dataset with detailed clinical data from over 1000 NSCLC patients. The analysis demonstrated that high expression score based on 12-genes are significantly associated with poor disease outcome (Figures 3A,B). The 12-gene signature separated the patients into high risk and low-risk groups. Patients in the high-risk group had a significantly worse prognosis than the low-risk group with p-value < 1 × 10⁻¹⁶ (Figure 3). Patients in the high-risk group were more than twice likely to die from the disease than those in the low-risk group (Figure 3A). Similarly, the progression-free survival also showed that patients in the high-risk group had a poorer progression-free survival than patients in the low-risk group (Figure 3B).

FIGURE 3 | Prognostic significance of the 12-gene signature. (A) Overall and (B) progression free survival (PFS) analysis using NSCLC tumor samples (n = 1,144 samples). (C) Multivariate analysis using histology (adenocarcinoma and squamous cell carcinoma), gender, and smoking history as covariates. (D) Classification results of our gene signature using nearest centroid with proportional prior probability algorithm.

Moreover, the multivariate analyses indicated that our 12-gene signature is prognosticating the outcome of the disease independent of other clinic-pathological variables, such as histology, smoking history, and gender (HR = 2.64, 95% CI = 1.72–4.07; p-value = 1.3 × 10⁻⁸) (Figure 3C). Furthermore, we designed the 12-gene classifier using nearest centroid with proportional prior probability algorithm that provided over 99% accuracy in classifying samples as tumors or normal controls (Figure 3D).

Validation in Blood and Functional and Network Analyses

The expression of 12-gene in blood samples from patients and healthy controls (GSE69732) were compared that revealed that 12-gene signature score is significantly higher in tumor compared to normal (p-value = 0.002, Figure 4A). Functional and gene network analyses of the gene signature were performed using IPA which indicated that 12 genes were significantly associated with cancer, cell cycle, cellular movement, molecular transport, RNA trafficking, cell morphology, organ development, and tumor morphology (Figure 4B). Moreover, gene interaction networks revealed several key genes and cancer-related pathways that may role for early NSCLC transformation and disease progression, including FAM83A, ZNF696, UBE2C, RECK, TIMM50, GEMIN7, and XPO5 (Figure 4C).

FIGURE 4 | (A) mRNA gene expression of 12-gene signature score in blood from tumor vs. normal. (B) Gene ontology and functional analysis of the 12-gene signature. X-axis represents the significance (–log10 (p-value)) of the functional term. p-value of 0.05 is indicated as the threshold line in the figure (C) Gene interaction network analyses of the 12-gene signature. Red/green indicates higher/lower expression in NSCLC compared to controls. Straight lines are for direct interactions and dashed lines for indirect ones.

DISCUSSION

In this study, we sought to identify blood-based biomarkers with diagnostic and prognostic potential for early lung cancer using integrated analysis of multiple high dimensional independent datasets of transcriptomic and genomic datasets that detect the disease in early stages in patients’ biological fluids rather than using invasive techniques.

We identified 12-gene signature using integrated omics approach and validated its diagnostic and prognostic significance for overall and recurrence-free survival using data from over 1000 lung cancer patients’ samples with detailed clinical data. The analysis demonstrated that high 12-gene signature score was significantly associated with poor disease outcome. Previous studies reported that the integrated analysis of transcriptomic and genomic data may lead to reliable biomarkers that are more robust in disease classification and may have role in tumorigenesis (Colak et al., 2010; Al-Harazi et al., 2016; Chakraborty et al., 2018; Al-Harazi et al., 2021b). Indeed, several potential cancer driver genes that are involved in tumor initiation and progression have been identified using this approach (Colak et al., 2010; Colak et al., 2013; Ohshima et al., 2017).

Functional, pathway, and gene network analyses revealed significant biological functions, including cancer, cell cycle, cellular movement, molecular transport, and RNA trafficking, as well as several key genes and cancer-related pathways that may have importance for NSCLC transformation, including FAM83A, ZNF696, UBE2C, RECK, TIMM50, GEMIN7, and XPO5. Indeed, some of the identified genes were reported to be associated with cancers, including lung cancer. For example, FAM83A was found to be highly expressed in lung tumors (Li et al., 2015; Snijders et al., 2017). RECK is downregulated in esophageal squamous cell carcinoma (ESCC) and associated with a poor survival in ESCC (Zhu et al., 2017). The UBE2C gene is overexpressed in different types of cancers and considered a new target for cancers therapies (Dastsooz et al., 2019). Moreover, we used a machine learning algorithm to develop a model using our 12-gene signature for performing classification and tested its classification accuracy using over 500 lung cancer patients’ data that resulted in 99% prediction accuracy.

In conclusion, the 12-gene signature that we identified in this study reveals several genes and pathways that may be essential for early NSCLC transformation and progression and has potential to detect the disease in patients’ blood instead of utilizing invasive techniques. The integrated omics and network analyses may lead to robust biomarkers for the detection of early lung cancer and may lead to improved diagnosis, prognosis and therapeutic options.

DATA AVAILABILITY STATEMENT

Publicly available datasets were analyzed in this study. These data can be found here: The Cancer Genome Atlas (TCGA), ArrayExpress, and the NCBI Gene Expression Omnibus.

AUTHOR CONTRIBUTIONS

DC conception, design, and supervision. DC, IHK, OA-H, and MTK collected, analyzed, interpreted the data, and drafted the manuscript. All authors read and approved the manuscript.

FUNDING

This study is funded by the Research Grant (RAC#2110006 to DC).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

ACKNOWLEDGMENTS

We would like to thank King Faisal Specialist Hospital and Research Centre (KFSH&RC) and our individual sponsor who generously donated to this research (RAC#2110006 to DC). We also would like to thank Ms. Sukina Qanbar for administrative assistance. This work was under an institutionally approved King Faisal Specialist Hospital and Research Centre project (RAC# 2110006).

REFERENCES

Al-Harazi, O., Al Insaif, S., Al-Ajlan, M. A., Kaya, N., Dzimiri, N., and Colak, D. (2016). Integrated Genomic and Network-Based Analyses of Complex Diseases and Human Disease Network. J. Genet. Genomics 43 (6), 349–367. doi:10.1016/j.jgg.2015.11.002

Al-Harazi, O., Kaya, I. H., Al-Eid, M., Alfantoukh, L., Al Zahrani, A. S., Al Sebayel, M., et al. (2021). Identification of Gene Signature as Diagnostic and Prognostic Blood Biomarker for Early Hepatocellular Carcinoma Using Integrated Cross-Species Transcriptomic and Network Analyses. Front. Genet. 12, 710049. doi:10.3389/fgene.2021.710049

Al-Harazi, O., Kaya, I. H., El Allali, A., and Colak, D. (2021). A Network-Based Methodology to Identify Subnetwork Markers for Diagnosis and Prognosis of Colorectal Cancer. Front. Genet. 12, 721949. doi:10.3389/fgene.2021.721949

Benjamini, Y., and Hochberg, Y. (1995). Controlling the False Discovery Rate: a Practical and Powerful Approach to Multiple Testing. J. R. Stat. Soc. Ser. B (Methodological) 57, 289–300. doi:10.1111/j.2517-6161.1995.tb02031.x

Bild, A. H., Yao, G., Chang, J. T., Wang, Q., Potti, A., Chasse, D., et al. (2006). Oncogenic Pathway Signatures in Human Cancers as a Guide to Targeted Therapies. Nature 439 (7074), 353–357. doi:10.1038/nature04296

Botling, J., Edlund, K., Lohr, M., Hellwig, B., Holmberg, L., Lambe, M., et al. (2013). Biomarker Discovery in Non-small Cell Lung Cancer: Integrating Gene Expression Profiling, Meta-Analysis, and Tissue Microarray Validation. Clin. Cancer Res. 19 (1), 194–204. doi:10.1158/1078-0432.CCR-12-1139

Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., and Jemal, A. (2018). Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer J. Clinicians 68 (6), 394–424. doi:10.3322/caac.21492

Cancer Genome Atlas Research Network (2012). Comprehensive Genomic Characterization of Squamous Cell Lung Cancers. Nature 489 (7417), 519–525. doi:10.1038/nature11404

Chakraborty, S., Hosen, M. I., Ahmed, M., and Shekhar, H. U. (2018). Onco-Multi-OMICS Approach: A New Frontier in Cancer Research. Biomed. Res. Int. 2018, 1–14. doi:10.1155/2018/9836256

Chen, R., Manochakian, R., James, L., Azzouqa, A.-G., Shi, H., Zhang, Y., et al. (2020). Emerging Therapeutic Agents for Advanced Non-small Cell Lung Cancer. J. Hematol. Oncol. 13 (1), 58. doi:10.1186/s13045-020-00881-7

Colak, D., Al-Harazi, O., Mustafa, O. M., Meng, F., Assiri, A. M., Dhar, D. K., et al. (2020). RNA-seq Transcriptome Profiling in Three Liver Regeneration Models in Rats: Comparative Analysis of Partial Hepatectomy, ALLPS, and PVL. Sci. Rep. 10 (1), 5213. doi:10.1038/s41598-020-61826-1

Colak, D., Chishti, M. A., Al-Bakheet, A.-B., Al-Qahtani, A., Shoukri, M. M., Goyns, M. H., et al. (2010). Integrative and Comparative Genomics Analysis of Early Hepatocellular Carcinoma Differentiated from Liver Regeneration in Young and Old. Mol. Cancer 9, 146. doi:10.1186/1476-4598-9-146

Colak, D., Nofal, A., Albakheet, A., Nirmal, M., Jeprel, H., Eldali, A., et al. (2013). Age-specific Gene Expression Signatures for Breast Tumors and Cross-Species Conserved Potential Cancer Progression Markers in Young Women. PLoS One 8 (5), e63204. doi:10.1371/journal.pone.0063204

Dastsooz, H., Cereda, M., Donna, D., and Oliviero, S. (2019). A Comprehensive Bioinformatics Analysis of UBE2C in Cancers. Ijms 20 (9), 2228. doi:10.3390/ijms20092228

Dennis, G., Sherman, B. T., Hosack, D. A., Yang, J., Gao, W., Lane, H. C., et al. (2003). DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 4 (5), P3. doi:10.1186/gb-2003-4-5-p3

Director's Challenge Consortium for the Molecular Classification of Lung Adenocarcinoma Shedden, K., Taylor, J. M., Enkemann, S. A., Tsao, M. S., Yeatman, T. J., et al. (2008). Gene Expression-Based Survival Prediction in Lung Adenocarcinoma: a Multi-Site, Blinded Validation Study. Nat. Med. 14 (8), 822–827. doi:10.1038/nm.1790

Győrffy, B., Surowiak, P., Budczies, J., and Lánczky, A. (2013). Online Survival Analysis Software to Assess the Prognostic Value of Biomarkers Using Transcriptomic Data in Non-small-cell Lung Cancer. PLoS One 8 (12), e82241. doi:10.1371/journal.pone.0082241

Hou, J., Aerts, J., den Hamer, B., van Ijcken, W., den Bakker, M., Riegman, P., et al. (2010). Gene Expression-Based Classification of Non-small Cell Lung Carcinomas and Survival Prediction. PLoS One 5 (4), e10312. doi:10.1371/journal.pone.0010312

Jabs, V., Edlund, K., König, H., Grinberg, M., Madjar, K., Rahnenführer, J., et al. (2017). Integrative Analysis of Genome-wide Gene Copy Number Changes and Gene Expression in Non-small Cell Lung Cancer. PLoS One 12 (11), e0187246. doi:10.1371/journal.pone.0187246

Jinhua Sheng, J., Hong-Wen Deng, H. W., Calhoun, V. D., and Yu-Ping Wang, Y. P. (2011). Integrated Analysis of Gene Expression and Copy Number Data on Gene Shaving Using Independent Component Analysis. Ieee/acm Trans. Comput. Biol. Bioinf. 8 (6), 1568–1579. doi:10.1109/TCBB.2011.71

Lee, E.-S., Son, D.-S., Kim, S.-H., Lee, J., Jo, J., Han, J., et al. (2008). Prediction of Recurrence-free Survival in Postoperative Non-small Cell Lung Cancer Patients by Using an Integrated Model of Clinical Information and Gene Expression. Clin. Cancer Res. 14 (22), 7397–7404. doi:10.1158/1078-0432.CCR-07-4937

Li, Y., Xiao, X., Ji, X., Liu, B., and Amos, C. I. (2015). RNA-seq Analysis of Lung Adenocarcinomas Reveals Different Gene Expression Profiles between Smoking and Nonsmoking Patients. Tumor Biol. 36 (11), 8993–9003. doi:10.1007/s13277-015-3576-y

Ohshima, K., Hatakeyama, K., Nagashima, T., Watanabe, Y., Kanto, K., Doi, Y., et al. (2017). Integrated Analysis of Gene Expression and Copy Number Identified Potential Cancer Driver Genes with Amplification-dependent Overexpression in 1,454 Solid Tumors. Sci. Rep. 7 (1), 641. doi:10.1038/s41598-017-00219-3

Raponi, M., Zhang, Y., Yu, J., Chen, G., Lee, G., Taylor, J. M. G., et al. (2006). Gene Expression Signatures for Predicting Prognosis of Squamous Cell and Adenocarcinomas of the Lung. Cancer Res. 66 (15), 7466–7472. doi:10.1158/0008-5472.CAN-06-1191

Santarpia, M., González-Cao, M., Viteri, S., Karachaliou, N., Altavilla, G., and Rosell, R. (2015). Programmed Cell Death Protein-1/programmed Cell Death Ligand-1 Pathway Inhibition and Predictive Biomarkers: Understanding Transforming Growth Factor-Beta Role. Transl Lung Cancer Res. 4 (6), 728–742. doi:10.3978/j.issn.2218-6751.2015.12.04

Snijders, A. M., Lee, S.-Y., Hang, B., Hao, W., Bissell, M. J., and Mao, J.-H. (2017). FAM83 Family Oncogenes Are Broadly Involved in Human Cancers: an Integrative Multi-Omics Approach. Mol. Oncol. 11 (2), 167–179. doi:10.1002/1878-0261.12016

Xie, H., and Xie, C. (2019). A Six-Gene Signature Predicts Survival of Adenocarcinoma Type of Non-small-cell Lung Cancer Patients: A Comprehensive Study Based on Integrated Analysis and Weighted Gene Coexpression Network. Biomed. Res. Int. 2019, 1–16. doi:10.1155/2019/4250613

Xie, Y., Xiao, G., Coombes, K. R., Behrens, C., Solis, L. M., Raso, G., et al. (2011). Robust Gene Expression Signature from Formalin-Fixed Paraffin-Embedded Samples Predicts Prognosis of Non-small-cell Lung Cancer Patients. Clin. Cancer Res. 17 (17), 5705–5714. doi:10.1158/1078-0432.CCR-11-0196

Yamauchi, M., Yamaguchi, R., Nakata, A., Kohno, T., Nagasaki, M., Shimamura, T., et al. (2012). Epidermal Growth Factor Receptor Tyrosine Kinase Defines Critical Prognostic Genes of Stage I Lung Adenocarcinoma. PLoS One 7 (9), e43923. doi:10.1371/journal.pone.0043923

Zhu, C.-Q., Ding, K., Strumpf, D., Weir, B. A., Meyerson, M., Pennell, N., et al. (2010). Prognostic and Predictive Gene Signature for Adjuvant Chemotherapy in Resected Non-small-cell Lung Cancer. Jco 28 (29), 4417–4424. doi:10.1200/JCO.2009.26.4325

Zhu, J., Ling, Y., Xu, Y., Lu, M., Liu, Y., and Zhang, C. (2017). Promoter Hypermethylation of the RECK Gene Is Associated with its Low Expression and Poor Survival of Esophageal Squamous Cell Carcinoma. Oncol. Lett. 13 (3), 1911–1918. doi:10.3892/ol.2017.5656

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Kaya, Al-Harazi, Kaya and Colak. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 11 March 2022
doi: 10.3389/fmolb.2022.687786

Conflicting Roles of ZFP36L1 in Regulating the Progression of Muscle Invasive Bladder Cancer

Simin Yuan1,²†, Yujia Zhai¹†, Tao Tao3,²†, Xiaolong Zhang2,⁴, Ghassan Bashir5,², Guangzhi Li3,², Gang Wang² and Song Wu3,5,²*

¹Department of Anesthesiology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, China

²Shenzhen Following Precision Medicine Research Institute (Luohu Hospital Group), Shenzhen, China

³Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, China

⁴State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China

⁵Medical College, Shenzhen University, Shenzhen, China

Edited by:
Ismail Hosen, University of Dhaka, Bangladesh

Reviewed by:
Saroj Kumari, Nation institute of immunology, India
Chien-Feng Li, National Health Research Institutes, Taiwan

* Correspondence: Song Wu, wusong@szu.edu.cn

†These authors have contributed equally to this work and share first authorship

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 30 March 2021
Accepted: 17 February 2022
Published: 11 March 2022

Citation: Yuan S, Zhai Y, Tao T, Zhang X, Bashir G, Li G, Wang G and Wu S (2022) Conflicting Roles of ZFP36L1 in Regulating the Progression of Muscle Invasive Bladder Cancer. Front. Mol. Biosci. 9:687786. doi: 10.3389/fmolb.2022.687786

As the most common carcinoma of the human urinary system, bladder cancer (BC) is characterized by high recurrence, and poor prognosis after metastasis. In the past decade, genome-wide expression and sequencing studies had identified key genes and pathways related to BC, and pictured the comprehensive molecular features of the disease. Our previous study indicated that the coding gene of zinc finger protein 36 like 1 (ZFP36L1) mutated frequently in bladder tumor tissues and may be a potential suppressor for BC. The present study aimed to further investigate the role of ZFP36L1 in BC, and the survival analysis based on TCGA dataset revealed that high expressing level of ZFP36L1 associated with poorer prognosis of the patients with muscle invasive bladder cancer (MIBC). The associations of ZFP36L1 expression to the clinicopathological and molecular biological features also implicated the high level of ZFP36L1 may related to worse outcomes of patients. Also, GSEA indicated that high expression of ZFP36L1 significantly associated with enhanced activity of cancer metastasis related pathways. Functions of ZFP36L1 in MIBC were investigated further, and it was found that while ZFP36L1 suppressed the self-renewal of bladder cancer cells, it promoted the invasiveness of the cells markedly. Taken together, these results led to the conflicting roles of ZFP36L1 in regulating the progression of MIBC, and revealed further researches are needed to clarify the functions of the gene in tumor initiation and recurrence.

Keywords: TCGA, invasiveness, self-renewal, muscle invasive bladder cancer, Zfp36l1

INTRODUCTION

Bladder cancer (BC) is known to be the most common carcinoma of urinary system, with an estimated 5,49,393 new cases and 1,99,922 deaths worldwide in 2018 alone (Bray et al., 2018). Approximately 75% of the new BC cases were diagnosed to be the early-stage (Ta-T1) non-muscle invasive bladder cancer (NMIBC), which is characterized by a good prognosis (5-years survival >90%) and a strong tendency of recurrence. (Flaig et al., 2020). However, about 10–15% of the NMIBC cases recur to more advanced stage (T2-T4), and should be redefined as muscle-invasive bladder cancer (MIBC) for their malignant extension into the muscle (Knowles and Hurst, 2015). MIBCs have less favorable prognosis (5-years survival <50%) and are common to metastasis (Knowles and Hurst, 2015; Flaig et al., 2020), and 5-years survival for metastatic cases is less than 5% (Knowles and Hurst, 2015; Akaza et al., 2016). Deeper understanding of molecular mechanisms under the progression of MIBC is urgently required for the development of improved treatments.

In a previous study, we had performed a deep whole-genome sequencing of bladder tumor biopsies from 65 individuals, and characterized the mutations associated with BC by comparing the tumor tissues to the matched peripheral blood samples (Wu et al., 2019). Frequent mutations were found in the protein-coding exons of a series known bladder tumorigenesis-related genes, such as FGFR3, TP53, PIK3CA, HRAS, and KDM6A, etc. ZFP36L1, which was included in the OncoKB Cancer Gene List (https://www.oncokb.org/) as a tumor suppressor gene (Chakravarty et al., 2017), mutated in 12.3% BC samples. The mutation types identified in ZFP36L1 included deletion, frameshift deletion, frameshift insertion, missense mutation, nonsense mutation, and synonymous mutation, most of which tended to damage the structure or function of the gene. Moreover, comparing the gene expression in six BC cell lines to that in an immortalized normal bladder urothelial cell line revealed the lowly expressed ZFP36L1 in BC cells (Wu et al., 2019). High mutation frequency of ZFP36L1 (7%) was also confirmed in the 412 BC cases from the Cancer Genome Atlas (TCGA) (Cancer Genome Atlas Research, 2014; Robertson et al., 2017). These results seemed to be proving the tumor-suppressor properties of ZFP36L1 in BCs as reported in a recent study (Loh et al., 2020).

ZFP36L1 encodes the protein of Zinc Finger Protein 36 Like 1 (ZFP36L1), which belongs to the tristetraprolin (TTP) protein family. Mechanistically, ZFP36L1 functions by binding to the AU-rich elements within the 3′-untranslated regions of its target mRNAs and thus resulting the decay of the latter (Saini et al., 2020). Germline deletion of Zfp36l1 in mice was embryonically lethal, most of the deaths were caused by the failure of chorioallantoic fusion while the others were likely caused by the decreased cell proliferation within placentas (Stumpo et al., 2004). In the last decade, roles of the TTP family proteins in carcinogenesis have been researched, and abundance of evidence suggests a protective role of the TTP proteins in tumorigenesis. Oncogenes like NOTCH1, NANOG, and BCL-2 were reported to be the targets suppressed by ZFP36L1 in several previous studies (Hodson et al., 2010; Tan and Elowitz, 2014; Zekavati et al., 2014). Downregulation of ZFP36L1 was considered to be associated with some cancers (Chen et al., 2015; Rataj et al., 2019; Loh et al., 2020; Saini et al., 2020). However, upregulation of ZFP36L1 was also proved to be related to the initiation and progression of cancers. For instance, ZFP36L1 was found overexpressed in lymph node positive primary breast tumors, and required for the gliomagenesis (Abba et al., 2007; Weng et al., 2019). The contradiction among the evidences reveals that conclusions about the role of ZFP36L1 in carcinogenesis should be made more carefully.

In the current study, we aimed to gain insight on the function of ZFP36L1 in bladder cancer and explore the correlation between ZFP36L1 expression and the prognosis of BC patients. The results suggest that ZFP36L1 may play opposite roles in the self-renewal and invasion of BC cells respectively.

RESULTS

The Expression of ZFP36L1 in MIBCs Compared to Normal Tissues

To investigate the role of ZFP36L1 in MIBCs, we analyzed the ZFP36L1 expression in the MIBC cohort (427 samples) obtained from the TCGA. As shown in Figure 1A, the expression of ZFP36L1 is significantly lower in tumor than that in normal tissues among the unmatched MIBC samples in TCGA (408 tumors vs. 19 normal tissues; p < 0.0001, Wilcoxon rank sum test). Comparison exclusively between the 19 paired matched samples of tumor and their adjacent normal tissues in TCGA also indicated the low expression of ZFP36L1 in MIBC, even with a less significant difference (p < 0.05, Paired Student’s t-test) (Figure 1B). Then we derived the expression data of ZFP36L1 from the transcriptome profiling array of bladder carcinoma contributed by Dyrskjot L et al in GEO database (GSE3167). The result suggested the significant down-regulation of ZFP36L1 in superficial and muscle-invasive bladder cancer (MIBC), respectively, and but not in the carcinoma in situ (CIS) (Figure 1C). Altogether, decreased ZFP36L1 expression in MIBCs compared to normal urothelium tissues was confirmed in our analysis.

FIGURE 1 | Expression of ZFP36L1 in different bladder cancer specimens and the correlation analysis of survival time with the expression level. (A) Comparison of the transcription of ZFP36L1 between the 408 bladder tumors and 19 bladder normal tissues from the database of TCGA; (B) Comparison of the transcription of ZFP36L1 exclusively between the 19 paired matched samples of bladder tumor and their adjacent normal tissues in TCGA; (C) Comparison of the transcription of ZFP36L1 among superficial bladder tumors (n = 28), invasive bladder tumors (n = 13), CIS (n = 5), and normal bladder tissues (n = 14) from the transcriptome profiling array of bladder carcinoma contributed by Dyrskjøt L et al in GEO database (GSE3167). Disease free survival (D) and overall survival (E,F) analysis of ZFP36L1 with the muscle invasive bladder cancer transcriptome data of TCGA and GSE5287 predicting the correlation between the expression of the gene and the prognosis of the patients with MIBC.

High-Expressed ZFP36L1 Suggests Worse Prognosis of MIBCs

To clarify the potential correlation of ZFP36L1 with the prognostics of the patients with MIBC, survival analysis was performed based on the expression data from TCGA and GSE5287. Interestingly, the ZFP36L1High patients had significantly lower disease-free survival rate (p < 0.05, Log-rank test) (Figure 1D) and lower overall-survival rate (p < 0.05, Log-rank test, based on the GSE5287 dataset) although not significantly in the TCGA dataset (Figures 1E,F). These results seemed to be contradicted with the tumor suppressor role of ZFP36L1 in bladder cancer suggested by previous studies.

The Associations of ZFP36L1 Expression to the Clinicopathological and Molecular Biological Features of MIBC

To gain a comprehensive understanding about the expression of ZFP36L1 in MIBC tumors of different clinicopathological features, we then investigated the relationships between ZFP36L1 expressing levels and clinicopathological parameters in the TCGA-BLCA patients. The expression of ZFP36L1 was significantly higher in patients with a high-grade tumor (p =0.048 < 0.05) (Figure 2A), a high-TNM stage tumor (p = 0.036 < 0.05) (Figure 2B), and a high-tumor (T) stage tumor (p = 0.017 < 0.05) (Figure 2C), but showed no association with metastasis (M) stage (p = 0.33) and node (N) stage (p = 0.42) (Figures 2D,E).

FIGURE 2 | Analysis of the associations of ZFP36L1 expression to the clinicopathological and molecular biological features of TCGA-BLCA patients. Comparison of the ZFP36L1 expression in different (A) tumor grades, (B) TNM (Tumor node metastasis) stages, (C) T (Tumor) stages, (D) M (Metastasis) stages, (E) N (Node) stages, and (F) molecular subtypes of MIBC. p < 0.05 was recognized as statistically significant.

To achieve an international consensus on MIBC molecular subtypes that reconciled the published classification schemes, Aure´lie Kamouna et al had identified a consensus set of six MIBC molecular subtypes based on 1750 MIBC transcriptomic profiles from 16 published datasets and two additional cohorts (Kamoun et al., 2020). These six subtypes, including Ba/Sq (basal/squamous), LumP (luminal papillary), LumNS (luminal nonspecified), LumU (luminal unstable), NE-like (neuroendocrine-like), and stroma-rich, differ regarding underlying oncogenic mechanisms, infiltration by immune and stromal cells, and histological and clinical characteristics. To investigate the expressions of ZFP36L1 in MIBCs of different molecular biological characteristics, we compared the expression levels of ZFP36L1 in the 6 subtypes of TCGA-BLCA patients (p = 5.5e-07, Kruskal–Wallis test) (Figure 2F; Supplementary Table S1). And the result showed that compared to in the subtype of LumP, the expressions of ZFP36L1 were significant higher in the subtypes of Ba/Sq (p = 0.015 < 0.05), LumU (p = 0.006 < 0.05), and stroma-rich (p = 0.001 < 0.05). The differences of ZFP36L1 expressions between the LumP and LumNS (p = 0.52) or NE-like (p = 0.1) were not significant. According to the report, while LumP tumors were characterized by high expression of a noninvasive Ta pathway signature, LumU tumors had a higher cell cycle activity. The Ba/Sq and stroma-rich subtypes were both characterized by immune infiltration, while the former was also associated hypoxic microenvironment (Kamoun et al., 2020).

High Expression of ZFP36L1 Is Associated With Enhanced Activity of Cancer Metastasis Related Pathways

To investigate the biology function of ZFP36L1 during cancer progression, we separated MIBCs from TCGA as ZFP36L1High and ZFP36L1low basing on the expression level of ZFP36L1 (top and bottom quartiles, respectively), and performed Gene Set Enrichment Analysis (GSEA) between these two subgroups. In this process, five databases including GO, KEGG, Reactome, CGP, and Hallmark, were included for enrichment analysis. 1,298 gene sets are significantly correlated with the expressions of ZFP36L1 (FDR <0.05) (Supplementary Table S2). For each database, the top 10 significantly enriched gene sets (defined and sorted by the value of FDR) were considered affected by the expression level of ZFP36L1 remarkably and were depicted further (Supplementary Figure S1). All these 60 gene sets were identified in the ZFP36L1High > ZFP36L1Low analysis (NES >0). Notably, 3 cancer associated gene sets (LINDGREN_BLADDER_CANCER_CLUSTER_2B, RICKMAN_HEAD_AND_NECK_CANCER_C, and KEGG_SMALL_CELL_LUNG_CANCER) (Figure 3A) and 4 metastasis gene sets (HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION, HALLMARK_TNFA_SIGNALING_VIA_NFKB, HOLLERN_EMT_BREAST_TUMOR_UP, and HALLMARK_TGF_BETA_SIGNALING) (Figure 3B) are enriched in the ZFP36L1 highly expressed group. Even though previous studies mostly suggested ZFP36L1 as a tumor-suppressor gene, the results of survival analysis and GSEA assay above raise the attention to the potential cancer promoting effect of ZFP36L1 in MIBCs.

FIGURE 3 | GSEA based on the TCGA bladder cancer data identified 3 cancer associated gene sets (A) and 5 metastasis associated gene sets (B) enriched in the ZFP36L1 high expressing group.

Invasiveness Promoting Effect of ZFP36L1 on MIBCs

To examine the metastasis related function of ZFP36L1 in MIBCs, we sought to assess the effect of ZFP36L1 on the invasion of bladder cancer cell lines established from MIBC in vitro. Stable overexpression of ZFP36L1 was achieved by transfecting the T24 and 5,637 cells with the ZFP36L1 harboring-lentivirus and confirmed by western blotting assay (Figure 4A). Matrigel-coated transwell assays were performed and showed that the ZFP36L1 overexpressing cells presented a higher capability of invasion compared to the control (Figures 4C,D). Complementary experiments were performed with the ZFP36L1-knocking down cells constructed by siRNAs (Figure 4B). As shown in Figures 4E,F, reduced ZFP36L1 attenuated the invasiveness of bladder cancer cells.

FIGURE 4 | Invasiveness promoting effect of ZFP36L1 on MIBCs. (A) Expression level of ZFP36L1 in the ZFP36L1-overexpressing bladder cancer cells (oeZFP36L1) compared to the control cells (oeVec); (B) Expression levels of ZFP36L1 in the ZFP36L1-knocked down bladder cancer cells (siZFP36L1 1 and siZFP36L1 2) compared to the control cells (NC); (C,D) Overexpression of ZFP36L1 promoted the invasiveness of the T24 and 5,637 bladder cancer cells; (E,F) Knocking down the ZFP36L1 with siRNA suppressed the invasiveness of the T24 bladder cancer cells significantly.

ZFP36L1 Suppresses the Self-Renewal of MIBC Cells

The regulation on the growth of MIBC cells by ZFP36L1 was then investigated in the in vitro system of this study. Overexpression of ZFP36L1 markedly repressed the proliferation of bladder cancer cells as shown in the colony formation assay (Figures 5A,B). Furthermore, in the oncosphere formation assay, the sphere forming efficiency of ZFP36L1 overexpressed cells is significantly lower than the control cells (Figures 5C–E), while ZFP36L1 knockdown increases both the quantity and size of the oncospheres formed by the same number of cells (Figures 5F–H). To sum up, corresponding to previous studies, ZFP36L1 played as a suppressor on the self-renewal ability of MIBC cells.

FIGURE 5 | ZFP36L1 suppresses the self-renewal of MIBC cells. (A,B) Overexpression of ZFP36L1 inhibited the colony-formation capability of bladder cancer cells; (C–E) Overexpression of ZFP36L1 inhibited the sphere-formation capability of bladder cancer cells. *p < 0.05 and ****p < 0.0001. Data are representative of three independent experiments; (F–H) ZFP36L1 knocking down enhanced the sphere-formation capability of bladder cancer cells significantly. *p < 0.05 and ***p < 0.001. Data are representative of three independent experiments.

DISCUSSION

While newly detected BC cases are commonly NMIBCs and usually followed by good prognosis, patients first diagnosed with or progressed to MIBC may have to accept cystectomy and followed by a high risk of relapse. Metastasis may occur in the relapse when the MIBC cells invade the lymphatic and blood vessels, and the 5 years-survival for these patients is only about 5%. Therefore, it is important to investigate the invasion mechanisms of cancer cells for prolonging the survival time of BC patients.

In the present study, we showed that even though the expression of ZFP36L1 is lower in BC tumor cells than in normal bladder cells, the high expressing level of ZFP36L1 is associated with worse prognosis of BC patients. In the comprehensive analysis of the associations of ZFP36L1 expression to the clinicopathological and molecular biological features of MIBC, we found high-grade and high-stage tumors expressing higher levels of ZFP36L1 than low-grade and low-stage tumors. Also, high expression of ZFP36L1 were found in the MIBC subtypes of Ba/Sq and LumU, which were reported associated with poorer prognosis of patients (Kamoun et al., 2020). Moreover, GSEA based on the bladder cancer RNA-Seq data identified 5 cancer metastasis associated pathways may be activated by the high level of ZFP36L1. While the tumor-suppressing function of ZFP36L1 was widely reported, results of the survival analysis and GSEA in BC above are needed to be further investigated. By in vitro experiments, ZFP36L1 was shown inhibiting the colony formation and oncosphere formation capability of BC cells while promoting their invasion capability. These results combined suggested the tumor initiation suppressing role as well as the tumor invasion promoting role of ZFP36L1 in bladder cancer.

Cell proliferation and invasion are two uncoupled processes during cancer progression, they were proved to be mutually exclusive in certain cellular models, and highlighting dynamics in cell plasticity (Gao et al., 2005). Comparison of the gene expression signatures between the proliferative and invasive melanoma cells showed two different transcription programs (Hoek et al., 2008). In basal cell carcinoma, infiltrative tumor cells do not actively proliferate, and a functional p16INK4a-cyclin D-Rb pathway tended to activate the invasion while cease proliferation (Svensson et al., 2003). Previous studies, involving cellular models of bladder cancer and prostate cancer, had shown that the EMT increases the invasion capability of cancer cells but non-EMT cells are more likely to establish new colonies (Tsuji et al., 2008; Celià-Terrassa et al., 2012). In our study, GSEA based on the expression profile of BC revealed that high-expressing ZFP36L1 enhanced the EMT associated pathways (HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION, HALLMARK_TNFA_SIGNALING_VIA_NFKB, HOLLERN_EMT_BREAST_TUMOR_UP, and HALLMARK_TGF_BETA_SIGNALING) (Figure 2). Therefore, we reckoned that high level of ZFP36L1 may suppress the self-renewal capability of BC while promote the invasion capability of the cells by activating the EMT associated pathways. Notably, it was reported that expression of ZFP36L1 is induced by abroad variety of growth factors including TGFβ and TNFα, and responses to scratch-wounding in keratinocytes in vitro (Hacker et al., 2010). It is still pending to distinguish the high expression of ZFP36L1 is a cause or effect of the EMT of bladder cancer cells in vivo.

