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ISSN 1664-8714
ISBN 978-2-8325-4795-3
DOI 10.3389/978-2-8325-4795-3

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Molecular markers and targeted therapy for hepatobiliary tumors

This ebook is split into several files; this is part two of the ebook, please see the overview text of the Research Topic page to access the first part of the ebook.

Topic editors

Yunfei Xu – Shandong University, China

Zongli Zhang – Qilu Hospital of Shandong University, China

Hongda Liu – Nanjing Medical University, China

Xuesong Gu – Beth Israel Deaconess Medical Center, Harvard Medical School, United States

Citation

Xu, Y., Zhang, Z., Liu, H., Gu, X., eds. (2024). Molecular markers and targeted therapy for hepatobiliary tumors. Lausanne: Frontiers Media SA. doi: 10.3389/978-2-8325-4795-3

Table of Contents

Clinicopathological Characteristics and Molecular Phenotypes of Primary Hepatic Lymphoma

Ai-Yan Xing, Xin-Zhe Dong, Liu-Qing Zhu, Long Liu, Dong Sun and Sen Guo

FBXO9 Mediates the Cancer-Promoting Effects of ZNF143 by Degrading FBXW7 and Facilitates Drug Resistance in Hepatocellular Carcinoma

Zhenyu Wang, Xiaoxia Chen, Lianer Zhou, Xinge Zhao, Chao Ge, Fangyu Zhao, Haiyang Xie, Taoyang Chen, Hua Tian, Hong Li and Jinjun Li

Effectiveness of Trastuzumab Combined With Capecitabine Treatment in a Patient With Hilar Cholangiocarcinoma Complicated by Liver Metastases With an ERBB2-Activating Mutation: A Case Report

Daobing Zeng, Xiaofei Zhao, Liang Di, Luyan Lou, Yanfang Song, Yanrui Zhang, Huanhuan Liu and Guangming Li

CSTF2 Promotes Hepatocarcinogenesis and Hepatocellular Carcinoma Progression via Aerobic Glycolysis

Zhimin Chen, Weijie Hao, Jingzhi Tang, Wei-Qiang Gao and Huiming Xu

ZFAND5 Is an Independent Prognostic Biomarker of Perihilar Cholangiocarcinoma

Pei Liu, Yijia Wang and Lingling Duan

Development and Validation of a 6-Gene Hypoxia-Related Prognostic Signature For Cholangiocarcinoma

Qi Sun, Huxia Wang, Baoan Xiao, Dong Xue and Guanghui Wang

Utilizing Gut Microbiota to Improve Hepatobiliary Tumor Treatments: Recent Advances

Hao Qin, Baowen Yuan, Wei Huang and Yan Wang

Research Progress on Long Noncoding RNAs and N6-Methyladenosine in Hepatocellular Carcinoma

Wenjie Zhang, Wenlong Wu, Qiang Meng, Long Yang, Juzheng Yuan, Zelin Tian, Rui Ding, Xuan Zhang, Jianlin Wang and Kaishan Tao

The expression, clinical relevance, and prognostic significance of HJURP in cholangiocarcinoma

Yang Yang, Jinyan Yuan, Zhenzhong Liu, Wenwen Cao and Pei Liu

Five-lipoxygenase-activating protein-mediated CYLD attenuation is a candidate driver in hepatic malignant lesion

Kun-kai Su, Xue-hua Zheng, Christian Bréchot, Xiao-ping Zheng, Dan-hua Zhu, Rong Huang, Yan-hong Zhang, Jing-jing Tao, Yi-jia Lou and Lan-juan Li

Design and Fabrication of a Liver-on-a-chip Reconstructing Tissue-tissue Interfaces

Jing Liu, Chong Feng, Min Zhang, Feng Song and Haochen Liu

Construction and validation of a prognostic model of pyroptosis related genes in hepatocellular carcinoma

Guanqun Li, Dongxin Zhang, Chaowei Liang, Chaojie Liang and Jixiang Wu

Parthenolide and arsenic trioxide co-trigger autophagy-accompanied apoptosis in hepatocellular carcinoma cells

Juan Yi, Xia Gong, Xiao-Yang Yin, Li Wang, Jin-Xia Hou, Jing Chen, Bei Xie, Gang Chen, Li-Na Wang, Xiao-Yuan Wang, Da-Chun Wang and Hu-Lai Wei

Prognostic significance of CDC20 expression in malignancy patients: A meta-analysis

Feng Xian, Xuegang Yang and Guohui Xu

KIAA1429 regulates alternative splicing events of cancer-related genes in hepatocellular carcinoma

Zhao-chen Liu, Lu-Hao Li, Ding-Yang Li, Zhi-Qiang Gao, Dong Chen, Bin Song, Bing-Hua Jiang and Xiao-wei Dang

Combining the SMAC mimetic LCL161 with Gemcitabine plus Cisplatin therapy inhibits and prevents the emergence of multidrug resistance in cholangiocarcinoma

Sunisa Prasopporn, Orawan Suppramote, Ben Ponvilawan, Chanette Jamyuang, Jantappapa Chanthercrob, Amphun Chaiboonchoe, Pimkanya More-Krong, Kamonchanok Kongsri, Monthira Suntiparpluacha, Rawisak Chanwat, Krittiya Korphaisarn, Seiji Okada, Somponnat Sampattavanich and Siwanon Jirawatnotai

WWOX-rs13338697 genotype predicts therapeutic efficacy of ADI-PEG 20 for patients with advanced hepatocellular carcinoma

Yu-De Chu, Hui-Fen Liu, Yi-Chen Chen, Chun-Hung Chou and Chau-Ting Yeh

GPBAR1 is associated with asynchronous bone metastasis and poor prognosis of hepatocellular carcinoma

Nan Chen, Jieqing Wang, Lei Zhou, Baiqiang Hu, Yinzhong Chen and Zhuangchen Zhu

SLC-0111, an inhibitor of carbonic anhydrase IX, attenuates hepatoblastoma cell viability and migration

Katja Eloranta, Marjut Pihlajoki, Emmi Liljeström, Ruth Nousiainen, Tea Soini, Jouko Lohi, Stefano Cairo, David B. Wilson, Seppo Parkkila and Markku Heikinheimo

Case report: Obstructive jaundice caused by biliary cystadenoma

Yun-cheng Li, An-da Shi and Kang-shuai Li

CEP55 as a promising biomarker and therapeutic target on gallbladder cancer

Maotuan Huang, Fuxiu Zhong, Mingyuan Chen, Lingju Hong, Weihong Chen, Xiahenazi Abudukeremu, Feifei She and Yanling Chen

System analysis based on the lysosome-related genes identifies HPS4 as a novel therapy target for liver hepatocellular carcinoma

Ke-Jie He and Zhiqiang Nie

Preclinical evidence for anaplastic lymphoma kinase inhibitors as novel therapeutic treatments for cholangiocarcinoma

Kyaw Zwar Myint, Mireia Sueca-Comes, Pamela Collier, Brinda Balasubramanian, Simran Venkatraman, John Gordan, Abed M. Zaitoun, Abhik Mukherjee, Arvind Arora, Noppadol Larbcharoensub, Chinnawut Suriyonplengsaeng, Kanokpan Wongprasert, Tavan Janvilisri, Dhanny Gomez, Anna M. Grabowska, Rutaiwan Tohtong, David O. Bates and Kiren Yacqub-Usman

Treatment with palbociclib and tislelizumab for CDKN2A-mutated and PD-L1-positive advanced intrahepatic cholangiocarcinoma: a case report and literature review

Yajun Liang, Liya Hu, Huanlei Wu, Tiejun Yin and Jun Zhang

ORIGINAL RESEARCH
published: 27 June 2022
doi: 10.3389/fonc.2022.906245

Clinicopathological Characteristics and Molecular Phenotypes of Primary Hepatic Lymphoma

Ai-Yan Xing ¹, Xin-Zhe Dong ², Liu-Qing Zhu ³, Long Liu ¹, Dong Sun ⁴* and Sen Guo ⁴*

¹ Department of Pathology, Shandong University Qilu Hospital, Jinan, China, ² Department of Radiation Oncology, Shandong University Qilu Hospital, Jinan, China, ³ Nanjing Geneseeq Technology Inc., Nanjing, China, ⁴ Department of General Surgery, Shandong University Qilu Hospital, Jinan, China

Edited by:
Xuesong Gu, Harvard Medical School, United States

Reviewed by:
Bing Han, The Affiliated Hospital of Qingdao University, China
Zongzhen Xu, The First Affiliated Hospital of Shandong First Medical University, China
Xiufeng Niu, Shandong University of Traditional Chinese Medicine, China

*Correspondence:
Dong Sun
22993681@qq.com
Sen Guo
gssduqlyy@163.com

Specialty section:
This article was submitted to Cancer Molecular Targets and Therapeutics, a section of the journal Frontiers in Oncology

Received: 28 March 2022

Accepted: 03 May 2022

Published: 27 June 2022

Citation:
Xing A-Y, Dong X-Z, Zhu L-Q, Liu L, Sun D and Guo S (2022) Clinicopathological Characteristics and Molecular Phenotypes of Primary Hepatic Lymphoma. Front. Oncol. 12:906245. doi: 10.3389/fonc.2022.906245

Primary hepatic lymphoma (PHL) is a rare malignant tumor, occurring in 0.016% of non-Hodgkin’s lymphoma (NHL). The common histological subtype is diffuse large B-cell lymphoma (DLBCL). Due to the rarity of tumor, clinicopathological characteristics and molecular phenotypes of PHL are limited. Seven patients with PHL (primary liver DLBCL) and 13 cases of liver involvement by DLBCL diagnosed between 2014 and 2021 in our hospital were included. The genetic features were also compared between the two groups by next-generation sequencing (NGS). Differential gene expression and pathway enrichment analysis were also performed. There were some discrepancies on presenting symptoms, pathological characteristics, laboratory data, and prognosis between PHL and DLBCL-liver groups. No same mutation was found between PHL and DLBCL-liver groups by NGS. Differential gene expression analysis discovered some up- and downregulated genes in PHL compared with the DLBCL-liver group. Upregulated genes were enriched in metabolic pathways, and downregulated genes were enriched in the HTLV-1 infection pathway. PHL is a distinct entity, with unique molecular features compared to liver involvement of systemic lymphoma. Kaplan–Meier analysis showed that the prognosis of the PHL group was better than that of the DLBCL-liver group. Understanding the clinicopathological and molecular features of PHL would help to direct clinical treatment.

Keywords: primary hepatic lymphoma, diffuse large B-cell lymphoma, gene features, next-generation sequencing, liver

Introduction

Primary hepatic lymphoma (PHL) is a very rare tumor accounting for 0.1% of all liver malignant tumors (1). For non-Hodgkin’s lymphoma (NHL), it constitutes about 0.016% of all NHLs and 0.4% of all primary extranodal NHLs (2). PHL is generally considered as a proliferative disease of lymphoid tissue that originates from intrahepatic lymphatic and residual hematopoietic tissue, and is characterized by no extrahepatic infiltration (3). Liver involvement is also common and present in more than 50% of cases of lymphoma (4). However, the prognosis of PHL patients is better than that of systemic lymphoma with liver involvement (5). We speculated that the clinical features and molecular phenotypes of PHL might be different, compared with liver involvement by lymphoma. Because of the rarity, there is a challenge on the diagnosis and therapy of PHL. The investigation of clinicopathological features and molecular phenotypes of PHL might have an important value on clinical management.

Diffuse large B-cell lymphoma (DLBCL) has been identified as the main type of PHL (6). Most reports of PHL published are mainly case reports, and there are few reports on the molecular characteristics of PHL at present. In this study, 7 cases of PHL (all were primary liver DLBCL) and 13 cases of liver involvement by DLBCL (DLBCL-liver) were analyzed retrospectively between 2014 and 2021 in our hospital. The pathological characteristics, etiologic factors, treatment, and follow-up were collected and compared between PHL and DLBCL-liver groups. We also attempted to reveal the discrepancy of molecular phenotypes between the two groups by NGS, which may help to facilitate an accurate diagnosis and to identify tumor biomarkers for contemporary systemic therapies of PHL.

Materials and Methods

Patients and Tissue Specimens

DLBCL was diagnosed according to the morphology and immunohistochemistry criteria by two pathologists in Shandong University Qilu Hospital between 2014 and 2021. Among them, 7 patients were described as having liver primary lymphoma at presentation with no involvement of spleen, lymph node, bone marrow, or other lymphoid tissues. The remaining 13 cases had DLBCL involvement of liver and extrahepatic tissue, such as spleen or lymph nodes. Clinicopathological information of the 20 patients was available from pathological records and patient files, including age, sex, presenting symptoms, laboratory examination, imaging, therapeutic regimens, and follow-up durations. Informed consents of all patients were obtained. The Ethics Committee of Shandong University approved this study, and the procedures involving human subjects were in accordance with the Declaration of Helsinki.

Nucleic Acid Extraction and Library Construction

Genomic DNA and RNA were extracted from tumor FFPE samples using the QIAamp DNA FFPE Tissue Kit and the miRNeasy FFPE Kit, respectively. The fragment DNA was generated with Bioruptor (Diagenode, Bioruptor UCD-200). Ribosomal RNA was removed using RNase H followed by library preparation using the KAPA Stranded RNA-seq Kit with RiboErase (HMR) (KAPA Biosystems). Libraries were constructed using the KAPA Hyper DNA Library Prep Kit (KAPA Biosystem, KK8504). The dual-indexed sequencing libraries were PCR amplified with KAPA HiFi Hot start-ready Mix (KAPA, KK2602) for 4–6 cycles, then cleaned up by purification beads (Corning, AxyPrep Fragment Select-I kit, 14223162). Library concentration and quality were determined by the Qubit 3.0 system (Invitrogen) and Bioanalyzer 2100 (Agilent, Agilent HS DNA Reagent, 5067-4627), respectively.

Hybrid Selection and Next-Generation Sequencing

The probes for targeted sequencing covered exons and selected introns of 475 leukemia- and lymphoma-related genes and were produced by Nanjing Geneseeq Biotechnology (Nanjing, China). RNA sequencing was performed by whole transcriptome RNASEQ detection (30M). The capture probes were added to the pre-library mixture in 5 min, and the solution hybridization was performed for 16–18 h at 65°C. After hybridization was completed, the captured targets were selected by pulling down the biotinylated probe/target hybrids using streptavidin-coated magnetic beads, and off-target library was removed using wash buffer. Then, the samples were purified by AMPure XP beads, quantified by qPCR (Kapa), and sized on Bioanalyzer 2100 (Agilent, Agilent HS DNA Reagent, 5067-4627). Libraries were normalized to 2.5 nM and were sequenced as paired 150-bp reads on Illumina HiSeq 4000 according to the manufacturer’s instrument.

Sequencing Data Processing

Base calling was performed on bcl2fastq v2.16.0.10 (Illumina, Inc.) to generate sequence reads in FASTQ format (Illumina 1.8+ encoding). High-quality reads were mapped to the human genome (hg19, GRCh37 Genome Reference Consortium Human Reference 37) using the BWA aligner 0.7.12 with the BWA-MEM algorithm with default parameters to create SAM files. Picard 1.119 was used to mark duplicate reads by generating BAM files that were then sorted according to chromosome coordinates. The Genome Analysis Toolkit (GATK, version 3.4-0) was used to locally realign the BAM files at intervals with indel mismatches and recalibrate base quality scores of reads in BAM files.

Differential Gene Expression Analysis

Quality control was performed with Trimmomatic (version 0.33 (7) STAR (version 2.5.3a) (8) was used for transcriptome mapping followed by isoform- and gene-level quantification, which was performed by RSEM (version 1.3.0 (9). Differential expression analysis was conducted by R packages DESeq2 (version 1.16.1 (10) and edgeR (version 3.18.1 (11) Differentially expressed genes were selected by fold change > 2 and p-value < 0.05. Corresponding volcano plots and heatmaps were generated by in-house R scripts. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed by ClusterProfiler (version 3.4.4 (12) Unpaired, two-tailed Student’s t-test was used to determine differences between two groups. p < 0.05 was considered statistically significant.

Statistical Analysis

Fisher’s exact probability tests were used for between-group comparisons. Overall survival (OS) analyses were performed using Kaplan–Meier plots and log-rank tests. Cox proportional hazards regression analysis was used for the evaluation of prognostic factors and calculation of hazard ratios (HRs) along with their 95% confidence intervals (CIs). OS was defined from the date of being diagnosed to the date of death. p < 0.05 was considered statistically significant. We used R (version 3.6.1) and R Bioconductor packages for all the above-mentioned analyses.

Results

Clinicopathological Characteristics

The clinicopathological characteristics of 7 patients with PHL and 13 patients with DLBCL-liver are summarized in Table 1. The median age of the patients in the two groups was 64 and 57 years, respectively (range, 31–78 and 29–71 years). Of the 7 patients with PHL, there were 4 male patients and 3 female patients. In the DLBCL-liver group, there were 10 male patients and 3 female patients. There were 4 patients (57.1%) with liver mass by health examination without any symptoms, and 3 patients (42.9%) with abdominal pain or distention in the PHL group. For the DLBCL-liver group, the common presenting symptoms were abdominal pain (8 cases; 61.5%), fever, and fatigue (4 cases; 30.8%). The remaining case showed no symptoms. The symptoms between the two groups showed statistical difference (Figure 1A; p = 0.031). Liver solitary or multiple masses were the main clinical image features of PHL, while liver and spleen (solitary or multiple) masses, hepatosplenomegaly, or lymphadenectasis were found in the DLBCL-liver group. As subtypes of DLBCL, the GCB (germinal center B cell) ratio and the non-GCB ratio were 4:3 and 11:2 in PHL and DLBCL-liver groups, respectively. A better prognosis of non-GCB was observed compared with the GCB subtype (Supplementary Figure 1A), which might be due to the fact that most non-GCBs were from PHL. The chemotherapy of the two groups was mainly the rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) regimen.

Table 1 | The main clinical characteristics of 7 patients (Case nos. 1–7) with PHL and 13 patients with liver involvement of lymphoma (Case nos. 8–20).

Figure 1 | Comparison of clinical parameters of the PHL and the DBCLC-liver group. Clinical symptom was significantly different between the PHL and the DLBCL-liver group (A; p = 0.031). More upregulated CA125 was found in the DLBCL-liver group, although there is no significant difference (p = 0.051). Kaplan–Meier analysis showed that the OS of PHL was better than that of the DBCLC-liver group (B). Compared with the CA125 upregulation group, the CA125 normal group showed better prognosis (C). The longer survival of the without symptom group was observed compared with the symptom group (D).

Laboratory data revealed moderate to severe elevation of aspartate aminotransferase (AST) and alanine aminotransferase (ALT) in 3 patients (42.9%) in the PHL group, and in 5 patients (38.5%) in the DLBCL-liver group. By comparative analysis, we found that both the AST/ALT ≤ 1.5 and AST/ALT ≤ 2.0 groups might appear to have a better prognosis than the AST/ALT>1.5 and AST/ALT>2.0 groups (Supplementary Figures 1B, C), partly suggesting the important role of AST and ALT in DLBCL. As an important prognostic pretreatment parameter (13), serum lactate dehydrogenase (LDH) level was at an average of 490 U/L in the PHL and 725 U/L in the DLBCL-liver group, but was not statistically different (p = 0.3712). However, the longer survival of the LDH ≤ 300 U/L group was observed compared with the LDH>300 U/L group (Supplementary Figure 1D). Hepatitis B viral (HBV) infections were found in both PHL (4 cases; 57.1%) and the systemic groups (5 cases; 38.5%). It seems that there is a higher HBV-positive rate in the PHL group, but Fisher’s exact test showed that there is no significant difference between the two groups (p = 0.332) due to the small sample size. The α-fetoprotein (AFP) and the carcinoembryonic antigen (CEA) levels were normal in all patients tested in both groups. Additionally, upregulated CA125 was found in 6 patients (46.2%) in the DLBCL-liver group, but none in the PHL group (Figure 1A; p = 0.051).

Study date ends on October 31, 2021; in the PHL group, 1 patient died of hepatic encephalopathy. Except for 1 patient lost to follow-up, the survival time ranged from 1 to 80 months. In the DLBCL-liver group, 5 patients died of lymphoma. Moreover, 2 patients were lost to follow-up, and the survival time was 19 months (range, 10 days to 52 months). Though median survival time has not been reached, of the total of 20 cases, Kaplan–Meier analysis showed a better prognosis of the PHL group than the DLBCL-liver group (Figure 1B). In addition, the normal CA125 group and the without symptom group showed a longer survival time (Figures 1C, D). The log-rank analysis was also performed. The results showed that CA125 was an independent prognostic factor (Supplementary Table 1; p = 0.0438). We also found that the upregulated level of AST/ALT, pathology subtypes, symptom, and LDH might be potential indicators of poor prognosis.

Landscape of Genomic Alterations in PHL

Because of sample scarcity, 2 cases of PHL and 2 cases of DLBCL-liver were sequenced using DNA-based 475 genetic testing (1000X) and RNA-based whole transcriptome RNASEQ detection (30M). The distribution of somatic mutations of these 4 cases is shown in Figure 2. There were 34 putative driver genes and 35 alterations were identified. The mean number of mutations per case was 9.25. Signal pathway involvement by the mutations was mainly on apoptosis/cell cycle, the B-cell receptor signaling pathway, the kinase signaling pathway, and transcriptional regulation. However, the heterogeneity was obvious, without consistent mutations found between PHL and DLBCL-liver groups. It was worth noting that there were no mutations related to the apparent regulatory pathway and the Fanconi anemia pathway (DNA repair pathway) in PHL. This result partly suggested that the molecular profiles of PHL might be different from DLBCL-liver.

Figure 2 | Genomic profiles of PHL and DCBCL-liver. Thirty-four putative driver genes and 35 alterations were identified between the PHL and the DCBCL-liver group. The upper bars represent the mutation number for each patient. The left table shows the apparent regulatory pathway to which the above genes belong.

Anupama et al. performed an integrative gene analysis of a cohort of 1,001 DLBCL patients by whole-exome and transcriptome sequencing (14). We also compared these high-frequency mutated genes of the above study with our panel and found that there were 36 mutated genes in our testing. Furthermore, we compared the differences in mutational genes among PHL, DLBCL-liver, and DLBCL. We found that the PHL and DLBCL groups shared 5 majority driver genes (Figure 3A), namely, TP53, GNA13, KLHL6, H1-4, and MYD88, and the DLBCL-liver and DLBCL groups shared 7 majority driver genes, namely, BTG2, KMT2C, MEF2B, TET2, CARD11, CREBBP, and EZH2; 27.8% (5/18) of the genes in PHL were mutated in DLBCL. This result demonstrated that PHL shared some molecular characteristics of DLBCL. Genetic sequencing might assist in the differential diagnosis of PHL.

Figure 3 | Mutated genes in PHL and DLBCL-liver compared with HCC and DLBCL in a previous study. Venn diagram demonstrates the number of altered genes shared in PHL, DLBCL-liver, HCC, and DLBCL (A). The table shows corresponding numbers and gene names (B).

The Genetic Characteristics of PHL Are Obviously Different From Hepatocellular Carcinoma

The main presenting symptoms and clinical features of PHL are usually similar to HCC. There was a risk of misdiagnosing PHL as HCC. James et al. (15) have found some frequently mutated genes (mutation frequency >5%), such as TP53, ARID2, ZRSR2, TERT, AXIN1, APC, CTNNB1, BAP1, SF3B1, TSC2, and JAK1, in HCC compared with normal tissues by NGS. Of these 14 mutated genes in HCC, compared with mutant genes in PHL in this study (ACTB, IRF4, CD79B, DNM2, CCT6B, ASXL3, RUNX1, PTPN6, PRDM1, PTPRD, FLT3, ERBB3, and TP53), we found no other same mutant gene between the PHL and HCC groups except for the TP53 gene. TP53 is a tumor suppressor gene, whose mutation is closely related with the occurrence of many tumors. Thus, we concluded that the gene phenotypes of HCC and PHL are significantly different (Figure 3B).

Specific Genes and Biological Pathways Were Enriched in PHL Compared to DLBCL-Liver

Differentially expressed genes were shown between the PHL and DCBCL-liver groups by cluster analysis (Figure 4A). Compared to the DLBCL-liver group, the top 10 upregulated genes in PHL were COL1A1, ADAMTS2, DCN, SERPING1, COL3A1, COL5A2, COL1A2, POSTN, LUM, and RAB31. The top 10 downregulated genes were IGHD, EML6, IGKV3-20, ZNF595, ANKRD36BP2, CNR1, SSBP2, PYGO1, TUBB2B, and RP11-768B22.2, both with identical statistical significance (p < 0.05, Figure 4B).

Figure 4 | Differential expressed gene analysis. Differential expressed genes were shown between the PHL and the DCBCL-liver group by cluster analysis (A). The column represents the sample; each row represents a gene; the right side is marked as the gene name; the color represents the expression, changing from red to blue as expression decreases. The top 10 up- and downregulated genes in PHL were showed compared with the DLBCL-liver group (B). Each point in the volcano map represents a gene, and the red point is a significantly different gene that meets the thresholds of |log2FC| > 1 and p-value < 0.05. The blue dots are genes that only satisfy p-value < 0.05.

Additionally, we conducted GO term, KEGG pathway enrichment, and differential gene expression analysis to see if specific genes are up- or downregulated and if those genes were enriched in specific GO terms and signaling pathways in PHL compared to the DLBCL-liver group. GO analysis showed the cellular component, molecular function, and biological process of these differentially expressed genes. The top 5 upregulated and downregulated GO terms involved cell, cell part, binding, cellular process, and signaling (Figure 5A). Pathway enrichment revealed that upregulated genes were more enriched in metabolic pathways, PI3K-AKT signaling pathway, pathways in cancer, cytokine–cytokine receptor interaction, and focal adhesion pathways (Figure 5B). Downregulated genes were more enriched in human T-cell lymphotropic virus type 1 (HTLV-1) infection, AGE-RAGE signaling pathways in diabetic complications, Wnt signaling pathway, cell cycle, and small cell lung cancer pathways (Figure 5C).

Figure 5 | GO and KEGG pathway enrichment analysis. GO analysis shows upregulated and downregulated GO terms mainly involving cell, cell part, binding, cellular process, and signaling (A). KEGG pathway enrichment analysis reveals upregulated genes mainly enriched in metabolic pathways, PI3K-AKT signaling pathway, pathways in cancer, cytokine–cytokine receptor interaction, and focal adhesion pathways (B). Downregulated genes are more enriched in HTLV-I infection, AGE-RAGE signaling pathways in diabetic complications, Wnt signaling pathway, cell cycle, and small cell lung cancer pathways (C).

Discussion

Because PHL is a rare tumor, little is known about its pathogenesis and molecular mechanism. In this study, we reviewed the clinicopathological characteristics and molecular phenotypes of 7 cases of PHL and 13 cases of DLBCL-liver. We aimed to find some discrepancy on pathological features and gene phenotype between these two diseases to guide clinical management.

PHL, as a proliferative disease of lymphoid tissue, is characterized by no extrahepatic infiltration. Currently, the diagnosis of PHL mainly depends on pathology and imaging. The early diagnosis of PHL is still difficult at present, especially distinguishing it from DLBCL-liver without biopsy. However, according to the results of our study, we need to consider the following possibilities for PHL in some conditions: no clinical symptoms but with liver masses, and no abnormal levels of serum AFP, CEA, and CA125, but slightly upregulated ALT/AST and LDH levels. As for DLBCL-liver, most patients have clinical symptoms, such as abdominal pain or distention, fever, and fatigue. Imaging reveals liver or spleen masses or multiple swollen lymph nodes. Levels of serum AFP and CEA are normal, but CA125, ALT/AST, and LDH levels could be obviously upregulated. The prognosis of patients with DLBCL-liver is worse than those with PHL. Our results suggest that a comprehensive test is necessary to diagnose patients with DLBCL-liver, if obvious symptoms and abnormal serum markers are observed. Circulating tumor DNA (ctDNA) detection is now used in a variety of tumors. It hopes that the role of ctDNA is defined on the auxiliary diagnosis of PHL and DLBCL-liver in the future.

At present, the overall prognosis of PHL is better, and there are few studies on the prognostic factors of PHL. Of the total of 20 cases, the log-rank analysis showed that CA125 was an independent prognostic factor. We also found that the upregulated levels of AST/ALT and LDH might be potential indicators of poor prognosis. These indicators are much higher in patient no. 5 of this study, the only patient who died in the PHL group. However, due to the small number of PHL samples, no detailed analysis was carried out. The clinician needs to pay more attention to these patients with higher levels of CA125, AST/ALT, and LDH.

DLBCL is a genetically heterogeneous malignant tumor, regardless of whether it has a liver origin or has a systemic involvement. Recent advancements in gene sequencing analysis have provided new opportunities for novel targeted therapy for patients with lymphoma (16). Thus, we further explored the molecular features of PHL and DLBCL-liver in order to reveal the discrepancy between the two groups at the genetic level. The DNA-based sequencing and RNA-based sequencing results showed that there were 18 mutations found in PHL and 17 mutations found in DLBCL-liver. No same mutation was observed between the two groups, perhaps due to the obvious tumor heterogeneity. However, compared with DLBCL-liver, no mutations related to the apparent regulatory pathway and the Fanconi anemia pathway (DNA repair pathway) were found in PHL, partly suggesting that the gene phenotype of PHL might be different from that of DLBCL-liver.

The mutated genes of PHL were also compared with the high-frequency mutated genes reported in HCC and DLBCL. Except for TP53, the mutated genes of PHL were significantly different from HCC, highlighting the possibility of differential diagnosis of PHL and HCC based on NGS. Five majority driver genes, namely, TP53, GNA13, KLHL6, H1-4, and MYD88, were shared in PHL and DLBCL groups, suggesting that PHL shared some molecular features with DLBCL. Thus, the similarities in therapeutic regimens of PHL and DLBCL were adopted by clinicians. More PHL cases need to be collected to further elaborate whether the mutation spectrum of PHL is different from DLBCL in the next study.

PHL showed distinct gene expression patterns compared to DCBCL-liver, although the pathology type is the same. Compared to DCBCL-liver, 10 upregulated genes (COL1A1, ADAMTS2, DCN, SERPING1, COL3A1, COL5A2, COL1A2, POSTN, LUM, and RAB31) and 10 downregulated genes (IGHD, EML6, IGKV3-20, ZNF595, ANKRD36BP2, CNR1, SSBP2, PYGO1, TUBB2B, and RP11-768B22.2) were detected in PHL. Many of these genes are also cancer-related. For example, COL1A1, reported as an oncogene, promotes metastasis in breast cancer (17) and colorectal cancer (18). The EML6-ALK fusion variant associates with crizotinib response in lung adenocarcinoma (19). Pathway enrichment revealed that upregulated genes are enriched in metabolic pathways, and downregulated genes are enriched in the HTLV-1 infection pathway. The genetic variations in metabolic pathways were reported to be predictable on prognosis and survival of NHL (20). HTLV-1 causes a lifelong infection and is associated with an aggressive form of leukemia (21). The distinct gene expression signature of PHL may not only aid differential diagnosis, but also provide insights into understanding the molecular pathways involved in PHL. However, there were some limitations in our research. First, due to the long history and scarcity of tissue samples, only 2 PHL and 2 DLBCL-liver samples were sequenced. We have not yet analyzed the correlation between the genetic changes and clinicopathological manifestations. Second, the pathogenic mechanisms of the above molecular phenotypes on PHL have not been further studied. More prospective studies are required to further verify the clinicopathological characteristics and molecular phenotypes of PHL.

In conclusion, we reported the clinicopathological characteristics of PHL in a Chinese population. PHL is a distinct entity, with unique molecular features compared to liver involvement of systemic lymphoma. The overall prognosis of PHL is relatively better after chemotherapy. Therefore, early accurate identification and individualized treatment are critical for clinical management.

Data Availability Statement

The original contributions presented in the study are publicy available. This data can be found here: https://ngdc.cncb.ac.cn/gsa-human/, HRA002577. Further inquries can be directed to the corresponding author/s.

Ethics Statement

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

Author Contributions

A-YX and SG were the principal investigators and designed the research. A-YX, X-ZD, LL, L-QZ, and DS performed the experiments. A-YX, L-QZ, DS, and SG analyzed the results and wrote the manuscript. All authors contributed to the article and approved the submitted version.

Funding

This work was supported by the Shandong Province Key R&D Program (No. 2021CXGC011105-7), the Beijing Medical Award Foundation (No. 6010121060), Chen Xiaoping Science and Technology Development Fundation (No. CXPJJH11800004-021) and the Natural Science Foundation of China (No. 81902884).

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2022.906245/full#supplementary-material

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Conflict of Interest: Author L-QZ is employed by Geneseeq Technology Inc.

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.

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Copyright © 2022 Xing, Dong, Zhu, Liu, Sun 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: 30 June 2022
doi: 10.3389/fonc.2022.930220

FBXO9 Mediates the Cancer-Promoting Effects of ZNF143 by Degrading FBXW7 and Facilitates Drug Resistance in Hepatocellular Carcinoma

Zhenyu Wang ¹, Xiaoxia Chen ¹,², Lianer Zhou ¹, Xinge Zhao ¹, Chao Ge ¹, Fangyu Zhao ¹, Haiyang Xie ³, Taoyang Chen ⁴, Hua Tian ¹, Hong Li ¹ and Jinjun Li ¹*

¹ State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, ² State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, ³ Department of General Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China, ⁴ Department of Pathology, Qi Dong Liver Cancer Institute, Qidong, China

Edited by:
Hongda Liu, Nanjing Medical University, China

Reviewed by:
Ravi Manoharan, University of Madras, India
Fan Feng, The 302th Hospital of PLA, China

*Correspondence:
Jinjun Li
jjli@shsci.org

Specialty section:
This article was submitted to Cancer Molecular Targets and Therapeutics, a section of the journal Frontiers in Oncology

Received: 27 April 2022

Accepted: 07 June 2022

Published: 30 June 2022

Citation:
Wang Z, Chen X, Zhou L, Zhao X, Ge C, Zhao F, Xie H, Chen T, Tian H, Li H and Li J (2022) FBXO9 Mediates the Cancer-Promoting Effects of ZNF143 by Degrading FBXW7 and Facilitates Drug Resistance in Hepatocellular Carcinoma. Front. Oncol. 12:930220. doi: 10.3389/fonc.2022.930220

F-box proteins are critical for malignancy because they control the turnover of key proteins that govern multiple cellular processes. F-box protein 9 (FBXO9) belongs to the F-box protein family and exhibits oncogenic properties in hematological malignancies. However, the function and molecular mechanism of FBXO9 in hepatocellular carcinoma (HCC) remain unclear. Here, we report that FBXO9 was remarkably overexpressed in HCC. Loss- and gain-of-function experiments showed that FBXO9 facilitates HCC cell proliferation and metastasis both in vitro and in vivo. Mechanistically, as a direct upstream transcription factor, FBXO9 is regulated by zinc finger protein 143 (ZNF143) and accelerates tumor growth and metastasis by targeting the F-box and WD repeat domain containing 7 (FBXW7) for ubiquitination and degradation. Additionally, we found that with FBXO9 knockdown, HCC cells were more sensitive to treatment with lenvatinib and sorafenib. In summary, our results demonstrate that a ZNF143-FBXO9-FBXW7 signaling regulatory axis may be involved in tumor progression in HCC, and suggest that FBXO9 could be a potential biomarker and therapeutic target for HCC.

Keywords: hepatocellular carcinoma, FBXO9, ZNF143, FBXW7, ubiquitin-proteasome mediated degradation, Lenvatinib, drug resistance

Introduction

Liver cancer is the fourth leading cause of cancer-associated decease worldwide (1, 2). Hepatocellular carcinoma (HCC) accounts for approximately four-fifths of primary liver malignancies, imposing a heavy disease burden worldwide (3, 4). Despite considerable progress in treatments for HCC, the limited effect of diagnosis and therapy remains to lead to an inferior prognosis in HCC patients (2, 5). Thus, it is imperative to find more effective diagnostic and treatment strategies through a deeper understanding of the molecular mechanisms underlying HCC progression.

Ubiquitin-mediated proteasomal degradation of the target protein is a critical step in various cellular processes that feature selective protein turnover (6). Dysfunction of the ubiquitin-proteasome system (UPS) usually leads to abnormal levels of oncoproteins or tumor suppressor proteins, in turn leading to carcinogenesis (6, 7). The UPS has three main enzymatic reactions to exert its biological functions, in which E3 ubiquitin ligases are crucial for the specific identification of substrates for ubiquitylation. Among the over 600 supposed E3 ubiquitin ligases in humans, the SKP1–cullin 1–F-box protein (SCF) complex is the most well-characterized (8, 9).

As a target-recognition subunit of SCF complexes, an F-box protein includes at least two primary domains: the F-box domain and the variable domain that bind to specific substrates (10). According to the variable domain, F-box proteins can be divided into three families: FBXO, FBXL, and FBXW (11). In recent years, a series of studies have demonstrated that the expression of F-box proteins can be closely correlated with the occurrence, development, and drug resistance of tumors (12, 13). For example, FBXL1 promotes tumorigenesis by facilitating the degradation of a series of tumor-suppressor proteins, such as such as p21 and p27 (12); FBXO22 degrades nuclear PTEN to promote tumorigenesis (10); and FBXW7 plays a tumor-suppressive role by degrading various cancer-promoting proteins such as mTOR, cyclin E, MCL-1, N-Myc, and SOX9 (12, 14, 15).

However, less attention has been paid to FBXO9 (16). To date, several substrates of FBXO9 have been identified, including Neurog2 (17), PRMT4 (18), p53 (19), PPARγ, BK channel β1 subunit, TEL2, and TTI1 (17, 20). FBXO9 participates in several cellular processes by degrading these substrates. However, the functions and underlying mechanisms of action of FBXO9 in HCC remain unclear. Here, we report that FBXO9, under the control of ZNF143, ubiquitylates and degrades FBXW7 and facilitates HCC proliferation, metastasis, and drug resistance. Our results uncover the pro-oncogenic effect of FBXO9 in HCC and demonstrate that the ZNF143–FBXO9–FBXW7 regulatory pathway is a prospective target for anti-HCC treatment.

Materials and Methods

Database Analysis

The data from The Cancer Genome Atlas (TCGA) (https://tcga-data.nci.nih.gov/tcga/), Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/, accession codes GSE14520 and GSE76427), and Gene Expression Profiling Interactive Analysis (GEPIA) (http://gepia.cancer-pku.cn) databases were downloaded from their website. The transcription factor-binding sites of FBXO9 were predicted using the UCSC Genome Browser (http://genome.ucsc.edu/index.html) and the JASPAR database (http://jaspar.genereg.net/). Protein interactions were predicted using STRING database (version 11.0b, https://version-11-0b.string-db.org/).

Human Specimens

Human HCC specimens and paired adjacent nontumorous liver tissues were obtained from Zhejiang University (Hangzhou, China), Guangxi Cancer Institute (Guangxi, China), and Qidong Liver Cancer Institute (Qidong, China) with informed consent. None of the patients had received chemotherapy or radiation therapy. Clinical characteristics of tissue donors are shown in Supplementary Table S1. The Research Ethics Committee of Renji Hospital reviewed and approved all the protocols involving human tissues used in this study.

Cells and Cell Culture

The HCC-LY10 and HCC-LY5 cell lines were established in our laboratory. THLE-3, MIHA, and L02 cells were provided by the Cell Bank of the Institute of Biochemistry and Cell Biology (Shanghai, China). Li7 was purchased from BioLeaf (Beijing, China). HEK-293T, PLC/PRF/5, and Hep3B cells were purchased from the American Type Culture Collection. Huh7 cells were purchased from the Riken Cell Bank (Tsukuba, Japan). MHCC-LM3, MHCC-97H, and MHCC-97L cells were kindly provided by Zhongshan Hospital (Shanghai, China). Cells were cultured at 37°C in a humidified atmosphere containing 5% CO₂ using complete Dulbecco’s modified Eagle medium (DMEM, #11965092; Gibco, Grand Island, NY, USA) containing 10% fetal bovine serum (FBS, #10100147C; Gibco) and 1% penicillin/streptomycin (#15070063, Gibco). The supplier authenticated and characterized the cells within the previous six months. All cells were free of mycoplasma contamination and tested using PCR.

Plasmid Constructs, Lentivirus Production, and Cell Transduction

Short hairpin RNA (shRNA) targeting FBXO9 (shFBXO9#1/shFBXO9#2), FBXW7 (shFBXW7#1/shFBXW7#2), and their negative control (shNC) were purchased from GeneCopoeia (Rockville, USA). Human FBXO9 cDNA (NM_033480.2) and FBXW7 cDNA (NM_033632.3) were subcloned into Lv201 or Lv105 vector (GeneCopoeia). The ZNF143 related plasmids (ZNF143/shZNF143#1/shZNF143#2) are as described in our previous study (21). All the shRNA target sequences are listed in Supplementary Table S2. Lentivirus production and cell transfection were performed as previously described (21, 22).

Quantitative Real-Time PCR

TRIzol reagent (15596026, Invitrogen, Carlsbad, CA, USA) and the PrimeScript RT Reagent Kit (RR037A, TaKaRa Bio, Japan) were used to obtain cDNA. TB Green Premix Ex Taq II (RR820A, TaKaRa Bio, Japan) and an Applied Biosystems 7500 real-time PCR platform (Thermo Fisher Scientific, USA) were used for qPCR assays, following the manufacturer’s instructions. Relative gene expression was quantified using the ΔΔCt method and was normalized to that of GAPDH. The primer sequences are listed in Supplementary Table S3.

Western Blotting

Cells or tissues were lysed using radio-immunoprecipitation assay (RIPA) buffer (#89901, Thermo Scientific, Waltham, MA, USA) containing a phosphatase inhibitor (#04906837001; Roche, Basel, Switzerland) and protease inhibitor cocktail (#04693132001; Roche). After denaturing and SDS-PAGE, the proteins were transferred to polyvinylidene difluoride membranes (#IPVH00010; Millipore, Bedford, MA, USA). The membranes were blocked and incubated with primary antibodies at 4°C overnight. HRP-conjugated secondary antibodies were used to probe the proteins, and the results were visualized with a chemiluminescent substrate (#34580; Thermo Scientific) using a chemiluminescence analyzer (Bio-Rad, Hercules, CA, USA). Antibody information is listed in Supplementary Table S4.

Cell Counting Kit-8 Assay

Cells were seeded in 96-well plates, and cell viability was measured using the CCK-8 assay (#B34304; Bimake, Houston, TX, USA) at the designated times.

Colony Formation Assay

For the colony formation assay, cells were seeded in 6-well plates and incubated for approximately 2 weeks. Colonies were fixed and stained using Giemsa stain (#G5637-5G; Sigma-Aldrich, St. Louis, MO, USA).

Drug Sensitivity Assay

For short-term viability assays, serially increasing dilution concentrations of lenvatinib (#S1164; Selleck, Houston, USA) and sorafenib (#S7397; Selleck) were prepared. The cells were seeded in 96-well plates and exposed to these drugs for 72 h. Cell viability was quantified using a CCK-8 assay, and the relative cell viability and IC₅₀ values were calculated. For long-term clonogenic assays, cells were seeded in 6-well plates and exposed to increasing concentrations of lenvatinib and sorafenib for 14 days. The colonies were then fixed and stained with Giemsa.

Flow Cytometry Analysis

Cells were seeded in 6-well plates. After adherence, the cells were treated with 10 μM lenvatinib for 24 h. The cells were digested with EDTA-free trypsin and stained using an eBioscienceTM Annexin V Apoptosis Detection Kit (#88-8007, Thermo Scientific) according to the manufacturer’s instructions. Then, Flow cytometry analysis was performed within 4 h.

EdU Incorporation Experiment and Annexin V Staining

EdU incorporation experiment and Annexin V staining were performed using the EdU-555 cell proliferation kit (#C0075S; Beyotime, Shanghai, China) and Annexin V-mCherry apoptosis detection kit (#C1069S, Beyotime), respectively, by following the manufacturer’s instructions. The images were captured using an Olympus CKX53 microscope.

In Vitro Transwell Assays

Transwell assays were performed to examine in vitro invasion and migration. Cells were seeded in the upper chamber of the transwell apparatus (8 μm, MCEP24H48, Sigma-Aldrich) with or without Matrigel coating (#354230; BD Biosciences, San Jose, CA, USA), and cultured in serum-free DMEM. Complete DMEM was added to the lower side of the chamber as a chemoattractant. The non-invaded or non-migrated cells were removed from the upper chamber after 24–48 hours of incubation. After fixing, crystal violet (#E607309-0100; Sangon Biotech, Shanghai, China) was used to stain the remaining cells, which were then counted in three randomly selected visual fields.

Mice

Five- to seven-week-old male BALB/c nude mice were purchased and housed in the SPF animal facility at our institute. For the mouse liver orthotopic transplantation assay, 2 × 10⁶ Li7 cells infected with FBXO9/vector, and 2 × 10⁶ MHCC-LM3 cells infected with shFBXO9#1/shFBXO9#2/shNC in 20 μl serum-free DMEM were mixed with 20 μl Matrigel in an ice bath. Under anesthesia, the cells were injected into the murine liver as quickly as possible. Mice were sacrificed after 6–8 weeks of housing and liver weights (including tumor weights) and tumor sizes were measured. Lung and liver tissues were collected from the mice and fixed for histological examination. For mouse subcutaneous tumor xenograft assays, 3 × 10⁶ MHCC-LM3 cells infected with shFBXO9#1/shNC were resuspended in 200 μl serum-free DMEM and injected into the left axillary fossa. Following tumor formation, the mice were randomly allocated and treated with 10 mg/kg lenvatinib orally every alternate day. The tumor size was measured during lenvatinib treatment. After three weeks of lenvatinib treatment, the mice were sacrificed, and tumors were removed and weighed. All experimental animal protocols were approved by the Animal Care and Use Committee of Renji Hospital and performed according to their guidelines.

Chromatin Immunoprecipitation Assay

The EZ-Magna ChIP assay kit (MAGNA0017, Millipore) was used for the ChIP assay. Briefly, PLC/PRF/5 and HCC-LY10 cells were cross-linked and lysed. The cell lysates were sonicated to shear the DNA to fragments of 500–1,000 bp. The DNA-containing complexes were immunoprecipitated using an anti-ZNF143 (#16618-1-AP; Proteintech, Rosemont, IL, USA) antibody or IgG (Millipore). After reverse crosslinking, qPCR was performed to probe the ZNF143-binding site in the FBXO9 promoter region. The primer sequences are listed in Supplementary Table S3 and the antibody information is listed in Supplementary Table S5.

Dual-Luciferase Assay

The FBXO9 promoter (bp -1150/+200 relative to the ATG start codon) and two 5’-truncated sequences of the FBXO9 promoter were cloned into the dual-luciferase reporter gene vector pEZX-FR01 (GeneCopoeia). Lipofectamine 2000 (11668030, Invitrogen) was used to transfect the corresponding dual-luciferase reporter along with ZNF143 or the vector plasmid into Li7 and HCC-LY10 cells. Two days later, luciferase activity of the cells was detected using a dual-luciferase reporter gene assay system (E1980, Promega, Madison, WI, USA).

Coimmunoprecipitation (Co-IP) Assay

Cells were harvested using RIPA lysis buffer, and cell lysates were immunoprecipitated with protein A/G agarose beads (#IP05-1.5ML; Sigma-Aldrich) and antibodies overnight with rotation at 4°C. The beads were then washed, and the immunoprecipitates were subjected to western blotting. Details of the antibodies used are listed in Supplementary Table S5.

Statistical Analysis

GraphPad Prism 7 software and ImageJ software were used for statistical analyses. Statistical comparisons of the data were performed using a two-tailed Student’s t-test for two-group comparisons or one-way ANOVA for multiple comparisons. Kaplan–Meier method was used to generate survival curves, and the log-rank test was used for comparison. The correlation between the two molecules was analyzed using Pearson correlation. All data are shown as mean ± SD. Statistical significance was set at P < 0.05.

Results

FBXO9 Is Upregulated in HCC

To study the functions of FBXO9 in HCC, we first investigated the mRNA transcription of FBXO9 in human primary HCC using the GEO and TCGA databases. The transcription of FBXO9 was remarkably increased in HCC tissues compared to that in nontumorous liver tissues (Figures 1A, B). Moreover, we found higher expression of FBXO9 in the cancer tissues of patients in the advanced pathological stages than in those in early pathological stages (Figure 1C). Next, we analyzed the relationship between FBXO9 expression and overall survival in HCC patients using GSE14520 dataset. The data showed that patients with FBXO9 overexpression exhibited worse overall survival (Figure 1D). We then evaluated the expression of FBXO9 in human HCC tissues and paired liver tissues from our laboratory. Aligned with the data from the databases, we found that human primary HCC tissues upregulated FBXO9 expression compared with noncancerous liver tissues (Figures 1E–G). Thus, these findings reveal that FBXO9 is overexpressed in HCC.

Figure 1 | FBXO9 is upregulated in HCC. (A, B) The expression of FBXO9 in HCC tissues and noncancerous liver tissues in GSE14520, GSE76427, and TCGA database. (C) Expression of FBXO9 in HCC tissues of early pathological stage (Stage I) and advanced pathological stage (Stage II-IV) in TCGA database. (D) Kaplan-Meier analysis of overall survival in patients with low/medium expression or high expression of FBXO9 in GSE14520 dataset. (E) qPCR analysis (above, two-sided unpaired t-test; below, two-sided paired t-test) of FBXO9 transcription in 35 paired HCC tissues and paired nontumorous tissues in our lab. (F) Western blot of FBXO9 expression in 20 paired HCC tissues (HCC) and normal (N) samples in our lab. (G) Waterfall plot showing the protein level of FBXO9 in HCC tissues compared with adjacent noncancerous tissues from 20 patients, as determined by western blotting. Red histogram, FBXO9 overexpressed more than two times in HCC tissues compared with normal tissues; blue histogram, FBXO9 decreased more than two times; grey histograms, FBXO9 overexpressed or decreased by no more than twice. The data are presented as mean ± SD, *, P < 0.05, **, P < 0.01 by two-tailed Student's t-test.

FBXO9 Accelerates HCC Cell Growth

To further reveal how FBXO9 affects HCC cell growth, we examined FBXO9 expression in a series of HCC cell lines and immortalized hepatocyte cell lines using qPCR and western blotting. HCC cells were selected for gain-of-function or loss-of-function experiments because of their high or low FBXO9 levels (Supplementary Figures S1A, B). Based on this, we established stable FBXO9-overexpressing (FBXO9/Vector) cell lines with HCC-LY10 and Li7 cells and FBXO9 knockdown (Mock/shNC/shFBXO9#1/shFBXO9#2) cell lines with PLC/PRF/5 and MHCC-LM3 cells via lentiviral transfection. The expression of FBXO9 in FBXO9-knockdown or overexpressing HCC cells was verified using qPCR and western blotting (Figures 2A, B). As shown in CCK-8 and colony formation assays, enhanced growth of HCC cells with FBXO9 overexpression and reduced growth of HCC cells with FBXO9 knockdown were observed (Figures 2C, D). Next, we performed EdU incorporation and annexin V staining (Supplementary Figure S2) to investigate whether FBXO9 accelerated HCC cell growth by promoting cell proliferation or inhibiting cell death. The results showed that FBXO9 markedly inhibited cell death and promoted cell proliferation. Next, we explored the effects of FBXO9 on HCC in vivo, using an orthotopic mouse xenograft model. In line with the in vitro results, the size and weight (liver with xenograft) of tumors derived from FBXO9-overexpressing Li7 cells were significantly increased compared with those of tumors derived from negative control cells. In contrast, tumors transfected with shFBXO9#1 or shFBXO9#2 showed lower weights and smaller sizes than tumors transfected with shNC in MHCC-LM3 cells (Figure 2E, Supplementary Figure S3). Taken together, our findings suggest that FBXO9 promotes HCC cell growth both in vitro and in vivo.

Figure 2 | FBXO9 increases HCC cell growth and mobility. (A, B) qPCR and western blotting of FBXO9 expression in FBXO9-overexpressing and FBXO9-knockdown HCC cells. (C) The growth curve of FBXO9 overexpressed or knocked down HCC cells was generated by CCK-8 assays. (D) Representative images and quantitative data of the colony formation of FBXO9 overexpressed or knock down HCC cells. (E) Liver tissues from xenograft-bearing animals. Quantitative analysis of liver weight was shown in the scatter plot graphs (n = 8). (F, G) Representative images of the transwell assays of FBXO9 function in HCC cell migration and invasion. Scatter plot graphs show the quantitative data. (H) H&E staining images of intrahepatic and lung metastatic nodules of the vector or FBXO9 overexpression groups. The numbers of metastatic nodules per mouse are presented in the scatter plot graphs (n = 8). Data are shown as the mean ± S.D, n = 3. Scale bars: 100 μm. *, P < 0.05, **, P < 0.01 by two-tailed Student’s t-test or one-way ANOVA.

FBXO9 Promotes HCC Metastasis

Next, we investigated whether FBXO9 affected HCC cell migration and invasion in vitro. The results of in vitro cell mobility (transwell) experiments (with or without Matrigel) showed that the overexpression of FBXO9 promoted the migration and invasion of HCC cells in vitro, whereas its knockdown suppressed HCC cell migration and invasion in vitro (Figures 2F, G).

To explore the effects of FBXO9 on HCC metastasis in vivo, paraffin sections of liver and lung tissues with xenograft and/or metastatic nodules were prepared, and hematoxylin-eosin staining was performed. Mice overexpressing FBXO9 exhibited more metastatic nodules in the liver and lungs than the control mice (Figure 2H). Collectively, our findings suggest that FBXO9 markedly facilitates HCC metastasis both in vitro and in vivo.

FBXO9 Is a Direct Downstream Target Gene of ZNF143

To further understand the role of FBXO9 in HCC, we attempted to identify the potential transcription factors of FBXO9 using the UCSC Genome Browser and JASPAR databases. As shown in Supplementary Figure S4, 19 common transcription factors were predicted by two databases, and only six transcription factors (SP1, CREB1, ZNF143, NRF1, RFX1, and YY1), which have a Pearson correlation coefficient (Pearson-CC) greater than 0.45 with FBXO9 in TCGA. Among these transcription factors, ZNF143 attracted our attention. The abnormal expression of ZNF143 is associated with tumor progression, as reported in our previous study (21) and other studies (23, 24). However, the relationship between ZNF143 and SCF complex has not been fully elucidated. Thus, we first investigated ZNF143 and FBXO9 expressions in HCC and matched non-tumorous liver tissues using the GEPIA database and our own samples. The transcription level of FBXO9 was positively correlated with ZNF143 expression (Figures 3A, B). We also found that the mRNA transcription of FBXO9 and ZNF143 was positively correlated in a series of HCC and immortalized liver cell lines (Supplementary Figures S1A, S5). We then established cell lines with stable ZNF143 overexpression (ZNF143/Vector) using Li7 and HCC-LY10 cells and stable cell lines with ZNF143 knockdown (Mock/shNC/shZNF143#1/shZNF143#2) in PLC/PRF/5 and MHCC-LM3 cells. Western blotting demonstrated that the overexpression of ZNF143 promoted the expression of FBXO9, whereas knockdown of ZNF143 inhibited the expression of FBXO9 (Figure 3C). Next, we analyzed the possible ZNF143 binding sites in the FBXO9 promoter using the JASPAR database. The data showed three putative binding sites (represented by ①, ②, and ③, respectively) located at -564 to -549 bp (①), -490 to -475bp (②), and +90 to +105 bp (③) relative to the TSS (Figure 3D). A ChIP assay demonstrated that ZNF143 could directly bind to the promoter of FBXO9 in HCC cells (Figures 3E, F). Furthermore, we constructed three different dual-luciferase reporter plasmids using a 1350 bp FBXO9 promoter region (−1150 to +200 bp relative to the TSS) or two truncated clones of the region (truncated#1:67 to 200 bp relative to the TSS; truncated#2: -541 to 200 bp relative to the TSS; Figure 3G). Luciferase assays revealed that compared with the control vector, all the constructs exhibited higher promoter activity in Li7 and HCC-LY10 cells and could be induced by ZNF143 overexpression, mainly in the region of -564 to -549 bp (Figure 3H). Taken together, our results indicate that ZNF143 is a transcription factor for FBXO9.

Figure 3 | ZNF143 binds to the FBXO9 promoter and promotes FBXO9 expression. (A) Verification of the correlation of ZNF143 and FBXO9 using the GEPIA database. (B) Scatter plots of the correlation between ZNF143 mRNA expression and FBXO9 mRNA expression in 64 HCC tissues from HCC patients. (C) The expression level of ZNF143 and FBXO9 as detected by western blotting in HCC cells after knockdown or overexpression of ZNF143. (D) Left, ZNF143 binding motif. Right, JASPAR analysis showed three potential ZNF143-binding sites of the promoter region of FBXO9. (E) Agarose gel electrophoresis for CHIP analysis of ZNF143 binding to the FBXO9 promoter in HCC-LY10 and PLC/PRF/5 cells. (F) CHIP-qPCR analysis of ZNF143 binding to the FBXO9 promoter in PLC/PRF/5 and HCC-LY10 cells. (G) Different luciferase reporter vectors containing truncation mutants of FBXO9 were constructed and transfected into Li7 and HCC-LY10 cells. (H) The corresponding luciferase activities were determined using dual-luciferase reporter assays. Error bars represent mean ± S.D, n = 3. R, Pearson correlation coefficient. *, P < 0.05; **, P < 0.01; ns, no significance by two-tailed Student’s t-test or one-way ANOVA.

FBXO9 Mediates the Cancer-Promoting Effect of ZNF143 in HCC

To explore whether the upregulation of FBXO9 expression is a key part of the cancer-promoting effect of ZNF143 in HCC, plasmids with shFBXO9 were transfected into ZNF143-overexpressing cells to silence FBXO9 expression. Western blotting showed that FBXO9 knockdown effectively reversed FBXO9 upregulation induced by the overexpression of ZNF143 (Figure 4A). The results of the CCK-8 cell proliferation assay and cell colony formation assay indicated that FBXO9 knockdown significantly reduced HCC cell proliferation induced by ZNF143 overexpression (Figures 4B, C). Furthermore, in vitro Transwell assays demonstrated that FBXO9 knockdown markedly inhibited the invasion and migration capacities of ZNF143-overexpressing HCC cells (Figure 4D). In addition, overexpression of FBXO9 rescued the proliferation and metastatic capacity of the ZNF143-knockdown HCC cells (Figures 4E–H). Thus, our results confirm that FBXO9 mediates the cancer-promoting effect of ZNF143 in HCC.

Figure 4 | ZNF143 promotes HCC cell growth and mobility by enhancing FBXO9 expression. (A) ZNF143-overexpressing HCC cells were transfected with shNC or shFBXO9, and the knockdown efficiency of FBXO9 was tested by western blotting. (B, C) The effects of FBXO9 silencing on ZNF143-induced cell growth in Li7 and HCC-LY10 cells were measured by CCK-8 and in vitro colony formation assays. (D) Transwell assays of cell migration and invasion in ZNF143-overexpressing HCC cells treated with shFBXO9. (E) FBXO9 plasmid was transfected into ZNF143 knockdown HCC cells, and the protein expression levels of FBXO9 and ZNF143 were detected using western blotting. (F, G) Effects of FBXO9 overexpression on the proliferation of ZNF143 knockdown HCC cells were measured by CCK-8 and colony formation assays. (H) Transwell assays of cell migration and invasion in ZNF143 knockdown HCC cells treated with FBXO9 plasmid. Data are shown as mean ± S.D, n = 3. Scale bars: 100 μm. *, P < 0.05; **, P < 0.01; ns, no significance, by two-tailed Student’s t-test or one-way ANOVA.

FBXO9 Promotes HCC Cell Growth and Metastasis by Targeting FBXW7 for Ubiquitination and Degradation

mTOR is a well-known oncogene that is activated in various tumors (25–27). A recent study showed that SCFFbxo9 ubiquitin ligase could regulate mTOR signaling to direct the cellular response to growth factor withdrawal and switch to a state of nutritional saving and maintenance of survival (20). Therefore, we assessed the protein levels of mTOR in FBXO9-knockdown or -overexpressing HCC cells. Our results showed that the overexpression of FBXO9 increased the expression of mTOR and activated the mTOR pathway; conversely, knockdown of FBXO9 decreased mTOR expression and inhibited the mTOR pathway (Figure 5A). Next, because FBXO9 is a subunit of the SCF complex, we determined whether the interaction between FBXO9 and mTOR could alter the ubiquitination level of mTOR. Our results showed that ubiquitination of mTOR in HCC cells was inhibited by FBXO9 overexpression, whereas facilitated by FBXO9 knockdown (Figures 5B, C). These results indicate that there may be other molecules that can ubiquitinate mTOR were ubiquitinated by FBXO9. To further study the mechanism underlying the function of FBXO9 in HCC, top 10 possible functional partners of FBXO9 were predicted using the STRING database (version 11.0b, Supplementary Figures S6A, B). Among these proteins, FBXW7 attracted the most attention (Supplementary Figure S6C). It is well known that FBXW7 serves as a tumor suppressor that promotes ubiquitination of a wide range of oncoproteins for proteasomal degradation, including mTOR (14, 15, 28–30). In line with this, our results also showed that FBXW7 overexpression significantly inhibited the proliferation and metastasis of HCC cells in vitro (Supplementary Figure S7).

Figure 5 | FBXO9 exerts its cancer-promoting effect by interacting with FBXW7 and promoting ubiquitination and degradation of FBXW7 in HCC. (A) Western blotting of the levels of p-mTOR, p-S6K, p-Akt, total mTOR, S6K, Akt, and FBXO9 in HCC cells with FBXO9 overexpression or knockdown. (B, C) HCC cell with FBXO9-overexpressing or FBXO9-knockdown were treated with 10 μM MG132 or DMSO for 6 h, lysates were immunoprecipitated with an anti-mTOR antibody under native conditions. The resultant immunocomplexes and lysates (Input) were immunoblotted with antibodies against indicated antibodies. (D, E) Endogenous FBXO9, mTOR, or FBXW7 were immunoprecipitated with antibodies as indicated. The proteins in immunocomplexes were respectively detected using anti-FBXO9, anti-FBXO9, or anti-FBXW7 antibody by western blotting. (F, G) Western blotting of the protein levels of FBXO9 and FBXW7 in FBXO9-overexpressing or knock down HCC cells or xenograft tumors. (H, I) HCC cell with FBXO9-overexpressing or FBXO9-knockdown were treated with 10 μM MG132 or DMSO for 6 h, lysates were immunoprecipitated with an anti-FBXW7 antibody under native conditions. The resultant immunocomplexes and lysates (Input) were immunoblotted with antibodies against indicated antibodies. (J) Western blotting showed the protein levels of FBXO9, FBXW7, p-mTOR, p-S6K, p-Akt, total mTOR, S6K, and Akt in FBXO9-overexpressing HCC cells after FBXW7-overexpression.

To explore the interaction between FBXO9, FBXW7, and mTOR, a Co-IP assay of endogenous proteins in HCC cells was performed. FBXW7 co-precipitated with antibodies targeting FBXO9 or mTOR (Figure 5D). FBXO9 and mTOR also co-precipitated with anti-FBXW7 antibodies (Figure 5E). We then investigated the protein expression levels of FBXW7 in FBXO9 overexpressing and knock down HCC cells. Western blotting revealed that FBXO9 overexpression decreased the protein level of FBXW7, and FBXO9 knockdown increased the expression of FBXW7, both in vitro (Figure 5F) and in vivo (Figure 5G). In addition, we found that the protein expression levels of FBXO9 and FBXW7 were opposite in most HCC cell lines (Supplementary Figure S8). Furthermore, we investigated whether FBXO9 regulated the protein level of FBXW7 by inducing FBXW7 ubiquitination and degradation. The ubiquitination of FBXW7 in HCC cells was facilitated by FBXO9 overexpression and inhibited by FBXO9 knockdown (Figures 5H, I). Moreover, our results demonstrated that FBXW7 overexpression effectively reversed mTOR upregulation and activation induced by FBXO9 overexpression (Figure 5J). These results indicate that FBXO9 can directly interact with FBXW7, leading to the ubiquitination and subsequent degradation of FBXW7.

To further investigate the effects of FBXW7 on FBXO9-promoted HCC cell proliferation and metastasis, we overexpressed FBXW7 under FBXO9 overexpression in HCC cells. We found that FBXW7 overexpression rescued FBXO9 overexpression-induced cell proliferation, migration, and invasion translocation in HCC cells (Supplementary Figures S9A–C). Taken together, these results indicated that FBXO9 promotes HCC cell growth and metastasis by targeting FBXW7 for ubiquitination and subsequent degradation.

Knockdown of FBXO9 Reduces Drug Resistance of HCC Cells

Many studies have shown that F-box proteins play indispensable roles in drug resistance (6, 12). As the target protein of FBXO9, FBXW7 has been reported to increase chemosensitivity of HCC cells (14, 31). Accordingly, we explored the effects of FBXO9 knockdown on the sensitivity of HCC cells to lenvatinib and sorafenib, the two first-line drugs for HCC therapy. HCC cells were exposed to increasing concentrations of lenvatinib or sorafenib, and cell viability was determined after 72 h of exposure. The results were calculated as the dose-dependent inhibition curves and IC₅₀ values. As shown in Figure 6A, knockdown of FBXO9 reduced the IC₅₀ of lenvatinib and sorafenib in HCC cells. Furthermore, flow cytometry analysis indicated that the increase in the percentage of apoptotic cells in FBXO9-knockdown HCC cells after lenvatinib treatment was higher than that in control cells (Supplementary Figure S10). Subsequently, we analyzed the effects of FBXO9 knockdown on long-term resistance of HCC cells to lenvatinib and sorafenib using clonogenic assays. HCC cells with FBXO9 knockdown were more vulnerable to the effects of lenvatinib and sorafenib than the corresponding control cells (Figures 6B, C).

Figure 6 | Knockdown of FBXO9 increases chemosensitivity of HCC cells. (A) FBXO9 knockdown HCC cells were treated with Lenvatinib (0–25 µM) or sorafenib (0–20 µM) for 72 h and cell viabilities were assayed. (B) Effects of lenvatinib and sorafenib on MHCC-LM3 cells with FBXO9 knockdown were analyzed by using long-term colony-formation assays. (C) Quantitative analysis of colony-formation assays. (D, E) MHCC-LM3-shFBXO9#1/MHC-LM3-shNC cells were subcutaneously injected into nude mice. After 10 days of tumor formation, the mice were treated with 10 mg/kg lenvatinib orally every other day, and the tumor growth curves of mice were analyzed. (F, G) Both lenvatinib treatment and FBXO9 knockdown inhibited tumor growth, and FBXO9 knockdown combined with lenvatinib treatment more effectively abolished tumor growth in mice (n = 8). Error bars are shown as the mean ± SD, n = 3. *, P < 0.05; **, P < 0.01 by two-tailed Student t-test or one-way ANOVA.

For further confirmation, MHCC-LM3 cells with a stable knockdown of FBXO9 and their control cells were subcutaneously injected into nude mice. Following tumor formation, mice were randomly allocated and treated with lenvatinib or water (Figure 6D). Our results showed that both FBXO9 knockdown and lenvatinib treatment inhibited tumorigenicity, and knockdown of FBXO9 enhanced the tumor growth inhibition of lenvatinib (Figures 6E–G). Taken together, these results provide strong evidence that FBXO9 knockdown sensitizes HCC cells to lenvatinib and sorafenib.