CONCLUSION

This study reveals a potential invasion promoting role of ZFP36L1 in bladder cancer the first time, which may explain the association of ZFP36L1 expression with the prognosis of BC patients somehow. Although limitations of this study, including a lack of in vivo experiments evidence, suggest further research is expected, we believe the results above raise attention to the tumor progression regulating role of ZFP36L1 in bladder cancer which may need to be reconsidered more carefully.

METHODS AND MATERIALS

Data Acquisition and Bioinformatics Analysis

TCGA normalized RNA sequencing (RNA-Seq) data sets with estimation of Fragments Per Kilobase of transcript per Million mapped reads (FPKM) and the clinicopathological characteristics information (Supplementary Table S3) from 408 tumor samples and 19 non-tumor samples were retrieved from UCSC Xena (https://xenabrowser.net/datapages/) for further analysis. Gene expression data in GSE3167 based on the Affymetrix Human Genome U133A Array platform were downloaded from gene expression omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/), including normal bladder tissues (9 samples) superficial transitional cell carcinoma with surrounding CIS (13 samples), without surrounding CIS lesions (15 samples), and in muscle invasive carcinomas (13 samples). Kastle–Meyer survival analysis based on the TCGA BLCA dataset and the GSE5287 dataset was performed using the web server named GEPIA (http://gepia.cancer-pku.cn/index.html) and PrognoScan (http://dna00.bio.kyutech.ac.jp/PrognoScan/), respectively. BLCA RNA-seq data was derived from TCGA and the samples were sorted into the top and bottom quartiles of ZFP36L1 expression (ZFP36L1High and ZFP36L1Low, respectively). Gene set enrichment analysis (GSEA) was performed via GSEApy (https://github.com/zqfang/GSEApy). We downloaded the terms of Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, and MSigDB Hallmark pathways, and conducted the GSEA analysis, respectively. The significant gene terms were considered as significantly enriched as false discovery rate (FDR) < 0.05.

Cells and Culture

Human bladder cancer cell lines 5,637 and T24 were obtained from the American Type Culture Collection (ATCC). The cells were cultured in Roswell Park Memorial Institute (RPMI) 1,640 medium (Gibco, Grand Island, NY) containing 10% fetal bovine serum (FBS), 100 U/mL penicillin, and 100 mg/ml streptomycin (Gibco, Grand Island, NY) at 37°C in a humidified atmosphere with 5% CO₂.

Generation of Stable Cell Lines With ZFP36L1 Overexpression

The coding sequence of ZFP36L1 (1,017 bp) was amplified from the total cDNA library of the immortalized normal bladder cell line SV-HUC-1, with the primers h-ZFP36L1-F (GAG​GAT​CTA​TTT​CCG​GTG​AAT​TCG​CCA​CCA​TGA​CCA​CCA​CCC​TCG​TGT) and h-ZFP36L1-R (TAG​TCA​CTT​AAG​CTT​GGT​ACC​GAG​GAT​CCG​TCA​TCT​GAG​ATG​GAA​AGT). The fragments then were cloned into the lentivirus plasmid vector pHBLV-CMV-MCS-3FLAG-EF1-ZsGreen-T2A-PURO (Hanbio Biotechnology Co.,Ltd). For virus generation, we transfected the 293T cells with the plasmid constructed above, along with package plasmids (pSPAX2 and pMD2G). Bladder cancer cell lines 5,637 and T24 were transfected by virus supernatants and then screened for the positive clones with 1–1.5 μg/ml puromycin. The stable ZFP36L1 overexpressing cells established then were used in following experiments.

Knock Down ZFP36L1 With siRNA in Bladder Cancer Cell Lines

3 types of siRNA oligos against the mRNA of ZFP36L1 were designed and purchased from GenePharma Co.,Ltd. T24 cells were transformed with the oligos using Lipofectamine RNAiMAX (Invitrogen, Thermo Fisher Scientific, Inc.), according to the manufacturers’ instructions. 18 h later, the total RNAs of the transformed cells were examined by RT-PCR to detect the ZFP36L1 knocking down efficiency. 2 efficient oligos were chose to be used in further experiments and their sequences were as follows: h-ZFP36L1-siRNA #1: sense 5′-CCC​UCG​UGU​CUG​CCA​CCA​UTT-3′, and antisense 5′-AUG​GUG​GCA​GAC​ACG​AGG​GTT-3’; h-ZFP36L1-siRNA #2: sense 5′- GGA​GGC​ACU​CAG​UCA​CCC​UTT-3′, and antisense 5′- AGG​GUG​ACU​GAG​UGC​CUC​CTT-3’.

Transwell Invasion Assay

For transwell invasion assay, the upper membrane of the chamber inserts of transwell (8 μm pore size; Corning Costar, Lowell, MA, United States) were precoated with 50 μl of 100 μg/ml Matrigel (BD Biosciences San Diego, CA, United States). Indicated cells (1 × 10⁴/chamber) were serum-starved for 6h first, and then were suspended in 100 μl FBS-free RPMI 1640 medium before be seeded into the upper chamber insert held in the lower well with 600 μl RPMI 1640 containing 20% FBS of the 24-well companion plate. After 24 h of incubation, invading cells at the bottom of the membrane filters were fixed in 4% paraformaldehyde and stained with 0.025% crystal violet, then were photographed under a light microscope. The experiment was repeated 3 separate times, and the number of invading cells was quantified by counting 5 fields in each chamber.

Oncosphere Formation Assay

5,000 indicated cells were cultured with Knock-Out DMEM/F-12 medium (Gibco, 12,660,012) supplemented with 20 ng/ml EGF (Invitrogen, PHG0314), 20 ng/ml bFGF (Invitrogen, 13256029), 1% N2 (Gibco, 17,502,048), and 2% B27 (Gibco, 17,504,044), in the 6 well ultra-low attachment culture dishes (Corning Costar, Lowell, MA, United States). 10 days later, spheres were counted and photographed under a light microscope.

Colony Formation Assay

The indicated cells were seeded to 6-well plates (500 cells/well) and cultured with complete medium. After 10 days incubation, the colonies formed were fixed with 4% paraformaldehyde and stained with 0.025% crystal violet solution, then were photographed under a light microscope. The experiment was repeated 3 separate times, and the number of colonies was quantified.

Statistical Analyses

The GraphPad Prism V6 software and R software v4.0.3 were used for data analysis. Differences between two groups of variables were compared using the two tailed unpaired t-test for variables with normal distributions, and the rank sum test for variables not normally distributed. And Kruskal–Wallis test was used to compare more than two groups of variables. p < 0.05 was considered statistically significant.

DATA AVAILABILITY STATEMENT

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.

AUTHOR CONTRIBUTIONS

SW, SY, and TT designed and elaborated this study. The data used was collected and analyzed by TT, YZ, and XZ. SY, GB, and GL worked on the experiments. The draft was written by SY, TT, and YZ, then revised by SW and GW.

FUNDING

This work was supported by the National Natural Science Foundation of China (82000732 and 82003620), the Guang Dong Basic and Applied Basic Research Foundation (2019A1515110151 and 2019A1515110176), the Shenzhen Basic Research Project (JCYJ20210324142413034), and the Guang Dong Pharmaceutical Association Foundation (2020XC13).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2022.687786/full#supplementary-material

REFERENCES

Abba, M. C., Sun, H., Hawkins, K. A., Drake, J. A., Hu, Y., Nunez, M. I., et al. (2007). Breast Cancer Molecular Signatures as Determined by SAGE: Correlation with Lymph Node Status. Mol. Cancer Res. 5 (9), 881–890. doi:10.1158/1541-7786.mcr-07-0055

Akaza, H., Jason Chia-Hsien, C., Byung Ha, C., Narmada, G., Shiro, H., Shigeo, H., et al. (2016). NCCN Guidelines Bladder Cancer. Philadelphia, PA: National Comprehensive Cancer Network (NCCN).

Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., and Jemal, A. (2018). Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer J. Clinicians 68 (6), 394–424. doi:10.3322/caac.21492

Cancer Genome Atlas Research (2014). Comprehensive Molecular Characterization of Urothelial Bladder Carcinoma. Nature 507 (7492), 315–322. doi:10.1038/nature12965

Celià-Terrassa, T., Meca-Cortés, Ó., Mateo, F., Martínez de Paz, A., Rubio, N., Arnal-Estapé, A., et al. (2012). Epithelial-mesenchymal Transition Can Suppress Major Attributes of Human Epithelial Tumor-Initiating Cells. J. Clin. Invest. 122 (5), 1849–1868. doi:10.1172/jci59218

Chakravarty, D., Gao, J., Phillips, S. M., Kundra, R., Zhang, H., Wang, J., et al. (2017). OncoKB: A Precision Oncology Knowledge Base. JCO Precis Oncol. 2017, 1–16. doi:10.1200/PO.17.00011

Chen, M.-T., Dong, L., Zhang, X.-H., Yin, X.-L., Ning, H.-M., Shen, C., et al. (2015). ZFP36L1 Promotes Monocyte/macrophage Differentiation by Repressing CDK6. Sci. Rep. 5, 16229. doi:10.1038/srep16229

Flaig, T. W., Spiess, P. E., Agarwal, N., Bangs, R., Boorjian, S. A., Buyyounouski, M. K., et al. (2020). Bladder Cancer, Version 3.2020, NCCN Clinical Practice Guidelines in Oncology. J. Natl. Compr. Canc Netw. 18 (3), 329–354. doi:10.6004/jnccn.2020.0011

Gao, C.-F., Xie, Q., Su, Y.-L., Koeman, J., Khoo, S. K., Gustafson, M., et al. (2005). Proliferation and Invasion: Plasticity in Tumor Cells. Proc. Natl. Acad. Sci. 102 (30), 10528–10533. doi:10.1073/pnas.0504367102

Hacker, C., Valchanova, R., Adams, S., and Munz, B. (2010). ZFP36L1 Is Regulated by Growth Factors and Cytokines in Keratinocytes and Influences Their VEGF Production. Growth Factors 28 (3), 178–190. doi:10.3109/08977190903578660

Hodson, D. J., Janas, M. L., Galloway, A., Bell, S. E., Andrews, S., Li, C. M., et al. (2010). Deletion of the RNA-Binding Proteins ZFP36L1 and ZFP36L2 Leads to Perturbed Thymic Development and T Lymphoblastic Leukemia. Nat. Immunol. 11 (8), 717–724. doi:10.1038/ni.1901

Hoek, K. S., Eichhoff, O. M., Schlegel, N. C., Döbbeling, U., Kobert, N., Schaerer, L., et al. (2008). In Vivo switching of Human Melanoma Cells between Proliferative and Invasive States. Cancer Res. 68 (3), 650–656. doi:10.1158/0008-5472.can-07-2491

Kamoun, A., de Reyniès, A., Allory, Y., Sjödahl, G., Robertson, A. G., Seiler, R., et al. (2020). A Consensus Molecular Classification of Muscle-Invasive Bladder Cancer. Eur. Urol. 77 (4), 420–433. doi:10.1016/j.eururo.2019.09.006

Knowles, M. A., and Hurst, C. D. (2015). Molecular Biology of Bladder Cancer: New Insights into Pathogenesis and Clinical Diversity. Nat. Rev. Cancer 15 (1), 25–41. doi:10.1038/nrc3817

Loh, X.-Y., Sun, Q.-Y., Ding, L.-W., Mayakonda, A., Venkatachalam, N., Yeo, M.-S., et al. (2020). RNA-binding Protein ZFP36L1 Suppresses Hypoxia and Cell-Cycle Signaling. Cancer Res. 80 (2), 219–233. doi:10.1158/0008-5472.can-18-2796

Rataj, F., Planel, S., Denis, J., Roelants, C., Filhol, O., Guyon, L., et al. (2019). Targeting AU-Rich Element-Mediated mRNA Decay with a Truncated Active Form of the Zinc-finger Protein TIS11b/BRF1 Impairs Major Hallmarks of Mammary Tumorigenesis. Oncogene 38 (26), 5174–5190. doi:10.1038/s41388-019-0784-8

Robertson, A. G., Kim, J., Al-Ahmadie, H., Bellmunt, J., Guo, G., Cherniack, A. D., et al. (2017). Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer. Cell 171 (3), 540–e25. doi:10.1016/j.cell.2017.09.007

Saini, Y., Chen, J., and Patial, S. (2020). The Tristetraprolin Family of RNA-Binding Proteins in Cancer: Progress and Future Prospects. Cancers (Basel) 12, 12. doi:10.3390/cancers12061539

Stumpo, D. J., Byrd, N. A., Phillips, R. S., Ghosh, S., Maronpot, R. R., Castranio, T., et al. (2004). Chorioallantoic Fusion Defects and Embryonic Lethality Resulting from Disruption of Zfp36L1 , a Gene Encoding a CCCH Tandem Zinc Finger Protein of the Tristetraprolin Family. Mol. Cel Biol 24 (14), 6445–6455. doi:10.1128/mcb.24.14.6445-6455.2004

Svensson, S., Nilsson, K., Ringberg, A., and Landberg, G. (2003). Invade or Proliferate? Two Contrasting Events in Malignant Behavior Governed by p16(INK4a) and an Intact Rb Pathway Illustrated by a Model System of Basal Cell Carcinoma. Cancer Res. 63 (8), 1737–1742.

Tan, F. E., and Elowitz, M. B. (2014). Brf1 Posttranscriptionally Regulates Pluripotency and Differentiation Responses Downstream of Erk MAP Kinase. Proc. Natl. Acad. Sci. U S A. 111, E1740. doi:10.1073/pnas.1320873111

Tsuji, T., Ibaragi, S., Shima, K., Hu, M. G., Katsurano, M., Sasaki, A., et al. (2008). Epithelial-mesenchymal Transition Induced by Growth Suppressor p12CDK2-AP1 Promotes Tumor Cell Local Invasion but Suppresses Distant colony Growth. Cancer Res. 68 (24), 10377–10386. doi:10.1158/0008-5472.can-08-1444

Weng, Q., Wang, J., Wang, J., He, D., Cheng, Z., Zhang, F., et al. (2019). Single-Cell Transcriptomics Uncovers Glial Progenitor Diversity and Cell Fate Determinants during Development and Gliomagenesis. Cell Stem Cell 24 (5), 707–723. doi:10.1016/j.stem.2019.03.006

Wu, S., Ou, T., Xing, N., Lu, J., Wan, S., Wang, C., et al. (2019). Whole-genome Sequencing Identifies ADGRG6 Enhancer Mutations and FRS2 Duplications as Angiogenesis-Related Drivers in Bladder Cancer. Nat. Commun. 10 (1), 720. doi:10.1038/s41467-019-08576-5

Zekavati, A., Nasir, A., Alcaraz, A., Aldrovandi, M., Marsh, P., Norton, J. D., et al. (2014). Post-transcriptional Regulation of BCL2 mRNA by the RNA-Binding Protein ZFP36L1 in Malignant B Cells. PloS one 9, e102625. doi:10.1371/journal.pone.0102625

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Yuan, Zhai, Tao, Zhang, Bashir, Li, Wang and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 17 March 2022
doi: 10.3389/fmolb.2022.743515

Siglec-9, a Putative Immune Checkpoint Marker for Cancer Progression Across Multiple Cancer Types

Yuliang Wu1,2,³†, Wei Huang1,³†, Yutong Xie², Chunyan Wang¹, Ning Luo1,³, Yingying Chen¹, Liefu Wang⁴, Zhongping Cheng1,³*, Zhengliang Gao2,4,⁵* and Shupeng Liu1,⁶*

¹Department of Obstetrics and Gynecology, Shanghai Tenth People’s Hospital, Tongji University, Shanghai, China

²Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), Tongji University School of Medicine, Shanghai, China

³Gynecologic Minimally Invasive Surgery Research Center, Tongji University School of Medicine, Shanghai, China

⁴Xinyang Vocational and Technical College, Xinyang, China

⁵Institute of Geriatrics (Shanghai University), Affiliated Nantong Hospital of Shanghai University (The Sixth People’s Hospital of Nantong), School of Medicine, Shanghai University, Nantong, China

⁶Department of Obstetrics and Gynecology, Putuo District People’s Hospital of Shanghai City, Shanghai, China

Edited by:
Ismail Hosen, University of Dhaka, Bangladesh

Reviewed by:
Rajesh Sinha, University of Alabama at Birmingham, United States
Tabish H. Khan, University of Virginia, United States

* Correspondence: Zhongping Cheng, mdcheng18@263.net; Zhengliang Gao, zhengliang_gao@tongji.edu.cn; Shupeng Liu, lshup@tongji.edu.cn

†These authors have contributed equally to this work and share first authorship

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 18 July 2021
Accepted: 08 February 2022
Published: 17 March 2022

Citation: Wu Y, Huang W, Xie Y, Wang C, Luo N, Chen Y, Wang L, Cheng Z, Gao Z and Liu S (2022) Siglec-9, a Putative Immune Checkpoint Marker for Cancer Progression Across Multiple Cancer Types. Front. Mol. Biosci. 9:743515. doi: 10.3389/fmolb.2022.743515

Siglec-9, a cell surface transmembrane receptor mainly expressed on B cells, CD56⁺ NK cells, and CD4⁺ and CD8⁺ T cells, is strongly related to the tumor immune microenvironment. However, the expression pattern of Siglec-9 and its prognostic potential have not been investigated in a pan-cancer perspective. This study aimed to explore the association of Siglec-9 with prognosis, tumor stage, molecular subtype, and the immune microenvironment in pan-cancer. The mRNA expression of Siglec-9 was obtained from The Cancer Genome Atlas (TCGA), the Broad Institute Cancer Cell Line Encyclopedia (CCLE), and Genotype-Tissue Expression (GTEx). The relationship between Siglec-9 mRNA expression and prognosis was evaluated by the Kaplan–Meier analysis. The correlation between Siglec-9 and tumor-infiltrating immune cells, immune subtype, and molecular subtype was evaluated on Tumor Immune Estimation Resource (TIMER) and Integrated Repository Portal for Tumor-Immune System Interactions (TISIDB). The correlation between Siglec-9 expression and immune checkpoint, mismatch repair (MMR), DNA methyltransferase (DNMT), tumor mutation burden (TMB), and microsatellite instability (MSI) was also analyzed. It showed that Siglec-9 expression was significantly altered in most TCGA tumors. Siglec-9 expression was associated with the prognosis of patients with adrenocortical carcinoma (ACC), lung adenocarcinoma (LUSC), thymoma (THYM), colon adenocarcinoma (COAD), glioblastoma multiforme (GBM), prostate adenocarcinoma (PRAD), esophageal carcinoma (ESCA), and brain lower-grade glioma (LGG). Particularly, increased Siglec-9 expression was strongly correlated with poor prognosis in LGG. Correlation between Siglec-9 expression and tumor stage was also observed in various cancers. In addition, Siglec-9 was positively associated with infiltration of immune cells including neutrophils, dendritic cells (DCs), macrophage, and CD4⁺ and CD8⁺ T cells. Moreover, a significant correlation between Siglec-9 and MSI, TMB, MMR, DNMT, immune checkpoint, immune subtype, molecular subtype, and immunomodulators was observed in multiple cancers. Specifically, poor prognostic value and strong correlation to immune cell infiltration were verified with the LGG dataset from the Chinese Glioma Genome Atlas (CGGA). These findings indicated that Siglec-9 can be a novel biomarker and a potential target for cancer immunotherapy.

Keywords: Siglec-9, the Cancer Genome Atlas, immune infiltration, pan-cancer, LGG, GSEA

INTRODUCTION

Tumors remain to be a medical problem seriously endangering human health (Bray et al., 2020). Therapies including surgery, chemotherapy, and radiotherapy improved the prognosis of patients, but millions of deaths were induced by tumor relapse and metastasis. Immunodeficiency and immunosuppression are among the main causes of tumorigenesis and recurrence (Mortezaee, 2020). Immunotherapy including immune checkpoint therapy and chimeric antigen receptor T (CAR-T) cell therapy has improved prognosis in tumors such as non-small-cell lung cancer (NLSLC), OV, and B-cell lymphoma (Neelapu et al., 2017; Galluzzi et al., 2018). However, immune therapy remains a poor therapeutic effect in solid tumors, with the underlying mechanism remaining unclear.

Immune checkpoints are involved in tumor immunosuppression and are supposed to be ideal modulation targets for tumor immunotherapy. The sialic acid-binding immunoglobulin-like lectins (Siglecs), novel kinds of immune checkpoints involved in tumor immunosuppression, may be new targets, or biomarkers or prognostic factors of immunotherapy (Liu et al., 2021). Siglecs family, belonging to the immunoglobulin superfamily, is a type I transmembrane protein of I-lectin that consists of a V-set immunoglobulin blind domain and a series of C2-set Ig-like domain in the extracellular zone⁷. Siglecs are mainly expressed on immune cells and manipulate immune function via recognizing sialylated glycans (Zheng et al., 2020). Siglecs can arouse inhibitory signals to immune cells in either an in-cis or an in-trans manner (Fraschilla and Pillai, 2017). Among Siglecs, Siglec-9, one of the Siglecs with ITIM domains, was found to be widely involved in tumor progression and tumor immunosuppression (Fraschilla and Pillai, 2017). It was reported to deliver activation signals to cancer cells and activate inhibitory signals in immune cells (Sabit et al., 2013). Siglec-9 promoted the growth of adenocarcinomas or hematological tumor cells via recruiting β-catenin to the C-terminal domain of Siglec-9 by interacting with MUC1 on tumor cells (Tanida et al., 2013). It also reported that Siglec-9 promoted cancer cell growth by interacting with MUC16 on epithelial ovarian cancer cells (Belisle et al., 2010). In addition to the direct effect on tumor cells, activation of Siglec-9 on NK cells, B cells, T cells, and monocytes was reported to restrain cytolytic capacity and helped tumor cells evade immune surveillance (Belisle et al., 2010). Combining with the finding that Siglec-9 was wildly expressed on immune cells including neutrophils, NK cells, B cells, macrophages, and monocytes, it suggests that Siglec-9 might be a potential target for cancer immunotherapy and a potential prognostic marker of cancer patients. However, little is known about the expression pattern and the prognostic value of Siglec-9 in cancers.

In the present study, datasets from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were obtained and used to assess the expression pattern of Siglec-9 and its correlation with prognosis and tumor immune microenvironment. It showed that the expression of Siglec-9 mRNA was altered in most cancers. The Siglec-9 mRNA expression level was correlated with the survival of patients. It was also correlated with other prognostic factors such as immune checkpoint, mismatch repair (MMR), DNA methyltransferase (DNMT), tumor mutation burden (TMB), and microsatellite instability (MSI). In addition, Siglec-9 expression was strongly correlated with the tumor immune microenvironment including infiltration of immune cells, immune score, immune subtypes, and immune checkpoint expression. Moreover, the correlation between Siglec-9 expression and prognosis or immune cell infiltration in LGG was further verified using the dataset from the Chinese Glioma Genome Atlas (CGGA).

The present study reported the expression pattern of Siglec-9 and its relationship with prognosis and tumor immune microenvironment in pan-cancer. It uncovered the prognostic value of Siglec-9 in some cancers including LGG and suggested that Siglec-9 can be a novel biomarker and a potential target for cancer immunotherapy.

MATERIALS AND METHODS

Data Collection

The mRNA expression, copy number alteration, and clinical information of more than 30 cancer types were obtained from The Cancer Genome Atlas (TCGA). The mRNA expression data from normal tissue sites across nearly 1,000 people were obtained from the Genotype-Tissue Expression (GTEx) to supplement the normal tissue mRNA sequencing data lacking in TCGA. Siglec-9 mRNA expression data of tumor cell lines were obtained from the Broad Institute Cancer Cell Line Encyclopedia (CCLE). The mRNA expression data and clinical information of glioma were obtained from the Chinese Glioma Genome Atlas (CGGA) to verify the result in TCGA-LGG. We used the “mRNAseq_693” dataset (Wang et al., 2015; Liu et al., 2018). WHO grade II and WHO grade III cases were defined as LGG to explore the role of Siglec-9 in LGG (Liu et al., 2018).

Correlation of Siglec-9 Expression to Tumor Clinical Characteristics

GEPIA2 (http://gepia2.cancer-pku.cn/#index) (Tang et al., 2019) was used to analyze the correlation between tumor stages and Siglec-9 mRNA expression using “major stage” and “log2 (TPM + 1) for log-scale”. The association between Siglec-9 mRNA expression and overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI) depended on TCGA databases and was analyzed on the DriverDBv3 (http://driverdb.tms.cmu.edu.tw/) (Liu et al., 2020) with mean of Siglec-9 expression as cutoff value. The online database UALCAN network (http://ualcan.path.uab.edu/) (Chandrashekar et al., 2017) was also used to verify the OS analysis with default settings. Siglec-9 expression in diverse molecular subtypes was analyzed on The Integrated Repository Portal for Tumor-Immune System Interactions (TISIDB, http://cis.hku.hk/TISIDB/index.php) (Ru et al., 2019).

Correlation of Siglec-9 Expression to Immunological Characteristics

TIMER (https://cistrome.shinyapps.io/timer/) (Li et al., 2017) and TIMER2.0 (http://timer.comp-genomics.org/) (Li et al., 2020) were used to assess the correlation between the Siglec-9 expression and immune cell infiltration for TCGA tumors and CGGA-LGG dataset with TIMER score and CIBERSORT score (Newman et al., 2015) using immune infiltrates query function and estimation function. The results of estimation were visualized by R package “psych”. TISIDB, a user-friendly web instrument to explore comprehensive investigation of tumor-associated immunity, was used to analyze the association between Siglec-9 mRNA expression and tumor immune subtypes, and specific types of immune cell infiltration. We estimated Siglec-9 expression in diverse molecular subtypes, and immune subtypes involving C1 (wound healing), C2 (IFN-γ dominant), C3 (inflammatory), C4 (lymphocyte deplete), C5 (immunologically quiet), and C6 (TGF-β dominant) subtypes. Specific types of immune cell infiltration of 28 TIL types were inferred by using gene set variation analysis (GSVA) based on the Siglec-9 expression profile on TISIDB.

Gene Set Enrichment Analysis

GSEA based on WEB-based Gene SeT AnaLysis Toolkit (WebGestalt, http://www.webgestalt.org/) (Liao et al., 2019) was performed with the TCGA-LGG dataset on LinkedOmics (Vasaikar et al., 2018) (http://www.linkedomics.org/login.php), a publicly available portal that includes multi-omics data from all 32 TCGA Cancer types. CGGA mRNA expression data were downloaded and LGG cases were selected to be analyzed with GSEA on WebGestalt. The parameters for the enrichment analysis were as follows: minimum number of IDs in the category: three; maximum number of IDs in the category: 2000; significance level: Top 25; number of permutation: 1,000. The top ten positive related categories are shown in the main figure.

Statistical Analysis

The expression of Siglec-9 in different tissue was used by Kruskal–Wallis test, and between tumor tissues and normal tissues were estimated by t-test. Additionally, the expression of Siglec-9 in different grades of glioma was utilized by t-test and ANOVA test. Kaplan–Meier curves were visualized to compare the survival patients stratified based on different levels of expression of Siglec-9. The relationship between Siglec-9 and TMB (tumor mutation burden), MSI (microsatellite instability), MMR gene mutation, immune checkpoints, DNMT, immune score, stromal score, ESTIMATE score, and immune cells was evaluated by Pearson and Spearman correlation analyses. p < 0.05 was recorded as statistically significant for all analyses unless otherwise specified.

RESULTS

The Expression Pattern of Siglec-9 mRNA in Human Cancers

The expression of Siglec-9 mRNA in normal tissue was firstly analyzed using the GTEx database. It showed that the level of Siglec-9 expressions was highest in tissues of blood, spleen, and lung and the lowest in muscle and bone marrow among all types of tissues assessed (Kruskal–Wallis test, p = 0) (Supplementary Figure S1).

Then, the expression of Siglec-9 in tumor and normal samples from various tissue types was assessed using TCGA database. Differential expression analysis was performed with normal tissues used as control. Siglec-9 was upregulated in tumor tissues from cancers including breast invasive carcinoma (BRCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), brain lower-grade glioma (LGG), stomach adenocarcinoma (STAD), thyroid carcinoma (THCA), and uterine corpus endometrial carcinoma (UCEC) (Figure 1A). Siglec-9 was downregulated in cancers including colon adenocarcinoma (COAD), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), and pancreatic adenocarcinoma (PAAD) (Figure 1A).

FIGURE 1 | The mRNA expression of Siglec-9 in various cancers according to The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx). (A) Siglec-9 expression in pan-cancer relied on TCGA. (B) Sigelc-9 expression in pan-cancers based on TCGA and the normal samples were enriched with the GTEx database. (t-test, p < 0.05 was considered to be significant, *p < 0.05, **p < 0.01, ***p < 0.001. N: Normal tissue; T: Tumor tissue.)

For the tumors lacking normal sample controls in TCGA, the corresponding normal samples from GTEx databases were included for further differential expression analysis. The result showed that Siglec-9 was upregulated in BRCA, cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), esophageal carcinoma (ESCA), acute myeloid leukemia (LAML), LGG, ovarian serous cystadenocarcinoma (OV), pancreatic adenocarcinoma (PAAD), skin cutaneous melanoma (SKCM), STAD, testicular germ cell tumors (TGCT), THCA, UCEC, and uterine carcinosarcoma (UCS), and was downregulated in adrenocortical carcinoma (ACC) (Figure 1B).

In addition, the expression pattern of Siglec-9 in the tumors of different clinal stages and molecular subtypes in pan-cancer was also investigated. It showed that Siglec-9 expression was significantly different among clinal stages in bladder urothelial carcinoma (BLCA), ESCA, KICH, THCA, and SKCM (Figure 2A). The different expression pattern was also observed among molecular subtypes in ACC, BRCA, COAD, HNSC, KIRP, LGG, LIHC, LUSC, OV, PCPG, PRAD, and STAD (Figure 2B). No different expression was found among tumor stages or molecular subtypes in other tumors such as ACC, BRCA, and CESC (Supplementary Figure S2A).

FIGURE 2 | The correlation between Siglec-9 mRNA expression and tumor stages and molecular subtypes. (A) The correlation between Siglec-9 expression and tumor stage in various cancers obtained from Gene Expression Profiling Interactive Analysis2.0 (GEPIA2.0). Tumors with a significant result were shown. [Vertical axis: log2 (TPM + 1); one-way ANOVA, Pr (>F) < 0.05 was considered to be significant. The larger the F value and the smaller the Pr (>F) value, the greater the difference in characteristics between groups.] (B) The correlation between Siglec-9 expression and different molecular subtypes from Integrated Repository Portal For Tumor-Immune System Interactions (TISIDB). Tumors with significant result were shown. (Kruskal–Wallis test, p < 0.05 was considered to be significant. CPM: count-per-million.)

Correlation Analysis of Siglec-9 Expression and Prognostic Value in Pan-Cancer

The prognostic value of Siglec-9 expression was assessed by investigating the relationship between Siglec-9 expression and clinical characteristics including OS, DSS, DFI, and PFI using DriverDBv3. Higher Siglec-9 expression level was found to be associated with shorter OS and 5-year OS (HR > 1) in LUSC, THYM, and LGG, but longer OS and 5-year OS (HR < 1) in ACC (Table 1; Figures 3A,B; Supplementary Figure S3A). DSS analysis was performed to further verify these correlations and found similar results (Figures 3C,D; Supplementary Table S1). High Siglec-9 expression correlated with the poor outcome of PFI in GBM, PRAD, and LGG, while it correlated with a good outcome of PFI in ACC (Supplementary Table S2; Figure 3E; Supplementary Figure S3B). DFI analysis found that increased Siglec-9 expression was associated with shorter OS and 5-year survival in ESCA only (Supplementary Table S3; Supplementary Figure S3C). OS analysis was also performed using UALCAN for verification. It showed that Siglec-9 was a poor prognostic factor in LGG, SKCM, UVM, and a good one in READ (Supplementary Table S4). A significant correlation of Siglec-9 with the prognosis in LGG was observed from both DriverDBv3 and UALCAN analysis (Table 1; Supplementary Table S4).

TABLE 1 | Overall survival analysis of TCGA tumors by DriverDBv3.

FIGURE 3 | Significant correlation between Siglec-9 expression and prognostic value in pan-cancer relied on DriverDBv3. (A) Expression of Siglec-9 was associated with overall survival (OS) in LGG, ACC, LUSC, and THYM. (B) Expression of Siglec-9 was associated with 5-year OS in LGG, ACC, LUSC, and THYM. (C) Expression of Siglec-9 was associated with disease-specific survival (DSS) in ACC, COAD, LGG, LUSC. (D) Expression of Siglec-9 was associated with 5-year DSS in ACC, COAD, LGG, and LUSC. (E) Expression of Siglec-9 was associated with progression-free interval (PFI) in ACC, GBM, LGG, and PRAD (p < 0.05 was considered significant. HR: hazard ratio).

Correlation Between Siglec-9 Expression and Tumor Immune Microenvironment

Previous research has identified six molecular immune subtypes containing wound healing (C1), IFN-γ dominant (C2), inflammatory (C3), lymphocyte depletes (C4), immunologically quiet (C5), and TGF-β dominant (C6) subtypes, which were related to tumor molecular characterization and prognosis of patients (Thorsson et al., 2018). We analyzed the expression pattern of Siglec-9 in six immune subtypes in TCGA pan-cancer. The Siglec-9 was differentially expressed in different immune subtypes in ACC, BLCA, BRCA, CESC, COAD, KICH, KIRC, LGG, LUSC, LIHC, LUAD, Mesothelioma (MESO), OV, PAAD, PCPG, PRAD, READ, SARC, SKCM, STAD, TGCT, THCA, UCEC, UCS, and UVM (Figure 4; Supplementary Figure S4A). No difference among different immune subtypes was observed in other tumors including CHOL, ESCA, GBM, HNSC, and KIRP (Supplementary Figure S4B).

FIGURE 4 | Correlation between Siglec-9 mRNA expression and tumor immune subtypes depended on TISIDB. Siglec-9 mRNA expression was related to immune subtypes in various cancers (top 15 tumors were shown). Immune subtypes included: C1, wound healing; C2, IFN-γ dominant; C3, inflammatory; C4, lymphocyte depletes; C5, immunologically quiet; and C6, TGF-β dominant. (Kruskal–Wallis test, p < 0.05 was considered to be significant. CPM: count-per-million.)

Then, the association between Siglec-9 expression and immune cell infiltration in tumor tissues was analyzed using TIMER. A positive correlation between Siglec-9 expression and the infiltration of B cells, CD4⁺ T cells, CD8⁺ T cells, neutrophils, macrophages, and dendritic cells was observed in most TCGA tumors including LGG, ACC, and LUSC (Figures 5A–C; Supplementary Figure S5). In THYM, a negative correlation was observed in B-cell, CD4⁺ T-cell, and CD8⁺ T-cell infiltration and a positive correlation was observed in neutrophil infiltration (Figure 5D). Besides, Siglec-9 expression was negatively correlated with CD8⁺ T cells in GBM, with B cells in STAD and UVM, and with macrophages in DLBC (Supplementary Figure S5). ESTIMATE analysis was applied to further investigate the correlation between Siglec-9 expression and the tumor microenvironment. A strong positive correlation was observed in immune scores (Supplementary Figure S6), stromal scores (Supplementary Figure S7), and ESTIMATE scores (Supplementary Figure S8) in all 33 tumor types in TCGA including ACC, LGG, LUSC, and THYM (Figures 6A–C). To explore the association between Siglec-9 expression and specific types of immune cell infiltration, the relative abundance of 28 TIL types was inferred by using gene set variation analysis (GSVA) based on the Siglec-9 expression profile on TISIDB. The result showed that Siglec-9 expression was strongly correlated with follicular helper T cells (Tfh), regulatory T cells (Treg), myeloid-derived suppressor cells (MDSC), and macrophages across 33 TCGA tumors (Figure 6D). Moreover, the correlation between Siglec-9 expression and the expression of 47 immune checkpoint genes was also analyzed. Siglec-9 was positively correlated with immune checkpoints including LAIR1, HAVCR2, CD80, PDCD1 (PD1), PDCD1LG2 (PD-L2), VSIR, and CD86 across most TCGA tumors (Figure 6E). Siglec-9 had a low correlation with immune checkpoints in tumors including ACC, CHOL, DLBC, LAML, MESO, THYM, and UCS. In particular, LAIR1, HAVCR2, LGALS9, and CD86 expression were strongly positively correlated with Siglec-9 expression, and only CD200 expression was negatively correlated with Siglec-9 expression in LGG. In LUSC, Siglec-9 expression was strongly positively correlated with LAIR1, HAVCR2, and CD86, and negatively correlated with VTCN1 and TNFRSF18.