Clinical Correlations Between FBXO9 With ZNF143 and FBXW7

To further understand the critical role of FBXO9, we used western blotting to analyze the protein expression of ZNF143, FBXO9, and FBXW7 in HCC clinical samples and matched non-cancerous liver tissues from 48 patients. Our results revealed that the protein levels of ZNF143 and FBXO9 were higher in most HCC tissues than in their paired noncancerous liver tissues, whereas the expression of FBXW7 was the opposite (Figure 7A). Furthermore, the expression trend of FBXO9 positively correlated with ZNF143 expression trend and negatively correlated with FBXW7. Meanwhile, the expression trend of FBXW7 was opposite to that of ZNF143 and FBXO9 (Figure 7B).

Figure 7 | Correlation of ZNF143, FBXO9 and FBXW7 in HCC samples. (A) Western blotting analysis of FBXO9, ZNF143, and FBXW7 expression in HCC and paired noncancerous tissues (N). (B) Correlation analysis of ZNF143, FBXO9, and FBXW7 expression trends shown in Western blotting analysis of panel (A). (C) Pattern diagram of the ZNF143-FBXO9-FBXW7 axis. Under the transcriptional regulation of the ZNF143, FBXO9 directly interacts with FBXW7 and induces ubiquitination and degradation of FBXW7 and thus leads to an increase in oncogenic substrates of FBXW7, which ultimately promotes the progression and drug resistance of HCC. R, Pearson correlation coefficients.

Discussion

In recent years, F-box proteins have been identified as essential factors for malignancies via target degradation (12, 28). FBXO9 is a mysterious member of the F-box protein family (16), and a few available studies have shown that FBXO9 is involved in the development of the nervous system or adipose tissue (17, 18), in pathological changes in the muscle in diabetes (17) and epithelial damage in respiratory distress syndrome (18), and in the adverse effects of zoledronic acid (19) or as a target of sepsis (18), and participates in disease progression in hematological tumors by regulating the activity of the mTOR pathway (20) and the proteasome (8). However, the biological functions and mechanisms underlying the role of FBXO9 in HCC have not been reported. In this context, we present a novel finding that FBXO9 acts as an oncoprotein in HCC. Here, we investigated the expression of FBXO9 using the databases and human HCC samples from our lab, and found that FBXO9 was upregulated in HCC. Furthermore, our functional research indicated that FBXO9 promotes HCC cell growth and metastasis in vitro and in vivo.

FBXW7 is a member of the F-box family. It is a well-established tumor inhibitor that targets a series of oncogenic proteins for degradation (14, 15, 32) and plays a powerful tumor suppressor role in a variety of tumors, including HCC (15, 29, 30, 32, 33). Some proteins have been reported to promote the degradation of FBXW7 through auto-ubiquitination, such as Pin1, ERK, PLK2, CSN6, and LSD1 (9, 15, 34). In addition, some studies have indicated that FBXO45-MYCBP2 E3 ubiquitin ligase and Parkin ubiquitin ligase can directly ubiquitinate and degrade FBXW7 (35). In this study, we demonstrated that FBXO9 directly interacted with FBXW7, which resulted in the ubiquitination and proteasome-mediated degradation of FBXW7. Furthermore, FBXO9 exerts a tumor-promoting effect in HCC by downregulating FBXW7, thereby increasing the protein levels of FBXW7 oncogenic substrates, such as mTOR. Therefore, through our work, we uncover the mechanism by which FBXO9 exerts a tumor-promoting effect in HCC and propose a novel FBXO9-involved degradation mechanism of FBXW7. In future studies, we will continue to examine how FBXO9 causes the ubiquitination and degradation of FBXW7.

Chemotherapeutic treatment is an effective form of cancer therapy. However, primary and acquired drug resistance contribute to poor prognosis in cancer patients (12, 26). Recently, F-box protein has been considered to play a pivotal role in the development of drug resistance (12), and previous studies have indicated that FBXW7 increases the chemosensitivity of HCC (14, 31). Therefore, we investigated the effect of FBXO9 knockdown on drug resistance in HCC. The knockdown of FBXO9 impaired the resistance of HCC cells to lenvatinib and sorafenib. These results imply that FBXO9-targeted therapy is synergistic with chemotherapy; thus, FBXO9 inhibitors may be valuable for the treatment of patients with HCC in the future.

Another interesting finding of the present study is that we identified ZNF143 as a transcription factor of FBXO9. Multiple studies have reported that ZNF143 has cancer-promoting capabilities in a variety of tumors (23, 24, 36). Our previous study showed that ZNF143 stimulates proliferation via the ZNF143–MDIG–CDC6 regulatory axis in HCC cells (21). Here, we demonstrated that ZNF143 could exert oncogenic functions through the ZNF143-FBXO9-FBXW7 axis. These findings will deepen our understanding of the role of ZNF143 and FBXO9 in HCC.

In conclusion, we have presented a novel finding for the ZNF143–FBXO9–FBXW7 axis (Figure 7C). Under the control of ZNF143, FBXO9 facilitates HCC cell proliferation, metastasis, and drug resistance by interacting with FBXW7. To the best of our knowledge, this study is the first to reveal the carcinogenic effect of FBXO9 in HCC, which may provide a novel understanding of the functions of FBXO9 in HCC diagnosis and therapy.

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 Renji Hospital. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

ZW: Conceptualization, methodology, validation, formal analysis, investigation, writing – original draft. XC, LZ, XZ, CG, FZ, HT, HL: Investigation, methodology. TC, HX: Resources. JL: Funding acquisition, project administration, conceptualization, methodology, investigation, formal analysis, supervision, writing – review & editing. All authors read and approved the final manuscript. All authors contributed to the article and approved the submitted version.

Funding

This study was supported, in part, by grants from the National Natural Science Foundation of China (82173331, 81972580, 81773152), the National Key Sci-Tech Special Project of China (2018ZX10723204-006) and the SKLORG Research foundation (zz-94-21-02).

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2022.930220/full#supplementary-material

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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.

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.

Copyright © 2022 Wang, Chen, Zhou, Zhao, Ge, Zhao, Xie, Chen, Tian, Li 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.

CASE REPORT
published: 07 July 2022
doi: 10.3389/fonc.2022.918297

Effectiveness of Trastuzumab Combined With Capecitabine Treatment in a Patient With Hilar Cholangiocarcinoma Complicated by Liver Metastases With an ERBB2-Activating Mutation: A Case Report

Daobing Zeng ¹,², Xiaofei Zhao ¹,², Liang Di ¹,², Luyan Lou ³, Yanfang Song ³, Yanrui Zhang ³, Huanhuan Liu ³ and Guangming Li ¹,²*

¹ General Surgery Department, Beijing Youan Hospital, Capital Medical University, Beijing, China, ² Clinical Center for Liver Cancer, Capital Medical University, Beijing, China, ³ Medical Affairs Department, Acornmed Biotechnology Co. Ltd., Beijing, China

Edited by:
Zongli Zhang, Qilu Hospital of Shandong University, China

Reviewed by:
Jiansheng Guo, First Hospital of Shanxi Medical University, China
Shanglei Ning, Shandong University, China

*Correspondence:
Guangming Li
liguangming917@163.com

Specialty section:
This article was submitted to Cancer Molecular Targets and Therapeutics, a section of the journal Frontiers in Oncology

Received: 12 April 2022

Accepted: 13 June 2022

Published: 07 July 2022

Citation:
Zeng D, Zhao X, Di L, Lou L, Song Y, Zhang Y, Liu H and Li G (2022) Effectiveness of Trastuzumab Combined With Capecitabine Treatment in a Patient With Hilar Cholangiocarcinoma Complicated by Liver Metastases With an ERBB2-Activating Mutation: A Case Report. Front. Oncol. 12:918297. doi: 10.3389/fonc.2022.918297

The identification of ERBB2 (HER2) alteration in some solid tumors has become critically important due to the actionable events predictive of response to anti-HER2 therapy. However, the efficacy of ERBB2 mutated hilar cholangiocarcinoma (hCCA) against ERBB2 is rarely reported. Here we report a 76-year-old female diagnosed with hCCA complicated by liver metastases after radical resection. The next-generation sequencing assay showed that the tumor had an ERBB2 mutation. Then, the patient was treated with trastuzumab plus capecitabine. After 2 months of treatment, she had a partial response. Until now, the patient is still alive. This study has shown the potential of trastuzumab combined with capecitabine as an effective treatment for hilar cholangiocarcinoma complicated by liver metastases harboring ERBB2 alterations.

Keywords: cholangiocarcinoma, ERBB2 mutation, treatment, trastuzumab, case report

Introduction

Cholangiocarcinoma (CCA) is a heterogeneous malignancy that can occur at every point in the biliary tree, from the canals of Hering to the main bile duct (1){Banales, 2016 #3}{Banales, 2016 #3}. CCAs are classified into intrahepatic (iCCA) and extrahepatic (eCCA) categories on the basis of anatomical origin (2). The eCCA can be further separated into two subtypes: the proximal or hilar (HCCA) accounts for 60–70% and the distal for 20–30% of CCA (3).

hCCA is a common malignant tumor with a relatively poor prognosis. The overall 5-year survival rate is approximately 13–40% (4). The gold standard of treatment for patients with unresectable hilar cholangiocarcinoma is palliative chemotherapy. However, there was a poor outcome with radiation and systemic chemotherapy, such as with gemcitabine and cisplatin (5). Thus, a novel therapeutic approach is warranted.

Targeted therapies are rapidly transforming the therapeutic paradigm for biliary tract cancer, particularly for iCCA, where targeting FGFR2 fusions, IDH 1/2 mutations, and BRAF V600E mutations are becoming a reality (6, 7). Unlike iCCA, targeted therapies for hCCA are rare.

Herein we report a rare case of a female patient with ERBB2-mutant hCCA who responds well to the treatment of trastuzumab in combination with capecitabine.

Case Presentation

A 76-year-old woman was admitted to a local hospital after she experienced itchy skin plus dark yellow urine for several days. The patient was diagnosed with Bismuth type IV hCCA after imageological diagnosis and received percutaneous transhepatic cholangiodrainage (PTCD) on February 5, 2021. The total bilirubin concentration was 400 µmol/L before PTCD, and the daily bile drainage was approximately 300-400 ml. The preoperative total bilirubin level was reduced to the normal level of 19.6 µmol/L. Then, she visited Beijing Youan Hospital, Capital Medical University, for further operative treatment. Enhanced CT of the upper abdomen and magnetic resonance cholangiopancreatography indicated high biliary obstruction and a high possibility of hilar cholangiocarcinoma, involving the upper to the middle common bile duct, and lesions after PTCD. On March 17, 2021, the patient underwent hilar radical resection, and the resection segments were reconstructed by “basin-shape” hepaticojejunostomy. The clinical data were collected and analyzed. A postoperative pathological examination revealed adenocarcinoma with lymph node metastasis. The patient fell accidentally after discharge from the hospital and experienced a fracture of the left femoral neck. Therefore, she did not undergo postoperative treatment until she came to the hospital for review in September 2021. Abdominal contrast-enhanced CT revealed a low-density mass in the left medial hepatic lobe of the liver. After receiving multidisciplinary treatment, the patient was considered unsuitable for surgical treatment, ablation therapy, and radiotherapy.

With the patient’s consent, the tissue sample obtained during surgery was subjected to next-generation sequencing (NGS) using an 808-gene panel in a College of American Pathologists (CAP)-certified lab. Somatic gene mutations have been detected in the tissue, including ERBB2 R678Q, PTCH1 c.747-2A>T, TP53 E286Q, ARID1A Q2176X, SMAD4 D493Y, and SMAD4 R361H (Table 1). The NGS results indicated low-level microsatellite instability and low tumor mutational burden (5.58 mutants/Mb).

Table 1 | Summary of gene test results and mutations that may have clinical significance.

With the patient’s informed consent, we considered trastuzumab combined with capecitabine as the first-line treatment strategy. The initial loading dose of trastuzumab was 420 mg, followed by 380 mg at 3 weeks later, concomitant with capecitabine 2,000 mg/m² for 14 days in 21-day cycles. Tumor response is “objectified” according to the response evaluation criteria in solid tumors, whereby the longest diameter of the target lesion that can be best visualized is monitored over time—depending on the clinical situation, e.g., every 2 months. After 2 months, the MR scan showed that the volume of some lesions was reduced by 51.9%, from 27 to 13 mm, indicating that the patient had a partial response (PR). We then continued to treat the patient with trastuzumab combined with capecitabine. After another 2 months, the lesion diameter was still 13 mm, which was considered a stable disease (SD), and no metastatic lesions in other organs were found. In addition, the levels of serum tumor markers were significantly reduced during the treatment (Figure 1). At the time of this report, the patient was alive.

Figure 1 | Tumor progression of the patient with postoperative liver metastases from hilar cholangiocarcinoma. (A) The timeline of therapies and tumor progression are indicated. (B) Line chart showing changes in the levels of the tumor marker CA 19-9 during the course of treatment. (C–E) MR scan of the lesion in the bile duct before and after 2 and 4 months of capecitabine plus trastuzumab treatment. MR, magnetic resonance; CA 19-9, cancer antigen 19-9.

Discussion

Advances in genomic research have promoted the development of personalized medicine for the treatment of cancer patients. Here we reported an hCCA patient harboring the ERBB2 R678Q mutation who acquired a response to trastuzumab and capecitabine treatment. To our knowledge, this is the first report to claim that ERBB2 mutations can be used as trastuzumab targets in hCCA.

ERBB2-targeted therapy has achieved remarkable success in the treatment of metastatic breast cancer and gastric cancer. Similarly, recent studies have identified that patients with ERBB2 overexpression or amplification could potentially benefit from ERBB2-targeted therapy in biliary tract cancer (trastuzumab, not lapatinib, has therapeutic effects on Chinese patients with HER2-positive cholangiocarcinoma) (8){Law, 2012 #21;Law, 2012 #21}.

Jeong reported that a biliary tract cancer patient with HER2 overexpression received trastuzumab-pkrb, gemcitabine, and cisplatin and showed an overall response rate of 50.0% (including SD and PR) (9). Yarlagadda et al. demonstrated that a CCA patient with ERBB2 amplification was treated with dual HER2-directed therapy (trastuzumab/pertuzumab) and responded very well with regression of tumor on imaging (10). Consistent with this result, our study showed that an hCCA patient with ERBB2 mutated can benefit from capecitabine combined with trastuzumab treatment. This may implicate that ERBB2-targeted therapy could be a favorable option in hCCA with ERBB2 variation.

ERBB receptors contain an extracellular domain (ECD), a transmembrane domain (TMD), an intracellular region that consists of a juxtamembrane domain (JMD), a kinase domain, and a carboxy terminal tail domain (11). Several studies reported that TMD variation and ECD of ERBB2 may stabilize ERBB2 heterodimerization with other EGFR family members and favor a kinase-active conformation (12). TMD variations are located within the glycine zipper motif at the N‐terminal portion of TMD, which is critically important to the dimerization of ERBB2 to other EGFR family members (12).

Trastuzumab is a monoclonal antibody specifically designed to target the ERBB2. The mechanisms of action of trastuzumab include antibody-dependent cellular cytotoxicity, antibody-mediated ERBB2 internalization followed by receptor degradation, and inhibition of ERBB2 dimerization (13). ERBB2 amplification with corresponding HER2 protein overexpression is associated with sensitivity to therapies targeting HER2, including lapatinib, pertuzumab, and trastuzumab, which were approved by the US Food and Drug Administration. Recent evidence has demonstrated that the presence of ERBB2 mutation can predict clinical responses to anti-HER2-targeted therapies (14).

The recent results from the SUMMIT study have shown that the clinical benefit from ERBB2-targeted therapies (neratinib) may be dependent on the type of ERBB2 alteration and type of tumor (15). Mou et al. reported that a CCA patient with ERBB2 gene S310F in the ECD received trastuzumab combined with chemotherapy and showed therapeutic effects, decreased tumor burden, and improved quality of life (16). Herein we found an hCCA patient, with Pahuja et al. indicating that the ERBB2 R678Q mutation located at the JMD might have a significant effect on TMD geometry and dimerization (17). Therefore, we speculated that the JMD mutant R678Q might respond to trastuzumab.

Concluding Remarks

In conclusion, we were able to identify ERBB2 JMD mutations on NGS in a patient with hCCA and found anti-HER2 therapy as an effective treatment strategy. This will have implications for other patients with hCCA in terms of the identification of this target and considerations toward anti-HER2 therapy on or off trial.

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

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

XZ and LD participated in collecting data. LL and YS drafted the manuscript. HL and YZ revised and commented on the draft. DZ and GL contributed to the scientific review and final approval of this manuscript. All authors read and approved the final manuscript.

References

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Conflict of Interest: Author LL, YS, YZ, and HL were employed by the company Acornmed Biotechnology Co., Ltd.

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.

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.

Copyright © 2022 Zeng, Zhao, Di, Lou, Song, Zhang, Liu 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 July 2022
doi: 10.3389/fonc.2022.897804

CSTF2 Promotes Hepatocarcinogenesis and Hepatocellular Carcinoma Progression via Aerobic Glycolysis

Zhimin Chen ¹†, Weijie Hao ¹†, Jingzhi Tang ², Wei-Qiang Gao ¹,³* and Huiming Xu ¹*

¹ State Key Laboratory of Oncogenes and Related Genes, Renji-Med X Clinical Stem Cell Research Center, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China, ² Department of Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China, ³ Med-X Research Institute and School of Biological Medical Engineering, Shanghai Jiao Tong University, Shanghai, China

Edited by:
Yunfei Xu, Shandong University, China

Reviewed by:
Hao Jia, Shanghai Jiao Tong University, China
Ding Li, Nationwide Children’s Hospital, United States

*Correspondence:
Huiming Xu
quxuhm123@163.com
Wei-Qiang Gao
gao.weiqiang@sjtu.edu.cn

†These authors have contributed equally to this work

Specialty section:
This article was submitted to Cancer Molecular Targets and Therapeutics, a section of the journal Frontiers in Oncology

Received: 16 March 2022

Accepted: 09 June 2022

Published: 08 July 2022

Citation:
Chen Z, Hao W, Tang J, Gao W-Q and Xu H (2022) CSTF2 Promotes Hepatocarcinogenesis and Hepatocellular Carcinoma Progression via Aerobic Glycolysis. Front. Oncol. 12:897804. doi: 10.3389/fonc.2022.897804

Background

The shortening of 3’ untranslated regions (3’UTRs) of messenger RNAs(mRNAs) by alternative polyadenylation (APA) is an important mechanism for oncogene activation. Cleavage stimulation factor 2 (CSTF2), an important regulator of APA, has been reported to have a tumorigenic function in urothelial carcinoma of the bladder and lung cancers. However, the tumor-promoting role of CSTF2 in hepatocellular carcinoma (HCC) and its underlying molecular mechanism remains unclear.

Methods

Multiple databases were used to analyze the expression level and prognostic value of CSTF2 in HCC. Function enrichment analysis was used to investigate the molecular mechanism of CSTF2 for the occurrence and development of HCC. The biological function in HCC cell lines in vitro was determined by CCK8, colony formation, Transwell migration, and invasion assay. Moreover, the tumorigenic function of CSTF2 in vivo was measured by a subcutaneous tumor formation or injecting four plasmids into a mouse tail vein within 5–7 s in an immunocompetent HCC mouse model. In addition, aerobic glycolysis in HCC cells was determined by measuring the extracellular acid rate (ECAR) and extracellular glucose and lactate levels.

Results

Bioinformatics analysis revealed that CSTF2 was overexpressed in HCC tissues. The high expression of CSTF2 was correlated with a poor prognosis and high histological grades. CSTF2 knockout inhibited the proliferation, migration, and invasion of HCC cells. In addition, CSTF2 knockout HCC cells failed to form tumors by a subcutaneous graft experiment. Furthermore, endogenous CSTF2 knockout attenuated hepatocarcinogenesis in an immunocompetent HCC mouse model. Function enrichment analysis suggested that the high expression of CSTF2 was associated with enhanced glycolysis. Moreover, we found that CSTF2 knockout reduced the level of the short 3’ UTR isoform of hexokinase 2 and increased its level of long 3’UTR. Furthermore, CSTF2 knockout inhibited ECAR levels, glucose uptake, and lactate production.

Conclusion

Our results indicated that CSTF2 is highly expressed in HCC and is correlated with a poor prognosis and high histological grade. The knockout of CSTF2 inhibits the tumorigenesis and procession of HCC both in vitro and in vivo. Moreover, CSTF2 is associated with enhanced glycolysis. Therefore, this study suggests that CSTF2 might be a new prognostic biomarker and therapeutic target for HCC.

Keywords: CSTF2, hepatocellular carcinoma, prognostic biomarker, aerobic glycolysis

Introduction

Liver cancer is one of the most common cancers in the world (1). Hepatocellular carcinoma (HCC) is the predominant histopathologic form of liver cancer, accounting for approximately 75% of total cases (2). Despite the advancement of the combination treatment of surgical resection and chemotherapy, recurrence and metastasis are still the main drawbacks of the curative treatment of HCC (3–5). Targeted therapy based on the dysregulation of gene expression and their associated molecular pathways is a novel and promising treatment strategy in advanced or metastatic HCC (6, 7). Thus, understanding the underlying molecular mechanisms of HCC is essential to develop novel treatment strategies.

It is well known that oncogene activation plays a crucial role in the pathogenesis of malignant tumors. Recently, the shortening of the 3’ untranslated regions (3’UTRs) of mRNAs by alternative polyadenylation (APA) is reported as an important mechanism for the upregulation and activation of oncogenes (8). As 3’UTRs contain regulatory sequence elements that are recognized by miRNAs and RNA-binding proteins, mRNA isoforms subjected to APA with different 3’UTRs lengths differ in their stability, translatability, and sublocation in the cells (9). In human cancers, the 3’UTR shortening of some oncogenes causes the upregulation of oncogene expression through the cis-acting mechanism and escape of microRNA-mediated inhibition (8, 10). In addition, the 3’UTR shortening of tumor-suppressor genes leads to their inactivation through a trans-acting mechanism (11). It is reported that the mRNAs of genes with shorter 3’UTR in tumors tend to be more aggressive and cause a poor prognosis (12). Up to now, it remains to be determined as to what extent APA occurs in HCC patients and what is the molecular mechanism of 3’UTR selection by APA in HCC.

The APA of pre-mRNA is carried out by a machinery composed of four protein complexes that recognize the conserved sequence around the cleavage site of 3’UTR (13). Among these complexes, the cleavage stimulation factor (CSTF) complex is related to the rapid initiation of the 3’-end processing of pre-mRNA, consisting of three different polypeptide subunits: CSTF1, CSTF2, and CSTF3 (14). CSTF2 (CstF64) is an important global regulator of APA. In the presence of limited CSTF2, the distal and stronger polyA signal sites (PASs) are preferred, and proximal PASs are less efficiently used. However, when CSTF2 is abundant, it promotes the selection of proximal and weaker PASs, which prevents the transcription and use of distal and stronger PASs (9, 15, 16). A recent study showed that high level of CSTF2 induces the shortening of RAC1 3’UTR, which is beneficial to the tumorigenesis of the urothelial carcinoma of the bladder. Furthermore, the high expression of CSTF2 with the short-3’UTR isoform of RAC1 is associated with a more invasive and poorer prognosis (17). In addition, the overexpression of CSTF2 appears to promote the growth and invasion of lung cancer cells (18). However, the tumor-promoting function and molecular mechanism of CSTF2 in HCC remain unclear.

In this study, we first comprehensively analyzed the expression of CSTF2 in HCC using multiple HCC databases and then investigated the association between the expression of CSTF2 and a prognosis using multiple bioinformatics analysis tools. Next, we performed in vitro and in vivo experiments to explore the function and molecular mechanism of CSTF2 for the tumorigenesis and development of HCC.

Materials and Methods

Data Collection

The gene expression data and corresponding clinical information were obtained from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/), Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/), and the International Cancer Genome Consortium (ICGC) portal (https://dcc.icgc.org/projects/LIRI-JP). In addition, the paired tumor and non-tumor liver tissues of 159 patients with liver cancer were the cohorts of Chinese patients with hepatitis B virus (HBV) infection (CHCC-HBV) (19) who underwent primary radical resection in Zhongshan Hospital (Fudan University, Shanghai, China) from 2010 to 2014. The data details are shown in Supplementary Table S1.

Functional Enrichment Analysis

CSTF2-related genes were obtained by Spearman correlation analysis with correlation coefficients greater than or equal to 0.5. Next, the “clusterProfiler” R software package (20) was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, and ρ < 0.05 indicated that the difference was statistically significant. In addition, the Cancer Genome Atlas-Liver hepatocellular carcinoma (TCGA-LIHC) tumor samples were divided into high- and low-expression groups according to the median value of CSTF2, and gene set enrichment analysis (GSEA) was performed using the GSEA software package (version 4.0.3). “h. all. v6.2. entrez. gmt” and “c2. cp. kegg. v6.2. symbols. gmt” were used as reference gene sets.

Cell Culture

HEK 293T cells and human hepatoma cell lines Huh7, MHCC-97H, and Hep3B were purchased from the Chinese Academy of Sciences Cell Bank (Shanghai, China). The cells were cultured in Dulbecco’s modified Eagle's medium (DMEM) (Gibco, USA) supplemented with 10% fetal bovine serum (FBS) (Sigma-Aldrich, MO,USA) and 1% penicillin/streptomycin (P/S, Gibco, USA) at 37°C with 5% CO₂.

Establishment of Stable CSTF2 Knockout and Overexpression of Hepatocellular Carcinoma Cell Lines

We constructed an sgRNA-targeting CSTF2 plasmid with the Lenti-CRISPR-V2 (Addgene #52,961) vector based on the CRIPR-Cas9 strategy using the following primers to construct a CSTF2-sgRNA plasmid: sgRNA-F: CACCGACAGGAAAGCCAAAGGTTA, sgRNA-R: AAACTAACCCTTTGGCTTTCCTGTC. The lenti-CRISPR-V2 empty vector was used as the control of CSTF2-sgRNA. In addition, we constructed a CSTF2 overexpression plasmid (pLVX-CSTF2-FLAG-puro). The empty vector pLVX-FLAG-puro was used as control. Next, CSTF2-sgRNA or overexpression plasmids together with the packaging plasmid psPAX2 (Addgene #12,260) and envelope plasmid pMD2.G (Addgene #12,259) were transfected into HEK-293T cells to be packaged into lentivirus. The virus supernatant was collected after 48 or 72 h, mixed with 8 μg/ml polybrene, and directly transfected the HCC cells. After 24 h of infection, 5 μg/ml puromycin was added for cell screening to establish a stable knockout or overexpression cell lines for cell screening. Finally, the efficiency of CSTF2 sgRNA or overexpression-stabilizing cell lines was identified by Western blotting.

Western Blotting

Western blotting was performed as previously described (21). Briefly, the cells were lysed using a RIPA lysis buffer (Thermo Fisher Scientific, Waltham, MA, USA) containing a protein inhibitor cocktail (Merck, Kenilworth, New Jersey, USA). The protein concentration was determined by the BCA kit (Thermo Fisher, Waltham, MA, USA). The polyvinylidene fluoride (PVDF) membranes were blocked with non-fat milk and incubated with the primary antibodies against β-actin mouse antibody (1:1,000, ABclonal, China) and CSTF2 rabbit pAbs (1:1,000, ABclonal, Wuhan, China) overnight at 4°C, washed with TBST and then incubated with the corresponding HRP-conjugated secondary antibodies (Thermo Fisher Science, USA) for 1 h at room temperature (RT) and washed with a Tris-buffered saline with Tween-20 (TBST) buffer. The BioRad VersaDoc 4000 imaging system was used for imaging, and the densitometric analysis of proteins was quantified by Quantity One software (BioRad, Berkeley, CA, USA).

CCK-8 Assay and Colony Formation Assay

Cell proliferation was measured by a CCK-8 (Dojondo Kumamoto, Japan) kit. The cells were inoculated with 10% CCK-8 at 37°C at 0, 24, 48, and 72 h; the optical density was measured at 450 nm in a microplate reader (BioTek, Winooski,Vermont, USA). Experiments were repeated three times independently. To assess the ability of the colony formation of HCC cells, the cells were seeded in 6-well plates at a density of 1,000 cells per well and cultured for approximately 14 days and formed an obvious colony. Then, the cells were fixed with 4% paraformaldehyde (PFA) for 15 min, stained with 0.2% crystal violet (Solarbio, China) for 30 min, and photographed.

Transwell Migration Assay

Cell migration was determined by Transwell chambers with an 8-μm pore size (Corning, Corning NY, USA). HCC cells (4×10⁴ cells) were seeded into the upper chamber of transwell in serum-free DMEM. DMEM medium containing 10% FBS was added into the lower chamber. After incubation for 48 h, the non-migrating cells on the upper chamber were carefully removed by cotton swabs. The migrated cells attached to the lower side of the membrane were fixed with 4% PFA for 15 min and stained with 0.2% crystal violet. Then, the cells were washed with PBS three times (5 min each time) and visualized using an inverted microscope (Leica, Germany) and counted.

Transwell Invasion Assay

Cell invasion was evaluated by a Transwell chamber with an 8-μm pore size (Corning, New York, USA). Firstly, Matrigel (1:8 dilution) precoated the upper chamber of the Transwell at 37°C for 2 h. Then, HCC cells (4 ×10⁴) were seeded into the upper chamber of transwell in a serum-free DMEM, and the DMEM medium supplemented with 10% FBS was added in the lower chamber. After incubation at 37°C for 48 h, the non-invasive cells on the upper chamber of the filter were removed and stained with 0.2% crystal violet and finally visualized using an inverted microscope and counted in the six random regions to quantify the number of invasive cells.

Xenografts

Four-week-old male BALB/c nude mice were purchased from Shanghai SLAC Co. Ltd. (Shanghai, China). The mice were randomly grouped into six mice for each group. Huh7 cells (1 × 10⁶) infected with lentivirus containing CSTF2-sgRNA or a vector were subcutaneously injected into the right back of each mouse. The mice were sacrificed after 28 days, and the tumors were isolated and photographed. All animals were approved by the Institutional Animal Care and Ethics Committee of Renji Hospital with a maximum allowable tumor volume of 2,000 mm³.

Hydrodynamic Injection Model

First, we constructed mouse CSTF2-sgRNA plasmids based on CRISPR-Cas9 methods using the following primers to construct CSTF2-sgRNA plasmids containing two sgRNAs plasmids: sgRNA-F1: CACCGCAGCGGGAGAGCACTTCGAG, sgRNA-R1: AAACTGCGGAACTTGAATGGGCGT, sgRNA-F2: ACGCCCATTCAAGTTCCGCA, sgRNA-R2: CTCGAAGTGCTCTCCCGCTGC. Then we confirmed the CSTF2-sgRNA plasmid by sequencing. Six-week-old female C57BL/6 mice were randomly grouped into six mice for each group. Approximately 2 ml of the plasmid mixture in 0.9% sodium chloride solution were prepared for the tail vein injection of each mouse. The plasmid mixture contained 13 μg pT3-EF1A-MYC-IRES-luc (Addgene 129775) (22), 13 μg pX330-p53 (Addgene 59910) (23), and 6.5 μg CMV-SB13 transposase. In the experimental group, 13 μg CSTF2-sgRNA (sgCSTF2) was added, while in the control group, a 13-μg empty vector lentiCRISPR v2 was mixed. Then, the plasmid mixture in sodium chloride solution was injected into the caudal vein of mouse within 5–7 s. Mice were monitored by abdominal palpation and euthanized when they had a high burden of liver tumors (i.e., large abdominal masses).

Immunohistochemistry

The liver cancer tissues of mice were fixed with 4% PFA and embedded in paraffin. Approximately 5-μm thick slices were prepared for immunohistochemical staining. The sections were blocked with 10% normal donkey serum in PBST (Gibco, USA) for 1 h and incubated with primary antibodies against CSTF2 (1:200, Proteintech, China) or Ki67 (1:1000, Abcam, Cambridge, United Kingdom, Rabbit) overnight at 4°C. After being washed with PBS, the sections were incubated with biotinylated anti-rabbit Immunoglobulin G (IgG) secondary antibodies for 1 h at RT (1:200, Vector, Peterborough, United Kingdom). Then, the sections were visualized with a Vectastain ABC kit (Vector) at RT for 30 min and developed with 3,3’-diaminobenzidine (DAB) (Genetech, Shanghai, China) at RT. The images were visualized using a microscope. Tissue array chips were purchased from Servicebio (ZL-LivHCC961, Shanghai, China), and included HCC tissues (n = 48) and adjacent normal liver tissues (n = 48). Immunohistochemistry was executed on a tissue array chip using CSTF2 (1:200, Proteintech, China).

Quantitative Real-Time PCR

Total RNA was extracted using TRIzol reagent (Life Technologies, #15596018) and reverse transcribed into cDNA using the PrimeScript RT reagent kit (Takara, Shiga, Japan). Quantitative real-time PCR (RT-PCR) were performed using Roche Light Cycler 480 detection system by SYBR Green PCR Master Mix kit (Toyobo, Osaka, Japan) and normalized by the expression of β-actin. The relative amount of each gene was quantified by the 2−ΔΔCt method. All quantitative RT-PCR experiments were performed at least three independent experiments for culture cells. The information of the primers was listed in Supplemental Table 2.

RNA 3’RACE

The total RNA of the cells was extracted using the TRIzol reagent (Thermo Fisher Scientific, Waltham, MA, USA), and the 3’end of cDNA was obtained by 3’RACE PCR using the 3’-RACE kit (Sangon Biotech, Shanghai, China). The semi-nest PCR approach was adopted to improve the accuracy of PCR, in which primers F1 and the 3’RACE outer primer were used in first-round PCR; primers F2 and the 3’RACE inner primer were used in the second-round PCR. The sequences of gene primers are listed below: ρ

hexokinase 2 (HK2) F1 (5’-3’): CCGGGGACGAGCTCTTTGACCACATT

HK2 F2 (5’-3’): GCTTTCACTCAACCCCGGCAAGCAGAG

Seahorse Analyses

The extracellular acidification rate (ECAR) was measured by the Seahorse XF96 Analyzer (Seahorse Bioscience/Agilent Technologies, North Billerica, MA, USA). In short, 1×10⁴ HCC cells were inoculated in a 96-well plate overnight at 37°C for adhesion. Before detection, the medium was replaced by a detection medium and performed an ECAR experiment with the EACR kit (Seahorse Bioscience, Agilent) according to the manufacturer’s protocol.

Glucose and Lactate Measurement

To test glucose uptake and lactate production, we cultured HCC cells up to 80% confluency. Then, the cells were changed to a fresh medium and cultured for 24 h, and the supernatant was collected for detection. Next, the concentration of glucose and lactate was measured by a glucose and lactate kit (Cedex Bio) using a Cedex Bio biochemical analyzer (Roche Diagnostics, Indianapolis, IN, USA), and normalization was performed according to the total protein levels in each group.

Statistical Analysis

A statistical analysis of bioinformatic analysis was performed by R software (version 4.0.3). The experimental data are presented as the mean ± SEM of at least three independent experiments, and the statistical analysis was assessed by SPSS software 22.0. Statistical significance was evaluated using Student’s t-test to compare two groups, and the chi-square test was used for the comparisons of categorical variables. Spearman correlation analysis was used to determine the relationship between CSTF2 and other genes. Kaplan–Meier survival analysis was used to analyze the effect of CSTF2 on OS and relapse-free survival (RFS) in patients with liver cancer. The log-rank test was used to determine the statistical significance. ρ < 0.05 indicated statistical significance. * ρ < 0.05, ** ρ < 0.01, *** ρ < 0.001, **** ρ < 0.0001.

Results

CSTF2 Is Upregulated in Hepatocellular Carcinoma

To explore the tumorigenic function of CSTF2 in various tumors, we analyzed various databases to identify the expression level of CSTF2 in human tumors. We found that CSTF2 was highly expressed in plenty of cancers by analyzing the Tumor IMmune Estimation Resource (TIMER) database (24) (Figure 1A). Furthermore, CSTF2 is highly expressed in multiple human tumors and was also verified on TCGA and GTEx data by Gene Expression Profiling Interactive Analysis (GEPIA) (25) (Figure 1B). Subsequently, we confirmed the high expression of CSTF2 in HCC using other HCC databases from TCGA-LIHC, ICGC-JP, CHCC-HBV, and GEO (GSE36376, GSE112790, GSE14520, GSE76427, GSE25097, GSE124535) (Figure 2A and Supplementary Figure S1). Furthermore, the tissue microarray analysis of 48 pairs of HCC tissues also showed that CSTF2 was more strongly expressed in tumor tissues than in adjacent non-tumor tissues (Figures 2B, 2C, Supplementary Figure S2). In addition, we determined the expression levels of CSTF2 in HCC cell lines by Western blot and found that CSTF2 was expressed at a higher level in Huh7 and MHCC-97H, Hep3B cells than control, non-tumorigenic LO2 liver cells (Figure 2D). Collectively, the above data indicate that CSTF2 is upregulated in human HCC.

Figure 1 | The expression levels of CSTF2 in human cancers. (A) The expression levels of CSTF2 in different tumor tissues compared to normal tissues in The Cancer Genome Atlas (TCGA) database. (B) The expression of CSTF2 in tumor tissues and matched TCGA normal in TCGA database and GTEx data by GEPIA analysis. * ρ < 0.05, ** ρ < 0.01, *** ρ < 0.001 compared to normal tissues.

Figure 2 | CSTF2 is highly expressed in HCC. (A) Analysis of CSTF2 mRNA expression levels in TCGA, International Cancer Genome Consortium (ICGC), and Chinese patients with hepatitis B virus (CHCC-HBV) databases (B) Representative images of the immunohistochemistry staining of CSTF2 in hepatocellular carcinoma (HCC) and adjacent non-tumor tissues from different HCC patients (tumor indicates HCC tissue; normal indicates adjacent normal tissue). (C) Quantitative analysis of sample numbers in different CSTF2 expression levels in 48 pairs of HCC tissues. (D) Western blot analysis of CSTF2 protein levels in non-tumorigenic LO2 liver cells, hepatocellular carcinoma cell lines Huh7, MHCC-97H, and Hep3B (left panel) and qualification of the relative intensity of CSTF2 protein levels (right panel). The levels of β-actin were used as an internal control. ** ρ < 0.01, *** ρ < 0.001.

High Expression of CSTF2 Is Associated With Patients’ Advanced Clinical Stages and a Poor Prognosis and Relapse in Hepatocellular Carcinoma

We further analyzed the correlation between high CSTF2 expression and the clinical malignant grades of HCC using the HCC databases from TCGA-LIHC, ICGC-JP, CHCC-HBV, and GEO (GSE14520, GSE76427, GSE36376). Figure 3 shows that the higher expression of CSTF2, the more advanced clinical stage for HCC patients (ρ < 0.05), suggesting that the expression of CSTF2 was related to the clinicopathological characteristics of HCC patients. In addition, Cox proportional hazard analysis using the TCGA database showed that HCC patients with higher CSTF2 expression had poor overall survival (OS) and relapse-free survival (RFS) (Figure 4A). We further confirmed the above conclusion with other liver cancer databases including CHCC-HBV, GSE14520, and ICGC databases (Figures 4B–D). Furthermore, multivariate Cox analysis demonstrated that CSTF2 expression was an independent prognostic factor for OS [hazard ratio (HR) = 1.671, 95% confidence interval (CI): 1.023–2.728, ρ = 0.040] (Figures 4E–F). Taken together, these results indicated that the higher expression of CSTF2 is related to the advanced clinical stage and a poor prognosis for HCC patients.

Figure 3 | The mRNA levels of CSTF2 were correlated with the advanced clinical stage in HCC in different databases. (A-F) The relationship between CSTF2 expression levels with a histological grade, a clinical stage in TCGA databases, ICGC databases, CHCC-HBV databases, GSE14520 databases, and GSE76427 databases and in GSE36376 databases. ** ρ < 0.01, *** p < 0.001 compared to normal.

Figure 4 | CSTF2 higher expression is associated with poor prognosis in HCC. (A-D) Overall survival and relapse-free survival in TCGA-LIHC, CHCC-HBV, GSE14520, and ICGC databases (E) Univariate and (F) multivariate Cox analyses of CSTF2 expression at different TNM stages, ages, and genders.

Knockout of CSTF2 Inhibits Cell Proliferation, Migration, and Invasion of Hepatocellular Carcinoma Cell Lines

To further determine the function of CSTF2 in HCC, we established stable CSTF2 knockout Huh7 and MHCC-97H cells by a CRISPR/Cas9 single-guide RNA (sgRNA) strategy. Western blotting showed that the expression levels of CSTF2 in CSTF2-sgRNA Huh7 and MHCC-97H cells were markedly lower than that in the vector control cells (Figure 5A). Figure 5B shows that knockout of CSTF2 significantly inhibited the cell proliferation in Huh7 and MHCC-97H cells by the CCk8 assay. Next, we studied the colony-forming capacity of HCC cells and found that CSTF2-sgRNA Huh7 cells and MHCC-97H cells formed far fewer colonies than the control vector group under low-density culture conditions (Figure 5C). Transwell assay showed that knockout of CSTF2 dramatically inhibited the migration and invasion of Huh7 cells and MHCC-97H cells (Figure 5D). These results together imply that the knockout of CSTF2 inhibits cellular proliferation, migration, and invasion of HCC cell lines.

Figure 5 | Knockout of CSTF2 inhibited the proliferation, migration, and invasion of HCC cells in vitro. (A) The stable CSTF2 knockout Huh7 and MHCC-97H cells were determined by Western blot (upper panel) and the qualification of the relative intensity of CSTF2 protein levels (bottom panel). The levels of β-actin were used as an internal control. (B) The cell proliferation ability of CSTF2 knockout Huh7 and MHCC-97H cells was measured by the CCK-8 assay. (C) Colony-forming assay of CSTF2 knockout Huh7 cells and MHCC-97H cells (left panel) and the qualification of the number of colonies in these cells (right panel). (D) The migration and invasion capacity were evaluated in CSTF2 knockout Huh7 cells and MHCC-97H cells by the Transwell assay (upper panel) and the qualification of migrated cells and invading cells per field (bottom panel). Scale bar, 50 μm. sgCSTF2 represents CSTF2-sgRNA. An empty vector was used as a control of CSTF2-sgRNA. * ρ < 0.05, ** p < 0.01, *** p < 0.001 compared to vector control.

Overexpression of CSTF2 Promotes Cell Proliferation, Migration, and Invasion in Hepatocellular Carcinoma Cells

To further verify the tumor-promoting function of CSTF2, we overexpressed CSTF2 in the HCC cell line Hep3B and confirmed that the expression level of CSTF2 was higher than that of vector control by Western blot (Figure 6A). Figures 6B–6C show that overexpression of CSTF2 evidently promoted the cell proliferation of Hep3B cells based on the CCk8 assay and colony-forming assay. Moreover, we confirmed the result in another HCC cell line sk-Hep1 cells (Supplementary Figure S3). In addition, the overexpression of CSTF2 also promoted cell migration and invasion in Hep3B cells by Transwell assays (Figure 6D). All in all, these results indicate that the overexpression of CSTF2 promotes cancer cell proliferation, migration, and invasion in HCC cells.

Figure 6 | Overexpression of CSTF2 promoted the cell proliferation, migration, and invasion of HCC cells in vitro. (A) The stable overexpression of CSTF2 in Hep3B cells was determined by Western blot (left panel) and the qualification of the relative intensity of CSTF2 protein levels (right panel). (B) CCK-8 assay of the cell proliferation rate of the overexpression of CSTF2 Hep3B cells. (C) Colony-forming assay of the overexpression of CSTF2 Hep3B cells (left panel) and the qualification of the number of colonies formed (right panel). (D) The migration and invasion were measured in the overexpression of CSTF2 Hep3B cells by the Transwell assay (left panel) and the qualification of migrated cells and invading cells per field (right panel). CSTF2 represents the overexpression of CSTF2. An empty vector was used as control. Scale bar, 50 μm. * ρ < 0.05, ** ρ < 0.01, *** ρ < 0.001 compared to the vector.

Knockout of CSTF2 Inhibits Tumorigenesis and Processing of Hepatocellular Carcinoma In Vivo

Next, we extended our studies to an in vivo setting. We first performed subcutaneous tumor formation experiments by injecting 1×10⁶ cells into the right back of BALB/c nude mice in the vector control group and CSTF2 sgRNA group, respectively. Approximately 28 days later, these mice were sacrificed, and the tumors were removed and photographed. Figure 7A shows that the knockout of CSTF2 in Huh7 cells completely inhibited tumor formation, while there were bigger tumors formed in the control group, indicating that the knockout of CSTF2 could completely inhibit the tumor formation and growth of human HCC cells in BALB/c nude mice. To further confirm the inhibitory effect of CSTF2 in HCC, we performed the endogenous CSTF2 knockout by mouse CSTF2 sgRNA in an immunocompetent HCC mouse model by the hydrodynamic tail vein injection (HTVI) delivery method according to a previously reported protocol (22, 26). Briefly, we simultaneously injected four plasmids including pT3-EF1A-MYC-IRES-luc, CMV-SB13 (transposase), px330-p53 (p53 sgRNA), sgCSTF2 (CSTF2 sgRNA containing double CSTF2 sgRNAs), or empty vector control (Control) into the mouse tail vein within 5–7 s (Figure 7B). Thirty-five to forty days later, tumors would be formed in the liver tissue of mice. As shown in Figures 7C–E, the endogenous CSTF2 knockout significantly reduced the size, numbers of tumor nodules, and liver weight compared with the control group. Moreover, the immunohistochemical staining of liver tumor nodules confirmed that the expression of CSTF2 in the CSTF2 sgRNA group was much lower than that in the control group. In addition, the expression of Ki67 in the CSTF2 sgRNA group was significantly lower than that in the control group (Figure 7F). Taken together, the above results demonstrate that CSTF2 might play a crucial role in the tumorigenesis and progression of HCC.

Figure 7 | Knockout of CSTF2 inhibited the tumorigenesis and progression of HCC. (A) In vivo tumor formation of CSTF2 sgRNA Huh7 and vector control Huh7. Tumorigenesis experiments were performed by subcutaneously injecting Huh7 cells into BALB/c nude mice. (B) Strategy for evaluating the hepatocarcinogenesis function of CSTF2 in immunocompetent C57BL/6 mice. Schematic representation of the hydrodynamic tail vein injection HCC model. Plasmids pT3-EF1A-MYC-IRES-luc, px330-p53 (p53-sgRNA), and CMV-SB13 transposase were delivered together with either CSTF2 sgRNA (sgCSTF2) or the control vector for HCC induction. (C) Representative images of dissected livers in CSTF2 sgRNA and the vector control group. (D) The number of tumor nodules in mice injected with sgCSTF2 was significantly lower than that in the control group. (E) Qualification of the liver weight, and the ratio of liver weight to body weight of these tumor nodules in CSTF2 sgRNA or control groups. At least 6 mice were used in every group. (F) Immunohistochemical staining formed tumors in the liver tissues using CSTF2 and Ki67 antibodies. Scale bar, 100 μm. sgCSTF2 represents CSTF2-sgRNA. The empty vector of sgRNA was used as control. * ρ < 0.05, ** ρ < 0.01 compared to control.

Biological Functions and Enrichment Pathways Related to CSTF2

To investigate the potential molecular mechanism of CSTF2 responsible for the tumorigenesis and progression of HCC, GO, KEGG, and GSEA enrichment analyses were performed based on the TCGA-LIHC database. GO and KEGG enrichment analyses showed that CSTF2 might be related to RNA splicing, DNA replication, chromosome segregation, and cell cycle–related biological processes involving cell proliferation (Figures 8A–B). Moreover, GSEA analysis indicated that CSTF2 high expression was also associated with cancer-related pathways, such as Myelocytomatosis (MYC) targets, P53 signaling, the Nod-like receptor signaling pathway, glycolysis, Wnt/beta-catenin, and PI3K/Akt/mTOR signaling, which play an important role in promoting tumorigenesis and the progression of HCC (27–31) (Figures 8C–D). Altogether, it appears that the high expression of CSTF2 activates many cancer-related pathways and cell-cycle and cell-proliferation-related pathways, contributing to the growth and development of HCC.

Figure 8 | Functional enrichment analysis of CSTF2 in the TCGA cohort. (A) GO classification analysis of gene changes in the biological process. (B) KEGG pathway enrichment analysis of upregulated pathways (C, D) GSEA analysis of activated pathways and hallmarks of CSTF2 related in HCC based on the TCGA database.

High Expression of CSTF2 Enhances Aerobic Glycolysis in Hepatocellular Carcinoma Mainly by Enhancing HK2 3’UTR Short Isoform

A growing body of evidence indicates that aerobic glycolysis or the Warburg effect plays an important role in the proliferation, invasion, and metastasis of HCC (31) and is also involved in the mechanism of sorafenib resistance in HCC patients (32). Here, our GSEA analysis showed that the high expression of CSTF2 is associated with the glycolysis pathway (Figure 8D). Considering that the high expression of CSTF2 is also associated with the activated PI3K/Akt/mTOR pathway and MYC target signals that promote glycolysis (31, 33, 34), we further analyzed the relationship between CSTF2 expression levels and glycolysis. According to the median value of CSTF2 expression, we divided the TCGA-LIHC data into two groups: high and low expression. The cluster heat map of glycolysis-related key enzymes and transporters including hexokinase 2 (HK2), phosphofructokinase 1 isoform found in muscle (PFKM), fructose-2,6-bisphosphatase 3 (PFKFB3), pyruvate kinase 2 (PKM2), L-lactate dehydrogenase A(LDHA), and glucose transporter member 1 (GLUT-1, also named SLC2A1) were highly expressed in the CSTF2 high-expression group (Figure 9A). Moreover, the expression of these genes was positively correlated with the expression of CSTF2 (Figure 9B). Next, we detected the expression of the above glycolysis-rated enzymes and transporters by quantitative RT-PCR and found that the levels of HK2, PKM, and SLC2A1 in CSTF2-sgRNA Huh7 and MHCC-97H cells were lower than that in the vector control cells, especially HK2 (Figures 9C, D). We further detected the protein levels of HK2 in the cells by Western blot. As shown in Figures 9E, F, knockout CSTF2 dramatically decreased the protein levels of HK2. Next, we wondered if the knockout of CSTF2 could decrease the levels of the short 3’UTR isoform of HK2. Interestingly, Figure 9G shows that that the levels shorter-3'UTR isoform were significantly decreased in CSTF2-sgRNA Huh7 cells compared to that in the vector control cells, but the longer-3’UTR isoform was increased in CSTF2-sgRNA Huh7 cells. These results suggest that high levels of CSTF2 enhance HK2 3’UTR shorting and promote the glycolysis process.

Figure 9 | High expression of CSTF2 enhances aerobic glycolysis in HCC. (A) Heat map of the cluster analysis of the relationship between the CSTF2 expression and glycolysis-related enzymes and glucose transporters. (B) The correlation analysis of CSTF2 expression and glycolysis-related enzymes and glucose transporters. (C-D) The relative expression levels of CSTF2, SLC2A1, HK2, LDHA, PKM, PFKFB3, and PFKM detected by quantitative RT-PCR in Huh7 cells and MHCC-97H cells. (E–F) The protein levels of HK2 in Huh7 cells and MHCC-97H cells determined by Western blot; the levels of β-actin were used as an internal control. (G) 3’ RACE HK2 fragments were amplified in Huh7 cells and detected by agarose gel electrophoresis. (H–J) The glycolytic function of HCC cells was measured by the extracellular acidification rate (EACR), relative glucose uptake, and lactate production in CSTF2 knockout Huh7 and MHCC-97H cells. (K-L) The glycolytic function of HCC cells was measured by EACR, the relative glucose uptake, and lactate production in CSTF2-overexpression Hep3B cells. sgCSTF2 represents CSTF2 sgRNA, CSTF2 represents CSTF2 overexpression, and the vector was used as control. Data were presented as the mean ± SEM. ** ρ < 0.01, *** ρ < 0.001 compared to the vector.

To confirm the glycolysis, we measured the extracellular acid rate (ECAR) of HCC cells using the Seahorse metabolic analyzer. Figures 9H–9J show that knockout of CSTF2 reduced the glycolytic function in both Huh7 and MHCC-97H cells, evidenced by decreased ECAR, a marked reduction in glucose uptake, and extracellular lactate levels in CSTF2 sgRNA Huh7 and MHCC-97H cells. By contrast, the overexpression of CSTF2 led to a significant increase in ECAR, glucose uptake, and extracellular lactate levels in Hep3B cells (Figures 9K, 9L). All in all, the above results indicate that the high expression of CSTF2 enhances aerobic glycolysis in HCC.

Discussion

In the current study, we demonstrated that CSTF2 is highly expressed in tumor tissues compared with that in normal tissues in a variety of malignant tumors based on the TCGA pan-cancer database. It is worth noting that CSTF2 is highly expressed in HCC tissues based on plenty of databases. Moreover, the HCC tissue microarray confirmed that the expression of CSTF2 in tumor tissues is much higher than that in adjacent non-tumor normal tissues from different HCC patients. More importantly, the high expression of CSTF2 is associated with advanced clinical stages and a poor clinical prognosis by univariate and multivariate Cox regression analyses. Therefore, the high expression of CSTF2 could be an independent prognostic biomarker for HCC patients.

The current study has investigated the tumorigenic function of CSTF2 not only in vitro but also in vivo. Firstly, the knockout of CSTF2 significantly inhibits the cell proliferation, migration, and invasion of HCC cells in vitro. Secondly, the overexpression of CSTF2 promotes the cell proliferation, migration, and invasion of HCC cells in cell cultures. Thirdly, Huh7 cells stably transfected with CSTF2-sgRNA are unable to form tumors by a subcutaneous graft experiment. Fourthly, an endogenous CSTF2 knockout distinctly reduces the carcinogenic ability in an immunocompetent HCC mouse model. Collectively, these results demonstrate clearly that CSTF2 plays an important role in the tumorigenesis and progression of HCC.

Of note, in the present study, we adopted an immunocompetent HCC mouse model by the hydrodynamic tail vein injection delivery method to measure the carcinogenic ability of CSTF2. In the model, by the overexpression of oncogenes (transposon-based vectors) and the deletion or mutation of tumor suppressor genes (using the CRISPR-Cas9 system), liver cancer models can be quickly established. Moreover, the model can replicate the interaction between tumor cells and the immune system, reproduce tumor development and immune response, and allow for interrogating the role of any genetic alteration in an antitumor immunity system (22).

In terms of the molecular mechanism of CSTF2 on HCC, GO and KEGG enrichment analyses revealed that the high expression of CSTF2 is related to the cell cycle and DNA replication, which contribute to tumor proliferation (27). The present GSEA analysis indicates that CSTF2 is involved in the Notch signaling pathway, P53 signaling pathway, glycolysis, PI3K/Akt/mTOR signaling pathway, and Wnt-β catenin signaling pathway, which is involved in the tumorigenesis and development of tumors (27–31). Of note, aerobic glycolysis, known as the Warburg effect, is the metabolic phenotype of cancer cells (35) and can meet the needs of the rapid growth of tumors (36). Previous studies have demonstrated that suppressing aerobic glycolysis can inhibit the growth of HCC (37, 38). In this regard, it is particularly important to note that the present work demonstrates the involvement of CSTF2 in glycolysis. The correlation analysis reveals that the high expression of CSTF2 is positively related to the transcription of glycolysis-related limiting enzymes and the key glucose transporter factor SLC2A1. In our study, we found that the expression of the rate-limiting enzyme HK2 in aerobic glycolysis was dramatically reduced during the knockout of CSTF2. Moreover, we found that the knockout of CSTF2 evidently reduced the levels of the shorter-3’UTR isoform of HK2 and increased the levels of the longer-3’UTR isoform of HK2. As the main regulator of 3’UTR shortening in many types of tumors, CSTF2 may induce the 3’UTR shortening of HK2 and make it escape the inhibition mediated by microRNA, leading to enhanced glycolysis. Therefore, the finding indicates that the high expression of CSTF2 could induce the HK2 3’UTR shortening of HK2 and promote the expression of HK2, resulting in tumorigenesis and progression in HCC. Supporting our finding, previous studies have demonstrated that HK2 is highly expressed in HCC and is directly related to the pathological stage and prognosis of patients (39). Finally, toward this direction, GSEA analysis showed that the high expression of CSTF2 is also associated with the activated PI3K/Akt/mTOR pathway and MYC target signals, which promote glycolysis by increasing the glucose uptake rate (40, 41) and upregulating the expression and activity of glycolytic enzymes and HIF-1a (31, 33, 34). However, how HK2 was regulated by CSTF2 needs more investigation in the future. Therefore, our results suggest that CSTF2 promotes the progression of HCC by enhancing the Warburg effect.

It is given that cancer stem cells (CSCs) prefer to take glycolysis for energy production (42), and the inhibition of glycolysis reduces the number of CSCs (43). Our GSEA analysis showed that the high expression of CSTF2 activates the Notch signaling and Wnt/β catenin, which play an important role in activating and maintaining the stemness of CSC in HCC (30). In addition, glycolysis contributes to cancer initiation, progression, chemoresistance, and relapse (42, 44). Therefore, our finding indicates that CSTF2 could play an important role in activating and maintaining the stemness of CSC in HCC, resulting in oncogenesis, processing, and relapse. However, whether CSTF2 influences the stemness of CSC in HCC remains to be determined in the future. Taken together, the above data indicate that the high expression of CSTF2 could enhance glycolysis, which might augment the stemness, promoting the tumorigenesis and progression of HCC.

Conclusions

Our findings reveal that the high expression of CSTF2 is associated with a poor prognosis in HCC patients. Moreover, the high expression of CSTF2 promotes hepatocarcinogenesis and HCC progression by enhancing HK2 3’UTR shorting and aerobic glycolysis. Therefore, this study indicated that CSTF2 might be a novel clinical biomarker for prognosis and a molecular therapeutic target for HCC patients.

Data Availability Statement

Publicly available datasets were analyzed in this study. This data can be found here: TCGA(https://portal.gdc.cancer.gov/),ICGC (https://dcc.icgc.org/projects/LIRI-JP), GEO (https://www.ncbi.nlm.nih.gov/geo/),MHCC-HBV(PMID: 31585088),All data generated and used in this study are available for anyone to utilize upon reasonable request.

Ethics Statement

The animal study was reviewed and approved by Institutional Animal Care and Ethics Committee of Renji Hospital.

Author Contributions

HX and W-QG conceived and designed the study. ZC and WH provided equal contributions to research design, data analysis, and article writing. JT helped to perform animal experiment. HX and W-QG reviewed the manuscript. The final manuscript has been read and approved by all authors.

Funding

This work was funded by the funding of the National Natural Science Foundation of China (81630073 and 81872406) and 111 project (B21024).

Acknowledgments

The authors gratefully acknowledge contributions from the TCGA, GEO, and ICGC network.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2022.897804/full#supplementary-material

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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 reviewer HJ declared a shared parent affiliation with the authors ZC, WH, W-QG, HX to the handling editor at the time of review.

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Copyright © 2022 Chen, Hao, Tang, Gao 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: 13 July 2022
doi: 10.3389/fonc.2022.955670

ZFAND5 Is an Independent Prognostic Biomarker of Perihilar Cholangiocarcinoma

Pei Liu ¹, Yijia Wang ¹ and Lingling Duan ²*

¹ Department of Plastic Surgery, Qilu Hospital Affiliated to Shandong University, Jinan, China, ² Department of Health Care, Central Hospital Affiliated to Shandong First Medical University, Jinan, China

Edited by:
Xuesong Gu, Beth Israel Deaconess Medical Center and Harvard Medical School, United States

Reviewed by:
Qi Sun, The First Affiliated Hospital of Xi’an Jiaotong University, China
Jianzhong Ai, Sichuan University, China

*Correspondence:
Lingling Duan
linglingdlld@126.com
duanll2005@163.com

Specialty section:
This article was submitted to Cancer Molecular Targets and Therapeutics, a section of the journal Frontiers in Oncology

Received: 29 May 2022

Accepted: 13 June 2022

Published: 13 July 2022

Citation:
Liu P, Wang Y and Duan L (2022) ZFAND5 Is an Independent Prognostic Biomarker of Perihilar Cholangiocarcinoma. Front. Oncol. 12:955670. doi: 10.3389/fonc.2022.955670

Background

Cholangiocarcinoma (CCA) is a highly aggressive malignancy with extremely poor prognosis. Perihilar CCA (pCCA) is the most common subtype of CCA, but its biomarker study is much more lagged behind other subtypes. ZFAND5 protein can interact with ubiquitinated proteins and promote protein degradation. However, the function of ZFAND5 in cancer progression is rarely investigated, and the role of ZFAND5 in pCCA is never yielded.

Materials and Methods

In this study, we established a pCCA cohort consisting of 72 patients. The expression of ZFAND5 in pCCAs, and the paired liver tissues, intrahepatic bile duct tissues and common bile ducts (CBD) tissues were detected with IHC. ZFAND5 mRNA in pCCAs and CBDs was detected with qRT-PCR. The pCCA cohort was divided into ZFAND5low and ZFAND5high subsets according to the IHC score. The correlations between ZFAND5 expression and clinicopathological parameters were assessed bychi-square test. The prognostic significance of ZFAND5 expression and clinicopathological parameters was estimated by univariate analysis with Kaplan-Meier method, and by multivariate analysis with Cox-regression model.

Results

Expression of ZFAND5 in pCCAs was substantially higher than that in interlobular bile ducts and common bile ducts, but lower than that in liver tissues. The ZFAND5low and ZFAND5high subsets accounted for 44.4% and 55.6% of all pCCAs respectively. ZFAND5 high patients had much lower survival rates than the ZFAND5low patients, with the average survival time as 31.2 months and 19.5 months respectively. ZFAND5 was identified as an independent unfavorable prognostic biomarker of pCCA with multivariate analysis.

Conclusion

ZFAND5 expression was up-regulated in pCCAs compared with the CBDs. We identified ZFAND5 as an independent biomarker of pCCA, which could provide more evidence for the molecular classification of pCCA, and help stratify the high-risk patients based on the molecular features.

Keywords: ZFAND5, prognosis, biomarker, perihilar cholangiocarcinoma, cohort

Introduction

Cholangiocarcinoma (CCA) is a highly aggressive malignancy originating from bile duct (1). CCA has several clinical manifestations such as dormant symptoms, severe malignancy, rapid progression, early metastasis, easier invasion and recurrence (2). These clinical features attribute to the poor prognosis of CCA. The 5-year survival rate ranging from 5% to 20% (3, 4). Base on anatomical locations, CCA can be classified as three subtypes, which are the intrahepatic CCA (iCCA), perihilar CCA (pCCA), and distal CCA (dCCA) (5). pCCA has the highest prevalence among CCAs, taking up about 50% of all CCAs. pCCA is not sensitive to systemic chemotherapy and patients always suffer early recurrence (6). Till 2020, the first targeted drug of CCA, pemigtinib, just emerged for treatment of patients with FGFR2 fusion, which only accounted for 10%-15% iCCA and 5% iCCA/dCCA (7). The treatment options of CCA are very dismal, and pCCA is the most less studied CCA subtype in the field of biomarker and target therapy (8). Therefore, identifying new biomarkers and therapies to expand the pCCA treatment options and improve life span of pCCA is very important.

ZFAND5 (AN1-type zinc finger protein 5, also named ZNF216) is a 23-kD cytosolic protein with one A20 zinc finger domain and one AN1-type zinc finger domain (9). It is a member of the zinc finger AN1-type domain family, which are comprised of 8 members (10). Among the AN1-type domain family, ZFAND2a and ZFAND5 are featured by their ability to increase the overall protein degradation (11). ZFAND5 interacts with poly-ubiquitinated proteins with the N-terminal A20-type zinc finger domain (12). ZFAND5 is highly expressed in the brain and skeletal muscle, and is detectable in many tissues, and is reported to participate in muscle atrophy and osteoclasts differentiation (13). However, the expression of ZFAND5 in many other tissues are not clear.

The function of ZFAND5 in cancer progression is rarely investigated. Previous study suggested ZFAND5 as a biomarker of hepatocellular carcinoma indicating favorable prognosis (14). However, other studies showed that ZFAND5 was highly expressed in more aggressive nasopharyngeal carcinoma cells (15), and ZFAND5 may be associated with proliferation of colon cancer (16), indicating that ZFAND5 may have pro-tumor effects. Taken together, the current knowledge of ZFAND5 role in tumor progression is very poor and controversial. Here we investigated the expression of ZFAND5 in pCCA with immunohistochemistry (IHC), and estimated the clinical significance of ZFAND5 detection by investigating the correlation between ZFAND5 and clinicopathological parameters. Moreover, we estimated the prognostic value and the independent prognostic significance of ZFAND5 in pCCA.

Materials and Methods

Patients Cohort

The retrospective cohort was comprised of 122 patients who underwent the radical surgery of pCCA from 2017.1 to 2021.6 in Qilu Hospital of Shandong University and the Central Hospital of Shandong First Medical University, which constituted the initial cohort. 72 patients were enrolled from the initial cohort into the validation cohort if they (1) had available specimens for IHC detection and available follow-ups, (2) survived the perioperative period, (3)had no other malignancies. Moreover, another consecutive perspective cohort comprised of 10 patients was established if the patients underwent radical surgery and had enough pCCA tissues and adjacent CBD tissues for quantified real-time PCR (qRT-PCR). The pCCAs were staged as the 8th American Joint Committee on Cancer/Union for International Cancer Control (AJCC/UICC) Tumor-Node-Metastasis (TNM) classification system.

All specimens were obtained with informed consent from patients. All experiments were approved and supervised by the Ethics Committee of Qilu Hospital of Shandong University and the Central Hospital of Shandong First Medical University.

Tissue Microarray Construction

The slides were stained with hematoxylin and eosin staining for double confirmation of diagnosis and selection for proper tumor area to make TMA. The tumor area for TMA construction was determined by a senior pathologist. The core biopsies with 1.5 mm in diameter were taken from the selected area and arranged into TMA slides.

qRT-PCR

In the perspective cohort, total mRNA was purified with TRIzol reagent (Thermo Fisher, Waltham, MA, USA). The extracted total RNAs were reversely transcribed into cDNA with SuperScript Reverse Transcriptase (Thermo Fisher). The quantified real-time PCR was performed with QuantiFast Probe RT-PCR Plus Kit (Qiagen, Germany). mRNA level of ZFAND5 was standardized with GAPDH as an internal control by the 2-ΔΔCt method. All primers were designed as follows:

ZFAND5: forward primer 5’-GCTAGTGGTTCCAACAGTCCT-3’,

reverse primer 5’-TCGGGGTAGTTATTTTGTCCTCT-3’;

GAPDH: forward primer 5’-TGTGGGCATCAATGGATTTGG-3’,

reverse primer 5’-ACACCATGTATTCCGGGTCAAT-3’.