FIGURE 5 | Correlation between Siglec-9 mRNA expression and immune cell infiltration (B cell, CD4⁺ T cell, CD8⁺ T cell, neutrophil, macrophage, and dendritic cell) in ACC (A), LGG (B), LUSC (C), and THYM (D) according to TIMER. (Spearman correlation, p < 0.05 was considered significant. The larger the R-value, the greater the correlation.)

FIGURE 6 | The relationship between Siglec-9 mRNA expression and immune score, stromal score, ESTIMATE score, specific immune cell infiltration, and immune checkpoints. (A) Correlation between Siglec-9 mRNA expression and Immune Score in ACC, LGG, LUSC, and THYM. (B) Correlation between Siglec-9 mRNA expression and Stromal Score in ACC, LGG, LUSC, and THYM. (C) Correlation between Siglec-9 mRNA expression and ESTIMATE score in ACC, LGG, LUSC, and THYM. (D) Correlation between immune cells and Siglec-9 mRNA expression according to TISIDB. (E) Correlation between the immune checkpoint and Siglec-9 mRNA expression. (A–D) Spearman correlation; (E) Pearson’s correlation (p < 0.05 was considered significant).

Correlation Between Siglec-9 Expression and Tumor Mutation Burden, Microsatellite Instability, DNA Mismatch Repair Genes, and DNA Methyltransferase

TMB, MSI, DNA MMR, and DNA methylation were considered immune-related and prognosis-related factors in tumors (Duffy and Crown, 2019). Therefore, we analyzed the correlation between the Siglec-9 expression and TMB, MSI, DNA MMR genes, and DNMT. Siglec-9 expression was found to be positively correlated with TMB in CESC, COAD, LGG, OV, PRAD, SARC, and THYM and negatively correlated with TMB in GBM and THCA (Figure 7A). Siglec-9 expression was negatively related to MSI in HNSC, LGG, LUAD, LUSC, PAAD, SKCM, STAD, and TGCT and positively related to MSI in COAD (Figure 7B). Five DNA MMR genes (MLH1, MSH2, MSH6, PMS2, and EpCAM) were included for analyzing the association between MMRs and Siglec-9. MLH1 was positively correlated with Siglec-9 in BLCA, BRCA, ESCA, HNSC, KIRC, LIHC, LUAD, LUSC, PAAD, PRAD, STAD, UCEC, and negatively correlated in GBM and SARC (Figure 7C). MSH2 had a positive correlation with Siglec-9 in KIRC, LIHC, and PRAD, and had a negative correlation in (Figure 7C). MSH6 showed a positive association with Siglec-9 in HNSC, KIRC, LGG, LIHC, PAAD, PRAD, and STAD, and a negative association in GBM, LAML, LUSC, SARC, TGCT, and UCS (Figure 7C). PMS2 was positively correlated with Siglec-9 in KIRC, LIHC, PAAD, PRAD, and STAD, and negatively correlated in GBM, LUSC, SARC, SKCM, and THCA (Figure 7C). Siglec-9 was negatively correlated to EpCAM in most tumors including BLCA, BRCA, CESC, CHOL, COAD, KIRC, KIRP, LAML, LGG, LUAD, LUSC, OV, PAAD, PRAD, STAD, TGCT, and THCA (Figure 7C). Collectively, Siglec-9 had a significant association with MMRs in KIRC, PAAD, and PRAD (Figure 7C).

FIGURE 7 | Correlation between Siglec-9 mRNA expression and tumor mutation burden (TMB), microsatellite instability (MSI), DNA methyltransferase (DNMT), and mismatch repair (MMR) in pan-cancer. (A) Relationship between Siglec-9 expression and TMB. (B) Relationship between Siglec-9 expression and MSI. (C) Correlation between Siglec-9 expression and MMR. (D) Correlation between Siglec-9 expression and DNMT (DNMT1 was red, DNMT2 was blue, DNMT3 was green, DNMT4 was purple). (Pearson’s correlation, p < 0.05 was considered significant, *p < 0.05, **p < 0.01, ***p < 0.001.)

DNA methyltransferase1 (DNMT1), DNA methyltransferase2 (DNMT2), DNA methyltransferase3 (DNMT3), and DNA methyltransferase3B (DNMT3B) were included for the analysis of the correlation between Siglec-9 expression and DNA methyltransferases. It showed that Siglec-9 expression was correlated with DNA methylation in various cancers, especially in BLCA, GBM, HNSC, KICH, KIRC, LGG, PAAD, PRAD, TGCT, and THCA (Figure 7D). In particular, Siglec-9 was positively correlated with DNMT1 (Pearson’s rho = 0.11, p = 0.029), DNMT2 (Pearson’s rho = 0.2, p = 8.5 e-6), and DNMT3 (Pearson’s rho = 0.21, p = 5.4 e-6). There was no significant correlation or less correlation between Siglec-9 expression and DNA methyltransferases in ACC, LUSC, and THYM.

Further Validation of Siglec9 Expression Pattern in Brain Lower-Grade Glioma

The analysis above showed that Siglec9 expression was correlated with the prognosis of patients, especially of ACC and LGG patients (Figure 3). Due to the larger sample size, similar survival pattern to other tumors, and closer association with the tumor immune microenvironment, TMB, MSI, MMRs, and DNMTs, we selected LGG for further analysis. Data from the Chinese Glioma Genome Atlas (CGGA) was employed for further verification. Expression of Siglec-9 in different grades of glioma (WHO II, WHO III, and WHO IV) was analyzed. The results showed that Siglec-9 expression was higher in WHO IV (GBM) than that in WHO II and WHO III (LGG) (Figure 8A). OS analysis showed that high expression of Siglec-9 was correlated with poor prognosis in primary WHO grade II and recurrent WHO grade III glioma (Figure 8B). There was no significant correlation between OS and Siglec-9 expression in recurrent WHO grade II (Supplementary Figure S9A) and primary WHO grade III glioma (Supplementary Figure S9B). In addition, Siglec-9 expression in IDH mutation and 1p/19q co-deletion status subtypes was analyzed, because IDH mutation status and 1p/19q co-deletion status were beneficial to diagnosis, prognosis prediction, and treatment of glioma (Aoki et al., 2018). No difference between IDH mutant and wild type in WHO grade II was observed, and higher Siglec-9 expression was observed in wild type compared to IDH mutant in WHO grade III (Figure 8C). Besides, Siglec-9 expression was significantly higher in the none 1p/19q co-deletion than in the 1p/19q co-deletion subtype in both WHO grade II and WHO grade III (Figure 8D), which coincided with the former result of low expression of Siglec-9 in Codel subtype in the TCGA-LGG (Figure 2B). Subsequently, Siglec-9 expression in subtypes that combined IDH mutation with 1p/19q was also analyzed in LGG and GBM. The result showed that Siglec-9 was significantly differentially expressed among these subtypes (Figure 8E). Then, TIMER was used to assess the correlation between Siglec-9 expression and immune cell infiltration in CGGA-LGG. Similar to TCGA-LGG, Siglec-9 expression was positively correlated with B cells, CD4⁺ T cells, neutrophils, macrophages, and myeloid dendritic cells except for CD8⁺ T cells according to TIMER immune score in CGGA-LGG (Figure 9A). A strong correlation was observed between Siglec-9 expression and immune score, stromal score, and ESTIMATE score (Figure 9B). Analysis of correlation between Siglec-9 expression and specific immune cells indicated that high Siglec-9 expression was accompanied by high infiltration level of M1/2 macrophages and neutrophils, and low infiltration level of activated NK cells, Tfh, and naïve CD4⁺ T cells (Figure 9C).

FIGURE 8 | The relationship between Siglec-9 mRNA expression and clinical characteristics in brain lower-grade glioma (LGG). (A) Siglec-9 mRNA expression in different glioma grades. (B) Expression of Siglec-9 was related to OS in WHO grade II (primary glioma) and WHO grade III (recurrent glioma). (C) Different Siglec-9 expression in WHO grade II and WHO grade III depended on IDH mutation status. (D) Different Siglec-9 expression in WHO grade II and WHO grade III depended on 1p/19q co-deletion status. (E) Different Siglec-9 expression in WHO grade II and WHO grade III depended on IDH mutation status combined with 1p/19q co-deletion status. (Statistical methods are shown in the figures. P < 0.05 was considered significant. IDH: isocitrate dehydrogenase.)

FIGURE 9 | Correlation between Siglc-9 expression and immune cell infiltration and immune score, stromal score, and ESTIMATE score in brain lower-grade glioma (LGG). (A) Relationship between Siglec-9 expression and immune cell infiltration (macrophage, B cell, CD4⁺ T cell, neutrophil, dendritic cell, and CD8⁺ T cell). (B) Correlation between Siglec-9 mRNA expression and ESTIMATE score, immune score, and stromal score. (C) Correlation between Siglec-9 expression and specific immune cell infiltration. (Spearman correlation, p < 0.05 was considered significant, *p < 0.05, **p < 0.01, ***p < 0.001. The larger the R-value, the greater the correlation.)

To clarify the specific mechanism by which siglec9 affected LGG survival, GSEA analysis was used to identify key biological functions of Siglec-9, between the low and high Siglec-9 subgroups in the TCGA-LGG and CGGA-LGG (WHO grade II and III). Results indicated that various pathways involved in adaptive immune response, cytokine production, inflammatory response, antigen processing, and presentation were enriched in the high Siglec-9 expression subgroup in Gene Ontology (GO) enrichment analysis (Figures 10A,B; Supplementary Figures S10A–F) and KEGG analysis (Figures 10C,D; Supplementary Figures S10G,H). Immune process-related cellular components, such as MHC protein complex, immunological synapse, and secretory granule membrane, were significantly enriched in GO enrichment analysis (Supplementary Figures S10E,F). Interestingly, neural cell function-related pathways were significantly enriched in low Siglec-9 expression in all analyses mentioned above.

FIGURE 10 | Significant pathways influenced by Siglec-9 based on Gene set enrichment analysis (GSEA) in TCGA-LGG and CGGA-LGG. (A) Correlation between Siglec-9 and GO BP terms in LGG from TCGA analyzed by GSEA. (B) Correlation between Siglec-9 and GO BP terms in LGG from CGGA analyzed by GSEA. (C) Correlation between Siglec-9 and KEGG pathways in LGG from TCGA analyzed by GSEA. (D) Correlation between Siglec-9 and KEGG pathways in LGG from CGGA analyzed by GSEA.

DISCUSSION

In our study, we investigated the expression pattern of Sigelc-9 in TCGA pan-cancer. Abnormal expression of Siglec-9 was observed in the tumor compared with normal tissues in the TCGA and GTEx database. Siglec-9 was upregulated in BRCA, ESCA, GBM, HNSC, KIRC, KIRP, LGG, STAD, UCEC, CESC, LAML, OV, PAAD, TGCT, THCA UCS, SKCM, and STAD (Figure 1), which was similar to the existing result that Siglec-9 was upregulated in colorectal and ovarian cancer (Stanczak et al., 2018). Downregulation of Siglec-9 was observed in ACC, COAD, LIHC, LUAD, LUSC, and PAAD (Figure 1). Differential Siglec-9 expression was observed in different stages and molecular subtypes in various tumors (Figure 2), which indicated that Siglec-9 expression was associated with tumor progression and tumor molecular subtypes. Importantly, high Siglec-9 expression predicted poor prognosis in LGG, LUSC, and THYM, and predicted good prognosis in ACC (Figure 3). The results above indicated that Siglec-9 mRNA expression was correlated with clinical characteristics in multiple tumors and could be a prognostic predictor in ACC, LGG, LUSC, and THYM.

It is well known that Siglec-9 was correlated with the tumor immune microenvironment (Beatson et al., 2016). Siglec-9 has been regarded as a regulator of the immune response in cancers including astrocytoma, epithelial ovarian cancer, colorectal cancer, adenocarcinomas, and hematological cancers (Fraschilla and Pillai, 2017). Our analysis uncovered the correlation between Siglec-9 expression and tumor immune microenvironment or tumor microenvironment in cancers (Figure 4), and further consolidated the pivotal roles of Siglec-9 in tumor immune regulation. Subsequently, we found that Siglec-9 expression was strongly correlated with B cells, CD4⁺ T cells, CD8⁺ T cells, neutrophils, macrophages, and DC cells in most tumors as expected. Other reports have shown that Siglec-9 was expressed on cytotoxic CD8⁺ T cells, neutrophils, and NK cells (Fraschilla and Pillai, 2017). Our findings confirmed the above results to some extent. Notably, Siglec-9 expression was positively correlated with LAIR1, HAVCR2, CD80, PDCD1 (PD1), PDCD1LG2 (PD-L2), VSIR, and CD86 in most tumors (Figure 6E). The upregulation of immune checkpoints and immune suppression molecules, such as LAIR1, HAVCR2, CD80, PD1, and PD-L2, has been described as one of the hallmarks of T-cell exhaustion and suggested T-cell dysfunctional status (Speiser et al., 2016; Thommen and Schumacher, 2018). T-cell exhaustion status is reported to be medication guidance for immune therapy (Thommen and Schumacher, 2018). Based on these findings, we could speculate that Siglec-9 might predict T-cell status according to our result and might be a molecular marker for immune therapy. It was worth pointing out that Siglec-9 had a low correlation with immune checkpoints in ACC, which might result from low expression in ACC and explain the beneficial role of Siglec-9 in the survival analysis of ACC (Figure 3).

The occurrence, progression, and prognosis of tumors were closely related to TMB, MSI, DNMT, and MMRs (Duffy and Crown, 2019). Recent studies have identified TMB, MSI, DNMT, and MMRs as immunotherapy and tumor immune microenvironment-related factors, and may predicate the outcome of immunotherapy (Mandal et al., 2019; Samstein et al., 2019; Segovia et al., 2019). Our study found that Siglec-9 expression was associated with these factors in pan-cancer (Figure 7). Therefore, we speculated that the expression of Siglec-9 could be a predictor of immunotherapy efficacy. There was no direct evidence that the correlations between Siglec-9 expression and these factors would influence the survival of tumor patients. Therefore, the causal relationship still needed to be explored.

Our findings that Siglec-9 expression was significantly correlated with clinical characteristics of LGG suggested the therapeutic roles of Siglec-9 in LGG. IDH mutation status and 1p/19q co-deletion status were beneficial to diagnosis, prognosis prediction, and treatment of glioma (Aoki et al., 2018). The wild type and none 1p/19q co-deletion status was reported to be more insensitive to drugs (Aoki et al., 2018). We found that Siglec-9 expression was higher in wild type and the none 1p/19q co-deletion subtype (Figures 8C,D), suggesting that Siglec-9 expression might participate in drug resistance in LGG. In addition, none 1p/19q co-deletion status was associated with an increased polarization of tumor-associated macrophages toward an M2 phenotype, and most of the TAMs in IDH mutated tumors express M1 activation markers (Zhang et al., 2021). We found that Siglec-9 expression was more positively correlated to M2 than M1 in tumor-associated macrophages just as previously reported (Laubli et al., 2014), suggesting the possible role of Siglec-9 in TAM polarization in LGG. As TAM polarization can exert opposing influence on the effectiveness of cytoreductive therapies (chemotherapy and radiotherapy) (Mantovani et al., 2017), targeting Siglec-9 might benefit cytoreductive therapies for LGG.

GSEA analysis between the low and high Siglec-9 subgroups in the TCGA-LGG and CGGA-LGG showed that various pathways involved in adaptive immune response, cytokine production, inflammatory response, antigen processing, and presentation were enriched in the high Siglec-9 expression subgroup in BP, MF, and KEGG analyses (Figure 10). Immune process-related cellular components, such as MHC protein complex, immunological synapse, and secretory granule membrane, were significantly enriched in CC analysis. Inflammation and adaptive immune response are considered as immunosuppression components in the suppression of effective antitumor immunity during LGG progression (Mantovani et al., 2008; Diakos et al., 2014). Thus, the GSEA results suggested that high Siglec-9 expression may result in activation of adaptive immune response and inflammatory response, and lead to immune suppression status in LGG. Targeting cancer-related inflammation and adaptive immune cells could benefit tumor treatment effectiveness, especially immunotherapy (Diakos et al., 2014). Despite the specific mechanism remaining to be explored and verified, we could speculate that targeting Siglec-9 may benefit immunotherapy of LGG. Interestingly, neural cell function-related pathways were significantly enriched in low Siglec-9 expression, indicating that high Siglec-9 expression was negatively correlated with the normal physiological activity of nerve cells. These results suggest that Siglec-9 may affect the survival time of LGG patients by modulating the immune microenvironment to immunosuppression status.

In conclusion, our study uncovered the correlations between Siglec-9 expression with clinical characteristics of tumor patients including cancer progression, prognosis, and immune status (Supplementary Figure S11). Siglec-9 expression was a prognostic factor for patients with ACC, ESCA, LGG, LUSC, and THYM. A strong correlation was observed between Siglec-9 expression and immune cell infiltration and immune checkpoint expression in most tumors. LGG, ACC, and LUSC showed significance in most analyses, suggesting that Siglec-9 might be more important in LGG, ACC, and LUSC (Supplementary Figure S11). In particular, increased Siglec-9 expression predicted poor prognosis and might influence LGG development through immune regulation and normal physiological activity interference. Collectively, our study indicated that Siglec-9 might be considered as a potential biomarker for predicting prognosis and immune infiltration in multiple tumor patients, especially in LGG. However, all the results above still need further experimental verification.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.

AUTHOR CONTRIBUTIONS

ZC, ZG, SL, and YW conceived and designed the research. YW, WH, YX, NL, and YC collected and conducted data under the instruction of SL and CW. ZC and ZG supervised the study and provided funds. LW provided funds and participated in the discussion. WH and YW wrote the initial paper and SL revised the paper. All authors read and approved the final manuscript.

FUNDING

This work was supported by funds from the National Key R&D Program of China (2019YFA0110300 and 2017YFA0104100), the National Natural Science Foundation of China (81874104 and 31600819), and Medical Guidance Science and Technology Project of Shanghai Science and Technology Commission (18411964100).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

ACKNOWLEDGMENTS

We would like to gratefully acknowledge contributions from TCGA, GTEx, and CGGA.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2022.743515/full#supplementary-material

REFERENCES

Aoki, K., Nakamura, H., Suzuki, H., Matsuo, K., Kataoka, K., Shimamura, T., et al. (2018). Prognostic Relevance of Genetic Alterations in Diffuse Lower-Grade Gliomas. Neuro Oncol. 20 (1), 66–77. doi:10.1093/neuonc/nox132

Beatson, R., Tajadura-Ortega, V., Achkova, D., Picco, G., Tsourouktsoglou, T.-D., Klausing, S., et al. (2016). The Mucin MUC1 Modulates the Tumor Immunological Microenvironment through Engagement of the Lectin Siglec-9. Nat. Immunol. 17 (11), 1273–1281. doi:10.1038/ni.3552

Belisle, J. A., Horibata, S., Jennifer, G. A., Petrie, S., Kapur, A., André, S., et al. (2010). Identification of Siglec-9 as the Receptor for MUC16 on Human NK Cells, B Cells, and Monocytes. Mol. Cancer 9, 118. doi:10.1186/1476-4598-9-118

Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., and Jemal, A. (2020). Erratum: Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA A. Cancer J. Clin. 70 (4), 313. doi:10.3322/caac.21609

Chandrashekar, D. S., Bashel, B., Balasubramanya, S. A. H., Creighton, C. J., Ponce-Rodriguez, I., Chakravarthi, B. V. S. K., et al. (2017). UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia 19 (8), 649–658. doi:10.1016/j.neo.2017.05.002

Diakos, C. I., Charles, K. A., McMillan, D. C., and Clarke, S. J. (2014). Cancer-related Inflammation and Treatment Effectiveness. Lancet Oncol. 15 (11), e493–e503. doi:10.1016/s1470-2045(14)70263-3

Duffy, M. J., and Crown, J. (2019). Biomarkers for Predicting Response to Immunotherapy with Immune Checkpoint Inhibitors in Cancer Patients. Clin. Chem. 65 (10), 1228–1238. doi:10.1373/clinchem.2019.303644

Fraschilla, I., and Pillai, S. (2017). Viewing Siglecs through the Lens of Tumor Immunology. Immunol. Rev. 276 (1), 178–191. doi:10.1111/imr.12526

Galluzzi, L., Chan, T. A., Kroemer, G., Wolchok, J. D., and López-Soto, A. (2018). The Hallmarks of Successful Anticancer Immunotherapy. Sci. Transl. Med. 10 (459), 14. doi:10.1126/scitranslmed.aat7807

Läubli, H., Pearce, O. M. T., Schwarz, F., Siddiqui, S. S., Deng, L., Stanczak, M. A., et al. (2014). Engagement of Myelomonocytic Siglecs by Tumor-Associated Ligands Modulates the Innate Immune Response to Cancer. Proc. Natl. Acad. Sci. USA 111 (39), 14211–14216. doi:10.1073/pnas.1409580111

Li, T., Fan, J., Wang, B., Traugh, N., Chen, Q., Liu, J. S., et al. (2017). TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res. 77 (21), e108–e110. doi:10.1158/0008-5472.CAN-17-0307

Li, T., Fu, J., Zeng, Z., Cohen, D., Li, J., Chen, Q., et al. (2020). TIMER2.0 for Analysis of Tumor-Infiltrating Immune Cells. Nucleic Acids Res. 48 (W1), W509–W514. doi:10.1093/nar/gkaa407

Liao, Y., Wang, J., Jaehnig, E. J., Shi, Z., and Zhang, B. (2019). WebGestalt 2019: Gene Set Analysis Toolkit with Revamped UIs and APIs. Nucleic Acids Res. 47 (W1), W199–w205. doi:10.1093/nar/gkz401

Liu, J., Zhang, S., Dai, W., Xie, C., and Li, J.-C. (2021). A Comprehensive Prognostic and Immune Analysis of SLC41A3 in Pan-Cancer. Front. Oncol. 10, 586414. doi:10.3389/fonc.2020.586414

Liu, S.-H., Shen, P.-C., Chen, C.-Y., Hsu, A.-N., Cho, Y.-C., Lai, Y.-L., et al. (2020). DriverDBv3: a Multi-Omics Database for Cancer Driver Gene Research. Nucleic Acids Res. 48 (D1), D863–D870. doi:10.1093/nar/gkz964

Liu, X., Li, Y., Qian, Z., Sun, Z., Xu, K., Wang, K., et al. (2018). A Radiomic Signature as a Non-invasive Predictor of Progression-free Survival in Patients with Lower-Grade Gliomas. NeuroImage: Clin. 20, 1070–1077. doi:10.1016/j.nicl.2018.10.014

Mandal, R., Samstein, R. M., Lee, K.-W., Havel, J. J., Wang, H., Krishna, C., et al. (2019). Genetic Diversity of Tumors with Mismatch Repair Deficiency Influences Anti-PD-1 Immunotherapy Response. Science 364 (6439), 485–491. doi:10.1126/science.aau0447

Mantovani, A., Allavena, P., Sica, A., and Balkwill, F. (2008). Cancer-related Inflammation. Nature 454 (7203), 436–444. doi:10.1038/nature07205

Mantovani, A., Marchesi, F., Malesci, A., Laghi, L., and Allavena, P. (2017). Tumour-associated Macrophages as Treatment Targets in Oncology. Nat. Rev. Clin. Oncol. 14 (7), 399–416. doi:10.1038/nrclinonc.2016.217

Mortezaee, K. (2020). Immune Escape: A Critical Hallmark in Solid Tumors. Life Sci. 258, 118110. doi:10.1016/j.lfs.2020.118110

Neelapu, S. S., Locke, F. L., Bartlett, N. L., Lekakis, L. J., Miklos, D. B., Jacobson, C. A., et al. (2017). Axicabtagene Ciloleucel CAR T-Cell Therapy in Refractory Large B-Cell Lymphoma. N. Engl. J. Med. 377 (26), 2531–2544. doi:10.1056/NEJMoa1707447

Newman, A. M., Liu, C. L., Green, M. R., Gentles, A. J., Feng, W., Xu, Y., et al. (2015). Robust Enumeration of Cell Subsets from Tissue Expression Profiles. Nat. Methods 12 (5), 453–457. doi:10.1038/nmeth.3337

Ru, B., Wong, C. N., Tong, Y., Zhong, J. Y., Zhong, S. S. W., Wu, W. C., et al. (2019). TISIDB: an Integrated Repository portal for Tumor-Immune System Interactions. Bioinformatics 35 (20), 4200–4202. doi:10.1093/bioinformatics/btz210

Sabit, I., Hashimoto, N., Matsumoto, Y., Yamaji, T., Furukawa, K., and Furukawa, K. (2013). Binding of a Sialic Acid-Recognizing Lectin Siglec-9 Modulates Adhesion Dynamics of Cancer Cells via Calpain-Mediated Protein Degradation. J. Biol. Chem. 288 (49), 35417–35427. doi:10.1074/jbc.M113.513192

Samstein, R. M., Lee, C.-H., Shoushtari, A. N., Hellmann, M. D., Shen, R., Janjigian, Y. Y., et al. (2019). Tumor Mutational Load Predicts Survival after Immunotherapy across Multiple Cancer Types. Nat. Genet. 51 (2), 202–206. doi:10.1038/s41588-018-0312-8

Segovia, C., San José-Enériz, E., Munera-Maravilla, E., Martínez-Fernández, M., Garate, L., Miranda, E., et al. (2019). Inhibition of a G9a/DNMT Network Triggers Immune-Mediated Bladder Cancer Regression. Nat. Med. 25 (7), 1073–1081. doi:10.1038/s41591-019-0499-y

Speiser, D. E., Ho, P.-C., and Verdeil, G. (2016). Regulatory Circuits of T Cell Function in Cancer. Nat. Rev. Immunol. 16 (10), 599–611. doi:10.1038/nri.2016.80

Stanczak, M. A., Siddiqui, S. S., Trefny, M. P., Thommen, D. S., Boligan, K. F., von Gunten, S., et al. (2018). Self-associated Molecular Patterns Mediate Cancer Immune Evasion by Engaging Siglecs on T Cells. J. Clin. Invest. 128 (11), 4912–4923. doi:10.1172/jci120612

Tang, Z., Kang, B., Li, C., Chen, T., and Zhang, Z. (2019). GEPIA2: an Enhanced Web Server for Large-Scale Expression Profiling and Interactive Analysis. Nucleic Acids Res. 47 (W1), W556–W560. doi:10.1093/nar/gkz430

Tanida, S., Akita, K., Ishida, A., Mori, Y., Toda, M., Inoue, M., et al. (2013). Binding of the Sialic Acid-Binding Lectin, Siglec-9, to the Membrane Mucin, MUC1, Induces Recruitment of β-Catenin and Subsequent Cell Growth. J. Biol. Chem. 288 (44), 31842–31852. doi:10.1074/jbc.M113.471318

Thommen, D. S., and Schumacher, T. N. (2018). T Cell Dysfunction in Cancer. Cancer Cell 33 (4), 547–562. doi:10.1016/j.ccell.2018.03.012

Thorsson, V., Gibbs, D. L., Brown, S. D., Wolf, D., Bortone, D. S., Ou Yang, T. H., et al. (2018). The Immune Landscape of Cancer. Immunity 48 (4), 812–830.e14. doi:10.1016/j.immuni.2018.03.023

Vasaikar, S. V., Straub, P., Wang, J., and Zhang, B. (2018). LinkedOmics: Analyzing Multi-Omics Data within and across 32 Cancer Types. Nucleic Acids Res. 46 (D1), D956–D963. doi:10.1093/nar/gkx1090

Wang, Y., Qian, T., You, G., Peng, X., Chen, C., You, Y., et al. (2015). Localizing Seizure-Susceptible Brain Regions Associated with Low-Grade Gliomas Using Voxel-Based Lesion-Symptom Mapping. Neuro Oncol. 17 (2), 282–288. doi:10.1093/neuonc/nou130

Zhang, Y., Xie, Y., He, L., Tang, J., He, Q., Cao, Q., et al. (2021). 1p/19q Co-deletion Status Is Associated with Distinct Tumor-Associated Macrophage Infiltration in IDH Mutated Lower-Grade Gliomas. Cell Oncol. 44 (1), 193–204. doi:10.1007/s13402-020-00561-1

Zheng, Y., Ma, X., Su, D., Zhang, Y., Yu, L., Jiang, F., et al. (2020). The Roles of Siglec7 and Siglec9 on Natural Killer Cells in Virus Infection and Tumour Progression. J. Immunol. Res. 2020, 1–9. doi:10.1155/2020/6243819

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Wu, Huang, Xie, Wang, Luo, Chen, Wang, Cheng, Gao and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 23 March 2022
doi: 10.3389/fmolb.2022.738711

LncRNA FAM13A-AS1 Promotes Renal Carcinoma Tumorigenesis Through Sponging miR-141-3p to Upregulate NEK6 Expression

Xin Jun Wang1,², Si Li1,², Jiang Fang1,²†, Zhi Jian Yan1,² and Guang Cheng Luo1,²*

¹Department of Urology, Zhongshan Hospital Xiamen University, School of Medicine, Xiamen University, Xiamen, China

²The Third Clinical Medical College, Fujian Medical University, Fuzhou, China

Edited by:
Ismail Hosen, University of Dhaka, Bangladesh

Reviewed by:
Larance Ronsard, Ragon Institute, United States
Tianxiang Chen, First Affiliated Hospital of Xi’an Jiaotong University, China

* Correspondence: Guang Cheng Luo, lgch@xmu.edu.cn

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

†ORCID: Jiang Fang, https://orcid.org/0000-0002-2026-8288

Received: 09 July 2021
Accepted: 03 March 2022
Published: 23 March 2022

Citation: Wang XJ, Li S, Fang J, Yan ZJ and Luo GC (2022) LncRNA FAM13A-AS1 Promotes Renal Carcinoma Tumorigenesis Through Sponging miR-141-3p to Upregulate NEK6 Expression. Front. Mol. Biosci. 9:738711. doi: 10.3389/fmolb.2022.738711

Long non-coding RNAs are a diverse catalog of RNAs that have been implicated in various aspects of tumorigenesis. Emerging evidence indicates that they play crucial roles in tumor growth, disease progression, and drug resistance. However, the clinical significance of lncRNAs in tumor behavior prediction and disease prognosis as well as the underlying mechanism in renal cell carcinoma (RCC) remains elusive. By analyzing the gene expression profiles of 539 RCC patients from the TCGA cohort and 40 RCC patients from an independent cohort, we identified FAM13A-AS1, a poorly studied lncRNA, upregulated in RCC patients. Knockdown experiments revealed that FAM13A-AS1 promotes cell proliferation, migration, and invasion by interacting with miR-141-3p. FAM13A-AS1 regulates the expression of NEK6 by decoying miR-141-3p. In addition, there was a strong positive correlation between the expression of FAM13A-AS1 and NEK6 in RCC patients. In summary, our results demonstrate the oncogenic role of FAM13A-AS1 in RCC and suggest that it promotes tumorigenesis by upregulating the expression of NEK6 by competitively binding to miR-141-3p.

Keywords: FAM13A-AS1, miR-141-3p, NEK6, prognostic markers, therapeutic targets, rcc

INTRODUCTION

Kidney cancer is among the 10 most common cancers in both men and women, representing 4% of all new cancer cases annually in United States (Escudier et al., 2019). Renal cell carcinoma (RCC) is the most dominant form of kidney cancer, accounting for up to 85% of all cases. RCC has the highest mortality rate of genitourinary cancers, and the incidence of RCC has risen steadily, especially in developed countries. It has been reported that approximately 295,000 new kidney cancer cases are diagnosed, and approximately 134,000 deaths are recorded worldwide (Hsieh et al., 2017; Escudier et al., 2019). Although the 5-year survival rate has recently increased, it is still ranked as the seventh leading cause of mortality in the United States (Jemal et al., 2007). Localized RCC can be treated with surgical resection, radiation, or local ablation. Despite advances in the curative treatment, more than 30% of patients would eventually develop metastases, requiring more complicated therapy and low mortality. The likelihood of successful treatment relied heavily on the early diagnosis (Barata and Rini, 2017). However, early clinical manifestations of RCC are diverse and may give rise to a range of non-specific and often misattributed symptoms. Therefore, developing better models for risk assessment, diagnosis, and prognosis has become an urgent need for managing RCC, requiring a fundamental understanding of the disease’s molecular mechanisms.

Once dismissed as “junk DNA,” lncRNAs are now implicated in various fundamental biological processes and disease development (Kopp and Mendell, 2018). Evidence accumulated over the past decade has revealed that lncRNAs function in diverse mechanisms, such as signals, RNA decay, miRNA sponging, guides, and RNA scaffolding. Notably, a growing number of lncRNAs are emerging as master regulators of cancer progression and tumor metastasis (Slack and Chinnaiyan, 2019). Some examples include HOTAIR (Gupta et al., 2010; Alves et al., 2013; Kim et al., 2013), FILNC1 (Xiao et al., 2017), and GAS5 (Zhang et al., 2017). Dysregulation of these genes is associated with renal tumor metabolism, pancreatic cancer metastasis, and prostate cancer proliferation.

In this study, we identified an RCC-associated lncRNA, FAM13A-AS1, through gene expression analysis on a cohort of 539 TCGA-KIRC datasets, whose function in oncogenesis has not yet been investigated. We found that FAM13A-AS1 expression was increased in both primary tumor and RCC cell lines. Elevated expression of FAM13A-AS1 is associated with poor prognosis in patients with RCC. We further demonstrated that FAM13A-AS1 promotes cell proliferation, migration, and invasion by targeting NEK6 when acting as a sponge for miR-141-3p. Thus, our study identified the FAM13A-AS1/miR-141-3p/NEK6 axis in prostate tumorigenesis and indicated them as promising prognostic markers and potential therapeutic targets for RCC patients.

METHODS

Patient Samples

RCC samples (n = 40) and their paired normal tissues (n = 40) were obtained from the Department of Urology, Zhongshan Hospital, Xiamen University (Xiamen, China). Informed consent was obtained from all patients who were without preoperative treatments before surgery, and the study was approved by the Institutional Review Board of Xiamen University.

Cell Culture

293 T cells were purchased from IMMOCELL (catalog number: IM-H222, Xiamen, China). The human kidney proximal tubule epithelial cell line HK-2 and malignant cancer cell lines 786-O, A-498, SN12-PM6, and Caki-1, were purchased from American Type Culture Collection (ATCC, Manassas, VA, United States). The authenticity of these cell lines was confirmed using STR profiling analysis. The cells were cultured in a medium consisting of 90% DMEM (Gibco, catalog number: 11965092, Shanghai, China) supplemented with 10% FBS (Gibco, catalog number: 12483020, Shanghai, China) in an incubator at 37 °C and 5% CO2.

Plasmid and Lentiviral Transduction

The human FAM13-AS1 fragment or human NEK6 3′ UTR containing the putative binding sites targeted by miR-141-3p were cloned into a pmirGLO vector (Promega, Madison, WI, United States), which were named as FAM13-AS1 WT and NEK6 3′ UTR WT, respectively. The mutant FAM13-AS1 or mutant NEK6-3′ UTR was also cloned into a pmirGLO vector (Promega, Madison, WI, United States), which were named as FAM13-AS1 MUT and NEK6 3′ UTR MUT, respectively.

For RNAi, short hairpin RNAs targeting FAM13-AS1 and its negative control were constructed using the pLKO.1-TRC vector (Anithela, Xiamen, Fujian, China). miR-141-3p mimics and inhibitors (Table 1) were purchased from Guangzhou RiboBio Co., Ltd, and the transfection was carried out using Lipofectamine™ 2000 Transfection Reagent (Invitrogen, catalog number: 11668500, Shanghai, China) following the manufacturer’s instructions. For the expression of NEK6 in cells, the coding sequence of NEK6 was cloned into the PCDH-EF1a-mcs-T2A-puro vector (Anithela, Xiamen, Fujian, China). Cloning was performed using the ClonExpress Ultra One Step Cloning Kit (Vaztme, C115-01, Nanjing, China), and the relevant primers are listed in Table 2.

TABLE 1 | Sequences of mimics and inhibitors.

TABLE 2 | Primers for plasmid construction.

Plasmid shFAM13-AS1 was co-transferred with pMD2G and pspax2 into 293 T cells to package the lentivirus. After 72 h, the supernatants containing lentiviruses were collected. Two days after lentiviral infection, 786-O cells were cultured in the presence of 1.0 μg/ml of puromycin (Sigma, San Francisco, CA, United States) for 8 days, which was named as shFAM13-AS1 cell.

RNA Extraction, Reverse Transcription and RT-qPCR

Prostate cancer cell lines, transfected cells, and tissue homogenates were harvested for RNA extraction. Total RNA was isolated using TRIzol reagent (Invitrogen, catalog number: 15596018, Grand Island, NY, United States) according to the manufacturer’s instructions, and 1 μg RNA was reverse transcribed into cDNA using the Superscript II First-Strand Synthesis System (Invitrogen, catalog number: 18080051, Shanghai, China). Primers used for RT-qPCR are listed in Table 3.

TABLE 3 | Primers for RT-qPCR.

Western Blotting

Cells and tissue homogenates were harvested and lysed in RIPA buffer (50 mM Tris-HCl [pH 7.4], 1% Nonidet P-40, 0.25% sodium deoxycholate, 150 mM NaCl, 1 mM EDTA, 1 mM PMSF, 1 mg/ml aprotinin, 1 mg/ml leupeptin, 1 mg/ml pepstatin, 1 mM Na₃VO₄, and 1 mM NaF). The protein concentration in the lysate was quantified using the BCA Kit. The cell lysates were loaded onto an SDS-PAGE gel for electrophoresis. After transfer of the proteins onto a piece of nitrocellulose membrane, targets were detected by western blotting using primary antibodies, including NEK6 antibody (1 : 2000, catalog number: 10378-1-AP, Proteintech, Wuhan, China), GAPDH antibody (1 : 10,000, catalog number: 10494-1-AP, Proteintech), secondary antibody, HRP-conjugated goat anti-mouse IgG (H + L) (1:10,000, catalog number: ab205719, Abcam, United States), HRP-conjugated goat anti-rabbit IgG (H + L) (1:10,000, catalog number: ab205718, Abcam), and an ECL Chemiluminescent Substrate Reagent Kit (Invitrogen, catalog number: WP20005, Shanghai, China). Protein expression levels were quantified using ImageJ software. The experiments were performed in triplicate.

MTT Assay

After seeding into 96-well plates at a density of 10³ cells/well and cultured overnight, the cells were treated as indicated. Subsequently, the CyQUANT™ MTT Cell Viability Assay (Invitrogen, catalog number: V13154, Shanghai, China) was used to detect cell proliferation according to the manufacturer’s instructions.

Invasion and Migration Assay

After transfection for 24 h, 3 × 10⁵ cells were seeded into the upper chambers of the transwell plates with and without matrigel for invasion and migration assays, respectively. After incubation in a serum-free medium for 24 h, the cells in the upper chambers were wiped away. The cells at the bottom of the upper chamber were fixed and stained with 4% paraformaldehyde and 0.5% crystal violet. The upper chamber was then dipped into distilled water to remove excess crystal violet. Migrated or invasive cells were counted under a microscope. The experiments were performed in triplicate.

Luciferase Reporter Assay

Plasmids, FAM13-AS1 WT, FAM13-AS1 MUT, NEK6 3′-UTR WT, and NEK6 3′-UTR MUT were co-transfected into kidney cancer cells with miR-141-3p mimics or the negative control (mimic NC). After 48 h, the relative luciferase activity was measured with the ONE-Glo luciferase Assay System (Proteintech, catalog number: E6110, Wuhan, China) according to the manufacturer’s instructions.

Analysis of the Cell Cycle

786-O or SN2-PM6 cells transfected with various plasmids were seeded at a density of 3×10⁵ cells/well in 6-well plates in a medium containing 90% DMEM plus 10% FBS. Upon receiving the desired treatments, the cells were trypsinized and fixed with 70% (v/v) ethanol at 4°◦C overnight. The fixed cells were stained with 50 μg/ml propidium iodide (PI) for 30 min at 37°C in the dark for cell cycle analysis. After incubation, the cells were analyzed using a FACScan flow cytometer (Beckman Coulter, United States) within 15 min. The fixed cells were stained with Annexin V-FITC and PI double staining solution for apoptosis analysis before being subjected to flow cytometry. The percentage of cells in each phase (G1, S, and G2/M) of the cell cycle and the percentage of cells undergoing apoptosis were determined using the Cell Quest software. The proliferation indexes were calculated using the following formula: PI=(S + G2/M)/(G1+S + G2/M).

Fluorescence in Situ Hybridization (FISH)

Cells were seeded and grown overnight on glass coverslips. After immobilization and permeabilization, the cells were subjected to hybridization with 20 μM Cy3-labeled FAM13A-AS1, 18S, or U6 FISH probes. The nuclei of the cells were stained with 6-diamidino-2-phenylindole (DAPI). All images were captured using a confocal laser-scanning microscope.

Animal Model

All animal experiments were approved by Institutional Review Board of ZhongShan Hospital, Xiamen University. Nude mice were purchased from Shanghai Lab at the Animal Research Center (Shanghai, China). Each mouse was injected with 5×10⁶ shFAM13-AS1 cells or negative control cells in the axilla. The long diameter 1) and short diameter 2) of tumors were measured every 4 days. The tumor volume (V) was calculated using formula V = ab (Hsieh et al., 2017)/2. The tumor was monitored for 38 days before the mice were sacrificed to dissect the tumor tissues. The tumor tissues were weighed and then ground using a grinder Tissuelyser-24 (Jingxin, Shanghai, China). The tissue homogenates were subjected to analyses of RT-qPCR and western blotting.

TCGA Data Processing

Gene expression and RCC patient survival data were downloaded from the GDC website (https://portal.gdc.cancer.gov/). The TCGA database provides multiple types of data for 539 RCC cases. The expression value of FAM13-AS1 was classified as either high or low. Survival probability was calculated in days from the date of diagnosis to the time of death.

Statistical Analysis

All image data were analyzed using GraphPad Prism 5 software and represented as mean ± standard deviation (SD). The Shapiro-Wilk test was performed to determine whether the data follows a normal distribution. The Levene’s test was used to ensure the homogeneity of variance. ANOVA followed by Tukey’s post-hoc test was used for multiple comparisons among three groups. Unpaired and paired student’ t-test were performed to compare the difference between two Unpaired and paired groups, respectively. Survival curves were calculated using the Kaplan-Meier method, and the significance was determined by the log-rank test. A Pearson correlation test was performed to assess the correlation among FAM13A-AS1, miR-141-3p, and NEK6 mRNA expression using software environment R, version 3.3.2. All results were considered statistically significant at p < 0.05.

RESULTS

Expression of lncRNA FAM13-AS1 Is Upregulated and Associated With Poor Clinical Outcomes in RCC Patients

FAM13-AS1 is a long non-coding RNA that was recently identified as a prognostic biomarker for thyroid cancer (Rao et al., 2020). However, its role in RCC has not yet been studied. We found that the expression level of FAM13-AS1 was significantly elevated in RCC by analyzing the expression profiles of 539 RCC samples and 72 normal tissue samples from the TCGA-KIRC database, indicated by the scatter plot generated from the whole dataset (p < 0.001, Figure 1A) and 72 paired tissue samples (p = 0.0083, Figure 1A). To further support this finding, we used RT-qPCR to measure the expression level of FAM13-AS1 in an independent RCC cohort, containing 40 RCC patients whose tumor and paracancerous tissues were sampled. Compared to normal renal tissues, FAM13-AS1 expression was significantly increased in tumor tissues, with p < 0.01 and p = 0.0012 for overall and paired scatterplots, respectively (Figure 1B). In addition, we tested FAM13-AS1 expression in multiple kidney cell lines. Three different human kidney cancer cell lines, 786-O, SN12-PM6, and Caki-1, showed significantly higher expression of FAM13A-AS1 than the normal renal epithelial cell line HK-2. No difference in FAM13A-AS1 expression was observed in the A-498 cells (Figure 1C). This evidence suggests that FAM13A-AS1 may be universally overexpressed in different types of RCC. Thus, we investigated whether the expression level of FAM13A-AS1 correlates with any clinical outcome. Kaplan-Meier survival analysis showed that patients with low expression of FAM13A-AS1 had significantly better overall survival rates (p = 0.0007) (Figure 1D), suggesting that FAM13A-AS1 could be a promising prognostic marker for RCC.

FIGURE 1 | FAM13A-AS1 is upregulated in RCC and associated with poor clinical outcome. (A) Scatter plots showing the expression levels of FAM13A-AS1 in RCC (n = 539) and normal tissues (n = 72) from TCGA cohort. (B) Scatter plots showing the expression levels of FAM13A-AS1 in RCC (n = 40) and normal tissues (n = 40) from our cohort. (C) Bar plot showing the fold change in FAM13A-AS1 expression in human kidney epithelia and cancer cell lines. (D) Kaplan-Meier analysis of overall survival of 515 RCC patients in TCGA-KIRC stratified by high and low FAM13A-AS1 expression.

FAM13A-AS1 Promotes Cell Proliferation, Migration, and Invasion in Kidney Cancer Cell Lines

Many long non-coding RNAs have been reported to promote tumorigenesis and the progression of various types of cancers³. To explore whether FAM13A-AS1 has similar functions in RCC, we examined cell proliferation, migration, and invasion in both 786-O and SN12-PM6 cells transfected with shRNA against FAM13A-AS1. The knockdown efficiency of shRNA was confirmed by RT-qPCR analysis (Figure 2A). MTT assays indicated that shRNA knockdown of FAM13A-AS1 significantly impaired cell growth and proliferation rate, compared to cells transfected with control shRNA (Figure 2B). To explore the possible mechanisms underlying the increase in cell proliferation rates, we performed flow cytometry to examine the cell cycle of 786-O and SN12-PM6 cells transfected with shFAM13A-AS1 or scramble control. We found that reduced FAM13A-AS1 expression was associated with cell cycle arrest at the G₀/G₁ stage (Figures 2C,D). In particular, the mean percentage of cells in the G₀/G₁ phase increased by 10% in the FAM13A-AS1 knockdown cell line compared to the control. Consistently, Annexin V staining indicated that FAM13A-AS1 inhibition promoted cell apoptosis (Figures 2E,F). The percentage of cells undergoing apoptosis was significantly higher in cells transfected with shFAM13A-AS1 than in cells transfected with scrambled control. Moreover, we found that cells with reduced FAM13A-AS1 expression displayed significantly less migration and invasion activities, as revealed by the Transwell assay (Figures 2G,H) and matrigel invasion assays, respectively (Figures 2I,J). Collectively, these results indicate that FAM13A-AS1 is a potential oncogenic lncRNA in RCC.

FIGURE 2 | FAM13A-AS1 promotes RCC cell proliferation, migration, and invasion. (A) Bar plot showing the knockdown efficiencies of shRNA targeting FAM13A-AS1. Expression levels were measured by RT-qPCR and calibrated using the expression level of 18S rRNA. Fold changes in expression were then computed against the scrambled control. (B) MTT assay of RCC cells transfected with shFAM13A-AS1 or scrambled control. (C,D) Representative histograms (C) and quantification (D) of cell cycle distribution of RCC cells transfected with shFAM13A-AS1 or scrambled control. X and Y axes in (C) denote DNA content and cell number, respectively. (E,F) Dot plot (E) and bar chart (F) of Annexin V/PI two-parameter flow cytometry analysis of RCC cells transfected with shFAM13A-AS1 or scrambled control. Bar plot (F) shows the percentages of cells that underwent apoptosis. (G,H) Representative images (G) and quantification (H) of the Transwell migration assay in RCC cells transfected with shFAM13A-AS1 or scrambled control. (I,J) Representative images (I) and quantification (J) of the matrigel invasion assay in RCC cells transfected with shFAM13A-AS1 or scrambled control. Data represent the mean ± SD. **: p < 0.01; ***: p < 0.001; ****: p < 0.0001.

FAM13A-AS1 Attenuates miR-141-3p Function by Acting as a ceRNA

To investigate how FAM13A-AS1 exerts the function, we first explored its subcellular locations in 786-O kidney cancer cells by FISH assay. FAM13A-AS1 transcripts were recognized and labeled by cy3, which mainly remained in the cytoplasm, almost exclusively from DAPI staining (Figure 3A). These results were further confirmed by RT-qPCR using subcellular fractionation. This analysis showed that 73% of FAM13A-AS1 transcripts resided in the cytoplasm pool, whereas only 27% were retained inside the nucleus (Figure 3B). Therefore, we hypothesized that FAM13A-AS1 might function as a ceRNA to regulate miRNA species. Using bioinformatics tools, we predicted potential interacting miRNAs from three different databases (Figure 3C). A total of 188 miRNA targets could interact with FAM13A-AS1, and five overlapped across all three databases. We further examined their expression levels in RCC by analyzing the published miRNA expression profiles. Among the five shared miRNA targets, we found that one miRNA, miR-141-3p, was negatively correlated with FAM13A-AS1 (Figure 3D, p < 0.0001). These findings suggested us that FAM13A-AS1 may interact with miR-141-3p. Using a luciferase reporter assay, we revealed that overexpression of miR-141-3p led to decreased luciferase activity of the wild-type FAM13A-AS1 reporter gene (Figures 3E,F). In contrast, the miR-141-3p mimic did not alter the luciferase activity of the FAM13A-AS1 mutant carrying mutations at the putative miRNA-binding site. Moreover, knockdown of FAM13A-AS1 could, in turn, elevate the expression level of miR-141-3p in both 786-O and SN12-PM6 cells (Figure 3G). In addition, the negative correlation between the expression of FAM13A-AS1 and miR-141-3p was also confirmed in our RCC patient cohort (Figure 3H, p = 0.031). These findings confirmed that FAM13A-AS1 serve as a ceRNA for miR-141-3p. Finally, we assessed the expression of miR-141-3p in patients from the TCGA and RCC cohorts. Expression of miR-141-3p was significantly repressed in RCC tumor samples compared to that in normal paracancerous tissues (Figures 3I,J). However, miR-141-3p alone is not a good estimator for overall patient survival, as the Kaplan-Meier survival analysis showed no difference in the overall survival between RCC patients classified into high and low miR-141-3p expression (Figure 3K). This may attribute to the limited number of samples. Taken together, these findings suggest that FAM13A-AS1 binds to miR-141-3p as a decoy to sequester the miRNA away from its target.

FIGURE 3 | FAM13A-AS1 functions as a ceRNA that competes for miR-141-3p. (A,B) Fish assay (A) and RT-qPCR (B) showing the subcellular expression (nucleus and cytoplasm) of FAM13A-AS1 in RCC cells, compared with 18S and U6 as the endogenous controls. (C) Venn diagram showing the intersection between computationally identified FAM13A-AS1 interacting miRNAs. (D) Scatter plot showing the co-expression pattern between miR-141-3p (Y-axis) and FAM13A-AS1 (X-axis) in TCGA. (E) Schematic showing the predicted miR-141-3p binding sites in FAM13A-AS1 wild-type transcript, and the FAM13A-AS1 mutant escaping the binding of miR-141-3p. The red nucleotides represent mutations on target sites. (F) Bar plot showing the luciferase activities in RCC cells co-transfected with wild-type (WT) or mutant (MUT) FAM13A-AS1 plasmid together with miR-141-3p mimic or negative control (NC). (G) Bar plots showing the miR-141-3p expression in RCC cells transfected with shFAM13A-AS1 or negative control (NC). Expression levels were measured using RT-qPCR. (H) Scatter plot showing the co-expression pattern between miR-141-3p (Y-axis) and FAM13A-AS1 (X-axis) in and our own cohorts. (I,J) Scatter plots showing the miR-141-3p expression in normal and tumor tissues of RCC patient from TCGA (H) and our own (I) cohorts. (K) Kaplan-Meier analysis of the overall survival rate of RCC patients stratified by the expression of miR-141-3p from TCGA cohort. Data represent the mean ± SD. ***: p < 0.001; ****: p < 0.0001.

To further establish the hypothesis that the carcinogenic effect of FAM13A-AS1 is mediated by negative regulation of miR-141-3p, we introduced miR-141-3p mimic or miR-141-3p inhibitor in kidney cancer cells transfected with shFAM13A-AS1 (Figure 4A). Consistent with our previous findings, silencing of FAM13A-AS1 led to elevated expression of miR-141-3p. In addition, we found that the miR-141-3p inhibitor fully rescued the phenotypic effects resulting from FAM13A-AS1 silencing. In particular, inhibition of miR-141-3p function promoted cell proliferation (Figures 4B–D), cell migration (Figures 4G,H), and cell invasion (Figures 4I,J). The miR-141-3p knockdown mediated increase in cell proliferation was associated with increased DNA synthesis and the release from the cell cycle arrest at the G₀/G₁ phase, as revealed by the EdU cell proliferation assay and flow cytometry analysis, respectively (Figures 4B–D). Similarly, Annexin V staining showed fewer cells underwent apoptosis when miR-141-3p silencing was introduced in RCC cells transfected with shFAM13A-AS1 (Figures 4E,F). Based on our findings, we conclude that FAM13A-AS1 functions as an oncogene in RCC, partially by negatively regulating the expression of miR-141-3p.

FIGURE 4 | miR-141-3p inhibits kidney cancer cell proliferation, migration, and invasion. (A) Bar plot showing the expression level of miR-141-3p in RCC cells co-transfected with shFAM13A-AS1 or scrambled control treated with either miR-141-3p. (B) MTT assay of RCC cells transfected with shFAM13A-AS1 treated with either miR-141-3p inhibitor or negative controls. (C,D) Representative images (C) and quantification (D) of cell proliferation assay in RCC cells. Edu-positive cells: green. Cell nuclei: blue. (C,D) Representative histograms (C) and quantification (D) of cell cycle distribution of RCC cells. X and Y axes (C) denote DNA content and cell number, respectively. Bar plot (D) shows the percentages of cells at different stages during the cell cycle. (E,F) Representative dot plots (E) and quantification (F) of Annexin V/PI two-parameter flow cytometry analysis of RCC cells. Bar plot (lower) shows the percentages of cells that undergo apoptosis. The RCC cell lines were transfected with shFAM13A-AS1 or scrambled control and treated with miR-141-3p inhibitor. (G,H) Representative images (G) and quantification (H) of the transwell migration assay in RCC cells transfected or treated with miR-141-3p inhibitor. (I,J) Representative images (I) and quantification (J) of the matrigel invasion assay in the above cells. Data represent the mean ± SD. ***: p < 0.001; ****: p < 0.0001.

FAM13A-AS1 Sponges miR-141-3p to Upregulate Its Target NEK6

To identify the genes regulated by miR-141-3p, we first predicted the target genes of miR-141-3p using five different computational software packages (Figure 5A). mirDIP recovered the largest number of target hits, a total of 448 predicted target genes, whereas miRTarbase only reported 48 potential targets. Notably, there were 41 common targets predicted by all the software. To further identify the most relevant gene targets, we performed a correlation analysis between the expression level of each candidate target and miR-141-3p or FAM13A-AS1 in tumor samples from patients with RCC. Among all the tested mRNAs, NEK6, a gene that encodes a kinase required for progression through the metaphase portion of mitosis, is of particular interest. We found that the expression level of NEK6 was significantly positively correlated with FAM13A-AS1 in the TCGA cohort (Figure 5B, p < 0.0001). In addition, a strong negative correlation was observed between NEK6 and miR-141-3p expression (p < 0.0001). RT-qPCR confirmed the results in 40 patients with RCC. These results together suggest that NEK6 is a direct target of miR-141-3p. Consistent with this, using a luciferase reporter assay, we showed that overexpression of miR-141-3p significantly inhibited the luciferase activity of RCC cells transfected with wildtype NEK6 3’ -UTR reporter. In contrast, no repression of luciferase activity was observed in cells transfected with the mutant reporter (Figures 5C,D). Next, we investigated whether endogenous expression of NEK6 is affected by the miR-141-3p mimic. RT-qPCR and western blotting revealed that both the mRNA and protein levels of NEK6 were reduced with miR-141-3p overexpression than the mimic negative control (Figures 5E,F). In summary, we identified NEK6 as a gene target of miR-141-39 in RCC.

FIGURE 5 | FAM13A-AS1 sponges miR-141-3p to upregulate its target NEK6. (A) Upset plot showing the intersections between five sets of computationally identified miR-141-3p targeting genes. (B) Correlation between NEK6 and miR-141-3p or FAM13A-AS1 expression in RCC from TCGA and our own cohorts. (C) Schematic showing the predicted miR-141-3p binding sites in 3′UTR of NEK6 wild-type and mutant escaping the binding of miR-141-3p. The red nucleotides represent mutations on target sites. (D) Bar plot showing the luciferase activities in 786-O and SN2-PM6 cells co-transfected with wild-type (WT) or mutant (MUT) NEK6 plasmid together with miR-141-3p mimic or negative control (NC). (E,F) Bar plots (E) and blotting image (F) showing the mRNA and protein levels of NEK6 expression in 786-O and SN2-PM6 cells transfected with miR-141-3p mimic, or negative controls (NC) from RT-qPCR and western blot analysis. Data represent the mean ± SD. **: p < 0.01; ***: p < 0.001; ****: p < 0.0001.

Expression of NEK6 is elevated and associated with poor clinical outcome in RCC patients.

The NEK6 transcript has been reported to be significantly upregulated in hepatocellular carcinoma. However, it is not clear how NEK6 functions in RCC. By analyzing the transcriptome of the TCGA cohort, we noticed that the expression level of NEK6 in RCC was significantly increased in tumor samples, compared with the adjacent normal tissues (Figure 6A). A similar observation was obtained from 40 additional RCC patients. RT-qPCR indicated that NEK6 expression was significantly elevated in RCC (Figure 6B). We further determined the protein level of NEK6 in all RCC biopsies. Of the 40 patients, 12 displayed decreased protein levels of NEK6 in tumors when normalized to their paired normal tissue. The rest had an elevated NEK6, with the highest showing more than an eight-fold change (Figures 6C,D). Surprisingly, Kaplan-Meier survival analysis suggested that NEK6 is a favorable estimator for the overall OS of RCC patients from the TCGA cohort. We found that patients with high NEK6 expression showed better survival rates than those with low NEK6 expression.

FIGURE 6 | Elevated expression level of NEK6 in RCC. (A) Scatter plots showing the expression of NEK6 in RCC (n = 539) and normal tissues (n = 72) from TCGA cohort. (B) Scatter plots showing the expression levels of NEK6 in RCC (n = 40) and normal tissues (n = 40) from our cohort. (C,D) Blotting images (C) and quantification (D) of protein level of NEK6 in RCC and normal tissues from our cohort. (E) Kaplan-Meier analysis of the overall survival rate of RCC patients stratified by the expression of NEK6 from TCGA cohort.

FAM13A-AS1 Exerts Oncogenic Effect by Regulating NEK6

To further establish our hypothesis that FAM13A-AS1 functions by negatively regulating miR-326, we used shRNA to knockdown FAM13A-AS1 in RCC cells, which resulted in a significant reduction in NEK6 RNA and protein levels, resembling the effect of transfection with miR-141-3p mimic (Figures 7A,B). The downregulation of NEK6 expression was reversed, and the expression level was comparable to that in RCC cells transfected with a scrambled control, with miR-141-3p inhibitor treatment. Overexpression of NEK6 restored its mRNA and protein levels (Figures 7A,B). In addition, we found that inhibition of cell proliferation (Figures 7C–E), apoptosis (Figures 7F,G), migration (Figures 7H,I), and invasion (Figures 7J,K) caused by the FAM13A-AS1 knockdown was rescued by NEK6 overexpression. These results together confirmed that FAM13A-AS1 enhances cell proliferation, migration, and invasion in a NEK6-dependent manner.

FIGURE 7 | NEK6 is responsible for FAM13A-AS1-mediated cell proliferation, migration, and invasion. (A) Bar plots showing the expression of NEK6 in 786-O and SN12-PM6 cells transfected with shFAM13A-AS1 or negative control (shNC), together with empty vector or NEK6 overexpression plasmid. Expression levels were measured by RT-qPCR. Fold changes of expression were then computed against the scrambled control. (B) Blotting images showing the protein level of NEK6 in the above cells. (C) MTT assay of 786-O and SN12-PM6 cells transfected with shFAM13A-AS1 or negative control (shNC), together with empty vector or NEK6 overexpression plasmid. (D–K) Representative images and quantifications of the flow cytometry analysis for cell cycle distribution (D,E), Annexin V/PI two-parameter flow cytometry analysis (F,G), transwell migration assay (H,I), and the matrigel invasion assay (G,K) of 786-O and SN12-PM6 cells transfected with shFAM13A-AS1 or negative control (shNC), together with empty vector or NEK6 overexpression plasmid. Data represent the mean ± SD. *: p < 0.05; **: p < 0.01; ***: p < 0.001; ****: p < 0.0001.

FAM13A-AS1 Promotes RCC Tumorigenesis in Vivo

We have demonstrated that FAM13A-AS1 exerts its carcinogenic function by forming the lncRNA/miRNA/target axis with miR-141-3p and NEK6 in kidney cancer cell lines. To confirm that a similar mechanism exists in vivo, we generated a transgenic mouse model carrying shFAM13A-AS1 to induce FAM13A-AS1 silencing (Figure 8A). Our results showed that FAM13A-AS1 dramatically inhibited tumor growth (Figures 8B,C). A significant reduction in both tumor volume and weight was observed in mice with FAM13A-AS1 silencing. The tumor volume decreased from 580 to 160 mm², and the tumor weight was reduced to only 35% on day 38 in shFAM13A-AS1 transgenic mice compared to scrambled shRNA. The decreased expression of FAM13A-AS1 was confirmed in the tumor tissues, which was accompanied by an increased level of miR-141-3p (Figures 8D,E). Moreover, FAM13A-AS1 knockdown led to the downregulation of NEK6 in vivo (Figures 8F–H). Both the mRNA (Figure 8F) and protein levels (Figures 8G,H) of NEK6 were reduced in shFAM13A-AS1 tumors. In addition, FAM13A-AS1 deficiency regulated the protein levels of EMT markers such as N-cadherin, E-cadherin, and vimentin (Figures 8G,H). Thus, our data suggest that FAM13A-AS1 promotes the tumorigenesis of RCC in vivo.

FIGURE 8 | FAM13A-AS1 promotes tumorigenesis in vivo. (A-C) Xenograft tumor model was constructed by transplanting the above 786-O cells into nude mice with representative images (A), measurement of tumor volume (B), and tumor weight (C). (D-F) Expression of FAM13A-AS1 (D), miR-141-3p(E), and NEK6 (F) in the tumors revealed by RT-qPCR, calibrated using the expression level of 18S rRNA or U6. Fold changes of expression were then computed against the scrambled control. (G,H) Blotting images (G) and quantification (H) of protein levels in the tumor tissues (n = 6). Data represent the mean ± SD. **: p < 0.01; ***: p < 0.001; ****: p < 0.0001.

DISCUSSION

RCC is one of the most common cancers worldwide. In contrast to breast cancer, which is frequently diagnosed at an early stage and is widely treated by conservative surgery, RCC has a high incidence of metastasis¹³. Its incidence is also increasing every year, especially among the elderly. The mainstay of treatment for metastatic RCC is androgen deprivation therapy (ADT), which reduces the levels of androgens in the body¹⁴. In the initial stages of ADT, most patients experienced improved symptoms and increased quality of life. However, ADT is associated with several adverse effects in advanced metastatic RCC. As a result, there is a need for novel and effective approaches to diagnose and treat RCC. In the present study, we identified FAM13A-AS1 as a potential biomarker for RCC. FAM13A-AS1 is an lncRNA that has been associated with non-small cell lung carcinoma (NSCLC) (Yu et al., 2015). Distinct expression of FAM13A-AS1 has been reported in NSCLC patients. In addition, Sun et al. built a five-lncRNA prognostic signature that contains FAM13A-AS1 to predict the survival outcome of bladder urothelial carcinoma patients (Sun et al., 2020). However, its role in RCC has never been studied before. We found that FAM13A-AS1 was significantly upregulated in RCC tissues and positively correlated with adverse clinical outcomes. We further demonstrated that FAM13A-AS1 is a ceRNA of miR-141-3p and that the expression levels of these two transcripts have a strong negative correlation in RCC patients. Moreover, we established that FAM13A-AS1 promotes the proliferation, migration, and invasion of RCC cells using overexpression and knockdown experiments. These results suggest that FAM13A-AS1 is involved in the tumorigenesis of RCC.

MicroRNAs (miRNAs) are endogenous, small non-coding RNAs that regulate gene expression by binding to the 3′-UTRs of their mRNA targets (Bartel, 2018). In the past decades, accumulating evidence has shown that aberrant miRNAs are associated with carcinogenesis (Peng and Croce, 2016). As such, miR-141-3p has been implicated in various malignant tumors. miR-141-3p was first identified as a negative regulator in stromal stem cells to regulate cell proliferation by targeting CDC25A (Qiu and Kassem, 2014). More recently, the potential oncogenic role of miR-141-3p in prostate cancer was revealed by Li et ai. (Li et al., 2017). The authors found that miR-141-3p

miR-141-3p promoted the stemness of prostate cancer cells by negatively regulating Krüppel-l-like factor 9. Additional evidence revealed that miR-141-3p could inhibit the expression of GLI2, downregulating parathyroid hormone-related protein 1 and promoting apoptosis in osteosarcoma cells (Wang et al., 2018). In this study, we showed that miR-141-3p was also downregulated in RCC. Our findings showed the significance of the interaction between FAM13A-AS1 and miR-141-3p in RCC. Treatment with miR-141-3p inhibitor fully rescued the suppression of cell proliferation, migration, and invasion resulting from FAM13A-AS1 silencing.

NEK6 is a protein kinase required for progression through the metaphase portion of mitosis. It is involved in the control of mitotic spindle formation, acting together in a mitotic kinase cascade with other NIMA-family kinases (O’Regan and Fry, 2009; Meirelles et al., 2011). Inhibition of the expression of NEK6 can lead to apoptosis. This protein has been implicated in the enhancement of tumorigenesis by suppressing tumor cell senescence. It was recently shown that NEK6 expression was significantly upregulated in hepatocellular carcinoma (Chen et al., 2006). Earlier studies have also demonstrated elevated expression of NEK6 in multiple malignancies, such as breast cancer, colorectal cancer, lung cancer, and laryngeal cancer (Capra et al., 2006). Similar mechanisms have been uncovered in advanced gastric cancers by Takeno et al. (Takeno et al., 2008). Using bioinformatics tools, we predicted NEK6 as a potential target of miR-141-3p, which was confirmed by a luciferase reporter assay. Furthermore, we showed that overexpression of miR-141-3p suppressed the expression of NEK6 at both the mRNA and protein levels. Finally, we showed that overexpression of NEK6 rescued the growth inhibition phenotype induced by FAM13A-AS1 knockdown. Collectively, these results suggest that FAM13A-AS1 promotes tumor growth by competing miR-141-3p with NEK6.

It should be noted that there are also several limitations for this study. For example, investigation the effect of FAM13A-AS1 overexpression on normal cells may further confirm our conclusion, which will be done in the next work. Moreover, the pulmonary metastatic model should be established to evaluate the effect of FAM13A SA1/miR-141/NEK6 axis on the metastatic ability of RCC cells.

In conclusion, FAM13A-AS1 serve as an oncogene through miR-141-3p/NEK6 axis and may be a potential target for RCC treatment.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

ETHICS STATEMENT

The studies involving human participants were reviewed and approved by Institutional Review Board of ZhongShan Hospital, Xiamen University. The patients/participants provided their written informed consent to participate in this study. The animal study was reviewed and approved by Institutional Review Board of Xiamen University.

AUTHOR CONTRIBUTIONS

GCL conceived and designed the study. GCL and XJW collected thedata, analyzed the data and reviewed the manuscript. SL, JF, and ZJY performed the experiments and wrote the draft. All authors read and approved the final manuscript.