IHC Staining

The streptavidin-peroxidase IHC was used to detect the ZFAND5 expression in pCCA tissues. In brief, the TMA was deparaffinized and rehydrated with xylene and graded alcohol, and then immersed in 3% hydrogen peroxide to inactivate the endogenous peroxidase. After that, the TMA slides were incubated in citrate buffer (pH = 6.0) and boiled using a microwave for optimal antigen retrieval. The unspecific antigen binding was blocked by bovine serum albumin incubation. TMA slides were incubated in the primary antibody of ZFAND5(NBP1-80609, Novus Biologicals, Minneapolis, MN, USA) at dilution as 1:100 overnight at 4°C, and then rinsed by phosphate buffered saline (PBS) for 3 times. After that, TMA was incubated in the secondary antibody labeled with biotin (Beyotime, Beijing, China) for 1 hour at room temperature. The antigen was finally visualized by peroxidase reaction with 3,3-diaminobenzidine (DAB) solution (Beyotime).

IHC Score Evaluation

The IHC results were semi-quantified by IHC scores which were determined by the staining intensity as well as the positive cell percentage. The staining intensity was defined as negative (score 0), weak (score 1), moderate (score 2), or strong (score 3). The positive cell percentage was calculated as <25% (score 1), 25%-50% (score 2), 50%-75% (score 3) and >75% (score 4). The final IHC scores were the products of the score (staining intensity) multiplied by the score (positive cell percentage), varying from 0 to 12. The IHC scores were further divided into the ZFAND5low and ZFAND5high scores based on the cut-off in the receiver operating characteristic (ROC) curve, which was set as the cases with the highest specificity plus sensitivity. In our study, the cut-off was 3.5, representing that scores ≥4 were set as high ZFAND5.

Statistical Analysis

All the statistical analyses were performed with software SPSS 22.0. The correlations between ZFAND5 and clinicopathological parameters were estimated by the chi-square test. The overall survival curves were plotted with Kaplan-Meier method and the statistical difference between different subsets were compared with the log-rank test. The Cox proportional hazard regression model was used to assess the independent prognostic significance of ZFAND5 and clinicopathological parameters. The statistical significance between IHC scores of different subtypes was analyzed with one-way ANOVA, and the statistical significance of qRT-PCR results was assessed with paired t test. P value less than 0.05 was considered as statistically significant.

Results

ZFAND5 is Highly Expressed in pCCA

Firstly, we established a cohort comprised of 72 pCCA patients. There were 31 male patients and 41 female patients, with an average age as 56.5 years old (Table 1). The expression of ZFAND5 in the 72 pCCAs, and their paired liver tissues, intrahepatic bile ducts tissues and common bile ducts (CBD) tissues were detected with IHC (Figure 1A). In these tissues, ZFAND5 was abundantly expressed in cell cytoplasm. Moreover, The IHC scores of pCCA were substantially higher than those of interlobular bile ducts tissues and CBD tissues, but lower than those of hepatic tissues(Figure 1B).

Table 1 | The basic infromation of the pCCA cohort.

Figure 1 | The expressions of ZFAND5 in pCCAs, liver tissues, interlobar bile ducts tissues, and common bile ducts tissues. (A) The expressions of ZFAND5 in pCCAs, liver tissues, interlobar bile ducts tissues, common bile ducts tissues were detected by IHC. Scale bar: 100 μm. The arrow indicates the interlobar bile duct. (B) The expressions of ZFAND5 in pCCAs, liver tissues, interlobar bile ducts tissues, and common bile ducts tissues were estimated by the IHC scores. *** represents P < with one-way ANOVA test. (C) mRNAs of ZFAND5 in pCCAs and common bile ducts were detected with qRT-PCR. The statistical significance was analyzed by paired t test.

Moreover, we established a perspective cohort including 10 consecutive pCCA patients, and detected the ZFAND5 mRNA of pCCAs and adjacent CBD tissues. As expected, the ZFAND5 mRNAs in pCCAs were extensively higher than those in CBDs, indicating a potential role of ZFAND5 in tumorigenesis and progression of pCCA(Figure 1C).

The Correlations Between ZFAND5 Expression and Clinicopathological Parameters

We divided the pCCA cohort into subsets with low or high expression of ZFAND5 based on the IHC score (Figure 2). There were 32 ZFAND5low patients and 40 ZFAND5high patients, accounting for 44.4% and 55.6% of all pCCAs respectively (Table 1). The correlations between ZFAND5 expression and clinicopathological parameters were further assessed with chi-square test. The enrolled clinicopathological parameters included the gender, age of patients, tumor size, histological grade, T stage, N stage and TNM stage. M stage was not included because all patients were present without metastasis. No significant correlation between ZFAND5 and these parameters was observed. The only potential ZFAND5-associated factor was tumor size. High expression of ZFAND5 appeared to be correlated with larger tumor size, though the statistical significance was not remarkable(P=0.155) (Table 2).

Figure 2 | The cohort was divided into ZFAND5low and ZFAND5high subsets. The representative images of low and high ZFAND5 expression. Scale bar: 100 μm.

Table 2 | The correlation between ZFAND5 and clinicopathological parameters.

ZFAND5 Was a Prognostic Biomarker of pCCA

With Kaplan-Meier methods, we plotted the survival curves of all clinical parameters and ZFAND5 (Table 3). Noticeably, high expression of ZFAND5 correlated with lower overall survival rates of pCCA, indicating that ZFAND5 was an effective prognostic biomarker of pCCA (Figure 3A). The average survival time of ZFAND5low and ZFAND5high patients were 31.2 months and 19.5 months, with the 5-year overall survival rates as 31.9% and 0% respectively. In addition to ZFAND5, the advanced histological grade could also predict a poor prognosis of pCCA (Figure 3B). Advanced T stage, TNM stage and tumor size tended to correlate with low survival rates, with a noteless significance (Figure 3C–E). Other parameters did not exhibit strong correlations with the overall survival rates.

Table 3 | The prognostic significance in univariate analysis.

Figure 3 | The correlation between clinicopathological factors and overall survival rates. The patients were divided into different subsets according to ZFAND5 expression (A), histological grade (B), T stage (C), TNM stage (D) and tumor size (E). The survival curves were plotted by Kaplan-Meier method, and the statistical significance was analyzed with the log-rank test.

The Independent Prognostic Risks of pCCA

Moreover, we enrolled all these clinicopathological parameters into the Cox-regression hazard model to identify the independent prognostic factors. In this multivariate analysis, ZFAND5 was identified as an independent prognostic biomarker of pCCA. The odds of ZFAND5high patients was 4.33 fold times higher than that of ZFAND5low patients to suffer from pCCA-related death (Table 4). Additionally, high histological grade was also an independent factor indicating the unfavorable outcome (P=0.039, HR=5.18).

Table 4 | The independent prognostic factors were determined by multivariate analysis.

Discussion

CCA is a highly heterogeneous and complicated malignancy. The three subtypes of CCA, iCCA, pCCA and dCCA, have distinct clinical manifestations, treatment options and outcomes (6). However, their biological characters are not as more distinguished as their clinical features. In recent years, many molecular fingerprints of CCA have been identified, defining different molecular CCA patterns (1, 17). However, most studies on molecular CCA patterns are focused on iCCA, because iCCA specimens are much easier to get, compared with pCCA. pCCA and dCCA were separated as distinct subtypes in 2007 according to the 7th AJCC/UICC (18). But the understanding of the molecular differences between pCCA and dCCA is extremely poor. More studies on the molecular characters of CCA, especially pCCA, could deepen the understanding of differences among CCA subtypes, and help discover prognostic biomarkers.

Even in the field of CCA biology, the studies of pCCA are much fewer compared with iCCA and dCCA. Although pCCA has substantially higher incidence, the radical surgery of pCCA is more difficult than iCCA and dCCA. Hepatectomy and pancreaticoduodenectomy are the radical surgical approaches for iCCA and dCCA respectively, but the radical surgery of pCCA is extensively lower compared with iCCA and dCCA (19). The anatomical complexity of hepatic portal determines that pCCA is much easier to invade the hepatic artery and portal vein, making the radical surgery extremely difficult, which further results in that the specimen obtainment of pCCA is difficult, and that large pCCA cohort study is rare. Our cohort consisting 72 pCCA patients is a relatively large-sample-sized cohort. Our results demonstrated that ZFAND5 was a highly effective biomarker indicating the unfavorable prognosis of pCCA (P=0.001). Our study could provide more evidence for the molecular classification of pCCA, and help stratify the high-risk patients based on the molecular features.

Cancer cells have many hallmarks in cell metabolism and energy utilization (20). For example, in shortage of available nutrients, cancer cells can exert adaptive metabolic pathways to maintain their viability by the self-catabolic processes such as autophagy. Protein ubiquitination and degradation are the key segments in the self-catabolic processes, and the molecular characters of proteasome mechanisms and regulation need further studies. ZFAND5 interacts with both Ub and proteasomes, and it may function as a linker between substrates and 26S proteasome. Previous study showed that ZFAND5 could increase multiple proteasomes activities and extensively induce proteolysis by the Ub-proteasome pathway (12). However, the exact mechanism of ZFAND5-induced protein ubiquitination is still unknown. For example, whether ZFAND5 interacts with the Ub-binding substrates, and whether ZFAND5 functions as a E3 ligase is still unknown. We speculated that the high expression of ZFAND5 may be attributed to the enhanced autophagy of CCA, which is a promising topic but requires further experiments.

Till now, the studies of ZFAND5 physiological function are very insufficient, and the reports on the oncogenic role of ZFAND5 are extremely poor. In addition to an activator of proteasome, other functions of ZFAND5 were also reported. Previous study demonstrated that ZFAND5 could bind and stabilize mRNAs with AU-rich elements in 3’-untranslated regions (21). In addition, ZFAND family are mainly stress-associated proteins. For example, expression of ZFAND2a (also named AIRAP) is increased in cases that misfolded proteins are accumulated, such as arsenite exposure, heat shock, or proteasome inhibition (22). ZFAND2a could promote proteins degradation by binding to proteasomes and thus increased their peptidase activity (21, 22). The exact mechanism of ZFAND5 up-regulation in pCCA, and the function of ZFAND5 in pCCA progression is worthy of further investigation.

The role of ZFAND5 in tumor immunity is not investigated. On one hand, ZFAND5 can be induced by cytokines including RANKL, TNFα, and IL-1β, on the other hand, ZFAND5 can inhibit the activation of transcription by NF-κB, TNFα, or IL-1β (13, 23). However, the exact mechanism of how ZFAND5 influences the cytokine and how it participates in inflammation is still unknown. Given that ZFAND5 affects cytokine expression and is involved in immunology, it would be a very interesting topic to further study the role of ZFAND5 in tumor response to immune checkpoint inhibitors.

In summary, here we established a pCCA cohort consisting of 72 patients, and identified ZFAND5 as an independent biomarker of pCCA. The odds of ZFAND5high patients was 4.33 fold times higher than that of ZFAND5low patients to suffer from pCCA-related death. Our results provide more evidence for the molecular classification of pCCA, and help stratify the high-risk patients based on the molecular features.

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

All experiments were approved and supervised by the Ethics Committee of Qilu Hospital of Shandong University and the Central Hospital of Shandong First Medical University. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

PL and YW performed the experiments, PL and LD designed the study, collected the specimens and wrote the paper. All authors contributed to the article and approved the submitted version.

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ORIGINAL RESEARCH
published: 18 July 2022
doi: 10.3389/fonc.2022.954366

Development and Validation of a 6-Gene Hypoxia-Related Prognostic Signature For Cholangiocarcinoma

Qi Sun ¹†, Huxia Wang ²†, Baoan Xiao ¹, Dong Xue ¹ and Guanghui Wang ¹*

¹ Department of General Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China, ² Mammary Department, Shaanxi Provincial Cancer Hospital, Xi’an, China

Edited by:
Xuesong Gu, Beth Israel Deaconess Medical Center and Harvard Medical School, United States

Reviewed by:
Tao Wei, Zhejiang University, China
Haibo Yu, Henan Provincial People’s Hospital, China

*Correspondence:
Guanghui Wang
amon@mail.xjtu.edu.cn

†These authors have contributed equally to this work

Specialty section:
This article was submitted to Cancer Molecular Targets and Therapeutics, a section of the journal Frontiers in Oncology

Received: 27 May 2022

Accepted: 21 June 2022

Published: 18 July 2022

Citation:
Sun Q, Wang H, Xiao B, Xue D and Wang G (2022) Development and Validation of a 6-Gene Hypoxia-Related Prognostic Signature For Cholangiocarcinoma. Front. Oncol. 12:954366. doi: 10.3389/fonc.2022.954366

Cholangiocarcinoma (CHOL) is highly malignant and has a poor prognosis. This study is committed to creating a new prognostic model based on hypoxia related genes. Here, we established a novel tumor hypoxia-related prognostic model consisting of 6 hypoxia-related genes by univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) algorithm to predict CHOL prognosis and then the risk score for each patient was calculated. The results showed that the patients with high-risk scores had poor prognosis compared with those with low-risk scores, which was verified as an independent predictor by multivariate analysis. The hypoxia-related prognostic model was validated in both TCGA and GEO cohorts and exhibited excellent performance in predicting overall survival in CHOL. The PPI results suggested that hypoxia-related genes involved in the model may play a central role in regulating the hypoxic state. In addition, the presence of IDH1 mutations in the high-risk group was high, and GSEA results showed that some metabolic pathways were upregulated, but immune response processes were generally downregulated. These factors may be potential reasons for the high-risk group with worse prognosis. The analysis of different immune regulation-related processes in the high- and low-risk groups revealed that the expression of genes related to immune checkpoints would show differences between these two groups. We further verified the expression of the oncogene PPFIA4 in the model, and found that compared with normal samples, CHOL patients were generally highly expressed, and the patients with high-expression of PPFIA4 had a poor prognosis. In summary, the present study may provide a valid prognostic model for bile duct cancer to inform better clinical management of patients.

Keywords: cholangiocarcinoma, hypoxia status, prognostic model, immune microenvironment, PPFIA4

Introduction

Cholangiocarcinoma (CHOL) is a malignant and aggressive disease with a poor prognosis, the median survival less than 24 months (1). Depending on the location of the tumor, CHOL is divided into intrahepatic cholangiocarcinoma (iCCA) and extra-hepatic cholangiocarcinoma (eCCA), and eCCA is divided into perihilar cholangiocarcinoma (pCCA) and distal cholangiocarcinoma (dCCA) (2). The early symptoms of CHOL are insidious and not easily detected, and most patients are diagnosed at an advanced stage (2). For patients with advanced CHOL, treatment options are limited. The median overall survival (OS) of patients treated with standard chemotherapy regimens (gemcitabine and cisplatin) is still less than 1 year (3). Therefore, it is clinically important to find the relevant factors affecting the prognosis of patients with CHOL. The American Joint Committee on Cancer (AJCC) staging manual has become a benchmark for classifying cancer patients, determining prognosis, and determining the best treatment (4). However, it has been discovered that the TNM staging approach is insufficient for determining prognosis and does not account for cancer’s biological heterogeneity. There is significant variation in prognosis and treatment response even among patients with the same stage, and other factors such as age, performance status, and tumor site might affect patient survival, therefore it only provides limited information on clinical prognosis (5). As a result, developing reliable prognostic biomarkers is critical in order to improve clinical prognostic value. With the continuous improvement and development of high-throughput sequencing technology (6), it is possible to exploit potential molecular targets in the CHOL genome using bioinformatics technology. The development of promising molecular targets is essential to improve the application of targeted therapy for CHOL.

The tumor microenvironment is a complex and variable cellular microenvironment, which can not only provide the material conditions required for the growth and proliferation of tumor cells, but also change its composition through autocrine and paracrine secretion of tumor cells (7). It was reported that CHOL cells can construct their own favorable environment by secreting tumor-related regulatory mediators through the extracellular matrix and stromal cells, thus promoting the proliferation of CHOL cells and enhancing their resistance to treatment (8). Most solid tumors include hypoxia as a micro environmental trait, and developing tumors frequently live in hypoxic environments due to limited blood supply (9). The hypoxic conditions could enhance the angiogenesis, proliferation and invasive abilities of tumor cells (10, 11). Similarly, in CHOL studies, hypoxia has been found to increase the aggressiveness of CHOL (12). In iCCA studies, hypoxic environment was able to promote the progression of CHOL through upregulation of Rab1a (13). It has been found that in CHOL, hypoxia-induced Sonic Hedgehog signaling pathway regulates cancer stemness, epithelial-to-mesenchymal transition and invasion (14).

In the present study, the hypoxia-related genes for CHOL were mainly screened by GEO and TCGA-CHOL dataset. The prognostic signature characterized by hypoxia-related genes were filtered according to univariate and LASSO regression analysis, and the risk score of each samples were obtained. Subsequently, CHOL samples in TCGA were classified into high- and low-risk subgroups according to the risk score, and the differences between high- and low-risk subgroups were further explored, such as survival differences, clinical characteristics differences, enrichment differences, somatic cell mutation differences, and tumor immunomodulatory gene expression differences.

Materials and Methods

Pre-Processing and Data Collection

We utilized the R package TCGAbiolinks (15) to download the 36 CHOL RNA-seq data [log2(FPKM+1)] and relevant clinical information from the TCGA database (https://portal.gdc.cancer.gov/). The 36 samples with both available RNA-seq data and clinical information were used as the training set for hypoxia-related markers. The GSE107943 dataset was obtained from the GEO database (https://www.ncbi.nlm.nih.gov/geo/), which were used as the external validation set, containing available microarray expression data and clinical data of bile duct cancer samples. The microarray data preprocessing process was as follows: remove null probes and probes corresponding to multiple genes at the same time, and use the median expression value of these probes as the expression value of the gene when multiple probes correspond to the same gene.

Prognostic Model Construction

The 200 potential hypoxia-related genes were retrieved from MSigDB’s HALLMARK HYPOXIA pathway (https://www.gsea-msigdb.org/gsea/index.jsp) (v7.4 version). All prognosis-associated hypoxia genes were screened using univariate Cox hazard analysis, with a p-value of 0.1 chosen as the cutoff. Utilizing the R package glmnet, the Lasso Cox regression model was built using all prognosis-related hypoxia genes, and candidate genes were penalized to exclude redundant components and reduce the risk of overfitting (16). A 10-fold cross-validation under the constraint of low bias is used to calculate the model’s penalty parameter (λ).

To determine the best prognosis-associated hypoxia signature, Lasso regression analysis was used, and the regression coefficients were calculated for each gene separately, and then the sample risk scores for multivariate Cox regression were calculated using the R package survminer (17), with the formula as follows:

βi denotes the weight of each gene in Lasso Cox regression; χi denotes the expression level of each gene.

The CHOL patients were divided into high-risk and low-risk groups based on Risk score. Kaplan-Meier curves of survival differences between samples in the high-risk and low-risk groups were plotted, and significant differences in OS between groups were assessed based on log-rank tests.

Correlation Analysis of Prognostic Models and Clinical Characteristics

The univariate and multivariate Cox hazard regression analysis were performed separately for risk scores and patient clinical characteristics (including age, gender, and stage) to determine whether the predictive power of the prognostic model was independent of clinical characteristics. Risk ratios (HR) and 95% confidence intervals were calculated for each candidate prognostic factor, with a p-value < 0.05 as the threshold of significance. In addition, differences in risk scores across age, sex, and stage subgroups were assessed based on the Wilcoxon rank-sum test.

Gene Interactions Analysis

The expression correlation between genes in the prognostic model was assessed based on Pearson correlation analysis. In addition, the interactions of proteins encoding hypoxia-related genes were obtained based on the STRING database (http://www.string-db.org/), and protein-protein interactions (PPI) network of hypoxia-related genes were constructed, and the network graphs were visualized using Cytoscape (v3.9.1) to assess the interactions of the genes in the prognostic model.

Functional Enrichment Analysis

Gene set enrichment analysis (GSEA) was used to identify biological processes that differed significantly between high- and low-risk group samples (18). We examined biological processes from the GO database and KEGG signaling pathways for significant variations between high- and low-risk group samples using the R tool ClusterProfiler (19). To screen the collection of substantially linked genes, a corrected p-value of less than 0.05 was employed as a criterion, and the p-value correction technique Benjamini Hochberg was applied.

Somatic Cell Mutations

TCGA provided the tumor mutational data. The mutation genes were calculated using tumor mutational burden (TMB). The mutational data in both the high-risk and low-risk groups were analyzed using the R maftools package (20). P-values less than 0.05 were deemed statistically significant.

Expression of Immune-Negative Regulatory Genes

The cancer immune cycle has emerged as the primary research framework for cancer immunotherapy. It describes a cyclical process in which the immune system eradicates cancer: Antigen Processing and Presentation (Category 1), Atimicrobials (Category 2), Chemokines (Category 3), Cytokine Recepors (Category 4), Cytokines (Category 5), Matrix remodeling (Category 6), and Nature Killing Cell Cytoxicity (Category 7) (21). In the low and high hypoxia risk groups, we looked at the expression of genes that negatively regulate these processes. Tracking Tumor Immunophenotype (http://biocc.hrbmu.edu.cn/TIP/index.jsp) was used to identify immunonegative regulation-related genes (22).

Tissue Specimens

From September 2021 to March 2022, 10 paired fresh cholangiocarcinoma tissues and neighboring non-tumor tissues were taken from patients who underwent radical surgery at Xi’an Jiaotong University’s First Affiliated Hospital. In addition, between December 2013 and April 2016, 62 patients with cholangiocarcinoma who had a curative procedure were enrolled in a cohort. The protocol was authorized by the First Affiliated Hospital of Xi’an Jiaotong University’s Institutional Medical Ethics Committee (No. XJTU1AF2021LSK-261).

RNA Isolation and RT-qPCR

The RNeasy Mini Kit (Qiagen) was used to extract total RNA, and the reverse transcription was done with the High Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific). A LightCycler480 PCR equipment was used to perform quantitative PCR using SYBR Green dye (Roche). GAPDH was used to standardize the relative fold change in expression.

Immunohistochemistry (IHC)

Following deparaffin deparaffinization, rehydration and antigen retrieval, the paraffin-embedded slides were incubated with primary anti-PPFIA4 antibody (1:100, Sigma) at 4°C overnight. HRP-conjugated secondary antibody and DAB peroxidase substrate were utilized for immunostaining.

Statistical Analysis

All statistical analyses were performed using R software (version 4.1.2; https://www.R-project.org). Wilcoxon rank-sum test was used to compare significant differences in continuous variables between the two groups. Pearson correlation analysis was used to analyze the expression correlation between genes. Differentially expressed genes between high and low risk groups were identified based on the R package limma (23) with a differentially expressed gene threshold of |log2FC| > 0.58 and p-values < 0.05. The univariate Cox regression analysis was performed using the R package survival, and multi-factor Cox regression analysis was performed on prognostic model scores to explore the the independent prognostic value of the score. Kaplan-Meier curves were plotted using the R package survminer, and the significance of differences between groups was assessed based on the log-rank test. And p-values < 0.05 were used as the threshold of significance for statistical analysis.

Results

Establishment of a Hypoxia-Related Prognostic Model for CHOL

We acquired 200 hypoxia-related genes from the MSigDB database to study the predictive efficiency of hypoxia-related genes in CHOL. EGFR, PFKL, CHST2, ADORA2B, EDN2, HMOX1, CP, PPFIA4, PLAC8, IDS, ALDOC, STBD1, BCL2 were among 13 hypoxia-related genes found to be substantially linked with overall survival (OS) in the TCGA CHOL cohort using univariate Cox hazard analysis (p-value < 0.1, Figure 1A). Based on the median value of gene expression (EGFR, PFKL, CHST2, ADORA2B, EDN2, HMOX1), we separated the samples into two groups, and Kaplan-Meier curve analysis revealed that gene expression affected patient survival (log-rank test p-value < 0.05, Figure 1B).

Figure 1 | Construction of a hypoxia-related prognostic model for CHOL. (A) A forest map showed 13 hypoxia-related signatures identified by univariate Cox proportional hazard regression. (B) OS curves of CHOL patients with different PPFIA4 expression. (C, D) The LASSO Cox regression model to identify the most robust hypoxia-related signatures. (E) Distribution of LASSO coefficients of the hypoxia-related gene signature. (F) The heatmap of the vital status, survival and gene expression.

Although we initially observed genes with prognostic efficacy in CHOL patients by univariate Cox regression analysis and log-rank test, we further performed Lasso regression analysis based on these 13 genes in order to integrate these informative genes to obtain a more optimal prognostic model, while removing redundant factors and controlling the risk of overfitting. Ten-fold cross-validation was performed under optimal conditions to determine the penalty parameter (λ) of the model. We screened out 6 most predictive factors for OS (Figures 1C, D). PPFIA4 and CP were retained as a valid prognostic risk factor, while BCL2, PLAC8, PFKL and EGFR were retained as a prognostic protective factor, Together, they constituted a prognostic risk model associated with hypoxia in CHOL. Based on the linear combination of the expression levels of these genes and the corresponding weights, we could assess the hypoxia-related prognostic risk score for each patient (Figure 1E). The corresponding association between the expression of 6 hypoxia-related signature in the prognostic model and the vital status and survival of patients were visualized in Figure 1F.

Independent Validation of the Hypoxia-Related Prognostic Model

The risk score for each patient with CHOL in the TCGA was determined using the hypoxia-related prognostic model for CHOL that was constructed. After obtaining each patient’s risk value, the samples were sorted from low to high in terms of risk score, and scatter plots were used to display the results. The median of all sample risk scores was used as the cutoff, and the samples were divided into two groups: high risk and low risk, the low-risk group’s OS was likewise considerably lower than the high-risk group’s (log-rank test p-value < 0.001, Figure 2A). We also calculated heat maps for display to investigate the expression of different genes in the risk model across all genes in different samples, and we discovered that the expression of six hypoxia-related genes in the model differed considerably in the high- and low-risk groups (Figure 2B).

Figure 2 | Independent validation of hypoxia-related prognostic models. (A, C) Survival curve for low-risk and high-risk subgroups in the training dataset and validation dataset. (B, D) The expression heat map of the 6 prognostic hypoxia-related signatures in low-risk and high-risk subgroups training dataset and validation dataset.

To assess the robustness and generalizability of the hypoxia-related prognostic model, we collected CHOL samples from GSE107943 as an independent validation cohort. Patients were divided into high-risk and low-risk groups according to the risk score of the prognostic model. We obtained similar results to the training cohort through an external validation cohort (Figures 2C, D).

Hypoxia-Related Risk Score Is an Independent Prognostic Factor

We then explored the independence of the prognostic model in patients with CHOL from TCGA. The univariate Cox regression analysis revealed that the hypoxia-related risk score was the only prognostic factor of OS (HR = 5.94, p-value < 0.001, Figure 3A). Further, the multivariate Cox regression analysis confirmed the independent prognostic value of the hypoxia-related risk score (HR = 74.71, p-value = 0.004, Figure 3B).

Figure 3 | The Hypoxia-related risk score is an independent prognostic factor. (A-D) The forest plot of the univariate and multivariate Cox regression analysis shows that the risk score was an independent risk factor for overall survival in training dataset and validation dataset. (E-G) The box line chart of the differences in the distribution of risk scores by grouping patients based on their age, gender and stage.

In the GEO cohort, univariate Cox analysis showed that hypoxia-related risk score (HR = 2.65, p-value = 0.046), stage (HR = 3.48, p-value = 0.015) were prognostic factors in patients with CHOL (Figure 3C). Hypoxia-related risk score (HR = 3.06, p-value = 0.032) and stage (HR = 3.88, p-value = 0.017) were found to be independent predictive markers in patients with CHOL in a multifactorial Cox analysis (Figure 3D), which is consistent with the TCGA cohort’s findings. The prognostic risk scores established in our study might be used as independent risk factors affecting patient prognosis based on the above investigations.

To further corroborate the independence of prognostic risk scores from clinical characteristics, we compared the differences in the distribution of risk scores by grouping patients based on their age, gender and stage, respectively. We found no significant differences in risk scores by age and tumor stage grouping (p-value > 0.05, Figures 3E, G), but slightly higher in male patients than in female patients (p-value < 0.05, Figure 3F).

The Interaction Mechanism Between Prognostic Genes

Although the 6 signature genes we obtained from the prognostic model were all involved in the tumor hypoxic state hallmark, but their interactions with other hypoxic genes were not clear, so we examined the co-expression of these genes. Overall, there was no strong co-expression among the hypoxia-related genes involved in the prognostic model (Pearson |r| < 0.34, Figure 4A), indicating that the redundant genes with strong associations had been filtered out through LASSO Cox analysis. In addition, we looked at how the hypoxia-related genes in the model interacted with other hypoxia-related genes. We built a PPI network connecting the genes in the prognostic model and other hypoxia-related genes using the interactions of gene-encoded proteins retrieved from the String database online tool (Figure 4B).

Figure 4 | The interaction mechanism between Prognostic Genes. (A) The spearman correlation analysis based on the expression of the 6 hypoxia-related signatures. (B) The Protein–Protein network interactions including 200 hypoxia-associated genes.

We found extensive interactions between EGFR, CP, PFKL genes and other hypoxia-associated genes in the model. The model includes hypoxia-related genes, which might play a key role in tumor hypoxia regulation.

Hypoxia-Related Risk Xcores Reveal Differences in Molecular Tumor Characteristics

To explore the underlying molecular mechanisms of hypoxia-related prognostic models, we first examined somatic mutations in different risk groups in the TCGA-CHOL cohort. Due to the small sample size of the TCGA-CHOL dataset, the comparison of differences in mutation frequencies failed to present significant results (Figure 5A, p-value < 0.05). However, we found four patients in the high-risk group enriched for the IDH1 mutation, while only one patient in the low-risk group was enriched for this mutation (Figure 5A).

Figure 5 | Hypoxia-related risk scores reveal differences in tumor immune microenvironment characteristics. (A) The heatmap for mutational gene and mutation type in the high- and low-risk groups. (B) The GSEA enrichment analysis for KEGG signaling pathways between the high- and low-risk groups. (C) The GSEA enrichment analysis for biological processes from the GO database between the high- and low-risk groups.

Using GSEA enrichment analysis, we looked into the biological pathways altered by the hypoxia-related predictive risk score in subgroups. Metabolic processes such as “Carbon metabolism”, “Glycolysis/Gluconeogenesis”, “Biosynthesis of amino acids”, “Cholesterol metabolism”, “PPAR signaling pathway” and “Fatty acid degradation” were dramatically increased in the high-risk group (NES > 0, FDR < 0.0001), whereas T cell receptor signaling route, natural killer cell mediated cytotoxicity, antigen processing and presentation, chemokine signaling pathway, and cytokine-cytokine receptor interaction were all significantly downregulated in the high-risk group (NES < 0, FDR < 0.0001, Figure 5B). In addition, GSEA enrichment analysis of GO-BP showed that metabolic processes such as “protein-lipid complex subunit organization”, “protein-lipid complex subunit organization”, “protein-lipid complex remodeling”, “protein-containing complex remodeling”, “organic acid catabolic process”, “requlation of lipid catabolic process”, “carboxylic acid catabolic process”, “neutral lipid metabolic process”, “sterol metabolic process”, “small molecule catabolic process”, “cellular amino acid metabolic process”, “secondary alcohol metabolic process”, “steroid metabolic process”, “organic acid biosynthetic process”, “lipid catabolic process” were increased dramatically in the high-risk group (NES > 0, FDR < 0.0001). We observed that “leukocyte migration”, “leukocyte mediated immunity”, “regulation of cell-cell adhesion”, “mononuclear cell migration”, “I-kappaB kinase/NF-kappaB signaling”, “adaptive immune response”, “leukocyte cell-cell adhesion”, “interleukin-12/10 production “, “cell activation involved in immune response”, “antigen processing and presentation”, “positive regulation of cell activation” were significantly downregulated in the high-risk group (NES < 0, FDR < 0.0001; Figure 5C). Taken together, we deciphered the tumor molecular signature of the differences between hypoxia-related risk subgroups, suggesting potential molecular mechanisms for the different risk subgroups.

Hypoxia-Related Risk Scores Reveal Differences in Tumor Immune Microenvironment Characteristics

Tumor-immune interactions are crucial in controlling the immune milieu because they incorporate the effects of several regulators. We looked examined how immune regulatory genes were expressed differently in high-risk and low-risk people. We discovered that genes involved in immune modulation were expressed differently in high and low risk groups (Figure 6A). We looked examined how divergent genes were expressed in distinct immune regulatory groups.

Figure 6 | Hypoxia-related risk scores reveal differences in tumor immune microenvironment characteristics. (A) The volcano plot of different expression immune-related genes. (B) The heatmap of immune-related genes and different immune categories in the high- and low-risk group.

The genes HLA-DPB1, HLA-DQA1, HLA-DRA, HLA-DRB1 and HLA-DRB5 in the “Antigen Processing and Presentation” category were considerably downregulated in the high-risk group. PLA2G2A, KNG1, HRG, and other genes associated to “antimicrobials” were considerably downregulated in the high-risk group. CCL17, a gene associated to “chemokines,” was considerably downregulated in the high-risk group. The gene EGFR, which belongs to the “Cytokine Receptors” category, was considerably downregulated in the high-risk group. The genes CTF1, GDNF, HAMP, IGF2, SAA1, SAA2, TGFA, which belong to the “cytokines” category, were considerably downregulated in the high-risk group. CA9 and PLOD2, which are connected to “matrix remodeling,” were considerably downregulated in the high-risk group. The gene PLCG2, which is connected to “Natural Killer Cell Cytotoxicity,” was considerably downregulated in the high-risk group. The above results are shown in figures (Figures 6A, B). These findings show that immune checkpoint expression differs under the effect of CHOL’s hypoxic condition, affecting the immunological microenvironment’s control.

The Expression of PPFIA4 Is Dramatically Increased in CHOL Tissues

To further corroborate these findings, PPFIA4 mRNA levels were measured in 10 matched fresh CHOL tissues with surrounding non-tumor tissues. When compared to non-tumor tissues, the amount of PPFIA4 mRNA in cholangiocarcinoma tissues became much higher (Figure 7A). In addition, we investigated PPFIA4 expression in a cohort of patient with cholangiocarcinoma samples from our hospital by IHC assay, which demonstrated predominant expression of PPFIA4 within tumor cells (Figure 7C, D). The expression of PPFIA4 was found in 26 of the 62 carcinoma samples (41.9%), whereas the remaining 36 samples (58.1%) were negative. Table 1 outlined the link between PPFIA4 expression and clinicopathological characteristics. The presence of PPFIA4 was significantly linked to large tumor size, advanced TNM stage, and lymph node metastasis, while there was no correlation of PPFIA4 expression with gender, age, differentiation and tumor stage. Additionally, individuals with high PPFIA4 expression had a poorer 5-year OS prognosis (Figure 7B, p-value = 0.03).

Figure 7 | PPFIA4 expression is significantly upregulated in cholangiocarcinoma tissues. (A) PPFIA4 mRNA levels were determined by RT-qPCR in 10 pairs of cholangiocarcinoma tissues and matched non-tumor tissues. NT, tumor adjacent non-tumor tissue; T, tumor. ***p-value < 0.001. (B) OS curves of cholangiocarcinoma patients with different PPFIA4 expression. (C) Representative IHC images of positive PPFIA4 expression. (D) Representative IHC images of negative PPFIA4 expression. Scale bar, 100 µm.

Table 1 | Relationships between PPFIA4 expression and clinicopathological characteristics.

Discussion

The molecular mechanisms underlying the development of CHOL are still unclear. It was reported that hypoxia enhances the aggressiveness of CHOL cells (12) and the malignant behavior of tumors can be regulated through hypoxia-related gene (13, 24). Gene signatures have been utilized to predict the prognosis of many malignancies in recent years, and they have proven to be more accurate than TNM staging and histological diagnosis (25, 26). Hypoxia has been identified as a factor impacting patient prognosis that is independent of clinically established prognostic variables such as tumor stage, lymph node status, and tumor grade (27). Although approaches such as nitroimidazole, PET imaging, and IHC biomarker expression have been used to determine the degree of hypoxia in patients’ tumors, the precise proven method remains unknown (28, 29). For a number of malignancies, such as prostate cancer, lung cancer, and breast cancer, prognostic models based on hypoxia-related gene expression have been described (30–32). In this study, we used a Cox and Lasso regression model to analyze 200 hypoxia-related genes, and then screened 6 prognosis-related hypoxia-related genes and constructed a prognostic prediction model for CHOL, and the OS of the high-risk group was significantly lower than that of the low-risk group, and we validated our model using the GEO CHOL date set. The prognostic model we constructed is an ideal model with better sensitivity and specificity.

Phosphofructokinase L (PFKL) includes Binding locations for hypoxia-inducible factor 1 (HIF-1) (33). HIF-1 is a nuclear transcription factor that is active in the oxygen state where the body is adapted to hypoxia or hypoxia. HIF-1α has a certain transcriptional activity and a broad target gene spectrum (34). In hypoxia, the transcriptional and translational levels of HIF-1α can increase exponentially with prolonged hypoxia (35). D-fructose 6-phosphate is converted to D-fructose 1,6-bisphosphonate, a crucial step in glucose metabolism, by an enzyme encoded by PFKL (glycolysis) (36). In the tumor hypoxic region, tumor cells translocate extracellular glucose into the cell via glucose transporter proteins on the cell membrane to produce pyruvates and ATP via the glycolytic pathway, and LDH-A acts to convert it to lactate (37). The production of lactate constitutes a local acidic environment for tumor cell proliferation and activates the cascade reaction of protein hydrolases to convert pre-matrix metalloproteinases into matrix metalloproteinases, which promote the degradation of the extracellular matrix of cancer cells and facilitate the invasion of surrounding tissues by tumor cells (38). A number of cancers have been identified to have PFKL as a component of the glycolysis process, including lung cancer (39), esophageal cancer (40), neuroblastoma (41) and hepatocellular carcinoma (42). The Bcl-2 protein family is one of the core regulatory mechanisms of apoptosis, which can receive and transmit intrinsic intracellular signals or external environmental stress signals, such as nutrient or hypoxic stress, DNA damage, oncogene over-activation, endoplasmic reticulum stress, etc (43). It was found that Bcl-2-associated transcription factor BCLAF1 was highly expressed under hypoxia with a time-dependent hypoxic effect and positively correlated with HIF1α expression (44). PTPRF Interacting Protein Alpha 4 (PPFIA4) was reported to be glycolysis-associated signature, involved in glycolysis of tumor microenvironment (45). Proliferation and migration of tumor cells in colon cancer are facilitated by PPFIA4’s ability to increase tumor glycolysis (46); PPFIA4 also increases mitochondrial metabolism via MTHFD2 in desmoplastic-resistant prostate cancer (47). In our study, we found that PPFIA4 was oncogenic in CHOL and its high expression was associated with poor prognosis of patients. We confirmed the high expression of PPFIA4 in the tumor tissues of CHOL patients through fresh tumor tissues and their corresponding paraneoplastic tissue and previous pathological slides collected clinically. Likewise, we confirmed that high PPFIA4 expression was significantly associated with poor patient prognosis. We hypothesized that the expression status of PPFIA4 in CHOL tumor tissues could reflect the hypoxic status in tumor tissues. It has been discovered that PLAC8 enhances human interstitial extravillous trophoblast cell invasion and migration, and hypoxia dramatically upregulates PLAC8 expression in this cell population (48). In hypoxic conditions, Ceruloplasmin (CP) induces generates oxygen radicals (49), while inducing activation of HIF-1α (50), Overexpression of CP in clear cell renal carcinoma is linked to oncogenic pathways and worse survival rates (51). Epidermal Growth Factor Receptor (EGFR) is a primary regulator of HIF-1 under hypoxic conditions, and mutant EGFR is a prominent regulator of HIF-1a in EGFR mutant NSCLC cells (52). In addition, EGFR was reported that it can blocked anti-cancer miRNA maturation under hypoxic status (53).

Somatic mutations between high- and low-risk groups were compared in this study. Among the high-risk individuals, we observed that Isocitrate Dehydrogenase (NADP(+)) 1 (IDH1) mutations were more common than expected. IDH1 has been linked to tumor hypoxic reprogramming, according to a study (54). Mutations in IDH1 reduce IDH1’s catalytic activity and activate HIF-1 α in glioblastoma cells (55). Wild-type IDH1 suppresses tumor development in renal cell carcinoma via degrading HIF-α (56). By using GSEA to examine the variations in cellular activities and pathways between high and low risk groups, we found that metabolic processes and pathways associated with hypoxic state were upregulated, but immune response processes were generally downregulated. Prognostic models developed by our team may be able to respond well to tumor microenvironment hypoxia.

Genes associated to the negative regulation of the tumor immune, such as checkpoints and suppressor-related genes, were examined between high and low risk groups to study the hypoxia/immunity association. Immune checkpoints are targets of immunotherapy, and researchers have found that patients with tumors co-expressing HIF-1α, a subtype of HIF, and PD-L1, an immune checkpoint, have a high risk of tumor recurrence and metastasis as well as lethality (57). In CHOL, hypoxic condition altered the regulatory impact on the immune milieu by modifying the expression of immunological checkpoints. In the high-risk scenario, the hypoxic condition could boost the ability of tumors to escape and invade.

It is possible that our research contained some flaws. In the first place, the research is a retrospective one with a relatively limited sample size. Therefore, the sparse mutation landscape was not suitable for further investigation of genetic factors associated with the hypoxia-related risk score. For example, only three samples showed KRAS and TP53 mutations. In addition, the insufficient clinical information of patients and lack of diversity also hindered the further in-depth analysis. In the present study, most (32/36) of the obtained TCGA-CHOL samples were intrahepatic cholangiocarcinoma, and cholangiocarcinoma at different histological sites may present different molecular features, therefore, study with large sample size would be needed in the future to further analyze cholangiocarcinoma samples from different sites. Nevertheless, the 6-gene hypoxia-related signature identified in our study demonstrated the value to better predict the prognostic status of patients and respond to the hypoxic status of tumor cells.

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 First Affiliated Hospital of Xi’an Jiaotong University’s Institutional Medical Ethics Committee. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

QS, HW, and GW undertook the data analysis and wrote the manuscript. BX and DX helped design the study and edit the manuscript. HW and BX aided in the methodology and data duration. BX and DX assisted in the conception and data representation. All the authors reviewed the data and analysis, read and approved the manuscript.

Funding

The present study was supported by the National Natural Science Foundation of China (Program No.: 82073271) and Institutional Foundation of The First Affiliated Hospital of Xi’an Jiaotong University (Program No.: 2022YQPY07).

Acknowledgments

The authors thank Beijing GAP BioTechnology Co.Ltd. for their help in supporting from the technique.

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REVIEW
published: 18 July 2022
doi: 10.3389/fonc.2022.924696

Utilizing Gut Microbiota to Improve Hepatobiliary Tumor Treatments: Recent Advances

Hao Qin ¹†, Baowen Yuan ¹†, Wei Huang ²* and Yan Wang ¹,²*

¹ Key Laboratory of Cancer and Microbiome, State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China, ² Beijing Key Laboratory of Cancer Invasion and Metastasis Research, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Capital Medical University, Beijing, China

Edited by:
Yunfei Xu, Shandong University, China

Reviewed by:
Peter Suenaert, A-Mansia Biotech, Belgium
Lutao Du, The Second Hospital of Shandong University, China
Di Zhang, Peking University, China

*Correspondence:
Wei Huang
weihuang@ccmu.edu.cn
Yan Wang
yanwang@cicams.ac.cn

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

Specialty section:
This article was submitted to Cancer Molecular Targets and Therapeutics, a section of the journal Frontiers in Oncology

Received: 20 April 2022

Accepted: 20 June 2022

Published: 18 July 2022

Citation:
Qin H, Yuan B, Huang W and Wang Y (2022) Utilizing Gut Microbiota to Improve Hepatobiliary Tumor Treatments: Recent Advances. Front. Oncol. 12:924696. doi: 10.3389/fonc.2022.924696

Hepatobiliary tumors, which include cholangiocarcinoma, hepatocellular carcinoma (HCC), and gallbladder cancer, are common cancers that have high morbidity and mortality rates and poor survival outcomes. In humans, the microbiota is comprised of symbiotic microbial cells (10-100 trillion) that belong to the bacterial ecosystem mainly residing in the gut. The gut microbiota is a complicated group that can largely be found in the intestine and has a dual role in cancer occurrence and progression. Previous research has focused on the crucial functions of the intestinal microflora as the main pathophysiological mechanism in HCC development. Intestinal bacteria produce a broad range of metabolites that exhibit a variety of pro- and anticarcinogenic effects on HCC. Therefore, probiotic alteration of the gut microflora could promote gut flora balance and help prevent the occurrence of HCC. Recent evidence from clinical and translational studies suggests that fecal microbiota transplant is one of the most successful therapies to correct intestinal bacterial imbalance. We review the literature describing the effects and mechanisms of the microbiome in the gut in the context of HCC, including gut bacterial metabolites, probiotics, antibiotics, and the transplantation of fecal microbiota, and discuss the potential influence of the microbiome environment on cholangiocarcinoma and gallbladder cancer. Our findings are expected to reveal therapeutic targets for the prevention of hepatobiliary tumors, and the development of clinical treatment strategies, by emphasizing the function of the gut microbiota.

Keywords: hepatobiliary tumors, gut microbiota, metabolites, probiotics, antibiotics, fecal microbial transplantation

Introduction

Hepatobiliary tumors are malignant space-occupying lesions that occur in hepatocytes, the gallbladder, and intrahepatic bile ducts. They are associated with a poor survival rate and an aggressive phenotype (1). Primary liver cancer is the second leading cause of cancer-related death worldwide. Approximately 75% of patients with primary liver cancer have hepatocellular carcinoma (HCC) (2), and it was estimated that, in 2030, more than one million patients will die from liver cancer (3). The major risk factors for HCC are alcohol intake, viral hepatitis, and nonalcoholic steatohepatitis (NASH), which occurs in chronic liver disease patients (4, 5). HCC is invasive and has a high level of metastasis; however, as it is asymptomatic in its early stages, it is frequently diagnosed by imaging at an advanced stage (6). Serum α-fetoprotein is commonly used as a screening biomarker for patients with HCC in developing countries (7, 8). However, there is controversy regarding the use of α-fetoprotein due to its poor specificity and sensitivity, limiting its application in clinical practice (9). There is thus an urgent need to investigate and identify combinations of tumor markers that have a high level of prediction.

The human body consists of trillions of microbes that mainly live in the digestive system, and these include bacteria, viruses, fungi, and archaea (10). The ratio of microbial cells to human cells is roughly equal and the total bacterial count in the colon, the organ with the most microorganisms, is approximately 3.8 × 10¹³ (11). The human gut microbiota has a close relationship with the homeostasis and health maintenance of the host (12, 13). Many probiotics exhibit an antitumor activity through apoptotic signaling pathway activation or immune response enhancement, and they can help improve intestinal health and maintain intestinal flora (14). At the same time, probiotic metabolites may regulate various cellular and molecular processes and the composition of the gut microbiome (15), which plays a critical role in cancer prevention and supports anticancer therapies. Bacterial fermentation of carbohydrates and proteins produces a range of metabolites that have been shown to influence human health and are associated with many diseases either directly or indirectly (16). These metabolites interact with numerous important targets in several metabolic pathways and the molecular processes of malignant tumors.

The largest proportion of bacteria is located in the colon and direct bacterial actions and metabolites promote colorectal cancer initiation and development (17). The hepatic portal vein transports venous blood to the liver, which is subjected continually to bacterial products derived from the gut (18). The gut-liver axis forms a reciprocal interaction between the intestine and the liver that involves the intestinal microbiome and hepatic immune system, which mediates hepatobiliary disorders (19–21). An increasing number of studies have recently shown that the microbiota of the gut, intestinal commensal bacterial species, and their metabolites and products, have a vital function in the pathogenesis of or protection against the development of HCC (22–24). Antibiotic treatments are connected to decreased commensal intestinal bacterial diversity, which, in combination with anticancer therapies, affects the outcome of patients with HCC (25, 26). The promising strategy of fecal microbiota transplantation (FMT) for improving the immune response against cancer has been examined (27), and the enhanced effectiveness of inhibitors of the immune checkpoints with FMT may be a potential strategy to prevent HCC.

Here, we summarize the current progress in our comprehension of the gut microbiota in the treatment of hepatobiliary tumors, including HCC, cholangiocarcinoma (CCA), and gallbladder cancer (GBC). The fundamental mechanisms of the effects of metabolites derived from the gut microbiota on the occurrence of HCC are also discussed. These potential effective therapeutic or preventive strategies, including probiotics, antibiotics, and FMT, may lead to new noninvasive and cost-effective approaches for preventing and treating HCC in the future.

Bacterial Metabolism and HCC Promotion

The gut-liver axis is an anatomical and physiological connection between the liver and gut. The gut microbiota, including metabolites, helps to maintain normal liver functions. Exposure of the liver with chronic lesions to bacterial metabolites results in liver damage and ultimately to the development of HCC (28). Synthesis of cholic acid, primary bile acids (PBAs), and chenodeoxycholic acid takes place via liver cholesterol, and several studies have examined the function of bile acid metabolism disorders in relation to the growth of HCC. Bile acids are categorized as PBAs and secondary bile acids (SBAs). SBAs are derived from PBAs that are converted by gut bacteria and microbial bile acid products and could, thus, be promoters of liver cancer (29). The SBA deoxycholic acid (DCA) is a byproduct of intestinal bacterial metabolism that promotes hepatocarcinogenesis, and the reduction of DCA levels prevents the formation of liver cancer in obese mice (30). The STHD-01 model of steatohepatitis induced by high-fat diet (HFD), which promotes HCC without the administration of chemical carcinogens, shows that the hepatic accumulation of SBAs, such as DCA, is regulated by the gut microbiota and could promote hepatocarcinogenesis by activating the pathway of mammalian target of the rapamycin in hepatocytes (31). The prevalence of nonalcoholic fatty liver disease (NAFLD) is mainly increased by obesity and type 2 diabetes mellitus, and high-fat or high-cholesterol diets are closely linked to NAFLD-associated HCC, which animal experiments and clinical research have confirmed (32, 33). The pathological mechanism associated with gut microbiota dysbiosis involves an increase in the gut bacterial metabolite taurocholic acid and a decrease in 3-indolepropionic acid, as observed in the metabolomic profiling of the serum in high-fat/high-cholesterol diet-fed mice (34). The underlying molecular mechanisms involved in HCC progression include the gut microbial dysbiosis that can lead to enhanced serum taurocholic acid and reduced 3-indolepropionic acid levels that can promote cell propagation and lipid accretion, leading to NAFLD-associated HCC (34). In addition, obesity-associated tumorigenesis develops via NAFLD, continuing to NASH and eventually to HCC (35). The constituent of gram-positive bacterial cell walls, lipoteichoic acid, cooperates with DCA to promote NASH-associated HCC development in the tumor microenvironment via the microbiota-driven cyclo-oxygenase 2 pathway under the gut-liver axis (36). This implies that the abundance of the PBAs and SBAs, which is influenced by changes in the gut microbiota, shows significant associations between risk factors and effective strategies for the prevention of HCC.

The key metabolites produced by gut microbial fermentation of dietary fiber are short-chain fatty acids (SCFAs), which have a vital function in maintaining intestinal homeostasis. An epidemiological study of 125,455 participants from two cohorts in the USA suggested that the increased dietary fiber and whole grains intake was linked to lower HCC development risk (37). Treatment with SCFAs significantly decreased the HCC-positive mouse numbers and prevented the chronic liver disease progression to HCC (McBrearty et al., 2021). The molecular mechanism behind this was that SCFAs upregulated tumor suppressor disabled homolog 2 expression and partially blocked the activity of hepatitis B X antigen (HBx) (38, 39). These results were consistent with those from an in vivo model of HBx transgenic mice in which all the mice developed hepatitis and steatosis, which progressed to dysplastic nodules and noticeable HCC by 1 year (40). Propionate is a health-promoting SCFA that is produced by gut bacterial fermentation of indigestible fiber and is a principal metabolite that maintains intestinal homeostasis (41). Kobayashi et al. found that propionate enhanced the sensitivity of cisplatin and cisplatin-induced apoptosis of HCC cell lines through a GPR41-dependent pathway (42). The above findings demonstrate that SCFAs could be a possible treatment target to delay the development of HCC (Table 1).

Table 1 | Dysbiosis of bacterial metabolism and hepatocarcinogenesis.

A cross-sectional case-control study involving patients with primary liver cancer (n = 671) and 671 controls was conducted to examine the relationship between the levels of metabolites dependent on the gut microbiota, trimethylamine N-oxide and choline, and risk of primary liver cancer. Serum trimethylamine N-oxide level was positively related to primary liver cancer development, particularly in patients with lower levels of choline in the serum (44). Several studies have examined the concentration of ammonia during end-stage liver disease, including NASH and cirrhosis, and hyperammonia is linked with liver disease severity while scavenging ammonia prevents the progression of fibrosis in NAFLD (45, 46). Ammonia is generated by gram-negative anaerobes or gram-positive non-sporing anaerobes in the gut. It can enter the bloodstream and activate hepatic stellate cells (HSCs) and contribute to HCC occurrence and development (47).

Probiotics in HCC

Probiotics are living microbes that confer beneficial effects to the host and help to maintain the stability of an organism as a whole (48). The well-known probiotic strains, e.g., Lactobacillus and Bifidobacterium, are involved in preserving gut microbiota homeostasis. Several beneficial strains for human health including Faecalibacterium prausnitzii, Akkermansia muciniphila, and other SCFA-producing bacteria require further investigation (48–51). Recent studies have revealed that various probiotic strains exert antiproliferative and proapoptotic effects on carcinoma cells and tumor-bearing or tumor-induced animal models (52, 53). The derangement of the gut flora has an important function in a variety of liver diseases, which includes HCC (54). Kumar et al. showed that probiotic fermented milk prepared with probiotic lactic acid bacteria Lactobacillus rhamnosus GG (LGG) and Lactobacillus casei strain Shirota could decrease the risk of HCC by regulating lower genotoxicity or oncogene expression and DNA damage levels (55). Furthermore, Prohep reduced subcutaneous HCC in a tumor model in mice, as there was a 40% reduction in tumor weight and size in comparison with that of the control group (56). Prohep is a novel probiotic blend that comprises three ingredients, LGG, heat-inactivated VSL#3, and viable Escherichia coli Nissle 1917. The possible mechanism of action of Prohep in preventing the development of HCC is the induction and emission of anti-inflammatory interleukin (IL)-10 and suppression of the differentiation of Th17 cells to regulate the gut microbiota and weaken tumor growth and angiogenesis (56). There have been several in vitro investigations of the effects of probiotics against fibrogenesis. Liver fibrosis is caused by an increase in extracellular interstitial protein levels, which leads to the cirrhosis and HCC development. Kanmani et al. reported that the probiotics (Weissella cibaria, Lactobacillus brevis, and Lactiplantibacillus plantarum) might reduce HSC activation and reduce collagen deposition in human LX-2 cell lines by modifying the signaling of transforming growth factor (TGF)-β/SMAD and inducing autophagy and apoptotic pathways (57). This study reported the beneficial role of probiotics in the inhibition of liver fibrogenesis and the prevention of liver cancer. Another study has shown the protective effects of LGG against patulin-induced apoptosis in HepG2 cells by regulating the levels of proapoptotic BCL2 proteins p53 upregulated modulator of apoptosis and BH3-interacting domain death agonist in vitro (58).

Several chemical-induced HCC mouse models have been used to investigate the effects of gut probiotics on healthy individuals. Diethylnitrosamine (DEN) is the most extensively used toxic chemical that induces human liver fibrosis and cirrhosis resulting in HCC development (59). Recent studies have reported that chronic DEN treatment of rats increases the number of cecal E. coli and Atopobium while inducing significant inhibition of the Lactobacillus spp., Bifidobacterium spp., Enterococcus spp., and Bacteroides fragilis groups. Probiotics alleviated hepatocarcinogenesis induced by DEN by re-establishing homeostasis of the gut and improving intestinal and hepatic inflammation. Administration of probiotics could inhibited liver cirrhosis progression to HCC through endogenous damage-associated molecular pattern and exogenous pathogen-associated molecular patterns inflammatory signaling pathways (60). Another study reported excessive E. coli growth in the guts of patients with HCC, which contributed to the hepatocarcinogenesis process (61). A rat model of DEN-induced hepatocarcinogenesis showed that probiotics could potentially be an effective treatment for HCC (62). Indeed, exopolysaccharides are highly viscous microorganism-produced and secreted carbohydrate polymers (63) that have immune, antitumor, and potent antioxidant properties. Lactobacillus acidophilus ATCC 4356 exopolysaccharides exhibit a preventive and therapeutic effect against DEN-induced HCC through the regulation of inflammatory biomarkers IL-17 and TGF-β1 and TLR2/STAT-3/P38-MAPK signaling pathways (62). This suggests that the potential mechanism of the effect of the gut microbiota on HCC includes changes in the intestinal microbiome composition that affects the function of hepatic immune cells. Several studies have shown that the gut microbiota modulates the immune system in patients with HCC by reducing CD8⁺ T cells (23), stimulating inflammatory response (64), and modulating immunosurveillance (22). Xie et al. explored the dynamic alterations in the microbiota of the gut in HCC development and progression using a mouse model of streptozotocin/HFD-induced NASH-HCC. Their work identified that the abundance of major bacterial species, including Bacteroides spp. and Desulfovibrio spp., were amplified in the group given streptozotocin/HFD, while the abundance of Barnesiella spp., Odoribacter spp., and Paraprevotella spp. was significantly decreased (65). The increased abundance of bacterial species correlated positively with lipopolysaccharide levels and the host pathophysiological features.

Clinical trials examining the connection between gut bacterial microbiota and HCC via the gut-liver axis are underway. Alterations in the populations of live bacteria in the intestines are linked to liver function, implying that changes in the gut microbiome serve as biomarkers for early detection of HCC (Table 2). A recent study reported that the presence of qualitative and quantitative differences between the microbiota of the gut in patients with HCC related to NAFLD when compared with that of the healthy controls. The numbers of Bacteroidales, Bacteroidaceae, Bacteroides, Gemellaceae, and Enterococcaceae identified in the fecal samples from cirrhotic patients with HCC were increased, while there was a decline in the numbers of Bifidobacteriaceae, Verrucomicrobiaceae, Akkermansia, Adlercreutzia, and Dialister when compared to those in the controls (66). In patients with HCC, Bifidobacterium was depleted and its abundance was correlated inversely with the concentration of calprotectin, thereby showing a link with intestinal inflammation. The results suggested that systemic inflammation and the profile of the gut microbiota correlate significantly in NAFLD-related cirrhosis and contribute to HCC development. Interestingly, in a large cohort study from three regions in China with a total of 486 fecal samples from 135 healthy controls, 311 patients with hepatic tumor and 40 patients with cirrhosis were used to clarify how the gut microbiome might be used to noninvasively identify biomarkers of HCC (67). In the study, 419 samples underwent 16S rRNA sequencing with MiSeq to construct an early HCC diagnostic model. The results indicated that the diversity of the gut microbes was increased from cirrhosis to early HCC. There was a decrease in the abundance of bacteria that produce butyrate, such as Oscillibacter, Ruminococcus, Clostridium cluster IV, Faecalibacterium, and Coprococcus, while there was an increase in lipopolysaccharide-producing bacteria, including Haemophilus and Klebsiella, in early HCC when compared with those of the healthy controls (67). In addition, it has recently been demonstrated via high-throughput amplicon sequencing of the 16S rRNA gene that patients with primary HCC exhibited an increased degree of dysbiosis, calculated based on abundance of probiotic and harmful bacterial genera, in comparison to the healthy controls. The patients’ fecal samples showed that the relative abundance of Desulfococcus, Enterobacter, Prevotella, and Veillonella was enhanced at all stages, while the patients with primary HCC (stages I-III) had reduced Cetobacterium (68). In line with this, several studies have noted a correlation between intestinal dysbiosis and chronic liver disease (69, 70) and have proposed a novel integrated index degree for dysbiosis (Ddys) as a tool for evaluating the discrepancies between the microbiota of the gut and the development of HCC.

Table 2 | Summary of the probiotics associated with HCC.

Antibiotics in HCC

Antibiotic treatments are related to decreased diversity of commensal bacteria, and the early use of antibiotics when treating malignancies is considered to be a disadvantage as it reduces the abundance of Bifidobacteria, Akkermansia, and Ruminococcus in the gut microbiota and increases that of the Bacteroides (71, 72). Clinical studies have also shown that antibiotic treatments are linked with a worse overall survival in patients with advanced HCC who received treatment with first-line sorafenib therapy. Antibiotics can cause changes in gut microbiota that influence reactions in systemic therapy (26). Prospective studies should focus on elucidating the mechanism behind the failure of the multitargeted tyrosine kinase inhibitors for HCC that caused by antibiotics which lead to gut microbiota dysbiosis. Moreover, immune checkpoint inhibitors (ICIs) that target programmed cell death-1 and its ligand are new promising therapies that extend the survival of patients with HCC. The humanized monoclonal antibodies target inhibitory receptors to provoke an antitumor immune response and modulate T-cell activation by interrupting the delivery of inhibitory signals (73). A retrospective cohort study investigated the impacts of antibiotics on the clinical trial results of ICIs for treating patients with HCC (74). Antibiotics against anaerobes were linked with a higher risk of HCC-related death during ICI therapy when compared with antibiotics against aerobes. In a retrospective study, 59 patients with advanced HCC from two academic centers were exposed to antibiotics while receiving ICI nivolumab treatment. Patients who received antibiotics had a shorter median overall survival than those who did not (P = 0.04). This suggests that patients with HCC receiving nivolumab have worse survival rates if they receive antibiotics, and that antibiotics should be used carefully during ICI treatments (75). In some other tumor types, multiple clinical trials have also demonstrated that early exposure to antibiotics was associated with poorer outcomes and reduced clinical benefits following ICI treatment, such as worse overall survival and increased disease refractory risk (76, 77). Future studies should include stool sampling, as well as biopsies and blood sampling, to focus on elucidating the mechanisms of the effect of antibiotics on the gut microbiota of patients with HCC treated with ICIs.

Some studies have assessed the effects of antibiotics in combination with anticancer therapies on the outcomes of patients with HCC. In an international multicenter cohort of 450 ICI recipients, early exposure to antibiotics was not disadvantageous to the therapeutic outcomes of immunotherapy in patients with HCC (78). Antibiotic exposure 30 days before or after ICI initiation during early immunotherapy showed no association with overall survival but was significantly associated with prolonged progression-free survival. The efficacy of ICI therapy, mainly anti-PD-1 monotherapy, in 414 patients from 11 centers, suggests that antibiotics (beta-lactams or quinolones) administered 30 days pre- or post-ICI initiation in HCC are linked with extended progression-free survival (79). It is recommend that patients with HCC should be given broad-spectrum antibiotics before surgery. Furthermore, Wang et al. used a meta-analysis to assess the influence of prophylactic antibiotic treatments on infections and complications in transarterially treated patients with HCC (80). They showed that antibiotic prophylaxis may not be routinely necessary for postoperative infectious complications in patients with HCC who have been treated with transarterial therapy. However, in patients with HCC who have undergone biliary tract disease or biliary reconstruction surgery and enterobiliary anastomosis, the prophylactic use of antibiotics could reduce the risk of infection. In a spontaneous HCC model using MYC transgenic mice, a cocktail treatment with three antibiotics was invented to remove gram-positive bacteria to provoke hepatic natural killer (NK) T cell accretion and prevent liver tumor growth (81). They also found that vancomycin or cefoperazone alone was sufficient to increase hepatic NKT cells, while the effect of neomycin was weakened. Abdel-Hamid et al. examined the anticancer activity of some macrolide antibiotics including clarithromycin, azithromycin, and erythromycin on HCC development both in vitro and in vivo (82). Treatment with clarithromycin inhibited the proliferation of HepG2 cells with an IC₅₀ of 3.13 μg/mL and induced the apoptosis pathway to suppress HCC in a DEN-induced rat model. The basic experimental research should include the mechanisms of antibiotic-mediated intestinal dysbiosis and anticancer immune response drivers.

FMT in HCC

FMT involves transferring healthy donor stool to the gut of a recipient with a physical illness (83). FMT is a safe and effective alternative to treat a recurrent Clostridium difficile infection (84). Multiple randomized clinical trials have confirmed that FMT had a success rate of nearly 92% when used as a second-line treatment of recurrent Clostridium difficile infection (85, 86). Preliminary studies have proposed that FMT is a potential treatment for a variety of other conditions, which include obesity, functional gastrointestinal disorders, inflammatory bowel disease, and metabolic syndromes (87). According to several randomized clinical trials, healthy donor FMT could possibly heal liver disease (88). FMT administered via an oral capsule was safe in patients with recurrent hepatic encephalopathy and cirrhosis (NCT03152188, Phase 1). There was increased abundance of Ruminococcaceae and Bifidobacteriaceae while that of Streptococcaceae and Veillonellaceae was decreased in the post-FMT microbiota, which is associated with improved duodenal mucosal diversity and hepatic encephalopathy (89). A characteristic of NAFLD is excess accumulation of fat in the hepatocytes and it is estimated to be present in 10-40% of adults worldwide (90). NAFLD is a risk factor of cirrhotic and HCC progression (91, 92). Craven et al. reported that patients with NAFLD demonstrating elevated permeability of the small intestinal at baseline reduced significantly 6 weeks after allogenic FMT (NCT02496390, Phase 1 and 2). However, there were no significant changes hepatic proton density fat fraction (allogeneic or autologous) or resistance to insulin (93). In most cases, the cause of HCC is the underlying liver damage due to chronic hepatitis, and in some developed countries, the mortality rate of HCC associated with cirrhosis is increasing (94). Previous researchers have evaluated the alterations in the gut microbiota using metagenomic analysis and found a decreased expression of the antibiotic resistance gene in cirrhosis patients who underwent FMT via capsule or enema. These genes could be potential targets with healthy donor FMT (NCT03152188, Phase 1) (95). Moreover, many clinical trials have investigated the preliminary benefits of FMT in various liver diseases, such as: the effect of consecutive FMT on NAFLD with 20 participants through a randomized-controlled trial (NCT04465032, Phase 4); FMT using stool from five lean donors to determine the efficacy and safety for patients with NASH through single group assignments of interventional clinical trial (NCT02469272, Phase 1); and FMT versus standard therapy in NASH related cirrhosis with 112 participants through a randomized control trial (NCT02721264) (Table 3).

Table 3 | Registered clinical trials and animal studies using FMTs to treat liver disease.

Data from animal studies have suggested that FMT alleviates steatohepatitis induced by HFD and has a beneficial outcome because of its alterations to the composition of the gut microbiota (96). In male C57BL/6 mice, FMT reverses gut microbiota dysbiosis and regulates SCFA levels by increasing the butyrate concentration in the cecum to correct the ratio of T-cell subsets that are beneficial to immunological balance. Bacteroidetes abundance was decreased whereas that of Firmicutes was increased after FMT when related with the HFD group (96). The direct effects of FMT on HCC growth and development have not been studied yet. The possible mechanism is that FMT regulates intestinal flora disorders and reduces cytotoxin production to inhibit the incidence of HCC in patients with hepatitis B cirrhosis (97). In addition, FMT can modulate the gut microbiome to abrogate ICI-associated colitis and toxicity (98), which implying the other mechanisms such as immune microenvironment could be involved in the effect of FMT on HCC. Thus, further investigations are needed to prove the safety of FMT in patients with HCC.