FUNDING

This work was supported by Project of Science and Technology Bureau of Xiamen (3502Z20184033 and 3502Z20194022), Natural Science Foundation of Fujian Province (2020J011213), and the Young/Middle-aged Talent Cultivation Project funded by Fujian Provincial Health and Family Planning Commission and Xiamen Health and Family planning Commission (2021GGB028).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2022.738711/full#supplementary-material

REFERENCES

Alves, C. P., Fonseca, A. S., Muys, B. R., Bueno, R. D. B. E. L., Bürger, M. C., de Souza, J. E. S., et al. (2013). Brief Report: The lincRNA Hotair Is Required for Epithelial-To-Mesenchymal Transition and Stemness Maintenance of Cancer Cell Lines. Stem Cells Dayt Ohio 31, 2827–2832. doi:10.1002/stem.1547

Barata, P. C., and Rini, B. I. (2017). Treatment of Renal Cell Carcinoma: Current Status and Future Directions. CA: A Cancer J. Clinicians 67, 507–524. doi:10.3322/caac.21411

Bartel, D. P. (2018). Metazoan MicroRNAs. Cell 173, 20–51. doi:10.1016/j.cell.2018.03.006

Capra, M., Nuciforo, P. G., Confalonieri, S., Quarto, M., Bianchi, M., Nebuloni, M., et al. (2006). Frequent Alterations in the Expression of Serine/Threonine Kinases in Human Cancers. Cancer Res. 66, 8147–8154. doi:10.1158/0008-5472.can-05-3489

Chen, J., Li, L., Zhang, Y., Yang, H., Wei, Y., Zhang, L., et al. (2006). Interaction of Pin1 with NEK6 and Characterization of Their Expression Correlation in Chinese Hepatocellular Carcinoma Patients. Biochem. Biophysical Res. Commun. 341, 1059–1065. doi:10.1016/j.bbrc.2005.12.228

Escudier, B., Porta, C., Schmidinger, M., Rioux-Leclercq, N., Bex, A., Khoo, V., et al. (2019). Renal Cell Carcinoma: ESMO Clinical Practice Guidelines for Diagnosis, Treatment and Follow-Up. Ann. Oncol. 30, 706–720. doi:10.1093/annonc/mdz056

Gupta, R. A., Shah, N., Wang, K. C., Kim, J., Horlings, H. M., Wong, D. J., et al. (2010). Long Non-coding RNA HOTAIR Reprograms Chromatin State to Promote Cancer Metastasis. Nature 464, 1071–1076. doi:10.1038/nature08975

Hsieh, J. J., Purdue, M. P., Signoretti, S., Swanton, C., Albiges, L., Schmidinger, M., et al. (2017). Renal Cell Carcinoma. Nat. Rev. Dis. Primers 3, 17009. doi:10.1038/nrdp.2017.9

Jemal, A., Siegel, R., Ward, E., Murray, T., Xu, J., and Thun, M. J. (2007). Cancer Statistics, 2007. CA: A Cancer J. Clinicians 57, 43–66. doi:10.3322/canjclin.57.1.43

Kim, K., Jutooru, I., Chadalapaka, G., Johnson, G., Frank, J., Burghardt, R., et al. (2013). HOTAIR Is a Negative Prognostic Factor and Exhibits Pro-oncogenic Activity in Pancreatic Cancer. Oncogene 32, 1616–1625. doi:10.1038/onc.2012.193

Kopp, F., and Mendell, J. T. (2018). Functional Classification and Experimental Dissection of Long Noncoding RNAs. Cell 172, 393–407. doi:10.1016/j.cell.2018.01.011

Li, J.-Z., Li, J., Wang, H.-q., Li, X., Wen, B., and Wang, Y.-j. (2017). MiR-141-3p Promotes Prostate Cancer Cell Proliferation through Inhibiting Kruppel-like Factor-9 Expression. Biochem. Biophysical Res. Commun. 482, 1381–1386. doi:10.1016/j.bbrc.2016.12.045

Meirelles, G. V., Silva, J. C., Mendonça, Y. d. A., Ramos, C. H., Torriani, I. L., and Kobarg, J. (2011). Human NEK6 Is a Monomeric Mostly Globular Kinase with an Unfolded Short N-Terminal Domain. BMC Struct. Biol. 11, 12. doi:10.1186/1472-6807-11-12

O’Regan, L., and Fry, A. M. (2009). The NEK6 and Nek7 Protein Kinases Are Required for Robust Mitotic Spindle Formation and Cytokinesis. Mol. Cel Biol 29, 3975–3990. doi:10.1128/MCB.01867-08

Peng, Y., and Croce, C. M. (2016). The Role of MicroRNAs in Human Cancer. Signal. Transduct Target. Ther. 1, 15004–15009. doi:10.1038/sigtrans.2015.4

Qiu, W., and Kassem, M. (2014). miR-141-3p Inhibits Human Stromal (Mesenchymal) Stem Cell Proliferation and Differentiation. Biochim. Biophys. Acta (Bba) - Mol. Cel Res. 1843, 2114–2121. doi:10.1016/j.bbamcr.2014.06.004

Rao, Y., Liu, H., Yan, X., and Wang, J. (2020). In Silico Analysis Identifies Differently Expressed lncRNAs as Novel Biomarkers for the Prognosis of Thyroid Cancer. Comput. Math. Methods Med. 2020, 3651051. doi:10.1155/2020/3651051

Slack, F. J., and Chinnaiyan, A. M. (2019). The Role of Non-coding RNAs in Oncology. Cell 179, 1033–1055. doi:10.1016/j.cell.2019.10.017

Sun, Z., Jing, C., Xiao, C., and Li, T. (2020). An Autophagy-Related Long Non-coding RNA Prognostic Signature Accurately Predicts Survival Outcomes in Bladder Urothelial Carcinoma Patients. Aging 12, 15624–15637. doi:10.18632/aging.103718

Takeno, A., Takemasa, I., Doki, Y., Yamasaki, M., Miyata, H., Takiguchi, S., et al. (2008). Integrative Approach for Differentially Overexpressed Genes in Gastric Cancer by Combining Large-Scale Gene Expression Profiling and Network Analysis. Br. J. Cancer 99, 1307–1315. doi:10.1038/sj.bjc.6604682

Wang, N., Li, P., Liu, W., Wang, N., Lu, Z., Feng, J., et al. (2018). miR-141-3p Suppresses Proliferation and Promotes Apoptosis by Targeting GLI2 in Osteosarcoma Cells. Oncol. Rep. 39, 747–754. doi:10.3892/or.2017.6150

Xiao, Z.-D., Han, L., Lee, H., Zhuang, L., Zhang, Y., Baddour, J., et al. (2017). Energy Stress-Induced lncRNA FILNC1 Represses C-Myc-Mediated Energy Metabolism and Inhibits Renal Tumor Development. Nat. Commun. 8, 783. doi:10.1038/s41467-017-00902-z

Yu, H., Xu, Q., Liu, F., Ye, X., Wang, J., and Meng, X. (2015). Identification and Validation of Long Noncoding RNA Biomarkers in Human Non-small-cell Lung Carcinomas. J. Thorac. Oncol. 10, 645–654. doi:10.1097/jto.0000000000000470

Zhang, Y., Su, X., Kong, Z., Fu, F., Zhang, P., Wang, D., et al. (2017). An Androgen Reduced Transcript of LncRNA GAS5 Promoted Prostate Cancer Proliferation. PLoS One 12, e0182305. doi:10.1371/journal.pone.0182305

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Wang, Li, Fang, Yan and Luo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 29 April 2022
doi: 10.3389/fmolb.2022.792482

Comprehensive Analysis of mTORC1 Signaling Pathway–Related Genes in the Prognosis of HNSCC and the Response to Chemotherapy and Immunotherapy

Zhao Ding1,2,³†, Hailong Shen1,2,³†, Ke Xu1,2,³, Yu Wu1,⁴, Shuhao Wang1,2,³, Fangzheng Yi1,2,³, Daming Wang¹ and Yehai Liu¹*

¹Department of Otorhinolaryngology-Head and Neck Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China

²Anhui Medical University, Hefei, China

³Graduate School of Anhui Medical University, Hefei, China

⁴Department of Otolaryngology, General Hospital of Anhui Wanbei Coal Power Group, Suzhou, China

Edited by:
Florence Le Calvez-Kelm, International Agency For Research On Cancer (IARC), France

Reviewed by:
Xiaoxiao Huang, Regeneron Pharmaceuticals, Inc., United States
Enfa Zhao, The First Affiliated Hospital of Xi’an Jiaotong University, China

* Correspondence: Yehai Liu, liuyehai@ahmu.edu.cn

†These authors have contributed equally to this work

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 10 October 2021
Accepted: 14 March 2022
Published: 29 April 2022

Citation: Ding Z, Shen H, Xu K, Wu Y, Wang S, Yi F, Wang D and Liu Y (2022) Comprehensive Analysis of mTORC1 Signaling Pathway–Related Genes in the Prognosis of HNSCC and the Response to Chemotherapy and Immunotherapy. Front. Mol. Biosci. 9:792482. doi: 10.3389/fmolb.2022.792482

Objective: The mammalian target of the rapamycin complex 1 (mTORC1) signaling pathway has emerged as a crucial player in the oncogenesis and development of head and neck squamous cell carcinoma (HNSCC), however, to date, no relevant gene signature has been identified. Therefore, we aimed to construct a novel gene signature based on the mTORC1 pathway for predicting the outcomes of patients with HNSCC and their response to treatment.

Methods: The gene expression and clinical data were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The key prognostic genes associated with the mTORC1 pathway were screened by univariate Cox regression analyses. A prognostic signature was then established based on significant factors identified in the multivariate Cox regression analysis. The performance of the multigene signature was evaluated by the Kaplan–Meier (K–M) survival analysis and receiver operating characteristic (ROC) analysis. Based on the median risk score, patients were categorized into high- and low-risk groups. Subsequently, a hybrid prognostic nomogram was constructed and estimated by a calibration plot and decision curve analysis. Furthermore, immune cell infiltration and therapeutic responses were compared between the two risk groups. Finally, we measured the expression levels of seven genes by quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC).

Results: The mTORC1 pathway–based signature was constructed using the seven identified genes (SEC11A, CYB5B, HPRT1, SLC2A3, SC5D, CORO1A, and PIK3R3). Patients in the high-risk group exhibited a lower overall survival (OS) rate than those in the low-risk group in both datasets. Through the univariate and multivariate Cox regression analyses, this gene signature was confirmed to be an independent prognostic risk factor for HNSCC. The constructed nomogram based on age, American Joint Committee on Cancer (AJCC) stage, and the risk score exhibited satisfactory performance in predicting the OS. In addition, immune cell infiltration and chemotherapeutic and immunotherapeutic responses differed significantly between the two risk groups. The expression levels of SEC11A and CYB5B were higher in HNSCC tissues than in normal tissues.

Conclusion: Our study established and verified an mTORC1 signaling pathway–related gene signature that could be used as a novel prognostic factor for HNSCC.

Keywords: signature, HNSCC, mTORC1, prognosis, nomogram

INTRODUCTION

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide, originating from epithelial cells in various areas, including, but not limited to, the nasopharynx, oropharynx, hypopharynx, oral cavity, and larynx (Jemal et al., 2011). HNSCC is an often-fatal malignancy that is responsible for 431,000 annual deaths, with more than 835,000 new cases diagnosed every year (Bray et al., 2018). Because no effective screening method presently exists for identifying cases in the early stages, more than 60% of patients with HNSCC have already progressed to an advanced stage (stage III or IV) at the time of diagnosis (Machiels et al., 2020). Treatment strategies for early-stage (stage I/II) HNSCC include surgery or radiotherapy alone, whereas advanced-stage diseases require a multidisciplinary treatment strategy that combines surgical resection, radiotherapy, and chemotherapy (Tijink et al., 2006). The 5-year overall survival (OS) rate of patients with HNSCC is approximately 60% because of recurrence and metastasis, although there have been great advances in the therapeutic strategies for treating HNSCC in recent years, including immunotherapy and targeted molecular therapy (Ferris, 2015; Polverini et al., 2018). Therefore, developing a prognostic model capable of predicting clinical end points is of great importance for guiding clinical decisions and improving the understanding of the pathophysiology of the disease.

Many studies have investigated the relationship between the expression of various biomarkers and HNSCC prognosis. For example, the overexpression of CD44, a transmembrane glycoprotein that acts as a cell-surface adhesion receptor, has been used as a prognostic marker in laryngeal and pharyngeal cancer and as a target of therapeutic strategies (Kokko et al., 2011). Aldehyde dehydrogenase 1 (ALDH1) belongs to a family of intracellular enzymes that are involved in cellular detoxification and mechanisms driving multidrug resistance. High levels of ALDH1 in patient samples were shown to be associated with drug resistance and poor prognosis in HNSCC (Yu and Cirillo, 2020). However, HNSCC cells are characterized by a high degree of genomic instability, and polygenic changes may be required to drive cancer progression and tumor metastasis (Califano et al., 1996). Therefore, compared with a single-gene biomarker, a multigene signature model would be expected to exhibit a better predictive value, and researchers have recently built some multigene signatures for predicting clinical outcomes and guiding the treatment of those with HNSCC. For instance, Liu et al. (2021) constructed a new prognostic signature based on six genes that was capable of predicting both the responses to immunological treatment and prognosis of patients with HNSCC. In addition, other studies have shown that a constructed gene signature based on seven ferroptosis-related genes can be used to predict the prognosis of those with HNSCC (He et al., 2021).

The mammalian target of rapamycin (mTOR) is a serine/threonine kinase that binds to different proteins and assembles into two complexes: mTOR complex 1 (mTORC1) and mTOR complex 2 (mTORC2) (Mossmann et al., 2018). The mTOR pathway governs the processes that mediate cell metabolism, proliferation, survival, immune function, and apoptosis, and the pathway plays a vital role in the pathophysiology of diverse malignant tumors (Caro-Vegas et al., 2019). Many studies have been implemented to explore how the mTOR pathway modulates angiogenesis and tumor immunity (Bazzichetto et al., 2020). Many previous studies have shown that mTORC1 is tightly related to cancer progression. The activation of mTORC1 phosphorylates the eukaryotic initiation factor 4E-binding protein 1 (4E-BP1) and the p70 ribosomal S6 kinase 1 (S6K1), affecting mRNA transcription and protein translation (Meric-Bernstam and Gonzalez-Angulo, 2009). Furthermore, mTORC1 regulates autophagy through ULK-complex phosphorylation (Saxton and Sabatini, 2017) and plays an important role in regulating apoptosis (Shao et al., 2017). So, a signature based on the mTORC1 signaling pathway established to predict the prognosis of HNSCC patients would have important clinical applications.

In this study, we constructed prognostic models using univariate and multivariate Cox regression analyses based on data from The Cancer Genome Atlas (TCGA) cohort. The performance of the gene signature was validated using the Gene Expression Omnibus (GEO) dataset. We also developed a nomogram that can be applied in clinical practice. Most importantly, our signature is capable of distinguishing patients with HNSCC who are likely to respond to chemotherapy and immunotherapy.

MATERIALS AND METHODS

Data Source and Processing

The gene expression profiles of patients with HNSCC, somatic mutations, and clinical data related to age, sex, stage, and follow-up time were downloaded from TCGA database on 10 July 2021. The data from patients with HNSCC in TCGA database were used as a training set to build the predictive gene signature. For further external validation, we used data from the GEO: GSE41613 cohort as a test group to certify the performance of the signature. Detailed clinical data for patients in TCGA and GEO cohorts are shown in Table 1. Related genes of the mTORC1 signaling pathway were collected from the Molecular Signatures Database (HALLMARK_MTORC1_SIGNALING). Gene Set Enrichment Analysis (GSEA) was used to assess whether mTORC1 signaling pathway–related genes were specifically enriched in the cancer tissues of patients with HNSCC.

TABLE 1 | Clinical data of patients in the TCGA and the GEO validation cohort.

Construction and Verification of the mTORC1 Signaling Pathway–Related Gene Signature

First, the expression matrix of all the genes involved in the mTORC1 pathway was extracted from TCGA dataset. Subsequently, a univariate Cox regression analysis was performed to screen for potential genes that were significantly associated with the prognosis of patients with HNSCC (p < 0.01). Subsequently, a multivariate Cox regression analysis was conducted to further screen for the key genes that could be used in the construction of the model. Based on the model gene coefficients and expressions calculated from the results of the multivariate regression analysis, an equation was constructed to calculate the risk score for each individual patient using the following formula:

where Coef i is the coefficient of gene i and Expr i represents the expression value of gene i. Based on this signature, a risk score was calculated for each patient in TCGA cohort and from this score, each patient was categorized into a high-risk or low-risk group, with the threshold being the median risk score. To further explore the prognostic value of the signature, the Kaplan–Meier (K–M) survival analysis and the log-rank test were utilized to investigate differences in survival between the high- and low-risk patients using the “survival” package in R statistical analysis software. Using the “timeROC” package in R, time-dependent receiver operating characteristic (ROC) curves were generated to investigate the reliability of the gene signature for predicting the prognosis. To further evaluate the robustness of the results, the risk model was validated using data from the GEO cohort.

Analysis of Differences and Mutations in Genes Related to Prognosis

The differences in the expression of 25 genes related to prognosis in normal and tumor tissues were statistically analyzed using the Wilcoxon test. Copy number variation (CNV) is the most frequent type of genetic variation occurring in cancers and is associated with the occurrence and progression of tumors. Thus, we computed the frequencies of copy number gains or losses using the Genomic Identification of Significant Targets in Cancer (GISTIC) algorithm (Mermel et al., 2011). The distributions of CNVs in 25 chromosomes were plotted using the “RCircos” package in R software. A waterfall plot of the mutational landscape was generated using the “maftools” package.

Independent Prognostic Analysis, Nomogram Construction, and Performance Assessment

Univariate and multivariate Cox regression analyses were performed on the data related to the signature and other clinical characteristics, including age, sex, and the American Joint Committee on Cancer (AJCC) stage in the training and test groups to clarify whether the risk score independently predicted clinical outcomes in patients with HNSCC. Parameters including the hazard ratio (HR), 95% confidence intervals (CIs), and p values were calculated using the “survival” package and visualized with the help of the “forestplot” package in R. The variables in the multivariate Cox regression analysis that independently predicted patient prognosis were used to construct the nomogram. Furthermore, the calibration plot was used to evaluate the calibration of the nomogram, and a decision curve analysis (DCA) was conducted to assess the clinical practicability of the nomogram by determining the net benefits at various threshold probabilities.

Exploration of Tumor-Infiltrating Immune Cells

An immune infiltration analysis was subsequently conducted to evaluate the association between immune cell characteristics and the risk score. We compared the infiltration status of immunocytes between the high- and low-risk groups by using presently acknowledged algorithms, including TIMER (Li et al., 2020), CIBERSORT (Chen et al., 2018), CIBERSORT-ABS (Tamminga et al., 2020), QUANTISEQ (Plattner et al., 2020), MCPCOUNTER (Dienstmann et al., 2019), XCELL (Aran, 2020), and EPIC (Racle et al., 2017). The differences in tumor-infiltrating immune cells between the risk groups identified by these methods were analyzed using the Wilcoxon test, and the results were visualized with a heatmap. The R “ggplot2” package was used to complete the process.

Prediction of Treatment Responses to Chemotherapy and Immunotherapy

This study compared the sensitivity of patients in the low- and high-risk groups to five chemotherapeutic agents commonly used in clinical practice (cisplatin, docetaxel, lapatinib, methotrexate, and paclitaxel). Immune checkpoint inhibitor drugs have revolutionized the treatment of multiple cancers, and the TIDE algorithm (http://tide.dfci.harvard.edu/) and subclass mapping (SubMap; https://cloud.genepattern.org/gp/) were used to predict the treatment responses to anti–programmed cell death 1 (PD1) and anti-cytotoxic T lymphocyte–associated protein 4 (CTLA4) antibodies (Wang et al., 2020a).

Collection of Tissue Specimens

An experienced pathologist acquired fresh HNSCC tissues and adjacent normal tissues from patients treated at the First Affiliated Hospital of Anhui Medical University, none of whom received anticancer treatments before surgery. The collected tissue samples were stored at −80°C until use. All patients signed informed consent forms, and the study was approved by the Ethics Committee of the First Affiliated Hospital of Anhui Medical University.

qRT-PCR and IHC

The TRIzol method was used to extract RNA from the tissue samples. The isolated RNA was used as a template for reverse transcription reactions using a RevertAid First-Strand cDNA Synthesis Kit (Thermo Fisher Scientific). A qRT-PCR analysis was performed using SYBR® Premix Ex TaqTM Ⅱ (TaKaRa) and a Real-Time System (Roche Life Science). The primer sequences used are shown in Supplementary Table S1. Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) was used as an internal reference. IHC was performed on formalin-fixed, paraffin-embedded specimens to quantify the levels of proteins encoded by the signature genes in cancerous and para-cancerous tissues. The HNSCC tissue sections were first deparaffinized in xylene and then dehydrated in ethanol. Antigen retrieval was performed using a citrate buffer (pH 6), followed by blocking with bovine serum albumin (BSA) for 1 h to prevent nonspecific binding of antibodies. The samples were then incubated in solutions containing specific primary antibodies (SEC11A, 1:150, Thermo Fisher Scientific; CYB5B, 1:100, Proteintech) followed by incubation in solutions containing secondary antibodies. Finally, the sections were visualized after staining with 3,3′-diaminobenzidine (DAB).

Statistical Analysis

Log2 conversion was performed on the expression values of the genes used for the differential expression analysis in TCGA dataset. The “survminer” and “survival” packages in R software were used to depict the survival curves of the model genes in TCGA cohort, and the log-rank test was used to test for statistical differences. P < 0.05 was considered statistically significant, unless stated otherwise in the text. All analyses and the generation of graphical representations were performed using R version 3.6.3 and the corresponding packages described earlier.

RESULTS

The mTORC1 Gene Set Significantly Differs Between HNSCC and Normal Tissues

The main workflow of our study is shown in Figure 1. We retrieved the HALLMARK_MTORC1_SIGNALING gene set from the Molecular Signature Database. The results of the GSEA showed that the gene set was significantly enriched in HNSCC tumor samples (normalized enrichment score (NES) = 2.01, false discovery rate (FDR) = 0.009; Supplementary Figure S1). The expression matrix of the 187 genes common to this gene set was then extracted from TCGA and GSE41613 cohort data for the subsequent analyses.

FIGURE 1 | Flow chart of the analyses performed in this study. (A) Transcriptome data and corresponding clinical information from TCGA and GEO databases were downloaded. (B) Univariate and multivariate Cox regression analyses were performed to select prognostic genes and build an optimal prognostic signature. (C) K–M survival curves and the log-rank test were used to analyze the differences in survival times between the low- and high-risk patients. An ROC curve was plotted to investigate the prediction accuracy of the risk model. The nomogram prediction model was constructed based on the risk score and independent clinical factors, including age and AJCC stage. (D) We compared the infiltration status of immunocytes between the low- and high-risk groups using the presently acknowledged algorithms. Chemotherapeutic and immunotherapeutic sensitivities of patients with HNSCC were calculated and compared between the high- and low-risk groups. Abbreviations: AJCC, American Joint Committee on Cancer; GEO, Gene Expression Omnibus; HNSCC, head and neck squamous cell carcinoma; K–M, Kaplan–Meier; ROC, receiver operating characteristic; TCGA, The Cancer Genome Atlas.

Identification of the Seven Gene Signature

The univariate Cox regression analysis in TCGA cohort showed that a total of 25 genes had a statistically significant correlation with the OS (p < 0.01, Figure 2A). These 25 genes were then included in the subsequent multivariate regression analysis, which resulted in the identification of seven genes that would be used to construct the model (Figure 2B). A risk score formula based on the regression coefficients and expression levels of the seven genes was used to determine the risk score for each patient (Figure 2C). The formula was as follows: risk score = (0.440) × expression level of SEC11A + (0.280) × expression level of CYB5B + (0.273) × expression level of HPRT1 + (0.232) × expression level of SLC2A3 + (0.214) × expression level of SC5D + (−0.124) × expression level of CORO1A+ (−0.231) × expression level of PIK3R3. According to this formula, the total risk score of all the patients was calculated, and then based on the median value, 499 patients were divided into two subgroups. For the training set, the distribution of the risk scores, each patient’s survival status, and the expression patterns of these seven risk genes in the two risk groups are presented in Figure 3A. The K–M survival analysis revealed that the OS was worse in the high-risk group than in the low-risk group (p < 0.001, Figure 3B), indicating that the risk score played an important role in prognostic prediction for patients with HNSCC. In the training group, the area under the curve (AUC) at 3 years was 0.728, and the 5‐year AUC was 0.706, which indicated a powerful prediction accuracy (Figure 3C).

FIGURE 2 | Construction of the prognostic signature of the mTOR signaling pathway–related genes in TCGA cohort. (A) Forest plot of the univariate Cox regression analysis of 25 prognosis-related genes. (B) Seven genes identified by the multivariate Cox regression analysis were used to construct a prognostic signature. (C) Coefficients of the seven genes included in the signature. Abbreviations: mTOR, mammalian target of rapamycin; TCGA, The Cancer Genome Atlas.

FIGURE 3 | Prognostic signature analysis of patients with HNSCC in TCGA and GEO GSE41613 cohorts. (A,D) Distributions of the risk score, survival status, and the expression heatmap of the seven identified genes in the high- and low-risk groups in TCGA (A) and GEO GSE41613 (D) cohorts. (B,E) K–M curves for the two subgroups in TCGA (B) and GEO GSE41613 (E) cohorts. (C,F) ROC curve analysis for predicting the survival according to the risk score in TCGA (C) and GEO GSE41613 (F) cohorts. Abbreviations: GEO GSE41613, Gene Expression Omnibus GSE41613 cohort; HNSCC, head and neck squamous cell carcinoma; TCGA, The Cancer Genome Atlas.

Validation of the Gene Signature Based on Data From the GSE41613 Cohort

To evaluate the reliability and robustness of the gene signature, we conducted validation tests using data from the GSE41613 cohort (test dataset). The risk score for the population in the validation cohort was calculated using the formula constructed using TCGA dataset. The patients with HNSCC in the GSE41613 cohort were categorized into different high- and low-risk groups based on the median value. Ultimately, 48 patients were allocated to the high-risk group and 49 were assigned to the low-risk group. Figure 3D shows the distribution of the risk scores, each patient’s survival status, and the expression patterns of the seven risk genes in the 97 patients. The K–M survival plots revealed that the high-risk group exhibited a poorer survival rate (p = 0.005, Figure 3E). The AUC at 3 years was 0.733, and the 5-year AUC was 0.734 (Figure 3F). These results demonstrate the robustness and stability of our gene signature.

Landscape of Genetic Variance of the 25 Prognosis-Related Genes in HNSCC

We first investigated the expression levels of the identified genes in HNSCC and normal tissues among patients with HNSCC in TCGA database. Overall, 25 genes were either up- or downregulated in those with HNSCC. More specifically, in patients with HNSCC, the expression levels of CCT6A, CORO1A, CYB5B, GAPDH, HPRT1, HSPA5, MTHFD2, P4HA1, PGK1, PLOD2, PSMB5, PSMD14, RDH11, SEC11A, SLC2A3, SQLE, STC1, STIP1, TRIB3, and UFM1 were higher in tumors than in normal tissues, whereas ELOVL6 and ME1 were more highly expressed in normal tissues than in tumors (Figure 4A, p < 0.01). We then analyzed the incidence of CNV and somatic mutations of these genes in patients with HNSCC, which revealed frequent CNV alterations in these genes, with a higher proportion of copy number gains than losses. More than half of the 25 prognosis-related genes exhibited significant copy number gains, along with widespread CNV deletion frequencies of PSMB5, SC5D, UFM1, and STC1 (Figure 4B). Figure 4C shows the locations of the CNV alterations of these genes on chromosomes. Gene mutations were found in 47 (9.29%) of the 506 samples. Indeed, we found that PLOD2 was the most frequently mutated gene in patients with HNSCC, followed by ASNS, CCT6A, P4HA1, STIP1, HSPA5, MTHFD2, and STC1; the other 17 genes showed no mutations or mutations in less than 1% of samples. The missense mutation ranked as the top variant classification, and C > T was the most common single-nucleotide variant (SNV) class (Figure 4D).

FIGURE 4 | Landscape of genetic and expression variations of 25 prognostic-related genes identified through the univariate Cox regression analysis. (A) Expression levels of the 25 genes in HNSCC (red) and normal (dew grass color) tissues. (B) CNV frequency of the 25 genes in TCGA cohort. The height of the column represents the alteration frequency. CNV gain, red; CNV loss, dew grass color. (C) Locations of the CNV alterations of the 25 genes on 23 chromosomes in TCGA cohort. (D) Mutation frequency and classification of the 25 genes in HNSCC (***p < 0.001 and **p < 0.01). Abbreviations: CNV, copy number variation; HNSCC, head and neck squamous cell carcinoma; TCGA, The Cancer Genome Atlas.

Prognostic Value of the Seven Genes Used to Construct the Signature

The results of the K–M survival analysis demonstrated that all identified genes had a major impact on the clinical outcome of patients with HNSCC in TCGA database (SEC11A: p < 0.001; SC5D: p < 0.001; CYB5B: p = 0.005; HPRT1: p = 0.006; SLC2A3: p = 0.007; CORO1A: p = 0.003; PIK3R3: p = 0.005). The K–M curves showed that patients with HNSCC who had high expression levels of SEC11A, SC5D, CYB5B, HPRT1, and SLC2A3 had lower survival rates, whereas those with HNSCC who had low expression levels of CORO1A and PIK3R3 had lower survival rates (Figures 5A–G).

FIGURE 5 | Kaplan–Meier survival analysis of the seven genes in TCGA cohort. (A) SEC11A, (B) SC5D, (C) CYB5B, (D) HPRT1, (E) SLC2A3, (F) CORO1A, and (G) PIK3R3.

Independent Prognostic Analysis and the Construction and Validation of the Nomogram

The results of the univariate Cox regression analysis in the training group revealed that age (HR = 1.022, 95% CI: 1.008–1.036), AJCC stage (HR = 1.454, 95% CI: 1.208–1.750), and risk score (HR = 1.436, 95% CI: 1.300–1.585) were related to significantly lower OS (Figure 6A). Similarly, the multivariate Cox regression analysis also revealed that age (HR = 1.021, 95% CI: 1.006–1.036), AJCC stage (HR = 1.442, 95% CI: 1.190–1.747), and risk score (HR = 1.378, 95% CI: 1.237–1.535) were associated with significantly worse OS (Figure 6B). For the test group, the results revealed that the AJCC stage (univariate: HR = 3.829, 95% CI: 1.959–7.485; multivariate: HR = 3.502, 95% CI: 1.774–6.912) and risk score (univariate: HR = 1.668, 95% CI: 1.163–2.393; multivariate: HR = 1.508, 95% CI: 1.039–2.190) were associated with worse OS among patients with HNSCC (Figures 6C, D). Collectively, these data revealed that the gene signature was an independent prognostic factor for HNSCC after adjustment for confounding factors (p < 0.05).

FIGURE 6 | Independent prognostic factors for the OS in patients with HNSCC. Univariate and multivariate Cox regression analyses were performed using TCGA dataset (A,B) and GEO dataset (C,D). Abbreviations: GEO, Gene Expression Omnibus; HNSCC, head and neck squamous cell carcinoma; OS, overall survival; TCGA, The Cancer Genome Atlas.

Subsequently, a nomogram based on the significant clinical parameters (age and AJCC stage) and risk score was constructed to forecast survival probability in patients with HNSCC in TCGA cohort. The overall score comprises the points for each included variable and can be used to forecast survival probability at 3 and 5 years (Figure 7A). The calibration plots showed that the nomogram performed well in comparison with an ideal model, indicating that the compound nomogram has excellent reliability and veracity (Figure 7B). The clinical practicability of the model was assessed through DCA. Compared with the performance of three other models based on a single factor (AJCC stage only or age only or signature only), the hybrid nomogram based on age, AJCC stage, and the multigene signature can achieve higher net benefits (Figure 7C).

FIGURE 7 | Construction of the predictive nomogram. (A) Nomogram predicting the OS probability for patients with HNSCC. (B) Calibration curves of the nomogram for predicting the OS at 3 and 5 years. (C) DCA analysis showing the performance of the nomogram. Abbreviations: HNSCC, head and neck squamous cell carcinoma; OS, overall survival.

Differences in Tumor Immune Infiltrating Cells Between the Two Risk Groups

As shown in Supplementary Figure S2, the heatmap demonstrates the levels of immune cell infiltration in the two risk groups based on seven algorithms. The infiltration levels of immune cells such as resting mast cells, resting natural killer (NK) cells, and M0 macrophages were all lower in the low‐risk group, whereas the levels of B cells and CD8⁺ T cells were lower in the high-risk group. Our findings suggest that the immune microenvironment differed between patients in the two risk groups, which may have implications for individualized treatment.

Prediction of Patient Response to Chemotherapy and Immunotherapy in the Two Risk Groups

This study evaluated and compared the response to treatment with cisplatin, docetaxel, lapatinib, methotrexate, and paclitaxel between the high- and low-risk groups. The IC₅₀ of each patient was estimated, and the results showed statistically significant differences between the low- and high-risk groups for all five chemotherapeutic agents. As shown in the box plots, patients with a high risk score had lower IC₅₀ values than those with a low risk score, which indicated that the high-risk group responded better to treatment with cisplatin, docetaxel, and lapatinib (Figures 8A–C), whereas the low-risk group was more sensitive to treatment with methotrexate and paclitaxel (Figures 8D, E). Recent studies have reported that treatment with anti–PD-1 and anti–CTLA-4 antibodies is an effective therapeutic option for patients with HNSCC (Italiano et al., 2021; Mao et al., 2021; Qin et al., 2021). The challenge of immunotherapy is that the response to a particular immunotherapeutic treatment varies among patients and can be hard to predict (Konstorum et al., 2017). Therefore, we assessed the therapeutic response of patients with HNSCC to PD-1 and CTLA-4 inhibitors. The results suggested that compared with those in the high-risk group, the low-risk group showed more promising responses to anti–PD-1 therapy (nominal p < 0.01, Bonferroni corrected p = 0.01, Figure 8F).

FIGURE 8 | Sensitivities of patients in the high- and low-risk groups to chemotherapy and immunotherapy. The differential chemotherapeutical sensitivities to cisplatin (A), docetaxel (B), lapatinib (C), methotrexate (D), and paclitaxel (E) in the two risk groups. (F) Sensitivity to PD1 and CTLA4 inhibitors of patients in the low- and high-risk groups. Abbreviations: CTLA4, cytotoxic T lymphocyte-associated protein 4; PD1, programmed cell death 1.

Expression of SEC11A and CYB5B Is Upregulated in HNSCC

For the purpose of detecting the difference in the mRNA expression of seven signature genes, we conducted a qPCR to detect 12 pairs of cancer and normal tissues. The mRNA expression levels of SEC11A and CYB5B in tumors were significantly higher than those in normal tissues. However, no obvious difference was observed for HPRT1, SLC2A3, SC5D, CORO1A, and PIK3R3 mRNA expression levels between HNSCC tissues and adjacent normal tissues (Supplementary Figure S3). We performed further IHC experiments to quantify the levels of the proteins encoded by the two genes for which the qRT-PCR results were significant. The protein levels of SEC11A and CYB5B were significantly increased in cancer tissues (Figure 9).

FIGURE 9 | SEC11A and CYB5B expression is upregulated in HNSCC. (A) qRT-PCR analysis of SEC11A mRNA levels in tumor and adjacent normal tissues. (B) qRT-PCR analysis of CYB5B mRNA levels in tumor and adjacent normal tissues. (C) IHC analysis of SEC11A protein levels in tumor and adjacent normal tissues. (D) IHC analysis of CYB5B protein levels in tumor and adjacent normal tissues. ***, p < 0.001. Abbreviations: CYB5B, cytochrome B5 type B; HNSCC, head and neck squamous cell carcinoma; IHC, immunohistochemistry; mRNA, messenger ribonucleic acid; qRT-PCR, quantitative real-time polymerase chain reaction; SEC11A, signal peptidase complex catalytic subunit.

DISCUSSION

Genetic factors play a vital role in the tumorigenesis and progression of HNSCC. Signatures based on certain features such as glycolysis (Chen et al., 2021) and immunity (Ma et al., 2020) have been discovered and may be useful for predicting the prognosis in patients with cancer. Compared with the mTORC1, little is known about the mechanisms regulating the mTORC2 and its biological function in the cell. In the present study, we constructed a novel gene signature capable of predicting the prognosis of patients with HNSCC based on specific genes related to the mTORC1 signaling pathway. A previous study showed that the phosphoinositide 3-kinase/Akt/mTOR pathway is frequently overactivated in more than 80% of individuals with HNSCC (Iglesias-Bartolome et al., 2013). Thus, the search for prognostic genes associated with the mTORC1 signaling pathway is important for guiding further research into therapeutic targets for patients with HNSCC.

In our study, a univariate Cox regression analysis was first used to identify 25 mTORC1 signaling pathway–related genes that could have important prognostic value in HNSCC. Subsequently, based on the multivariate Cox regression analysis, we identified seven key genes for constructing the signature. A worse prognosis was observed in patients with HNSCC who exhibited high levels of expression of SEC11A, SC5D, CYB5B, HPRT1, and SLC2A3 and low levels of expression of CORO1A and PIK3R3. Furthermore, we conducted qRT-PCR and IHC analyses and concluded that the mRNA and protein expression levels of SEC11A and CYB5B were higher in HNSCC tissues than in adjacent normal tissues. Among the genes included in the signature, five were found to be closely associated with HNSCC in previous studies. For example, Yao et al. (2021) found that SPC18 encoded by SEC11A is upregulated in tongue squamous cell carcinoma, and miR-873-5p may promote tumor cell apoptosis by targeting SEC11A. A recent study suggests that the overexpression of HPRT1 is correlated with the poor clinical outcome of HNSCC and can be used as a meaningful prognostic marker and therapeutic target (Ahmadi et al., 2021). Moreover, HPRT may promote the epithelial–mesenchymal transition (EMT) process and cell proliferation through direct interaction with STAT3 in HNSCC (Wang et al., 2021). Similarly, SLC2A3, which is related to glycolysis, can be used as a marker for the invasion of laryngeal squamous cell carcinoma (Starska et al., 2015). Song et al. (2019) identified 16 hub genes involved in the tumorigenesis of HNSCC by using the weighted gene coexpression network analysis, including CORO1A, and validated these genes at the transcription and translation levels. Moreover, a study showed that miR-367 can suppress the proliferation and invasion of oral squamous cell carcinoma by regulating PIK3R3 (Sun and Feng, 2020). However, the relationship between CYB5B and SC5D and HNSCC had not been clarified before, and further studies are expected to verify their prognostic value.

It is well known that most malignancies are heterogenous, and HNSCC is no exception. Significant investment to fund the study of tumor heterogeneity would undoubtedly be one of the most effective ways in improving patient survival. Prognostic models based on gene expression levels can, to some extent, compensate for the shortcomings of AJCC staging in predicting patient prognosis. The signature developed in this study exhibited strong performance in predicting the prognosis of patients with HNSCC, with the DCA results showing that the net benefit of the risk score is greater than that of the AJCC stage, and the predictive performance of the nomogram is greatly enhanced when both were used in combination with age.

Resection, radiotherapy, and systemic therapy have been established as effective treatment modalities for HNSCC (Johnson et al., 2020). Neoadjuvant chemotherapy, also known as primary chemotherapy, is widely used to reduce tumor bulk and convert unresectable tumors into resectable ones. Single drugs that are efficacious in the treatment of HNSCC include docetaxel, methotrexate, cisplatin, 5-fluorouracil (5-FU), and paclitaxel (Specenier and Vermorken, 2018). In addition, a recent phase II clinical trial demonstrated that a novel induction regimen that included the administration of lapatinib before transoral surgery was feasible, and positive results were achieved in patients with HNSCC (Hackman et al., 2020). In this study, we assessed the chemotherapeutic sensitivity to five drugs in patients with HNSCC and compared it between the two risk groups by using IC₅₀ values as an evaluation index. The patients in the high-risk group exhibited higher chemotherapeutic sensitivity to cisplatin, docetaxel, and lapatinib, whereas patients in the low-risk group showed higher sensitivity to methotrexate and paclitaxel.

The activity of mTORC1 is critical for maintaining immune cell function, and the inhibition of mTORC1 can significantly alter or completely ablate the immune response (Abraham and Wiederrecht, 1996). Wang et al. (2020b) found that mTORC1 was a promising target for tumor blood vessel normalization, which could reinforce antitumor immunity. Tumor immune cell infiltration reflects the immune microenvironment around the tumor tissues and can impact the response to immunotherapy (Liu et al., 2020). In squamous cell carcinomas, low immune infiltration was found to be correlated with worse prognosis of patients (Balermpas et al., 2014). Moreover, immune checkpoint (PD-1 and CTLA-4) blockade is gaining recognition as a paradigm for effective cancer treatment (Ribas and Wolchok, 2018). In this study, we compared the infiltration status of immunocytes between the two risk groups and assessed the therapeutic response to PD-1 and CTLA-4 inhibitors. Significant differences in the abundance of immune infiltrating cells such as quiescent mast cells, quiescent NK cells, M0 macrophages, B cells, and CD8⁺ T cells were observed between the high- and low-risk groups. This finding explains the difference in the prognosis of patients with HNSCC in terms of the changes in the immune microenvironment of tumors. Finally, the comparison of the ability to predict immunotherapy responses between both groups revealed a greater benefit for patients in the lower-risk group when receiving anti–PD-1 therapy, which could be helpful in guiding clinical treatment.

The most important contribution of this study is the development of a novel and potentially applicable tool that is not only capable of predicting the prognosis of patients with HNSCC but can also help identify individuals who would benefit most from chemotherapy and immunotherapy. However, there are still some limitations to this study. First, it involved a bioinformatics analysis of database information, and the findings need to be validated using data from multiple centers. Second, the potential mechanisms through which the seven genes in the prognostic signature are involved in the occurrence or development of HNSCC are not entirely understood, and further investigation would require a series of experiments, both in vivo and in vitro.

CONCLUSION

We have developed a prognostic gene signature based on the mTORC1 signaling pathway that can be used as a helpful tool in predicting the prognosis of patients with HNSCC and their sensitivity to chemotherapy and immunotherapy.

DATA AVAILABILITY STATEMENT

Publicly available datasets were analyzed in this study. The raw data of this study are derived from the TCGA database (https://portal.gdc.cancer.gov/) and GEO data portal (https://www.ncbi.nlm.nih.gov/geo/), which are publicly available databases.

ETHICS STATEMENT

The studies involving human participants were reviewed and approved by the Ethics Committee of the First Affiliated Hospital of Anhui Medical University. The patients provided their written informed consent to participate in this study.

AUTHOR CONTRIBUTIONS

ZD and HS conceived this research and drafted the manuscript. KX performed the experiments. KX, YW, SW, FY and DW analyzed and/or interpreted the data. YL provided study supervision and revised the manuscript. All authors contributed to this manuscript and approved the final version.

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

ACKNOWLEDGMENTS

We sincerely acknowledge the publicly available TCGA and GEO databases.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2022.792482/full#supplementary-material

REFERENCES

Abraham, R. T., and Wiederrecht, G. J. (1996). Immunopharmacology of Rapamycin. Annu. Rev. Immunol. 14, 483–510. doi:10.1146/annurev.immunol.14.1.483

Ahmadi, M., Eftekhari Kenzerki, M., Akrami, S. M., Pashangzadeh, S., Hajiesmaeili, F., Rahnavard, S., et al. (2021). Overexpression of HPRT1 Is Associated with Poor Prognosis in Head and Neck Squamous Cell Carcinoma. FEBS Open Bio 11, 2525–2540. doi:10.1002/2211-5463.13250

Aran, D. (2020). Cell-Type Enrichment Analysis of Bulk Transcriptomes Using xCell. Methods Mol. Biol. 2120, 263–276. doi:10.1007/978-1-0716-0327-7_19

Balermpas, P., Michel, Y., Wagenblast, J., Seitz, O., Weiss, C., Rödel, F., et al. (2014). Tumour-infiltrating Lymphocytes Predict Response to Definitive Chemoradiotherapy in Head and Neck Cancer. Br. J. Cancer 110, 501–509. doi:10.1038/bjc.2013.640

Bazzichetto, C., Conciatori, F., Falcone, I., and Ciuffreda, L. (2020). Translational Landscape of mTOR Signaling in Integrating Cues between Cancer and Tumor Microenvironment. Adv. Exp. Med. Biol. 1223, 69–80. doi:10.1007/978-3-030-35582-1_4

Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., and Jemal, A. (2018). Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer J. Clinicians 68, 394–424. doi:10.3322/caac.21492

Califano, J., van der Riet, P., Westra, W., Nawroz, H., Clayman, G., Piantadosi, S., et al. (1996). Genetic Progression Model for Head and Neck Cancer: Implications for Field Cancerization. Cancer Res. 56, 2488–2492.

Caro-Vegas, C., Bailey, A., Bigi, R., Damania, B., and Dittmer, D. P. (2019). Targeting mTOR with MLN0128 Overcomes Rapamycin and Chemoresistant Primary Effusion Lymphoma. mBio 10. doi:10.1128/mBio.02871-18

Chen, B., Khodadoust, M. S., Liu, C. L., Newman, A. M., and Alizadeh, A. A. (2018). Profiling Tumor Infiltrating Immune Cells with CIBERSORT. Methods Mol. Biol. 1711, 243–259. doi:10.1007/978-1-4939-7493-1_12

Chen, L., He, X., Yi, S., Liu, G., Liu, Y., and Ling, Y. (2021). Six Glycolysis-Related Genes as Prognostic Risk Markers Can Predict the Prognosis of Patients with Head and Neck Squamous Cell Carcinoma. Biomed. Res. Int. 2021, 8824195. doi:10.1155/2021/8824195

Dienstmann, R., Villacampa, G., Sveen, A., Mason, M. J., Niedzwiecki, D., Nesbakken, A., et al. (2019). Relative Contribution of Clinicopathological Variables, Genomic Markers, Transcriptomic Subtyping and Microenvironment Features for Outcome Prediction in Stage II/III Colorectal Cancer. Ann. Oncol. 30, 1622–1629. doi:10.1093/annonc/mdz287

Ferris, R. L. (2015). Immunology and Immunotherapy of Head and Neck Cancer. Jco 33, 3293–3304. doi:10.1200/jco.2015.61.1509

Hackman, T. G., Patel, S. N., Deal, A. M., Neil Hayes, D., Chera, B. S., Paul, J., et al. (2020). Novel Induction Therapy Transoral Surgery Treatment Paradigm with Risk-Adapted Adjuvant Therapy for Squamous Cell Carcinoma of the Head and Neck - Mature Clinical and Functional Outcomes. Oral Oncol. 110, 104957. doi:10.1016/j.oraloncology.2020.104957

He, F., Chen, Z., Deng, W., Zhan, T., Huang, X., Zheng, Y., et al. (2021). Development and Validation of a Novel Ferroptosis-Related Gene Signature for Predicting Prognosis and Immune Microenvironment in Head and Neck Squamous Cell Carcinoma. Int. immunopharmacology 98, 107789. doi:10.1016/j.intimp.2021.107789

Iglesias-Bartolome, R., Martin, D., and Gutkind, J. S. (2013). Exploiting the Head and Neck Cancer Oncogenome: Widespread PI3K-mTOR Pathway Alterations and Novel Molecular Targets. Cancer Discov. 3, 722–725. doi:10.1158/2159-8290.cd-13-0239

Italiano, A., Cassier, P. A., Lin, C. C., Alanko, T., Peltola, K. J., Gazzah, A., et al. (2021). First-in-human Phase 1 Study of Budigalimab, an Anti-PD-1 Inhibitor, in Patients with Non-small Cell Lung Cancer and Head and Neck Squamous Cell Carcinoma. Cancer Immunol Immunother 71, 417-431. doi:10.1007/s00262-021-02973-w

Jemal, A., Bray, F., Center, M. M., Ferlay, J., Ward, E., and Forman, D. (2011). Global Cancer Statistics. CA: A Cancer J. Clinicians 61, 69–90. doi:10.3322/caac.20107

Johnson, D. E., Burtness, B., Leemans, C. R., Lui, V. W. Y., Bauman, J. E., and Grandis, J. R. (2020). Head and Neck Squamous Cell Carcinoma. Nat. Rev. Dis. Primers 6, 92. doi:10.1038/s41572-020-00224-3

Kokko, L.-L., Hurme, S., Maula, S.-M., Alanen, K., Grénman, R., Kinnunen, I., et al. (2011). Significance of Site-specific Prognosis of Cancer Stem Cell Marker CD44 in Head and Neck Squamous-Cell Carcinoma. Oral Oncol. 47, 510–516. doi:10.1016/j.oraloncology.2011.03.026

Konstorum, A., Vella, A. T., Adler, A. J., and Laubenbacher, R. C. (2017). Addressing Current Challenges in Cancer Immunotherapy with Mathematical and Computational Modelling. J. R. Soc. Interf. 14. doi:10.1098/rsif.2017.0150

Li, T., Fu, J., Zeng, Z., Cohen, D., Li, J., Chen, Q., et al. (2020). TIMER2.0 for Analysis of Tumor-Infiltrating Immune Cells. Nucleic Acids Res. 48, W509–W514. doi:10.1093/nar/gkaa407

Liu, R., Hu, R., Zeng, Y., Zhang, W., and Zhou, H.-H. (2020). Tumour Immune Cell Infiltration and Survival after Platinum-Based Chemotherapy in High-Grade Serous Ovarian Cancer Subtypes: A Gene Expression-Based Computational Study. EBioMedicine 51, 102602. doi:10.1016/j.ebiom.2019.102602

Liu, Z., Zhang, D., Liu, C., Li, G., Chen, H., Ling, H., et al. (2021). Comprehensive Analysis of Myeloid Signature Genes in Head and Neck Squamous Cell Carcinoma to Predict the Prognosis and Immune Infiltration. Front. Immunol. 12, 659184. doi:10.3389/fimmu.2021.659184

Ma, B., Li, H., Qiao, J., Meng, T., and Yu, R. (2020). Immune-related miRNA Signature Identifies Prognosis and Immune Landscape in Head and Neck Squamous Cell Carcinomas. Biosci. Rep. 40. doi:10.1042/BSR20201820

Machiels, J.-P., René Leemans, C., Golusinski, W., Grau, C., Licitra, L., Gregoire, V., et al. (2020). Squamous Cell Carcinoma of the Oral Cavity, Larynx, Oropharynx and Hypopharynx: EHNS-ESMO-ESTRO Clinical Practice Guidelines for Diagnosis, Treatment and Follow-Up. Ann. Oncol. 31, 1462–1475. doi:10.1016/j.annonc.2020.07.011

Mao, L., Xiao, Y., Yang, Q.-C., Yang, S.-C., Yang, L.-L., and Sun, Z.-J. (2021). TIGIT/CD155 Blockade Enhances Anti-PD-L1 Therapy in Head and Neck Squamous Cell Carcinoma by Targeting Myeloid-Derived Suppressor Cells. Oral Oncol. 121, 105472. doi:10.1016/j.oraloncology.2021.105472

Meric-Bernstam, F., and Gonzalez-Angulo, A. M. (2009). Targeting the mTOR Signaling Network for Cancer Therapy. Jco 27, 2278–2287. doi:10.1200/jco.2008.20.0766

Mermel, C. H., Schumacher, S. E., Hill, B., Meyerson, M. L., Beroukhim, R., and Getz, G. (2011). GISTIC2.0 Facilitates Sensitive and Confident Localization of the Targets of Focal Somatic Copy-Number Alteration in Human Cancers. Genome Biol. 12, R41. doi:10.1186/gb-2011-12-4-r41

Mossmann, D., Park, S., and Hall, M. N. (2018). mTOR Signalling and Cellular Metabolism Are Mutual Determinants in Cancer. Nat. Rev. Cancer 18, 744–757. doi:10.1038/s41568-018-0074-8

Plattner, C., Finotello, F., and Rieder, D. (2020). Deconvoluting Tumor-Infiltrating Immune Cells from RNA-Seq Data Using quanTIseq. Methods Enzymol. 636, 261–285. doi:10.1016/bs.mie.2019.05.056

Polverini, P. J., D’Silva, N. J., and Lei, Y. L. (2018). Precision Therapy of Head and Neck Squamous Cell Carcinoma. J. Dent Res. 97, 614–621. doi:10.1177/0022034518769645

Qin, Y., Zheng, X., Gao, W., Wang, B., and Wu, Y. (2021). Tumor Microenvironment and Immune-Related Therapies of Head and Neck Squamous Cell Carcinoma. Mol. Ther. - Oncolytics 20, 342–351. doi:10.1016/j.omto.2021.01.011

Racle, J., de Jonge, K., Baumgaertner, P., Speiser, D. E., and Gfeller, D. (2017). Simultaneous Enumeration of Cancer and Immune Cell Types from Bulk Tumor Gene Expression Data. Elife 6. doi:10.7554/eLife.26476

Ribas, A., and Wolchok, J. D. (2018). Cancer Immunotherapy Using Checkpoint Blockade. Science 359, 1350–1355. doi:10.1126/science.aar4060

Saxton, R. A., and Sabatini, D. M. (2017). mTOR Signaling in Growth, Metabolism, and Disease. Cell 168, 960–976. doi:10.1016/j.cell.2017.02.004

Shao, Y., Wolf, P. G., Guo, S., Guo, Y., Gaskins, H. R., and Zhang, B. (2017). Zinc Enhances Intestinal Epithelial Barrier Function through the PI3K/AKT/mTOR Signaling Pathway in Caco-2 Cells. J. Nutr. Biochem. 43, 18–26. doi:10.1016/j.jnutbio.2017.01.013

Song, Y., Pan, Y., and Liu, J. (2019). The Relevance between the Immune Response-Related Gene Module and Clinical Traits in Head and Neck Squamous Cell Carcinoma. Cmar 11, 7455–7472. doi:10.2147/cmar.s201177

Specenier, P., and Vermorken, J. B. (2018). Optimizing Treatments for Recurrent or Metastatic Head and Neck Squamous Cell Carcinoma. Expert Rev. Anticancer Ther. 18, 901–915. doi:10.1080/14737140.2018.1493925

Starska, K., Forma, E., Jóźwiak, P., Bryś, M., Lewy-Trenda, I., Brzezińska-Błaszczyk, E., et al. (2015). Gene and Protein Expression of Glucose Transporter 1 and Glucose Transporter 3 in Human Laryngeal Cancer-The Relationship with Regulatory Hypoxia-Inducible Factor-1α Expression, Tumor Invasiveness, and Patient Prognosis. Tumor Biol. 36, 2309–2321. doi:10.1007/s13277-014-2838-4

Sun, H., and Feng, X. (2020). MicroRNA-367 Directly Targets PIK3R3 to Inhibit Proliferation and Invasion of Oral Carcinoma Cells. Biosci. Rep. 40. doi:10.1042/BSR20193867

Tamminga, M., Hiltermann, T. J. N., Schuuring, E., Timens, W., Fehrmann, R. S., and Groen, H. J. (2020). Immune Microenvironment Composition in Non-small Cell Lung Cancer and its Association with Survival. Clin. Transl Immunol. 9, e1142. doi:10.1002/cti2.1142

Tijink, B. M., Buter, J., de Bree, R., Giaccone, G., Lang, M. S., Staab, A., et al. (2006). A Phase I Dose Escalation Study with Anti-CD44v6 Bivatuzumab Mertansine in Patients with Incurable Squamous Cell Carcinoma of the Head and Neck or Esophagus. Clin. Cancer Res. 12, 6064–6072. doi:10.1158/1078-0432.ccr-06-0910

Wang, L., Wang, Y., Han, N., Wang, X., and Ruan, M. (2021). HPRT Promotes Proliferation and Metastasis in Head and Neck Squamous Cell Carcinoma through Direct Interaction with STAT3. Exp. Cel Res. 399, 112424. doi:10.1016/j.yexcr.2020.112424

Wang, S., Raybuck, A., Shiuan, E., Cho, S. H., Wang, Q., Brantley-Sieders, D. M., et al. (2020). Selective Inhibition of mTORC1 in Tumor Vessels Increases Antitumor Immunity. JCI Insight 5. doi:10.1172/jci.insight.139237

Wang, Z., Guo, X., Gao, L., Wang, Y., Ma, W., and Xing, B. (2020). Glioblastoma Cell Differentiation Trajectory Predicts the Immunotherapy Response and Overall Survival of Patients. Aging 12, 18297–18321. doi:10.18632/aging.103695

Yao, Y., Liu, X. Q., Yang, F. Y., and Mu, J. W. (2021). MiR-873-5p Modulates Progression of Tongue Squamous Cell Carcinoma via Targeting SEC11A. Oral Dis. 2021. doi:10.1111/odi.13830

Yu, S. S., and Cirillo, N. (2020). The Molecular Markers of Cancer Stem Cells in Head and Neck Tumors. J. Cel Physiol 235, 65–73. doi:10.1002/jcp.28963

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Ding, Shen, Xu, Wu, Wang, Yi, Wang and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 02 June 2022
doi: 10.3389/fmolb.2022.756772

The Prognostic Model and Drug Sensitivity of LKB1-Mutant Lung Adenocarcinoma Based on Immune Landscape

Guanghui Wang1,²†, Haotian Zheng¹†, Xiaogang Zhao³, Yadong Wang¹, Yukai Zeng¹ and Jiajun Du1,²*

¹Institute of Oncology, Shandong Provincial Hospital, Shandong University, Jinan, China

²Department of Thoracic Surgery, Shandong Provincial Hospital, Shandong University, Jinan, China

³Department of Thoracic Surgery, The Second Hospital of Shandong University, Jinan, China

Edited by:
Florence Le Calvez-Kelm, International Agency For Research On Cancer (IARC), France

Reviewed by:
Shuijun Zhang, First Affiliated Hospital of Zhengzhou University, China
Lifeng Li, First Affiliated Hospital of Zhengzhou University, China

* Correspondence: Jiajun Du, dujiajun@sdu.edu.cn

†These authors have contributed equally to this work

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 11 August 2021
Accepted: 21 April 2022
Published: 02 June 2022

Citation: Wang G, Zheng H, Zhao X, Wang Y, Zeng Y and Du J (2022) The Prognostic Model and Drug Sensitivity of LKB1-Mutant Lung Adenocarcinoma Based on Immune Landscape. Front. Mol. Biosci. 9:756772. doi: 10.3389/fmolb.2022.756772

Background: Lung cancer is the most common cause of cancer-related deaths worldwide. LKB1-mutant lung adenocarcinoma (LUAD) is a unique subtype of this deadly cancer. LKB1 mutations cause functional changes in a variety of cell processes, including immune functions, that affect prognosis. To date, the potential role of immunity in the prognosis of LKB1-mutant LUAD is not well understood.

Methods: We systematically analyzed immune-related genes in LUAD samples from The Cancer Genome Atlas (TCGA) database. ESTIMATE and CIBERSORT algorithms were used to explore the immune microenvironment. A prognostic risk model was constructed, and prognostic, immune function, drug sensitivity, and model specificity analyses were performed to identify the effectiveness of the model.

Results: Our results showed that LKB1 mutations suppressed immune function in LUAD. A three-gene signature was constructed to stratify patients into two risk groups. The risk score was an independent predictor for overall survival (OS) in multivariate Cox regression analyses [hazard ratio (HR) > 1, p = 0.002]. Receiver operating characteristic (ROC) curve analyses confirmed that the risk score has better performance than clinicopathological characteristics. Functional analysis revealed that the immune status was different between the risk groups. ZM.447439 was an appropriate treatment for the high-risk group of patients. This risk model is only suitable for LKB1-mutant tumors; it performed poorly in LUAD patients with wild-type LKB1.

Conclusion: Our findings indicate the potential role of immunity in LKB1-mutant LUAD, providing novel insights into prognosis and guiding effective immunotherapy.

Keywords: LKB1, lung adenocarcinoma, prognostic model, TCGA, Immune, drug sensitivity

INTRODUCTION

Lung cancer is the most common cause of cancer-related deaths worldwide, with more than 40% of cases being lung adenocarcinoma (LUAD) (Jordan et al., 2017). Genomic alterations have important impacts on tumor cell-intrinsic and non-cell-autonomous cancer hallmarks; accordingly, genomic alterations underlie the molecular and clinical heterogeneity of LUAD (Skoulidis and Heymach, 2019). The gene encoding liver kinase B1 (LKB1) is the third most commonly mutated gene in LUAD (after KRAS and TP53), with approximately 19% of LUAD cases involving LKB1 mutations (Ding et al., 2008; Cancer Genome Atlas Research, 2014). Moreover, LKB1 is involved in many cell processes essential for cell survival, such as metabolic balance, maintenance of DNA integrity, proliferation, and polarity establishment (Shackelford and Shaw, 2009). Notably, therapeutic approaches targeting oncogene driver mutations, for example, activating mutations in the epidermal growth factor receptor or rearrangements in anaplastic lymphoma kinase, have been recently shown to elicit dramatic clinical responses (Paez et al., 2004; Soda et al., 2007; Bergethon et al., 2012). However, there are no routinely used clinical drugs that specifically target LUAD with LKB1-inactivating mutations.

The tumor microenvironment (TME) refers to the local biological environment including cancer cells, stromal cells, and distant recruited cells, such as infiltrating immune cells (myeloid cells and lymphocytes), bone marrow-derived cells, and secreted factors such as cytokines and chemokines (Wang et al., 2017; Zhu et al., 2020). The status of the TME plays a bidirectional role in tumor progression and often impacts the effectiveness of targeted drugs (Topalian et al., 2012; Tamborero et al., 2018). While the complexity of TME components has proven to be a barrier to research, some new algorithms, including ESTIMATE and CIBERSORT, that are based on bulk RNA sequencing have dealt with this problem effectively (Yoshihara et al., 2013; Newman et al., 2015a). These algorithms can quantify the degree of stromal cell and immune cell infiltration in tumor tissues, and their accuracy has been verified in breast cancer and liver cancer models (Newman et al., 2015a).

Recent work has shown that oncogenic mutations shape the TME and determine its immune context (Akbay et al., 2013). Because oncogenes directly activate immune checkpoints to impact immune-evading mechanisms and cause immune suppression (Akbay et al., 2013), immunotherapies for immune checkpoint blockade and other aspects efficiently target tumors and show an association with the survival of cancer patients (Taube et al., 2014; Tumeh et al., 2014; Drerup et al., 2020). It is unclear whether the inactivation of tumor suppressor genes such as LKB1 exerts similar effects, even though recent studies have opened new perspectives in this field.

Thus, there is an urgent need to elucidate the underlying mechanisms influencing immune activity and the TME in LKB1-mutant LUAD and to explore potential prognostic biomarkers. Understanding these factors could provide better therapeutic strategies for LUAD. Furthermore, in addition to conventional biomarkers, the multi-faceted roles of long non-coding RNA (lncRNA) and microRNA (miRNA) in the progression of LUAD, including in determining immune and microenvironmental conditions (Shen et al., 2020; Sun et al., 2020), mean that these molecules may also serve as useful indicators of disease progression. Therefore, we have included all kinds of RNA in our research.

To this end, we aim to explore the impact of LKB1 mutations on the immune microenvironment of LUAD and the mechanisms that underlie this impact. In addition, we constructed a prognostic model based on immune-related RNAs to promote the development of therapies in LKB1-mutant LUAD and to provide novel prognostic biomarkers.

MATERIALS AND METHODS

Data Downloading and Preprocessing

LUAD gene expression data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov), and 14,086 lncRNAs, 19,605 mRNAs, and 2,191 miRNAs were obtained by data annotation. According to the mutation status of LKB1 obtained from the cBioPortal database (https://www.cbioportal.org), we separated TCGA tumor samples into LKB1-mutant (LKB1-mu) and LKB1-wild-type (LKB1-wt) groups. A total of 1,959 immune-related genes were retrieved from the ImmPort database (https://www.immport.org/resources), and two immune-related gene sets (immune system process M13664 and immune response M19817) were extracted from the Molecular Signatures Database v4.0 (http://www.broadinstitute.org/gsea/msigdb/index.jsp).

We identified 89 patients with LKB1 mutations by searching the 31 pan-cancer studies (excluding LUAD) in the cBioPortal database. The miRNA expression data of these patients were obtained from the TCGA database, and 74 non-LUAD LKB1-mutated patients were ultimately included as a validation cohort.

Effect of the LKB1-Mutation on the Immune Landscape of Lung Adenocarcinoma

The LKB1 gene mutation status of LUAD was analyzed by probing the cBioPortal database. The difference in the expression level of LKB1 between the LKB1-wt and LKB1-mu groups was verified by t-test. The Gene Set Enrichment Analysis (GSEA) was performed to explore the biological function regulated by the LKB1 mutant in LUAD. The stromal score, immune score, and ESTIMATE score were also analyzed by the ESTIMATE algorithm based on transcriptome expression profiles of LUAD to examine the effect of the LKB1 mutant (Yoshihara et al., 2013). The CIBERSORT algorithm was used to accurately determine the composition of 22 kinds of immune cells in a large tumor sample dataset, and the impact of the LKB1 mutant on these immune cells was determined (Newman et al., 2015b). The TIMER database (https://cistrome.shinyapps.io/timer/) was used to explore the correlation between LKB1 and immunity. The differential expression of 46 immune checkpoint genes between LKB1-mu and LKB1-wt cells was further explored.

Identifying Candidate Genes to Construct a Prognostic Risk Model

Expression levels of mRNAs, lncRNAs, and miRNAs in the LKB1-wt and LKB1-mu groups were analyzed by the edger package with the difference screening parameters |logFC| > 1 and false discovery rate (FDR) < 0.05. Utilizing a total of 1959 immune-related genes extracted from the databases, immune-related lncRNAs were identified according to the criteria of |correlation coefficient| > 0.8 and p < 0.001 by the Pearson correlation analysis (Schober et al., 2018). The Kaplan–Meier analysis and univariate Cox regression analysis were used to further identify differentially expressed RNA (DERNA) molecules with prognostic values in LKB1-mutant LUAD.

To reduce the risk of overfitting, the LASSO-Cox regression analysis was applied to construct a prognostic model (Tibshirani, 1997; Simon et al., 2011). The LASSO algorithm was used for variable selection, and the penalty parameter (λ) for the model was determined by a ten-fold cross validation following the minimum criteria. A multivariate regression analysis was then conducted on LKB1-mutant LUAD survival-related genes to obtain a risk gene signature, and the risk score based on the signature was formulated as follows: risk score = expX1 × coefX1 + expX2 × coefX2 +… + expXi × coefXi, where, coefXi represents the synergetic coefficient and expXi represents the relative expression of RNA. The median risk score of all samples was used as the critical value to form high-risk and low-risk groups.

Survival and Prognostic Analysis of the Prognostic Risk Model

The survival difference between the high-risk and low-risk groups was estimated by the K–M survival analysis. Time-dependent receiver operating characteristic curves were drawn, and the area under the curve (AUC) was calculated to estimate and compare the predictive accuracy for survival time by the risk score and different clinical-pathological factors. Univariate and multivariate Cox regression analyses were performed to find independent prognostic factors. The relationship between the clinicopathological characteristics and risk score was identified by a correlation analysis. A nomogram was then drawn to predict 1-, 3-, and 5-year mortality. This nomogram utilized both clinical information and risk scores because both factors have probable impacts on survival. The predictive performance of the nomogram was identified using calibration curves, K–M survival analyses, and ROC curves.

Because of the inaccessibility of other data relating to LKB1-mutated LUAD and the similarity of tumor characteristics in the same mutational background, we used patients with other cancers who had LKB1 mutations as a validation cohort. K–M survival analyses and ROC curves were used to validate the accuracy of the model.

Immune Function Analysis of the Prognostic Risk Model

The Pearson correlation analysis was performed to assess the relationship between the risk score and immune cell infiltration. For detailed information about immune functions, the single-sample Gene Set Enrichment Analysis (ssGSEA) was performed to calculate the infiltrating score of 16 immune cells and the activity of 13 immune-related pathways (Rooney et al., 2015). A Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) were performed to explore the model’s ability to distinguish LKB1-mutant LUAD patients, based on the expression of genes.

Drug Sensitivity Analysis of the Prognostic Risk Model

To assess the relationship between the chemotherapeutic response and the risk score, we performed a drug sensitivity analysis by using the pRRophetic package based on gene expression data (Geeleher et al., 2014a; Geeleher et al., 2014b). The pRRophetic algorithm has been used extensively in medical studies (Liu et al., 2021a; Liu et al., 2021b; Liu et al., 2022a; Liu et al., 2022b; Liu et al., 2022c). We further explored the expression of targets of immune checkpoint inhibitors and other drugs between high-risk and low-risk groups to search for the potential functions of the signatures in the responses to immunotherapy and chemotherapy.

Model Specificity Analysis of the Prognostic Risk Model

We used the constructed prognostic model for wild-type LKB1 LUAD. The same analysis used in LKB1-mutant LUAD was performed in wild-type LKB1 LUAD to examine if the prognostic risk model was only applicable to LKB1-mutant cancers.

All statistical and modeling analyses were performed using Perl, GSEA (GSEA_4.1.0), and R (version 4.0.2) software.

RESULTS

The flow chart of this study is shown in Figure 1.

FIGURE 1 | Flow chart of this study.

LKB1 Mutations Suppress the Immune Landscape of Lung Adenocarcinoma

In TCGA cohort, 19% of LUAD patients were found to possess an LKB1 mutation (Figure 2A). Missense mutations and truncating mutations were the main mutation types, and these mutations were mostly located in the kinase domain (Figures 2A,B). These mutations significantly affected the expression level and function of LKB1 in cells. These findings were consistent with our hypothesis that the expression level of LKB1 is low in LKB1-mutant LUAD (Figure 2C).

FIGURE 2 | From the cBioPortal database, (A) LKB1 genetic alterations (B) and mutation positions in LUAD patients of TCGA database. (C) Difference in the expression level of LKB1 between the LKB1-wt and LKB1-mu groups. (D) GSEA in LKB1-mutant LUAD. (E) ESTIMATE score, immune score, and stromal score and (F) infiltration abundance of six kinds of immune cells in the LKB1-wt and LKB1-mu groups.

Cell adhesion, natural killer cell-mediated cytotoxicity, toll-like receptor signaling pathways, and other immune-related pathways were suppressed in LKB1-mu LUAD as compared with LKB1-wt LUAD (Figure 2D). A lower stromal score, immune score, and ESTIMATE score (all p < 0.001) in the LKB1-mu group also suggested a lower degree of immune cell infiltration (Figure 2E, Supplementary Figure S1A). Because different immune cells play different roles in tumor progression, we further explored the differential degrees of infiltration of different types of immune cells. The TME of the LKB1-mu group possessed fewer B cells, CD8⁺ T cells, CD4⁺ T cells, macrophages, neutrophils, and dendritic cells (Figure 2F). Additional details regarding immune cell infiltration are given in Supplementary Figure S1B.

Of the 46 known immune checkpoint genes, 17 were found to be significantly differentially expressed between the two groups of LUAD. These differentially expressed genes included CD274, which codes for the programmed death ligand 1 (PD-L1), CD40, and CD44 (Supplementary Figure S1C). Notably, the high expression of PD-L1 LKB1-mutant LUAD is consistent with the results that demonstrate low infiltration of T cells. These results showed that the mutation of LKB1 was a common event in LUAD and that these mutations suppressed the immune landscape.

Prognostic Risk Model of a Three-Gene Signature

The differential analysis results uncovered a total of 1,869 DEmRNAs, 1,549 DElncRNAs, and 105 DEmiRNAs (Figure 3A). According to the correlation analysis, the differential expressions of 228 immune-related mRNAs were found to intersect with the differential expressions of 488 immune-related lncRNAs. The K–M analysis and univariate Cox regression analysis further identified a total of 26 mRNAs, 9 lncRNAs, and 8 miRNAs (p < 0.05, Figure 3B, Supplementary Tables S1, S2, and S3), for a total of 43 RNAs.

FIGURE 3 | (A) Differentially expressed mRNAs, lncRNAs, and miRNAs between the LKB1-wt and LKB1-mu groups. (B) Kaplan–Meier survival analysis of candidate signatures. (C) Result plots of the LASSO regression analysis.

The LASSO-Cox regression analysis was applied to establish a prognostic model using the expression profiles of these 43 differentially expressed genes. A signature including three genes (CHST4, GLP1R, and hsa-miR-582-5p) was identified based on the optimal value of λ (Figure 3C). The risk score was calculated as follows: risk score = 0.0113 × Ce—0.0817 × Ge + 0.0016 × He, where Ce is the expression level of CHST4, Ge is the expression level of GLP1R, and He is the expression level of hsa-miR-582-5p. The LKB1-mutant patients were stratified into a high-risk group (n = 37) and a low-risk group (n = 37) according to the median cut-off value of the risk score.