Microbiome and CCA

After HCC, CCA is the most common malignant tumor and epithelial cell malignancy that accounts for almost 15% of all primary liver cancers. More than two-thirds of patients with CCA are diagnosed at an advanced disease stage with poor prognosis, as there is an absence of obvious symptoms to facilitate earlier diagnosis (99). Based on the anatomical sites of origin, CCA is categorized into three subtypes: intrahepatic, perihilar, and distal (100). Microbiota dysbiosis is closely linked with CCA progression from precancerous diseases (101). Gut microbiota can be used as a potential biomarker for CCA. In a case-control study, Zhang et al. performed high-throughput sequencing of the 16S rRNA from the fecal samples from 53 patients with CCA, 47 patients with cholelithiasis, and 40 healthy controls, and proposed a predictive model based on four enriched genera (Bacteroides, Muribaculaceae_unclassified, Muribaculum, and Alistipes) for the better diagnosis of CCA (102). Similarly, a classification model consisting of eight genera was constructed to classify CCA, HCC, and healthy controls (103).

In healthy people, the gut microbiota is ecologically balanced and meanwhile the link between gut microbiota and the pathogenesis of CCA is attracting more attention (104, 105). Biliary obstruction caused by CCA leads to cholestasis, which provides a culture medium for bacteria. Decreased bile secretion, low immunity of patients with CCA, and surgical trauma can promote overgrowth of the gut microbiota, which can result in the overgrowth of small intestinal bacteria, increased abundance of pathogenic bacteria, and change in microbiota abundance (105, 106). The overgrowth of gut microbiota can damage the intestinal mucosal barrier, which can lead to increased permeability of the intestinal mucosa; thus, a large amount of endotoxin and pathogenic bacteria permeate into the blood, resulting in intestinal endotoxemia and movement of the microbiota (107). This process leads to the inflammatory mediator release such as IL-1, TNF-α, and IL-6 which induces a series of inflammatory immune responses (108, 109). Moreover, long-term low-grade chronic inflammation is related to cancer incidence and development. At the same time, the gut microbiota can enter the biliary tract through the blood, lymph, or in a retrograde manner leading to a biliary infection (110).

Bile duct stones are a risk factor for intrahepatic CCA development, as a systematic review reported that there was a strong correlation between hepatolithiasis and intrahepatic CCA (111). Liver flukes are linked to bile duct infection and proinflammatory cytokine production which was consistent with the inflammatory phenotype in CCA development (112–114). Chng et al. reported that the Stenotrophomonas species abundance was significantly increased in tumor tissues of non-fluke-related CCA (115). The pathogenesis of CCA may involve the microbial community, as reported by Saab et al., who found that Streptococcus, Enterococcus, Klebsiella, Bacteroides, and Pyramidobacter were the most abundant in the bile of patients with extrahepatic CCA. The bile of patients with CCA had lower Firmicutes abundance and higher Bacteroidetes abundance than those of benign biliary pathology (116). The microbiota of the bile from patients with distal CCA was highly enriched with Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria. Moreover, the abundance of phyla Nitrospirae, Gemmatimonadetes, Latescibacteria, Chloroflexi, and Planctomycetes were significantly increased in patients with distal CCA when compared with patients with cholelithiasis (117). However, when compared with HCC, liver cirrhosis, and healthy controls, Jia et al. reported that patients with intrahepatic CCA demonstrated the highest levels of species richness, with four highly enriched genera in the gut microbiota, Lactobacillus, Actinomyces, Peptostreptococcaceae, and Alloscardovia (109). The Ruminococcaceae family is also enriched in patients with intrahepatic CCA who have vascular invasion. Patients with intrahepatic CCA have high plasma-stool ratios of tauroursodeoxycholic acid and glycoursodeoxycholic acid, which can be used as biomarkers to distinguish intrahepatic CCA from HCC (109). These studies indicate the microbiome diversity in patients with CCA and the heterogeneity among individuals.

A potential risk in CCA development is choledocholithiasis (CDL), which is a consequence of bacterial infection. Dangtakot et al. performed a sequencing study of the 16S rRNA gene, including samples of bile from 30 patients with CDL and 30 with CCA. Results showed higher abundance of Enterobacter, Pseudomonas, and Stenotrophomonas species in CCA when related with CDL (P < 0.05), while the Pyramidobacter, Cetobacterium, and Streptococcus species were less abundant in CCA when compared with CDL (P < 0.05) (118). The chronic liver disease, primary sclerosing cholangitis is accompanied by inflammation and scarring and is an important risk factor in CCA (119). It was recently reported that the gut microbiome directs hepatocytes through myeloid cell accumulation to control CCA; and when treated with neomycin to eliminate gram-negative gut bacteria, CCA inhibition could be achieved in the bile duct ligation-modeled mice (120). The bacteria that commonly exist in the environment although not in humans, such as Nesterenkonia, Methylophilaceae, and Mesorhizobium, were found in the microbiota of patients with extrahepatic CCA and benign biliary pathology. In a multicenter case-control study, the abundance of Actinomyces, Novosphingobium, Prevotella, and Fusobacterium were significantly increased in extrahepatic CCA, while that of Nesterenkonia and Rothia were reduced (121). Serra et al. found in bile samples that Pseudomonas was a significant positive prognosticator for CCA (122), and Lee et al. reported that Pseudomonas was a significant negative biomarker for CCA using plasma samples (123). These contrasting results might be due to the differences in sample types and study objects. When compared with bile samples, the blood microbiome of patients with CCA requires further investigation to explore its conformation and mechanisms.

The functional mechanisms of the microbiota of the gut in CCA have been inadequately studied. The gut microbiome directs hepatocytes and promotes CXCL1 expression in a TLR4-dependent manner to recruit myeloid-derived suppressor cells and promote CCA (120). Using large-scale proteogenomics, Dong et al. yielded insights into intrahepatic CCA etiology and taxonomy. They identified four intrahepatic CCA subgroups that were associated with different molecular and clinical features and selected HKDC1 and SLC16A3 as prognostic biomarkers (124).

Microbiome and GBC

GBC is a common malignant tumor with poor prognosis and almost all types are adenocarcinomas that are usually diagnosed at an advanced stage (125). GBC is recognized as a highly lethal disease due to a lack of predictive radiological features and accurate identification, which leads to only 10% of patients presenting at a stage suitable for surgical resection (126). Multiple investigations have demonstrated that bacterial infections are related to higher GBC risk. Choi et al. reported the bacterial species present in the bile microbiome, through metagenomic sequencing from patients with normal gallbladder, chronic cholecystitis, and GBC. They showed significantly increased Escherichia, Enterobacter, and Klebsiella in GBC samples when compared with that in normal bile samples. Klebsiella abundance between patients with chronic cholecystitis and GBC was significantly different (127), which indicates a link between microbiological disorders and GB carcinogenesis. Another study clarified the microbial communities between patients with GBC and cholelithiasis using next-generation-based 16S rRNA sequencing. Enterobacter sp. and Fusobacterium nucleatum were predominant and identified in the GBC samples, while Salmonella sp. and Enterococcus gallinarum were prevalent in the bile of cholelithiasis patients (128). A chronic Salmonella carrier state is related to GB carcinogenesis, especially in areas with high typhoid endemicity (129). This is consistent with the finding that Salmonella typhi in the gallbladder of gallstone patients may be a cause for the development of GBC (130). A recent study investigated if Salmonella infection is a risk factor for GBC in genetically susceptible mice through the regulation of AKT and MAPK pathways during infection (131). The results indicated that Salmonella enterica can promote carcinogenesis and is a causative agent of GBC. Thus, eradicating bacterial infection via immunotherapy, anti-inflammatory therapy, and hygienic practices might help in reducing the incidence of GBC.

Discussion

There is growing proof for the profound impact of gut homeostasis on HCC development, and consequently, gut microflora may embody a novel target for prevention of primary HCC. Due to its anatomical position, the liver is subjected to enteric bacterial components, meaning intestinal bacterial products could spread to the liver through the portal vein (132). Many have examined the alterations that occur in the microbiomes of patients with CCA and GBC, and it was found that dysbiosis of the microbiome could promote tumorigenesis and GBC and CCA development (101, 128). In this review, we have summarized the data regarding the bacteria that are altered in the microbiome, such as Enterobacter and Klebsiella and the gram-negative bacteria that co-occur in cirrhotic patients with HCC using samples from patients with GBC (Figure 1). There was a significant increase in Enterobacter in GBC samples when compared with the normal bile samples, and its abundance was enhanced in the fecal samples of patients with HCC at all disease stages (68, 127). It has been reported that Enterobacter is related to a proinflammatory response (133). The above phenotype was consistent with an investigation, which found that inflammation is central to GBC development and that reducing inflammation-related pathways was associated with increased survival during GBC outcomes (134). Inflammation is a critical component of HCC progression and anti-inflammatory drugs are reported to enhance the prognosis of patients with HCC (135). There was increased Bacteroides, Prevotella, Klebsiella, and Enterobacter in the stool samples from cirrhotic patients with HCC and the biliary microbiota of patients with CCA as compared with the control groups (66, 116). Gram-negative Bacteroides are correlated with chronic intestinal inflammation and tissue injury, which are colorectal cancer risk factors (136). Chronic and unresolved inflammation is regarded as the main carcinogenic mechanism promoting GBC development (137), as it provides a possible underlying mechanism, in which Bacteroides have essential roles in HCC and GBC incidence through gut-derived inflammation. In this context, human bacteria, such as Enterobacter and Bacteroides, coexist with their host and facilitate multiple types of carcinogenesis. Other studies have also reported that augmented Actinomyces abundance in the bile ducts of patients with extrahepatic CCA may have originated from the intestine (109).

Figure 1 | Contributions of the microbiota of the gut to hepatobiliary tumors. There is an anatomical and physiological connection between the liver and the intestinal tract. Potential effective treatments or preventive strategies for HCC, including probiotics, antibiotics, and FMT, may be cost-effective. HCC, hepatocellular carcinoma; CCA, cholangiocarcinoma; GBC, gallbladder cancer; FMT, fecal microbiota transplant; ↑ = upregulated; ↓ = downregulated.

Probiotics are used to modulate the intestinal microflora balance and confer health benefits to the host. LGG is a widely used probiotic strain that has been evaluated for the treatment of diarrhea (138), antiproliferative effects on the cells of colon adenocarcinoma (139), suppressive effects on inflammation during Helicobacter infection in gastric adenocarcinoma cells (140), and high-fructose diet-induced NAFLD (141). In the present review, we found that probiotic LGG can decrease the risk of HCC through multiple pathways, including DNA damage (55), inflammatory and cell differentiation (56), and human hepatoma cell apoptotic regulation (58). Probiotics interact with the host and the microbiome via molecular effectors as metabolic products through the gastrointestinal microenvironment and mediate health benefits (142). Intestinal dysbiosis influences HCC development partly via microbial and gut metabolites and products conveyed through the portal vein to the liver, which could be a key regulator of tumorigenicity and HCC aggressiveness (143). This was consistent with a previous study that explored the molecular mechanisms of gut dysbiosis on gut barrier and inflammation in relation to carcinogenesis, such as immune responses, toxic metabolites, and signaling pathway intervention (144). The metabolic byproduct of intestinal bacteria, DCA, could contribute to HCC via multiple signaling pathways such as the activation of mammalian targets of rapamycin signaling or the gut microbiota-driven cyclo-oxygenase 2 pathway (31, 36). It was recently reported that DCA induced human HSC malignant behavior of HCC by inducing senescence-associated secretory phenotype factors, e.g., TGF-β and IL-8 (145). The above investigations suggest that multiple actions of gut metabolites could control cancer growth and progression in different HCC models.

In conclusion, we have summarized the role of microbiota of the gut in hepatobiliary tumor incidence and development, including HCC, CCA, and GBC. The potential effectiveness of the treatments and representative novel strategies, including probiotics, antibiotics, and FMT, focus on fecal microbiome modulation, which provides alternative therapeutic methods for HCC. There are still many questions regarding microbiota function in the occurrence of HCC that remain unclear. There are limitations to the gut microbiome as a biomarker or therapeutic target for hepatobiliary tumors, since the gut microbiome is easily disturbed by several confounding factors, such as diet, medication (especially probiotics and antibiotics), mental health, and environmental factors, which make this type of biomarker not robust or reliable. In addition, interruption of the balance of the gut microbiome could lead to a series of problems, thus a global view should be taken before using the gut microbiome as a therapeutic target. The gut microbiome and hepatobiliary tumors are not directly linked spatially, which means that the gut microbiome does not reflect hepatobiliary tumors straightforwardly. The gut microbiome is involved in hepatobiliary tumor development through complex mechanisms, and there is an urgent need for more laboratory investigations and extensive human clinical trials to assess the gut microbiota abundance and screen for beneficial strains.

Author Contributions

YW designed the content of the review with input from all the co-authors. HQ and BY drafted the manuscript. YW and WH revised the manuscript. All authors contributed to the article and approved the submitted version.

Funding

This study was supported by grants from The National Natural Science Foundation of China (42125707, 41931291, 81902960), the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (2019PT310027), the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (2019-I2M-1-003, 2021-1-I2M-018, 2021-RC310-018).

Abbreviations

HCC, hepatocellular carcinoma; FMT, fecal microbiota transplantation; CCA, cholangiocarcinoma; GBC, gallbladder cancer; PBA, primary bile acids; SBA, secondary bile acid; DCA, deoxycholic acid; NAFLD, nonalcoholic fatty liver disease; NASH, nonalcoholic steatohepatitis; SCFA, short-chain fatty acid; HSC, hepatic stellate cell; LGG, Lactobacillus rhamnosus GG; TGF, forming growth factor; DEN, diethylnitrosamine; ICI, immune checkpoint inhibitors; CDL, choledocholithiasis.

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REVIEW
published: 22 July 2022
doi: 10.3389/fonc.2022.907399

Research Progress on Long Noncoding RNAs and N6-Methyladenosine in Hepatocellular Carcinoma

Wenjie Zhang ¹,²†, Wenlong Wu ²†, Qiang Meng ²†, Long Yang ², Juzheng Yuan ³, Zelin Tian ², Rui Ding ², Xuan Zhang ²*, Jianlin Wang ²* and Kaishan Tao ²*

¹ Chinese Education Ministry’s Key Laboratory of Western Resources and Modern Biotechnology, Key Laboratory of Biotechnology Shaanxi Province, College of Life Sciences, Northwest University, Xi’an, China, ² Department of Hepatobiliary Surgery, Xijing Hospital, The Fourth Military Medical University, Xi’an, China, ³ Xi’an Medical University, Xi’an, China

Edited by:
Zongli Zhang, Qilu Hospital of Shandong University, China

Reviewed by:
Jie Li, Chinese Academy of Medical Sciences and Peking Union Medical College, China
Shihai Liu, The Affiliated Hospital of Qingdao University, China

*Correspondence:
Kaishan Tao
taokaishan0686@163.com
Jianlin Wang
wangjianlin217@163.com
Xuan Zhang
zhangxuantj@163.com

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

Specialty section:
This article was submitted to Cancer Molecular Targets and Therapeutics, a section of the journal Frontiers in Oncology

Received: 29 March 2022

Accepted: 20 June 2022

Published: 22 July 2022

Citation:
Zhang W, Wu W, Meng Q, Yang L, Yuan J, Tian Z, Ding R, Zhang X, Wang J and Tao K (2022) Research Progress on Long Noncoding RNAs and N6-Methyladenosine in Hepatocellular Carcinoma. Front. Oncol. 12:907399. doi: 10.3389/fonc.2022.907399

N6-methyladenosine (m6A) is an epigenetic modification that widely exists in long noncoding RNAs (lncRNAs) and is involved in the regulation of oncogenes or tumor suppressor genes that form complex enzymes to affect the occurrence of tumors. The abnormal modification of m6A methylation can alter the overall m6A level and thus contribute to the malignant biological behaviors of hepatocellular carcinoma (HCC). LncRNAs related to m6A methylation are involved in lipogenesis, the proliferation, migration and invasion of HCC cells, the stemness of tumor cells and sorafenib resistance. In this review, we systematically elaborated the occurrence mechanism of lncRNA and m6A methylation modification in HCC and the effect of m6A methylation modification of lncRNA on the occurrence of HCC, suggesting that the combination of m6A methylation modification and lncRNA will be more meaningful as molecular markers or prognostic markers. It is helpful to provide further ideas for exploring the pathogenesis of HCC and identifying new targets for HCC treatment and diagnosis and achieve precise individual treatment of liver cancer.

Keywords: hepatocellular carcinoma, long noncoding RNAs, epigenetic modification, methylation modification, N6-methyladenosine

1 Introduction

Liver cancer is the third most common cause of cancer death in the world, after lung cancer and colorectal cancer, with 905,677 new cases and 830,180 deaths each year, accounting for 8.3% of the total number of deaths from HCC (1). HCC is the most common pathological type of primary liver cancer, accounting for 75%-85% of cases. China is a high-risk area for HCC, with 50% of the new cases diagnosed annually worldwide, showing an obvious upward trend (2). In recent years, the overall prognosis of HCC is still poor, with a 5-year survival rate of only 15-38%, despite the improved survival of patients with multidisciplinary comprehensive treatment and personalized treatment programs (3–5).

In recent years, studies on the pathogenesis of HCC have focused on oncogenes, tumor suppressor genes, epigenetic modification and noncoding RNA (ncRNA). With the development of high-throughput sequencing technology, researchers have found that m6A modification sites are widely present on messenger RNA (mRNA) and lncRNA (6, 7). m6A modification affects gene expression by regulating mRNA and lncRNA and abnormal expression of lncRNA levels and m6A methylase complexes associated with various human cancers, including HCC (8, 9). These findings suggest that these differentially expressed genes may provide guidance for the study of the pathogenesis of HCC and provide a new target for the diagnosis or treatment of HCC. Therefore, this paper reviews the mechanism of m6A modification and the lncRNAs involved in the pathogenesis and development of HCC. A detailed study of the distribution and function of lncRNAs and chemical modifications as well as their association with related proteins will contribute to a comprehensive understanding of the multilevel gene expression regulation mechanism in mammalian cells. The potential role of m6A modification at the level of lncRNA may provide additional mechanisms for transcriptome gene regulation during body development. The study of lncRNA and m6A can further explore tumor markers and contribute to the detection and diagnosis of HCC. The combination of m6A methylation modification of lncRNA with clinical drug therapy and emerging immunotherapy provides new opportunities for early diagnosis, effective treatment and even disease prognosis of cancer.

2 m6A Methylation Modification

2.1 Overview of m6A

The m6A methylation modification refers to methylation of the N atom at the sixth position of adenine (A) and was first discovered in 1974. m6A methylation sites mostly appeared on the common sequence RRACH (R = A/G; H for A/C/U), and there were more 3’ -UTR (3’-untranslated regions), CDS (coding sequence), intron and stop codon regions and fewer 5’ UTR regions (10, 11) (Figure 1).

Figure 1 | Mechanism of the function of the m6A methylation-related enzyme complex.

2.2 m6A Methylation-Related Enzymes

2.2.1 Methyltransferase Complex (Writers)

Methyltransferase complexes include METTL3 (methyltransferase-like 3), METTL14 (methyltransferase-like 14), WTAP (Wilms tumor 1-associated Protein), KIAA1429 (also called VIRMA, vir-like m6A methyltransferase associated), RBM15/15B (RNA-binding motif protein 15/15B), METTL16 (methyltransferase-like 16) and so on. Its function is to add methyl groups on the S-adenosyl methionine transferase to the target RNA. METTL3, as the catalytic core of methylase, has methylase activity (12). As an RNA binding domain, METTL14 can enhance methylase activity by binding METTL3 (13). WTAP stabilizes the METTL3/METTL14 complex, helps METTL3/14 locate nuclear spots, and maintains the catalytic activity of m6A methyltransferase in vivo(14). RBM15/RBM15B recruits METTL3-related methylation complexes to act on specific methylation sites in a WTAP-dependent manner. METTL16 acts as a methyltransferase, catalyzing m6A methylation of U6 snRNA and ncRNA (15).

2.2.2 Demethylases (Erasers)

Fat mass and obesity-associated protein (FTO) and a-ketoglutarate-dependent dioxygenase alkB homolog 5 (ALKBH5) belong to the α-ketoglutarate-dependent dioxygenase family and remove m6A methylation in an Fe2+- and α-ketoglutarate-dependent manner (16). FTO plays a key role in regulating m6A modification of mRNA transcription and is an important m6A demethylase associated with metabolic disorders such as diabetes and obesity (17). In addition to m6A demethylation, FTO also acts on m6Am of mRNA/snRNA (small nuclear RNA) and m1A of tRNA (18, 19). ALKBH5, a member of the alkB family, demethylates m6A on single-stranded RNA/DNA and can directly remove methyl groups from m6A-methylated adenosine (20). ALKBH5 is expressed differently in different tissues, with high expression in the testis but low expression in the heart and brain. ALKBH5 plays a role in nuclear RNA output and metabolism, gene expression and mouse fertility (21).

2.2.3 m6A Methyl Recognition Protein (Readers)

The methylated molecules combine with the m6A methyl recognition protein “readers” (YTHDF1/2/3, YTHDC1/2, HNRNPA2B1, HNRNPC and HNRNPG, IGF2BP1/2/3) to perform their epigenetic modification (22, 23). It affects alternative splicing, output, translation and stability of organisms. IGF2BP1/2/3 mainly plays a role in maintaining RNA stability (12). YTHDC1 exists in the nucleus, regulates the selective splicing of RNA, maintains the stability of RNA, and has a certain nuclear output function (22). YTHDF1/YTHDF3/YTHDC2 can interact with promoters and participate in RNA translation, while YTHDF2 regulates RNA degradation (23).

Abnormal expression of m6A methylation regulators can change the overall intracellular m6A level, affect RNA splicing, stabilization, output, and transcription, and affect the expression of downstream target genes by regulating the binding of RNA binding proteins (RBPs) or miRNAs (22–24). Furthermore, it can regulate the cell cycle, cell differentiation and development, and carcinogenesis.

2.3 Abnormal Expression of m6A Affects HCC Processes

2.3.1 Impact of the Abnormal Expression of “Writers” in HCC

Studies have shown that METTL3 expression in HCC is significantly higher than that in normal tissue, and METTL3 plays an important role in HCC. Overexpression of METTL3 can increase the m6A modification of SOCS2 (suppressor of cytokine signaling 2) mRNA, reducing the stability of the downstream target SOCS2 mRNA and inhibiting the expression of SOCS2, thus promoting the proliferation and migration of HCC cells (25). In addition, METTL3 is associated with a poor prognosis in HCC patients (26). It has been reported that METTL14 is expressed at low levels in HCC and inhibits HCC metastasis by interacting with DGCR8 (DiGeorge syndrome critical region gene 8) and promoting the maturation of pri-miRNA126. It can thus be used as one of the predictors of liver cancer recurrence and metastasis (27).

2.3.2 Impact of the Abnormal Expression of “Erasers” in HCC

The expression of FTO has been found to be upregulated in HCC tissues and cells, and high expression of FTO is associated with a poor prognosis in HCC patients (28). FTO knockdown obstructs the transition from G0 phase to G1 phase and inhibits the growth of tumor cells (17). Highly expressed FTO acts as a m6A demethylase to induce the demethylation of PKM2 (pyruvate kinase M2) mRNA and promote the production of translation products, thus affecting the occurrence of HCC (29). As a tumor suppressor, ALKBH5 is high-expressed in HCC tissues and cells. ALKBH5-mediated m6A demethylation can lead to posttranscriptional inhibition of LYPD 1 (LY6/PLAUR domain containing 1). LYPD1 can then be recognized and stabilized by the m6A effector IGF2BP1, exerting its oncogene function to promote the proliferation and invasion of HCC (30).

2.3.3 Impact of the Abnormal Expression of “Readers” in HCC

Notably, both the oncogene YTHDF1 and the suppressor factor YTHDF2 are involved in the occurrence and development of HCC. YTHDF1 is significantly overexpressed in HCC and is closely related to the pathological stage and survival rate of patients (28). Studies have found that c-Myc is the upstream gene regulating YTHDF1, and it is speculated that the potential target gene regulating YTHDF1 protein may be related to the tumor cell cycle, the degradation of various amino acids and lipid metabolism, and their abnormal expression in cells may lead to the occurrence of HCC (31). IGF2BPs are highly expressed in hepatocellular carcinoma cells and are associated with poor prognosis. IGF2BP1 promotes the expression of serum response factor (SRF) and its target genes in a m6A-dependent manner, enhances the expression of tumor genes and promotes the proliferation, migration and invasion of HCC cells (32).

3 LncRNA

3.1 Overview of LncRNAs

In the past few years, with the rapid development of second-generation sequencing technology, it has been determined that most of the genome is transcribed into RNA, while only 1-2% of the RNA with protein coding ability is RNA, most of which is ncRNA (33). According to the number of ncRNA nucleotides, ncRNAs with less than 200 nucleotides are called short noncoding RNAs (including microRNAs, circRNAs, siRNAs and piRNAs), and ncRNAs with a length greater than 200 nucleotides are called lncRNAs (34).LncRNAs were classified based on the relationship between the genome and protein-coding genes: a) justice lncRNAs, which overlap with one or more exons of coding genes; b) antisense lncRNAs, which are partially or completely complementary to the transcriptional products on the opposite chain; c) intronic lncRNAs, which are derived from introns of genes; d) bidirectional transcripts, which share the same promoter with protein-coding genes but are transcribed in opposite directions; and e) intergenic lncRNAs, which are independently transcribed by sequences located between protein-coding genes (34–36) (Table 1).

Table 1 | LncRNA classification.

LncRNAs exist in different subcellular structures, including the cell membrane, cytoplasm and nucleus, and their functions and regulatory mechanisms are closely related to their localization in cancer cells. In the nucleus, lncRNAs can act as scaffolders to recruit regulatory proteins and interact with mRNAs or as competing endogenous RNAs (ceRNAs) to regulate mRNA translation and stability (35). Moreover, lncRNAs may be involved in maintaining the stability of m6A-related proteins. LncRNAs are diverse and have complex functional mechanisms. They play vital regulatory roles in gene expression, species evolution, embryonic development, the metabolism of substances and tumorigenesis and are important branches and hotspots of transcriptional regulation (37). Studies have shown that lncRNAs are abnormally expressed in a variety of cancers and play a role as key tumor promoters or inhibitors, affecting cell growth, apoptosis and metastasis (36).

3.2 The Role of LncRNAs in HCC

It has been confirmed that lncRNAs can perform their functions through epigenetic modification, transcription, splicing and degradation and can interact with downstream target molecules (mRNA, microRNA and protein) directly or with the help of transcription factors to regulate the expression of oncogenes or tumor suppressor genes and activate intracellular signaling pathways (38). They can affect cell proliferation, differentiation, metastasis and apoptosis, further affecting the occurrence and development of HCC (39).

LncRNA-CDKN2B (cyclin-dependent kinase inhibitor 2B) expression is upregulated and changes the chromatin structure by recruiting chromatin modification factors to regulate the expression of target genes, ultimately promoting the proliferation of HCC cells (40). LncRNA-HULC (highly upregulated in liver cancer) can interact with the promoter of protein-coding genes to promote the expression of the autophagy protein P62 and activate the AKT-PI3K-mTOR pathway (41, 42). At the same time, it synergizes with lncRNA-MALAT1 (metastasis-associated lung adenocarcinoma transcript 1) to upregulate the transcriptional expression of telomerase repeat binding protein 2, which jointly enhances the immortalization of this protein and reduces the methylation of the telomerase RNA promoter region (43). LncRNAs can promote transcription in this region, thus maintaining telomere length and influencing the occurrence of HCC.

There are miRNA-binding sites on lncRNAs, which can act as ceRNAs to regulate the expression of downstream target genes and corresponding signaling pathways through competitive binding miRNAs, thus affecting the proliferation, cycle and metastasis of HCC (39). LncRNA-GAS5 (growth arrest-specific transcript 5) is overexpressed in liver cancer tissues and cells and negatively regulates miR-21, indirectly affecting the expression of the downstream target gene PTEN and thus regulating the proliferation of HCC. Meanwhile, the high expression of lncRNA-GAS5 was correlated with tumor stage (TNM), overall survival (OS), disease-free survival (DFS) and metastasis, suggesting that GAS5 may be a potential diagnostic and prognostic biomarker in HCC (44). LncRNA-H19 was significantly correlated with vascular infiltration and promoted the expression of alpha-fetoprotein (AFP) (45). LncRNA-CCAT1 (colon cancer-associated transcript 1) may play a role as a proto-oncogene in HCC and is upregulated in cancer tissues. The high level of its expression is correlated with tumor size, vascular invasion and serum AFP level and may be related to the prognosis of HCC patients (46). LncRNA-TUC338 and lncRNA-VLDLR are highly expressed in HCC cells and tissues and are associated with sorafenib resistance, suggesting a role for lncRNAs in clinical treatment (47, 48).

4 Current Research Progress on LncRNA and m6A Methylation in HCC

Early studies on m6A methylation focused on mRNA. With the rapid development of high-throughput sequencing, lncRNA CHIPS and MeRIP technology, researchers have found that lncRNAs have important value in tumors. Studies on the m6A methylation of lncRNAs have become the focus of attention (24). Dysregulation of lncRNA and m6A regulator factors in cancer has been described in a large number of studies in gastric cancer, non-small-cell lung cancer, and prostate cancer (49–51). However, there are few studies on how lncRNA and m6A methylation modification regulate the pathogenesis of HCC. An increasing number of reports have proven that lncRNAs and m6A regulatory factors can be used as biomarkers or therapeutic targets for liver cancer (52). This suggests that it is of great significance to explore the m6A methylation of lncRNAs in the pathogenesis of HCC. Currently, the effects of lncRNAs and m6A on the occurrence and progression of HCC are shown in Figures 2, 3 and Table 2.

Figure 2 | Mechanism of antisense lncRNA and m6A methylation affecting HCC. (A) GATA3-AS with the help of methylase KIAA1429 promotes the degradation of GATA3 mRNA. (B) ILF3-AS with the help of METTL3 improves ILF3 mRNA stability.

Table 2 | The mechanism of m6A regulatory factors and their influence on HCC.

4.1 Promotion of Lipogenesis

LncRNAs are involved in many processes of liver fat metabolism, including lipolysis, fatty acid β oxidation and fat differentiation, leading to the occurrence of diseases. During HCC, organisms acquire nutrients through reprogramming and metabolic pathways that affect the metabolism of glucose, fatty acids and amino acids to satisfy the growth and development of cancer cells (21).

It has been reported that the lncRNA and m6A methylation recognition protein “reader” IGF2BP2 plays a role in the proliferation and metabolism of colorectal cancer. LINRIS inhibits ubiquitination and enhances the stability of IGF2BP2 by binding to K139 of IGF2BP2. IGF2BP2 binds to the m6A site of Myc mRNA, enhancing Myc axis-mediated glycolysis and promoting colorectal cancer cell proliferation (65). However, the mechanism of lncRNA and m6A regulatory factors in HCC is different from that in colorectal cancer (31). m6A methylation affects the stability of lncRNAs, and lncRNAs competitively bind miRNAs through a ceRNA-dependent mechanism to promote fat generation and metabolism. METTL3 methylase regulates the downregulation of lncRNA-MEG3, which acts as a molecular sponge to promote liver lipogenesis and metabolism by competitively binding miR-21 and LRP6 (53). METTLL3-dependent m6A modification increases the stability of LINC00958, inhibits miR-3619-5p expression, increases HDGF expression and promotes lipogenesis (55) (Figure 3A).

Figure 3 | The mechanism of lncRNA m6A regulatory factors and their influence. (A) m6A of lncRNAs promote lipogenesis. (B) m6A of lncRNAs effect HCC Processes. (C) m6A of lncRNAs enhance tumor stemness. (D) m6A of lncRNAs influence drug resistance on tumor cells.

These results suggest that m6A modification may be closely related to lipid metabolism in metabolic reprogramming, which provides the possibility to search for tumor metabolic targets. However, there are few studies on the role of m6A modification in tumor metabolic reprogramming (eg. fatty acid and amino acid metabolism), so more regulatory details need to be explored in this area.

4.2 Effects on HCC Processes

The interaction between m6A modification and lncRNA involves two aspects. M6A modification affects RNA–protein interactions, participates in lncRNA-mediated ceRNA models, and participates in lncRNA-miRNA interactions. In addition, lncRNAs can also regulate the expression and function of m6A modification by regulating the m6A modification factor (Figure 3).

MEG3 is expressed at low levels in HCC, and m6A methylation occurs in the presence of METTL3. Through the ceRNA mechanism, MEG3 competitively promotes the expression of miR-544b, downregulates the target gene BTG2 and inhibits the proliferation of HCC (54). LNCAROD is highly expressed in HCC patients with a poor prognosis. Increased m6A methylation of METTL3-IGF2BP1 mediates the stability of LNCAROD, thus maintaining its high expression in HCC. In the nucleus, LNCAROD directly binds with serine and arginine-rich splicing factor 3 (SRSF3) to induce the conversion of PKM1 to PKM2. However, LNCAROD in the cytoplasm acts as the molecular sponge of miRNA. The inhibitory effect of miR-145-5p on PKM2 levels was alleviated through the ceRNA mechanism. The expression of hypoxia-inducing factor 1α (HIF1α), which can be triggered by hypoxia, enhances the PKM2-mediated glycolysis process and further promotes the migration and invasion of HCC cells (56). YTHDF2 is expressed at low levels in HCC, and m6A methylation mediated by YTHDF2 can improve the half-life of lncCY, further promoting the stability of the downstream gene BMI1 mRNA and activating the Wnt-β-catenin signaling axis to affect the proliferation, migration and invasion of HCC (57) (Figure 3B).

KIAA1429 is highly expressed in HCC and is associated with prognosis. KIAA1429 induces m6A methylation at the 3’ end of GATA3 premRNA, leading to the separation of HuR and degradation of GATA3 premRNA. As a cis-acting element, lncRNA GATA3-AS preferentially interacts with KIAA1429 and GATA3 premRNA, inhibits the expression of GATA3 and leads to the proliferation and metabolism of HCC (58, 59) (Figure 2A). ILF3-AS1 is highly expressed in HCC and can be used as a regulatory factor to recruit METTL3 and form the ILF3-AS/METTL3/ILF3 complex, thereby improving the m6A methylation level and stability of ILF3 mRNA. ALKBH5, as a demethylase, is downregulated in HCC and inhibited by ILF3-AS1, which reduces the demethylation process of ILF3 mRNA. ILF3-AS1 enhances the stability of ILF3 mRNA through m6A methylation mediated by the METTL3-IGF2BP1 axis and promotes cell proliferation, migration and invasion, leading to the occurrence of HCC and further affecting the prognosis of patients (60) (Figure 2B).

4.3 Enhancement of Tumor Stemness

Tumor stemness plays an important role in cancer progression and has been one of the hot topics in cancer research in recent years. It is an important cause of chemoradiotherapy tolerance, tumorigenesis, metastasis, self-replication, metabolic reprogramming and changes in the immune microenvironment (66, 67). However, the exact mechanism is unclear. Studies have shown that lncRNAs play an important role in the regulation of hepatocellular carcinoma stemness.

Abnormal expression of lncRNAs and m6A methylases has been reported to affect tumor cell stemness in pancreatic cancer and breast cancer (68, 69). However, there are few reports that abnormal m6A regulatory factors of lncRNAs affect the stemness of HCC cells.LncRNA DUXAP8 increases the m6A level through METTL3 and its own stability and activates downstream target genes MAPK1 and MAPK-ERK signaling pathways through the competitive binding of miR-584-5p to promote the process and stem cell characteristics of HCC (61). METTL3/IGF2BP1-mediated m6A modification promotes LINC00106 expression. Then, increased LINC00106 competitively binds to let7f through a ceRNA mechanism, affects downstream periostin mRNA and the PI3K-AKT signaling pathway and enhances stemness and metastasis of HCC cells (62) (Figure 3C). This evidence suggests that m6A can control the expression and function of lncRNAs, thus regulating tumor stemness. It is worth pointing out that lncRNA and m6A modification molecules have potential clinical value in regulating tumor immunity and immune invasion patterns, have potential as tumor stemness markers and therapeutic targets in many cancer species and contribute to improving diagnostic accuracy. At the same time, the study of the drug resistance mechanism of tumor stem cells has good application prospects and significance, which is of great significance to the adjuvant drug therapy of HCC patients.

4.4 Influence of Drug Resistance on Tumor Cells

Drug resistance of tumor cells to chemotherapeutic drugs is one of the main reasons for failure of tumor chemotherapy. After the tumor is resistant to chemotherapy, it easily progresses to tumor recurrence and metastasis, so 80% ~ 90% of the deaths of tumor patients can be directly or indirectly attributed to drug resistance (59). The generation of drug resistance is a complex process caused by a variety of factors, among which abnormal methylation of lncRNAs and m6A can also lead to drug resistance in HCC patients (70).It has been found that lncRNA-TSUC7 modified by the m6A methylated binding protein YTHDF2 through the Notch signaling pathway affects erlotinib resistance in lung cancer (71). METTL3 promotes the stability of lncRNA-TRIM11 transcripts through the m6A-IGF2BP2-dependent pathway and enhances cisplatin resistance in nasopharyngeal carcinoma (72). In HCC, abnormal m6A methylation modification of lncRNAs mainly causes sorafenib resistance.

LINC01273 is highly expressed in HCC and sorafenib-resistant patients and affects tumor drug resistance through a dual feedback mechanism. On the one hand, there were m6A methylation modification sites on LINC01273, which were recognized by METTL3/YTHDF2 and degraded, leading to the combination of miR-600 and METTL3 mRNA, and the high expression of METTL3 promoted oncogene and resistance to sorafenib. On the other hand, LINC01273 colocalizes with miR-600 in the HCC cell cytoplasm and acts as a molecular sponge of miRNA, which increases the stability of miR-600 and promotes the inhibition of miR-600 on METTL3 mRNA, leading to the downregulation of METTL3 expression. HCC cells undergo autophagy, which affects resistance to sorafenib (63). NIFK-AS1 expression was upregulated in HCC cells and tissues. The stability of NIFK-AS1 RNA was enhanced in the process of m6A methylation mediated by the METTL3/IGF2BP1 axis, which promoted the high expression of NIFK-AS1. Through the ceRNA mechanism, the miR-637/AKT/MMP-7/MMP-9 axis is affected to drive the malignant characteristics of HCC. The high expression of NIFK-AS1 reduced the uptake and transport of the transporter OATP1B1/OATP1B3 to sorafenib and enhanced the resistance of HCC patients to sorafenib (64) (Figure 3D).

5 Discussion

In summary, m6A regulatory factors (including “Writer”, “Reader” and “Eraser”) and lncRNAs are closely related to HCC processes, including tumor cell proliferation, metastasis, cancer cell stemness and tumor drug resistance. Currently, the m6A methylation modification of lncRNAs in HCC mainly focuses on METTL3 and IGF2BP1/2/3 m6A regulatory factors. LncRNAs modified by m6A methylase contain methylation sites, which can affect their stability and expression. MiRNA competitively binds lncRNAs through the ceRNA mechanism to modulate the expression of downstream target molecules and promote the occurrence of HCC. Some antisense lncRNAs, which serve as regulatory factors, affect the binding of m6A methylase to mRNA, which has an impact on the pathogenesis of HCC. However, how other types of lncRNAs play a role in the HCC process has not been reported. How m6A demethylation participates in lncRNA expression has not been reported, partly due to the controversy over the expression level of FTO in HCC, and only a few kinds of demethylases have been described. Whether there are other types of demethylases remains to be further explored. Currently, some m6A inhibitors (such as FTO class inhibitors) have shown important functions in tumors. They alter the overall level of m6As in tumor cells by targeting the inhibition of m6A regulatory factors, providing new therapies for tumors. New methods and technologies need to be developed so that the dynamic modification process of m6A regulatory factors and the influence of m6A methylation modification on lncRNA subcellular localization can be investigated, along with the underlying mechanism of action. It is crucial to reveal how epigenetic transcriptomic modification affects the occurrence of diseases in lncRNAs.

At the same time, changes in lncRNA and m6A levels under hypoxia can activate aerobic glycolysis in tumor cells and may alter the intracellular tumor microenvironment or cellular metabolism, suggesting that the occurrence of HCC is the result of multiple factors. LncRNA and m6A methylation mutually regulate the occurrence and development mechanism of HCC, and such multidirectional regulation may help clinicians and researchers gain a better understanding of HCC.

LncRNAs and m6A regulatory factors play significant roles in a variety of biological processes of cells, and their abnormal expression is associated with the diagnosis and prognosis of a variety of diseases, including cancer. LncRNAs and m6A-regulatory factors can be used as specific molecular markers or prognostic markers for HCC. The combination of lncRNAs and m6A regulatory factors can be more sensitive and accurate for the detection of HCC, which can help improve the prognosis of patients and reduce the mortality of patients. Both lncRNA and m6A methylated modified protein are expected to be tumor markers, but lncRNA is far less unstable than m6A methylase. m6A methylation can regulate the stability of lncRNA and improve the effectiveness and accuracy of tumor markers by adding labels to lncRNA. Some lncRNAs are expressed differently in serum. Studies have shown that the combination of the two growth factors in serum provides a new auxiliary method for the diagnosis of HCC, and its combination with AFP has a higher predictive value for HCC (73, 74),the clinical application of AFP and other m6A regulatory proteins can be used for the early detection and diagnosis of HCC. It has been reported that the therapeutic efficacy of PD-1 checkpoint blockade is diminished in METTL3 deficient mice, thus identifying Mettl3 as a potential therapeutic target for tumor immunotherapy, targeted intervention of lncRNA can affect the signaling pathway of immune cells to improve the chemotherapy sensitivity of tumors (75). The development of siRNA(lncRNA)-NPS nanomaterials based on PLGA-PEG can increase the uptake of drugs by tumor cells, reduce the biological function of tumor cells, and significantly improve the tumor targeting ability in the body, which can be used as an important target for clinical treatment of cancer patients (55). In-depth understanding and monitoring of the progression of cancer is a crucial link in cancer treatment, which can provide a theoretical basis for new targeted therapy and immunotherapy and design individualized treatment plans for the benefit of liver cancer patients.

Although two newly developed high-throughput deep sequencing technologies (MeRIP and m6A sequencing) play a key role in revealing the functional significance of m6A modification, there are still many unknown lncRNAs to be excavated due to the variety and complexity of lncRNAs, and multiple m6A methylation modification sites on lncRNAs. The effect of single base modification sites or multiple base modification sites interaction on lncRNAs remains to be studied. At present, most studies are devoted to elucidating the downstream mechanism of differential expression of lncRNAs in tumors, while its upstream regulatory mechanism has not attracted attention. m6A inhibitors or activators interfere with lncRNA expression levels, and new methods to regulate lncRNAs with different expressions are yet to be explored. The current development of the possible combination of lncRNAs and m6A as potential targets for tumor diagnosis and treatment is mostly confined to mechanism studies, and further validation is needed for clinical application.

Author Contributions

WJZ, WLW, and QM contributed equally to this work. LY, JZY, and ZLT draw the figures. XZ and JLW designed the subject. KST revised the article. All authors contributed to the article and approved the submitted version.

Funding

This study was supported by the Major Military Logistics Research Projects (AKJ19J001), Xijing Hospital Disciplinary Boost program (XJZT19MJ09), Key Research and Development program of Shaanxi province (2020SF-066) and the National Natural Science Foundation of China (81970566, 82170667,82070681, 81900571).

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ORIGINAL RESEARCH
published: 28 July 2022
doi: 10.3389/fonc.2022.972550

The expression, clinical relevance, and prognostic significance of HJURP in cholangiocarcinoma

Yang Yang ¹, Jinyan Yuan ¹, Zhenzhong Liu ¹, Wenwen Cao ² and Pei Liu ³*

¹ Department of Hepatobiliary Surgery, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, China, ² Department of Respiratory Medicine, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, China, ³ Department of Burn and Plastic Surgery, Qilu Hospital Affiliated to Shandong University, Jinan, China

Edited by:
Xuesong Gu, Beth Israel Deaconess Medical Center and Harvard Medical School, United States

Reviewed by:
Xianqiang Wang, PLA General Hospital, China
Zhuonan Zhuang, Tsinghua University, China

*Correspondence:
Pei Liu
liupei_peiliu@126.com

Specialty section:
This article was submitted to Cancer Molecular Targets and Therapeutics, a section of the journal Frontiers in Oncology

Received: 18 June 2022

Accepted: 04 July 2022

Published: 28 July 2022

Citation:
Yang Y, Yuan J, Liu Z, Cao W and Liu P (2022) The expression, clinical relevance, and prognostic significance of HJURP in cholangiocarcinoma. Front. Oncol. 12:972550. doi: 10.3389/fonc.2022.972550

Background

Cholangiocarcinoma (CCA) is the malignancy originating from the biliary epithelium, including intrahepatic (iCCA), perihilar (pCCA), and distal (dCCA) CCA. The prognosis of CCA is very poor, and the biomarkers of different CCA subsets should be investigated separately. Holliday junction recognition protein (HJURP) is a key component of the pre-nucleosomal complex, which is responsible for normal mitosis. The ectopic expression of HJURP has been reported in several cancers, but not CCA.

Materials and methods

In our study, we investigated the expression of HJURP in 127 CCA patients which were composed of 32 iCCAs, 71 pCCAs, and 24 dCCAs with immunohistochemistry and divided these patients into subgroups with a low or high expression of HJURP. With chi-square test and univariate and multivariate analyses, we evaluated the clinical relevance and prognostic significance of HJURP in iCCAs, pCCAs, and dCCAs.

Results

HJURP was ectopically upregulated in CCAs compared with the para-tumor tissues based on TCGA and other mRNA-seq databases. A high expression of HJURP was correlated with low overall survival rates of iCCA and pCCA, but not in dCCA. Moreover, HJURP was an independent prognostic biomarker in both iCCA and pCCA. Patients with high HJURP were more likely to suffer CCA-related death after operation.

Conclusions

HJURP was an independent prognostic biomarker in both iCCA and pCCA, but not in dCCA. Our results provide more evidence of the molecular features of different CCA subsets and suggest that patients with high HJURP are more high-risk, which can guide more precision follow-up and treatment of CCA.

Keywords: HJURP, cholangiocarcinoma, prognosis, biomarker, CCA subsets

Introduction

Cholangiocarcinoma (CCA) is the malignancy originating from the biliary epithelium and has molecular features of cholangiocyte differentiation (1, 2). CCA is categorized as intrahepatic (iCCA), perihilar (pCCA), and distal (dCCA) CCA according to different anatomical locations, which have distinct epidemiology, oncological behaviors, treatment options, and prognoses (3, 4). The pCCA and dCCA were also defined as extrahepatic CCA (ehCCA), till the 7th AJCC/UICC separated them as distinct CCA subtypes. The outcome of CCA is very poor because the early symptoms of CCA are silent and CCA is not very sensitive to radiotherapy and chemotherapy. The studies of CCA on the field of biomarkers, genetic alterations, drug therapies, and treatment strategies are far away behind other tumors such as hepatocellular carcinoma. Recent advances in high-throughput sequencing have depicted the genetic landscape of CCA and identified different CCA molecular patterns in the genome and transcription levels (5–7). However, the changes at protein levels and posttranscriptional levels of CCA have not been well elucidated.

The centromere is essential for precision and equal segregation, and centromere protein-A (CENP-A) is a unique histone H3 variant responsible for organizing functional centromeres (8, 9). As a molecular chaperone of CENP-A, holliday junction recognition protein (HJURP) mediates CENP-A deposition by forming a pre-nucleosomal complex with the CENP-A/H4 heterodimer via its highly conserved Scm3 domain (10–13). The function of HJURP in centromeres is essential to maintaining chromosome precision separation and mitosis (12). As for cancer progression, HJURP was reported to promote progression in several cancer types including hepatocellular carcinoma, prostate cancer, pancreatic cancer, and glioblastoma (14–16). As a diagnostic or prognostic biomarker, HJURP expression is relevant with the prognosis of several cancer types including colon cancer, breast cancer, and hepatocellular carcinoma (17–19). However, the expression, clinical relevance, and prognostic significance of HJURP in CCA are still totally unknown.

In our study, we investigated the expression of HJURP in 127 CCA patients comprising 32 iCCAs, 71 pCCAs, and 24 dCCAs with immunohistochemistry (IHC) and divided these patients into subgroups with a low or high expression of HJURP. With chi-square test and univariate and multivariate analyses, we evaluated the clinical correlation and prognostic significance of HJURP in iCCAs, pCCAs, and dCCAs.

Materials and methods

Patients and ethics

Our cohort was composed of 127 consecutive CCA patients who underwent radical surgery and received regular follow-up. The cohort consisted of 67 male patients and 60 female patients. All the specimens were obtained with the prior consent of patients, and the study was approved with the Ethics Committee of Qilu Hospital of Shandong University and the Second Hospital Affiliated to Shandong First Medical University. The tumors were classified and staged according to the 8th edition of the AJCC/UICC TNM classification system.

TMA and IHC detection

Tissue microarray (TMA) was used for IHC detection. Hematoxylin and eosin (HE) staining was performed to confirm the diagnosis and design the expected area for TMA histological features of all samples. Core biopsies 1.0 mm in diameter were taken from each sample and arranged into TMA slides.

For IHC, TMA slides were first de-paraffined and rehydrated and then incubated in sodium citrate buffer (pH = 6.0), which was boiled by a microwave for optimal antigen retrieval. After that, TMA slides were immersed in hydrogen peroxide for 15 min to minimize the endogenous peroxidase activity. After incubation in 5% bovine serum albumin for 30 min to inhibit the unspecific binding, the TMA was incubated in a primary antibody (Abcam, Cambridge, UK, catalog: ab100800) at 1:50 overnight at 4°C and then in a secondary antibody (Beyotime, Beijing, China) at room temperature for 1 h. DAB was finally applied for the visualization of antigen.

IHC results evaluation

The tumor area was selected by a senior pathologist, and the IHC results of the tumor area were semi-qualified by the IHC score, which was the product of stained cells and staining intensity. The stained cells were classified as <25%, 25%–50%, 50%–75%, and ≥75%, representing scores 1, 2, 3, and 4, respectively. The staining intensity was stratified as negative, weak, moderate, and strong staining, representing scores 0, 1, 2, and 3, respectively. Therefore, the final IHC score was set from 0 to 12. The cutoff was defined with the receiver operating characteristic (ROC) curve and applied to divide the cohort into high- or low-HJURP subgroup.

Statistical analysis

Statistical significance was analyzed with software SPSS 22.0. The correlation between HJURP and clinicopathological factors was assessed by the chi-square test. The Kaplan–Meier method was applied to plot the survival curves, and the log-rank test was administrated to generate the statistical significance of different subgroups. The independent prognostic significance of HJURP and other clinicopathological factors was estimated with multivariate analysis by the Cox proportional hazards regression model. P value < 0.05 was considered statistically significant.

Results

Basic information of the CCA cohort

From 2014 December to 2019 May, a retrospective CCA cohort was established, consisting of 127 CCA patients who underwent radical surgery. This cohort was composed of 32 iCCAs, 71 pCCAs, and 24 dCCAs (Table 1). A total of 67 male patients and 60 female patients were enrolled, with the average age of 55.8 years and the average follow-up time of 15.8 months. The 3-year overall survival (OS) rate of this cohort was 16.1%, and the average OS time was 20.7 months.

Table 1 | Basic information of the CCA cohort.

The expression of HJURP in different CCA subtypes

According to TCGA database and GEPIA, HJURP was upregulated in many types of cancers compared with the para-tumor tissues, including bladder urothelial carcinoma, breast cancer, and colon cancer (20). In CCA, HJURP was also highly expressed than para-tumor tissues (Figure 1A). Moreover, we retrieved the mRNA-seq data of iCCA (GSE33327) and ehCCA (GSE132305) from the GSE database (5, 6). Interestingly, HJURP was overexpressed in ehCCA than para-tumor tissues, but this tendency was not statistically significant as to iCCA (P = 0.114)(Figure 1B).

Figure 1 | The expression of HJURP in CCA. (A) The fragments per kilobase of exon model per million mapped fragments (FPKMs) of HJURP in CCAs and para-tumor tissues in TCGA database. (B) The FPKMs of HJURP in iCCAs, ehCCAs, and their para-tumor tissues in the database with GSE33327 and GSE132305. (C) HJURP expression in CCAs was detected with IHC, dividing the patients into low and high HJURP expressions. (D) The IHC scores of pCCAs and dCCAs were higher than iCCAs. * represents P < 0.05, and n.s. represents not significant. In A, B, and D, statistical significance was analyzed by t test.

Furthermore, the expression of HJURP was detected with IHC, and the patients were divided into subgroups with a low or high expression of HJURP. The ratios of low or high expression of HJURP in iCCA, pCCA, and dCCA were 46.88% vs 53.12%, 32.39% vs 67.61%, and 29.17% vs. 70.83%, respectively (Table 1). It is interesting to note that most cases had an HJURP expression in the cytoplasm instead of the nucleus, which may be in conflict with its function as a CENP-A chaperone (Figure 1C). Moreover, the expression of HJURP was semi-qualified by IHC scores. Intriguingly, the IHC scores of pCCA and dCCA were significantly higher than the HJURP scores of iCCA (Figure 1D), indicating that iCCA, pCCA, and dCCA had different oncological characters.

Clinical relevance of HJURP in CCA

Several clinicopathological factors including the gender and age of patients, tumor size, histological grade, tumor infiltration (T stage), lymphatic invasion (N stage), and TNM stage were enrolled to investigate the potential HJURP-associated factors in CCA (Table 2). As a result, a high HJURP was significantly associated with the advanced T stage of iCCA, suggesting that HJURP may participate in iCCA proliferation and infiltration. Moreover, the associations of HRJUP with patients’ gender (P = 0.077) and histological grade (P = 0.059) in iCCA, tumor size (P = 0.076), and N stage (P = 0.089) in pCCA as well as tumor size in dCCA (P = 0.058) approached to be statistically significant.

Table 2 | The correlation between clinicopathological parameters and HJURP expression.

Prognostic significance of HJURP in CCA

The OS curves of different expression patterns of HJURP were plotted with the Kaplan–Meier method, and the prognostic significance of HJURP expression in iCCA, pCCA, and dCCA was evaluated with the log-rank test (Table 3). In iCCA and pCCA, patients with high HJURP had much poorer prognoses compared with others with low HJURP, suggesting that HJURP was a prognostic biomarker of iCCA and pCCA (Figures 2A, B). As for dCCA, the correlation between HJURP and outcome was not remarkably significant (P = 0.184), which may be a result of that the sample size of the dCCA cohort was not that large (n = 24)(Figure 2C).

Table 3 | The prognostic significance of HJURP and clinicopathological factors of CCA.

Figure 2 | The prognostic significance of HJURP in iCCA, pCCA, and dCCA. The patients with iCCA (A), pCCA (B), and dCCA (C) were divided into low- or high-HJURP subgroups, and the statistical significance between subgroups was analyzed with the log-rank test.

Prognostic significance of other clinicopathological factors of CCA

The correlations between the clinicopathological factors and OS rates were also assessed with the Kaplan–Meier method. In iCCA, advanced histological grade, T stage, N stage, and TNM stage were all defined as the factors associated with poor prognosis (Figures 3A–D). In pCCA, the outcome-associated factors were tumor size, T stage, and TNM stage (Figures 4A–C). In dCCA, only TNM stage indicated the unfavorable outcome (Figure 5).

Figure 3 | The prognostic significance of clinicopathological factors in iCCA. In iCCA, the prognosis-relevant factors included histological grade (A), T stage (B), N stage (C), and TNM stage (D). The statistical significance between subgroups was analyzed with the log-rank test.

Figure 4 | The prognostic significance of clinicopathological factors in pCCA. In pCCA, the prognosis-relevant factors included tumor size (A), T stage (B), and TNM stage (C). The statistical significance between subgroups was analyzed with the log-rank test.

Figure 5 | The prognostic significance of clinicopathological factors in dCCA. In dCCA, the prognosis-relevant factors included TNM stage. The statistical significance between subgroups was analyzed with the log-rank test.

HJURP is an independent prognostic biomarker of CCA

All the above clinicopathological factors were selected into the Cox-regression hazard model for multivariate analysis (Table 4). HJURP was identified as an independent prognostic biomarker in iCCA (P = 0.041, HR = 5.23, 95% CI = 1.24–22.65) and pCCA (P = 0.009, HR = 3.25, 95% CI = 1.33–7.91). The odds of CCA-related death for patients with high HJURP was 5.23- and 3.25-fold higher than those for patients with low HJURP. In dCCA, the independent prognostic significance of HJURP was not notable (P = 0.157). In addition, large tumor size (P = 0.012) and advanced T stage (P = 0.033) were also defined as independent factors suggesting poor outcome in iCCA. In pCCA and dCCA, no other clinicopathological factors were identified as independent prognostic factors in the Cox-regression hazard model.

Table 4 | The independent prognostic factors of CCA.

Discussion

The centromere is a conserved eukaryotic protein machinery required in precision segregation of the parental genome into two daughter cells during mitosis. HJURP is a key member of the CENP-A pre-nucleosomal complex, essential for the deposition of CENP-A at the centromeres. Accordingly, HJURP should be mainly expressed in the nucleus; however, we mainly observed HJURP in cell cytoplasm. We also noticed this inconsistency in HJURP sub-localization in previous studies. In hepatocellular carcinoma, prostate cancer, ovarian cancer, and breast cancer, IHC showed that HJURP was mainly expressed in cell cytoplasm (16, 21–24); nevertheless, HJURP was mainly observed in cytoplasm in pancreatic cancer and colorectal cancer (15, 17). The underlying mechanism of this expression phenotype is still unknown. Whether HJURP has functions other than the CENP-A chaperon requires further experiments to investigate.

Emerging molecular differences between iCCA, pCCA, and dCCA were revealed, thanks to the new high-throughput techniques such as whole-genome sequencing and transcriptome sequencing. There are several molecular classifications of CCA, especially iCCA, based on the genetic alterations (25, 26). However, these molecular patterns should be verified by prognostic significance evaluation. The prognostic validation requires the cohort study of large sample size, which is difficult for CCA because of its poor radical surgical rate (27, 28). Moreover, the biological differences between pCCA and dCCA remain unclear. Interestingly, we showed that HJURP was a prognostic biomarker of iCCA and pCCA, but the prognostic significance was not observed in dCCA. Maybe the sample size of dCCA was not large enough to generate statistical difference, but this may also be a true molecular difference between pCCA and dCCA. The molecular and biological difference between pCCA and dCCA still requires a multicenter large-sample cohort study to verify.

CCA is featured with the poor prognosis and limited treatment options (29). To identify more prognostic biomarkers and promote more biomarker-driven studies is urgently needed. Till now, there is only one Food-and-Drug-Administration-approved target drug of CCA, which is pemigatinib (30). Pemigatinib is recommended to be administrated for CCA patients with FGFR2 fusion, which only accounts for 10%–15% iCCA and approximately 5% pCCA (31). Most CCA patients had no target drugs with well-accepted effect. The precision treatment of CCA requires more investigations and long-term attention. Here we identified HJURP as a prognostic biomarker of iCCA and pCCA, indicating that patients with high HJURP are high-risk and should receive more rigor post-operational examination. Our results could provide evidence for screening high-risk patients and promote the precision treatment of CCA.

In this study, we did not further investigate the molecular mechanism of HJURP-involved CCA prognosis and the role of HJURP in CCA progression. The molecular mechanism of how HJURP participated in cancer progression had various explanations. Previous studied reported that several key proteins and signaling pathways were involved in HJURP-associated cancer progression. The involved proteins include CDKN1A, SPHK1, p21, and p53, and the signaling pathways contain GSK3β/JNK signaling, MAPK/ERK1/2 signaling, AKT/GSK3β signaling pathways, and so on (16, 21, 22). The exact molecular functions and HJURP-regulated signaling in CCA need further investigations.

In summary, we detected the expression of HJURP in 127 CCA patients and demonstrated that HJURP was an independent prognostic biomarker in both iCCA and pCCA, but not in dCCA. Our results can provide more evidence of the molecular characters of different CCA subsets and suggest that patients with high HJURP are high-risk, which can guide more precision follow-up and treatment of CCA.

Data availability statement

Publicly available datasets were analyzed in this study. This data can be found here: GSE33327,GSE132305.

Ethics statement

This study was reviewed and approved by the study was approved with the Ethics Committee of Qilu Hospital of Shandong University and the Second Hospital Affiliated to Shandong First Medical University. The patients/participants provided their written informed consent to participate in this study.

Author contribution

YY, JY, ZL, and WC performed the experiments, YY and PL collected the specimens and analyzed the data, and PL wrote the paper. All authors contributed to the article and approved the submitted version.

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.

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ORIGINAL RESEARCH
published: 01 August 2022
doi: 10.3389/fonc.2022.912881

Five-lipoxygenase-activating protein-mediated CYLD attenuation is a candidate driver in hepatic malignant lesion

Kun-kai Su ¹,², Xue-hua Zheng ³,⁴, Christian Bréchot ⁵, Xiao-ping Zheng ⁶, Dan-hua Zhu ¹,², Rong Huang ⁶, Yan-hong Zhang ¹,², Jing-jing Tao ¹,², Yi-jia Lou ³ and Lan-juan Li ¹,²*

¹ State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China, ² National Clinical Research Center for Infectious Diseases, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China, ³ Institute of Pharmacology and Toxicology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China, ⁴ Department of Pharmacology, Shengjing Hospital, China Medical University, Shenyang, China , ⁵ Institut Pasteur, Paris, France, ⁶ Department of Pathology, Shulan (Hangzhou) Hospital, Hangzhou, China

Edited by:
Zongli Zhang, Qilu Hospital of Shandong University, China

Reviewed by:
Xiang Zhang, The Chinese University of Hong Kong, China
Jiabin Cai, Fudan University, China

*Correspondence:
Lan-juan Li
ljli@zju.edu.cn

Specialty section:
This article was submitted to Cancer Molecular Targets and Therapeutics, a section of the journal Frontiers in Oncology

Received: 05 April 2022

Accepted: 30 June 2022

Published: 01 August 2022

Citation:
Su K-k, Zheng X-h, Bréchot C, Zheng X-p, Zhu D-h, Huang R, Zhang Y-h, Tao J-j, Lou Y-j and Li L-j (2022) Five-lipoxygenase-activating protein-mediated CYLD attenuation is a candidate driver in hepatic malignant lesion. Front. Oncol. 12:912881. doi: 10.3389/fonc.2022.912881

Hepatocellular carcinoma (HCC) is an inflammation-associated cancer. However, the lipid pro-inflammatory mediators have only been seldom investigated in HCC pathogenesis. Cylindromatosis (CYLD) attenuation is involved in hepatocarcinogenesis. Here, we aimed to evaluate the significance of hepatic lipid pro-inflammatory metabolites of arachidonate-affected CYLD expression via the 5-lipoxygenase (5-LO) pathway. Resection liver tissues from HCC patients or donors were evaluated for the correlation of 5-LO/cysteinyl leukotrienes (CysLTs) signaling to the expression of CYLD. The impact of functional components in 5-LO/CysLTs cascade on survival of HCC patients was subsequently assessed. Both livers from canines, a preponderant animal for cancer research, and genetic-modified human HCC cells treated with hepatocarcinogen aristolochic acid I (AAI) were further used to reveal the possible relevance between 5-LO pathway activation and CYLD suppression. Five-LO-activating protein (FLAP), an essential partner of 5-LO, was significantly overexpressed and was parallel to CYLD depression, CD34 neovascular localization, and high Ki-67 expression in the resection tissues from HCC patients. Importantly, high hepatic FLAP transcription markedly shortened the median survival time of HCC patients after surgical resection. In the livers of AAI-treated canines, FLAP overexpression was parallel to enhanced CysLTs contents and the simultaneous attenuation of CYLD. Moreover, knock-in FLAP significantly diminished the expression of CYLD in AAI-treated human HCC cells. In summary, the hepatic FLAP/CysLTs axis is a crucial suppressor of CYLD in HCC pathogenesis, which highlights a novel mechanism in hepatocarcinogenesis and progression. FLAP therefore can be explored for the early HCC detection and a target of anti-HCC therapy.

Keywords: hepatocellular carcinoma, lipid pro-inflammatory mediators, 5-lipoxygenase pathway, median survival time, candidate biomarker

Introduction

Hepatocellular carcinoma (HCC) is one of the most lethal malignancies and the second leading cause of cancer death worldwide (1–3). HCC surveillance is associated with early tumor detection and improved survival in patients with liver diseases (4). To date, it is not fully elucidated how initial molecular events and signaling involve the onset and progression of HCC. HCC is defined as an inflammation-associated cancer (5). Hepatocarcinogenesis and progression may evolve from the inflammation. However, the lipid pro-inflammatory mediators have only been seldom investigated in HCC pathogenesis. Cysteinyl leukotrienes (CysLTs), including LTC₄, LTD₄, and LTE₄, are lipid-signaling molecules that mediate both acute and chronic inflammation (6). CysLTs constitute the major products of arachidonic acid (AA) metabolism via the 5-lipoxygenase (5-LO) pathway (7, 8), forming 5-LO/CysLTs cascade. The integral membrane protein 5-LO-activating protein (FLAP) is an essential partner of 5-LO for the first step in LTs synthesis (9, 10); it selectively transfers AA to 5-LO and enables the sequential oxygenation of AA (11). Any factor inducing FLAP expression can lead to the enhancement of CysLTs synthesis. The primary metabolite LTA₄ is further converted to LTC₄ via LTC₄ synthase (LTC₄S) or its isoenzyme, membrane-embedded microsomal glutathione-S-transferase 2 (mGST2) (12–14). CysLTs bind at different affinities to two classical G-protein-coupled receptors: CysLTR1 and CysLTR2 (8, 15) (Figure 1, an overview from the available references). Activation of CysLTRs exerts downstream effects, including chemokine production, immune cell activation, and tissue inflammation (16). Cancer cells may utilize the 5-LO pathway to interact with tumor microenvironment during the development and progression of a tumor (8, 17). Chemical-induced liver damage in animal models displays the 5-LO-pathway activation and CysLTs content elevation (7, 18, 19). Hepatitis B virus X protein enhances proliferation of hepatoma cells via AA metabolism-associated pathways in vitro (20). However, the underlying mechanism by which CysLTs contribute to HCC pathogenesis is so far not well documented. Whether 5-LO/CysLTs cascade also takes a critical role in promoting hepatocarcinogenesis and development is still elusive.

Figure 1 | The overview diagram of arachidonic acid-derived metabolite pathways. Left: COX/PGE₂ pathway, COX: Cyclooxygenase, mPGES-1: microsomal prostaglandin E synthase-1.. Middle: 5-LO/CysLTs cascade and the receptors [AA is catalyzed by 5-LO/FLAP to form primary metabolite LTA₄, and then converted to LTC₄ via LTC₄S (in leukocytes) or mGST2 (in a wide variety of cells); subsequently, LTC₄ exports from cells, and extracellularly cleaves and sequentially forms CysLTs]. Right: LTB₄ bypass, BLT1/2: LTB₄ receptor 1/2. (Summarized from the available references).