Survival and Prognostic Analysis Using the Risk Model

The K–M curve demonstrated that patients in the high-risk group had a significantly worse OS than patients in the low-risk group (Figure 4A, p < 0.001). The 1-, 3-, and 5-year AUC of the risk score for OS were 0.843, 0.916, and 0.8, respectively (Figure 4B). Moreover, the predictive performance of the risk score (AUC = 0.845) was better than the performance of age, gender, and stage (AUC = 0.613, 0.597, and 0.699, respectively) (Figure 4B). As shown in Figure 4C, patients in the high-risk group had a higher probability of death and shorter survival time.

FIGURE 4 | (A) Kaplan–Meier survival analysis in the high-risk and low-risk groups. (B) ROC curves of the risk score and clinical-pathological factors with OS. (C) Risk factor analysis and (D) independent prognostic analysis of risk scores in LKB1-mutant LUAD. (E) Heatmap and (F) proportion diagram of clinical correlation analyses.

Univariate and multivariate Cox regression analyses also indicated that the risk score could be used as an independent prognostic factor for LKB1-mutant LUAD (Figure 4D). The risk score was found to be significantly positively associated with the stage (Figures 4E,F). Figure 4F indicates that higher expression levels of CHST4 and hsa-miR-582-5p had higher stage patients. In addition, the expression level of hsa-miR-582-5p was found to be associated with T and N (p < 0.05, Supplementary Figure S1D).

To help clinicians better predict the prognosis of LUAD patients with mutated LKB1, a nomogram was constructed using the risk score plus stage (Figure 5A). The prognostic capability of the nomogram was further evaluated by ROC curves, K–M survival analyses, and calibration plots. The degree of fit of the calibration curves corresponding to 1-, 3-, and 5-year survival shows good consistency (Figure 5B). The AUC values of ROC curves corresponding to 1-, 3-, and 5-year survival in LKB1-mutant LUAD were 0.826, 0.946, and 0.819, respectively (Figure 5C). The K–M curve demonstrated that patients in the high-risk group had a significantly worse OS than patients in the low-risk group (Figure 4D, p < 0.001). These results demonstrated that the risk score and nomogram had a good prognostic ability.

FIGURE 5 | (A) Nomogram of LKB1-mutant LUAD. (B) 1-, 3-, and 5-year calibration plots, (C), 1-, 3-, and 5-year ROC curves, and (D), Kaplan–Meier survival analysis of the nomogram. (E), 1-, 3-, and 5-year ROC curves, and (F), Kaplan‐Meier survival analysis of the validation cohort.

When the validation cohort was divided into high- and low-risk groups according to the constructed model, there was a significant difference in the overall survival (p = 0.005, Figure 5F). The 1-, 3-, and 5-year AUC were 0.678, 0.781, and 0.757, respectively (Figure 5E). These indicated that this prognostic model had a good survival prediction ability for LKB1-mutated cancers.

Immune Function Analysis of the Prognostic Risk Model

The Pearson correlation analysis indicated the risk score and the three other genes were significantly related to CD8⁺ T cell infiltration (R = 0.49, 0.47, 0.26, and −0.28, respectively). Among these factors, GLP1R showed a significant negative correlation (p < 0.05, Figure 6A). The risk score was associated with four immune cells and five immune-related pathways according to the ssGSEA (Figure 6B). CHST4, GLP1R, hsa-miR-582-5p, and risk scores were significantly associated with multiple immune checkpoints (Figure 6C). Of the 46 immune checkpoints, 10 were significantly differentially expressed between the high- and low-risk groups of patients with LKB1-mutant LUAD (Figure 6D and Supplementary Figure 1E). the PCA and t-SNE analysis demonstrated that the patients were well distinguished according to the risk score (Figures 6E,F).

FIGURE 6 | (A) Pearson correlation analysis between CD8⁺ T cell and the risk score and three other signatures. (B) Boxplots of ssGSEA. (C) Correlation analysis and (D) boxplots of immune checkpoints. (E) PCA and (F) t-SNE analysis of risk scores.

Drug Sensitivity Analysis of the Prognostic Risk Model

We calculated the half-maximal inhibitory concentration (IC₅₀) of 138 chemotherapy drugs in the high- and low-risk groups. The associations of the values of IC₅₀ of 64 chemotherapy drugs with the groups were statistically significant (p < 0.05), and the p values associated with 15 chemotherapy drugs were less than 0.001 (Figures 7A,B and Supplementary Figure S2A). For these chemotherapy drugs, we analyzed the relationship between the risk score and the expression of genes coding for immune checkpoint-related drug targets. The expressions of AURKA, CD274, KIF11, and PLK1, which encode targets of ZM.447,439, PD-L1 immunotherapy, S-trityl-l-cysteine, and BI.2536, respectively, were associated with the risk score (Figure 7A, Figure 6D). However, the expression level and mutation status of EGFR were not associated with the risk score (Supplementary Figure S2B). The risk score was not statistically correlated with the targets of other drugs and HLA-related antigens (Supplementary Figure S2B).

FIGURE 7 | (A,B) Drug sensitivity analysis and (C) expression level correlation analysis in the high-risk and low-risk groups.

Model Specificity Analysis of the Prognostic Risk Model

When we applied the same risk model to the LKB1-wt group, the K–M curve demonstrated that patients in the high-risk group had a worse OS than patients in the low-risk group (Figure 8A, p = 0.012). However, the 1-, 3-, and 5-year AUC of the risk score for the OS were 0.52, 0.603, and 0.51, respectively (Figure 8B), which showed that the predictive performance of the risk score was poor. According to univariate and multivariate Cox regression analyses, the risk score could not be used as an independent prognostic factor for LUAD patients with wild-type LKB1 (Figures 8C,D). LUAD patients with wild-type LKB1 could not be distinguished according to the risk score (Figure 8F). There was no obvious relationship between the risk score and probability of death (Supplementary Figure S3A).

FIGURE 8 | Model specificity analysis of the prognostic risk model: (A) Kaplan–Meier survival analysis , (B) ROC curves , (C) univariate and (D) multivariate Cox regression analyses. (E) Nomogram of LKB1-wild-type LUAD. (F) PCA.

The same nomogram was also constructed using the risk score plus stage in LKB1-wild-type patients (Figure 8E). However, the impact of the risk score on patient prognosis was much smaller than the impact of the stage. The calibration curves corresponding to 1-, 3-, and 5-year survival rates show consistency (Supplementary Figure S3B). The AUC values of ROC curves corresponding to 1-, 3-, and 5-year survival in LKB1-wt UAD were 0.701, 0.718, and 0.691, respectively (Supplementary Figure S3C). The K–M curve demonstrated that patients in the high-risk group had a significantly worse OS than patients in the low-risk group (Supplementary Figure S3D, p < 0.001). But depending on the nomogram, the stage plays a more significant role than the risk score, resulting in the nomogram showing better predictive ability. These results demonstrated that the risk score was not applicable to wild-type LKB1 tumors.

DISCUSSION

LUAD patients diagnosed at the same stage according to the current TNM classification system can have different clinical outcomes, due to heterogeneity at both the molecular and the genetic levels (Navin et al., 2011; Gerlinger et al., 2012). Notably, LKB1-inactivating mutations are important cancer drivers and have a significant impact on the prognosis (Tsai et al., 2014). Because of the extensive tumor suppressor function and unique effect on the immunity of LKB1, the treatment of LKB1-mutant LUAD is progressing slowly. We therefore conducted this research to explore the immune landscape, construct an immune-related prognostic risk model, and predict effective drugs for the treatment of LKB1-mutant LUAD.

As a major driver of the cold non-T cell-inflamed microenvironment, inactivating LKB1 genomic alterations cause a decrease in the infiltration of CD3⁺, CD4⁺, and CD8⁺ T cells and low expression levels of PD-L1 (Skoulidis et al., 2015; Scheel et al., 2016; Kadara et al., 2017; Skoulidis et al., 2018). These findings are consistent with our research. Mechanistically, loss of LKB1 correlates with increases in the expression of IL-1β, IL-6, CXCL7, and G-CSF that promote myeloid cell recruitment and induce epigenetic repression of STING (Koyama et al., 2016; Kitajima et al., 2019). Corresponding increases in the expression of T-cell exhaustion markers and tumor-promoting cytokines result in the inhibition of T cells under the condition of LKB1 deficiency (Koyama et al., 2016). Thus, loss of LKB1 is not only related to the resistance to PD-1 and PD-L1 inhibition, but also to the resistance to combined anti-PD-1 and anti-CTLA4 therapy with nivolumab and ipilimumab (Hellmann et al., 2018). According to a research work by Koyama et al. (2016), treatment with an IL-6-neutralizing antibody or a neutrophil-depleting antibody yielded therapeutic benefits in LKB1-deficient patients. These results indicate that LKB1 has an important impact on immune function, and further exploration is necessary.

Therefore, we endeavored to identify specific RNAs associated with immunity and the survival prognosis. In this study, we identified a three-gene prognostic model associated with immunity that was constructed using TCGA datasets to predict the OS outcomes of patients with LKB1-mutant LUAD. These same three genes have been identified as prognostic biomarkers in multiple tumor types, and have been found to play important roles in the occurrence and development of various diseases by regulating immune function (Moschovaki Filippidou et al., 2020; Zhang et al., 2020; Hu et al., 2021; Ren et al., 2022). The AUC values of ROC curves suggested that our risk model had a superior prognostic value to other clinicopathological characteristics, including age, gender, and stage. According to univariate and multivariate Cox regression analyses, the prognostic risk model that included the three immune-related RNAs was the only independent prognostic factor for patients with LKB1-mutant LUAD.

Two of the three immune-related RNAs and the risk score had significant associations with the stage. More interestingly, the expression of hsa-miR-582-5p was associated with tumor size and lymph node status, implying that the risk score is not only strongly linked to the intrinsic biological characteristics of LKB1-mutant LUAD, but also that it has a close relationship with clinicopathological characteristics. The same analysis in LKB1-wt lung adenocarcinoma showed a poor survival predictive power. It is worth noting that the importance of the risk score and stage in influencing the prognosis was opposite in nomograms created for LKB1-mutated and wild-type LUAD.

In addition to excellent prognostic prediction ability, the risk score is closely related to immune function. The upregulation of natural killer cells in the high-risk group might be because of the dual role of these cells regarding both cancer progression and the boosting of the onset of immuno-suppressant TMEs (Di Vito et al., 2019). Increased levels of CD8⁺ cells have been shown previously to be associated with better outcomes in non-small-cell lung carcinoma, and CD8⁺ cells were an independent prognostic factor in this analysis (Fehniger et al., 2003). However, in our analysis, more CD8⁺ cells and higher cytolytic activity were found in the high-risk group. More detailed research thus needs to be performed to explore the underlying mechanisms. Moreover, higher risk scores correlated with protumor immunity, including the activity of inflammation-promoting and APC co-inhibition, and these factors may provide an explanation for their poor prognosis.

We used a drug sensitivity analysis to compare the IC₅₀ values of the high-risk and low-risk groups to confirm the ability of the risk score as a treatment-guiding biomarker. Among the three EGFR tyrosine kinase-inhibitors investigated, gefitinib, erlotinib, and afatinib (BIBW2992), only afatinib was found to have a statistically significant difference in efficacy between the high- and low-risk groups. However, the expression level and mutation status of EGFR were not related to the risk score. We speculated that this disparate result may be caused by the additional activity of afatinib against Her2, which has been investigated in the context of breast cancer and other EGFR and Her2-driven cancers (Lin et al., 2012). According to the value of IC₅₀ and the correlation between drug targets and risk scores, ZM.447,439, an aurora kinase-inhibitor, is the most appropriate treatment for patients in the high-risk group. Meanwhile, we noticed that the IC₅₀ of the high-risk group was often lower than that of the low-risk group, and the expression level of PD-L1 is also higher in the high-risk group; this correlation means that patients in the high-risk group are more sensitive to some drugs despite having more malignant characteristics. This conclusion demonstrates the importance of identifying high-risk patients through risk scores to identify effective treatments and to investigate prognoses of LUAD patients with LKB1 mutations.

However, there are several limitations to our study. First, because of the lack of data on patients with LKB1-mutant LUAD, the sample size of the present study is not as large as other studies, which may influence the precision of the results. Second, the underlying molecular mechanisms that would explain why the risk score can predict the prognosis of patients with LKB1-mutant LUAD have not been fully clarified. It is necessary for us to explore the links between the risk score and immune activity by conducting experiments. In addition, large-scale and multi-center studies are needed to confirm our results.

CONCLUSION

The risk model we constructed provides a new promising biomarker that can provide precise clinical insights into the progression of LUAD in patients with LKB1 mutations and can serve as an efficient guide for the treatment of this challenging disease.

DATA AVAILABILITY STATEMENT

Publicly available datasets were analyzed in this study. These data can be found at: https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga.

AUTHOR CONTRIBUTIONS

All authors contributed to the study conception and design. HZ, GW, and JD carried out the design of the study, analyses of the statistics, creation of new software used in the work, and drafting of the manuscript. HZ, GW, YZ, YW, and XZ carried out the collection of the statistics and the preparation of the manuscript. All the authors read and approved the final version of the manuscript.

FUNDING

This study was supported by the National Natural Science Foundation of Shandong Province [Grant number: ZR2021MH192] and Jinan Clinical Medical Science and Technology Innovation Plan [Grant number 202019058].

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

ACKNOWLEDGMENTS

The authors are grateful for the public databases used in this study; they are especially grateful for the large amount of valuable datasets provided by the TCGA platform.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2022.756772/full#supplementary-material

ABBREVIATIONS

AUC, area under the curve; FDR, False discovery rate; GSEA, gene set enrichment analysis; HR, hazard ratio; IC50, half maximal inhibitory concentration; ICI, immune checkpoint; LncRNA, long non-coding RNA; LUAD, lung adenocarcinoma; miRNA, microRNA; OS, overall survival; PCA, principal component analysis; ROC, receiver operating characteristic; ssGSEA, single-sample gene set enrichment analysis; TCGA, The Cancer Genome Atlas; TME, tumor microenvironment; t-SNE, t-distributed stochastic neighbor embedding.

REFERENCES

Akbay, E. A., Koyama, S., Carretero, J., Altabef, A., Tchaicha, J. H., Christensen, C. L., et al. (2013). Activation of the PD-1 Pathway Contributes to Immune Escape in EGFR-Driven Lung Tumors. Cancer Discov. 3, 1355–1363. doi:10.1158/2159-8290.cd-13-0310

Bergethon, K., Shaw, A. T., Ignatius Ou, S.-H., Katayama, R., Lovly, C. M., McDonald, N. T., et al. (2012). ROS1 Rearrangements Define a Unique Molecular Class of Lung Cancers. Jco 30, 863–870. doi:10.1200/jco.2011.35.6345

Di Vito, C., Mikulak, J., Zaghi, E., Pesce, S., and Marcenaro, E., Mavilio D: NK Cells to Cure Cancer.2019:41:101272. doi:10.1016/j.smim.2019.03.004

Cancer Genome Atlas Research, N. (2014). Comprehensive Molecular Profiling of Lung Adenocarcinoma. Nature 511, 543–550. doi:10.1038/nature13385

Ding, L., Getz, G., Wheeler, D. A., Mardis, E. R., McLellan, M. D., Cibulskis, K., et al. (2008). Somatic Mutations Affect Key Pathways in Lung Adenocarcinoma. Nature 455, 1069–1075. doi:10.1038/nature07423

Drerup, J. M., Deng, Y., Pandeswara, S. L., Padrón, Á. S., Reyes, R. M., Zhang, X., et al. (2020). CD122-Selective IL2 Complexes Reduce Immunosuppression, Promote Treg Fragility, and Sensitize Tumor Response to PD-L1 Blockade. Cancer Res. 80, 5063–5075. doi:10.1158/0008-5472.can-20-0002

Fehniger, T. A., Cooper, M. A., Nuovo, G. J., Cella, M., Facchetti, F., Colonna, M., et al. (2003). CD56bright Natural Killer Cells Are Present in Human Lymph Nodes and Are Activated by T Cell-Derived IL-2: a Potential New Link between Adaptive and Innate Immunity. Blood 101, 3052–3057. doi:10.1182/blood-2002-09-2876

Geeleher, P., Cox, N. J., and Huang, R. S. (2014). Clinical Drug Response Can Be Predicted Using Baseline Gene Expression Levels and In Vitro Drug Sensitivity in Cell Lines. Genome Biol. 15, R47. doi:10.1186/gb-2014-15-3-r47

Geeleher, P., Cox, N., and Huang, R. S. (2014). pRRophetic: an R Package for Prediction of Clinical Chemotherapeutic Response from Tumor Gene Expression Levels. PLoS One 9, e107468. doi:10.1371/journal.pone.0107468

Gerlinger, M., Rowan, A. J., Horswell, S., Larkin, J., Endesfelder, D., Gronroos, E., et al. (2012). Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing. N. Engl. J. Med. 366, 883–892. doi:10.1056/nejmoa1113205

Hellmann, M. D., Nathanson, T., Rizvi, H., Creelan, B. C., Sanchez-Vega, F., Ahuja, A., et al. (2018). Genomic Features of Response to Combination Immunotherapy in Patients with Advanced Non-small-cell Lung Cancer. Cancer Cell 33, 843–852. doi:10.1016/j.ccell.2018.03.018

Hu, B., Ma, X., Fu, P., Sun, Q., Tang, W., Sun, H., et al. (2021). miRNA-mRNA Regulatory Network and Factors Associated with Prediction of Prognosis in Hepatocellular Carcinoma. Genomics Proteomics Bioinforma. S1672-0229 (21). doi:10.1016/j.gpb.2021.03.001

Jordan, E. J., Kim, H. R., Arcila, M. E., Barron, D., Chakravarty, D., Gao, J., et al. (2017). Prospective Comprehensive Molecular Characterization of Lung Adenocarcinomas for Efficient Patient Matching to Approved and Emerging Therapies. Cancer Discov. 7, 596–609. doi:10.1158/2159-8290.cd-16-1337

Kadara, H., Choi, M., Zhang, J., Parra, E. R., Rodriguez-Canales, J., Gaffney, S. G., et al. (2017). Whole-exome Sequencing and Immune Profiling of Early-Stage Lung Adenocarcinoma with Fully Annotated Clinical Follow-Up. Ann. Oncol. 28, 75–82. doi:10.1093/annonc/mdw436

Kitajima, S., Ivanova, E., Guo, S., Yoshida, R., Campisi, M., Sundararaman, S. K., et al. (2019). Suppression of STING Associated with LKB1 Loss in KRAS-Driven Lung Cancer. Cancer Discov. 9, 34–45. doi:10.1158/2159-8290.cd-18-0689

Koyama, S., Akbay, E. A., Li, Y. Y., Aref, A. R., Skoulidis, F., Herter-Sprie, G. S., et al. (2016). STK11/LKB1 Deficiency Promotes Neutrophil Recruitment and Proinflammatory Cytokine Production to Suppress T-Cell Activity in the Lung Tumor Microenvironment. Cancer Res. 76, 999–1008. doi:10.1158/0008-5472.can-15-1439

Lin, N. U., Winer, E. P., Wheatley, D., Carey, L. A., Houston, S., Mendelson, D., et al. (2012). A Phase II Study of Afatinib (BIBW 2992), an Irreversible ErbB Family Blocker, in Patients with HER2-Positive Metastatic Breast Cancer Progressing after Trastuzumab. Breast Cancer Res. Treat. 133, 1057–1065. doi:10.1007/s10549-012-2003-y

Liu, Z., Guo, C., Li, J., Xu, H., Lu, T., Wang, L., et al. (2021). Somatic Mutations in Homologous Recombination Pathway Predict Favourable Prognosis after Immunotherapy across Multiple Cancer Types. Clin. Transl. Med. 11, e619. doi:10.1002/ctm2.619

Liu, Z., Guo, Y., Yang, X., Chen, C., Fan, D., Wu, X., et al. (2021). Immune Landscape Refines the Classification of Colorectal Cancer with Heterogeneous Prognosis, Tumor Microenvironment and Distinct Sensitivity to Frontline Therapies. Front. Cell Dev. Biol. 9, 784199. doi:10.3389/fcell.2021.784199

Liu, Z., Guo, C., Dang, Q., Wang, L., Liu, L., Weng, S., et al. (2022). Integrative Analysis from Multi-Center Studies Identities a Consensus Machine Learning-Derived lncRNA Signature for Stage II/III Colorectal Cancer. EBioMedicine 75, 103750. doi:10.1016/j.ebiom.2021.103750

Liu, Z., Liu, L., Weng, S., Guo, C., Dang, Q., Xu, H., et al. (2022). Machine Learning-Based Integration Develops an Immune-Derived lncRNA Signature for Improving Outcomes in Colorectal Cancer. Nat. Commun. 13, 816. doi:10.1038/s41467-022-28421-6

Liu, Z., Xu, H., Weng, S., Ren, Y., and Han, X. (2022). Stemness Refines the Classification of Colorectal Cancer with Stratified Prognosis, Multi-Omics Landscape, Potential Mechanisms, and Treatment Options. Front. Immunol. 13, 828330. doi:10.3389/fimmu.2022.828330

Moschovaki Filippidou, F., Kirsch, A. H., Thelen, M., Kétszeri, M., Artinger, K., Aringer, I., et al. (2020). Glucagon-Like Peptide-1 Receptor Agonism Improves Nephrotoxic Serum Nephritis by Inhibiting T-Cell Proliferation. Am. J. Pathology 190, 400–411. doi:10.1016/j.ajpath.2019.10.008

Navin, N., Kendall, J., Troge, J., Andrews, P., Rodgers, L., McIndoo, J., et al. (2011). Tumour Evolution Inferred by Single-Cell Sequencing. Nature 472, 90–94. doi:10.1038/nature09807

Newman, A. M., Liu, C. L., Green, M. R., Gentles, A. J., Feng, W., Xu, Y., et al. (2015). Robust Enumeration of Cell Subsets from Tissue Expression Profiles. Nat. Methods 12, 453–457. doi:10.1038/nmeth.3337

Paez, J. G., Jänne, P. A., Lee, J. C., Tracy, S., Greulich, H., Gabriel, S., et al. (2004). EGFR Mutations in Lung Cancer: Correlation with Clinical Response to Gefitinib Therapy. SCIENCE 304, 1497–1500. doi:10.1126/science.1099314

Ren, Y., Lyu, J., Guo, Y., Yao, Y., and Hu, L. (2022). Long Noncoding RNA TUG1 Inhibits Tumor Progression through Regulating Siglec-15-Related Anti-immune Activity in Hepatocellular Carcinoma. J. Immunol. Res. 2022, 9557859. doi:10.1155/2022/9557859

Rooney, M. S., Shukla, S. A., Wu, C. J., Getz, G., and Hacohen, N. (2015). Molecular and Genetic Properties of Tumors Associated with Local Immune Cytolytic Activity. Cell 160, 48–61. doi:10.1016/j.cell.2014.12.033

Scheel, A. H., Ansén, S., Schultheis, A. M., Scheffler, M., Fischer, R. N., Michels, S., et al. (2016). PD-L1 Expression in Non-small Cell Lung Cancer: Correlations with Genetic Alterations. Oncoimmunology 5, e1131379. doi:10.1080/2162402x.2015.1131379

Schober, P., Boer, C., and Schwarte, L. A. (2018). Correlation Coefficients. Anesth. Analgesia 126, 1763–1768. doi:10.1213/ane.0000000000002864

Shackelford, D. B., and Shaw, R. J. (2009). The LKB1-AMPK Pathway: Metabolism and Growth Control in Tumour Suppression. Nat. Rev. Cancer 9, 563–575. doi:10.1038/nrc2676

Shen, Q., Sun, Y., and Xu, S. (2020). LINC01503/miR-342-3p Facilitates Malignancy in Non-small-cell Lung Cancer Cells via Regulating LASP1. Respir. Res. 21, 235. doi:10.1186/s12931-020-01464-3

Simon, N., Friedman, J., Hastie, T., and Tibshirani, R. (2011). Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent. J. Stat. Softw. 39, 1–13. doi:10.18637/jss.v039.i05

Skoulidis, F., Byers, L. A., Diao, L., Papadimitrakopoulou, V. A., Tong, P., Izzo, J., et al. (2015). Co-occurring Genomic Alterations Define Major Subsets of KRAS-Mutant Lung Adenocarcinoma with Distinct Biology, Immune Profiles, and Therapeutic Vulnerabilities. Cancer Discov. 5, 860–877. doi:10.1158/2159-8290.cd-14-1236

Skoulidis, F., Goldberg, M. E., Greenawalt, D. M., Hellmann, M. D., Awad, M. M., Gainor, J. F., et al. (2018). STK11/LKB1 Mutations and PD-1 Inhibitor Resistance in KRAS-Mutant Lung Adenocarcinoma. Cancer Discov. 8, 822–835. doi:10.1158/2159-8290.cd-18-0099

Skoulidis, F., and Heymach, J. V. (2019). Co-occurring Genomic Alterations in Non-small-cell Lung Cancer Biology and Therapy. Nat. Rev. Cancer 19, 495–509. doi:10.1038/s41568-019-0179-8

Soda, M., Choi, Y. L., Enomoto, M., Takada, S., Yamashita, Y., Ishikawa, S., et al. (2007). Identification of the Transforming EML4-ALK Fusion Gene in Non-small-cell Lung Cancer. Nature 448, 561–566. doi:10.1038/nature05945

Sun, C.-C., Zhu, W., Li, S.-J., Hu, W., Zhang, J., Zhuo, Y., et al. (2020). FOXC1-mediated LINC00301 Facilitates Tumor Progression and Triggers an Immune-Suppressing Microenvironment in Non-small Cell Lung Cancer by Regulating the HIF1α Pathway. Genome Med. 12, 77. doi:10.1186/s13073-020-00773-y

Tamborero, D., Rubio-Perez, C., Muiños, F., Sabarinathan, R., Piulats, J. M., Muntasell, A., et al. (2018). A Pan-Cancer Landscape of Interactions between Solid Tumors and Infiltrating Immune Cell Populations. Clin. Cancer Res. 24, 3717–3728. doi:10.1158/1078-0432.ccr-17-3509

Taube, J. M., Klein, A., Brahmer, J. R., Xu, H., Pan, X., Kim, J. H., et al. (2014). Association of PD-1, PD-1 Ligands, and Other Features of the Tumor Immune Microenvironment with Response to Anti-PD-1 Therapy. Clin. Cancer Res. 20, 5064–5074. doi:10.1158/1078-0432.ccr-13-3271

Tibshirani, R. (1997). The Lasso Method for Variable Selection in the Cox Model. STATISTICS Med. 16, 11. doi:10.1002/(sici)1097-0258(19970228)16:4<385:aid-sim380>3.0.co;2-3

Topalian, S. L., Hodi, F. S., Brahmer, J. R., Gettinger, S. N., Smith, D. C., McDermott, D. F., et al. (2012). Safety, Activity, and Immune Correlates of Anti-PD-1 Antibody in Cancer. N. Engl. J. Med. 366, 2443–2454. doi:10.1056/nejmoa1200690

Tsai, L.-H., Chen, P.-M., Cheng, Y.-W., Chen, C.-Y., Sheu, G.-T., Wu, T.-C., et al. (2014). LKB1 Loss by Alteration of the NKX2-1/p53 Pathway Promotes Tumor Malignancy and Predicts Poor Survival and Relapse in Lung Adenocarcinomas. Oncogene 33, 3851–3860. doi:10.1038/onc.2013.353

Tumeh, P. C., Harview, C. L., Yearley, J. H., Shintaku, I. P., Taylor, E. J. M., Robert, L., et al. (2014). PD-1 Blockade Induces Responses by Inhibiting Adaptive Immune Resistance. Nature 515, 568–571. doi:10.1038/nature13954

Wang, Q., Hu, B., Hu, X., Kim, H., Squatrito, M., Scarpace, L., et al. (2017). Tumor Evolution of Glioma-Intrinsic Gene Expression Subtypes Associates with Immunological Changes in the Microenvironment. Cancer Cell 32, 42–56. doi:10.1016/j.ccell.2017.06.003

Yoshihara, K., Shahmoradgoli, M., Martínez, E., Vegesna, R., Kim, H., Torres-Garcia, W., et al. (2013). Inferring Tumour Purity and Stromal and Immune Cell Admixture from Expression Data. Nat. Commun. 4, 2612. doi:10.1038/ncomms3612

Zhang, L., Fan, Y., Wang, X., Yang, M., Wu, X., Huang, W., et al. (2020). Carbohydrate Sulfotransferase 4 Inhibits the Progression of Hepatitis B Virus-Related Hepatocellular Carcinoma and Is a Potential Prognostic Marker in Several Tumors. Front. Oncol. 10, 554331. doi:10.3389/fonc.2020.554331

Zhu, X., Xie, X., Zhao, Q., Zhang, L., Li, C., and Zhao, D. (2020). Potential Prognostic Value and Mechanism of Stromal-Immune Signature in Tumor Microenvironment for Stomach Adenocarcinoma. Biomed. Res. Int. 2020, 4673153. doi:10.1155/2020/4673153

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Wang, Zheng, Zhao, Wang, Zeng and Du. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 02 June 2022
doi: 10.3389/fmolb.2022.730213

Rare c-KIT c.1926delA and c.1936T>G Mutations in Exon 13 Define Imatinib Resistance in Gastrointestinal Stromal Tumors and Melanoma Patients: Case Reports and Cell Experiments

Chi Yan1,²*, Chengzhi Zhao1,², Ke Yang1,², Hongyan Zhou³, Limin Jing⁴, Weixing Zhao⁵, Wenguang Dou⁶, Qingxin Xia⁷, Jie Ma1,², Bing Wei1,² and Yongjun Guo1,²*

¹Department of Molecular Pathology, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China

²Henan Key Laboratory of Molecular Pathology, Zhengzhou, China

³Department of Pathology, Xinxiang First People’s Hospital, Xinxiang, China

⁴Computed Tomography Room, Xinxiang First People’s Hospital, Xinxiang, China

⁵Department of Pathology, The First Affiliated Hospital of Xinxiang Medical University, Xinxiang, China

⁶Department of Radiology, The First Affiliated Hospital of Xinxiang Medical University, Xinxiang, China

⁷Department of Pathology, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China

Edited by:
William C. Cho, QEH, Hong Kong SAR, China

Reviewed by:
Leping Li, National Institute of Environmental Health Sciences (NIH), United States
Lucia Taja-Chayeb, National Institute of Cancerology (INCAN), Mexico

* Correspondence: Chi Yan, yanchi_2005@126.com; Yongjun Guo, yongjunguo@hotmail.com

Specialty section: This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Received: 24 June 2021
Accepted: 15 April 2022
Published: 02 June 2022

Citation: Yan C, Zhao C, Yang K, Zhou H, Jing L, Zhao W, Dou W, Xia Q, Ma J, Wei B and Guo Y (2022) Rare c-KIT c.1926delA and c.1936T>G Mutations in Exon 13 Define Imatinib Resistance in Gastrointestinal Stromal Tumors and Melanoma Patients: Case Reports and Cell Experiments. Front. Mol. Biosci. 9:730213. doi: 10.3389/fmolb.2022.730213

Background: Target therapies play more and more important roles in gastrointestinal stromal tumors (GISTs) and melanoma with the advancement of clinical drugs that overcome the resistance caused by gene mutations. c-KIT gene mutations account for a large portion of GIST patients, which are known to be sensitive or resistant to tyrosine kinase inhibitors. However, the role rare mutations play in drug efficacy and progression-free duration remains elusive.

Methods: Two rare mutations were identified using Sanger sequencing from the GIST and melanoma cases. Cell experiments were further carried out to demonstrate their role in the imatinib resistance.

Results: c-KIT c.1926delA p.K642S*FS mutation in primary and recurrent GIST patients and c-KIT c.1936T>G p.Y646D point mutation in melanoma patients in exon 13 were first demonstrated to be novel targets resistant to imatinib agent.

Conclusion: c-KIT mutations c.1926delA and c.1936T>G in exon 13 are clinically significant targets that exhibit resistance to imatinib. This study provides guidance to GIST and melanoma treatments.

Keywords: GIST, c-KIT, melanoma, rare mutations, imatinib resistance

INTRODUCTION

Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal malignancy of the gastrointestinal tract, with an incidence of 7,000–8,000 cases per year in China (Yuval and Elazary, 2018). Over 90% of GISTs stained positive for c-KIT by immunohistochemistry, and around 70–85% of tumors had a functional c-KIT mutation by sequencing technology (Lee et al., 2013; Shi and Liang, 2014; Li and Raut, 2019). Using multi-gene panels by NGS, APC, CDH1, and TP53 mutations were found in multiple hereditary cancers such as gastric cancer and colorectal cancer (Slavin et al., 2015); however, they were not the priority in GISTs. Typically, c-KIT and PDGFRA gene mutation tests were recommended to assist molecular diagnosis and target therapy by the guidelines, and other mutations would be considered, given the absence of known positive variations. Over 60% of c-KIT alterations in GISTs are located in exon 11, followed by exon 9, which encodes the juxtamembrane domain responsible for inhibiting receptor dimerization and activation in the absence of the SCF ligand (Tan et al., 2018). Less common mutations have also been reported in exons 13, 17, or other parts of c-KIT, although they are usually regarded as secondary mutations resulting from tyrosine kinase inhibitor actions (Kobayashi et al., 2015; Nakano et al., 2017).

c-KIT mutations were found not only in GIST but also in melanoma patients (Mazurenko et al., 2017). c-KIT-mutated melanoma represented various frequencies in different populations and histological types, with the highest rate in Asians (Moltara et al., 2018; Gutiérrez- Castañeda et al., 2020). c-KIT mutations were present in 36% of acral lentiginous melanomas in cutaneous melanoma (Kong et al., 2011; Gutiérrez- Castañeda et al., 2020). Approximately 70% of mutations were identified to reside in exons 11 and 13 of the c-KIT gene, of which the commonest one was L576P for the former and K642E for the latter (Reddy et al., 2017; Davis et al., 2018). These mutations led to the activation of MAPK and other cell signaling pathways causing tumor cell proliferation and cancer progression (Calipel et al., 2014). Thus, activating c-KIT mutations act as the target of clinical therapies in melanoma (Jiang et al., 2008). However, it remains to be demonstrated whether the rare c-KIT alterations are effective to tyrosine kinase inhibitors in malignant melanoma.

Considering the crucial roles of c-KIT in GIST and melanoma tumorigenesis, targeted inhibitors were thus used as the treatments for c-KIT-positive patients. The most widely investigated c-KIT inhibitor was imatinib, which served as the first-line drug in GIST (Blay et al., 2015), whereas as an alternative treatment beyond immunotherapy in melanoma (Carvajal et al., 2011; Guo et al., 2011; Delyon et al., 2020). For patients with resistant c-KIT mutation, a second-generation tyrosine kinase inhibitor such as nilotinib was proven more efficient than imatinib in GISTs and metastatic melanoma (Tran and Tawbi, 2012). Although somatic mutations in c-KIT were present over the entire gene, it appeared to be different for the efficacy of c-KIT inhibitors in patients owning mutations residing in exons 11 and 13. Improvement of the efficacy of c-KIT inhibition will require a better understanding of the mechanisms underlying response and resistance to treatments (Jiang et al., 2008). The roles of the novel mutations in exon 13 of the c-KIT gene in the tolerance to imatinib in GISTs or melanoma remain uncertain.