Cylindromatosis (CYLD), a deubiquitination enzyme, negatively regulates cancer progression (21). Livers in CYLD-deficient mice display spontaneous Kupffer cell activation, inflammatory cell infiltration, nuclear factor (NF)-κB activation, oncoprotein c-Myc expression, and potentiated tumor development induced by diethylnitrosamine (22, 23). CysLTs can initiate NF-κB constitutively active in up to 80% of colorectal tumors (24, 25). However, it is still unclear whether 5-LO/CysLTs cascade is required for CYLD suppression. Understanding the relevance of the 5-LO pathway to the CYLD expression, and the signal cooperativity might provide a deeper look into the mechanism in hepatocarcinogenesis and HCC development.

Short-term aristolochic acid I (AAI) exposure leads to NF-κB signaling activation and c-Myc expression in kidneys (AAI-DNA adduct target) and livers (AAI-DNA adduct nontarget) of human TP53 knock-in mice (26). Owing to the inherent biological nature of dogs, canine and human share the remarkable similarity in cancers (27, 28). Our previous study confirms that hepatic premalignant lesion appears in canines after a 10-day AAI oral administration. It is featured by the overexpression of c-Myc and oncofetal RNA-binding protein Lin28B, the enhancement of Il6 [interleukin (IL)-6] transcription, and the increase of supernatant IL-6 level (29). The premalignant node involves dysregulations of microRNAs, as well as activation of IL-6 receptor, p-STAT3, and NF-κB (29). Therefore, it is extremely valuable to further explore whether 5-LO/CysLTs cascade is associated with AAI-induced hepatocarcinogenesis and involved in CYLD suppression.

In the present study, we report that FLAP highly expresses in the resection tissues from HCC patients. Furthermore, high transcription of FLAP in resection tissue significantly shortens the median survival time (MST) of HCC patients after surgical resection. Using an AAI-treated canine model and human HCC cell lines, we further highlight the potential role of the hepatic FLAP/CysLTs cascade in CYLD attenuation. This finding provides the hopeful target for early HCC detection and anti-HCC drug development.

Materials and methods

Human subjects

Human liver samples were obtained from HCC patients undergoing surgical resection or liver transplantation, and healthy donors for liver transplantation in the form of histologic section at Shulan (Hangzhou) Hospital. Protocols for patient tissue section usage were reviewed and approved by the Ethics Committees of Shulan (Hangzhou) Hospital (№ 20170207). Written informed consent was obtained. All experiments were done in accordance with the governmental and institutional guidelines of the ICH-GCP (according to the principles of the Declaration of Helsinki). Histopathological grading was performed at the Department of Pathology in Shulan (Hangzhou) hospitals. All the participants are anonymized and summarized in Supplementary Table 1.

Bioinformatics analysis of gene transcriptions

MRNA sequencing data of HCC tissues and clinical follow-up record of the HCC cohort were downloaded from The Cancer Genome Atlas (TCGA) in accordance with the data usage policy. MRNA expression of the indicated genes was retrieved (30). Patients were allocated into dichotomized subgroups based on processed expression for the survival analysis. Kaplan–Meier plots were used for comparison of survival curves, and log-rank tests were applied for p-value calculation. The correlation between the transcription level of FLAP in resection tissue and the MST of HCC patients after surgical resection was evaluated.

Animal experimentation

Ten-month-old male beagle canines were purchased from the Nanjing Anlimo Tech. Co. Ltd. (Nanjing, China) and maintained in a specific pathogen-free environment, since they were a preponderant animal for cancer research (27, 28). The protocols were approved by the Animal Care and Use Committee of Zhejiang University (№ ZJU2009108012Y), and all procedures were performed in accordance with the institutional guidelines.

AAI (purity >98%, HPLC, Delta) was mixed with filler and filled into capsules. Canines were randomly assigned into two groups and given capsules with AAI filler (3 mg/kg/day) or control filler only for 10 days (29). Canines were sacrificed 11 days after the initiation of the treatment. Livers were excised immediately after sacrifice. Part of the liver was fixed in 4% (wt/vol) neutral buffered formalin (pH 7.4) and embedded in paraffin for histologic analyses. The remaining liver tissues were immediately snap-frozen in liquid nitrogen and kept at −80°C until use.

Antibodies

Immunohistochemistry: Anti-FLAP (sc-28815), mGST2 (sc-65130), and LTC₄S (sc-22564) were purchased from Santa Cruz Biotechnology (California, USA). CysLTR2 (ab32536) was purchased from Multisciences Biotech (Hangzhou, China). CYLD (#8462) was purchased from Cell Signaling Technology (Danvers, USA). 4′,6′-Diamidino-2-phenylindole (DAPI) (ZLI-9557) was purchased from Zhongshan Jinqiao Biotechnology (Beijing, China). Goat Anti-Rabbit IgG Dylight 549 (GAR5492) was purchased from Multisciences Biotech. Goat Anti-Rabbit IgG/Alexa Fluor 488 (ZF-0511) was purchased from Yeasen Biotech.

Western blot: Anti-FLAP, mGST2, LTC₄S, CysLTR2, and CYLD were the same as mentioned above. 5-LO (sc-8885) was purchased from Santa Cruz Biotechnology. LTB₄ receptor 1 (BLT1) (ab131041) and BLT2 (ab84600) were purchased from Abcam (Cambridge, England). CysLTR1 (ab32534) was purchased from Multisciences Biotech (Shanghai, China). Antibodies of c-Jun N-terminal kinase (JNK) (#9252), phospho-JNK (p-JNK) (#9251), eukaryotic translation initiation factor 2 alpha (eIF2a) (#2103), p-eIF2a (#3398), 78-kDa glucose-regulated protein (GRP78) (#3177), and 94-kDa Glucose-Regulated Protein (GRP94) (#2104) were purchased from Cell Signaling Technology. Microsomal prostaglandin E synthase-1 (mPGES-1) (#160140) was purchased from Cayman Chemical (Ann Arbor, USA). Β-Actin (Mab1445), GAPDH (Mab5465-040), α-Tubulin (ab36864), β-Tubulin (ab012), and the HRP-conjugated secondary antibodies (goat anti-mouse, LK-GAM007; goat anti-rabbit, LK-GAR007; and rabbit anti-goat, LK-RAG007) were purchased from Multisciences Biotech.

Histological assessments of liver tissues

Human liver paraffin-embedded consecutive 4-μm-thick sections were used for immunohistochemical analysis. Anti-Ki-67 (IR098) and CD34 (IM034) were purchased from LBP (Guangzhou, China). Canine liver sections were prepared as described previously (29) for hematoxylin and eosin (H&E) or immunohistochemical stains, and consecutive 8-µm-thick frozen sections (cut by a freezing microtome, CM1950, Leica, Wetzlar, Germany) were used for immunofluorescent staining. Immunofluorescence analysis was performed using a confocal microscope (TCS SP8 MP, Leica) or a microscope (Nikon Eclipse 80i). Both human and canine liver sections were stained with the indicated antibodies to distinguish the immunohistochemical or immunofluorescence changes in expression and distribution in situ. For Ki-67 tissue evaluation, sections were graded based on the percentage of Ki-67 positively stained nuclei, using the range 0%–100%.

Western blot analysis and ELISA

Western blot assay was performed as described previously (29). Briefly, samples were homogenized with RIPA buffer (P0013K, Beyotime, Shanghai, China) for protein extraction. Whole blotting gel quantified with a fluorescence scanner imaging system (Bio-Rad, Hercules, California, USA) and densitometry was performed using ImageJ software (https://imagej.nih.gov/ij, NIH, Bethesda, MD, USA).

Supernatant containing CysLTs was extracted from liver tissue homogenate of canines (1:10 wt/vol, 50 mmol/L Tris-HCl, pH 7.5). CysLTs level was measured by ELISA Kit (Cayman, USA) on a DTX 880 Multimode Detector (Beckman Coulter, USA).

Real-time qRT-PCR analysis

Real-time qRT-PCR analysis was conducted according to the protocol (31). Briefly, total RNAs from human HCC cell Lines and canine livers were isolated with TRIzol reagent (Gibco BRL, USA). After 2 min of initial denaturation at 95°C, amplification used 40 cycles following 15 s at 95°C and 1 min at 60°C. The primer sequences (5’-3’) for FLAP, Lta4h (LTA₄ hydrolase), and GAPDH (in human HCC cells) were documented in Supplementary Table 2.

Cell culture and genetic handling of FLAP in human HCC cells

The human HCC cell lines HepG2, Hep3B2.1-7 (Hep3B), and PLC/PRF/5 were purchased from Shanghai Institute of Cell Bank, Chinese Academy of Sciences (№ 22008). Cells were maintained in DMEM (12800-017, Life Technologies, USA) supplemented with 10% fetal bovine serum (FBS; 10099-141, Gibco, Germany).

Overexpression of FLAP in HepG2 or Hep3B cells was achieved by lentiviral infection. Lentiviruses (lv) were constructed, concentrated, and purified by Genechem (Shanghai, China). HepG2 or Hep3B cells were infected with lv-Control (lv-Con) or lv-FLAP (HepG2lv-Con, HepG2lv-FLAP, Hep3B lv-Con, and Hep3B lv-FLAP) (MOI = 10), and then selected for 3 days in the presence of 2.5 μg/ml puromycin (Gibco, Germany). Knockdown of FLAP in HepG2 or Hep3B cells was conducted by short hairpin RNA (shRNA). Sh-Con and sh-FLAP plasmids (Sangon Biotech, Shanghai, China) were transfected with Lipofectamine 2000 transfection agent (HepG2sh-Con, HepG2sh-FLAP, Hep3Bsh-Con, and Hep3Bsh-FLAP) (Invitrogen, Life Technologies) according to the manufacturer’s instructions. Sequences for shRNA are shown in Supplementary Table 3. Lv-RNA or shRNA interfering efficiency was validated by qRT-PCR.

All the knock-in or knock-down processed cells together with wild-type (WT) HCC cell lines were further incubated with AAI (final concentration, 1.25 μM) for another 7 days. Cells were harvested on day 10 after initiation of infection or transfection. The expression of FLAP in HepG2 cells was examined by flow cytometry according to commercial instructions. Finally, cells were analyzed by a BD FACSCount II Flow Cytometer (BD Biosciences, San Jose, USA) or a microscope (Nikon Eclipse 80i) after immunofluorescent staining. Stunning (for Hep3B cells).

Statistical analysis

Pairwise comparisons between continuous data were analyzed using an unpaired two-tailed Student t-test, and multiple comparisons were analyzed by one-way ANOVA. All data were expressed as mean ± SD, and p < 0.05 was considered statistically significant. Significant p-values are indicated by asterisks in the individual figures.

Results

FLAP overexpression is paralleled to CYLD suppression in resection tissues from HCC patients

We explored the impact of functional components in CysLTs biosynthesis on CYLD expression in the resection tissues from 10 HCC patients. CysLTs biosynthesis exhibits AA metabolism through the 5-LO pathway. To investigate the involvement of FLAP in the progression of HCC, we examined its expression and localization in the resection tissues. Patients’ characteristics, concerning etiology, gender, age, and tumor stage, were summarized (Supplementary Table 1). Immunohistochemical staining revealed that the FLAP level was considerably higher in the resection tissues from HCC patients as compared to the samples from donor controls (Figure 2A, upper left panel). All the HCC resection tissues displayed a higher FLAP expression, but only 1 in 10 liver tissues from donors expressed marginal FLAP (Figure 2B). In contrast, the resection tissues from HCC patients had a markedly lower CYLD expression than the samples from donor controls (Figure 2A, bottom right panel). Considering that both mGST2 and CysLTR2 in the 5-LO pathway were basically required in the lipid pro-inflammatory mediators’ signaling transduction, we subsequently observed their abundance, and further clustered all the four molecular events according to their expression grades in resection tissues from HCC patients or donors (Figures 2A, B). In parallel to high FLAP expression, 8 in 10 resection HCC tissues showed the absence of CYLD expression (Figure 2B). As to the two cases with CYLD expression, one showed CYLD nuclear translocation together with a much higher cytosolic CysLTR2 expression within poorly differentiated HCC lesions (Supplementary Figure 1, left panel), and the other one displayed slight cytosolic CYLD expression together with a much higher nuclear CysLTR2 expression in paracancerous tissue (Supplementary Figure 1, right panel). Furthermore, compared to the ratio of overexpression of mGST2 (3/10) and CysLTR2 (2/10) in donor livers, there are much more overexpression of mGST2 (8/10) and CysLTR2 (10/10) in HCC tissues (Figure 2B). According to the clustered results, the hepatic mGST2 and CysLTR2 have a basal expression level under physiological conditions, but their expressions were robustly enhanced in the malignant lesion, depending on the upstream element FLAP level. Strikingly, there was less significance of high expressions of mGST2 and CysLTR2 to CYLD suppression in the livers of donors, implying that mGST2 and CysLTR2 were not prerequisite for inhibiting CYLD expression. Our data strongly suggested a negative correspondence between FLAP overexpression and CYLD attenuation in HCC tissues (Figure 2B), suggesting that overexpressed FLAP was a potential hallmark of HCC. We further identified the effects of overexpressed FLAP on HCC invasiveness and development, as evidenced by evaluating the downstream effectors. We found that CD34, a marker of hematopoietic stem cells, and Ki-67, a marker of cell proliferation, were highly expressed in resection tissues of HCC in contrast to donors (Figure 2C). Of 10 resection tissues of HCC, 6 showed CD34 neovascular localization (including tumor angiogenesis), and 9 of 10 resection tissues of HCC showed Ki-67 overexpression (Figure 2C and Supplementary Table 1). Importantly, the HCC tissues that exhibited FLAP overexpression with ++ grades (≥50% positive rate) also showed Ki-67 expression with ++ grades. In contrast, the donor livers without FLAP expression showed neither CD34 nor Ki-67 expression (Figure 2C). These data implied that overexpressed FLAP was correlated with CYLD suppression, and involved in neovascularization and tumor cell proliferation in HCC tissues.

Figure 2 | Overexpression of FLAP correlates to CYLD suppression in resection tissues of HCC patients. (A) Representative immunohistochemistry (IHC) images of FLAP, MGST2, CysLTR2, and CYLD in each subject using the indicated antibodies. Scale bar = 20 μm, bar = 10 μm (magnification). (B) The expression levels of FLAP, mGST2, CysLTR2, and CYLD were clustered. Patient and donor codes are indicated. Grades of the indicated markers (Red: ++ = brown, Blue: + = golden, Green: ND = not detected). (C) The expression levels of CD34 localization and positive rate (%) for Ki-67 were clustered. Patient and donor codes are matched with each other. For each analysis, n = 10/per group.

Enhanced FLAP transcription shortens the median survival time of HCC patients after surgical resection

To support the lines of evidence for the role of FLAP on the clinical findings described above, we took advantage of the long-term clinical follow-up information provided by TCGA for bioinformatics analysis. We obtained a 417-patient HCC cohort, which was well documented with survival information. We subsequently analyzed the relationships between the transcription levels of hepatic FLAP, mGST2, CysLTR2 and CYLD and the MST in HCC patients after surgical resection, respectively. Notably, MST was only 47 months for high FLAP mRNA in HCC patients (p = 0.04), in contrast to 104 months for low FLAP mRNA (Figure 3A). Conversely, there was only a slightly higher effect between high and low mGST2 mRNA on MST in HCC patients (p = 0.27) after surgical resection (Figure 3B). On the other hand, higher CysLTR2 mRNA significantly shortened MST in HCC patients (p = 0.04) after surgical resection (Figure 3C). Furthermore, MST was as low as 34 months for low CYLD mRNA and 56 months for high CYLD mRNA in HCC patients after surgical resection (p = 0.04) (Figure 3D). Remarkably, the effects of hepatic FLAP or CysLTR2 and CYLD transcription on MST also displayed a negative correlation in HCC patients after surgical resection. Obviously, the patients with low FLAP mRNA had a much longer survival period (MST, 104 months) than those with low CysLTR2 mRNA (MST, 84 months), indicating that the transcription level of FLAP is one of the most crucial impacts on survival of HCC patients after surgical resection. From the MST point of view, enhanced FLAP appeared much earlier than attenuated CYLD in HCC patients (Figures 3A, D); therefore, FLAP possessed the crucial value to evaluate the progress and recrudescence of HCC patients after surgical resection.

Figure 3 | Enhanced hepatic FLAP mRNA shortens median survival time of HCC patients after surgical resection. Data for either HCC patients after surgical resection or liver transplantation from the TCGA Research Network (http://cancergenome.nih.gov/) were eligible for survival analysis. (A–D) Left panels: Kaplan–Meier survival curves in coordinate axis for comparing survival rates between high- and low-gene transcription subgroups from the HCC cohort. Horizontal dotted line: 50% survival. Vertical red/blue dotted lines: median survival time (MST). (A) FLAP mRNA (p = 0.0470). (B) mGST2 mRNA (p = 0.2697). (C) CysLTR2 mRNA (p = 0.0418). (D) CYLD mRNA (p = 0.0456). Right panels: Scattered plots of transcription levels for indicating genes from the HCC cohort. Student unpaired t-test was used to compare groups and p-value less than 0.05 was considered as statistically significant.

Hepatic CYLD attenuation and CysLTs increasement after AAI treatment

To determine whether 5-LO/CysLTs cascade influences CYLD depression, we first assessed the effect of AAI on CYLD expression in livers of canines. Compared to control, both the abundance of CYLD in hepatocytes and the numbers of CYLD-positive hepatocytes by AAI treatment extremely decreased using immunofluorescence staining (Figure 4A). Western blot analysis further confirmed that AAI treatment significantly attenuated the expression of CYLD in liver tissues (Figure 4B). Next, H&E-stained liver sections displayed inflammatory cell infiltration around the central veins after AAI treatment (Figure 4C, Supplementary Figure 2), which was similar to the feature that spontaneously appeared in the livers of CYLD-deficient mice (32). It was also well matched with our previous findings of an increase in serum ALT and AST, as well as in Il6 transcription and supernatant IL-6 level in AAI-treated livers (29). We then further examined the effect of AAI on CysLTs biosynthesis in canine livers. As determined by ELISA, AAI treatment significantly raised the content of CysLTs in liver homogenate supernatant compared to control (Figure 4D). These data suggested that CysLTs generation was parallel to CYLD attenuation accompanied by the CYLD-deficient feature of inflammatory cell infiltration around the central veins in AAI-treated canine livers.

Figure 4 | Hepatic CYLD attenuation is parallel to increased CysLTs production after AAI treatment in canines. Liver tissues were obtained from canines after treatment with AAI (3 mg/kg/day) or filler only (negative control, Con) for 10 days. (A) Representative photomicrographs of CYLD (green: distribution in cytoplasm, DAPI: nuclei) in liver sections stained by immunofluorescence histochemistry. Bar = 50 μm, bar = 5μm (magnification). (B) Immunoblot analysis of hepatic CYLD expressions. β-Tubulin was used as an internal loading control. (C) Representative photomicrographs of liver sections stained with H&E (inflammatory cells, arrowheads). Bar = 100 μm, bar = 50 μm (magnification). (D) Supernatant CysLTs level in liver tissue homogenate detected by ELISA. Error bars represent the mean value ± SD; Student unpaired t-test was used to compare groups, and p-value less than 0.05 was considered as statistically significant. *p < 0.05 vs. control. For each analysis, n = 4/per group.

Hepatic FLAP overexpression contributes to CysLTs increase

To obtain functional insights into hepatic 5-LO/CysLTs cascade in AAI-induced CYLD suppression, we observed the expression of each component in the 5-LO pathway. Using Western blotting, we evaluated the expressions of four catalytic proteins involved in the LTC₄ biosynthetic process, and found that FLAP and mGST2 were significantly higher in expression compared to control (p < 0.01 and p < 0.05, respectively) in AAI-treated livers (Figure 5A). In particular, we individually identified the abundance and distribution of each protein involved in LTC₄ biosynthesis in the liver tissues with immunohistochemical staining. We observed that, compared to control, AAI induced considerable overexpression of FLAP in both hepatocytes and Kupffer cells. MGST2 was highly expressed in hepatocytes, but the expression of LTC₄S did not change after AAI treatment (Figure 5B, the positive or negative control for immunohistochemical analysis saw in Supplementary Figure 3). In contrast, AAI failed to increase the transcription of hepatic Lta4h at the LTA₄-derived LTB₄ step in the 5-LO bypass (Figure 5C) (33). Conversely, AAI significantly inhibited the expression of hepatic mPGES-1 (p < 0.05) (Figures 5A, B), which catalyzes prostaglandin (PG) E₂ generation at the terminal steps of the cyclooxygenase (COX) pathway. These data indicated that neither the 5-LO/LTB₄ bypass nor the COX/PGE₂ pathway contributed to AAI-induced hepatic CYLD suppression. Therefore, AAI-treated livers underwent FLAP/CysLTs cascade-associated pathogenesis.

Figure 5 | AAI treatment leads to FLAP overexpression in canine livers. Liver tissues were obtained from canines after treatment with AAI (3 mg/kg/day) or filler only (negative control, Con) for 10 days. (A) Representative Western blotting of the catalytic proteins in 5-LO/CysLTs cascade and COX pathway. (B) Representative photomicrographs with immunohistochemical stains using indicated antibodies in 5-LO/CysLTs cascade and COX pathway. FLAP in hepatocytes (dark brown, green arrowheads), Kupffer cells (dark brown, red arrowheads), mGST2 (light brown, arrowheads), and LTC₄S (arrowheads) in liver tissues. MPEGS-1 (slight brown round central veins, disappearing in elsewhere). Bar = 100 μm, bar = 50 μm (magnification). (C) The transcription of hepatic Lta4h was analyzed by real-time quantitative PCR. Error bars represent the mean value ± SD; Student unpaired t-test was used to compare groups, and p-value less than 0.05 was considered as statistically significant. *p < 0.05, **p < 0.01 vs. control. For each analysis, n = 4/per group.

Endoplasmic reticulum stress contributes to CysLTs/CysLTR2 signaling transduction

To understand the underlying molecular mechanism of CysLTs associated with CYLD attenuation after AAI treatment, we subsequently focused on the connection of CysLTs/CysLTRs signaling in canine livers. Meanwhile, we also checked endoplasmic reticulum (ER) stress, since ER stress inducers showed the ability to reduce CYLD expression (34). Using Western blotting, we detected the expressions of the hepatic CysLTR1, CysLTR2, BLT1 (the high-affinity receptor of LTB₄), BLT2, Grp78, and Grp 94 (the early events of ER stress), as well as the phosphorylation of eIF2α (the late event of ER stress) once stimulated with AAI. We observed that, compared to control, AAI significantly upregulated CysLTR2 expression (p < 0.05), but did not influence the expressions of CysLTR1 (Figure 6A), BLT1, and BLT2 (Supplementary Figure 4). These data indicated that, among the hepatic receptors associated with LTs, only CysLTR2 displayed overexpression in response to AAI treatment. Furthermore, AAI increased in both expression of Grp78 (p < 0.05) and phosphorylation of eIF2α (p < 0.01) (Figure 6B), suggesting that a possible relevance existed in ER stress and CysLTR2 activation. We next measured the phosphorylation of JNK, one of the downstream effectors of the CYLD attenuation, and found that JNK was significantly phosphorylated (p < 0.01) (Figure 6C). Taken together, AAI-induced high expression of CysLTR2 was accompanied by ER stress and phosphorylation of JNK, implying that the ER stress/CysLTR2 axis probably promoted the FLAP/CysLTs/CYLD/p-JNK signaling transduction in AAI-treated livers.

Figure 6 | CysLTR2 overexpression along with ER stress and JNK phosphorylation in AAI-treated livers of canines. Liver tissues were obtained from canines after treatment with AAI (3 mg/kg/day) or filler only (negative control, Con) for 10 days. Immunoblot analyses of (A) CysLTRs, (B) molecular events for ER stress, and (C) phosphorylated JNK. GAPDH was used as loading control. Error bars represent the mean value ± SD. Student unpaired t-test was used to compare groups, and p-value less than 0.05 was considered as statistically significant. *p < 0.05 vs. control. **p < 0.01 vs. control. For each analysis, n = 4/per group.

Knock-in FLAP diminishes CYLD expression in human HCC cell lines

To further confirm the impact of FLAP on CYLD expression, we explored the abundance of CYLD in WT, lv-FLAP, and sh-FLAP human HCC cell lines according to the scheme of the experimental procedure (Figure 7A). Among human HCC cell lines HepG2, Hep3B, and PLC/PRF/5, we validated the favorable nature of FLAP expression in HepG2 cells in response to AAI exposure (Figure 7B). We therefore modulated the abundance of FLAP in HepG2 or Hep3B cells to generate HepG2lv-FLAP, Hep3B lv-FLAP or HepG2sh-FLAP1, HepG2sh-FLAP2, and Hep3Bsh-FLAP cells. Compared to WT HCC cell lines, HepG2lv-Con or HepG2sh-Con cells, we observed that either FLAP mRNA or FLAP-positive (FLAP⁺) cell ratio was remarkably high in HepG2lv-FLAP cells (p < 0.01) and decreased in HepG2sh-FLAP cells (p < 0.01) using qRT-PCR (Supplementary Figure 5) or flow cytometry assays (Figure 7C), indicating that infection or transfection efficiency was significant. Notably, there was an associated reduction of CYLD in HepG2lv-FLAP cells compared to the WT HCC cell line and HepG2lv-Con cells (p < 0.05), while the expression of CYLD in HepG2sh-FLAP2 cells was significantly higher than WT and HepG2sh-Con cells (p < 0.05), as evidenced by Western blot assay (Figures 7D, E). Using immunofluorescent staining, similar outcomes appeared in Hep3B cells. The negatively correlated tendency was the same as that in HepG2 cells. The immunofluorescent stained characteristic was quite similar to the liver tissues of AAI-treated canines by immunofluorescence assay (Supplementary Figure 6). These results intensively revealed that FLAP was required for negatively regulating CYLD expression in AAI-treated human HCC cells. Given that CysLTR2 was more expressed in the tumor vasculature compared to CysLTR1 (35), we further evaluated CysLTR2 expression in WT, HepG2lv-FLAP, or HepG2sh-FLAP HCC cells. Unlike AAI-treated livers of canines, the expression of CysLTR2 did not change in HepG2lv-FLAP or HepG2sh-FLAP cells in contrast to WT HCC cells, HepG2lv-Con, and HepG2sh-Con cells (Figures 7D, E). These data implied that HepG2 cells probably only kept limited expression of CysLTR2, and that its abundance was not significantly affected by FLAP. We suggested that the inherent CysLTR2 was enough to make CysLTs signal transduction, thereby negatively modulating CYLD expression. A schematic representation of the hepatic FLAP/CysLTs cascade contributing to the attenuation of CYLD in AAI-induced hepatic malignant alterations was illustrated in Figure 7F.

Figure 7 | Overexpression of FLAP diminishes CYLD expression in AAI-treated HepG2lv-FLAP cells. (A) Scheme of the experimental procedure. (B) FLAP-positive (FLAP⁺) cell ratio in human HCC cell lines HepG2, Hep3B, and PLC/PRF/5 in response to AAI by flow cytometry assay, respectively. WT HCC cells without AAI treatment were used as control. (C) FLAP-positive (FLAP⁺) cell ratio in WT, HepG2lv-FLAP, and HepG2sh-FLAP cells by flow cytometry assay. (D, E) Immunoblotting analyses of CYLD and CysLTR2 in WT, HepG2lv-FLAP, or HepG2sh-FLAP cells. β-Tubulin was used as loading control. (F) Schematic representation of FLAP overexpression results in CYLD attenuation in the HCC canceration process. Error bars, mean value ± SD; Student unpaired t-test was used to compare groups, and p-value less than 0.05 was considered as statistically significant. *p < 0.05 vs. control, **p < 0.01 vs. control. For each analysis, n = 3.

Discussion

The prognosis of HCC patients is poor, with only limited treatment options (36). There is an urgent need to seek novel targets for the early diagnostic and preventive strategies of HCC patients. Hepatocarcinogenesis and HCC development are a multistep process, and probably evolve from the inflammation. In the current study, we reveal that all the resection tissues from HCC patients show significant overexpression of FLAP in both malignant lesion and paracarcinoma tissues in contrast to those from donors. Furthermore, FLAP is closely associated with neovascularization (including tumor angiogenesis) and cell proliferation in resection tissues of HCC patients, evidenced by high expression of FLAP or Ki-67, and neovascular localization of CD34 in parallel. Remarkably, the effect of mRNA level of hepatic FLAP on MST also displayed a close correlation in HCC patients after surgical resection. Of additional interest is that the high transcription of FLAP leads to significant shortening of the MST of HCC patients after surgical resection, indicating that FLAP high expression plays a crucial role in HCC development or recrudescence. Therefore, our findings that hepatic FLAP increased in expression could be a valuable target for both therapeutics and predicting survival of HCC patients after surgical resection.

Since canine and human share a remarkable similarity in cancers (27, 28), the present study takes it as a preponderant animal model for onset and progression research of HCC. Our previous study confirms that hepatic premalignant lesion appears in canines after a 10-day AAI oral administration. It is featured by an increase in Il6 [interleukin (IL)-6] transcription and supernatant IL-6 level, as well as the overexpression of c-Myc and oncofetal RNA-binding protein Lin28B (29). The premalignant node involves the dysregulation of microRNAs, as well as the activation of IL-6 receptor, p-STAT3, and NF-κB (29). By studying the impact of AAI treatment on 5-LO/CysLTs cascade, the present study further looks into the mechanism that links FLAP to CYLD in canine liver, and we identify a novel candidate driver for hepatic malignant lesion. Following AAI administration of canines, the livers show a significant overexpression of FLAP accompanied by markedly higher CysLTs content. The enhanced hepatic FLAP/CysLTs cascade displays a negative correspondence to CYLD expression and a positive correspondence to its downstream effector JNK phosphorylation. The present study reveals that both the 5-LO/LTB₄ bypass and the COX/PGE₂ pathway in AAI-treated livers of canines do not change in expression, indicating that both pathways contribute less to the generation of lipid pro-inflammatory mediators (33). These data convincingly indicate that the more reduced CYLD is associated with increased CysLTs. Most importantly, the data from both HepG2lv-FLAP, Hep3Blv-FLAP and HepG2sh-FLAP, Hep3Bsh-FLAP cells directly confirm the impact of FLAP abundance on CYLD expression after AAI treatment. The in vitro results intensively support that hepatic FLAP overexpression is required for suppressing CYLD. Strikingly, our study highlights the fact that the enhanced hepatic FLAP/CysLTs cascade is involved in CYLD suppression. In the liver, CYLD acts as an important regulator of hepatocyte homeostasis (23). In HCC pathogenesis, CYLD is involved in negatively regulating apoptosis, regeneration, NF-κB signaling, and inflammation. Consistent with our previous findings, such as apoptosis, regeneration, NF-κB signaling, and inflammation, as well as an increase in Il6 transcription and supernatant IL-6 level in AAI-treated livers of canines (29), here, the further evidence that AAI-induced CYLD suppression accompanies the inflammatory cell infiltration around the central veins is quite similar to the features spontaneously appearing in the livers of CYLD-deficient mice (32). Owing to the FLAP overexpression-induced CYLD suppression, we consider that the hepatocyte homeostasis suffers from significant disorders, including all the features described above and the malignant progression in canine livers.

Conversely, the higher abundances of hepatic mGST2 and CysLTR2 do not cooperate with CYLD suppression in donors, although both of them highly express in the HCC tissues, similar to the livers in AAI-treated canines. Furthermore, the overexpression of mGST2 mRNAs does not influence the MST of HCC patients after surgical resection, suggesting that it is at least not involved in inhibiting expression of CYLD. These results intensively indicate that FLAP itself, rather than mGST2 and CysLTR2, is crucially important to HCC development and recrudescence in patients and AAI-induced hepatocarcinogenesis in canines. The compelling findings highlight the possible therapeutic implications of FLAP in human HCC development. Therefore, FLAP overexpression is a noteworthy index parameter running throughout the initiation and termination in liver cancer pathology.

In rat liver, hepatocytes accounted for the highest ability to metabolize and produce CysLTs from the LTA₄ (37). Using the FLAP as a marker, we confirm that FLAP overexpresses in both hepatocytes and Kupffer cells in canine liver after AAI treatment. Since LTC₄S only expresses in cells of hematopoietic lineage, the present study does not show that it highly expresses in canine livers, and we believe that majority of CysLTs come from hepatocytes. Given that FLAP aids to catalyze LTA₄ production, our data reveal that AAI-induced overexpression of FLAP is a major element contributing to CysLTs generation in canine livers. Hepatocyte-derived CysLTs mediate hepatic vascular tone abnormalities in cirrhosis (38). The 5-LO pathway has been indicated to have a role in different cancers (39). CysLTs involve the microenvironment, influencing local risk of malignant transformation (40). To date, the signaling effects of CysLTs/CysLTR2 on oncogenesis and development are controversial. High CysLTR2 expression is correlated with a good prognosis in patients with colorectal cancer (41) and breast cancer (42). However, constitutive activation of CysLTR2 in uveal melanoma acts as an oncogene (43). CysLTR2 expression in stromal cells, rather than tumor cells, is essential for enhanced invasiveness (35). Although high expression of CysLTR2 appears in resection tissues of HCC patients and in AAI-induced premalignant livers of canines, the data from both HepG2lv-FLAP and HepG2sh-FLAP cells do not demonstrate the impact of FLAP abundance on CysLTR2 expression. We therefore focus on the hepatic ER stress/CysLTRs axis, since ER stress triggers CysLTRs internalization (14) and ER stress inducers show the ability to reduce CYLD expression (34). Our data demonstrate that AAI treatment triggers the early and late events of ER stress in canine livers, as evidenced by high Grp78 expression and eIF2α phosphorylation, thereby probably promoting CysLTR2 internalization. Of CysLTs, LTC₄, LTD₄, and LTE₄ interact with their own specific receptors. LTC₄ possesses CysLTR2-specific affinity and potency (16). LTC₄ is the only intracellular CysLTs; it also outputs and exists in the extracellular space with a very short lifetime. ER stress induces CysLTR2 internalization, making LTC₄ much easier to activate CysLTR2 intracellularly. Our results confirm that a high FLAP expression is involved in CysLTs generation in vivo. Once ER stress triggers CysLTR2 internalization, increased CysLTs have a strong tendency to allow CysLTR2 activation. We consider that it is enough for CysLTs/CysLTR2 signaling transduction to take a local or transient effect on inhibiting CYLD expression in liver cancer pathology. The hepatic ER stress/CysLTR2 axis probably contributes to the FLAP/CysLTs/CYLD/p-JNK signaling transduction in vivo.

Nuclear expressions of CysLTRs are potential prognostic indicators of colorectal cancer. High CysLTR2 nuclear expression has the best survival expectancy (41). Our data show that a high FLAP expression along with high nuclear CysLTR2 nuclear expression results in CYLD expression in paracarcinoma tissues, while a high FLAP expression along with high cytosolic CysLTR2 expression leads to CYLD nuclear translocation in malignant lesion. Although our data link CysLTR2 nuclear distribution to CYLD expression in HCC, we cannot confirm whether different styles of CysLTR2 distribution directly affect CYLD expression from the only two HCC patients.

Strikingly, we reveal that FLAP overexpression is correlated with CD34 neovascular localization and high Ki-67 expression in HCC tissues of HBV-infected patients. That means, once hepatic FLAP is detected and inhibited, neovascularization and tumor cell proliferation can probably be inhibited in HCC tissues. Recently, the 5-LO pathway has been highlighted as a promising therapeutic direction in different diseases (8, 9, 44). Our data intensively suggest that despite the fact that the resection tissues from HCC patients or AAI-induced premalignant lesion in canine livers share the same characteristics, FLAP overexpression is parallel to CYLD attenuation. Based on our knowledge, we report here for the first time that FLAP overexpression exists throughout the hepatic premalignant and the terminal HCC period. FLAP inhibition, therefore, may provide the foundation for new therapeutic strategies of HCC patients and could be more significant in prolonging the life span of HCC patients after surgical resection.

Data availability statement

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

Ethics statement

Protocols for patient tissue section usage were reviewed and approved by the Ethics Committees of Shulan (Hangzhou) Hospital (№ 20170207). The patients/participants provided their written informed consent to participate in this study. T The protocols were approved by Animal Care and Use Committee of Zhejiang University (№ ZJU2009108012Y), and all procedures were performed in accordance with the institutional guidelines.

Author contributions

K-kS designed and conducted clinical data and in vitro experiments, bioinformatics analysis, statistical analysis, and drafted the manuscript. X-hZ conducted most of the animal experiments and data analysis. CB provided guidance on conception, and reviewed and edited the manuscript. X-hZ. and RH conducted the acquisition of clinical data of HCC patients and donors. D-hZ and J-jT aided in bioinformatics analysis. Y-jL and Y-hZ organized the animal study and acquisition of animal data. L-jL provided guidance on conception, supervised the study for clinic pathological feature and pathological analysis of HCC patients, and granted final approval of the version to be published. All authors contributed to the article and approved the submitted version.

Funding

This work was supported by the National Key Research and Development Program of China (№ 2019YFC0840609 to K-kS), the National Natural Science Foundation of China (№ 81401707 to K-kS, № 81790630 to L-jL, and № 91229124 to Y-jL), the Zhejiang Provincial Natural Science Foundation (№ LY20H030006 to K-kS), and the Fundamental Research Funds for the Central Universities (Nō 2022ZFJH003 to K-kS and L-jL).

Acknowledgments

Bang-xin Chen aided Western blotting experiments. Data were eligible for HCC patient survival analysis from the TCGA Research Network.

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.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2022.912881/full#supplementary-material

Supplementary Table 1 | The information for all the participants donating resection tissues of livers. The characteristics were summarized, concerning etiology, gender, age, and tumor stage.

Supplementary Table 2 | Primer sequences (5’-3’) for qRT-PCR.

Supplementary Table 3 | shFLAP sequences.

Supplementary Figure 1 | Two HCC cases with CYLD expression in resection tissues. One showed CYLD nuclear translocation together with a much higher cytosolic CysLTR2 expression within poorly differentiated HCC lesions (left panel), and the other one displayed slight cytosolic CYLD expression together with a much higher nuclear CysLTR2 expression in paracancerous tissue (right panel).

Supplementary Figure 2 | The inflammatory cell infiltration were evaluated by counting the numbers of inflammatory cells in each section field.

Supplementary Figure 3 | The positive and negative controls were set up for immunohistochemical analysis of all observed subjects.

Supplementary Figure 4 | Expressions of receptors in LTB4 bypass. Immunoblot analyses of BLTs.β-Actin and GAPDH were used as loading control. Error bars represent the mean value ± SD; A Student unpaired t test was used to compare groups, (BLT2 control, n=3).

Supplementary Figure 5 | Relative expression of FLAP mRNAs in Knock-in or Knock-down of FLAP in HepG2 cells at the indicating times. Overexpression of FLAP in HepG2 cells was achieved by lentiviral infection. Lentivirus-Control (lv-Con) or lv-FLAP (HepG2lv-Con, HepG2lv-FLAP) were selected for 3 days. Knockdown of FLAP in HepG2 cells was conducted by short hairpin RNA(shRNA). HepG2lv-FLAP and HepG2sh-FLAP, and wild type (WT), HepG2lv-Con or HepG2sh-Con cells were further incubated with AAI (final concentration 1.25 μM) for another 7 days. Error bars, mean value ± SD; A Student unpaired t test was used to compare groups, and a P value less than 0.05 was considered statistically significant. *P < 0.05 vs control, **P<0.01 vs control. For each analysis, n=3.

Supplementary Figure 6 | Overexpression of FLAP diminishes CYLD expression in AAI-treated Hep3Blv-FLAP cells. WT HCC cells without AAI treatment were used as control. In the case of AAI treatment, representative photomicrographs of CYLD abundance (red: distribution in cytoplasm, blue: nuclei with DAPI staining) was indicated by immunofluorescence histochemistry. Bar=100 μm, bar=20 μm (magnification).

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ORIGINAL RESEARCH
published: 05 August 2022
doi: 10.3389/fonc.2022.959299

Design and Fabrication of a Liver-on-a-chip Reconstructing Tissue-tissue Interfaces

Jing Liu ¹, Chong Feng ¹, Min Zhang ¹, Feng Song ²* and Haochen Liu ³*

¹ School of Biology, Food and Environment, Hefei University, Hefei, China, ² Shandong Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou, China, ³ Department of Cardiovascular Surgery, Xi’an Children’s Hospital, Xi’an, China

Edited by:
Yunfei Xu, Shandong University, China

Reviewed by:
Yongquan Gu, Capital Medical University, China
Keqing Shi, Wenzhou Medical University, China

*Correspondence:
Haochen Liu
haochen1010@163.com
Feng Song
sfeng-08@163.com

Specialty section:
This article was submitted to Cancer Molecular Targets and Therapeutics, a section of the journal Frontiers in Oncology

Received: 01 June 2022

Accepted: 24 June 2022

Published: 05 August 2022

Citation:
Liu J, Feng C, Zhang M, Song F and Liu H (2022) Design and Fabrication of a Liver-on-a-chip Reconstructing Tissue-tissue Interfaces. Front. Oncol. 12:959299. doi: 10.3389/fonc.2022.959299

Despite the rapid advances in the liver-on-a-chip platforms, it remains a daunting challenge to construct a biomimetic liver-on-a-chip for in vitro research. This study aimed to reconstruct the tissue-tissue interfaces based on bilayer microspheres and form vascularized liver tissue. Firstly, we designed a tri-vascular liver-on-a-chip (TVLOC) comprising a hepatic artery, a portal vein and a central vein, and theoretically analyzed the distribution of velocity and concentration fields in the culture area. Secondly, we designed a bilayer microsphere generating microsystem based on the coaxial confocal principle, which is primarily used to produce bilayer microspheres containing different kinds of cells. Finally, the bilayer microspheres were co-cultured with endothelial cells in the cell culture area of the TVLOC to form vascularized liver tissue, and the cell viability and vascular network growth were analyzed. The results revealed that the TVLOC designed in this study can provide a substance concentration gradient similar to that of the liver microenvironment, and the bilayer microspheres can form a three-dimensional (3D) orderly liver structure with endothelial cells. Such a liver-on-a-chip is capable of maintaining the function of hepatocytes (HCs) pretty well. This work provides full insights into further simulation of the liver-on-a-chip.

Keywords: tissue-tissue interfaces, vascularized liver tissue, substance concentration gradient, bilayer microspheres, organ-on-chip

Introduction

Drug development is costly and typically suffers from colossal failure during clinical trials. This is partially due to the fact that animal models fail to precisely predict the efficacy and safety of drugs in humans (1, 2). Thus, recapitulating the complex tissue structures in vitro according to their native morphologies is significant to drug screening and toxicity testing. Advances in microengineering-related technologies have opened up brand-new possibilities for creating in vitro models, being capable of reconstructing more complex 3D in vitro models and incorporating vital dynamic mechanical and chemical factors (3). Especially, micro-fluidic perfusion systems have emerged as a promising platform for the culture of such engineered tissues offering the ability to properly supply nutrients, oxygen, and growth factors, accurately control the flow of fluid, as well as timely remove waste (4), and have been demonstrated to facilitate the remarkable advance of organs-on-chips (5–7). Among various organs, the liver, as the largest inner organ of the human body, exerts a central function in drug metabolism and detoxification, which is critical to survival and cannot be compromised. Drug-induced hepatotoxicity or drug-induced liver injury (DILI) is the primary reason for discontinuing clinical trials and for drug withdrawal from the market, thus highlighting a pressing need for developing in vitro liver tissue models for drug screening and evaluation (8). Most recently, with the accumulating popularity of organs-on-chips, liver-on-a-chip, particularly, is highly expected to become a promising alternative to animal models (9, 10).

Liver lobules, as the basic structural unit of the liver, are typically hexagonal and each of them is supplied with dual blood vessels. The hepatic artery provides blood rich in oxygen, and the hepatic portal vein provides blood rich in nutrients but low in oxygen. These two types of blood converge in the liver sinusoids between the hepatocytes (HCs) and then flow out from the central vein. The hyperoxia blood in the hepatic artery merges with the hypoxic blood in the hepatic portal vein and brings about oxygen concentration gradients, which promotes the formation of liver zonation (11). Liver zonation is characterized by regional changes in hepatocyte function along the direction of liver sinusoid. The heterogeneity of the liver region enables the liver to maintain stable blood glucose levels during carbohydrate metabolism, whether during eating or fasting (12). Due to differences in gene expression and microenvironment regions, the adverse effects of drugs or toxins (such as carbon tetrachloride) are region-specific in the liver. The concentration gradients of oxygen, hormones, glucose and extracellular matrix are regarded as the main regulators for the formation of heterogeneous regions (13). In a static culture system, liver cells are exposed to a homogeneous environment of oxygen and hormone, which cannot simulate the concentration gradient of substances in vivo. Accordingly, the realization of physiological substance concentration gradients in the microsystem is conducive to further elucidating the regulatory functions of the liver and the role of oxygen in normal pathophysiological processes. Currently, the liver-on-a-chip has made tremendous progress, and the structure and function thereof are getting closer to that of the liver in vivo. Many a researcher has been dedicated themselves to fabricating a robust liver-on-a-chip platform. Banaeiyan et al. (14) developed a lobule-like bioinspired liver-on-a-chip in vitro microdevice composed of two independent layers, with the bottom layer carrying the culture chambers and diffusion channels and the top layer possessing a dual seed-feed functionality. This tissue-like hexagonal architecture and diffusion channels recreated the convection-diffusion of blood circulation in the liver. Plus, 3D tissue-like structure and bile-canaliculi network formation were observed in the chips. In a study by Du et al. (15), an in vitro liver sinusoid chip was developed by integrating murine primary HCs, liver sinusoidal endothelial cells (LSECs), Kupffer cells (KCs), and hepatic stellate cells (HSCs) into two adjacent fluid channels separated by a porous permeable membrane, mimicking its key structures and configurations. Such a liver chip was then demonstrated to have enhanced secretion of albumin, production of HGF, and metabolic activity of CYP450 under the exposure of shear flow, replicating primary immune responses. In our previous study (16), we proposed a simple method involving a liver microsystem to produce a mimicked SBF in the absence of vascular networks and successfully cultured HCs on a large, high-density scale. Despite the fact that these in vitro microfluidic liver-on-a-chip can provide cells with a microenvironment similar to that in vivo, simulating the anatomy, physiology and function of the liver, the tissue-tissue interfaces recreated in the state-of-the-art liver-on-a-chip platforms are essentially the orderly co-culture of diverse cells based on scaffolds such as porous membranes, which is detrimental to later organ transplantation research. Besides, there is no liver-on-a-chip that can enable genuine vascular networks. As such, further studies on the liver-on-a-chip remain in dire need.

In this study, we designed a tri-vascular liver-on-a-chip (TVLOC) comprising a hepatic artery, a portal vein, and a central vein. HCs and HSCs are hierarchically wrapped in bilayer microspheres to form liver microstructure, and the microspheres were surrounded with endothelial cells to generate a vascular network. Such a design is aimed at reconstructing liver lobules in vitro and providing cells with a substance concentration gradient to imitate in vivo microenvironment. Firstly, the design of TVLOC was proposed and the distributions of culture medium flow field and nutrient concentrations (oxygen and glucose) thereof were analyzed theoretically. Secondly, a high-throughput bilayer microsphere generating microsystem based on the coaxial confocal principle was developed. Lastly, cell viability and vascular network growth were observed and analyzed by means of experiments. This work will not only help predict drug hepatotoxicity and drug metabolism, but also facilitate the study of tumor invasion mechanisms in vitro.

Materials and methods

Materials and Instruments

The animal study was reviewed and approved by the Hefei University Ethics Committee. Type I collagen was prepared from the tail tendons of rats. Sodium alginate, 75% alcohol, glacial acetic acid, sodium chloride, disodium hydrogen phosphate, sodium dihydrogen phosphate, potassium dihydrogen phosphate, anhydrous sodium carbonate, bovine serum albumin (BSA), 4% paraformaldehyde, Triton X-100, Tween 20 were all purchased from Sangon Biotech Co., Ltd. (Shanghai, China). Beakers, ophthalmic Scissors, and tweezers were all obtained from the school reagent library; primary antibodies and fluorescent-labeled secondary antibodies were purchased from Wuhan Sanying Biology Technology Co., Ltd. Inverted fluorescence microscopes (IX53; Olympus, Japan), CO2 incubators (Thermo 3131), and computer-controlled microfluidic pumps (Harvard Apparatus PUMP33; Harvard Instruments, USA) were employed in this study.

Design and Fabrication of the TVLOC

The core concept of the liver-on-a-chip designed in this study lies in the introduction of the tri-vascular system, which imitates the hepatic artery, hepatic portal vein and central vein, respectively (Figure 1A). This liver-on-a-chip microsystem was composed of three polymethyl methacrylate (PMMA) stamps engraved with microchannels. The dimensions of such PMMA stamps were as follows: both the length and width were 52 mm×45 mm; the thicknesses of the top stamp, the middle stamp and the bottom stamp were 4 mm, 3 mm and 2 mm, respectively (Figure 1B). This system was fabricated by Qingsheng Information Technology Co., Ltd., Hefei, China. The top stamp was engraved with six fluid microchannels, wherein in an attempt to ensure that the flow resistance of each microchannel was uniform, and as per Kirchhoff’s Laws of current and voltage, these microchannels were designed to possess identical lengths from the inlet to the outlet. In contrast, apart from six fluid microchannels with the same flow resistance, the middle stamp harbored a hexagonal cell culture area. Plus, each fluid microchannel on the upper stamp had a corresponding through hole on the middle stamp, allowing the fluid to enter the cell culture area (Figure 1B). In a bid to arrive at the real-time observation, the bottom layer should be made from a transparent and thin PMMA stamp. However, the PMMA stamp, if too thin (1mm), was readily subjected to deformation, and thus did harm to the liquid sealing; and if too thick, could result in the failure of real-time observations owing to the limited height of the microscope. As such, the thickness of the bottom stamp was selected to be 2 mm.

Figure 1 | Design and operation of the tri-vascular liver-on-a-chip (TVLOC). (A) Schematic of the in vivo hepatic lobule; (B) Schematic components of the TVLOC; (C) Schematic of the assembled TVLOC; (D) The physical picture of the assembled TVLOC; (E) Schematic of the TVLOC operation system; (F) Schematic of the cultivation at early stage in the culture area; (G) Schematic of the formation of vascularized liver tissue in the culture area.

The assembled TVLOC was schematically displayed in Figure 1C. During the operation, the venous medium from the inlet (IN-1) traveled through the six microchannels on the top stamp, and subsequently entered the hexagonal cell culture zone via the six through holes carved on the middle stamp, wherein the upper ends of such through holes were connected with the six output ends of the microchannels, and the lower ends thereof were connected to the cell culture zone. By contrast, the arterial medium from the inlet (IN-2) moved through the six microchannels on the middle stamp before entering the cell culture zone. Upon converging in the cell culture area, the arterial medium and the venous medium formed substance concentration gradients and ultimately flowed out from the outlet (OUT). The physical picture shown in Figure 1D represented the assembled TVLOC perfused with red and green liquids, which represented the arterial medium and the venous medium, respectively. It can be seen that the liquids of distinct colors moved in their respective microchannels, converged in the cell culture area, and eventually were drained in the central vein. After being assembled, this system was fixed with stainless steel screws.

Set-up of the TVLOC System

As depicted in Figure 1E, the experimental operation platform for the TVLOC microsystem primarily consisted of two microinjection pumps and the TVLOC. DMEM high-glucose complete medium (4.5 g/L glucose) contained in the syringe on pump 1 entered into the chip from IN-1 by virtue of the driving force of pump 1, moved through the six microchannels on the upper stamp followed by entering the hexagonal cell culture zone on the middle stamp via the corresponding through holes. In the meantime, DMEM low-glucose complete medium (1 g/L glucose) contained in the syringe on pump 2 flowed into the chip from IN-2 and then entered the hexagonal cell culture zone via the six microchannels on the middle stamp. After converging in the cell culture area, these two culture media flowed into the liquid collecting bottle through the outlet OUT, with both the pumps operated at a flow rate of 200 μL/h. During cell culture, 200 μL samples were taken from the liquid collecting bottle every 24 h to be used to analyze and detect the concentrations of metabolites therein. HCs, HSCs and LSECs (the three cell lines were obtained from ATCC) in the cell culture zone were orderly distributed in three dimensions (Figure 1F), with HCs embedded in the inner layer of the bilayer microspheres, HSCs located at the outer layer, and LSECs encapsulated in the collagen outside the microspheres. After being cultured for a period of time, LSECs would form a vascular network-like structure, thereby resulting in the formation of vascularized liver tissue (Figure 1G).

Set-up of Experimental Platform for the Microsphere-generating System

As illustrated in Figure 2A, the experimental operation platform for the microsphere-generating system was mainly composed of the core device for microsphere formation, silicone tubes and four microinjection pumps. Specifically, the syringe on pump 1 was loaded with the mixture of 0.5% collagen and HCs-laden 0.5% sodium alginate, the syringe on pump 2 contained HSCs (LX2 cells)-embedded 1% sodium alginate, and the syringes on pumps 3 and 4 were filled with oil for shearing liquid. During the operation, the flow velocities used by pumps 1 and 2 were both 2 mL/h, and those adopted by pumps 3 and 4 were identical and set to be in the range of 20-60 mL/h. Herein, the oil was referred to as the focusing phase, while the liquids flowing in the inner needle and the outer needle were named the inner phase and the outer phase, respectively. The focusing phase focused the inner and outer phases to be in a cone shape (Figures 2B, C), which can be controlled by the flow velocity of the focusing phase.

Figure 2 | Design and Fabrication of the Microspheres-generating Chip. (A) Schematic of the bilayer microspheres-generating system; (B) Schematic of focusing the inner and outer phases to be cone-shaped; (C) The physical picture of focusing the inner and outer phases to be cone-shaped; (D) The physical picture of the microspheres-generating chip; (E) The exploded view of the coaxial needle; (F) Image of the assembled coaxial needle.

The core device for microsphere formation (i.e. the microsphere-generating chip) was made in accordance with the principle of compound flow focusing, and primarily composed of a coaxial needle, three ordinary needles and a cross-shaped chip (Figure 2D). The coaxial needle consisted of an inner needle (inner diameter: 0.41 mm, outer diameter: 0.71 mm) and an outer needle (inner diameter: 1 mm, outer diameter: 1.48 mm) (Figure 2E). The inner needle was 0.2 mm longer than the outer needle, and these two needles were integrated through a perforated silicone plug (Figure 2F). To guarantee the coaxiality of the needles, a plurality of metal wires were welded on the outer wall of the inner needle. The cross-shaped chip, made from PMMA, was transparent and thus enabled the real-time observation of fluid flow under the microscope. With a dimension of 20 mm × 20 mm × 10 mm, the cross-shaped chip was provided with a round hole on each of its four sides. Such four holes possessed an initial diameter of 3 mm, became narrower (0.3 mm) as they extended towards the center of the chip, and then formed a criss-cross connected with each other there, wherein the peripheral round hole was used to fix the needle, and the central criss-cross part was where the microspheres were produced by shearing (Qingsheng Information Technology Co., Ltd., Hefei, China).

Preparation of Rat Tail Collagen

Collagen used herein was derived from rat tails. The detailed preparation procedures were as follows (17): rat tails were soaked in 75% alcohol for disinfection before their skin was ripped off. Tail tendons were then pulled out after the tailbones were broken in sections. Subsequently, the thus-obtained tail tendons were sterilized and cut into pieces, followed by being soaked in the sterile 0.1% acetic acid and placed at 4 °C for about 7 days until fully dissolved. After being subject to lyophilization, 10 mg/mL collagen was formulated with 0.1% glacial acetic acid and stored at 4 °C for later use.

Loading of Cells into the TVLOC Microsystem

Liver sinusoidal endothelial cells (LSECs) at a final concentration 2×10⁴ cells/mL were firstly mixed with the prepared extracellular matrix (ECM) consisting of the neutralized collagen (final concentration: ~3 mg/mL), bovine fibrinogen (final concentration: ~2.5 mg/mL) and thrombin (final concentration: ~1.5 U/mL) (Of note, this step should be conducted on ice). Following that, the LSECs-laden ECM solution was mixed with the bilayer microspheres (total number: roughly 1×10³) at a volume ratio of 1:1, and a 50 μL mixture was then pipetted to the cell culture area on the TVLOC. Finally, the TVLOC was transferred to an incubator for 30 min. After the collagen was completely solidified, the TVLOC was connected with the micropump and placed in the incubator for culture (see Figures 1F, G). It is worth noting that HCs, HSCs and LSECs were all cultured in DMEM medium (Gibco) supplemented with 10% fetal bovine serum (Lonsera) and 1% penicillin-streptomycin (Gibco) at 37°C and 5% CO₂ before being used within passage 4-12.

Immunofluorescence Staining

For immunofluorescence assay, cells were fixed with 4% paraformaldehyde for 20min and washed with PBST three times. After being permeabilized in 0.1% Triton X-100 at room temperature (RT) for 7 min and washed with PBST three times, cells were blocked with 2% BSA in PBS at RT for 2h, followed by incubation with the primary antibody at RT for 2 h or at 4 °C overnight and then with the secondary antibody in the dark at RT for 40 min. Ultimately, cells were washed with PBST three times once again and then observed under an inverted fluorescence microscope (Olympus IX73).

Cell Viability Assay

To assess the viability of cells during the culture period, a standard Live/Dead assay kit (Invitrogen, USA) containing Hoechst 33342 and propidium iodide (PI) (18, 19) was employed pursuant to the product instruction. Briefly, cells were stained with 10 μg/mL Hoechst 33342 and 10 μg/mL PI for ~30 min at 37°C. The individual blue cells (viable) and red cells (dead) were counted by Image J.

Assessment of Albumin and Urea Production

The concentrations of urea and albumin in the samples collected from the liquid collecting bottle were analyzed using clinical biochemistry methods. To be specific, albumin secretion and urea production were measured by the Beckman Coulter AU480 biochemistry analyzer. The rates of change in the absorbance at 600 nm and 340 nm, which are proportional to the albumin and urea concentration, respectively, were recorded. In addition, to recheck the accuracy of the measured data, urea and albumin were further detected for their concentrations at 450 nm by ELISA.

Statistical Analysis

Data were expressed as the means ± standard deviation. P-values were analyzed using the two-tailed unpaired Student’s t test. The significance level was indicated as * for P<0.05, ** for P<0.01, and N.S. for no significance.

Modeling

In our TVLOC, the two inlets simulated the hepatic artery and portal vein respectively, and the outlet mimicked the central vein. Considering that the convergence of the hepatic artery and portal vein would result in concentration gradients of nutrients, to further figure out such gradients, a model was established using COMSOL Multiphysics software (Burlington, MA, USA) based on the following hypothesises: (1) the effect of cell metabolites on the flow velocity is negligible; (2) The flow in all areas conforms to Newton’s theory; (3) the properties of the medium (such as density and viscosity) are the same as those of water at 37°C; and (4) the effect of cells on the porosity of ECM is negligible.

Geometric Model

The geometric model established herein is a three-dimensional one, the top view of which was exhibited in Figure 3A. This model was divided into three parts: the fluid area on the top stamp, the fluid area and the cell culture area on the middle stamp, wherein both of the fluid areas were utilized to observe whether the fluids from the inlets could be evenly distributed to the corresponding output ends, and then to the cell culture area. Additionally, the fluid combination is partly aimed at observing the concentration diffusion of oxygen and glucose in the cell culture area.

Figure 3 | Simulation of flow and concentration distribution profiles in the TVLOC. (A) The top view of the geometric model; (B) Flow distribution in the TVLOC; (C) The distribution of oxygen concentration in the TVLOC; (D) The distribution of glucose concentration in the TVLOC.

Fluid Equation

The fluids in the TVLOC adopted the mode of laminar flow, and thus were described by the Naiver-Stokes equation (14, 20):

Here, ρis the fluid density (kg/m3), μis the fluid viscosity (N s/m2), uis the fluid velocity vector (m/s), p is the pressure, ∇ is the del-operator, and superscript T denotes the matrix transpose.

Concentration Equations

In terms of substance concentrations, herein, the oxygen and glucose concentrations were simulated respectively. Specifically, at the inlet of the hepatic artery, the oxygen partial pressure was set to 90 mmHg, and the concentration of glucose was set to 1.0 g/L, while at the inlet of the portal vein, the oxygen partial pressure was set to 30 mmHg, and the concentration of glucose was set to 4.5 g/L. Additionally, the diffusion coefficient of oxygen in water at 37 °C was 2.92×10⁻⁹ m2s⁻¹ (21), and the diffusion coefficient of glucose was 9×10⁻¹⁰ m2s⁻¹ (14). Diffusion was described by the following standard stationary convection-diffusion equations:

for oxygen,

for glucose,

Here, the subscripts o and g represent oxygen and glucose, respectively, crepresents the oxygen concentration (mol/m³), Dis the diffusion coefficient (m²/s), Vrepresents the oxygen consumption rate (mol/1/s), and u is the flow velocity (m/s). In the above two equations, the first and second items on the left represent the oxygen transport caused by convection and diffusion respectively. The oxygen partial pressure Pis routinely indirectly replaced by oxygen concentration (22, 23). Besides, the relationship between oxygen concentration and oxygen partial pressure can be depicted by Henry’s law (24): c = αP (here αis the oxygen dissolution coefficient).

Boundary Conditions

The velocity and pressure boundary conditions adopted in the simulation were as follows: at the inlets of the TVLOC, a uniform velocity was utilized, and the perfusion flow rate of the culture medium was given; at the outlet, the pressure was set to 0 Pa, and the concentration condition, ∂c/∂n = 0 was adopted.

Numerical Simulation

In this study, as mentioned above, COMSOL Multiphysics software was employed to conduct simulation. Firstly, to render the calculation more realistic, a three-dimensional geometric model, just as shown in Figure 3A, was established in the software. Secondly, relevant parameters and boundary conditions were set. Thirdly, the meshes were divided, and finer meshes were used herein. Fourthly, numerical simulations were performed to obtain the fluid distribution in the TVLOC and the distribution of oxygen and glucose concentrations in the mixed region. Of note, in the calculation process, to quickly converge, the velocity field was calculated before the concentration field was assessed.

Results and discussion

On-chip Flow Distribution

The design of the fluid channels was in accordance with Kirchhoff’s Laws of current and voltage. To ensure identical fluid resistance, the six fluid microchannels on the upper stamp and those on the middle stamp must be kept consistent in length as far as possible. With the fluidic behavior of laminar flow adopted in the numerical simulation, the fluid distribution on the TVLOC was demonstrated in Figure 3B. When the arterial and venous inlet velocities were both 200 μL/h, the flow velocities at the six microchannel output ends on the upper stamp are 2.1705×10⁻⁴, 2.1716×10⁻⁴, 2.1780×10⁻⁴, 2.1751×10⁻⁴, 2.1734×10⁻⁴, and 2.1764×10⁻⁴ m/s, respectively, and the flow velocities at the six microchannel output ends on the middle stamp are 2.1727×10⁻⁴, 2.1796×10⁻⁴, 2.1743×10⁻⁴, 2.1764×10⁻⁴, 2.1749×10⁻⁴, and 2.1799×10⁻⁴ m/s, respectively. Consequently, on account of the relatively uniform flow velocity and close flow resistance in the twelve microchannels, the culture medium therein would be less inclined to flow out from just one or several channels, thereby ensuring the flow of the medium into the hexagonal cell culture area through each of these microchannel output ends around it. It can be explicitly seen that the cell culture area got a flow velocity of 0.5-5×10⁻⁵ m/s and thus produced a relatively low flow shear force. As such, cells cultured in this area would not suffer from the damage of shear force and could be supplied with adequate nutrients.

Noticeably, the above simulation was based on the initial cell density. When just loaded into the culture area, the cells were present at a low level, relatively dispersed, and failed to form cell clusters or vascular networks; therefore, the flow distribution in the culture area was relatively uniform. As the culture time went by, HCs would gradually form cell clusters, LSECs would link with each other to form a vascular network, and changes would necessarily occur to the flow distribution. Given that the in-depth analysis of the changes of fluids with culture time needs the establishment of extremely complicated models, in this work, merely a simple model was created to observe the initial flow changes in the culture area.

On-chip Oxygen/glucose Concentration Profile

To better understand the distribution of substance concentration gradients in the cell culture area, COMSOL simulation software was used herein to mimic the distribution of oxygen and glucose concentration gradients. Now that the hepatic artery is rich in blood oxygen, the arterial medium was set to be a high-oxygen low-glucose one (oxygen level: 90 mmHg, glucose concentration: 1 g/L). By contrast, the hepatic vein is full of blood rich in nutrients from the digestive tract, spleen, among others; therefore, the venous medium was set to be a low-oxygen high-glucose one (oxygen level: 30 mmHg, glucose concentration: 4.5 g/L). Here, with the flow velocities at the inlets of the hepatic artery and portal vein set to be 200 μL/h, the changes of substance concentrations in the cell culture area over time were observed (Figures 3C, D). From the results, it can be clearly seen that remarkable concentration gradient distribution in the cell culture area occurred to both the oxygen and glucose. Specifically, the initial concentrations of oxygen and glucose were at low levels without the formation of concentration gradients. After 100 s, both of them developed a stable concentration gradient.

Accordingly, this TVLOC was capable of providing a 3D cell growth condition imitating the liver microenvironment, and thus could be used for the subsequent study on the functional mechanism of liver zonation. It is worth mentioning that, in the practical experiments below, the effect of oxygen concentration gradients on hepatocyte function was not investigated due to no dissolved oxygen controlling system available. In our future work, an oxygen concentration regulating chip will be designed to precisely control the dissolved oxygen concentration of the arteriovenous culture medium so as to further mimic the microenvironment in vivo.

Effect of Preparation Speed on the Size of Bilayer Microspheres

The morphology and size distribution of bilayer microspheres were closely associated with the fluid velocity adopted during the preparation. In this respect, Figures 4A, B indicated the relationship between the size of the bilayer microspheres and the flow rate of the focusing oil phase. In the case that the inner and outer flow velocities in the coaxial needle were set to be 2 mL/h, the size of microspheres gradually decreased with the increase of the flow rate of the oil phase. Here, for the convenience of observation, the inner and outer sheath fluids in the coaxial needle were added with green fluorescent particles and sodium alginate, respectively. As illustrated in Figure 4A, the green fluorescent particles were well wrapped in the microspheres without leakage, and as the flow rate of the oil phase increased, these microspheres tended to get smaller and possess better uniformity. The specific changes obtained through measurement and statistical analysis were as follows (Figure 4B): when the inner and outer flow velocities in the coaxial needle were both 5 mL/h, and the flow rate of the oil phase was 20 mL/h, the diameter of these microspheres was approximately 487 μm. When the flow rate of the oil phase rose to 60 mL/h, the microspheres were reduced roughly by half in size, with the diameter being only 270 μm.

Figure 4 | Preparation of Bilayer Microspheres. (A) Morphology diagram of the size of the microspheres as a function of the oil flow rate; (B) Statistical diagram of the size of the microspheres as a function of the oil flow rate; (C) Morphology images of the bilayer microspheres.