In this study, it is found that c-KIT c.1926delA p.K642S*FS mutation in primary and metastatic tumors of the GIST patients and c.1936T>G p.Y646D point mutation in melanoma cases were rare targets resistant to imatinib mesylate, which was identified using Sanger sequencing and cell culture experiments. This study was capable of providing valuable treatment or diagnosis information for GIST and melanoma patients harboring either mutation.

MATERIALS AND METHODS

Treatment of the Gastrointestinal Stromal Tumors Patient

Patient 1, a woman at the age of 49, was first diagnosed with gastric GISTs at Xinxiang First People’s Hospital in August 2017 using a biopsy specimen of the primary tumor from the stomach. After imatinib treatment for approximately four and a half months, there was a symptom of disease progression, and thereafter, the primary tumor was resected by surgery in the First Affiliated Hospital of Xinxiang Medical University at the end of December 2017. The surgical specimen of the primary tumor was pathologically diagnosed as gastric wall plexiform fibromyxoma. Postsurgical drugs were not taken until the new tumor was present at the right bottom of the stomach during the routine follow-up examination around seven and a half months later. Subsequently, the patient was orally treated by Gleevec for 1 year until the second resection of the metastatic tumor in the Affiliated Cancer Hospital of Zhengzhou University. All the pathological sections obtained by hematoxylin–eosin staining and immunohistochemistry were reviewed by two pathologists.

Treatment of the Melanoma Patient

Patient 2, a man aged 69 years, suffered from a metastatically malignant melanoma at the right thigh that originated from the primary tumor at the bottom of the right foot and had a novel mutation in exon 13 of the c-KIT gene with the polymorphic alteration of c.1936T>G and amino acid change p.Y646D as well as the wild-type PDGFRA. None of the c-KIT-targeted treatments were performed on this patient, except that he was prescribed 10 cycles of immunotherapy for anti-PD-1 antibody.

The retrospective study was approved by the Ethics Committee of the Affiliated Cancer Hospital of Zhengzhou University according to the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Written informed consent was obtained by both patients.

c-KIT Mutation Analysis

The formalin-fixed and paraffin-embedded specimens from the biopsy and the surgeries of both patients were sectioned, and genomic DNA was isolated by using a kit (Qiagen, Cat. No. 56404). As the leukocytes from both patients were not preserved in the regular physical examination, the FFPE tissues after surgery were taken from the normal part of the patients. The normal marginal counterparts matched with the tumor FFPE tissues that were resected from the two patients were sectioned to isolate the genomic DNA as well. c-KIT mutations in exons 9, 11, 13, and 17 and PDGFRA mutations in exons 12 and 18 were analyzed by using the commercial kits (Yuanqi Bio, Shanghai) including primer pairs used to amplify the entire sequence for each exon. Primer sets (forward: 5′-CAC​CCT​GTT​CAC​TCC​TTT​G-3′, reverse: 5′-GGT​ATG​TCC​TGG​GCT​GTT​C-3′) were employed to amplify the regions spanning exons 11 and 13 to determine the relative location of different variations on the same chromosome. The PCR amplicons were sequenced using Sanger sequencing.

c-KIT Tyrosine Phosphorylation Analysis

The wild-type and mutated c-KIT sequences with a notation of WT, M1, M2, M3, and M4 (Table 1) were cloned into the expression vectors with the GFP tag, as reported previously (Isotani et al., 2010). Specifically, the total length of the c-KIT coding sequence was amplified and ligated to the mammalian expression vector pcDNA3.1 (+) tagged with the GFP coding sequence at its 5′ terminus. The site-directed mutants of c.1668_1679delGTGGAAGGTTGT and c.1926delA as well as c.1936T>G were introduced to the wild-type expression plasmid.

TABLE 1 | Genomic changes of the c-KIT gene for plasmid construction.

c-KIT tyrosine phosphorylation was detected, as published previously (Nakano et al., 2017). In brief, SK-MEL-28 cells were plated at a density of 5 × 10⁵ cells on the 6-well plate. After cell adherence, c-KIT-GFP expression vectors producing wild-type c-KIT or the c-KIT with mutated c.1668_1679delGTGGAAGGTTGT or both, and c.1936T>G were transiently transfected into these cells using Lipofectamine 2000 Reagent (Invitrogen, Cat. No.11668-019). After growing for another 30 h, these cells were treated with or without 100 ng/ml human stem cell factor (SCF, PeproTech, Cat. No. 300-07) for 20 min. In order to analyze the tyrosine kinase inhibition, the cells were treated with imatinib mesylate at concentrations of 0.01, 0.1, 1, and 10 μM, individually. The harvested cells were subject to Western blotting using monoclonal mouse anti-human c-KIT antibody (Santa Cruz, Cat. No. sc-13508) and rabbit anti-human phospho-c-KIT antibody (Thermo Fisher Scientific, Cat. No. 44-490G), as well as monoclonal mouse anti-human phosphotyrosine antibody (Millipore, Cat. No. 05-321X).

Cell Proliferation and Apoptosis Assay

MTT assay was employed to evaluate cell proliferation. Briefly, after being transfected by plasmids WT, M3, or M4, SK-MEL-28 cells were incubated for 30 h. Then, the cells were digested and plated on the 96-well plate at a density of 5 × 10³ per well, followed by the imatinib treatment for 24 h or 48 h at concentrations of 0, 20, 40, 80, and 100 μM, respectively. At the individual time point, 10 µl of the stock MTT solution (5 mg/ml, MCE, Cat. No. HY-15924) was added to each well, and the cells were incubated for 4 h at 37°C. Then the supernatant was discarded, and 150 µl of DMSO was added to dissolve the formazan crystal produced by the cells. Finally, the absorbance was determined at a 568-nm wavelength on a microplate reader.

The proliferation rate was equal to the percentage of the mean OD from each treated group divided by the mean OD from MOCK. A remarkable difference between the two groups was analyzed by Student’s t-test. A p-value < 0.05 was considered statistically significant. Each data group was representative of three replicates unless otherwise indicated.

Cell apoptosis assay was performed using the Annexin V-APC/7-AAD Apoptosis Detection Kit (KeyGEN BioTECH, Cat. No. KGA 1026). Specifically, the cells were transfected by plasmids WT, M1, M3, or M4, followed by incubation at 37°C for 30 h. Then 100 μM imatinib was added to the cells, which were cultured for another 72 h. Subsequently, the cells were collected and further resuspended by 500 μl binding buffer. Next, 5 μl Annexin V-APC and 5 μl 7-AAD were sequentially added to the cell suspension. Finally, the samples were examined by flow cytometry (BECKMAN) after incubation for 5–15 min at room temperature under dark conditions.

RESULTS

Medical Imaging and Immunohistochemistry of the Gastrointestinal Stromal Tumor Patient

To make it clear what caused the stomachache of the female patient, enhanced computed tomography (CT) was carried out, and a biopsy specimen of the primary tumor was obtained from her stomach in the Xinxiang First People’s Hospital. The imaging results showed that the bottom of the stomach was occupied by lumpy soft tissue density shadow, with the likelihood of the GISTs (Figure 1A). To further prove this, immunohistochemistry was performed using the biopsy specimen. The data indicated that CD117 and CD34, as well as DOG-1 and Ki-67, were positively stained with negative expressions of SMA and S-100 proteins (Table 2); thus, the patient was diagnosed with gastric gastrointestinal stromal tumor.

FIGURE 1 | CT imaging of the female patient at different phases of the tumor progression. (A) CT imaging of the female patient at the initial diagnosis in the Xinxiang First People’s Hospital (photo taken on 2017-08-06). (B) Primary tumor shrinking after imatinib treatment for around four and a half months (photo taken on 2017-12-27). (C) Recurrent tumor was observed seven and a half months after primary tumor resection (photo taken on 2018-08-13). PS: plain scanning, AP: arterial phase, VP: venous phase, DP: delay phase.

TABLE 2 | Clinical and pathological characteristics in the GIST patient.

After imatinib treatment for four and a half months based on the mutated c-KIT gene in the biopsy specimen, the primary tumor shrank (Figure 1B), followed by surgical resection of the tumor at a size of 8 × 5 × 5 cm (Table 2). Immunohistochemistry staining showed that CD117, CD34, DOG-1, and Ki-67 were expressed at a negligible level. As c-KIT and PDGFRA gene sequencing was not performed then, this specimen was thus diagnosed as gastric wall plexiform fibromyxoma based on its morphology. However, the patient relapsed seven and a half months later, and the tumor metastasized to other parts of the stomach (Figure 1C). The metastatic tumor was excised in the Affiliated Cancer Hospital of Zhengzhou University at the size of 7 × 6 × 6 cm and was pathologically diagnosed with GISTs in combination with the mutation status of the c-KIT gene (Table 2).

Mutational Analysis on c-KIT and PDGFRA Genes for the Gastrointestinal Stromal Tumor patient

To determine whether the patient was sensitive to imatinib treatment, the mutational status of c-KIT and PDGFRA genes was analyzed on the biopsy specimen of the primary tumor by direct sequencing. The data indicated that partial deletion mutation of c-KIT c.1668_1679delGTGGAAGGTTGT causing the amino acid loss of p.557_560delWKVV and gain of p.Q556H mutation was present in exon 11, as well as a second novel single-nucleotide deletion mutation of c.1926delA in exon 13, resulting in the production of the stop codon (p.K642S*FS) (Figure 2A). The PDGFRA gene showed a wild-type genotype (not shown). All the aforementioned data suggested that the patient could benefit from the imatinib treatment, despite the unclear significance of this frameshift alteration in c-KIT exon 13.

FIGURE 2 | Mutation analysis of the c-KIT gene in different specimens. (A) Biopsy specimen of the primary tumor. c-KIT c.1668_1679del GTGGAAGGTTGT mutation in exon 11 and c.1926delA mutation in exon 13 were detected. The mutated nucleotides are shown in red colors. (B) Primary tumor after surgical resection. The same mutations were found in this specimen through biopsy. (C) Metastatic tumor after surgical resection. Similarly, the two mutations were detected in this metastatic tumor. (D) Detection of the two mutations in surgically primary tumor specimen in a transacting manner. (E) Normal marginal tissue matched with the recurrent tumors. The two mutations showed a wild type in the normal tissue.

Additionally, the surgically primary tumor specimen and the metastatic tumor specimen were sequenced, and it was discovered that mutations of c-KIT c.1668_1679delGTGGAAGGTTGT in exon 11 and c.1926delA in exon 13 were present in both the surgically primary and metastatic tumors (Figures 2B,C, respectively). To further clarify if both deletion mutations were arranged in a cis or trans manner, PCR was used to simultaneously amplify the fragment in the forward and reverse directions. Results showed that both deletion mutations act in a trans manner (Figure 2D). Also, the sequencing map showed that no mutations were detected in genomic DNA from the normal marginal tissue for both c-KIT and PDGFRA genes, which indicated the two mutations in c-KIT exon 11 and exon 13 found in this GIST patient were somatic (Figure 2E).

Presence of a Novel Mutation in c-KIT Exon 13 for the Melanoma Patient

One year after the initial surgical resection of the melanoma from the right foot, patient 2 was found to show metastatic melanoma in his right thigh, and the genetic mutations of c-KIT and PDGFRA showed that a novel mutation c.1936T>G in c-KIT exon 13, causing the production of missense amino acid mutation p.Y646D (Figure 3A). In addition, c-KIT exhibited a wild type in normal marginal tissue (Figure 3B), suggesting this de novo mutation was a somatic variant. The PDGFRA gene had a wild-type genotype (not shown). It was not clear whether this mutation was sensitive to tyrosine kinase inhibitors.

FIGURE 3 | Mutation analysis on the c-KIT gene for surgical melanoma specimen. (A) Tumor FFPE tissue. (B) Normal marginal tissue.

Two Rare Mutations in c-KIT Exon 13 Were Imatinib-Resistant

To further demonstrate if the two novel mutations discovered in exon 13 of the c-KIT gene could result in imatinib resistance, the monoclonal mouse anti-human c-KIT antibody and rabbit anti-human phospho-c-KIT antibody, as well as anti-phosphotyrosine antibody, were employed to examine c-KIT protein levels following the imatinib mesylate treatment on SK-MEL-28 cells after the transfection of pcDNA 3.1 plasmids, which comprised M1 (c.1668_1679delGTGGAAGGTTGT), M2 (c.1668_1679delGTGGAAGGTTGT/c.1926delA), M3 (c.1936T>G), and M4 (c.1926delA) or wild-type sequence. The relative phosphorylation levels were calculated as the values of p-cKIT to cKIT and p-Tyr to cKIT, as shown in Figures 4, 5 respectively. The results indicated that c-KIT expression was relatively invariable for wild-type or mutant sequences, regardless of whether the cells were activated by SCF, while phospho-c-KIT levels were reduced with the elevated dosages of imatinib mesylate (Figure 4). However, the levels of phospho-c-KIT decreased below the levels of that in cells transfected by wild-type sequences when the dosage of mesylate imatinib reached a certain concentration (0.01, 0.1, 1, and 10 μM) for M3 (c.1936T>G) and M4 (c.1926delA) (Figures 4E–G). For M1 with c-KIT c.1668_1679delGTGGAAGGTTGT mutation alone, phospho-c-KIT showed a decreased level dependent on the imatinib mesylate concentration (Figures 4C,G). For M2 (c.1668_1679delGTGGAAGGTTGT/c.1926delA), phospho-c-KIT was shown with the intermediate levels between M1 and M4 at the imatinib concentrations of 1 and 10 μM, followed by the levels no greater than those in cells transfected by the wild-type sequence at 1 and 10 μM (Figures 4D,G). The phosphotyrosine levels decreased with the increased concentration of imatinib, which was close to that in wild-type cells for mutant M3 at 10 μM (Figure 5F). The phosphotyrosine level in mutant M4 was less than that in wild type at the highest imatinib concentration.

FIGURE 4 | Western blotting detection in SK-MEL-28 cells treated by SCF or imatinib at different concentrations for each plasmid. The ratios of p-cKIT to cKIT accurate to 0.01 are shown at the bottom of each panel. (A) c-KIT and phospho-c-KIT levels after SCF treatment for wild type, M1, M2, M3, and M4. The values of p-cKIT to cKIT are shown in the bar chart for these plasmids treated (+SCF) or not treated by SCF (-SCF). (B) c-KIT and phospho-c-KIT levels in wild-type-transfected cells after imatinib treatment at concentrations of 0, 0.01, 0.1, 1, and 10 μM. (C) c-KIT and phospho-c-KIT levels in M1-transfected cells after imatinib treatment at concentrations of 0, 0.01, 0.1, 1, and 10 μM. (D) c-KIT and phospho-c-KIT levels in M2-transfected cells after imatinib treatment at concentrations of 0, 0.01, 0.1, 1, and 10 μM. (E) c-KIT and phospho-c-KIT levels in M3-transfected cells after imatinib treatment at a concentration of 0, 0.01, 0.1, 1, and 10 μM. (F) c-KIT and phospho-c-KIT levels in M4-transfected cells after imatinib treatment at concentrations of 0, 0.01, 0.1, 1, and 10 μM. (G) Values of p-cKIT to cKIT are displayed in the line chart for each plasmid at imatinib concentrations of 0, 0.01, 0.1, 1, and 10 μM relative to the individual value at 0 μM.

FIGURE 5 | Phosphotyrosine levels were analyzed in SK-MEL-28 cells transfected with various plasmids using Western blotting. p-Tyr and cKIT levels were indicated in cells transfected by wild-type plasmid (A), M1 (B), M2 (C), M3 (D), and M4 (E), respectively, which were treated with imatinib at concentrations of 0, 0.01, 0.1, 1, and 10 μM. The ratios of p-Tyr to cKIT are shown at the bottom of each panel. As shown in the line chart (F), the values of p-Tyr to cKIT were compared to the individual value at 0 μM for each plasmid at the individual concentration of imatinib. p-Tyr denotes phosphotyrosine.

To investigate the effect of M3 or M4 on cell proliferation, MTT assay was performed to assess the proliferation rate. As shown in Figure 6, the proliferation rate was reduced with the enhanced imatinib concentration regardless of whether the treatment duration was 24 h or 48 h in cells with M3 or M4 mutations. While the imatinib concentration reached 100 μM, the proliferation rate no longer decreased (Figure 6). Furthermore, at this point, the proliferation rate in M3- or M4-mutated c-KIT cells was significantly higher than that in cells with wild-type c-KIT (p < 0.01) when they were treated with imatinib for 24 h. However, the proliferation rate was still higher, though not markedly (p > 0.05), in cells with M3- or M4-mutated c-KIT compared to that in wild-type cells when the treatment time was 48 h at the highest imatinib concentration. It is noteworthy that the proliferation rate was remarkably improved in M3-mutated cells treated by imatinib for 48 h at a concentration of 80 µM. The data suggest that M3 or M4 mutation promoted cell proliferation as the imatinib concentration was increased to a relatively high level.

FIGURE 6 | Effects of imatinib at different concentrations (0, 20, 40, 80, and 100 μM, respectively) on the cell proliferation rate of SK-MEL-28 cells transfected with wild-type (WT) or mutated (M3 or M4) c-KIT. (A) Proliferation rate decreased with the increase in imatinib concentration when the cells were treated for 24 h. The proliferation rate was equal to the percentage of the mean OD from each treated group divided by the mean OD from MOCK. (B) Similar trend was discovered when the cells were treated with imatinib for 48 h.

To determine the effect of M3 or M4 on cell apoptosis, SK-MEL-28 cells were transfected by plasmids harboring WT, M1, M3, or M4 mutation, and the cell apoptotic rate was measured by flow cytometry. As shown in Figure 7, at the highest imatinib concentration (100 μM), the late apoptotic rate in M3- or M4-mutated cells was lower than that in M1-mutated cells. This indicated that M3 or M4 mutation inhibited cell apoptosis when the imatinib concentration was at a high level.

FIGURE 7 | Cell apoptotic rate was assessed by flow cytometry analysis in cells transfected with wild-type or mutated (M1, M3, or M4) c-KIT genes after imatinib treatment. Q1-LL indicates the live cells, Q1-LR early apoptotic cells, Q1-UR late apoptotic cells, and Q1-UL dead cells. The data showed that the late apoptotic rate of M3- or M4-mutated cells was lower than that of M1-mutated cells when they were treated with 100 μM imatinib.

These results suggest that the novel mutations in exon 13 of c-KIT present intolerance to imatinib treatment.

DISCUSSION

Targeted drugs played a key role in adjuvant or neoadjuvant treatments of GIST patients aiming at the c-KIT or PDGFRA gene for the high mutation frequency in China. Despite the fact that c-KIT mutation occurred at a lower incidence than BRAF and NRAS genes in most of the melanoma patients, it represented 36% of acral lentiginous melanomas in cutaneous melanoma (Ponti et al., 2017). c-KIT-targeted therapies enabled the single or combined treatments by receptor tyrosine kinase inhibitors such as imatinib or sunitinib in primary or metastatic melanoma patients. Resistance must be taken into account in the process of using c-KIT-targeted drugs. Mutations in exons 13 and 17 of the c-KIT gene account for most of the resistance to imatinib mesylate, which was considered one of the most common receptor tyrosine kinase inhibitors. However, whether the novel mutations in exon 13 of the c-KIT gene were responsible for imatinib intolerance in the GIST or melanoma patients remains elusive.

Using Sanger sequencing, patient 1 was found to own a rare mutation c.1668_1679delGTGGAAGGTTGT, causing the amino acid loss of p.557_560del WKVV and gain of p.Q556H mutation in c-KIT exon 11, as well as a rare deletion mutation c.1926delA in exon 13, leading to the production of the stop codon (p.K642S*FS). These alterations were present in the biopsy specimen and the surgical specimen of the primary tumor, which might be related to the intrinsic resistance to tyrosine kinase inhibitors. It is thus predicted that intolerance to imatinib mesylate for this patient resulted from the inherent genotype status, instead of that from the acquired drug-induced mutation. It is reported that the latter single-nucleotide deletion causing codon 644 frameshift mutation was only detected in the recurrent duodenal GISTs as the secondary mutation (Vu et al., 2005). Here is the first demonstration of the role of the single-nucleotide deletion in the primary and recurrent tumors of the gastric GIST patient. For patient 2, a point mutation c.1936T>G in c-KIT exon 13 produces the missense amino acid alteration p.Y646D, which was not reported in GISTs or melanoma previously. Considering that the effective therapies were lacking and the outcome was routinely poor for metastatic melanoma, determination of the c-KIT mutation status was an alternative to the treatment.

In order to further determine whether both rare mutations contributed to the efficacy of receptor tyrosine kinase inhibitors, the wild-type plasmid and the mutant plasmids harboring either c.1926delA or c.1668_1679delGTGGAAGGTTGT or their combination as well as the c.1936T>G point mutation were prepared, as described previously (Isotani et al., 2010). SK-MEL-28, one of the melanoma cell lines, was used as the cell model to be transfected by these plasmids accordingly. The results showed that imatinib mesylate was able to respond to the c-KIT antibody at the phosphorylated state, instead of the unphosphorylated state, for both wild-type and mutated sequences of the c-KIT gene. p-cKIT levels were not correlated with SCF stimulation for mutated c-KIT. The c-KIT protein could be activated automatically without SCF for mutated isoforms such as those translated by M3 and M4 mutants. These data were proved by additional literature (Kim et al., 2021). Thus, the levels in those cells would not change upon SCF stimulation. In addition, the cells harboring the mutation c.1668_1679delGTGGAAGGTTGT alone made response to imatinib mesylate in a concentration-dependent way, whereas those with c.1926delA or c.1936T>G produced gradually decreased levels of the phosphorylated c-KIT compared to those in cells transfected by M1, despite the elevated drug concentration. The phosphotyrosine level in M3-transfected cells declined to a level close to that in WT-transfected cells, while the cells with M4 mutation exhibited the c-KIT expression under the level in WT-transfected cells with the increase in imatinib concentration. Our study also showed that M3 or M4 mutation promoted cell proliferation and inhibited cell apoptosis. This is suggestive of the resistance to imatinib mesylate for cells with c.1926delA or c.1936T>G mutation, which is the first demonstration that the mutations are imatinib-intolerant. In addition, according to the published study (Lee et al., 2006), patients with the wild-type c-KIT were able to show certain sensitivity to imatinib treatment; our data were concordant with this. The results showed that the cells with M3 and M4 mutations tended to suppress the phosphorylation level of c-KIT in cells with M1 or M2 mutation, which indirectly mirrored the resistance to imatinib. This was in agreement with the previous report that mutations in c-KIT exon 13 typically represented inhibition by imatinib (Gajiwala et al., 2009). Patients with high phosphorylation levels are prone to descend continuously, while those with low phosphorylation levels have a tendency to fall to the bottom. For that p-cKIT declined significantly at the low concentration of imatinib, it was representative of the low level at the high concentration of imatinib, which on the other hand suggests the very little space to the bottom level. Interestingly, our data also suggest that either c.1926delA alone or concurrent with c.1668_1679delGTGGAAGGTTGT could contribute to imatinib resistance. Therefore, it was reasonable that the tumor of patient 1 had progressed with a shorter interval since imatinib mesylate was given after surgical resection of the primary tumor. At the same time, it implies that the next generation of receptor tyrosine kinase inhibitors such as sunitinib could be the alternative for patients with either of the alterations.

It is worthy to be noted that the metastatic tissue was removed from case 1 after the primary surgery; however, either the c-KIT or PDGFRA gene was not examined at that time. This could to, some extent, lead to the conclusion that this recurrent tissue was diagnosed as gastric wall plexiform fibromyxoma at the very beginning, which was not concordant with the report that gastric plexiform fibromyxomas were typical of benign tumors free from recurrence or metastasis (Pei et al., 2020; Arslan et al., 2021). This diagnosis would result in different treatments for GISTs. It should be fully realized that sequencing c-KIT gene mutations was inevitable in the assisted diagnosis of GIST patients. qPCR acts as a sensitive method to quantitatively analyze point mutations and short sequence deletions, and it was not suitable for examining longer fragment loss. Sanger sequencing or NGS may be an alternative to confirm the presence of c-KIT or PDGFRA mutations, whereas Sanger sequencing was selected for its cost-effectiveness in this study. Thus, the identification of the novel c-KIT alterations in this study provides potential and actionable biomarkers for GIST diagnosis and therapy.

However, limitations were not ignored as well, one of which was that certified GIST cell lines that currently were not available from the cell bank in and out of China. The primary GIST and melanoma cells should be a preferable option. The coding region between exons 11 and 13 represents amino acids from 550 to 663, whereas the phosphorylated c-KIT antibody contains tyrosine 568 and 570. So the plasmids used to express the mutated c-KIT protein could induce the alteration of c-KIT phosphorylation. The mutations in c-KIT exons 11 and 13 reside at the cytoplasmic tyrosine kinase domains, which are activated by extracellular ligand dimerization or directly autophosphorylated. The active conformation could phosphorylate c-KIT or other cell growth- or inhibition-associated intermediates, resulting in the sensitivity or desensitivity of imatinib. Thus, it is very important to test this hypothesis in GIST or melanoma-derived cells. This was another drawback of our study. It would be better for the two novel mutations to be introduced into the cells using CRISPR-Cas9 technology to directly target the chromosome, which avoided the background of the wild type. These point out the potential directions for investigation into the c-KIT-inducing drug resistance mechanism.

In summary, our study could provide significant guidance for clinical drug management in GIST or melanoma patients with these mutations in the c-KIT gene. However, it is necessary to study a greater number of samples and analyze the frequency of the mutations studied in the patients.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.

ETHICS STATEMENT

The studies involving human participants were reviewed and approved by the Ethics Committee of the Affiliated Cancer Hospital of Zhengzhou University. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

AUTHOR CONTRIBUTIONS

CY and YG designed the study. HZ, WZ, and QX provided the pathological samples. LJ and WD analyzed the imaging of the patients. CY, CZ, KY, BW, and JM performed the molecular experiments and analyzed the data. CY and YG drafted the manuscript. All authors reviewed the manuscript and agreed to present this work.

FUNDING

This study was supported by the Nursery Foundation of Henan Cancer Hospital and grants from Key Project of Science and Technology of Henan Province (Grant No. 172102310018) and Major Program of Science and Technology of Henan Province (Grant No. 161100311500).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

REFERENCES

Arslan, M. E., Li, H., Fu, Z., Jennings, T. A., and Lee, H. (2021). Plexiform Fibromyxoma: Review of Rare Mesenchymal Gastric Neoplasm and its Differential Diagnosis. Wjgo 13 (5), 409–423. doi:10.4251/wjgo.v13.i5.409

Blay, J.-Y., Shen, L., Kang, Y.-K., Rutkowski, P., Qin, S., Nosov, D., et al. (2015). Nilotinib versus Imatinib as First-Line Therapy for Patients with Unresectable or Metastatic Gastrointestinal Stromal Tumours (ENESTg1): a Randomised Phase 3 Trial. Lancet Oncol. 16 (5), 550–560. doi:10.1016/S1470-2045(15)70105-1

Calipel, A., Landreville, S., De La Fouchardière, A., Mascarelli, F., Rivoire, M., Penel, N., et al. (2014). Mechanisms of Resistance to Imatinib Mesylate in KIT-Positive Metastatic Uveal Melanoma. Clin. Exp. Metastasis 31 (5), 553–564. doi:10.1007/s10585-014-9649-2

Carvajal, R. D., Antonescu, C. R., Wolchok, J. D., Chapman, P. B., Roman, R. A., Teitcher, J., et al. (2011). KIT as a Therapeutic Target in Metastatic Melanoma. JAMA 305 (22), 2327–2334. doi:10.1001/jama.2011.746

Davis, E. J., Johnson, D. B., Sosman, J. A., and Chandra, S. (2018). Melanoma: What Do All the Mutations Mean?Cancer 124 (17), 3490–3499. doi:10.1002/cncr.31345

Delyon, J., Lebbe, C., and Dumaz, N. (2020). Targeted Therapies in Melanoma beyond BRAF: Targeting NRAS-Mutated and KIT-Mutated Melanoma. Curr. Opin. Oncol. 32 (2), 79–84. doi:10.1097/CCO.0000000000000606

Gajiwala, K. S., Wu, J. C., Christensen, J., Deshmukh, G. D., Diehl, W., DiNitto, J. P., et al. (2009). KIT Kinase Mutants Show Unique Mechanisms of Drug Resistance to Imatinib and Sunitinib in Gastrointestinal Stromal Tumor Patients. Proc. Natl. Acad. Sci. U.S.A. 106 (5), 1542–1547. doi:10.1073/pnas.0812413106

Guo, J., Si, L., Kong, Y., Flaherty, K. T., Xu, X., Zhu, Y., et al. (2011). Phase II, Open-Label, Single-Arm Trial of Imatinib Mesylate in Patients with Metastatic Melanoma Harboringc-KitMutation or Amplification. Jco 29 (21), 2904–2909. doi:10.1200/JCO.2010.33.9275

Gutiérrez-Castañeda, L. D., Nova, J. A., and Tovar-Parra, J. D. (2020). Frequency of Mutations in BRAF, NRAS, and KIT in Different Populations and Histological Subtypes of Melanoma: a Systemic Review. Melanoma Res. 30 (1), 62–70. doi:10.1097/CMR.0000000000000628

Isotani, M., Yamada, O., Lachowicz, J. L., Tamura, K., Yagihara, H., Fujino, Y., et al. (2010). Mutations in the Fifth Immunoglobulin-like Domain of Kit Are Common and Potentially Sensitive to Imatinib Mesylate in Feline Mast Cell Tumours. Br. J. Haematol. 148 (1), 144–153. doi:10.1111/j.1365-2141.2009.07926.x

Jiang, X., Zhou, J., Yuen, N. K., Corless, C. L., Heinrich, M. C., Fletcher, J. A., et al. (2008). Imatinib Targeting of KIT-Mutant Oncoprotein in Melanoma. Clin. Cancer Res. 14 (23), 7726–7732. doi:10.1158/1078-0432.CCR-08-1144

Kim, J. O., Kim, K. H., Baek, E. J., Park, B., So, M. K., Ko, B. J., et al. (2021). A Novel anti‐c‐Kit Antibody-Drug Conjugate to Treat Wild‐type and Activating‐mutant c‐Kit‐positive Tumors. Mol. Oncol. 16, 1290–1308. doi:10.1002/1878-0261.13084

Kobayashi, M., Kuroki, S., Tanaka, Y., Moriya, Y., Kozutumi, Y., Uehara, Y., et al. (2015). Molecular Changes Associated with the Development of Resistance to Imatinib in an Imatinib-Sensitive Canine Neoplastic Mast Cell Line Carrying aKITc.1523A>T Mutation. Eur. J. Haematol. 95 (6), 524–531. doi:10.1111/ejh.12526

Kong, Y., Si, L., Zhu, Y., Xu, X., Corless, C. L., Flaherty, K. T., et al. (2011). Large-scale Analysis of KIT Aberrations in Chinese Patients with Melanoma. Clin. Cancer Res. 17 (7), 1684–1691. doi:10.1158/1078-0432.CCR-10-2346

Lee, J.-L., Kim, J. Y., Ryu, M.-H., Kang, H. J., Chang, H. M., Kim, T.-W., et al. (2006). Response to Imatinib in KIT- and PDGFRA-Wild Type Gastrointestinal Stromal Associated with Neurofibromatosis Type 1. Dig. Dis. Sci. 51 (6), 1043–1046. doi:10.1007/s10620-006-8003-1

Lee, J. H., Kim, Y., Choi, J. W., and Kim, Y. S. (2013). Correlation of Imatinib Resistance with the Mutational Status of KIT and PDGFRA Genes in Gastrointestinal Stromal Tumors: a Meta-Analysis. J. Gastrointestin Liver Dis. 22 (4), 413–418.

Li, G. Z., and Raut, C. P. (2019). Targeted Therapy and Personalized Medicine in Gastrointestinal Stromal Tumors: Drug Resistance, Mechanisms, and Treatment Strategies. Ott Vol. 12, 5123–5133. doi:10.2147/OTT.S180763

Mazurenko, N. N., Gulyaeva, L. F., and Kushlinskii, N. E. (2017). The Genetic Markers and Targets of Target Therapy of Melanoma. Klin Lab. Diagn. 62 (6), 363–371. doi:10.18821/0869-2084-2017-62-6-363-371

Moltara, M. E., Novakovic, S., Boc, M., Bucic, M., Rebersek, M., Zadnik, V., et al. (2018). Prevalence of BRAF, NRAS and C-KIT Mutations in Slovenian Patients with Advanced Melanoma. Radiol. Oncol. 52 (3), 289–295. doi:10.2478/raon-2018-0017

Nakano, Y., Kobayashi, M., Bonkobara, M., and Takanosu, M. (2017). Identification of a Secondary Mutation in the KIT Kinase Domain Correlated with Imatinib-Resistance in a Canine Mast Cell Tumor. Vet. Immunol. immunopathology 188, 84–88. doi:10.1016/j.vetimm.2017.05.004

Pei, J.-Y., Tan, B., Liu, P., Cao, G.-H., Wang, Z.-S., and Qu, L.-L. (2020). Gastric Plexiform Fibromyxoma: A Case Report. Wjcc 8 (22), 5639–5644. doi:10.12998/wjcc.v8.i22.5639

Ponti, G., Manfredini, M., Greco, S., Pellacani, G., Depenni, R., Tomasi, A., et al. (2017). BRAF, NRAS and C-KIT Advanced Melanoma: Clinico-Pathological Features, Targeted-Therapy Strategies and Survival. Ar 37 (12), 7043–7048. doi:10.21873/anticanres.12175

Reddy, B. Y., Miller, D. M., and Tsao, H. (2017). Somatic Driver Mutations in Melanoma. Cancer 123 (S11), 2104–2117. doi:10.1002/cncr.30593

Shi, Y., and Liang, X. (2014). Drug Resistance Mechanism and New Therapy Strategy Progression in Targeted Treatment of Gastrointestinal Stromal Tumors. Zhonghua Wei Chang Wai Ke Za Zhi 17 (4), 321–325. doi:10.3760/cma.j.issn.1671-0274.2014.04.006

Slavin, T. P., Niell-Swiller, M., Solomon, I., Nehoray, B., Rybak, C., Blazer, K. R., et al. (2015). Clinical Application of Multigene Panels: Challenges of Next-Generation Counseling and Cancer Risk Management. Front. Oncol. 5, 208. doi:10.3389/fonc.2015.00208

Tan, S., Chen, P., Ji, J., Guo, S., Yu, D., Asakawa, T., et al. (2018). Genomic Subtypes of GISTs for Stratifying Patient Response to Sunitinib Following Imatinib Resistance: A Pooled Analysis and Systematic Review. Dis. Markers 2018, 1–9. doi:10.1155/2018/1368617

Tran, A., and Tawbi, H. A. (2012). A Potential Role for Nilotinib inKIT-Mutated Melanoma. Expert Opin. Investig. Drugs 21 (6), 861–869. doi:10.1517/13543784.2012.679341

Vu, H. A., Xinh, P. T., Kikushima, M., Zhu, Y., Tokuhara, M., Tani, M., et al. (2005). A Recurrent Duodenal Gastrointestinal Stromal Tumor with a Frameshift Mutation Resulting in a Stop Codon inKIT Exon 13. Genes Chromosom. Cancer 42 (2), 179–183. doi:10.1002/gcc.20110

Yuval, J. B., and Elazary, R. (2018). The True Incidence of Gastric GIST: an Update. Obes. Surg. 28 (6), 1787. doi:10.1007/s11695-018-3235-7

Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Yan, Zhao, Yang, Zhou, Jing, Zhao, Dou, Xia, Ma, Wei and Guo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.