It should be readily understood that the size of the microspheres could also be adjusted by regulating the inner and outer flow velocities in the coaxial needle. Likewise, the liquid viscosity also posed impacts on the size of the microspheres, which can be modulated as required. In addition to the aforementioned control on the size of such microspheres, the shell thickness of microspheres could be regulated by adjusting the velocity ratio of the inner and outer sheath fluids. Since the size of the microspheres affects the transport of oxygen and nutrients, this work merely took into account the relationship between the size of microspheres and the flow velocity of the oil phase. In an effort to ensure the survival and proliferation of cells in microspheres, the bilayer microspheres prepared herein harbored a diameter of about 270 μm. In the follow-up work, the outer shell thickness of the microspheres will be further optimized to make the shell as thin as possible, which is more conducive to the simulation of space of Disse. Plus, the bilayer microspheres prepared in this paper are not round enough, which is likely owing to the different viscosities of the inner and outer sheath fluids. In future work, to further beef up the quality and yield of such microspheres, adjusting the viscosities of the inner and outer sheath fluids, and submerging the outlet into the CaCl₂ solution, among others, should be given priority.

Cell Distribution in the Bilayer Microspheres

To observe the distribution of HCs and HSCs in the bilayer microspheres, HCs and HSCs were pre-stained with DiI (Orange) and DIO (green) prior to the formation of the microspheres. As demonstrated in Figure 4C, HCs (HepG2 cells) were colored green and located at the inner layer of the microspheres, while HSCs (LX2 cells) were colored and embedded in the outer layer of the microspheres. Of note, in the inner sheath fluid, HepG2 cells were suspended in a mixture of collagen and sodium alginate (0.5 mg/mL for collagen and 0.5 mg/mL for sodium alginate) at a final density of 6 × 10⁶ cells/mL. In the outer sheath fluid, LX2 cells were suspended in 1% sodium alginate solution at a final density of 5 × 10⁵ cells/mL. Such a cell density ratio of roughly 10:1 was set based on the actual ratio of HCs to HSCs in vivo.

Off-chip Culture of the Bilayer Microspheres

To better observe the changes of cell growth in the bilayer microspheres, herein, the bilayer microspheres were firstly cultured in a Petri dish, and the morphological changes of the cells were observed every day. Results from Figure 5A showcased that initially in the bilayer microspheres, most of the cells were singly distributed. With the extension of culture time, the volume of cell clusters gradually increased, and the cells had formed a compact tissue structure by Day 10, with the size of microstructure reaching 120 μm. Using Hochest/PI staining, almost all the cells in the microspheres were shown to be alive. Notably, considering that the diffusion distance of oxygen and nutrients is less than 200 μm, the bilayer microspheres possessing an average diameter of roughly 270 μm were employed in this experiment. These small microspheres could allow for sufficient material transport, thereby enabling the survival and proliferation of cells encapsulated in the microspheres.

Figure 5 | Off-chip and on-chip culture of the bilayer microspheres. (A) Off-chip cell growth in the microspheres as a function of time; (B) Brightfield image of the vascularized live microtissue cultured on chip; (C) Fluorescence image of the vascularized live microtissue cultured on chip.

On-chip Co-culture of Cells

After being generated and verified to be feasible as mentioned above, the bilayer microspheres were mixed in equal volume with the prepared ECM containing LSECs (the final concentrations of type I rat tail collagen and bovine fibrinogen were roughly 1.5 mg/mL and 1.25 mg/mL, respectively, and the cell density of LSECs was about 2×10⁴ cells/mL) and then loaded into the cell culture area of the TVLOC for solidification, followed by long-term culture. The formation of vascularized liver microstructure was depicted in Figure 5B. It can be observed that in the first four days of the co-culture, LSECs gradually elongated and linked with each other to form the preliminary structure of vascular network. On Day 6, a distinct vascular network was ultimately formed. The bilayer microspheres were wrapped with the vascular network, thereby resembling the liver microtissues in vivo to some extent.

To further demonstrate the vascularization of the liver microtissue, LSECs were labeled with CD31, and HCs in the microspheres were pre-stained with DiI in advance. As validated by the immunofluorescence staining shown in Figure 5C, a vascular network was resulted from the connection of LSECs with each other, and the microspheres were then surrounded by the vascular network, forming a vascularized liver tissue. Additionally, the analysis of cell survival through staining with Hochest/PI revealed that nearly all the cells in the cell culture area were alive, indicating the superb growth and proliferation ability of the vascularized liver microtissue in the TVLOC. Here, it’s worth noting that, DiI instead of specific antibodies was adopted to pre-stain HCs due to the poor specificity of the antibodies available; however, over a long period of cultivation, the fluorescence of DiI was gradually quenched, resulting in less distribution of liver microtissue observed under the microscope field than in the actual situation. To address such an issue, in the follow-up work, preparing stably transfected cell lines with fluorescence should be taken into consideration.

On-chip Analysis of Synthetic and Metabolic Functions of HCs

To further assess the performance of our TVLOC, the changes of liver-specific metabolite concentrations over time were studied herein. As is well-known, albumin is one of the liver-specific biomarkers, and urea is a primary measure of hepatocyte functionality. Therefore, in the present study, albumin synthesis and urea production were detected to analyze the viability of HCs in the TVLOC. During the long-term (8-day) co-culture of HCs, HSCs and LSECs, as depicted in Figures 6A, B, both the concentrations of the albumin and urea increased with time, suggesting that in this TVLOC, these cells could grow and proliferate stably, and the specific functions of HCs could be strongly maintained for a long time.

Figure 6 | Liver-specific metabolite concentrations as a function of time. (A) The change of albumin concentration in the medium over time; (B) The change of urea concentration in the medium over time; (C) The effect of glucose concentration gradients on the albumin synthesis of HCs; (D) The effect of glucose concentration gradients on the urea metabolism of HCs. **p < 0.01.

It is remarkable, however, that all the cells used in this work were cell lines, primary cells should be considered to be used for verifying the effectiveness of this system in future research. In this connection, how to efficiently separate all the four types of cells from the primary cells remains elusive, and hence the experimental conditions such as the antibody concentration in flow sorting need to be explored. Despite this, the excellent performance of the TVLOC could be highly expected. Furthermore, by way of example, the performance of this TVLOC could be further improved by adjusting the concentrations of collagen and sodium alginate to control the stiffness of the ECM and providing the culture medium with hormone gradients.

Effect of Glucose Concentration Gradients on Hepatocyte Function

In light of the fact that glucose metabolism is one of the vital functions of the liver, and the external glucose concentration inevitably poses a certain impact on the function of HCs, this study adopted the TVLOC to assess the effect of different glucose concentrations on HCs. To be specific, the changes of liver function over time under three different sugar concentrations, namely high sugar, low sugar and high-low sugar, were investigated. From Figures 6C, D, it can be seen that, after 4 days of culture, no significant difference occurred to albumin concentration in the cases of the three sugar concentrations, which was most likely owing to the non-interference between glucose metabolism and protein synthesis. That is, the external glucose concentration failed to affect the albumin synthesis. By contrast, the urea nitrogen synthesis capacity of HCs under the condition of glucose concentration gradients (i.e. the high-low sugar concentration) was enormously higher than that of HCs under the high or low glucose condition. We speculate that since HCs in different regions exert different liver functions due to the existence of liver zonation along the hepatic sinusoid, the presence of glucose concentration gradients here might stimulate the generation of corresponding hepatocyte zonation and result in a microenvironment closer to in vivo one, thereby improving the corresponding urea synthesis function of HCs.

Conclusions

Currently, the rapid development of organ-on-chips has shed light on human organ reproduction (25, 26). Such in vitro models pose a subversive impact on conventional 2D cell culture, and play central roles as supplements or alternatives to animal models in drug screening and toxicity testing.

In the current study, we proposed a tri-vascular liver-on-a-chip (TVLOC) reconstructing the tissue-tissue interfaces based on bilayer microspheres. This TVLOC primarily contains a hepatic artery fluid supply channel, a portal vein fluid supply channel and a hepatic lobular hexagonal cell culture area. With HCs located at the inner layer and HSCs cells embedded in the outer layer, HCs and HSCs were wrapped in the bilayer microspheres at a cell density ratio of 10:1 (based on the actual ratio in vivo). In this way, the tissue-tissue interfaces were reconstructed by virtue of the bilayer microspheres, and the layer of hydrogel where HSCs are encapsulated simulated the space of Disse. In addition, LSECs formed a vascular network surrounding the bilayer microspheres, thereby creating a vascularized liver microtissue in the cell culture area. To analyze the performance of this TVLOC, firstly, a theoretical model was established to observe the fluid distribution and the oxygen/glucose concentration distribution in the TVLOC respectively. Secondly, the relationship between the size of microspheres and the flow rate of the oil phase was analyzed to select microspheres of appropriate size. As the oxygen diffusion distance does not exceed 200 μm, the size of the microspheres to be used shall not exceed 400 μm. Lastly, to verify the performance of the TVLOC, the concentrations of metabolites (albumin and urea) in the culture medium were detected. It was found that the concentrations of these two substances increased with time, indicating that this chip can well maintain the specific function of hepatocytes. Collectively, the TVLOC designed in this work provides insights into further development of the liver-on-a-chip and holds great potential in drug screening and evaluation. Besides, the TVLOC, as a vascularized liver chip, can be used to research the progress of tumor metastasis because the tumor metastasis typically occurs through blood vessels.

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 animal study was reviewed and approved by Hefei University Ethics Committee.

Author Contributions

Conceptualization, JL and HL; methodology, JL and MZ; software, CF; formal analysis, HL; investigation, HL; resources, FS; data curation, JL and CF; writing—original draft preparation, JL; writing—review and editing, HL; supervision, FS; project administration, CF. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Natural Science Foundation of Anhui Province (grant number 2008085QC116), the University Natural Sciences Research Project of Anhui Province (grant number KJ2019A0824), the Talent Research Foundation of Hefei University 20RC41 and Taishan Young Scholars Program of Shandong Province (grant number tsqn20161049).

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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.

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ORIGINAL RESEARCH
published: 21 October 2022
doi: 10.3389/fonc.2022.1021775

Construction and validation of a prognostic model of pyroptosis related genes in hepatocellular carcinoma

Guanqun Li ¹†, Dongxin Zhang ¹†, Chaowei Liang ², Chaojie Liang ²* and Jixiang Wu ¹*

¹ Department of General Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China, ² Department of Hepatobiliary and Pancreatic Surgery, First Hospital of Shanxi Medical University, Taiyuan, China

Edited by:
Zongli Zhang, Qilu Hospital of Shandong University, China

Reviewed by:
Shaocheng Lyu, Beijing Chaoyang Hospital, Capital Medical University, China
Xiangyu Wang, Chinese Academy of Medical Sciences and Peking Union Medical College, China

*Correspondence:
Chaojie Liang
liangchaojie8@126.com
Jixiang Wu
trwujixiang2016@126.com

†These authors share first authorship

Specialty section:
This article was submitted to Cancer Molecular Targets and Therapeutics, a section of the journal Frontiers in Oncology

Received: 17 August 2022

Accepted: 03 October 2022

Published: 21 October 2022

Citation:
Li G, Zhang D, Liang C, Liang C and Wu J (2022) Construction and validation of a prognostic model of pyroptosis related genes in hepatocellular carcinoma. Front. Oncol. 12:1021775. doi: 10.3389/fonc.2022.1021775

Pyroptosis plays an important role in the occurrence and development of cancer. We are interested in determining the prognostic value of pyroptosis-related genes in hepatocellular carcinoma (HCC). In this study, we searched the original transcriptome data of The Cancer Genome Atlas (TCGA) and identified the related expressed genes by co-expression analysis. Differentially expressed genes were identified by using univariate analysis, the least absolute shrinkage and selection operator (LASSO) and multivariate analysis to screen for genes related to prognosis of HCC. Ultimately, we established a prognostic model for five genes, namely GSDME, DHX9, TREM2, SQSTM1 and GLMN. Survival analysis showed that the overall survival rate of HCC patients with high risk score was significantly lower than that of HCC patients with low risk score, and this signal could be used as an independent prognostic indicator of HCC. Receiver operating characteristic curve analysis confirmed the accuracy of this prognostic signal, and was further verified in a Gene Expression Omnibus (GEO) dataset (GSE14520) and the International Cancer Genome Consortium (ICGC) databases. In addition, nomograms based on the five identified prognostic genes were established and verified internally in TCGA cohort. Additionally, we also analyzed the gene mutations of the model genes and the correlation between immune cells of the model genes. In summary, this study identified for the first time a 5-gene prognostic signature associated with pyroptosis, which can be used as a promising prognostic biomarker and provide some potentially useful therapeutic targets for HCC.

Keywords: hepatocellular carcinoma, pyroptosis, prognostic signature, nomogram, The Cancer Genome Atlas (TCGA)

Introduction

HCC is a common malignant tumor of the digestive system. According to global statistics, in 2020 the incidence of HCC ranks sixth among malignant tumors, and its mortality rate ranks third among cancer-related deaths. HCC is common in men, and in some areas, the morbidity and mortality rates among men are 2-3 times higher than that among women. Among men, the incidence and mortality rates of HCC rank fifth and second, respectively, among all malignant tumors (1). The onset of HCC is hidden, and most of the patients with HCC are in the middle and late stage when the disease is discovered and lose the opportunity of radical surgical resection (2, 3). Despite the continuous development of medicine in recent years, which have led to improvements in the diagnosis and treatment of HCC in recent years, the overall quality of life of patients with HCC remains unsatisfactory, and the intrahepatic recurrence rate of HCC after surgical resection is still very high, and the prognosis is poor. Therefore, it is urgent to find an effective prognostic model (4).

Pyroptosis is a kind of inflammatory programmed cell death, which was first discovered in 1986 by Friedlander et al. (5), who found that the lethal toxin from Bacillus anthracis can cause the death of mouse macrophages. However, this mode of death has long been mistaken for apoptosis. The concept of inflammasome separates pyroptosis cell death from apoptotic cell death (6). The typical morphological features of pyroptosis are cell swelling, cell membrane perforation, cell lysis and content release (7). To date, mainly two types of pyroptosis have been reported, namely GSDMD- and GSDME-dependent pyroptosis. GSDMD-dependent pyroptosis primarily occurs in immune cells, through the formation of inflammasomes to activate immune caspase-1 or lipopolysaccharide (LPS) to directly activate immune caspas-4/5/11, and then cleave the GSDMD protein, resulting in pyroptosis (8). GSDME-dependent pyroptosis is triggered by apoptosis-inducing factors, DNA-damaging chemotherapeutic drugs, etc., which activate caspase-8 or caspase-9, and then activate caspase-3, to further cleave the GSDME protein, release its active N-terminal, and ultimately cause pyroptosis (9).

The mechanism of pyroptosis in hepatocellular carcinoma (HCC) has been extensively studied. Chu et al. (10) found that pyroptosis was inhibited in HCC tissues and cells. Zhang et al. (11) found that the apoptosis inducing factor (AIF) can inhibit the growth and metastasis of HCC cells by inducing NLRP3 inflammasome-mediated pyroptosis. Wei et al. (12) found that the 17 β-estradiol (E2)-induced activation of NLRP3 inflammasomes triggers pyroptotic cell death and inhibits protective autophagy, which play inhibitory roles in the progression of HCC. Zhang et al. (13) found that GSDME-dependent pyroptosis can be induced by mitroxone and lead to pyroptotic hepatocyte death. However, it is not known whether pyroptosis-related genes are related to the prognosis of patients with HCC.

In this study, the RNA-seq data and corresponding clinical information of patients with HCC were obtained from TCGA database, and the differentially expressed genes (DEGs) in tumor tissues and normal liver tissues were screened. Then, the survival-related genes were screened and a prognostic model was established to predict the clinical outcome of HCC patients. Additionally, we performed external validation in the Gene Expression Omnibus (GEO) dataset. The flowchart of this study is summarized in Figure 1.

Figure 1 | The flowchart of this study.

Materials and methods

Data acquisition

Transcriptome data and clinical information were obtained from TCGA HCC dataset (https://portal.gdc.cancer.gov/). The external validation dataset was obtained from the GEO (GSE14520) database (https://www.ncbi.nlm.nih.gov/geo/) and the LIRI-JP HCC cohort of the International Cancer Genome Consortium (ICGC) database. Raw count data was first normalized using the transcripts per million (TPM) method and log2 transformed. 19,654 protein-coding genes were then annotated. Pyroptosis-related genes were obtained from the human genetic database (https://www.genecards.org/). Ultimately, 120 genes associated with pyroptosis were included in the analysis and are listed in Supplementary Material 1.

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment

The data from the Gene Ontology (GO) enrichment analysis of blepharoptosis-related DEGs, including biological process (BP), cellular component (CC) and molecular function (MF), and the enrichment of the Kyoto Encyclopedia of Genes and Genomes (KEGG)pathways and gene sets were analyzed with the R software package “clusterprofiler”. The filter conditions were pvalueCutoff=0.05, qvalueCutoff=0.05.

PPI network

The protein-protein interaction (PPI) network of DEGs was constructed using the STRING database and visualized using the Cytoscape software. We used the molecular complex detection (MCODE) algorithm of the Cytoscape plugin to detect the important modules in the PPI network, and analyzed it by GO and KEGG enrichment analysis to further investigate its molecular function in HCC. MCODE plug-in is to find out the key sub-networks and genes in the huge network according to the relationship between edges and nodes, so as to facilitate downstream analysis. The parameters are set to degree cutoff =2, node score cutoff = 0.2, k-core =2, and max. depth =100

Establishment and verification of prognostic model

First, the pyroptosis-related DEGs in HCC tissues were identified by using the “limma” package in R software. The critical value of false discovery rate (FDR) was 0.05 and the critical value of log fold change (logFC) was 0.5. Then univariate analysis was performed with “survival” package to screen pyroptosis-related genes significantly associated with overall survival (OS) in TCGA HCC database. The least absolute shrinkage and selection operator (LASSO) regression analysis was performed using the “glmnet” software package, and significantly related genes were screened. The optimal value for the penalization coefficient λ was determined by running 1,000 cross-validation likelihood degrees. This method can avoid overfitting of signatures. Then the prognostic model was constructed using multivariate analysis. The risk score for each patient was calculated using the following formula: risk score =esum (each gene’s expression×corresponding coefficient). The median risk score of patients was divided into high risk group and low risk group.

We used the Kaplan-Meier method to evaluate the difference of survival time between the high-risk and low-risk groups, to assess the usefulness of the risk score in predicting the clinical prognosis of HCC patients. Additionally, we used the “SurvivalROC” package to plot the receiver operating characteristic (ROC) curve, compare the predictive ability of the risk score and other clinical features, and evaluate its sensitivity and specificity by calculating the area under the curve (AUC) value. For external validation, we downloaded the GSE14520 dataset from the GEO database. After processing the GSE14520 data set in R, the gene expression data of 225 patients with HCC and the survival information of 209 patients were obtained. The risk score of each included patient was calculated using the same prognostic model based on the identified gene signature. Second, the ROC curve and Kaplan-Meier curve were used to test the predictive value of the prognostic gene markers. The same verification method was used for verification in the ICGC dataset. In order to determine whether the pyroptosis-related risk index in TCGA dataset of patients with HCC can be used as an independent predictor of OS, we used univariate and multivariate Cox regression analysis, with the risk score, age, sex, tumor subtype, pathological stage and histological grade as covariates. In addition, we also used the online database cBioPortal to detect gene copy number changes and mutations in the prognostic model. The protein expression of the genes in the predictive gene signatures was studied using the human protein map (http://www.proteinatlas.org) online database.

The construction of nomogram

The nomogram and related calibration curve were constructed according to the age, sex, stage, grade, T, N, M staging and risk score, based on TCGA cohort. Finally, the ROC curve of the nomogram changing with time was plotted, and the AUC value was calculated. It lays a foundation for further clinical application.

Analysis of the correlation between the risk score model and immune cell infiltration

To study the relationship between the prognostic model and immune infiltration, we used the tumor immune assessment resource (TIMER), which can help users estimate the composition of six tumor infiltrating immune cell subsets (B cells, CD4+T cells, CD8+T cells, macrophages, neutrophils and dendritic cells). We estimated the level of immune cell infiltration in patients with HCC and evaluated the correlation between these tumor-infiltrating immune cells and the prognostic model in R.

Statistical analysis

Data standardization and further analysis were carried out in R software (V4.0.2).To screen the differentially expressed pyroptosis related genes in HCC tumors and adjacent normal tissues, we used Wilcoxon rank sum (Mann-Whitney) test. Univariate and multivariate Cox regression analyses were performed to determine the independent predictors of OS and the association of risk scores with clinical information and prognosis. Kaplan-meier method was used to compare the differences in OS between high risk group and low risk group, and log-rank sum test was used to calculate P values. T test was used to compare the differences in risk scores between different groups of clinical characteristics. P <0.05 was statistically significant if not explicitly mentioned.

Result

Identification and enrichment analysis of DEGs

Transcriptome sequencing data and the corresponding clinicopathological data of 374 HCC tissue samples and 50 normal liver tissue samples were retrieved from TCGA database. Four of the 374 patients were removed from the study due to lack of OS information. Therefore, ultimately, the mRNA expression and survival of 370 HCC patients were analyzed. After sorting out and excluding information on sex, age, histological grade, and pathological staging, and TNM staging, 235 patients with HCC were eventually used for subsequent analysis. The results showed that among these 120 pyroptosis-related genes there were 42 DEGs in HCC tissues (Figure 2A), of which 31 and 11 genes were upregulated and downregulated, respectively (Figure 2B).

Figure 2 | Identification of differential expressed genes (DEGs) in HCC and normal tissues. (A) Heatmap of 42 DEGs in TCGA. Red upregulation; Green downregulation. The abscissa represents the type, N normal; T Tumor; ordinate represents the gene. (B) Volcano plots of the distributions of 42 DEGs. The abscissa represents logFC and the ordinate represents -log10 (FDR).

Through GO enrichment analysis of the DEGs, it was found that these genes were mainly enriched in pyroptosis and positive regulation of cytokine production (Figure 3A). The KEGG enrichment analysis showed that these differential genes were mainly enriched in the NOD-like receptor signaling pathway and Toll−like receptor signaling pathway (Figure 3B).

Figure 3 | DEG enrichment analysis. (A) The results of the GO enrichment in TCGA cohort. “BP” stands for “biological process”, “CC” stands for “cellular component” and “MF” stands for “molecular function”. The abscissa represents the gene ratio. (B) The results of the KEGG enrichment in TCGA cohort. The abscissa represents the gene ratio.

PPI network analysis

We use the STRING database and Cytoscape software to further study the role of the identified DEGs in HCC development and progression. A PPI network consisting of 32 nodes and 99 edges was constructed (Figure 4A). The MCODE algorithm in the Cytoscape plugin was used to identify the key module in the PPI network. This key module includes 4 upregulated genes and 4 downregulated genes (Figure 4B). The GO and KEGG analysis showed that the module was mainly enriched in the positive regulation of cytokine production and the NOD-like receptor signaling pathway, respectively (Supplementary Materials 2, 3).

Figure 4 | PPI network analysis and analysis of modules. (A) PPI network for DEGs. (B) Key module in the PPI network. Red: upregulation, Green: downregulation.

Construction and verification of pyroptosis-related prognostic genes

Univariate Cox regression analysis identified 11 DEGs that had a significant influence on HCC prognosis (Figure 5A). LASSO regression analysis revealed that 9 candidate genes reduced the signature overfitting, namely GSDME, GSDMC, DHX9, TREM2, SQSTM1, MKI67, GLMN, ANXA2 and IL1RN (Figures 5B, C). Ultimately, a prognostic model containing five genes was determined by multivariate Cox regression analysis (Table 1). The risk score was calculated as follows: risk score = (0.371*GSDME expression) + (0.431* DHX9 expression) + (0.287* TREM2 expression) + (0.292* SQSTM1 expression) + (0.470* GLMN expression). The risk score of each HCC patient was calculated according to the expression level of these 5 genes, and the patients were divided into a high risk group and a low risk group according to the median risk score. A heat map was used to show the gene expression profiles of the high-risk group and the low-risk group (Figure 6A). The risk score distribution of the HCC patients is presented in Figure 6B, which shows that it increases gradually from left to right, and divides the patients into two groups. The distribution of the survival status and survival time of patients with different risk scores is shown in Figure 6C.

Figure 5 | Selection of prognosis-related DEGs in TCGA cohort. (A) Univariate Cox regression analysis. (B, C) LASSO regression analysis.

Table 1 | Multivariate Cox regression analysis to screen out the key RBPs most relevant to prognosis.

Figure 6 | Characteristics of the prognostic gene signatures. (A) The heatmap of the expression profiles of the ten DEGs in the high- and low-risk HCC patients. The abscissa represents risk types, the ordinate represents the gene. (B) Distribution of risk scores of the high- and low-risk HCC patients. The abscissa represents the patients (increasing risk score), the ordinate represents the risk score. (C) Scatter plot shows the correlation between survival time and risk score. The abscissa represents the patients (increasing risk score), the ordinate represents survival time (years).

Validation of the 5-gene prognostic signature

We used univariate and multivariate analysis to evaluate the independent predictive values of the five gene characteristics in patients with HCC. As shown in Figure 7A, univariate Cox regression revealed that the risk score, pathological stage, T stage and M stage had prognostic value. In addition, multivariate Cox regression analysis revealed that only the risk score was an independent prognostic factor associated with OS (Figure 7B). The Kaplan-Meier accumulation curve in TCGA HCC dataset showed that patients with high risk score had shorter survival time than patients with low risk score (Figure 7C). The AUC of the ROC curve of the risk score was higher than that of other individual indexes (AUC=0.786), which demonstrated that our prognostic model is superior to other single indexes in predicting prognosis (Figure 7D). Additionally, for in vitro verification, we calculated the risk score of the patient using the same formula used in the GSE14520 dataset. The OS of the high-risk group was found to be lower than that of the low-risk group (P < 0.05) (Figure 7E), according to the Kaplan-Meier and ROC curves. The AUC of 1 year, 3 years and 5 years were determined to be 0.591, 0.617 and 0.609, respectively (Figures 7F–H). We also effectively validated the model in ICGC datasets (Figures 7I–L). To sum up, these five genetic characteristics can predict the OS of patients with HCC.

Figure 7 | Validation of the prognostic signature of the 5 signature DEGs. (A) Forest plot shows the univariate Cox analysis of the relationship between the clinical features, risk score and OS of HCC patients in TCGA cohort. (B) Forest plot shows the multivariate Cox analysis of the relationship between the clinical features, risk score and OS of HCC patients in TCGA cohort. (C) Kaplan-Meier survival curve of patients with HCC in the High-risk and low-risk groups in TCGA dataset. The abscissa represents time (years), the ordinate represents survival probability. (D) ROC curve for predicting total survival time in TCGA dataset. The abscissa represents false positive rate, the ordinate represents true positive rate. (E) Kaplan-Meier survival curve of patients with HCC in the high-risk and low-risk groups in the GSE14520 dataset. The abscissa represents time (years), the ordinate represents survival probability. (F–H) The ROC curve for predicting the overall survival time of patients at 1, 3, and 5 years in the GSE14520 data set (I) Kaplan-Meier Survival Curve of patients with HCC in High-risk and low-risk groups in ICGC data set. (J–L) The ROC curve for predicting the overall survival time of patients at 1, 3, and 5 years in the ICGC data set.

Model verification of clinical grouping

We divided the clinical characteristics of the patients with TCGA HCC into groups and plotted survival curves to determine whether our model is equally applicable to each group. The Kaplan-Meier curve showed that our model is suitable for > 65 years old, ≤65years old, male, female, G1-2, G3-4, M0, N0, Stage I-II, Stage III-IV, T1-2 and T3-4 (P<0.05) (Figure 8).

Figure 8 | The correlation between the clinical features and risk score of HCC patients. The abscissa represents time (years), the ordinate represents survival probability.

Expression and alteration of the five prognosis-related RBP genes

In order to evaluate the expression of these five genes, we conducted a paired t-test. The results revealed that there was a significant difference in the expression of these five genes in tumor tissues, and the expression in tumor tissues was significantly higher than that in normal tissues (Figure 9). The protein expression level can be studied using the human protein map database. A typical immunohistochemistry analysis of five genes in tumor and normal liver tissues is shown in Figure 10A, (images are available from https://www.proteinatlas.org/). Using the cBioPortal online database (http://www.cbioportal.org/), we found that amplification was the main change of the five genes (Figure 10B).

Figure 9 | Differential expression of five DEGs in HCC and adjacent normal tissues.

Figure 10 | Expression and alteration of the five prognosis-related DEGs. (A) The representative protein expression of the five genes in HCC and normal tissue. Data were from the Human Protein Atlas (http://www.proteinatlas.org) online database. (B) The expression change profiles of the five genes in TCGA HCC RNA-seq dataset.

Construction and verification of nomogram

Data from 235 HCC patients with complete clinical information from TCGA datasets were used to establish a prognostic nomogram based on stepwise Cox regression model to predict 1-year and 3-year OS. The risk score, age, grade, pathological stage and TMN stage were included in the map (Figure 11A). When the calibration curve is closer to the diagonal, it means that the prediction result is more accurate (Figures 11B, C). The AUC of the nomograph’s 1-year and 3-year OS prediction was 0.787 and 0.779 respectively, which demonstrate that its predictive ability is good (Figure 11D).

Figure 11 | Construction and validation of the nomogram. (A) Calculation of the scores of each item of HCC patients according to the nomogram, and the total scores obtained after addition can predict the 1- and 3-year survival probability. (B, C) The 1- and 3-year calibration curves of the nomogram. (D) The ROC curves of the 1-and 3-year nomogram (AUC = 0.787 of 1 year, AUC = 0.779 of 3 years). The abscissa represents false positive rate, the ordinate represents true positive rate.

Analysis of immune cell infiltration

HCC The level of immune infiltration in patients with HCC was obtained from the TIMER website, and the correlation between 6 tumor-infiltrating immune cells and prognostic model was analyzed in the R software. The results showed that macrophages and neutrophils were positively significantly correlated with the risk score, but not dendritic cells, B cells, CD8+T cells and CD4+T cells (Figure 12A). The results also revealed that the expression of TREM2 and GSDME was positively correlated with the six types of immune cells, while the expression of GLMN and DHX9 was only independent of CD8+ T cells, and the expression of SQSTM1 was positively correlated with macrophages, myeloid dendritic cells and neutrophils (Figure 12B).

Figure 12 | Analysis of Immune Cell Infiltration. (A) Correlation plot between risk score and immune cells infiltration. The abscissa represents risk score, the ordinate represents infiltration abundances of immune cells. (B) A heat map of the relationship between five risk genes and immune cell infiltration. Different colors represent the correlation coefficient, and the darker the color is, the stronger the correlation is, *p < 0.05, **P < 0.01, and the asterisk represents the degree of importance (*p).

Discussion

The molecular mechanism of the occurrence and development of HCC is extremely complex, which is the reason why it remains one of the most threatening malignant tumors in the world. Therefore, it is urgent to find biomarkers to predict prognosis and develop individualized treatment plans for patients with this cancer. The development of gene sequencing technology and bioinformatics opens the possibility to find a prognostic gene model. However, the markers found in the current study are still limited. More accurate biomarkers need to be found to predict the prognosis of HCC.

In this study, we identified 120 pyroptosis-related genes and analyzed the transcriptome and clinical information of patients with HCC in TCGA dataset. Through univariate analysis, we found 42DEGs related to prognosis. The biological function and signal transduction pathway of DEGs in HCC were examined by GO and KEGG enrichment analysis. Then, a prognostic model consisting of 5 genes (GSDME, DHX9, TREM2, SQSTM1 and GLMN) was established by LASSO regression analysis and multivariate analysis. By calculating the risk score of each patient, patients with HCC were divided into two subgroups, namely the high-risk group and low-risk group. The prognosis of patients in the high-risk group was poor, and its robustness was verified using a GEO dataset and ICGC datasets. As for the low AUC values in the validation cohort, we believe that confounding factors such as race and geography in different datasets may also be related, and indeed there are many published studies in which the model performance is worse than ours. In future studies we will aim to collect clinical cases for further studies to validate our model. A nomogram was constructed to predict the OS of patients with HCC. The correction curve and ROC curve revealed that the nomogram has better prediction accuracy. The predicted results are in good agreement with the actual results.

Among the five genes in our prognostic model, DHX9 is a multi-domain and multi-functional protein, which plays a regulatory role in DNA replication, transcription, translation, RNA processing and transport, microRNA processing and maintenance of genomic stability (14). High-risk human papillomavirus (HPV) impairs the interaction between MDM2 and DHX9 and the degradation of DHX9 by inhibiting the expression of lnc-CCDST, thereby promoting the movement and angiogenesis of cervical cancer (15). Downregulation of SRSF3 or hnRNPM can inhibit the expression of DHX9 and the proliferation of Ewing’s sarcoma cells, and enhance the sensitivity of Ewing’s sarcoma cells to chemotherapy (16). LINC00460 plays a role in the proliferation and metastasis of colorectal cancer through direct interaction with IGF2BP2 and DHX9 (17). LNC-UCID promotes the growth of HCC by blocking the interaction between DHX9 and CDK6 (18). GLMN was initially identified by its relationship with glomerular venous malformations and was later identified as an inhibitor of cullin-RBX1E3 ligase (19). GLMN seems to have a cytoprotective effect on macrophage death induced by activation of NLRP inflammasomes (20). The expression of GLMN is upregulated in differentiated prostate tumors (21). GSDME, which belongs to the gasdermin family, is an important protein mediating pyroptosis, inducing cell death and promoting the release of inflammatory factors (9). The expression of GSDME has been shown to be associated with a good prognosis after chemotherapy, thus it may be a potential predictive biomarker (22). GSDME has been shown to be involved in tumorigenesis and by mediating pyroptosis in a variety of cancers (13, 23–26). SQSTM1 is a ubiquitin-binding protein that regulates a variety of physiological and pathological processes, especially autophagy. The SQSTM1/p62 is a well-known macroautophagy/autophagy receptor, which is a fatal inflammatory mediator in sepsis and septic shock (27). Several studies have studied the role of SQSTM1 in tumors. For example, Ashik et al. (28) found that SQSTM1/p62 plays a role in interstitial transformation and invasion of glioblastoma, and Saito et al. (29) found that its molecular targeting is a potential anti-HCC chemotherapy pathway. Kosumi et al. (30) found that cancer cells expressing SQSTM1 may play a role in regulatory T cells in the tumor microenvironment. TREM2 is an important pathologically induced immune signal hub (31). It is a receptor that interacts with a variety of ligands, many of which are markers of tissue injury (32). Some studies have shown that TREM2 has an inhibitory effect on HCC (33, 34) and colorectal cancer (35) through a variety of mechanisms. Also, the expression of TREM2 in gastric cancer is closely related to the prognosis of patients (36).

Our study also found that macrophages and neutrophils were positively correlated with the risk scores. The abundance of tumor-associated macrophages (TAMs) is often related to the acquisition of specific pathological features of the tumor, such as immunosuppression, neovascularization, invasion, metastasis and poor response to treatment, which indirectly suggests that TAMS may play a role in tumor promotion (37). Macrophage targeting therapy may provide a new therapeutic approach for patients with HCC (38–40). Neutrophils can participate in different stages of tumor formation, including tumor initiation, growth, proliferation or metastasis, and can promote tumor proliferation by weakening the immune system (41). Previous studies have shown that neutrophils play an important role in the occurrence and development of HCC (42–44).

This study has some limitations. For example, our analysis is based on publicly available datasets that have been reported by other researchers and have not been validated in the prospective cohort. The race factors related to the sequencing samples and some potential prognostic factors may not be included in the model, which limits its predictive ability. In the future, we plan to use more suitable bioinformatics strategies to improve the model.

To sum up, our study successfully identified a prognostic model comprising five genes to predict the OS, of patients with HCC and constructed a nomogram to predict the 1- and 3-year survival rate of patients with HCC. Our study can be used to guide the clinical application of pyroptosis and individualized treatment for patients with HCC. The future research on the molecular mechanism of this signal and prospective randomized clinical trials will have important clinical significance.

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

All authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.

Funding

This research was supported by the Shanxi Province Applied Basic Research Program (201901D211480).

Acknowledgments

We would like to thank everyone who took part in this study.

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.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2022.1021775/full#supplementary-material

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ORIGINAL RESEARCH
published: 24 October 2022
doi: 10.3389/fonc.2022.988528

Parthenolide and arsenic trioxide co-trigger autophagy-accompanied apoptosis in hepatocellular carcinoma cells

Juan Yi ¹*†, Xia Gong ²†, Xiao-Yang Yin ¹†, Li Wang ¹, Jin-Xia Hou ¹, Jing Chen ¹, Bei Xie ¹, Gang Chen ¹, Li-Na Wang ¹, Xiao-Yuan Wang ¹, Da-Chun Wang ³ and Hu-Lai Wei ¹,⁴*

¹ School of Basic Medical Sciences, Lanzhou University, Lanzhou, Gansu, China, ² Geriatrics Department, The First Hospital of Lanzhou University, Lanzhou, Gansu, China, ³ Biochemistry Department, LanZhou Ke Bao Biotechnology Co., Ltd., Lanzhou, Gansu, China, ⁴ Key Laboratory of Preclinical Study for New Drugs of Gansu Province, Lanzhou, Gansu, China

Edited by 
Yunfei Xu, Shandong University, China

Reviewed by 
Yaacov Ben-David, Guizhou Medical University, China
Yingyu Chen, Fujian Medical University, China

*Correspondence 
Hu-Lai Wei
weihulai@lzu.edu.cn 
Juan Yi
yij@lzu.edu.cn

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

Specialty section:
This article was submitted to Cancer Molecular Targets and Therapeutics, a section of the journal Frontiers in Oncology

Received: 07 July 2022

Accepted: 03 October 2022

Published: 24 October 2022

Citation:
Yi J, Gong X, Yin X-Y, Wang L, Hou J-X, Chen J, Xie B, Chen G, Wang L-N, Wang X-Y, Wang D-C and Wei H-L (2022) Parthenolide and arsenic trioxide co-trigger autophagy-accompanied apoptosis in hepatocellular carcinoma cells. Front. Oncol. 12:988528. doi: 10.3389/fonc.2022.988528

Although arsenic trioxide (ATO) shows a strong anti-tumor effect in the treatment of acute promyelocytic leukemia, it does not benefit patients with hepatocellular carcinoma (HCC). Thus, combination therapy is proposed to enhance the efficacy of ATO. Parthenolide (PTL), a natural compound, selectively eradicates cancer cells and cancer stem cells with no toxicity to normal cells. In this study, we chose PTL and ATO in combination and found that nontoxic dosage of PTL and ATO co-treatment can synergistically inhibit the in vitro and in vivo proliferation activity of HCC cells through suppressing stemness and self-renewal ability and inducing mitochondria-dependent apoptosis. More importantly, USP7-HUWE1-p53 pathway is involved in PTL enhancing ATO-induced apoptosis of HCC cell lines. Meanwhile, accompanied by induction of apoptosis, PTL and ATO evoke autophagic activity via inhibiting PI3K/Akt/mTOR pathway, and consciously controlling autophagy can improve the anti-HCC efficacy of a combination of PTL and ATO. In short, our conclusion represents a novel promising approach to the treatment of HCC.

Keywords: arsenic trioxide, parthenolide, apoptosis, autophagy, hepatocellular carcinoma

Introduction

In the 1970s, the therapeutic potential of arsenic trioxide (As₂O₃, ATO) against acute promyelocytic leukemia (APL) was first recognized in China (1). Subsequently, the enormous clinical studies conducted in China and US confirmed that a low dose of ATO has shown strong therapeutic efficacy with little toxicity when used in the treatment of refractory and relapsed APL (2, 3). Thus, US Food and Drug Administration (FDA) approved ATO as a chemotherapeutic drug to treat APL in 2000 (4), which prompted researchers to examine the availability of ATO to various solid tumors (5–7). Many reports have shown that ATO exerts the antitumor effect in cell lines from solid tumors through multiple mechanisms, including inducing apoptosis (8–10), dampening proliferation (11–13), triggering partial differentiation (14, 15), directly damaging DNA (16, 17), suppressing tumor angiogenesis (18, 19) and metastasis (20, 21) and so on. However, our study and other studies proved that the effective dose of ATO used in solid tumors is much higher than that in APL (22–24). Furthermore, clinical investigations have revealed that ATO can be removed quickly in blood circulation and fail to accumulate effectively in solid tumors, leading to no response to the treatment with a nontoxic dose of ATO in patients with solid tumors (25). However, it is encouraging that patients with solid tumors, such as breast cancer, colorectal cancer, head-neck cancer and gliomas, benefited from ATO and other drug combination therapy in some completed clinical trials (26).

Human hepatocellular carcinoma (HCC) remains a severe global health threat by reason of high mortality and recurrence. Although surgery is an option as first-line therapy for patients with HCC, systemic chemotherapy is still critical to keep the patients from relapsing after surgery. Given the powerful effects of ATO against APL, ATO was introduced to treat HCC cell lines in laboratory and found to be able to induce apoptosis and cell growth. But in fact, a phase II clinical trial verified that high dose ATO administered was not good for HCC patients and adversely caused grade 4 hematological toxicities (27). Hence, the establishment of therapeutic combination is required to improve the efficacy of nontoxic dose of ATO in HCC.

Parthenolide (PTL), a natural herbal compound from feverfew (28), has a significant anti-tumor effect in all types of cancers, but hardly affects normal cells (29, 30). More importantly, PTL is the first agent that specifically kills tumor stem cells (29, 31). As for the anti-tumor mechanisms of PTL, existing evidences have shown that it can directly suppress NF-κB pathway and deubiquitinase USP7 activated abnormally in cancer cells and tumor stem cells (32, 33). Of note, our previous study and other group study showed that PTL can enhance the sensitivity of tumor cells to drugs (34–36), indicating PTL may be an ideal candidate to sensitize solid tumor cells to ATO. As expected, Wang et al. (22) elucidated that the combination treatment with PTL and ATO significantly reduced growth of pancreatic cancer compared with those treated with either PTL or ATO alone. Thus, it is worth to explore whether PTL improves the efficacy of ATO in HCC.

In clinical chemotherapy, drug resistance is the most common event and causes recurrence and failure of treatment in HCC. Autophagy, a complex process of transferring cellular substances to lysosomes for degradation in normal or stressed conditions, contributes to the occurrence of drug resistance. Mounting studies have shown that almost all of chemotherapeutics cause changes in the activity of autophagy, and inhibiting autophagy can improve the efficacy of drugs (37–40). However, the existing reports mainly focus on the single drug-induced autophagy, and the roles of autophagy in combination of drugs is unclear.

Herein, we found that PTL combined with ATO synergistically inhibits the in vitro and in vivo proliferation activity of HCC cells through suppressing stemness and self-renewal ability and inducing mitochondria-dependent apoptosis, in which USP7-HUWE1-p53 pathway involved. Accompanied by induction of apoptosis, PTL and ATO evoke autophagic activity by inhibiting PI3K/Akt/mTOR pathway, and consciously controlling of autophagy can accelerate the anti-HCC efficacy of combination of PTL and ATO. In short, our conclusion represents a novel promising approach in the treatment of HCC.

Materials and methods

Drugs and antibodies

Drugs used in this study: Chloroquine (CQ; Sigma-Aldrich, C6628), Arsenic trioxide (ATO; Beijing Chemical, GB673-77) and Parthenolide (BIOMOL, P8522f). Antibodies specific against Caspase3 (9662), cleaved Caspase3 (9661), Pho-p53 (9284), Cdc25 (3652), p21 (2947), CyclinB (12231), Pho-S6K (323), CyclinE1 (20808S), ATG5 (12994), PI3K (4263), Akt (9272), Pho-Akt (13038), Pho-mTOR (5536), Pho-4EBP1 (2855) and BCL-2 (15071) were purchased from Cell Signaling Technologies. Antibodies specific against BAX (sc493), p53 (sc126), catalase (sc50508) and SOD1 (sc11407) were purchased from Santa Cruz. Antibodies specific against HUWE1 (ab70161) and USP7 (ab4080) were purchased from Abcam. Other antibodies were used in this study: CD133 (Immunway, YT5192), GFP (Thermo fisher scientific, MA5-15256), LC3B (Sigma, L7543), mTOR (Genetex, 41731) and GAPDH (Proteintech, 60004-1-Ig). Secondary antibodies used in this study: Peroxidase-conjugated affinity pure goat antimouse IgG, light chain specific (Jackson Immuno Research, 115–035-174), peroxidase-conjugated IgG fraction monoclonal mouse antirabbit, light chain specific (Jackson Immuno Research, 211–032-171).

Cell culture

HEK293T, HepG2, MHCC 97H and Huh7 cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM; HyClone, SH30022.01)) supplemented with 10% fetal bovine serum (FBS; YEASEN, 40130ES76) and 1% Penicillin/Streptomycin, LO2 cells and H22 cell were cultured in Roswell Park Memorial Institute 1640 (RPMI1640; HyClone, SH30027.01) supplemented with 10% FBS and 1% Penicillin/Streptomycin. All cells were incubated in a humidified 5% CO₂ incubator.

Cell viability assay

5×10³ cells were seeded in 96-well plates. After treatment with ATO and/or PTL for the indicated times, 10 μL MTT solution was added to each well. Plates were incubated for an additional 4 h at 37°C,100 μL % SDS was added to each well to solubilize formazan crystals. Each sample point was assayed with 4 replica points. Optical density was measured at 570nm using a microplate reader (Powerwave X, Bio-Tek) to calculate inhibition rate for cell proliferation and 50% inhibitory concentration (IC50).

Cell apoptosis

After the designated treatment, cells were washed and labeled with Annexin V/PI kit (eBioscience, 800-8005-72) for 15 min at room temperature in the dark, according to the manufacturer’s instructions. The images were captured using fluorescence microscopy (OLYMPUS, IX81), and the percentage of apoptosis was measured by Flow cytometry (Beckman-Coulter Epics XL).

Cell cycle analysis

Cells were treated with drugs at the indicated concentrations for 24 h, and then collected and fixed with 75% ethanol at -20°Covernight. Cells were washed twice with cold PBS and resuspended in propidium iodide (PI; Solaibio, C0080)) staining buffer containing RNAse. Following incubation for 30 min at room temperature in the dark, cells were analyzed by Flow cytometry.

Mitochondrial membrane potential

Cells were seeded into a 96-well plate and treated for 24 h with drugs at indicated concentrations. After treatment, cells were incubated at 37°C with TMRE (100 nM) (Thermo Fisher Scientific, I 34361) in growth media for 30 min in a CO₂ incubator. After incubation, cells were analyzed using fluorescence microscopy to determine the fluorescence intensity. Measurement of TMRE integrated intensity was done using Image J software.

Mitochondrial reactive oxygen species measurement

Cells were seeds into 96-plate and treated with ATO, PTL and ATO plus PTL for 24 h, followed by incubation with MitoSOX™ red mitochondrial superoxide indicator (Invitrogen, M36008) for 15min at 37°C. Cellular fluorescence intensity was detected using fluorescence microscope. The fluorescence intensity quantified by ImageJ software.

Colony formation assay

HpeG2 cells were seeded in six-well plates at low density (2,000 cells per well). In next day, ATO, PTL and ATO plus PTL was added directly into each well at indicated concentration. After 12 days, the plates were washed with PBS and stained with coomassie brilliant blue. The number of colons as counted and the morphology of single clone was observed under the microscope (>50 clones validated).

Plasmids

EYFP-USP7 constructs was generated using PCR amplification and subcloned into pEYFP-C1 vectors using ClonExpress II one step cloning Kit (Vazyme, C112-01). pEYFP-C1was obtained from Clontech. LentiCRISPRv2 puro (Addgene, 98290) was a gift from Brett Stringer (41). PMD2.G (Addgene, 12259) and psPAX2 (Addgene, 12260) were gifts from Didier Trono.

Establishment of ATG5 KO cell lines using CRIPSR/Cas9 system

ATG5 guide RNAs were designed with the online CRISPR design tool. The gRNA sequences (sg-ATG5-1: 5′-caccggatggacagttgcacacact-3′, sg-ATG5-2: 5′-aaacagtgtgtgcaactgtccatcc-3′) were purchased from Sangon Biotech. These sgRNAs were cloned into LentiCRISPRv2 according to the protocol provided by the Zhang Lab (42). These two plasmids containing the guide RNA of interest and packaging plasmids (PMD2.G and psPAX2) were transfected into HEK293T cells. After 96 h, virus supernatant was harvested, and then concentrated using Lentivirus concentration solution (YEASEN, 41101ES50). HepG2 cells were infected with concentrated virus supernatant for 48 h. Followed by puromycin (400 ng/ml; Merck, P9620) selection for 2 weeks. The ATG5 knockout effect in puromycin-resistant cells was then verified by immunoblotting.

Western blotting

After designated treatment, Laemmli buffer (62.5 mM Tris-HCl, pH 6.8, 20% glycerol, 2% SDS, 2 mM dithiothreitol (DTT), phosphatase inhibitor and proteinase inhibitor cocktail [Thermo Fisher Scientific, 78446]) was used to lyse the harvested cells. An equal amount of protein was separated on SDS-PAGE gels and then transferred onto PVDF membranes (Bio-Rad Laboratories, 1620177). After blocking with 5% nonfat milk in TBST, membranes were incubated with the indicated primary antibodies and secondary antibodies, and then visualized using Super ECL Detection Reagent (YESEN, RPN2232) with CLINX Hemiscope (QinXiang, China).

Animal studies

Kunming mice and BALB/c mice (8 weeks, 200-250g) were approved by Laboratory Animal Science and Technology Work Management Committee, School of Basic Medicine, Lanzhou University, which is fully accredited by the Institutional Animal Care and Use Committee. For xenograft experiments, H22 cell were injected into the abdominal cavity of 8-week-old Kunming mice. One week later, we collected ascites and counted 5×10⁵ cells in 200 μL PBS for subcutaneous injection into left flanks to establish tumors. Treatment was started when tumor volumes reached a minimal size (notable by sight, approximately a week after injection). Mice were treated with drugs by intraperitoneal injection at the indicated concentrations for 14 times in 28 days, and mice showed no toxicity to all drugs after injection. After 28 days, the mice were sacrificed and tumors were harvested.

Statistical analysis

All numerical data are expressed as the mean ± SD from at least three independent experiments. Statistical significance of differences between groups was evaluated using Student’s t-test. Statistical analysis was performed using SPSS statistical software (version 21.0). P<0.05 was considered to be statistically significant.

Results

Further suppression of proliferation of HCC cells by combination of PTL and ATO

Based on metastatic properties, three different HCC cell lines, HepG2 cells, MHCC 97H cells and Huh7 cells, were used to separately evaluate the effect of ATO or PTL on HCC cells growth. MTT assay showed that both drugs dramatically inhibited the proliferation of three cell lines in a dose and time dependent manner. The half inhibitory concentrations (IC50) of ATO are 21.2 μM and 9.0 μM (HepG2), 43.2 μM and 23.4 μM (MHCC 97H), 20.5 μM and 6.9 μM (Huh7) for 24 h and 48 h respectively (Figure 1A). The IC50 of PTL are 24.5 μM and 7.4 μM (HepG2), 39.9 μM and 21.4 μM (MHCC 97H), 20.3 μM and 7.1 μM (Huh7) for 24 h and 48 h (Figure 1B). According to our previous studies and other groups evidences, we found that HCC cell lines were less sensitive to ATO or PTL than leukemia cells (the value of IC50 is 3.4 uM for 24 h) (24). In a separate study, we assessed the cellular toxicity of ATO and PTL to human hepatocyte LO2 cells. The results indicated that ATO had appreciable toxicity at the doses treated HCC cell lines, whereas, we observed no obvious effect to LO2 cells at the doses that HCC cell lines demonstrated strong specific toxicity in response to PTL. In following experiments, the influences of PTL on ATO-mediated cytotoxicity was separately investigated in three HCC cell lines and LO2 cells using MTT assay. The data showed that the combination of ATO and PTL did not increase the ATO-induced toxicity to LO2 cells (Figure 1C). Conversely, HCC cell lines showed more remarkable suppression of cell growth in response to ATO/PTL combined group than each agent alone group. Consistent with this result, we observed combined treatment yielded a significantly higher reduction in cell numbers than those obtained with either drug alone using contrast microscope (Figures 1D, E). Taken together, these results suggested that PTL may decrease the effective dose of ATO required for treatment of hepatocarcinoma cells.

Figure 1 | PTL enhances the effect of growth suppression of HCC cell lines mediated by ATO without influence on the normal hepatocyte LO2 cells. (A, B) MTT assay showing the viability of HepG2 cells, MHCC97H cells and Huh7 cells after exposure to increasing concentrations of ATO or PTL for 24 h and 48 h. The half-maximal inhibitory concentration (IC50) of ATO or PTL was calculated (P < 0.0001 vs. Control). (C) LO2 cells were treated different concentrations of ATO, PTL or ATO plus PTL for 24 h or 48 h, and the cell viability was measured by MTT assay. (D) After exposure to indicated concentrations of ATO, PTL and ATO plus PTL for 24 h, the cell viability was tested by MTT assay and IC50 was calculated (**P < 0.01; ***P < 0.001; ****P < 0.0001). (E) After exposure to indicated concentrations of ATO, PTL and ATO plus PTL for 24 h, the changes of cell morphology were observed under microscope. Scale bar: 20 μm.

PTL and ATO co-induces mitochondria-dependent apoptosis in HCC cells

To ascertain the consequence of combined treatment with ATO/PTL in HCC cell lines, we assessed the changes of cell apoptosis. Following a 24h-treatment with ATO and/or PTL, the apoptotic rate was examined using Annexin-V/PI double staining. Fluroscence image showed that apoptotic cells were significantly elevated in ATO plus PTL group in three HCC cell lines, and FCM analysis demonstrated that 16.37%, 24.53% and 8.27% apoptotic rate in 2.5 μM alone, 5 μM ATO alone and 10 μM PTL alone treatment group, whereas combined treatment resulted in a 2~3-fold increase in the apoptotic rate of the HepG2 cells. Consistent results were also observed in MHCC 97H and Huh7 cells (Figure 2A). Subsequently, we also observed the significant elevation of cleaved caspase-3 accompanied with a reduction in the level of the total caspase-3 after the combination treatment in HCC cell lines. Based on these evidences, we concluded that combined treatment of ATO and PTL is more effective for induction of apoptosis in HCC cell lines (Figure 2B). Additionally, compared with alone reagent treatment, co-treatment with ATO and PTL showed a more obvious decline in anti-apoptotic protein BCL-2 and a more dramatic enhancement in pro-apoptotic protein BAX (Figures 2B, C). In follow-up investigation about the mechanisms of ATO/PTL-induced apoptosis, we observed that PTL/ATO combined treatment led to an appreciable reduction of the mitochondrial membrane potential (ΔΨm), an early trigger of mitochondrial apoptotic pathway, using TMRM staining (Figures 2D, E). Next, we identified the intracellular ROS using mitoSOX, and the images showed an increase in the release of ROS after treatment with ATO and PTL together (Figures 2F, G). To confirm this increase, the expression of superoxide dismutase 1 (SOD1) and catalase were examined using western blotting. The results indicated that combined treatment caused a more visible decrease in these two protein levels than the reagent alone (Figure 2H).

Figure 2 | PTL promotes the cell apoptosis of HCC cell lines mediated by ATO via weakening the mitochondrial membrane potential and elevating ROS. Exposure to indicated concentrations of ATO, PTL and ATO plus PTL for 12 h or 24 h. (A) Apoptotic cells were analyzed by fluorescence microscope and Flow cytometry (**P < 0.01; ***P < 0.001). (B) The cells were harvested and the indicated proteins were analyzed using western blotting, the expression of proteins were quantified as in (C); Columns, mean; bars, ± S.D. of 3 independent experiments (**P < 0.01; ***P < 0.001; n.s, no significance). (D) HepG2 cells were stained with TMRM and the images were captured using fluorescence microscope. Scale bar: 10 μm. (E) The relative intensity of TMRM in the samples was quantified (n = 200). Columns, mean; bars, ± S.D. of 3 independent experiments (***P < 0.001). (F) HepG2 cells were stained with mitoSOX and observed using fluorescence microscope. Scale bar: 50 μm. The relative intensity of mitoSOX in the samples was quantified (n = 200) as shown in (G); Columns, mean; bars, ± S.D. of 3 independent experiments (**P < 0.01; ***P < 0.001). (H) HepG2 cells were harvested and analyzed using indicated antibodies.

PTL promotes the blockage of cell cycle in G2/M phase endowed by ATO

PTL or ATO are known to affect cell proliferation by arresting cell cycle progression (23, 43). To test whether combination of ATO and PTL more effectively blocked cell cycle than each reagent alone, we next performed the cell cycle analyses following treatment with ATO and/or PTL for 24h and observed a slight block at the G1 phase for PTL alone, a significant arrest at G2/M phase for ATO alone, and a much more block at G2/M for co-treatment with ATO/PTL (Figures 3A, B). p53 negatively regulates cell cycle progression in response to different cellular stresses, whereas phosphorylated p53 represents the activation of p53 (44). Thus, we examined the changes in expression of p53 and phosphorylated p53. The western blotting results showed that there was a dose-dependent increase in p53 and phosphorylated p53 in response to ATO alone, whereas combination with ATO and PTL showed approximate two folds improvement in the protein level of both compared with ATO alone (Figures 3C, D). In further study, the expression of proteins involved in the G1 and G2/M checkpoint was examined. After treatment with ATO and PTL alone, or co-treatment with ATO and PTL, the expression of Cdc25C, Cyclin B, Cyclin E, and p21 was measured by western blotting (Figures 3E, F). The results showed that the expression of Cdc25C were significantly reduced and p21 protein levels were enhanced in the combination group. Cyclin E, a checkpoint protein of G1 phase, was remarkably reduced after treatment with PTL alone, but had no obvious differences between PTL alone group and co-treatment group. However, the checkpoint protein of G2/M phase, Cyclin B, was enhanced after treatment with ATO alone or ATO and PTL. The probability that upstream Cdc25C was dramatically down-regulated, leading to the accumulation of Cyclin B. Taken together, these results demonstrated that combination with ATO and PTL is more effective to arrest cell cycle progression at G2/M phase.

Figure 3 | PTL worsened blockage of the cell cycle induced by ATO. Exposure to indicated concentrations of ATO, PTL and ATO plus PTL for 24 h. (A) The distribution of cell cycle in HepG2 was measured using Flow cytometry after PI staining. The percentage of G₀G₁, S and G₂M were quantified as in (B). (C) HepG2 cells were harvested and analyzed with indicated antibodies, and the expression of proteins were quantified as in (D); Columns, mean; bars, ± S.D. of 3 independent experiments (**P < 0.01; n.s., no significance). (E) HepG2 cells were harvested and analyzed with indicated antibodies, and the expression of proteins were quantified as in (F); Columns, mean; bars, ± S.D. of 3 independent experiments (**, P < 0.01; ***P < 0.001; ****P < 0.0001; n.s., no significance).

PTL and ATO synergistically suppress stemness and self-renewal ability of HepG2 cells

PTL is known as the first natural compound that specially eradicated cancer stem cells (30). And self-renewal is the ability by which stem cells divide to make more stem cells. In next experiment, clone formation assay was performed to test the changes in self-renewal ability to assess the anti-tumor of PTL/ATO combination in vitro. The CBB staining showed a significant reduction in the number of clone in ATO/PTL combined treatment group compared to ATO alone treatment group. Using microscope, we also observed the distinct shrinkage in the morphology of clone in combination group compared to each reagent alone group (Figures 4A, B). CD133 is one of the most well-characterized bio-markers used for the isolation and identification of cancer stem cells. In further study, we assessed the changes of CD133 protein level to clarify the effect of combined treatment on cancer stem cells. As we expected, the combination of two reagents apparently lowered the protein level of CD133 in contrast to each reagent (Figures 4C, D). Altogether, these data indicated that self-renewal ability is more effectively inhibited by ATO co-treated with PTL in HCC cell lines, suggesting that PTL may eliminate the cancer stem cells to exert the effect of ATO-mediated anti-tumor.

Figure 4 | Suppression of stemness and self-renewal ability of HepG2 cells by combination of ATO with PTL. (A) HepG2 cells were treated with ATO, PTL and ATO plus PTL. After 12 days, cell clones were stained with coomassie brilliant blue. The number of colones was counted and the morphology of single clone was observed under the microscope (>50 clones validated). (B) Graphic quantitation of clone was obtained from at least three independent experiments. Columns, mean; bars, ± S.D. (***P < 0.001; ****P < 0.0001). (C, D) HepG2 cells and Huh7 cells were treated as (A), and then subjected to western blotting analysis.

USP7-HUWE1-p53 axis is involved in PTL enhancing ATO-induced apoptosis of HCC cell lines

The latest findings proved that PTL can directly interact with USP7 and inhibit its protein activity. HUWE1, an E3 ligase, is one of the substrates of USP7, and is responsible for the degradation of p53 (33, 45, 46). Therefore, we next explored whether USP7-HUWE1-p53 axis is involved in ATO/PTL -mediated apoptosis. The expression of USP7, HUWE1 and p53 was first detect in these cells treated with ATO or PTL alone at different concentrations using Western blot. The results showed that ATO had no apparent effect on expression of HUWE1 and only increased the protein level of p53, whereas, PTL exhibited a significantly decrease in protein level of HUWE1 going with enhancement of p53 (Figure 5A). These data gave us a hint that the inhibition of USP7 by PTL may improve the sensitivity of ATO. To confirm this hypothesis, we tested the changes of HUWE1 and p53 after treatment with ATO and PTL together. The results showed that compared with reagent alone, the combined use of ATO and PTL made the expression of HUWE1 decrease (Figure 5B), indicating ATO combination with PTL promotes the apoptosis of liver cancer cells through the USP7-HUWE1-p53 axis. At the same time, we evaluated the interaction between USP7 and HUWE1 after the combined action of drugs through the immunoprecipitation experiment (Figure 5C). The results showed that the interaction between USP7 and HUWE1 was significantly weakened after the co-treated with PTL compared with ATO alone. The above results prove that the effect of PTL on ATO-induced apoptosis of HCC cells may be achieved by regulating the USP7-HUWE1-p53 axis.

Figure 5 | USP7-HUWE1 is related to PTL enhancing ATO-induced apoptosis of HCC cell lines. (A) HepG2 cells and MHCC 97H cells were treated with indicated concentrations of ATO or PTL for 24 h. The cells were collected and subjected to western blotting. (B) HepG2 cells, MHCC97H cells and Huh7 cells were treated with ATO, PTL and ATO plus PTL for 24 h, followed by analysis with western blotting. (C) Designated plasmids were transfected into HepG2 cells for 24 h, and then cells were treated with indicated concentrations of ATO or TL for 24 h. Harvested cells were lysed with IP lysis buffer and subjected to IP using GFP-agrose and western blotting analysis with the indicated antibodies.

PTL enhances ATO-triggered protective autophagy by PI3K/Akt/mTOR pathway in HCC cell lines

To identify whether the combination of ATO/PTL has an impact on activity of autophagy, we first determined the best time point of activation of autophagy via examining the conversion of autophagy marker LC3. As shown by western blot, the best time point of autophagy activation is at 24 h after treatment with the combination ATO/PTL (Figure 6A). In follow-up investigation, we observed that the combination treatment had more higher conversion of LC3Ito LC3II compared with reagent alone in HCC cell lines (Figure 6B), indicating much stronger ability of autophagic induction in ATO/PTL combination group. LC3 immunostaining showed the similar consequence (Figure S1A). Next, we investigated the role of this increased autophagy in the process of drug combination. Cells were treated with ATO and PTL in absence or presence of CQ, and western blot results demonstrated that suppression of autophagy showed a significant increase in the level of cleaved caspase3, suggesting that activated autophagy have a protective effect on tumor cells as well as drug alone (Figure 6C). In addition, we further confirmed this conclusion in the ATG5- KO HepG2 cells (Figure 6D). Subsequently, we assessed the anti-tumor effect of drugs in vitro and observed that knock out ATG5 had a remarked promotion of cell death in whatever either drug group or combination of both drugs (Figure 6E). Finally, to elucidate the underlying mechanism of autophagy activation, we investigated the changes of some vital proteins in PI3K/Akt/mTOR signal pathway, an important pathway in the treatment of liver cancers and autophagy (47, 48). Our results showed that the combination of both drugs produced an abrogation in the expression levels of PI3K, AKT, mTOR, phosphorylated Akt and phosphorylated mTOR compared with either reagent. Furthermore, phosphorylation of S6K1 and 4EBP1, the downstream effectors in PI3K/AKT/mTOR signal pathway, also decreased in the combined group (Figure 6F). Taken together, these data indicated that the combination of ATO/PTL endows the HCC cell lines with much stronger autophagic activity than each reagent via inhibiting the PI3K/Akt/mTOR pathway, which may weaken the anti-tumor efficacy of the combination of PTL and ATO.

Figure 6 | PTL enhances ATO-triggered protective autophagy by PI3K/Akt/mTOR pathway in HCC cell lines. (A) HepG2 cells were treated with ATO, PTL and ATO plus PTL for 6 h, 12 h and 24 h. Cells were harvested and subjected to western blotting analysis. (B) HepG2 cells, MHCC97H cells and Huh7 cells were treated with ATO, PTL and ATO plus PTL for 24 h. Cells were harvested and subjected to western blotting analysis. (C) HepG2 cells were treated with ATO, PTL and ATO plus PTL for 24 h with or without 50 μM CQ. Cells were harvested and subjected to western blotting analysis. (D) HepG2 cells and HepG2 ATG5 KO cells were treated with ATO, PTL and ATO plus PTL for 24 h. Cells were harvested and subjected to western blotting analysis. (E) HepG2 cells and HepG2 ATG5 KO cells were treated with ATO, PTL and ATO plus PTL for 24 h. The changes of cell morphology were observed under microscope. Scale bar: 20 μm. (F) HepG2 cells were treated with ATO, PTL and ATO plus PTL for 24 h. Cells were harvested and subjected to western blotting analysis.

PTL synergizes ATO to inhibit the in vivo growth of hepatoma H22 cells in mouse HCC model

In the present study, we intended to evaluate the potential therapeutic efficacy of combination treatment with ATO, PTL and CQ in a xenograft Balb/c tumor model with subcutaneously implanted H22 cells, a mouse HCC cell line. Before establishing the animal model, we wondered whether PTL enhances the susceptibility of H22 cells to ATO as well as human HCC cell lines. As we expected, ATO or PTL significantly inhibited the proliferation of H22 cells, and the inhibition of cell growth by ATO was further enhanced after the addition of PTL (Figure 7A). Thus, H22 cells is suitable for the establishment of xenograft in this study. After the xenograft Balb/c tumor model was subjected to 28-day-treatment of ATO or PTL alone, the tumor growth was partially delayed compared with controls, whereas ATO combined with different concentrations of PTL strongly suppressed tumor growth (Figures 7B, C). Further, we observed that application of CQ significantly accelerated the decrease of the tumor growth caused by ATO and PTL (Figures 7D, E). Anyway, the observed data suggested that PTL synergistically accelerated the in vivo anti-HCC efficacy of ATO, and suppression of autophagic activity further promoted the therapeutic effects of PTL and ATO.

Figure 7 | Effects of the combination of ATO with PTL on hepatoma growth. (A) MTT assay showing the viability of H22 cells after exposure to increasing concentrations of ATO or PTL for 24 h and 48 h (P < 0.0001 vs. Control). After exposure to indicated concentrations of ATO, PTL and ATO plus PTL for 24 h, the cell viability was tested by MTT assay. Columns, mean; bars, ± S.D. (***P < 0.001; ****P < 0.0001). (B, D) Photographs of harvested xenograft tumors prior to processing. (C, E) The weight of xenograft tumors in mice are shown. Columns, mean; bars, ± S.D.(n=3) (**P < 0.01; ***P < 0.001).

Discussion

ATO, a Chinese traditional medicine, has shown substantial efficacy in treating patients with APL. More interestingly, our previous study revealed that multidrug resistant leukemia cells have much more sensitivity to ATO than the parallel sensitive cells, showing the unique feature of ATO distinguished from other chemotherapeutics (1, 24). In view of these, our attention is attached to the application of ATO in solid tumors. HCC is a malignant tumor with poor prognosis. The selection of chemotherapeutic agents is an important part of therapy to patients with HCC. In this study, ATO was introduced to assess the efficacy of anti-HCC. During research, however, we found IC50 of ATO in HCC is about ten times higher than that in leukemia cells, suggesting high dose of ATO required for HCC have the risk of toxicity and single-agent do not benefit patients with HCC. This urges us to consider whether treatment benefit emerges when ATO is combined with other agents. At this moment, we face a critical problem: How to choose the drugs combined with ATO? Ideally, this kind of drugs should have the features of nontoxicity and antitumor activity. PTL, a natural compound, has been reported to possess the powerful ability of killing cancer cells and cancer stem cells, and have no toxicity to normal cells. In published paper, we elaborated that PTL can increase the sensitivity of drug resistant leukemia cells to doxorubicin via impeding the expression of multi-drug resistance proteins (34). Therefore, we selected the combination of PTL with ATO, and expected an improvement in the anti-HCC effect of ATO. Unexpectedly, we found that PTL significantly promotes the anti-HCC effect of ATO in vivo and in vitro by further inhibiting the proliferation and self-renewal ability of HCC, blocking cell cycle, enhancing the production of ROS, and inducing mitochondrial-dependent apoptosis. Furthermore, low dose of PTL (10 μM) used does not dramatically affects the human hepatocyte LO2 cells and HCC cell lines, instead, enhances the efficacy of ATO, indicating that PTL may be a more advantageous agent combined with ATO than other chemotherapeutics in treatment of HCC.

PTL is an inhibitor of NF-kB pathway and USP7 (33, 49), but ATO activates or inhibits NF-kB activity, depending on a cell type and the drug dose used (33, 50–52). Thus, we first focused on USP7-HUWE1-p53 axis to explore the mechanisms of combination therapy. Our results elucidated that single-agent ATO has no effect on the expression of USP7 and HUWE1, and the accumulation of p53 by ATO is evoked at transcript, but, PTL leads to the accumulation of p53 via weakening the interaction between USP7 and HUWE1, not at transcript (Figure 5, S2A). This conclusion revealed that combination of PTL and ATO can synergistically facilitate accumulation of p53 though two different pathways. Additionally, we found that PTL can not lead to the accumulation of p53 in combination with ATO in LO2 cells, and the detailed mechanism need to be further investigated (Figure S2B). On the other hand, the specific killing effect of PTL on cancer stem cells also contributes to the improvement of ATO in treatment of HCC. In this part, we just preliminarily explore the mechanism of combination therapy, and not further verify the conclusion in USP7-deficient cells or USP7+HUWE1 - deficient cells.

It is an effective strategy to eliminate the effect of antagonistic factors to antitumor drugs to improve clinical chemotherapy. Autophagy protects cancer cells from the application of chemotherapeutics, usually resulting in failure of therapy (38). Here, we first elucidated that combination of PTL and ATO can more obviously trigger autophagy in contrast with single agent, and weakening the autophagy further activates apoptosis induced by combined treatment. In addition, the conversion of LC3I to LC3II is not so obvious in MHCC 97H cell line, which was due to less sensitivity to drugs than other two cell lines (Figures 1A-D, 2A, B). To verify the explanation, we increased the concentration of ATO in MHCC 97H cell line, and the results showed that combination of PTL and ATO observably increase the conversion of LC3I to LC3II (Figure S1B). CQ, a traditional antimalarial drug, is widely used as inhibitor of autophagy to treat various solid tumors in clinic. In animal experiment, application of CQ helps PTL and ATO further shrink the size of tumor, meaning that PTL and CQ combined with nontoxic dosage of ATO may benefit patients with HCC. Moreover, we confirmed that ATO combined with PTL has more significant ability of inhibiting cell growth than ATO combined with CQ (Figure S2C). PI3K/AKT/mTOR pathway, a critical pathway of treatment in HCC, is similarly involved in the activation of autophagy caused by combination of ATO/PTL, which provides possible directions for exploring the drugs aiming at this pathway, thereby enhancing the efficacy of ATO/PTL combination therapy.

In future work, we will further go on exploring the relationship between autophagy and apoptosis, and optimizing the combination of autophagy inhibitors and ATO/PTL to achieve the transformation from basic research to clinical application.

Conclusions

All in all, our study demonstrated that PTL is the best candidate for optimizing the anti-tumor efficacy of ATO in application of hepatocellular carcinomas in vitro and in vivo. In addition, we also found USP7, a target protein of PTL, participates in the effect of PTL enhancing ATO-induced apoptosis to HCC cell lines via USP7-HUWE1-p53 axis. Meanwhile, our findings showed that the treatment of drug combination can more significantly enhance the activity of autophagy than that of each reagent, and the inhibition of autophagy using genetic or pharmacological methods can further improve the anti-tumor efficacy of a combination of ATO with PTL.

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 animal study was reviewed and approved by Laboratory Animal Science and Technology Work Management Committee, School of Basic Medicine, Lanzhou University.

Author contributions

Conceptualization, JY, XG, X-YY, and H-LW; methodology, JY and X-YY; soft-ware, J-XH and GC; validation, L-NW, LW, and X-YY; formal analysis, BX; investigation, JC; resources, JY; data curation, XG; writing—original draft preparation, JY, and X-YY; writing—review and editing, JY, XG, and H-LW; visualization, JY; supervision, JY, and H-LW; project administration, JY, XG, X-YY and H-LW; funding acquisition, JY, XG, and D-CW. All authors contributed to the article and approved the submitted version.

Funding

This work was supported by the National Natural Science Foundation of China (NO.31701206); Natural Science Foundation of Gansu Province, China (No. 20JR5RA281 and NO. 21JR11RA089); Open Project of State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical Colleges (No.GTZK 202006).

Acknowledgments

We thank State Laboratory of Bioactive Substance and Function of Natural Medicines, Institution of Material Medica, Chinese Academy of Medical College for providing partial funding.

Conflict of interest

D-CW was employed by LanZhou Ke Bao Biotechnology Co., Ltd.

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.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2022.988528/full#supplementary-material

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SYSTEMATIC REVIEW
published: 21 November 2022
doi: 10.3389/fonc.2022.1017864

Prognostic significance of CDC20 expression in malignancy patients: A meta-analysis

Feng Xian ¹,², Xuegang Yang ³ and Guohui Xu ³*

¹ School of Medicine, University of Electronic Science and Technology of China, Chengdu, China, ² Oncology Department, Nanchong Central Hospital, The Second Clinical Institute of North Sichuan Medical College, Nanchong, China, ³ Department of Interventional Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China

Edited by:
Hongda Liu, Nanjing Medical University, China

Reviewed by:
Shan Lei, Guizhou Medical University, China
Shuai Wang, University of Pittsburgh Medical Center, United States

*Correspondence:
Guohui Xu
xfyixue@sina.com

Specialty section:
This article was submitted to Cancer Molecular Targets and Therapeutics, a section of the journal Frontiers in Oncology

Received: 12 August 2022

Accepted: 26 October 2022

Published: 21 November 2022

Citation:
Xian F, Yang X and Xu G (2022) Prognostic significance of CDC20 expression in malignancy patients: A meta-analysis. Front. Oncol. 12:1017864. doi: 10.3389/fonc.2022.1017864

Background

Cell Division Cycle Protein 20(CDC20) is reported to promote cancer initiation, progression and drug resistance in many preclinical models and is demonstrated in human cancer tissues. However, the correlation between CDC20 and cancer patients’ prognosis has not yet been systematically evaluated. Therefore, this present meta-analysis was performed to determine the prognostic value of CDC20 expression in various malignancy tumors.

Methods

A thorough database search was performed in EMBASE, PubMed, Cochrane Library and Web of Science from inception to May 2022. Stata14.0 Software was used for the statistical analysis. The pooled hazard ratios(HRs) and their 95% confidence intervals (95% CIs) were used to analysis of overall survival (OS), recurrence-free survival (RFS), distant-metastasis free survival (DMFS). Qualities of the included literature were assessed by JBI Critical appraisal checklist. Egger’s test was used to assess publication bias in the included studies.

Results

Ten articles were selected, and 2342 cancer patients were enrolled. The cancer types include breast, colorectal, lung, gastric, oral, prostate, urothelial bladder cancer, and hepatocellular carcinoma. The result showed strong significant associations between high expression of CDC20 and endpoints: OS (HR 2.52, 95%CI 2.13-2.99; HR 2.05, 95% CI 1.50-2.82, respectively) in the multivariate analysis and in the univariate analysis. Also, high expression of CDC20 was significantly connected with poor RFS (HR 2.08, 95%CI 1.46-2.98) and poor DMFS (HR 4.49, 95%CI 1.57-12.85). The subgroup analysis was also performed, which revealed that CDC20 upregulated expression was related to poor OS in non-small cell lung cancer (HR 2.40, 95% CI 1.91-3.02).

Conclusions

This meta-analysis demonstrated that highly expressing CDC20 was associated with poor survival in human malignancy tumors. CDC20 may be a valuable prognostic predictive biomarker and a potential therapeutic target in various cancer parents.

Keywords: CDC20, prognosis, malignancy, meta-analysis, overall survival

Introduction

Cancer is a common cause of morbidity and mortality throughout the world (1). Worldwide, an estimated 19.3 million new cancer cases and almost 10.0 million cancer deaths occurred in 2020 (2). In spite of desperate development of new remedies in recent years, the prognosis of cancer remains bleak (3). Therefore, detection of new biomarkers related to the progression of cancer is essential for improving clinical outcomes.

It is known that CDC20 consists of 499 amino acids with WD40 repeats at its C-terminus for protein binding, serving as the substrate recognizing subunit of Anaphase Promoting Complex (APC) (4). CDC20 has been found to play critical roles in regulating timely cell cycle progression in both the G2 and M phases (5). Multiple studies from various groups have demonstrated that CDC20 targets several key substrates including Securin, Cyclin B1, Cyclin A, Nek2A, p21 and Mcl-1 for degradation to govern cell cycle progression (6–11). Moreover, some studies show that p53, Mad2, RASSF1A and APC15 could inhibits tumors cell growth through regulation of CDC20 (12–15). CDC20 have been proven to have kinds of functions, including regulation of cell cycle,and regulation of apoptosis. Mounting evidence has revealed that CDC20 plays an oncogenic role in human tumorigenesis, and increased CDC20 expression is associated with clinical progression in human cancers, such as poor differentiation and poor recurrence-free survival rates (16, 17). However, it remains unclear whether CDC20 is associated with a worse outcome across solid cancer patients. These conflicting results may be due to the small sample size among individual studies and limitation of current technology.

Therefore, we present a meta-analysis evaluating the prognostic value of CDC20 over-expression in solid tumor. The purpose of this study was to estimate the correlation of CDC20 over-expression with survival in solid tumors, thereby shed more light on the development of CDC20 targeted therapy and prognostic prediction.

Materials and methods

Search strategy

This study followed the Preferred Reporting Items for Systematics Reviews and Meta-Analysis (PRISMA) (18). We utilized a systematic search based on the PubMed, Embase, Web of Science and Cochrane Library from the establishment of data to May 2022. The search terms included (“cell division cycle protein 20” or “CDC20”) and (“neoplasms” or “tumors” or “cancers” or “carcinoma” or “malignancies”) and (“prognosis” or “survival”). All potentially eligible studies were retrieved, and their bibliographies were carefully scanned to identify other eligible studies and extra studies were identified by a hand search of the references cited in the original studies. When multiple studies of the same patient population were identified, we included the published report with the largest sample size. The above search process was done by two reviewers independently.

Inclusion criteria

To be eligible for inclusion in this meta-analysis and data extraction, studies had to: (1) Patients were pathologically diagnosed with any type of malignancy. (2) The expression levels of CDC20 were identified in tissues samples. (3) Patients were classified into negative and positive expression or low and high expression group in line with the CDC20 of expression levels, the connection between expressing level of CDC20 and survival results was examined. (4) HR and 95% confidence intervals (CI) for survival times were computed by included articles which can provide enough data or survival curves. (5) Officially published and English-written literatures.

Exclusion criteria

Exclusion criteria were: (1) Duplicated articles; (2) literatures published as letters, editorials, abstracts, reviews, case reports and export opinions; (3) experiments performed in vitro or in vivo, but not based on patients; (4) insufficient data about survival analysis; (5) the follow-up duration was shorter than 3 years.

Data extraction

Two reviewers extracted related data from the articles independently and came to an agreement on the following items. Original data of elementary demographic characteristics (authors of article; year of publication; detection method; age; region; CDC20 level; the category of carcinoma; follow-up duration; endpoints; the Joanna Briggs Institute (JBI) score) were exhaustively extracted from included literature involving Kaplan-Meier curves, test words and tables. In term of endpoints, OS, RFS and DMFS were considered as terminal events. For some articles, HR can be directly obtained; for the studies in which survival data are presented only with K-M curves, Tierney’s method was employ was used to calculate the HR and 95% CI (19).

Methodological assessment

Two investigators individually assessed qualities of all enrolled studies by utilizing the JBI (20). The JBI Critical appraisal checklist for observational cohort study included 11 items. We regarded the included studies with at least 15 score as high-quality in methodology. And if the score were less than 15, those articles were considered as low-quality studies.

Statistical analysis

Our quantitative calculation was conducted based on Stata Software 14.0. We applied pooled HRs (high/low) along with its related 95% CIs to evaluate the association between the prognostic value and the expression levels of CDC20 in different malignancies. By utilizing Cochran’s Q and I² statistics, the heterogeneity of enrolled literatures can be evaluated precisely. Additionally, Chi square-based Chochran Q test and I² test were calculated to determine the heterogeneity among these articles (21). Heterogeneity was considered insignificant when p>0.10 or I² < 50%, and then fixed-effects was employed to pool the HRs and 95% CIs; Otherwise, a fixed-effects model was used.

In order to explore the source of heterogeneity, we also performed subgroup analysis and meta-regression. Furthermore, sensitivity analysis was implemented to confirm the steadiness of collected results. Finally, we assessed publication bias by means of utilizing Egger’s test. What’s more, if the p-value is no more than 0.05, the results above all can be regarded as statistical significance.

Results

Characteristics of studies

Eventually, ten studies (22–31) involving a total of 2342 patients were used for the meta-analysis (Figure 1). The included studies are summarized in Table 1. Two studies evaluated lung cancer, two studies evaluated breast cancer, and one each evaluated colorectal cancer, hepatocellular carcinoma, gastric cancer, oral cancer, urothelial bladder cancer, prostate cancer. The studies were performed in six countries (People’s Republic of China, Finland, United King, Gandra, Korea, Japan) and published prior to May 2022.

Figure 1 | The flow chart of the selection process in our meta-analysis.

Table 1 | Characteristics of the included studies.

Relationship between CDC20 expression level and OS of malignancy patients

There were eight studies that reported OS data with multivariate analysis. The relevant results showed that CDC20 overexpression in human tumor tissues was associated with a decrease in survival among malignancy patients (HR 2.52, 95% CI 2.13-2.99, p <0.001) in the multivariate analysis and (HR 2.05, 95% CI 1.50-2.82, p <0.001) in the univariate analysis (Figures 2A, B). There was slight heterogeneity among the eight studies mentioned (P =0.18, I² = 30.8%). Subgroup analyses were conducted as different factors including type of cancer (lung cancer or other cancers), follow-up duration (over 60 or less than 60 months), country (China or other country), and the pooled HRs for OS were shown in Figures 3A–C. The results of subgroup analysis showed that the high expression of CDC20 was associated with poor OS of malignancy patients (Table 2).

Figure 2 | Forest plot of meta-analysis of the relationship between CDC20 expression and OS of malignancy patients. (A) HR of OS in the multivariate analysis, (B) HR of OS in the univariate analysis.

Figure 3 | Forest plot of meta-analysis of the relationship between CDC20expresison and OS of malignancy patients as different factors. (A) Subgroup analysis stratified by type of cancers [lung cancer (group 1) and other cancers (group 2)]. (B) Subgroup analysis stratified by follow-up time [over 60 months (group1) or less than 60 months (group2)]. (C) Subgroup analysis stratified by country [China (group1) or other countries (group2)].

Table 2 | Subgroup analysis of pooled HRs for OS in cancer patients with abnormal expression level of CDC20.

Relationship between the expression of CDC20 and OS of lung cancer patients

Additionally, the prognosis role of the expression levels of CDC20 in lung cancer was assessed systemically. The results suggested that elevated CDC20 level implicated an unfavorable OS in lung cancer (HR 2.40, 95% CI 1.91, 3.02, p <0.001) (Figure 4).

Figure 4 | Forest plot of meta-analysis of the relationship between CDC20 expression and OS of lung cancer patients.

Relationship between CDC20 expression level and RFS, DMFS of malignancy patients

Among the included studies, two research estimated the relevance between CDC20 expression level and RFS, DMFS, respectively. The results showed that CDC20 increasingly expression was significantly related with poor RFS (HR 2.08, 95% CI 1.46, 2.98, p <0.001) (Figure 5A) and DMFS (HR 4.49, 95% CI 1.57, 12.85, p <0.001) (Figure 5B). However, we did not perform subgroup analysis for other types of cancer because of the insufficient numbers of trials included.

Figure 5 | Forest plot of meta-analysis of the relationship between CDC20 expression and RFS (A) and DMFS (B) of malignancy patients.

Sensitivity analysis

In order to assess the impacts of single study on the total outcomes, sensitivity analysis was conducted. AS to OS, our result of sensitivity analysis revealed that all the outcomes could not influence consequences remarkably, which means that the outcomes of OS were stable. The list of pooled HRs and 95% CIs after excluding single study one by one indicated the robustness of our results (Figures 6A, B). Furthermore, the sensitivity analysis RFS (Figure 6C) and DMFS (Figure 6D) identified that each included study influenced outcomes greatly, which suggested that the results of RFS and DMFS were not stable because of the limited number of studies included in each analysis. Thus, more related studies were needed to explore the effects of CDC20 on RFS and DMFS in human malignancy.

Figure 6 | Sensitivity analysis of the meta-analysis. (A) OS of CDC20 expression levels in the multivariate analysis. (B) OS of CDC20 expression levels in the univariate analysis. (C) RFS of CDC20 expression levels. (D) DMFS of CDC20 expression levels.

Publication bias

By Egger’s test, we systemically assessed publication bias of all above included studies. The result of Egger’s test (p =0.664) (Figures 7A, B) about OS revealed that there existed no significant publication bias among enrolled documents. We didn’t perform the publication bias of RFS and DMFS because of no more than four studies included in each analysis.

Figure 7 | Egger’s funnel plots for the studies involved in the meta-analysis. (A) OS in the multivariate analysis. (B) OS in the univariate analysis.

Discussion

The results of the study illustrated that elevated CDC20 expression indicated unfavorable prognosis OS of various malignancy patients, which was consistent with Wang et al.’s study (32). Our study further found that high CDC20 expression was connected with poor RFS and DMFS in malignancies. What’s more, we did more subgroup analyses stratified by follow-up time, type of cancers, different countries, respectively. Then we discovered that high expressing CDC20 was related to poor OS in lung cancer.

The above conclusions appear to be rational and understandable in line with the current agreement that as a chief cancer promoter, CDC20 can promote the abnormal growth and tumorigenesis of different kinds of tumors, such as lung cancer, which can serve as a promoter for regulating the progression of G1/S transition and the survival of cancer cells (10, 33). What’s more, Zheng et al. had demonstrated that besides the initiation, the presence of CDC20 is essential for tumor maintenance (34), which jointly contributes to the unfavorable prognosis in patients with elevated CDC20 expression level. Furthermore, Gao et al. and Li et al. showed that CDC20 could be useful for the treatment of osteosarcoma and might be a promising solution for the treatment of osteosarcoma with some chemotherapeutics insensitivity (35, 36). Notably, creasing studies found that CDC20 played a critical role in hematological malignancies as a prognostic factor and therapeutic target (37). Thus, CDC20 is likely to act as a prognosis factor for the occurrence, maintenance, drug resistance of malignancy tumors.

Furthermore, we also explored the association between the CDC20 expressing levels with the prognostic value among various cancers. But just on account of the restricted amounts of selected research, we only evaluated the prognostic value of CDC20 in lung cancer. And the result showed that higher CDC20 level implicated an unfavorable OS in lung cancer. Moreover, although we found that there existed sight heterogeneity among non-lung cancer group, it presented significant correlation between the elevated expression levels of CDC20 and the poor OS of non-lung cancer (HR 2.67, 95%CI 2.08-3.44, p <0.001). Based on the above, we believe that the prognostic role of CDC20 in diverse cancers is significant.

With regard to RFS, DMFS, disease free survival (DFS) and progression- free survival (PFS), these are all essential parameters reflecting the procession of malignancy. As all the included studies did not present any data about DFS and PFS, we just made analysis about RFS and DMFS. The outcomes of this meta-analysis revealed that higher CDC20 level implicated a poor RFS and DMFS in tumor patients. What’s more, due to the fact that only two researched were enrolled to appraise the connection among CDC20 expressing levels and RFS, DMFS respectively, more researched are essential to investigate the connection about CDC20 and the development of cancer.

Except for the encouraging results, there are several limitations among this quantitative meta-analysis. First, there was a risk of publication bias, as some studies with small sample sizes or negative results may not have been published. Second, there may be a certain publication bias within some of the included studies, as any negative results are less likely to have been reported. Finally, the results cannot fully represent all solid tumors and hematological malignancies since the types of cancer covered by included trials are incomplete, and further clinical trials are needed to explore.

Conclusions

In sum up, this meta-analysis suggested that higher expressing levels of CDC20 was correlative to poor prognosis of OS, RFS and DMFS among difference kinds of malignancy patient. In brief, our current study is the most comprehensive meta-analysis that systemically explores the incontrovertible evidence of the prognosis value of CDC20 in various malignancy patients. More related works still need to improve the understanding of CDC20 expression and prognosis in difference cancer types.

Data availability statement

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

Author contributions

FX and GX designed this study. FX and XY contributed to the literature search, review, and data extraction. FX and GX conducted the statistical analyses. FX and XY contributed to the manuscript drafting. FX and GX contributed to the manuscript revision. XY offered the funding. All authors accepted the eventual manuscript. All authors read and approved the final manuscript.

Funding

The meta-analysis was supported by Beijing Medical Award Foundation (YXJL-2020-0972-0424), Natural Science Foundation of Sichuan (No. 2022NSFSC0837) and Science and Technology Project of Chengdu (No. 2022-YF05-01811-SN).

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.

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ORIGINAL RESEARCH
published: 25 November 2022
doi: 10.3389/fonc.2022.1060574

KIAA1429 regulates alternative splicing events of cancer-related genes in hepatocellular carcinoma

Zhao-chen Liu ¹, Lu-Hao Li ¹, Ding-Yang Li ¹, Zhi-Qiang Gao ¹, Dong Chen ², Bin Song ², Bing-Hua Jiang ³ and Xiao-wei Dang ¹*

¹ Department of Hepatobiliary and Pancreatic Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China, ² Center for Genome Analysis, Wuhan Ruixing Biotechnology Co. Ltd, Zhengzhou, China, ³ Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China

Edited by:
Xuesong Gu, Beth Israel Deaconess Medical Center and Harvard Medical School, United States

Reviewed by:
Zequn Li, The Affiliated Hospital of Qingdao University, China
Tianpei Guan, Guangzhou Medical University Cancer Hospital, China

*Correspondence:
Xiao-wei Dang
dangxw1001@zzu.edu.cn

Specialty section:
This article was submitted to Cancer Molecular Targets and Therapeutics, a section of the journal Frontiers in Oncology

Received: 03 October 2022

Accepted: 02 November 2022

Published: 25 November 2022

Citation:
Liu Z-c, Li L-H, Li D-Y, Gao Z-Q, Chen D, Song B, Jiang B-H and Dang X-w (2022) KIAA1429 regulates alternative splicing events of cancer-related genes in hepatocellular carcinoma. Front. Oncol. 12:1060574. doi: 10.3389/fonc.2022.1060574

Hepatocellular carcinoma (HCC) remains one of the most fatal malignancies with high morbidity and mortality rates in the world, whose molecular pathogenesis is incompletely understood. As an RNA-binding protein participating in the processing and modification of RNA, KIAA1429 has been proved to be implicated in the pathogenesis of multiple cancers. However, how KIAA1429 functions in alternative splicing is not fully reported. In the current study, multi-omics sequencing data were used to analyze and decipher the molecular functions and the underlying mechanisms of KIAA1429 in HCC samples. RNA sequencing data (RNA-seq) analysis demonstrated that in HCCLM3 cells, alternative splicing (AS) profiles were mediated by KIAA1429. Regulated AS genes (RASGs) by KIAA1429 were enriched in cell cycle and apoptosis-associated pathways. Furthermore, by integrating the RNA immunoprecipitation and sequencing data (RIP-seq) of KIAA1429, we found that KIAA1429-bound transcripts were highly overlapping with RASGs, indicating that KIAA1429 could globally regulate the alternative splicing perhaps by binding to their transcripts in HCCLM3 cells. The overlapping RASGs were also clustered in cell cycle and apoptosis-associated pathways. In particular, we validated the regulated AS events of three genes using clinical specimens from HCC patients, including the exon 6 of BPTF gene and a marker gene of HCC. In summary, our results shed light on the regulatory functions of KIAA1429 in the splicing process of pre-mRNA and provide theoretical basis for the targeted therapy of HCC.

Keywords: alternative splicing, KIAA1429, HCC, RIP, BPTF

Introduction

Liver cancer ranks sixth among the most frequently-diagnosed cancers and fourth in terms of death ranks, with 75%-85% of cases being caused by hepatocellular carcinoma (HCC) (1). The majority of patients are diagnosed in later stages with poor prognosis. Apart from Hepatitis C virus and Hepatitis B virus infection, HCC progression was associated with several important factors, including inflammation, various molecular events, and different cellular signaling pathways (2). Although there are a variety of therapies, targeted treatment and immunotherapeutic drugs for HCC, drug resistance, tumor recurrence and metastasis still greatly limit the efficacy (3, 4). Therefore, deciphering the key events in cancer progression and thoroughly understanding the mechanisms of liver carcinogenesis will promote the discovery of new targeted drugs.

In pre-messenger RNA (pre-mRNA) splicing, which occurs in post-transcription, introns are spliced to create mature mRNA molecules. Simultaneously, different exons are combined to produce various transcripts. Alternative splicing (AS) defects that are regulated by mutation or in splicing regulatory factors and pre-mRNA sequences, were related to numerous pathologies (5). Dysregulation of AS is a hallmark of human tumors. Moreover, in tumors, these splicing variants specific to cancer are often up-regulated, contributing to cancer development and cancer cell survival, and predicting overall survival time in cancer patients (6, 7). Aberrant alterations of RNA splicing events and splicing factors have been involved in multiple cancers. In cervical neoplasia, the aberrant activity of serine/arginine-rich (SR) proteins and heterogeneous nuclear ribonucleoproteins (hnRNPs) was found to initiate the generation of cancer-causing proteins through processing pre-mRNA transcripts, which were generated from human papillomaviruses genomes or human genes (8). In gastric cancer, overexpressing PTBP3, which was implicated in alternative splicing, may cause inhibition of the differentiation and malignant proliferation of these cells through disrupting the feedback regulation among Hes1, Id1, and PTBP3 (9). Differential AS events (ASEs), which are prevalent in HCC, are largely influenced by the binding relations, expression variations, and even mutations of RNA binding proteins (RBPs) (10). Nuclear-enriched RBP-PTBP3, improves HCC cell metastasis and growth by balancing the splicing variants (NEAT1_1, NEAT1_2 and miR-612) (11). MTR4 drives the metabolic activity of cancer by ensuring that the differential splicing of pre-mRNAs of key glycolytic genes such as GLUT1 and PKM2 is correct (12). Furthermore, prognostic AS signatures have been constructed to predict HCC prognosis and have showed excellent performance in predicting HCC prognosis. Similarly, prognostic AS events were reported to be clustered in metabolism-associated pathways (13, 14).

Among the AS regulators, KIAA1429 is known as an RBP and an important methyltransferase participating in mRNA processing and splicing and m⁶A modification (15). In the cells of mammals, N6-methyladenosine (m⁶A) modification is reversibly regulated by m⁶A writers, erasers, and readers (WERs). Notably, KIAA1429 knockdown is reported to cause m⁶A peak scores to decrease by a median ∼4-fold, which is more conspicuous than that achieved when METTL3 or METTL14 was knocked down, demonstrating that KIAA1429 was essential in the methyltransferase complex (16). Strikingly, accumulating evidence proves that RNA m⁶A modification affects AS events. The m⁶A reader YTHDC1 mediates mRNA splicing by recruiting or blocking the pre-mRNA splicing factors, including SRSF3 and SRSF10 (17), so that those splicing factors can gain or lose access to the binding areas of targeted mRNAs. These discoveries proved the importance of RNA m⁶A modification in regulating AS.

KIAA1429 is involved in the pathogenesis of multiple cancers and its expression is related to the prognostic effect in patients (15, 18, 19). Scientists began to study the functions of KIAA1429 in HCC in recent years. KIAA1429 promoted the invasion and migration of HCC through the inhibition of ID2 via the upregulation of m⁶A modification of ID2 mRNA (20). And KIAA1429 was closely associated with the prognostic effect of HCC. A prognostic model which included m⁶A genes (ZC3H13 YTHDF1, YTHDF2, METTL3 and KIAA1429) was developed (21). KIAA1429 facilitates the development of liver cancer through regulating the expression of GATA3 by m⁶A methylation modification (22).

Up until now, the functions of KIAA1429 in AS, tumor genesis and associated mechanisms have not been fully studied. Since methylation is inseparable from alternative splicing and KIAA1429 can bind to RNA and cause m⁶A methylation, we predict that the binding of KIAA1429 to RNAs may regulate the alternative splicing of bound RNAs, thus playing an important role in HCC. Therefore, we downloaded GSE134776 data, which is the transcriptome data obtained from silencing KIAA1429 in HCC cell line HCCLM3. Differentially expressed genes (DEGs) and AS analysis were performed to obtain KIAA1429-regulated AS profile. Then we downloaded GSE134978 data, which is RIP-seq data of KIAA1429 in HCC cell line HCCLM3 with 2 biological duplications. Analysis of KIAA1429-bound RNA was performed. The two sets of data were overlapped to obtain genes that can combine with and be regulated by KIAA1429 for the occurrence of AS events. We extensively studied the function and mechanism of KIAA1429 in liver cancer by deciphering its important roles in AS regulation. This study extended our understanding of KIAA1429 and provided novel insights into the new treatments for HCC in the future.

Material and methods

Access to and processing of public data

We used KIAA1429-regulated transcriptome sequencing data (RNA-seq) and its associated RNA sequencing data by the method of RNA sequencing (RIP-seq) and immunoprecipitation (22). The accession numbers of Gene Expression Omnibus (GEO) database were GSE134978 (RIP-seq) and GSE134776 (RNA-seq), respectively. Public sequencing data were obtained from the Sequence Read Archive (SRA). SRA Run data files were transformed into fastq format with NCBI SRA Tool fastq-dump. Low-quality bases were discarded using a FASTX-Toolkit (v.0.0.13). Then the clean reads were analyzed with FastQC.

Reads alignment and differentially expressed gene analysis

We aligned quality-filtered reads to the human genome (GRCH38) with TopHat2 with at most 4 mismatches (23). Then we used uniquely mapped reads to count the read number and reads per kilobase of exon per million fragments mapped (RPKM) of each gene. Next, we used RPKM to calculate the expression levels of genes. The R package edge R (24), whose function is to identify which genes are differentially expressed, was applied to identify DEGs from RNA-seq data. The false discovery rate (FDR ≤ 0.05) and fold change (FC≥2 or ≤0.5) were applied to determine whether a gene was significantly differential between siKIAA1429 and control.

Alternative splicing analysis

ABLas pipeline was applied to define and quantify alternative splicing events (ASEs) and regulated alternative splicing events (RASEs) by siKIAA1429 as described previously (25, 26). Briefly, on the basis of the splice junction reads, we detected ten types of alternative splicing events, including exon skipping (ES), alternative 3’splice site (A3SS), alternative 5’ splice site (A5SS), A3SS&ES, A5SS&ES, mutually exclusive exons (MXE), intron retention (IR), cassette exon, mutually exclusive 5’UTRs (5pMXE), and mutually exclusive 3’UTRs (3pMXE).

For sample pair comparison, Fisher’s exact test was applied to determine statistical significance. The p-value <=0.05 and RASE ratio >=0.2 were set as the threshold to detect RASEs. For repetition comparison, Student’s t-test was conducted to evaluate the significance of AS ratio alteration. Those events with p-value <= 0.05 were regarded as RASEs.

RIP-seq data analysis

For KIAA1429 RIP-seq data, the reads-mapping and quality-filtering methods were the same as RNA-seq data. After the uniquely mapped reads were aligned onto the genome sequences, we used random IP method to detect the binding sites (peaks) of KIAA1429 on transcripts, which had been fully described in a previous study (26). After filtering the binding peaks of KIAA1429 with p-value < 0.05 as criterion, we extracted the sequences of peaks and detected the enriched motifs using HOMER software (27).

Functional enrichment analysis

We used Gene Ontology (GO) terms, KEGG pathways and Reactome pathways to identify functional categories of DEGs via KOBAS 2.0 server (28). The statistical significance was adjusted by Benjamini-Hochberg FDR.

Validating alternative splicing events by qPCR

We further validated the alternative splicing events of AS genes (URI1, MTMR14, BPTF) regulated by KIAA1429 in clinical samples using RT-qPCR. We extracted 13 pairs liver cancer tissues and adjacent normal tissues of HCC patients from the First Affiliated Hospital of Zhengzhou University and examined the AS levels of three genes selected. The studies involving human participants were reviewed and approved by the ethics committee of the First Affiliated Hospital of Zhengzhou University. All methods were employed according to the regulations and guidelines. We strictly conformed to the biosecurity law and followed institutional safety procedures in China.All the specimens were processed instantly after being collected, and then stored at − 80°C for RNA extraction. TRIzol reagent (Invitrogen) was used to extract toal RNAs. Then PrimeScript RT reagent Kit (Takara) was used to convert 10 μg RNA into complementary DNA (cDNA). RT-qPCR was conducted according to the published method (29, 30).

Other statistical analysis

After the reads of each gene were normalized by TPM (Tags per Million), in-house script (sogen) was used to visualize genomic annotations and next-generation sequence data. Based on Euclidean distance, the pheatmap package (https://cran.rproject.org/web/packages/pheatmap/index.html) in R was used to perform the clustering the two groups were compared using Student’s t-test.

Result

KIAA1429 is highly expressed in HCC and negatively associated with the prognosis of patients

To have an overview of the expression level and prognosis effect of KIAA1429 in liver cancer, we downloaded RNA-seq expression data of 419 samples in TCGA (The Cancer Genome Atlas) database, including 369 tumor samples and 50 normal samples. Then the expression level of KIAA1429 was analyzed. The result showed that KIAA1429 was higher expressed in 369 tumor samples compared with in 50 normal tissue samples with significance (Figure 1A). We further identified the relationship between the survival rate in HCC patients and the expression level of KIAA1429. The overall survival (OS) rate of patients in low KIAA1429 group was higher than that in high KIAA1429 group (Figure 1B), demonstrating that high expression of KIAA1429 may lead to a poor prognosis inHCC. To explore the expression level of KIAA1429 in multiple cancers, we used GEPIA2 online tool (31) and found that the expression levels of KIAA1429 were higher in multiple tumors (Figure S1A). Survival rate analysis showed that higher expression of KIAA1429 was correlated with worse prognosis in multiple cancer types (Figure S1B). These results indicate that KIAA1429 performs important functions in the progression of HCC.

Figure 1 | Analysis of KIAA1429 expression levels and prognosis in liver hepatocellular carcinoma samples from TCGA database. (A) Boxplot showing the expression level of KIAA1429 in 419 liver hepatocellular carcinoma samples from TCGA database, including 50 normal samples and 369 tumor samples. Error bars represent mean ± SEM. *p < 0.05. (B) Overall survival (OS) rate of HCC patients with high expression of KIAA1429 (top 25% of the expression range) versus low expression of KIAA1429 (bottom 25% of the expression range).

RNA Immunoprecipitation sequencing analysis showed the RNA binding features of KIAA1429 in HCCLM3 Cells

To further investigate how KIAA1429 functions in HCC, we downloaded RIP-seq data of KIAA1429 in HCC cell line HCCLM3 (GSE134978), and analyzed the global RNA binding features of KIAA1429. After the quality-filtered reads were aligned onto human genome, we found that KIAA1429 showed higher percentage in coding DNA sequence (CDS) and 3’ untranslated regions (UTR) regions compared with IgG control, and reads aligned in 3’UTR and CDS regions occupied over 70% of total aligned reads (Figure 2A). We then divided CDS, 5’UTR, and 3’UTR into 100 portions for each region and analyzed the reads distribution in these regions. The results showed that there was one peak around start codon and one around stop codon. Meanwhile, CDS and 3’UTR regions also showed higher levels in KIAA1429 IP samples compared with in IgG (Figure 2B), consistent with the results in Figure 2A. We then systematically identified the binding sites (peaks) of KIAA1429 using previously described method (26). A total of 28896 and 16544 peaks were detected from replicate 1 and 2, respectively; and 3895 peaks were shared by the two replicates (Figure 2C). Following analysis of overlapping genes bound by KIAA1429 in two biological replicates revealed that up to 5524 genes were shared by the two replicates (Figure 2D). After functional enrichment analysis was conducted, these overlapping genes were found to be clustered in GO BP pathways including RNA metabolism, mRNA metabolism, cell protein metabolism, mitosis, mitotic cell cycle, viral reproduction, nuclear mRNA splicing via spliceosome, RNA splicing, and DNA repair (Figure 2E), indicating KIAA1429 could bind to numerous RNAs with essential functions in HCC HCCLM3 cells. Similar analysis using KEGG pathway database also revealed that bound genes were highly enriched in RNA splicing and cell cycle-associated pathways (Figure S2A). Furthermore, we analyzed the enriched motif sequences among the KIAA1429-bound peaks using HOMER software (27), the results of which showed that the specific binding motif of KIAA1429 was UCGAUG in two biological replicates (Figure S2B). In summary, the RIP-seq confirmed that KIAA1429 had the ability to bind multiple target RNAs and had potential effects on the targeted RNAs in HCC cells.

Figure 2 | RIP-seq data demonstrated the RNA interactome of KIAA1429 in HCCLM3 cells. (A) The distribution of genomic region of KIAA1429-bound peaks. were shown by bar plot. (B) The peak reads distribution on transcripts was shown by line plot. Three regions (5’UTR, CDS, and 3’TUR) in each gene were divided into 100 bins. The KIAA1429 peak reads of each bin were counted. The reads density of KIAA1429 peaks in each gene was presented. (C) The overlapping peaks in two replicate RIP-seq samples were shown by venn plot. (D) The overlapping KIAA1429-bound genes in two replicate RIP-seq samples were shown by venn plot. (E) The top 10 enriched GO biological processes of genes bound by KIAA1429 were shown by bar plot.

KIAA1429-mediated alternative splicing events in HCCLM3 cells

To further explore the outcoming influence of KIAA1429 on its bound transcripts in HCCLM3 cells, we downloaded the global transcriptional sequencing (RNA-seq) data (GSE134776) of KIAA1429-silenced and control HCCLM3 cells with 2 biological duplicates, which were generated using the same cell batch of HCCLM3 RIP-seq data. After aligning RNA-seq data onto genome and calculating gene expression levels and splicing junction reads, we performed DEGs and alternative slicing AS analyses to obtain DEGs and AS events regulated by KIAA1429. Since the previous study of the RNA-seq data focused on DEGs (22), in this study we mainly investigated the RASEs of KIAA1429. Using ABLas program (26), we analyzed the alternative splicing events of the RNA-seq dataset and investigated the ratio changes in AS occurrence. Besides the known AS events previously annotated in the annotation file, we discovered plenty of novel alternative splicing events and classified them into ten canonical AS types. Dominant alternative splicing types include IR, ES, A5SS and A3SS (Figure 3A). Then we extracted RASEs with significant differences (p-value < 0.05) between siKIAA1429 samples and control samples, and detected hundreds of RASEs after silencing KIAA1429 (Figure 3B). The changed ratios of these RASEs showed consistent patterns in the two biological replicates (Figure S3A). Functional enrichment analysis of genes from RASEs revealed that these genes mediated by KIAA1429 at AS level were highly enriched in the positive regulation of fibroblast proliferation and the decomposition of cell components during apoptosis (Figure 3C), which were closely related to tumor progression. Transcription-related pathways were also enriched (Figure 3C). KEGG and Reactome pathway analyses demonstrated several pathways associated with tumor progression (Figure 3D, Figure S3B). Our DEG and RASG overlapping analyses showed that only 5 genes are both DEGs and RASGs, including CPZ, CYP27B1, GRB10, RP11-656D10.3, and TENM1 (Figure 3E). We then illustrated the expression levels and splicing ratio changes of these 5 genes and found consistent changes between siKIAA1429 and control (Figure 3F). In summary, these results indicate that KIAA1429 could regulate transcriptome profile through transcriptional and post-transcriptional regulation manners.

Figure 3 | KIAA1429-regulated alternative splicing events in HCCLM3 cells. (A) Classification of all the AS events detected. X-axis: Percentage. (B) Classification of all the AS events (RAS) regulated by KIAA1429. X-axis: AS number. up: alternative splicing pattern was up-regulated compared with model splicing pattern. down: alternative splicing pattern was down-regulated compared with model splicing pattern. (C) The top 10 enriched GO biological processes of alternative splicing genes regulated by KIAA1429. (D) The top 10 enriched KEGG pathways of alternative splicing genes regulated by KIAA1429. (E) Venn diagram displaying the overlap of differential expression genes (DEGs) and alternative splicing genes (RASGs) regulated by KIAA1429. (F) Box plots shows expression levels (left) and PSI profiles of two examples of overlapping genes from (E).

KIAA1429 selectively binds to mRNAs to regulate alternative splicing of cancer-associated genes

To further explore if there is an association between KIAA1429-bound transcripts and KIAA1429-regulated ASEs, we conducted an interaction analysis. Although there were only four RASEs that had KIAA1429-bound peaks around their genomic locations (Figure S4A), we found that about 67% (340/502) of alternative splicing genes regulated by KIAA1429 overlapped with genes bound by KIAA1429 (p-value ≤ 0.05, Hypergeometric test, Figure 4A), suggesting that KIAA1429 might regulate a large number of alternative splicing events through directly binding to RNA targets. Functional enrichment analysis of these overlapping genes demonstrated that they were highly enriched in several functional pathways, including decomposition of cellular components during apoptosis, cell cycle block, etc., which were closely related to liver cancer (Figure 4B). Functional enrichment analysis of KEGG and Reactome databases also revealed several enriched pathways (Figure S4B, C). We then selected and presented the splicing ratio changes of several RASGs that were from apoptotic process and were also bound by KIAA1429, including ACIN1, CASP8, GSN, and CAPN10 (Figure 4C). The splicing reads number and ratio changes of URI1 (ES event), MTMR14 (A5SS event), and BPTF (ES event) were also presented to show the significant differences between siKIAA1429 and control (Figure 4D and Figure S4D).

Figure 4 | KIAA1429 selectively binds to mRNA to regulate alternative splicing of cancer related genes. (A) Venn diagram showed the overlap of KIAA1429-bound genes and KIAA1429-regulated alternatively splicing genes. (B) The top 10 enriched GO biological processes of the genes of overlap of KIAA1429-bound peaks and -regulated alternatively splicing events. (C) Bar plots shows PSI profile of the four overlapped KIAA1429-regulated alternative splicing events by RNA binding in HCCLM3 Cells. (D) IGV-sashimi plot showed the KIAA1429-regulated alternative splicing events across mRNA of URI1 and MTMR14. Reads distribution of each alternative splicing event was plotted in the left panel with the transcripts of each gene shown below. The schematic diagrams depict the structures of ASEs at the top of the right panel. RNA-seq quantification of ASEs is shown at the bottom of the right panel. Error bars represent mean ± SEM. *p < 0.05.

Validation of alternative splicing events regulated by KIAA1429 in liver cancer using clinical specimens

We then validated the identified RASEs using clinical specimens from liver cancer patients by RT-qPCR experiment. We randomly selected three alternative splicing events regulated by KIAA1429, including URI1 (ES event), MTMR14 (A5SS event), and BPTF (ES event), which were shown in Figure 4 and Figure S4. Specific primers for RASEs were designed according to the exact splicing junction sequences (32). The AS ratios of these three AS events all increased in siKIAA1429 samples, indicating that they should increase in normal clinical specimens from which the expression level of KIAA1429 decreased compared with that of HCC samples (Figure 1A). The RT-qPCR results revealed that AS ratios of these three genes significantly decreased in cancer samples (p < 0.001, Figures 5A-C), conforming to the RNA-seq results in KIAA1429-silenced HCCLM3 cells.

Figure 5 | The validation of the alternative splicing events in clinical specimens. ***p < 0.001. (A–C): The RT-qPCR results revealed that AS ratios of these three genes significantly decreased in cancer samples (p < 0.001).

Discussion

As a multifunctional RNA binding protein and an important RNA methyltransferase, KIAA1429 is implicated in mRNA splicing and processing and m⁶A modification (15), and the altered KIAA1429 function enhances the proliferation, migration and invasion abilities of HepG2 cells through the inhibition of ID2 via the upregulation of m⁶A modification of ID2 mRNA (20). Additionally, increasing studies proved that KIAA1429 is related to the progression of multiple cancers, such as gastric cancer (19), osteosarcoma (18), and breast cancer (15). Thus, high-throughput methods have been adopted to identify targets of KIAA1429. KIAA1429 facilitates liver cancer progression by regulating the expression of GATA3 through m⁶A methylation modification (22). In this study, RNA immunoprecipitation sequence (RIP-seq) and RNA-seq data were applied to draw a comparison between the expression profiles of control and KIAA1429 stable knockdown HCC cells, the analyzing results of which, demonstrated that KIAA1429 can affect the alternative splicing patterns of genes implicated in various pathways by binding to numerous RNAs. Our results highlight the important regulatory roles of KIAA1429 in AS pattern. Thus, KIAA1429 might affect the development of HCC and could serve as a therapeutic target in the future.

The RIP-seq method was an approach to systematically identify the transcripts to which an RBP binds (33). In this study, RIP-seq analysis demonstrated that the RNA binding motif of KIAA1429 is UCGAUG. AS genes bound by KIAA1429 are enriched in functional pathways that are closely related to tumors, including mRNA metabolism, RNA metabolism, cell protein metabolism, mitosis, mitotic cell cycle, viral reproduction, nuclear mRNA splicing via spliceosome, RNA splicing, and DNA repair. This discovery greatly broadens our understanding of the functions of KIAA1429 in various biological processes. These results demonstrated that KIAA1429 functions in liver cancer mainly by regulating AS events. Previous studies have proved that KIAA1429 plays a vital part in the progression of liver cancer, but the functions of KIAA1429 in AS and tumorigenesis and associated mechanisms are still unclear. Our DEG and AS analysis shows that only 5 genes were differentially expressed and variably spliced simultaneously, indicating that KIAA1429 regulates distinct gene sets through transcriptional and post-transcriptional regulation. We hypothesized that KIAA1429 may, through its methylation, alter the variable splicing of key tumor genes and generate tumor-promoting spliceosomes, thus promoting tumor progression. In the present study, AS events regulated by KIAA1429 occurring in these genes were greatly enriched in the positive regulation of fibroblast proliferation and the decomposition of cell components during apoptosis, which were closely related to tumor progression. Meanwhile, a recent study demonstrated that key RNA methyltransferase METTL3 could promote tumorigenesis by enhancing translation of epigenetic factors in the absence of m⁶A (34). We propose that KIAA1429 may also regulate AS patterns independent of m⁶A modification in primary-RNAs.

Furthermore, recent study demonstrated that neoantigens for therapies based on immune in cancer can arise from dysregulated splicing (35). Since immune checkpoint inhibitors (e.g., ipilimumab and nivolumab) and chimeric antigen receptors (CAR-T) have succeeded in increasing patient survival, cancer therapies had been revolutionized by immunotherapy-based treatments (36, 37). Using RBP-focused CRISPRCas9 screening, researchers uncovered an essential role of YTHDF2 in breast cancer driven by MYC, highlighting the important functions of RBPs serving as effective therapeutic targets (38).Their studies revealed that RBP-RNA interactions promoted the progression of diseases and facilitated the growth and survival of tumor cells instead of somatic tissues, and that targeting RBPs is expected to be safe and effective and precise treatment modalities in particular cancer subtypes (38).For this reason, more studies are urgently needed to examine the immunogenicity of underlying neoantigens derived from AS events regulated by KIAA1429 for immunotherapies in liver cancer.

In addition, due to dysregulated splicing in cancer, oncogenes have undergone isoform switching as a mechanism through which cancer cells developed drug resistance to cancer treatments. For example, melanoma patients acquire resistance to RAF inhibitors through generating spliced isoforms of BRAF V600E in the absence of RAS-binding domain (39). Additionally, chronic myeloid leukemia patients with BCR-ABL chromosomal translocation displayed alternative splicing variants of BCR-ABL when treated with imatinib, the tyrosine kinase inhibitor (40). AS events regulated by KIAA1429 occurring in these genes were clustered in the positive regulation of the decomposition of cell components during apoptosis, suggesting that KIAA1429 may play a vital part in tumor drug resistance through regulating the splicing of related genes, which requires further exploration.

Our study also demonstrated that by directly binding to RNA targets, KIAA1429 might regulate many alternative splicing events. Several pathways emerged from the functional analysis of these genes: decomposition of cellular components during apoptosis, cell cycle block, etc., which are closely related to liver cancer. The 3 randomly selected alternative splicing genes (URI1, MTMR14 and BPTF) were further validated by qPCR in HCC clinical samples and alternative splicing events of these three genes are significantly changed (p < 0.001). Among them, BPTF is a protein-coding gene, about which researchers in neurodegenerative diseases are extremely concerned (such as Alzheimer’s disease) (41). In patients with neurodegenerative diseases, high expression levels of BPTF have been detected. In recent years, the role of BPTF in tumor has caught the attention of a large number of researchers. BPTF is found to be highly expressed in HCC. And high expression of BPTF leads to poor overall survival, which may be related to epithelial-mesenchymal transition (EMT) (42). Previous studies have shown that BPTF knockout also inhibits the growth and metastasis of HCC tumors in xenograft mouse models, and that BPTF may have the potential to be a new target for hepatocellular carcinoma therapy (43). Zhang et al. recently investigated the vital role of m⁶A modification and the METTL14/BPTF axis in the epigenetic and metabolic remodeling of metastasis of renal cell carcinoma, highlighting the BPTF inhibitor-AU1 as a key therapeutic candidate (44). Our preliminary study found that KIAA1429 inhibited the generation of BPTF variant splice subtype BPTF-A, but promoted the generation of BPTF-B, so we speculated that BPTF-B could have greater influence on HCC metastasis than BPTF-A, which needs future validation. However, our study is based on the RNA-seq and RIP-seq data from single cell line, which should be validated experimentally for further analysis. Further studied are urgently needed to reveal its specific and detailed mechanism, by which KIAA1429 regulate many alternative splicing events.

In summary, as an RBP, KIAA1429 is aberrantly expressed in HCC, whereas its role as a safe and effective drug target remains largely unexplored. Our study revealed that KIAA1429 regulated alternative splicing events by binding to transcripts which were tightly associated with cell cycle and apoptosis in live cancer. This functional manner of KIAA1429 may be achieved by m⁶A modification of RNAs, or perhaps by regulating m⁶A modification of host RNAs. Our results extended the understanding of the molecular mechanisms of KIAA1429 in HCC cells, demonstrating that KIAA1429 may be a potential molecular target for the development of new therapeutics for liver cancer treatment in the future.

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 University. The patients/participants provided their written informed consent to participate in this study.

Author contributions

B-HJ and XD designed and coordinated the project. D-YL and Z-QG performed the experimental research and analyzed the data. DC and BS performed bioinformatics analysis. ZL and L-HL wrote the manuscript. All authors commented on or contributed to the final manuscript.

Funding

This study was supported by Joint project of Henan Province Medical Science and Technology Program for Tackling Key Problems (LHGJ20190035).

Acknowledgments

The authors acknowledge the infrastructure and staff support provided by Center for Genome Analysis, Wuhan Ruixing Biotechnology Co. Ltd.

Conflict of interest

Authors DC and BS were employed by Wuhan Ruixing Biotechnology Co. Ltd

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.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2022.1060574/full#supplementary-material

Supplementary Figure 1 | Analysis of the expression levels and prognosis of KIAA1429 in different tumor samples from TCGA database. (A) Relative expression (TPM) of KIAA1429 in tumor samples (red) from 16 cancer types versus normal samples (green) *P < 0.05. (B) Correlation of KIAA1429 expression with the survival rates in multiple cancer types.

Supplementary Figure 2 | RIP-seq data showed the RNA interactome data of KIAA1429 in HCCLM3 cells. (A) the top 10 enriched GO biological processes of KIAA1429-bound genes were shown by bar plot. (B) Top five motifs of KIAA1429-bound peaks using HOMER software.

Supplementary Figure 3 | KIAA1429-mediated alternative splicing events in HCCLM3 cells. (A) PSI heatmap of all significantly regulated alternative splicing events (intron retention events, IR RAS) among KIAA1429-KD and control samples. (B) The top 10 enriched Reactome pathways of alternative splicing genes regulated by KIAA1429.

Supplementary Figure 4 | KIAA1429 selectively binds to mRNA for regulating the alternative splicing of cancer-related genes. (A) The overlap of peaks bound by KIAA1429 and alternatively spliced events regulated by KIAA1429 was shown in venn diagram. (B) The top 10 enriched KEGG pathways of the overlapping genes of peaks bound by KIAA1429 and alternatively spliced events regulated by KIAA1429. (C) The top 10 enriched Reactome pathways of the overlapping genes of peaks bound by KIAA1429 and alternatively spliced events regulated by KIAA1429. (D) The alternative splicing events regulated by KIAA1429 across mRNA of BPTF were shown in IGV-sashimi plot. In the left panel was the reads distribution of each alternative splicing event and the transcripts of each gene were shown below. At the top of the right panel were the schematic diagrams depicting the structures of ASEs. At the bottom of the right panel was RNA-seq quantification of ASEs. Error bars represent mean ± SEM. *p < 0.05.

Abbreviations

AS, alternative splicing, RASGs, Regulated AS genes, RNA-seq, Transcriptome sequencing data, RIP-seq, RNA Immunoprecipitation sequencing, HCC, hepatocellular carcinoma, pre-mRNA, pre-messenger RNA, DEG, differentially expressed gene, RBPs, RNA binding proteins, m6A, N6-methyladenosine, GEO, Gene Expression Omnibus, ES, exon skipping, A3SS, alternative 3’splice site, A5SS alternative 5’ splice site, MXE, mutually exclusive exons, IR, intron retention, 5pMXE, mutually exclusive 5’UTRs, 3pMXE, mutually exclusive 3’UTRs, ASEs, alternative splicing events, LIHC, liver hepatocellular carcinoma.

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ORIGINAL RESEARCH
published: 30 November 2022
doi: 10.3389/fonc.2022.1021632

Combining the SMAC mimetic LCL161 with Gemcitabine plus Cisplatin therapy inhibits and prevents the emergence of multidrug resistance in cholangiocarcinoma

Sunisa Prasopporn ¹,², Orawan Suppramote ²,³, Ben Ponvilawan ², Chanette Jamyuang ², Jantappapa Chanthercrob ¹, Amphun Chaiboonchoe ¹, Pimkanya More-Krong ¹, Kamonchanok Kongsri ¹, Monthira Suntiparpluacha ¹, Rawisak Chanwat ⁴, Krittiya Korphaisarn ⁵, Seiji Okada ⁶, Somponnat Sampattavanich ¹,² and Siwanon Jirawatnotai ¹,²*

¹ Siriraj Center of Research Excellence for Precision Medicine and Systems Pharmacology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand, ² Department of Pharmacology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand, ³ Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy, Bangkok, Thailand, ⁴ Hepato-Pancreato-Biliary Surgery Unit, Department of Surgical Oncology, National Cancer Institute, Bangkok, Thailand, ⁵ Division of Medical Oncology, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand, ⁶ Division of Hematopoiesis, Joint Research Center for Human Retrovirus Infection, Kumamoto University, Kumamoto, Japan

Edited by:
Zongli Zhang, Qilu Hospital of Shandong University

Reviewed by:
Kishor Pant, University of Minnesota Twin Cities, United States
Laisheng Li, The First Affiliated Hospital of Sun Yat-sen University, China

*Correspondence:
Siwanon Jirawatnotai
siwanon.jir@mahidol.ac.th

Specialty section:
This article was submitted to Cancer Molecular Targets and Therapeutics, a section of the journal Frontiers in Oncology

Received: 17 August 2022

Accepted: 11 November 2022

Published: 30 November 2022

Citation:
Prasopporn S, Suppramote O, Ponvilawan B, Jamyuang C, Chanthercrob J, Chaiboonchoe A, More-Krong P, Kongsri K, Suntiparpluacha M, Chanwat R, Korphaisarn K, Okada S, Sampattavanich S and Jirawatnotai S (2022) Combining the SMAC mimetic LCL161 with Gemcitabine plus Cisplatin therapy inhibits and prevents the emergence of multidrug resistance in cholangiocarcinoma. Front. Oncol. 12:1021632. doi: 10.3389/fonc.2022.1021632

Cholangiocarcinoma (CCA) is a highly lethal gastrointestinal malignancy that has one of the worst prognoses among solid tumors. The combination of Gemcitabine + Cisplatin (GEM/CIS) remains the standard first-line treatment for advanced stage CCA. However, this drug combination yields only a modest objective response rate, and in cases that initially respond to this treatment, drug resistance commonly rapidly develops. To improve the efficiency of GEM/CIS therapy for CCA, a thorough understanding of the mechanism of GEM/CIS resistance in CCA is required. To that end – in this study, we developed several acquired GEM/CIS-resistant CCA cell lines and we screened those cell lines for acquired vulnerability. The screening process revealed that subset of CCA with GEM/CIS resistance acquired vulnerability to the small-molecule second mitochondrial-derived activator of caspases (SMAC) mimetics LCL161 and Birinapant. The observed acquired vulnerability was found to be associated with upregulation of an inhibitor of apoptosis protein 2 (cIAP2), a known target of SMAC mimetics. LCL161 or cIAP2-shRNA downregulated cIAP2 and restored the sensitivity to GEM/CIS in GEM/CIS-resistant CCA cell lines and in in vivo GEM/CIS-resistant xenograft models. A strong synergic effect was observed when LCL161 was added to GEM/CIS. Interestingly, this synergism was also observed in drug-naïve CCA cell lines, xenografts, and patient-derived organoids. This triplet therapy also prevented the emergence of multidrug-resistant CCA in in vitro and in vivo models. Our findings suggest that activation of cIAP2 allows CCA to escape GEM/CIS, and that suppression of cIAP2 reestablishes the apoptotic profile of CCA, thus restoring its vulnerability to GEM/CIS. The results of this study indicate that combining the SMAC mimetic LCL161 with GEM/CIS inhibits and prevents the emergence of multidrug resistance in CCA.

Keywords: SMAC mimetics, LCL161, Gemcitabine plus Cisplatin therapy, multidrug resistance, cholangiocarcinoma, acquired vulnerability

Introduction

CCA is a difficult to treat cancer with high rates of recurrence and mortality. Factors that influence the unfavorable outcomes of treatment in this disease include delayed diagnosis, lack of a key molecular target, and lack of an effective and enduring pharmacologic treatment. The 5-year rate of overall survival in patients with advanced disease was reported to be as low as 2% (1). Drug resistance, both inherited and acquired, is a major problem that is responsible for the vast majority of treatment failure in CCA patients (2).

In recent years, a few oncogenic drivers have been identified as valid drug targets in CCA (i.e., mutant fibroblast growth factor receptors [FGFRs] or the isoforms 1 and 2 of isocitrate dehydrogenase [IDH1/2]); however, chemotherapy remains the key treatment modality for this disease (3). Based on the results of the Advanced Biliary Cancer (ABC)-02 trial, the combination of Gemcitabine + Cisplatin (GEM/CIS) has become the preferred first-line therapy for locally advanced or metastatic biliary tract cancer. Nonetheless, the outcome of GEM/CIS therapy is modest with an unacceptably low 28% response rate (RR) (4). The low RR is attributable to inherited or adoptive drug resistance after the treatment (5). Continued urgent study in GEM/CIS resistance in CCA is, therefore, essentially necessary, and a better understanding of the underlying mechanism of drug resistance will facilitate the development of both improved and novel therapies for CCA.

The complex mechanisms of chemoresistance in CCA facilitate the escape of cancer cells from the intended effect of anticancer agents (6, 7). Well-documented cell-intrinsic mechanisms of Gemcitabine resistance include altered drug metabolism, decreased intracellular drug concentration, and activation of prosurvival pathways, and most of these mechanisms are also observed in Cisplatin-resistant cancers (6, 8). Cisplatin resistance is also augmented by activated DNA damage repair (9). Preclinical and clinical evidence indicates that the combination of Gemcitabine and Cisplatin results in drug synergy that makes this combination therapy highly effective against several types of cancer. It has been demonstrated that Gemcitabine inhibits Akt serine/threonine kinase activity, and increases platinum-adduct retention due to decreased DNA repair when compared with Cisplatin alone (10, 11). The addition of Gemcitabine to the platinum-based therapy was shown to reverse Cisplatin resistance in several types of cancer (10, 12–14). Despite the high effectiveness of GEM/CIS therapy, the resistance to this duplet that often develops is effectuated by unknown or insufficiently well-understood mechanisms. The search for a third active drug that can synergize with GEM/CIS to prevent GEM/CIS resistance and improve treatment outcomes has started and is ongoing (4).

In the present study, we set forth to investigate the mechanism of GEM/CIS resistance, and to identify solutions that can overcome GEM/CIS resistance in CCA based on the principle of acquired vulnerability. It has been demonstrated that cancer cells under drug treatment attempt to reprofile their molecular network to survive and proliferate despite the presence of anticancer drug(s). However, this adaptation comes at a fitness cost to some collateral physical characters, which may result in an acquired vulnerability within the drug-resistant cell (15). We hypothesized that while under pressure from GEM/CIS treatment, CCA cells would rewire their molecular networks and become dependent on a new converged biological process/pathway to survive the chemotherapy. We aimed to identify the newly emerging therapeutic targets in GEM/CIS-resistant CCA cells by leveraging the acquired vulnerability of the CCA cells that grew under GEM/CIS treatment in the hope that inhibition of the acquired target would synergize with GEM/CIS and simultaneously block the escape of CCA from GEM/CIS treatment.

Our results revealed an acquired vulnerability in GEM/CIS-resistant CCA cells to second mitochondrial-derived activator of caspases (SMAC) mimetics, especially LCL161 and Birinapant. We also observed strong drug synergism between GEM/CIS and LCL161 in various CCA models, and uncovered the mechanistic basis for the observed drug synergism, which facilitates direct translation for clinical investigation of these findings.

Materials and methods

Cell culture and the construction of resistant cell lines

TFK1, RBE, SSP25, HuCCT1, HuCCA1, HuH28, KKU213A, KKU213B, KKU213C, KKU068, KKU131, KKU138, KKU055, KKU100, and YSCCC cell lines were obtained from the Japanese Collection of Research Bioresources (JCRB; Osaka, Japan). TFK1, RBE, SSP25, and YSCCC cells were maintained in Roswell Park Memorial Institute 1640 medium (RPMI 1640) (Gibco; Thermo Fisher Scientific, Waltham, MA, USA). HuCCA1 and KKU100 cells were maintained in Ham’s F-12 medium (Gibco; Thermo Fisher Scientific). HuCCT1, HuH28, KKU213A, KKU213B, KKU213C, KKU068, KKU131, KKU138, and KKU055 cells were maintained in Dulbecco’s Modified Eagle’s Medium (DMEM) (Gibco; Thermo Fisher Scientific). All cell lines were routinely tested for mycoplasma. For the construction of resistant cells, parental cells (KKU213C, TFK1, KKU068, and SSP25) were seeded in 12-well plates at 5 x 10⁴ cells/well in 1 milliliter (ml) of growth medium. Cells were treated with GEM/CIS using a stepwise dose-induction protocol (16).

Drug response of CCA cell lines

Gemcitabine (cat# S1714) and Cisplatin (cat# S1166) were purchased from Selleck Chemicals (Houston, TX, USA), and LCL161 (cat# HY-15518) and Birinapant (cat# HY-16591) were from purchased from MedChemExpress (Monmouth Junction, NJ, USA). Drug response tests were performed as previously described (17).

Drug library and high-throughput drug screening

Cells were seeded in 384-well plates at 1,000 cells/well in 40 µL of growth medium. After 24 hours, cells were treated with varied concentrations of drugs from a small Food and Drug Administration (FDA)-approved cancer drug library consisting of 62 cancer drugs (purchased from Selleck Chemicals or MedChemExpress) with 28 specific targeted molecules for 5 days. Cell numbers were determined by the Operetta® High Content Screening System (PerkinElmer, Waltham, MA, USA). The images were used to calculate growth rate inhibition (GR) values using the following equation (18).

For high-throughput drug screening, cells were seeded in 384-well plates using a MultiFlo FX Multimode Dispenser (BioTek Instruments, Winooski, VT, USA) at 1,000 cells/well in 40 µl of growth medium. After 24 hours, cells were treated with the drugs in the drug library (at their respective GR₇₅ values) in 20 µl of the total growth medium and using an EpMotion pipetting robot (Eppendorf, Hamburg, Germany). The cells were incubated for 5 days in a 37°C 5% CO₂ environment. Cells were stained with 4,6-diamidino-2-phenylindole (DAPI) and imaged using an Operetta CLS™ high-content system (PerkinElmer). The nuclei number was analyzed using a Columbus Image Data Storage and Analysis System (PerkinElmer), and plotted using MATLAB 2017a software (MathWorks, Natick, MA, USA).

Crystal violet staining

For visualization of cancer colonies, cells were washed in precooled phosphate-buffered saline (PBS), fixed with 10% neutral formaldehyde buffer for 30 minutes, and stained cells with crystal violet for 30 minutes. After staining, the cells were washed with water, air-dried for 24 hours, and imaged using a VersaDoc™ MP 4000 system (Bio-Rad Laboratories, Hercules, CA, USA).

Drug synergistic effect

Cells were seeded in 384-well plates at 1,000 cells/well, treated with LCL161 + GEM/CIS for 5 days, stained with DAPI and imaged using an Operetta CLS™ high-content system. The nuclei number was analyzed using a Columbus Image Data Storage and Analysis System (PerkinElmer). The combination index (CI) was calculated using CompuSyn software version 1.0 (ComboSyn, Inc., Paramus, NJ, USA) and Chou-Talalay’s equation (19). The CI < 1, CI =1, and CI > 1 indicate synergism, additive effect, and antagonism, respectively.

Western blotting and antibodies

Western blotting was performed as previously described (17). The following specific antibodies were used: XIAP (1:1000; cat. no. 2042; Cell Signaling Technology, Danvers, MA, USA), cIAP2 (1:1000; cat. no. 3130; Cell Signaling Technology), p-NF-kB (1:1000; cat. no. 3033; Cell Signaling Technology), NF-kB (1:1000; cat. no. 8242; Cell Signaling Technology), cIAP1 (1:1000; cat. no. sc-271419; Santa Cruz Biotechnology, Dallas, TX, USA), and β-actin (1:1000; cat. no. sc-47778; Santa Cruz Biotechnology).

siRNA knockdown

Small interfering RNAs (siRNAs) were transfected using RNAiMAX Reagent (Thermo Fisher Scientific) according to the manufacturer’s protocol. The target sequences for cIAP2 genes were (a) 5’-AATTGGGAACCGAAGGATAAT-3’, (b) 5’-CAAGAACATGATGTTATTAAA-3’, and (c) 5’-CACTACAAACACAATATTCAA-3’ (Qiagen, Hilden, Germany). AllStars Negative Control siRNA was used as nontargeting control (cat. no. 1027281; Qiagen, Hilden, Germany). At 48 hours after sicIAP2 transfection, the resistant cells were treated with GEM/CIS for 72 hours.

Apoptosis detection

To evaluate caspase 3 activity, cells were seeded in 12-well plates at 1.5-1.7 x 10⁵ cells/well in 1 ml of growth medium before treated with LCL161. After 72 hours, caspase3⁺ cells were detected using an FITC Active Caspase 3 Apoptosis Kit (BD Biosciences, San Jose, CA, USA) and a CytoFLEX Flow Cytometer (Beckman Coulter, Brea, CA, USA). The data analysis was performed using FlowJo™ software version 10.7.1 (FlowJo, LLC, Ashland, OR, USA). For propidium iodide (PI) (Abcam, Cambridge, UK, ab14085) permeabilization assay, cells were seeded in 12 well-plate approximately 1.5-1.7 x 10⁵ cells/well in 1 ml of growth medium. After 36 hours of LCL161 treatment, cells were harvested and washed in pre-cooled PBS 2 ml, centrifuging at 300 x g, 4°C for 5 minutes and then decanting, 2.5 μL of PI (50 μg/mL) were added and incubated in the dark for 5 minutes before proceeding to fluorescence detection by flow cytometry using CytoFLEX Flow Cytometer. Data were analyzed using CytExpert software version 2.1.0.92 (Beckman Coulter Life Sciences, Indianapolis, IN, USA).

ROS measurement

ROS measurement was performed in 12-well plates. Cells were seeded at a density of 5×10⁴ cells/well. At the detection time, cells were washed twice with PBS, and carboxy-DCFDA dye in serum-free medium was added at a final concentration of 10 µM. Plates were incubated at 37°C for 30 min, before removal of the dye. Cells were then washed twice with PBS, and immediately detection by flow cytometry using CytoFLEX.

Focus formation by immunofluorescence

The cells were fixed with 4% paraformaldehyde in PBS, washed and permeabilized by 0.1% Triton X-100 in PBS, and non-specific binding was blocked by the Odyssey® blocking buffer. Anti γH2AX, Ser139 (Cell Signaling Technology) was used to detect γH2AX⁺ cells. Alexa fluor 647 donkey anti-mouse IgG (Thermo Fisher Scientific) was used as secondary antibody. The nuclei were counter-stained using DAPI. Images were acquired using the Operetta CLS™ high-content system (PerkinElmer). At least 300 cells were counted. Positive foci formation of γH2AX, cells were determined by more than 3 foci per cell.

Reverse phase protein array (RPPA)

Preparation of cell lysates was performed following the protocol by RPPA Core Facility at MD Anderson Cancer Center (Houston, TX, USA). Positive control lysate was prepared from mixed cell lysates. Dilution buffer was used as negative control. Serially diluted lysates were spotted onto sixteen pad nitrocellulose‐coated slides (Grace Bio-Labs, Bend, OR, USA) by Arrayjet (Edinburgh, UK). Each pad was probed with a validated primary antibody (20). The antibodies used in this work were selected from the list from the RPPA Core Facility at MD Anderson Cancer Center. The relative fluorescence intensities of the antibody signals were detected by InnoScan 710-IR (Innopsys Inc., Chicago, IL, USA) using the Mapix software (Innopsys Inc). Signal intensities were normalized spot-by-spot division of antibody signal intensity by a housekeeping protein using RPPanalyzer (21). To analyze the results, binary-logarithm transformed median fluorescence intensities with background correction were performed a data normalization by spot-by-spot division of housekeeping protein intensities [x]. Subsequently, to investigate essential antibodies for LCL161 responses in the late time phase, partial least square discriminant analysis or PLS-DA (plsda function of mixOmics package version 6.20.0) was performed by utilizing all antibodies’ area under the curve (AUC) values calculated by using trapezoidal integration (trapz function of pracma library version 1.9.9). Then, the antibodies with VIP (variable importance in projection) scores which is close to or more than one was identified as meaningful variables for LCL161 responses among these five cell lines.

Drug response in primary cell line, and patient-derived organoids

The study protocols were approved by the Institutional Review Board for Human Research of the Faculty of Medicine Siriraj Hospital, Mahidol University, and The National Cancer Institute, Thailand (SI494/2019 and NCI006/2020). Primary cell SiK03 was isolated from a fresh specimen that was minced and incubated with collagenase for 1 hour at 37°C, with 5% CO2. The cells were cultured in DMEM. Organoid preparation was performed according to a previous published protocol (17). Briefly, the CCA tissues Si_003 and NCI_001 were washed with cold PBS containing 10% Antibiotic/Antimycotic (Thermo Fisher Scientific) 5-10 times. The tissues were then minced using surgical blades and washed using washing media (Advanced DMEM/F12 containing 1X Glutamax™, 1X HEPES, and 1X Antibiotic/Antimycotic) (all purchased from Thermo Fisher Scientific). The tissue paste was collected via centrifugation at 400 x g at 4°C for 5 minutes, and further digested with 2 mg/ml collagenase D at 37°C with 5% CO₂ for 30 minutes. Undigested tissues were filtered out using 100 µm cell strainers (Thermo Fisher Scientific). The cells were collected by centrifugation and embedded in 60% Matrigel® (Thermo Fisher Scientific). The gel was solidified at 37°C with 5% CO₂ for 30 minutes, and then organoid culture media (22) was added to each well. The cells were then incubated at 37°C with 5% CO₂ until the organoids formed.

For the drug testing experiment, the organoids were collected and dissociated into smaller sizes via incubation with TrypLE™ Express Enzyme (12604021; Gibco; Thermo Fisher Scientific) at 37°C with 5% CO₂ for 5 minutes. After incubation, the enzyme was diluted with wash media, and the cells were collected by centrifugation and counted. The cells were seeded in 384-well plates at 1,000 cells/well, suspended in organoid culture media, and incubated for 72 hours. The following final concentrations of GEM/CIS at 10/100, 1/10, 0.1/1, 0.01/0.1, 0.001/0.01, 0.0001/0.001, or 0.00001/0.0001 µM; LCL161 at 160, 16, 1.6, 0.16, 0.016, 0.0016, or 0.00016 µM; or, LCL161 + GEM/CIS at a 1:1 ratio was added and incubated under for 5 days. Cell viability was measured using an ATPlite™ Luminescence Assay System (PerkinElmer) according to the manufacturer’s instructions.

In vivo studies

The protocols for all in vivo experiments in this study were approved by the Faculty of Medicine Siriraj Hospital, Mahidol University – Institute Animal Care and Use Committee. The BALB/CRag2-/-,Jak3-/- mice used in this study were obtained from Kumamoto University. The mice were maintained in a 12 hour-12 hour light-dark cycle, 25°C environment, and mice were given free access to standard mouse pellets and water. CCA cell lines were subcutaneously injected into the anesthetized mice. The animals were anesthetized by intraperitoneal (I.P.) injection of 100 mg/kg ketamine: 10 mg/kg xylazine. GEM/CIS (20 mg/kg/2.5 mg/kg dose) (23) treatment was administered via intraperitoneal injection twice a week, and LCL161 (10 mg/kg dose) (24) was administered via I.P. injection every two days. Tumor length, width, and body weight were measured every other day. Tumor volume was calculated using the equation: V = ½ (Length × Width²). Toxicity was evaluated by mean percentage of weight loss in each group. At the experimental endpoint (when tumor size reaches 2 cm in diameter, or the animal loss 20% of the body weight), the mouse will be euthanized by I.P. injection of 300 mg/kg ketamine: 30 mg/kg xylazine.

Statistical analysis

Data are shown as mean ± standard deviation (SD) from at least 3 experiments. All statistical analyses and paired t-tests were performed using GraphPad Prism version 7.0 (San Diego, CA, USA). A p-value of less than 0.05 was considered to be statistically significant.

Results

Acquired vulnerability screening in GEM/CIS-resistant CCA cell lines revealed the hypersensitivity of drug resistant CCA to small molecule SMAC mimetics

To develop GEM/CIS-resistant CCA models, we grew several CCA cell lines (i.e., TFK1, KKU213C, KKU068, and SSP25) under increasing doses of GEM/CIS for 4 months (Figure 1A). The developed GEM/CIS-resistant cell lines (TFK1R, KKU213CR, KKU068R, and SSP25R) were resistant to GEM/CIS and proliferated under higher doses of GEM/CIS compared to the parental cell lines (Figure 1B–E, and Supplementary Table 1A). GR50s of GEM and CIS in parental CCA cell lines are provided in Supplementary Table 1B. We performed acquired vulnerability screening on the resistant cell lines in parallel with the parental drug-sensitive cell lines. To facilitate a clinical translation, we selected 62 anticancer drugs that are in clinical trial or on the FDA-approved drug list for our drug screenings (17) (Supplementary Figure 1A). An acquired vulnerability hit was defined as a drug being 2 times more effective at inhibiting GEM/CIS-resistant cells compared to its GEM/CIS-sensitive counterpart.

Figure 1 | Acquired vulnerability of GEM/CIS-resistant cells to LCL161. (A) Parental CCA cells were cultured under increasing doses of GEM/CIS until becoming GEM/CIS-resistant cells using a step-wise dosing protocol. (B–E) Dose-response curves for TFK1 and TFK1R cells, KKU213C and KKU213CR cells, KKU068 and KKU068R cells, and SSP25 and SSP25R cells, respectively. The cell lines were treated with different concentrations of GEM/CIS for 5 days to generate the dose-response curves. The error bars represent the SD of four cultures. (F) TFK1R cells and (G) KKU213CR cells were screened with a variety of drugs at GR₇₅ for 5 days, and the result shows the percent survival of the resistant CCA cell lines normalized to their parental drug-naïve counterparts. LCL161, which was shown to be the most effective drug, is highlighted in blue. (H, I) Dose-response curves for TFK1, TFK1R, KKU213C, and KKU213CR cells under LCL161 or Birinapant treatment. The error bars represent the SD of four cultures.

From our screenings, we found LCL161 (25), which is an SMAC mimetic drug, to be a positive hit in 2 pairs of cells in our screening model (TFK1/TFK1R and KKU-213C/KKU213CR) (Figure 1F, G).

We confirmed acquired vulnerability to LCL161 in TFK1R and KKU213CR cells via colony survival assay (Supplementary Figure 1B), and generated dose-response curves in those cell lines (Figure 1H). TFK1R and KKU213CR also acquired sensitivity to another SMAC mimetic drug – Birinapant (26) (Figure 1I, Supplementary Table 2), which suggests that the newly acquired sensitivity is to the SMAC mimetic drug class and not specific to only LCL161. Of note, the other two evaluated cell line pairs (KKU-068/KKU-068R and SSP25/SSP25R) did not yield acquired vulnerability to LCL161, Birinapant (Supplementary Figures 1C–E and Supplementary Table 2), or to any drug in our small drug library (data not shown), implying that other unknown mechanisms of drug resistance may play roles in these cells.

LCL161 synergizes with GEM/CIS in inhibiting both GEM/CIS-resistant and parental CCA cell lines

Since the screening results showed the GEM/CIS-resistant CCA cells to be hypersensitive to SMAC memetics, we investigated whether the addition of SMAC mimetics to GEM/CIS produces a synergistic effect. As shown in Figures 2A, B, the parental cells (TKF1 and KKU-213C) were not sensitive to LCL161, and the resistant CCA cells (TFK1R and KKU-213CR) were resistant to GEM/CIS. Addition of LCL161 to the GEM/CIS duplet effectively inhibited both parental GEM/CIS-sensitive cells and GEM/CIS-resistant cells. Similar results were observed when Birinapant was added to the GEM/CIS duplet (Figures 2C, D). These results suggest that when under pressure from GEM/CIS treatment, these cell lines reprofile themselves to survive the killing effect exerted by GEM/CIS and become reliant on the small molecule SMAC mimetic targets – inhibitor of apoptosis (IAP) proteins.

Figure 2 | LCL161 synergistically enhances the drug efficacy of GEM/CIS. (A, B) Triplet LCL161 + GEM/CIS therapy effectively inhibits multidrug-resistant cells. Percent cell survival of TFK1, TFK1R, KKU213C, and KKU213CR cells treated with LCL161 at 2.5 µM, or GEM/CIS at 0.01/0.1 µM, or LCL161 + GEM/CIS, and of (C, D) TFK1, TFK1R, KKU213C, and KKU213CR cells treated with Birinapant at 2.5 µM, GEM/CIS at 0.01/0.1 µM, or Birinapant + GEM/CIS for 5 days. The bars represent the averages of 4 replicates ± SD. Analysis for statistical significance was performed using Student’s t-test, and the p-values are shown above the bars in all 4 images. (E) Dose-response curves for TFK1 and TFK1R cells treated with LCL161, GEM/CIS, or LCL161 + GEM/CIS. (F) Dose-response curves for TFK1 and TFK1R cells treated with Birinapant, GEM/CIS, or Birinapant + GEM/CIS. (G) Dose-response curves for KKU213C and KKU213CR cells treated with LCL161, GEM/CIS, or LCL161 + GEM/CIS. (H) Dose-response curves for KKU213C and KKU213CR cells treated with Birinapant, GEM/CIS, or Birinapant + GEM/CIS. (I–L) The combination indexes (CIs) of LCL161 + GEM/CIS in TFK1, TFK1R, KKU213C, and KKU213CR cells. The colors indicate the proportion of respondents. A CI < 1, CI = 1, and CI > 1 indicate synergism, additive effect, and antagonism, respectively.

To examine whether the interaction between SMAC mimetics and GEM/CIS reflects synergism, we constructed dose-response curves for LCL161 + GEM/CIS or Birinapant + GEM/CIS. We found that the addition of LCL161 or Birinapant to GEM/CIS therapy significantly reduced the GR₅₀ of GEM/CIS in the resistant cells, as well as in the parental cells (Figures 2E–H). The combination indexes (CIs) revealed that almost all of the evaluated doses of the LCL161 + GEM/CIS combination generated synergistic effect (all CI<1) (Figures 2I–L). The observed synergism was found to be stronger in the GEM/CIS-resistant cells (TFK1R and KKU213CR) (Figures 2J, L) than in the parental cells (Figures 2I, K).

Upregulation of cIAP2 in GEM/CIS-resistant CCA facilitates acquired vulnerability to SMAC mimetics

Since GEM/CIS-resistant CCA cell lines were found to have acquired vulnerability to SMAC mimetics, we examined the expressions of the SMAC mimetic target proteins XIAP, cIAP1, and cIAP2 in the resistant CCA cell lines via immunoblotting. We found that the resistant cell lines TFK1R and KKU213CR showed upregulation of cIAP2 when compared to their parental counterparts (Figures 3A, B). The upregulation of cIAP2 in TFK1R and KKU213CR cells was found to be correlated with the upregulation and activation of NF-kB, which is a major IAP target protein (27, 28) (Supplementary Figure 2A). In contrast, the expression of cIAP2 and NF-kB in the GEM/CIS-resistant cell lines that did not develop acquire vulnerability to LCL161 (SSP25R and KKU068R) remained unchanged (Figures 3A, B, Supplementary Figure 2A). Using RPPA, we found that upregulation of cIAP2 in the LCL161-sensitive cell lines also associated with the activations of pathways implicated in cell survival under DNA damage-induced cellular stress, such as apoptosis, DNA damage respond (DDR), PI3K/AKT, and mTOR/TSC (Supplementary Figure 2B).

Figure 3 | cIAP2 overexpression mediates GEM/CIS resistance in CCA. (A) Western blot analysis of cIAP2 in parental cells (TFK1, KKU213C, KKU068, and SSP25 cells), compared to their resistant counterparts (TFK1R, KKU213CR, KKU068R, and SSP25R cells). β-actin was used as a protein loading control. A representative result is shown. (B) The bar graphs show the average intensities of 3 replicates ± SD of cIAP2 expression in the indicated cell lines from (A) P-values are shown above the bars. (C, D) Western blot analysis of cIAP2 in TFK1 vs. TFK1R, and SSP25 vs. SSP25R cells under 72 hours of treatment with LCL161, GEM/CIS, or LCL161 + GEM/CIS, as indicated. (E) Western blot analysis of the cIAP2 protein in TFK1R cells transfected with the indicated siRNAs. β-actin was used as a protein loading control. A representative result is shown. (F) The bar graph shows the average intensities of 3 replicates ± SD of the results in (E) P-values are shown above the bars. (G) GEM/CIS dose-response curves for siRNA-mediated cIAP2-depleted TFK1R cell lines. The curves represent the averages of 3 replicates ± SD. P-values are shown at the lower right quadrant of the image. (H) Western blot analysis of the cIAP2 protein in KKU213CR cells transfected with the indicated siRNAs. β-actin was used as a protein loading control. A representative result is shown. (I) The bar graph shows the average intensities of 3 replicates ± SD of the results in (H) P-values are shown above the bars. (J) GEM/CIS dose-response curves for siRNA-mediated cIAP2-depleted KKU213CR cells. The curves represent the averages of 3 replicates ± SD. P-values are shown at the lower right quadrant of the image. NS, not statistically significant.

LCL161 treatment alone or in combination with GEM/CIS significantly suppressed cIAP2 expression in the resistant TFK1R cells (Figure 3C). In contrast, LCL161 had no effect in the resistant cell line SSP25R, which was not sensitive to LCL161 (Figure 3D).

To validate whether the expression of cIAP2 is the key mediator that facilitates the survival of GEM/CIS-resistant cells, we depleted cIAP2 expression by using cIAP2-specific siRNAs. We found that 3 independent sequences of cIAP2 siRNA, which knocked down cIAP2 expression by more than 80% (Figures 3E, F), reproducibly suppressed TFK1R cell survival under GEM/CIS treatment (Figure 3G) and lowered the GEM/CIS GR₅₀ values when compared to the control non-target siRNA (Supplementary Table 3). Similar results were confirmed in KKU213CR cells (Figures 3H–J, and Supplementary Table 4). Of note, although the reversals of GEM/CIS sensitivity were significant and reproducible, they were not complete reversals (Figures 3G, J: compared to parental cells in black lines), which suggests that there might be other mechanisms that influence GEM/CIS resistance. From these results, we concluded that cIAP2 is a key protein in CCA that, at least partially, facilitates CCA survival under GEM/CIS treatment. Suppression of cIAP2 via siRNA or SMAC-dependent degradation was enough to resensitize the CCA cells to GEM/CIS treatment. Consistent with the role of cIAP2 as an anti-apoptotic protein, we found that LCL161 promoted substantial apoptosis in the cells as demonstrated by the increase in both the number of cells with activated caspase 3 and the number of propidium iodide⁺ cells (Figures 4A–C).

Figure 4 | LCL161-induced apoptosis in TFK1R and KKU213CR cells. (A, B) Percentage of activated caspase 3⁺ in TFK1, TFK1R, KKU213C, and KKU213CR cells treated with LCL161 or vehicle at 72 hours. (C) Percentage of PI cell permeability in KKU213CR and TFK1R cells treated with LCL161 at 36 hours. (D) Percentage of DCFDA⁺ TFK1 and TFK1R cells, with GEM/CIS or vehicle at 72 hours. (E) Percentage of DCFDA⁺ KKU213C and KKU213CR cells, with GEM/CIS or vehicle at 72 hours. (F, G) Percentage of γ-H2AX⁺ TFK1, TFK1R, and KKU213C, KKU213CR cells. Bars show the mean of 3 replicates ± SD. P-values are shown. NS, not statistically significant.

To investigate the cause of apoptosis prone phenotype of these cells, we examined the levels of reactive oxygen species (ROS) and DNA damage in the drug resistant cells and compared to the parental cells. We found that GEM/CIS treatment resulted in accumulations of ROS in the parental, as well as in the GEM/CIS resistant CCA cell lines (Figures 4D, E). Interestingly, the GEM/CIS resistant CCA cell lines accumulated significantly higher levels of ROS compared to the parental drug sensitive cell (Figures 4D, E). We also detected significantly elevated level of γ-H2AX, a DNA damage marker, in the GEM/CIS resistant cell lines (Figures 4F, G), which was associated with activation of the DDR pathway (Supplementary Figure 2B).

Therefore, these results indicate that the drug resistant cells adapted to survive under the persistent ROS/DNA damage-induced cellular stress and are more prone to the apoptosis imbalance. Hence, upregulations of IAPs is an essential step for the cells to evade apoptosis signal under the GEM/CIS treatment.

Triplet LCL161 + GEM/CIS effectively prevented the emergence of GEM/CIS resistance

Since LCL161 treatment inhibited CCA cells with GEM/CIS resistance, we examined whether LCL161 can prevent the development of GEM/CIS resistance. We found that although GEM/CIS initially inhibited the growth of TFK1 and KKU213C cells, GEM/CIS-resistant cells emerged at 8 weeks, and more profoundly at 10 weeks (Figures 5A–D). Even though LCL161 monotherapy had no inhibitory effect on the parental TFK1 and KKU213C cells, the addition of LCL161 to GEM/CIS completely suppressed the emergence of drug-resistant cells in the long-term culture (Figures 5A–D).

Figure 5 | The combination of LCL161 + GEM/CIS prevents the emergence of drug-resistant cells. (A–D) The emergence of multidrug-resistant TFK1 and KKU213C cells under various drug treatments. TFK1 and KKU213C cells were treated with GEM/CIS, LCL161, or LCL161 + GEM/CIS for 4 weeks, 8 weeks, or 10 weeks. Emerging colonies were stained with crystal violet and imaged. The number of emerging multidrug-resistant clones in 96-well plates are shown in images B–D. (E, F) The effectiveness of triplet combination and sequential regimen is compared in TFK1R and KKU213R cells. Multidrug-resistant cells were stained with crystal violet and imaged every 5 days. (G, H) The effectiveness of the triplet therapy in TFK1, TFK1R, KKU213C, and KKU213CR cells with and without drug holiday. Cells were treated with LCL161, GEM/CIS, or LCL161 + GEM/CIS and stained with crystal violet at the indicated time points.

We then compared whether LCL161 was better given sequentially or in combination with GEM/CIS (29). The results showed that LCL161 was more effective in killing drug-resistant CCA cells when given as a triplet combination compared to when given as a sequential regimen (Figures 5E, F).

Under drug holiday, which is defined as a period during which no cancer drugs are given, we found acquired vulnerability to LCL161 in our model cell lines to be partly transient and reversed after 4 or 6 weeks of drug holiday (Figures 5G, H). Importantly, the triplet completely suppressed all of the cells, indicating that it was effective against both transient or stable drug resistance (at 4 and 6 weeks of drug holiday – Figures 5G, H, respectively). These results indicate that acquired vulnerability to LCL161 can be applied to transient and stable GEM/CIS-resistant cells, and that consistent killing pressure from GEM/CIS is required to render CCA vulnerable to LCL161 treatment. These results strongly suggest the effectiveness of triplet LCL161 + GEM/CIS treatment in inhibiting and preventing an emergence of GEM/CIS-resistant CCA.

Synergistic effect of the triplet LCL161 + GEM/CIS on drug-naïve CCA cell lines

Since synergistic effect was observed in both GEM/CIS-resistant CCA and parental CCA counterpart cell lines, we set forth to determine whether the SMAC mimetic LCL161 synergizes with GEM/CIS in other CCA cell lines, especially in GEM/CIS-naïve CCA cells. We treated 13 CCA cell lines (YSCCC, KKU213A, KKU213B, KKU100, HuCCA1, KKU055, HuH28, KKU138, HuCCT1, SSP25, RBE, KKU131, and KKU068) with GEM/CIS, LCL161, or LCL161 + GEM/CIS for 5 days. We found that LCL161 exerted mild to no effect on most cell lines, whereas GEM/CIS was effective against most of the CCA cell lines (Figures 6A–M, blue and green line, respectively). Remarkably, combining LCL161 with GEM/CIS (LCL161 + GEM/CIS) potentiated GEM/CIS cytotoxicity in all CCA cells, except KKU055 and RBE, which were already hypersensitive to GEM/CIS treatment. Remarkably, combining LCL161 to GEM/CIS (LCL161 + GEM/CIS) potentiated GEM/CIS cytotoxicity in most CCA cells (except KKU055, and RBE, which are hypersensitive to GEM/CIS treatment. Addition of LCL161 mildly reduced GR50s of GEM/CIS in these 2 cell lines and the synergistic effect was observed only in some dosages), and significantly reduced the GR50s of both GEM/CIS and LCL161 (Figures 6A–M, Supplementary Table 5). In most of the CCA cell lines tested, we identified a broad range of dosages in which the triplet yielded the observed synergism (Figure 6N), which strongly suggests that the triplet combination yielded a synergistic effect in a broad cellular context. These results indicate that combining LCL161 with the standard GEM/CIS regimen should be expected to yield improved CCA treatment outcomes.

Figure 6 | LCL161 synergistically enhances the drug efficacy of GEM/CIS in drug-naïve cell lines. (A–M) Dose-response curves for YSCCC, KKU213A, KKU213B, KKU100, HuCCA1, KKU055, HuH28, KKU138, HuCCT1, SSP25, RBE, KKU131, and KKU068 cell lines under LCL161, GEM/CIS, or LCL161 + GEM/CIS treatment. The triplet combination of LCL161 + GEM/CIS was more effective than LCL161 or GEM/CIS treatment. (N) The combination indexes (CIs) of various LCL161 + GEM/CIS combinations generated in RBE, KKU100, HuCCA1, KKU138, KKU131, KKU055, YSCCC, SSP25, HuH28, KKU068, KKU213B, KKU213A, and HuCCT1 cell lines. A CI less than 1 indicates synergism.

LCL161 synergizes with GEM/CIS in in vivo drug-resistant xenograft models and patient-derived organoids

To investigate whether the triplet combination overcomes GEM/CIS resistance under the physiological condition, we generated the GEM/CIS-resistant CCA animal models by subcutaneously implanting GEM/CIS-resistant CCA cells into severely immunocompromised BALB/CRag2-/-,Jak3-/- mice. After tumor formation was confirmed, the animals were treated with vehicle, GEM/CIS, LCL161, or the triplet (LCL161 + GEM/CIS) (Figure 7A). As expected, GEM/CIS had no effect on TFK1R tumor growth (Figures 7B, C); however, LCL161 monotherapy significantly delayed the growth of the GEM/CIS-resistant TFK1R tumors. Strikingly, we found the triplet therapy to be superior to LCL161 monotherapy as demonstrated by complete suppression of the TFK1R tumor, i.e., no tumor growth was observed under the triplet therapy until the end of the experiment at 63 days.

Figure 7 | The effectiveness of triplet LCL161 + GEM/CIS therapy in in vivo models of GEM/CIS resistance. (A) BALB/CRag2-/-/Jak3-/- mice were subcutaneously injected with GEM/CIS-resistant cells. When the tumor reached 3 mm in diameter, the mice were intraperitoneally treated with vehicle, LCL161, GEM/CIS, or triplet LCL161 + GEM/CIS. (B) Growth of TFK1R tumors from the beginning to the end of the experiment under various treatments. The lines show the averages of tumor size ± SD. P-values are shown at the right lower quadrant of the image. (C) Pictorial images of the tumor from the beginning to the end of treatment for each drug regimen from image (B, D) Growth of TFK1R tumors from the beginning to the end of the experiment under various treatments. The lines show the averages of tumor size ± SD. P-values are shown at the right lower quadrant of the image. (E) Pictorial images of the tumor from the beginning to the end of treatment for each drug regimen from image (D, F) Growth of KKU213CR tumors from the beginning to the endpoint, on which the untreated controls reach the critical size. The lines show the averages of tumor size ± SD. P-values are shown at the right lower quadrant of the image. (G) KKU213CR tumor weight compared among the 4 treatments. The bars indicate the average tumor weight ± SD. P-values are shown at the top of the 4 columns. (H) Growth of KKU213C tumors from the beginning to the endpoint, on which the untreated controls reach the critical size. The lines show the averages of tumor size ± SD. P-values are shown at the right lower quadrant of the image. (I) KKU213C tumor weight compared among the 4 treatments. The bars indicate the average tumor weight ± SD. P-values are shown at the top of the 4 columns. (n = number of tumors).

Consistent with the in vitro results, growth of the parental TFK1 tumors was effectively delayed by GEM/CIS, and LCL161 monotherapy had no effect in these tumors (Figures 7D, E). The triplet therapy effectively inhibited the growth of drug-naïve TKF1 tumors, and this effect persisted until the end of the study (day 55). Similar results were observed in another in vivo GEM/CIS-resistant model (KKU213CR; Figures 7F, G), and its drug-naïve counterpart (KKU213C cells) (Figures 7H, I). Therefore, the triplet was shown to be effective in inhibiting both GEM/CIS-resistant CCA and drug-naïve CCA cells, also that it was able to suppress the emergence of multidrug resistant cells in in vivo mouse models.

To extend our observation of the observed synergism of this triplet therapy in other drug-naïve CCA tumors, we tested it in two other drug-naïve xenograft models (KKU068 and KKU131). We found the triplet to be superior to GEM/CIS in inhibiting drug-naïve KKU068 and KKU131 tumors, and that it also prevented the emergence of multidrug resistant tumors (Supplementary Figures 3A–D). Concerning toxicity, there was no difference in weight loss between the triplet therapy and GEM/CIS therapy (Supplementary Figures 4A, B).

Lastly, to evaluate the clinical relevance of our findings, we tested the effect of triplet therapy in a primary CCA cell line, and in 2 models of patient-derived organoids. We found the triplet to be more effective than GEM/CIS or LCL161 monotherapy in the primary CCA cell line and in both organoid models (Figures 8A–E). These results demonstrated the triplet therapy to be more effective in inhibiting drug-naïve CCA tumors, primary cell line, and organoids, as well as GEM/CIS-resistant tumors. In addition, the triplet was effective in preventing the emergence of multidrug resistant tumors.

Figure 8 | Anti-cancer effect of triplet LCL161 + GEM/CIS therapy in CCA PDOs. (A) Dose-response curves for LCL161, GEM/CIS, or LCL161 + GEM/CIS in a primary CCA cell line SiK03. (B, C) Dose-response curves for LCL161, GEM/CIS, or LCL161 + GEM/CIS in two models of PDOs (Si_003 and NCI_001). The lines represent the averages of triplicate data ± SD. The PDOs were treated for 5 days, the GR₅₀ values were obtained, and (D) the morphologies of Si_003 PDOs treated with indicated therapies were visualized. (E) The 5-day GR₅₀ values for LCL161, GEM/CIS, and LCL161 + GEM/CIS in PDOs. (F) Horizontal flow diagram describing our findings. Under GEM/CIS treatment, CCA cells adapted in an effort to survive the drug treatment by upregulating cIAP2, which resulted in a newly acquired vulnerability to SMAC mimetics, such as LCL161. Combining SMAC mimetics with the standard GEM/CIS treatment regimen was shown to promote synergistic inhibition of multidrug-resistant CCA.

Discussion

Despite the great efforts devoted to developing new pharmacological therapies in CCA, the clinical outcomes of CCA patients remain to be poor. The resistance to anticancer treatment is a major reason for the difficulty in CCA treatment. CCA is characterized by high interpatient and intratumor heterogeneity, which makes it difficult to overcome the problem of inherited drug resistance of subclones. In addition, the acquired resistance is known to contribute to failure in the treatment of CCA in the clinic. To design new combination therapy that can reverse the mechanisms underlying multidrug resistance and effectively and ubiquitously kill or stop tumor growth, without inducing acquired resistance in CCA, seem to be an ideal therapeutic strategy.

Here our acquired vulnerability screening revealed that CCA, which acquired a resistance to GEM/CIS, developed a collateral vulnerability to the pro-apoptotic drugs, the SMAC mimetics such as LCL161 and Birinapant (Figure 8F). This suggests that among several possible mechanisms of chemotherapy resistance, altered cell death response may be the main resistant mechanism for the duplet GEM/CIS therapy in CCA.

Among SMAC mimetics, LCL161 and Birinapant are currently the most well-studied; their therapeutic effectiveness and the molecular mechanism of actions have been studied extensively in pre-clinical and clinical studies. LCL161 is being tested in combination with Gemcitabine + Nab-Paclitaxel in a phase I clinical trial in pancreatic cancer (NCT01934634). In a phase II study, LCL161 + paclitaxel in triple-negative breast cancer has demonstrated some promising results (30). The anti-cancer potency of Birinapant is also under investigation in several types of tumor, including colorectal cancer, hepatocellular carcinoma, glioblastoma, breast cancer (31).

From our results, addition of LCL161 to the duplet GEM/CIS can reverse the GEM/CIS resistance, and suppress emergence of multi-drug resistant CCA, in vitro and in vivo. We hypothesize that, under GEM/CIS treatment CCA cells are heavily pressured and are reliant on anti-apoptotic proteins IAPs to survive. In agreement with this hypothesis, we found that the SMAC mimetics worked best when the pressure from GEM/CIS was kept on. This explains why the triplet therapy showed the best anti-tumor result compared to GEM/CIS duplet, LCL161 monotherapy, or sequential treatment. Interestingly, we found that the GEM/CIS resistance in our cell line models may be partially transient, as shown by the reversal of the phenotypes from LCL161-sensitive to LCL161-resistance after drug holiday. Nevertheless, the triplet was still able to eliminate the cells with reversed phenotype, indicating that the triplet is effective against both reversible and non-reversible cell re-profiling. This is in keeping with the results that the triplet therapy was effective in many drug-naïve cell lines, and PDOs that we tested.

Further work is needed to explain why the triplet was very effective in drug-naïve CCA models, in which LCL161 monotherapy had mild or no effect. One possible explanation is that in most of the CCA cells, a temporal expression of IAP proteins may be needed under GEM/CIS therapy for the CCA to escape cell death (32). SMAC mimetics may interfere with that transient expression, therefore, prohibiting the survival of the CCA cells.

The treatment of many tumors involves drug combinations. Such combinations are commonly chosen primarily due to their non-overlapping mechanism of action or toxicity. These approaches may need to be revised, since virtually patients only benefit from independent drug action, without drug synergy or additivity (33). We argue here that the candidate selection based on acquired vulnerability screening may be an attractive alternative approach. All in all, from our findings, we proposed that the triplet LCL161 + GEM/CIS therapy may be attractive as a first-line therapy for cholangiocarcinoma.

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 animal study protocol #009/2564 was reviewed and approved by Faculty of Medicine Siriraj Hospital, Mahidol University – Institute Animal Care and Use Committee.

Author contributions

SP, OS, BP, CJ, JC, AC, PM-K, KMK, and MS performed the experiments. KK, SO, SS, and SJ supervised and provided critical comments, the experimental framework, and the experimental design. RC, and SO provided important resources and clinical specimens. SP, CJ, JC, AC, and SJ analyzed and interpreted the data. SP and SJ wrote and edited the manuscript. SJ obtained financial support for the study. All authors contributed to the article and approved the submitted version.

Funding

This study was funded by grants from the National Science and Technology Development Agency (NSTDA) of Thailand, the Japan Science and Technology Agency, and the National Institute of Allergy and Infectious Diseases of the United States as part of the e-ASIA Joint Research Program (e-ASIA JRP); the NSTDA (P-15-50208), the National Research Council of Thailand (NRCT) R016441034, the Advanced Research in Pharmacology Fund, Siriraj Foundation (D003421), and by Mahidol University (Basic Research Fund, and the grant of fiscal year 2022).

Acknowledgments

SJ, SS, KK, and AC are supported by the R.E.D program, Faculty of Medicine, Siriraj Hospital, Mahidol University.

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.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2022.1021632/full#supplementary-material

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ORIGINAL RESEARCH
published: 02 December 2022
doi: 10.3389/fonc.2022.996820

WWOX-rs13338697 genotype predicts therapeutic efficacy of ADI-PEG 20 for patients with advanced hepatocellular carcinoma

Yu-De Chu ¹, Hui-Fen Liu ², Yi-Chen Chen ², Chun-Hung Chou ²,³* and Chau-Ting Yeh ¹,⁴*

¹ Liver Research Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan, ² Polaris Pharmaceuticals, Inc., Polaris Group, Taipei, Taiwan, ³ Ph.D. Program for Biotechnology Industry, College of Life Sciences, China Medical University, Taichung, Taiwan, ⁴ Molecular Medicine Research Center, College of Medicine, Chang Gung University, Taoyuan, Taiwan

Edited by:
Xuesong Gu, Beth Israel Deaconess Medical Center and Harvard Medical School, United States

Reviewed by:
Jungshan Chang, Taipei Medical University, Taiwan;
Qianqian Song, Wake Forest University, United States

*Correspondence:
Chun-Hung Chou
sampsonchou@polarispharma.com 
Chau-Ting Yeh
chautingy@gmail.com

Specialty section:
This article was submitted to Cancer Molecular Targets and Therapeutics, a section of the journal Frontiers in Oncology

Received: 18 July 2022

Accepted: 14 November 2022

Published: 02 December 2022

Citation:
Chu Y-D, Liu H-F, Chen Y-C, Chou C-H and Yeh C-T (2022) WWOX-rs13338697 genotype predicts therapeutic efficacy of ADI-PEG 20 for patients with advanced hepatocellular carcinoma. Front. Oncol. 12:996820. doi: 10.3389/fonc.2022.996820

Background

Previous studies have identified three single nucleotide polymorphisms (SNPs): GALNT14-rs9679162, WWOX-rs13338697 and rs6025211. Their genotypes are associated with therapeutic outcomes in hepatocellular carcinoma (HCC). Herein, we examined whether these SNP genotypes could predict the clinical outcome of HCC patients treated with ADI-PEG 20.

Methods

Totally 160 patients with advanced HCC, who had previously been enrolled in clinical trials, including 113 receiving ADI-PEG 20 monotherapy (cohort-1) and 47 receiving FOLFOX/ADI-PEG 20 combination treatment (cohort-2), were included retrospectively.

Results

The WWOX-rs13338697-GG genotype was associated with favorable overall survival in cohort-1 patients (P = 0.025), whereas the rs6025211-TT genotype was associated with unfavorable time-to-tumor progression in cohort-1 (P = 0.021) and cohort-1 plus 2 patients (P = 0.008). As ADI-PEG 20 can reduce plasma arginine levels, we examined its pretreatment levels in relation to the WWOX-rs13338697 genotypes. Pretreatment plasma arginine levels were found to be significantly higher in patients carrying the WWOX-rs13338697-GG genotype (P = 0.006). We next examined the association of the WWOX-rs13338697 genotypes with WWOX tissue protein levels in 214 paired (cancerous/noncancerous) surgically resected HCC tissues (cohort-3). The WWOX-rs13338697-GG genotype was associated with decreased tissue levels of WWOX and ASS1. Mechanistic studies showed that WWOX and ASS1 levels were downregulated in hypoxic HCC cells. Silencing WWOX to mimic low WWOX protein expression in HCC in patients with the WWOX-rs13338697-GG genotype, enhanced HIF1A increment under hypoxia, further decreased ASS1, and increased cell susceptibility to ADI-PEG 20.

Comclusion

In summary, the WWOX-rs13338697 and rs6025211 genotypes predicted treatment outcomes in ADI-PEG 20-treated advanced HCC patients. The WWOX-rs13338697-GG genotype was associated with lower tissue WWOX and ASS1 levels and higher pretreatment plasma arginine levels, resembling an arginine auxotrophic phenotype requires excessive extracellular arginine supply. Silencing WWOX to mimic HCC with the WWOX-rs13338697-GG genotype further stimulated HCC cell response to hypoxia through increased HIF1A expression, leading to further reduction of ASS1 and thus increased cell susceptibility to ADI-PEG 20.

Keywords: single nucleotide polymorphism, Advanced HCC, arginine deprivation therapy, ASS1, HIF1A

Introduction

In recent decades, cancer has become one of the leading causes of death in humans (1). Among the most common cancers, hepatocellular carcinoma (HCC) ranks the third leading cause of cancer-related death (2). Major risk factors for HCC include chronic hepatitis B or C infection and alcoholic liver diseases (3). Among all the therapeutic modalities for these patients, surgical removal of the liver tumor remains the most effective treatment (2). However, only a small number of HCC patients with acceptable liver function and in the early cancer stage without extrahepatic metastasis or large vessel invasion are qualified for surgical resection (4). In patients with unresectable HCC, the mainstay therapies include transcatheter arterial chemoembolization (TACE) using traditional or new embolization materials (for intermediate-stage HCC), oral targeted drugs, immunotherapy, radiotherapy, and chemotherapy (for advanced-stage HCC) (5). Despite the availability of these therapeutic treatments, a wide range of therapeutic responses has been observed in HCC patients, primarily due to the heterogeneity of their oncogenic pathways, resulting in significant changes in biological characteristics and genetic profiles (5, 6). Therefore, there is an urgent need for comprehensive therapeutic strategies to optimize responses to the therapies.

Depletion or reduction of exogenous amino acid sources using the amino acid-degrading enzymes has emerged as a new strategy to treat cancers possessing amino acid auxotroph phenotypes, such as arginine deprivation therapy (7–9). Two enzymatic drugs have been developed to achieve arginine deprivation in HCC patients: recombinant human arginase 1 (rhArg1) (10, 11) and pegylated arginine deiminase (ADI-PEG 20) (12, 13). Among these, the ADI-PEG 20 alone or in combination with other anticancer drugs has been demonstrated in several clinical trials as a safe and tolerable compound, including for advanced HCC (12, 14, 15). However, their efficacy in improving overall survival (OS), progression-free survival (PFS), time-to-tumor progression (TTP), and time-to-tumor response (TTR) remains unsatisfactory (12, 14–19). Hence, we sought an alternative approach to optimize the therapeutic efficacy of ADI-PEG 20 through re-evaluating the prognostic role of genetic markers in patients enrolled in clinical trials.

Previously, using a genome-wide association study (GWAS) followed by prospective validation, the single nucleotide polymorphism (SNP) genotypes of polypeptide N-acetyl-galactosaminyl-transferase 14 (GALNT14) have been identified as a predictor of response to chemotherapy in advanced HCC. Among them, a prominent SNP, rs9679162, was demonstrated to be associated with chemotherapeutic outcomes in HCC patients at Barcelona Clinic Liver Cancer (BCLC) stage C and TACE response in HCC patients at BCLC stage B. Subsequently, the same SNP has been identified as an outcome predictor for multiple gastrointestinal cancers, including colorectal cancer (for a review, see reference (20)). Furthermore, the GALNT14 mutations have been associated with an increased risk of hereditary neuroblastoma (21).

In previous GWAS results, two other SNPs, WWOX-rs13338697 and rs6025211, have been shown to be assistive in predicting therapeutic responses in advanced HCC (22). Particularly, the WWOX-rs13338697 and rs6025211 genotypes have been associated with TTP and OS, respectively. Thus, a post-hoc analysis was conducted to re-evaluate the therapeutic efficacy of ADI-PEG 20 in advanced HCC patients enrolled in previous trials, in relationship to these SNPs.

Materials and methods

Patients enrolled in this study

Three cohorts were retrospectively enrolled in this study under the approval of the Institutional Review Board of Chang Gung Memorial Hospital in Taiwan [Approval number: 100-3119A1 (09 Nov 2011), 201601426A0 (02 Dec 2016), and 201900261B0 (04 Mar 2019)]. Patients enrolled in cohort-1 and -2 were diagnosed with advanced-stage HCC and had participated in clinical trials of ADI-PEG 20 (NCT01287585 and NCT02102022) in Taiwan. The cohort-1, containing 113 patients, received ADI-PEG 20 monotherapy, while cohort-2, including 47 patients, experienced ADI-PEG 20 plus modified FOLFOX6 (mFOLFOX6) chemotherapy (14, 15). In cohort-3, 214 HCC patients who had undergone surgical resection for HCC at Chang Gung Memorial Hospital and their paired liver tissue samples (cancerous/noncancerous) were included. Anonymous samples, including the blood samples of the cohort-1 and 2, and the resected tissues of the cohort-3, were retrospectively retrieved from the Polaris group (cohort-1 and 2) and the Tissue Bank of Chang Gung Memorial Hospital (cohort-3) for post-hoc analysis. Their de-linked clinical parameters, including age, gender, hepatitis virus infection, extrahepatic spread, macrovascular invasion, tumor number, largest tumor size (diameter), alpha-fetoprotein (AFP), albumin, bilirubin, creatinine, aspartate aminotransferase (AST), alanine aminotransferase (ALT), Child-Pugh score, albumin-bilirubin (ALBI) score, ALBI grade, and Eastern Cooperative Oncology Group (ECOG) performance status, were also retrieved. Specifically, de-linked clinical parameters for cohort-1 and 2 were obtained from the Polaris group, and those for cohort-3 were obtained retrospectively from electronic medical record review.

Therapeutic outcomes evaluation

The duration of OS was calculated from the date on which the patients were included for randomization to the date of death, regardless of any causes, or the date of loss to follow-up. The objective tumor response was evaluated according to the Response Evaluation Criteria in Solid Tumors criteria (23). A >30% decrease in tumor volume, without progression in any target lesion or appearance of a new lesion, was defined as “response”. The duration of TTR was derived from the date on which the patients were included for randomization to the date of tumor response, which was censored at the date of leaving the trial. Progressive disease was defined as a >20% increase in the total tumor volume or appearance of a new lesion. The duration of TTP was calculated from the date of patient inclusion for randomization to that of disease progression, which was censored at the date of the last radiological assessment.

SNP genotyping

SNP genotyping was conducted as previously described (22). Briefly, blood sample-derived genomic DNA was isolated using the QIAamp DNA mini kit (QIAGEN, Hilton, Germany, Cat: 51306) according to the instruction provided by the manufacturer. Semi-nested polymerase chain reaction (PCR) was conducted using the primers, Rs9679162-F1: 5’-TCACGAGGCCAACATTCTAG-3’; Rs9679162-R1: 5’-TTAGA-TTCTGCATGGCTCAC-3’; Rs9679162-R2: 5’-TCCCTCCTACTGAACCTCTCC-3’; Rs13338697-F1: 5’-ACTTCTGACAGCCATCCAGA-3’; Rs13338697-F2: 5’-ATCCTGCTAGCATGTTGACT-3’; Rs13338697-R2: 5’-ACTGTAGATGCCTTCCATCT-3’; Rs6025211-F1: 5’-ACATTCACAGAGAACTTGGC-3’; Rs6025211-R1: 5’-CAAGCAGTCCTTCCACCTTG-3’; Rs6025211-R2: 5’-AAAGTGCTGGGATTACAGG-T-3’. Amplified PCR products were gel-purified using the EasyPrep Gel & PCR Extraction Kit (BIOTOOLS, New Taipei City, Taiwan, Cat: TT-B14-3).

Western blot analysis

Western blot analysis was performed as previously described (24). Briefly, the protein lysates were extracted by adding a sufficient amount of radio-immunoprecipitation assay (RIPA) buffer [20 mM Tris-HCl (pH 7.5), 150 mM NaCl, 1 mM Na₂EDTA, 1 mM EGTA, and 1% NP-40] containing 1× protease inhibitor cocktail (BIOTOOLS, Cat: TAAR-BBI2) to the collected cells in tubes. The tubes were placed in a cold room at 4°C with continuous rotation for 30 min. The mixture was then centrifuged at 13,000 rpm for 15 min at 4°C. Protein concentration was assessed using a protein assay reagent (Bio-Rad, Hercules, CA, USA, Cat: 5000001). For each lane, 20 μg of denatured protein was loaded and separated on a 10% Bis-Tris-buffered polyacrylamide gel and transferred onto PVDF membranes for western blotting. The membranes were blocked with 5% milk before adding primary and secondary antibodies. Rabbit polyclonal antibodies against WW domain-containing oxidoreductase (WWOX) (Proteintech, Rosemont, IL, USA, Cat: 15299-1-AP; 1:1000 dilution), argininosuccinate synthase 1 (ASS1) (Proteintech, Cat: 16210-1-AP; 1:5000 dilution), hypoxia-inducible factor 1-alpha (HIF1A) (Proteintech, Cat: 20960-1-AP; 1:1000 dilution), and glyceraldehyde 3-phosphate dehydrogenase (GAPDH) (Proteintech, Cat: 10494-1-AP; 1:10000 dilution) were used in this study.

Immunohistochemical staining

IHC was assayed as described previously (25). Briefly, after deparaffinization, rehydration, antigen retrieval, and endogenous peroxidase blockage, the tissue sections were hybridized with a specific primary antibody, followed by treatment with a horseradish peroxidase (HRP)-conjugated secondary antibody. The final signals were visualized by incubating the slides with the DAB substrate and counterstaining with hematoxylin solution. Rabbit polyclonal antibodies against WWOX (Proteintech, Cat: 15299-1-AP), and ASS1 (Proteintech, Cat: 16210-1-AP) were employed at a 1:100 dilution. Intensity of IHC images was acquired using ImageJ software (Fiji version).

Cell culture and lentivirus-mediated silencing WWOX

HCC cell lines, Alexander [Research Resource Identifier (RRID): CVCL_0485], Huh7 (RRID: CVCL_0336), and HepG2 (RRID: CVCL_0027), were utilized in this study. All these cells were gifts from Dr. Kwang-Huei Lin at Chang Gung University, Taoyuan, Taiwan. These cells were maintained in Dulbecco’s Modified Eagle Medium (DMEM; for Huh7 cells) or alpha-MEM (for Alexander and HepG2 cells) with 10% fetal bovine serum in a humidified 37°C incubator with 5% CO₂. Lentivirus-mediated gene silencing was conducted as previously described (26). An shRNA against LacZ was used. The WWOX shRNA clones, TRCN0000428967 and TRCN0000430007, were purchased from Academia Sinica, Taipei City, Taiwan.

Cell viability assay

Cell viability assay was performed as previously mentioned (27). For the cell viability assay, 3×10³ cells were seeded for at least 16 h before adding ADI-PEG 20 to the culture medium. Alarmar Blue Cell Viability Reagent (Invitrogen, Waltham, MA, USA, Cat: DAL100) was used according to the instructions provided by manufacture.

Statistical analysis

Dichotomized data are expressed as ratios (%) and compared using χ2 or Fisher’s exact test. Parametric data are expressed as mean ± standard deviation and compared using a two-sample t-test. Nonparametric or non-normally distributed data are expressed as median (range) and compared using the Mann–Whitney test. Univariate and multivariate Cox proportional hazards models were used to estimate survival for clinical and genotypic variables. After categorization, the Kaplan–Meier method was employed to estimate the survival probability between groups. The log-rank test was used to compare the survivals. A P < 0.05 was considered statistically significant. Statistical analyses were conducted using SPSS (IBM, Armonk, NY, USA, version 18.0) or Prism software (GraphPad Software, La Jolla, CA, USA, version 8.0).

Results

Baseline characteristics of ADI-PEG 20-treated patients included in this study

Two independent cohorts with a total of 160 patients with advanced-stage HCC were included in the post-hoc analysis. Among these, 113 and 47 who received ADI-PEG 20 monotherapy and ADI-PEG 20 plus mFOLFOX6 combination therapy were assigned to cohorts-1 and 2, respectively. Table S1 summarizes and compares baseline clinical and genotypic characteristics of the two cohorts. Except for the distribution of tumor numbers, AST levels, and ECOG performance status, the differences in the remaining parameters, including the genotypic distribution of SNPs, were not statistically different. In cohort-1, patients had a higher number of tumors, a higher AST levels, and more patients with ECOG performance status “0” (P < 0.001, 0.003, and 0.002, respectively). However, the differences in tumor numbers and AST levels might originate from the inclusion criteria of the trials, in which patients with higher AST levels were excluded from the cohort-2 (14, 15).

Clinicopathological factors and SNP genotypes are associated with OS, TTR, and TTP in advanced HCC patients receiving ADI-PEG 20 monotherapy

To investigate whether any of the parameters, including baseline characteristics and SNP genotypes, were correlated with OS in cohort-1, analyses using the Cox proportional hazards model were performed. Univariate analysis showed that patients with ≦ 4 tumors had a longer OS than those with > 4 tumors (mean OS, 10.6 months [95% CI: 8.5-12.7] versus 6.2 months [95% CI: 5.0-7.3]; P = 0.001). Additionally, patients with the WWOX-rs13338697-GG genotype had a longer OS compared to those with the WWOX-rs13338697-non-GG genotype (mean OS, 15.1 months [95% CI: 6.7-23.4] versus 8.1 months [95% CI: 6.8-9.3], P = 0.025) (Table S2). Furthermore, these two factors were independent predictors of OS in patients receiving ADI-PEG 20 monotherapy (adjusted P value = 0.003 and 0.045, respectively). The results of Kaplan-Meier analysis confirmed that tumor number and the WWOX-rs13338697-GG genotype could serve as independent factors to predict OS in cohort-1 (Figures 1A, B). However, the rs6025211 and GALNT14-rs9679162 genotypes had no predictive value for OS (Figures 1C, D).

Figure 1 | Analysis of clinicopathological factors and SNP genotypes in association with OS in HCC patients treated with ADI-PEG 20 monotherapy. Kaplan-Meier analysis for subgroups of patients with HCC stratified by (A) tumor number, (B) WWOX-rs13338697, (C) rs6025211, and (D) GALNT14-rs9679162 genotypes. P values were obtained by log-rank test, and < 0.05 was considered significant.

Similar analyses were performed to understand whether these factors were associated with TTR and TTP in cohort-1. No correlation was observed between any of these parameters and TTR using the Cox proportional hazards model (Table S3) or Kaplan-Meier analysis (Figure S1).

Notably, extrahepatic spread, tumor number, and another factor, the rs6025211-TT genotype, were linked to TTP using univariate analysis in a Cox proportional hazards model (Table S4, P = 0.030, 0.011 and 0.021, respectively). Patients with extrahepatic spread were correlated with a shorter TTP compared with those without extrahepatic spread (mean TTP, 2.7 months [95% CI: 2.2-3.2] versus 4.5 months [95% CI: 2.5-6.4]). Also, patients with tumor number > 4 were associated with a shorter TTP compared with those with tumor number ≦ 4 (mean TTP, 2.1 months [95% CI: 1.7-2.5] versus 3.8 months [95% CI: 2.8-4.8]). For the rs6025211 genotypes, patients with the TT genotype had a shorter TTP than those with the non-TT genotype (mean TTP, 2.0 months [95% CI: 1.3-2.6] versus 3.3 months [95% CI: 2.6-4.0]). Multivariate analysis using a Cox proportional hazards model further demonstrated that only tumor number and the rs6025211-TT genotype were independent predictors of TTP in cohort-1 (P = 0.047 and 0.013, respectively). Kaplan-Meier analysis also revealed distinguishable stratification of patients by use of tumor number (P = 0.008) or the rs6025211-TT (P = 0.015 when compared to non-TT) in cohort-1 (Figures 2A, B). No associations were found for the other SNP genotypes (Figures 2C, D).

Figure 2 | Analysis of clinicopathological factors and SNP genotypes in association with TTP in HCC patients treated with ADI-PEG 20 monotherapy. Kaplan-Meier analysis of subgroups of patients with HCC stratified by (A) tumor number, (B) rs6025211, (C) WWOX-rs13338697, and (D) GALNT14-rs9679162 genotypes. P values were obtained by log-rank test, and < 0.05 was considered significant.

Collectively, the WWOX-rs13338697-GG and rs6025211-TT genotypes could predict the outcome of ADI-PEG 20 monotherapy independent of clinicopathological parameters, such as tumor number.

Neither clinicopathological factors nor SNP genotypes are associated with clinical outcomes in patients receiving ADI-PEG 20 plus mFOLFOX6 combined therapy

To examine whether parameters including baseline characteristics and SNP genotypes correlated with OS, TTR and TTP in cohort-2, the aforementioned analyses were performed. However, no association was found between these parameters and prognoses was present, except for the Child-Pugh score (Tables S5–7 and Figures S2–4). Unexpectedly, patients with a Child-Pugh score > 5 were associated with a shorter TTR (P = 0.039) (mean TTR, 2.1 months [95% CI: 0.0-8.2] versus 5.4 months [95% CI: 4.0-6.7]). However, only two patients had a Child-Pugh score > 5 (Table S6). Thus, there could be a bias.

Clinicopathological factors and SNP genotypes are associated with OS, TTR and TTP in advanced HCC patients treated with ADI-PEG 20 monotherapy or combination therapy

To understand whether parameters including baseline characteristics and SNP genotypes correlated with OS, TTR and TTP in all patients in cohort-1 and -2 combined, the aforementioned analyses were performed. The Cox proportional hazards model followed by Kaplan-Meier analysis demonstrated that only tumor number (Table S8 and Figure 3A), but not other clinicopathological factors and SNP genotypes, served as an independent predictor for OS (Figures 3B–D). Except for ECOG performance status, where a higher status was associated with a shorter TTR (P = 0.024) (mean TTR, 3.8 months [95% CI: 3.0-4.6] versus 4.2 months [95% CI: 3.5-4.9]), no correlation was observed between the clinical/genetic parameters and TTR by using the Cox proportional hazards models (Table S9) and Kaplan-Meier analysis (Figure S5). However, extrahepatic spread (P = 0.030), tumor number > 4 (P = 0.005) and the rs6025211-TT genotype (P = 0.008) predicted unfavorable TTP, as analyzed by the univariate Cox proportional hazards model (Table S10). Multivariate analysis further showed that only tumor number (P = 0.047) and the rs6025211-TT genotype (P = 0.013) were independent predictors of TTP in all ADI-PEG 20-treated HCC patients. Kaplan-Meier analysis also confirmed this notion (P = 0.004 for comparison between patients stratified by tumor numbers, and P = 0.006 for comparison between subgroups of patients with the rs6025211-TT and non-TT genotypes) (Figures 4A, B).

Figure 3 | Analysis of clinicopathological factors and SNP genotypes in association with OS in all 160 HCC patients experiencing ADI-PEG 20 therapy. Kaplan-Meier analysis of subgroups of patients with HCC stratified by (A) tumor number, (B) WWOX-rs13338697, (C) rs6025211, and (D) GALNT14-rs9679162 genotypes. P values were obtained by log-rank test, and < 0.05 was considered significant.

Figure 4 | Analysis of clinicopathological factors and SNP genotypes in association with TTP in all 160 HCC patients experiencing ADI-PEG 20 therapy. Kaplan-Meier analysis of subgroups of patients with HCC stratified by (A) tumor number, (B) rs6025211, (C) WWOX-rs13338697, and (D) GALNT14-rs9679162 genotypes. P values were obtained by log-rank test, and < 0.05 was considered significant.

The WWOX-rs13338697 genotype is not associated with clinical outcomes in HCC patients received surgical resection

To examine whether the WWOX-rs13338697 genotype is a prognostic factor but not a response predictor, tissue samples derived from 214 surgically resected HCC patients were retrieved for genotyping and prognosis analysis. As shown in Figures 5A–C, no correlation was observed between the WWOX-rs13338697 genotypes and the prognosis of patients with HCC in cohort-3, implying that this SNP predictor was specifically associated with the therapeutic efficacy of ADI-PEG 20.

Figure 5 | Analysis of the WWOX-rs13338697 genotypes in association with OS, RFS, and MFS in all 214 HCC patients receiving surgical resection. Kaplan-Meier analysis of (A) OS, (B) RFS, and (C) MFS of subgroups of patients with HCC stratified by the WWOX-rs13338697 genotypes. P values were obtained by log-rank test, and < 0.05 was considered significant.

HCC patients with the WWOX-rs13338697-GG genotype have reduced tissue levels of WWOX and ASS1 and increased plasma arginine concentrations

To further understand whether the WWOX-rs13338697 genotype affects the gene expression of WWOX and the expression of the critical enzyme ASS1 in HCC, western blot analysis and IHC staining were performed. As demonstrated in Figures 6A, B, the ratio of WWOX expression in tumorous/nontumorous (T/N) tissues was significantly lower in patients with the WWOX-rs13338697-GG genotype than in those with other genotypes (P = 0.031 compared to patients with the non-GG; P = 0.045 and 0.039 compared to patients with the AA and AG genotypes, respectively). Simultaneous examination of the T/N ratios of ASS1 levels further uncovered that it was also markedly decreased in patients with the WWOX-rs13338697-GG genotype compared to those with other genotypes (P = 0.037 compared to patients with the non-GG; P = 0.048 and 0.029 compared to patients with the AA and AG genotypes, respectively). IHC staining results also confirmed these findings that the WWOX-rs13338697-GG genotype correlated with a lower T/N ratios for WWOX (P = 0.020 compared to patients with the non-GG; P = 0.040 and 0.023 compared to patients with the AA and AG genotypes, respectively) and ASS1 (P = 0.001 compared to patients with the non-GG; P = 0.005 and 0.001 compared to patients with the AA and AG genotypes, respectively) in HCC (Figures 6C, D). Pearson correlation analysis of western blotting and IHC staining results further demonstrated that the T/N ratios of WWOX and ASS1 were positively correlated (Figures 6E, F).

Figure 6 | The WWOX-rs13338697 genotypes correlates with WWOX and ASS1 expression in HCC. (A) Western blot analysis of WWOX and ASS1 in patient-derived tissues. NG, Non-WWOX-rs13338697-GG genotype; GG, WWOX-rs13338697-GG genotype; N, Non-tumor; T, Tumor. Quantification results generated from (A) are summarized in (B). T/N, Tumor/Nontumor ratio. (C) IHC analysis of WWOX and ASS1 in patient-derived sections. Quantification results generated from (C) are summarized in (D). P values were obtained by two-tailed unpaired student t-test, and < 0.05 was considered significant. Pearson correlation of levels of ASS1 T/N and WWOX T/N generated using (E) Western blot analysis (WB) and (F) IHC are shown. (G) Arginine and Citrulline levels measured using plasma derived from cohort-1 patients. P values were obtained by two-tailed unpaired student t-test, and < 0.05 was considered significant. *P < 0.05; **P < 0.01; ***P < 0.001.

To test whether the WWOX-rs13338697 genotypes correlates with plasma arginine or citrulline levels at baseline, plasma samples from cohort-1 patients were analyzed. Patients with the WWOX-rs13338697-GG genotype were associated with higher baseline (pretreatment) plasma arginine levels but not citrulline levels (P = 0.006) (Figure 6G).

Silencing WWOX enhances HIF1A increase and ASS1 decrease in HCC cells in response to hypoxia

To understand possible relationships between WWOX and ASS1 expression, a literature review was conducted. Notably, the effects of these molecules on hypoxia-mediated responses have been reported in several studies. In particular, Abu-Remaileh et al. showed that WWOX loss-of-function promotes HIF1A expression in HCC (28). Silberman et al. showed that increasing HIF1A suppressed ASS1 expression (29). Collectively, we hypothesize that lower levels of WWOX may suppress ASS1 expression through upregulating HIF1A in HCC cells. To test this hypothesis, HCC cells, with or without WWOX silencing, were treated with the hypoxia inducer, CoCl₂, and harvested for western blot analysis. As shown in Figures 7A, B, HIF1A was undetectable without the addition of CoCl₂. ASS1 levels remained similar in the three different HCC cell lines even after WWOX silencing (no statistical difference). However, a significant increase in HIF1A and a decrease in WWOX and ASS1 levels were noticed after CoCl₂-induced hypoxia. These effects were further enhanced by WWOX silencing, particularly after 48 h of CoCl₂ treatment. These findings indicate that silencing WWOX enhanced hypoxia-induced HIF1A expression, thereby repressing ASS1 expression in HCC cells.

Figure 7 | WWOX modulates HIF1A expression under hypoxia in HCC. (A) Western blot analysis of HIF1A, WWOX, and ASS1 expression in samples from HCC cells with the indicated treatments. Their quantitative results are summarized in (B). P values were obtained by two-tailed paired student t-test, and < 0.05 was considered significant. *P < 0.05; **P < 0.01; ***P < 0.001.

Silencing WWOX under hypoxic conditions enhances the susceptibility of HCC cells to ADI-PEG 20

Although silencing WWOX indeed suppressed ASS1 expression in HCC cells, it remains unclear whether it alters the susceptibility of HCC cells to ADI-PEG 20. To answer this question, HCC cells, with or without WWOX silencing and CoCl₂ treatment, were treated with different ADI-PEG 20 concentrations and then used for the cell viability assays. As shown in Figure 8A, growth curves of cells, with and without WWOX silencing, were not statistically different without the addition of ADI-PEG 20 (black, blue, and green lines for ADI-PEG 20 concentration at 0 μg/mL). Cells continued to grow under ADI-PEG 20 treatment. However, growth rates were attenuated (black lines for ADI-PEG 20 concentrations at 1, 2, 4, 8, and 16 μg/mL), suggesting that ADI-PEG 20 inhibited cell growth. Furthermore, ADI-PEG 20 had no or only weak cytotoxicity, and another combination agent was required to achieve better cytotoxic effects, as reported previously (30). Notably, silencing WWOX further attenuated cell growth when cells were treated with different concentrations of ADI-PEG 20 (blue and green lines for ADI-PEG 20 concentrations at 1, 2, 4, 8, and 16 μg/mL), indicating that reduced ASS1 expression after hypoxia induction and under WWOX knockdown enhanced the susceptibility of HCC cells to ADI-PEG 20.

Figure 8 | Silencing WWOX sensitizes HCC cells to ADI-PEG 20 under hypoxic conditions. (A) HCC cell viability when treated with the indicated concentrations of ADI-PEG 20, all under 100 μM CoCl₂ treatment. P values were obtained by two-way ANOVA and < 0.05 was considered significant. *P < 0.05; **P < 0.01; ***P < 0.001. (B) The proposed working model of this study. HCC patients with the WWOX-rs13338697-GG genotype were associated with lower WWOX and ASS1 levels and higher plasma arginine levels. Decreased ASS1 and elevated extracellular arginine demand resemble the arginine auxotrophic phenotype. Low WWOX protein expression in HCC in patients with the WWOX-rs13338697-GG genotype enhances the cellular hypoxic response through an increase in HIF1A. Since HIF1A is a known repressor of ASS1, increasing HIF1A facilitates further decrease in ASS1. Subsequently, decreased in ASS1 increases the sensitivity of HCC cells to ADI-PEG 20. This model suggests a supportive role of hypoxia in ADI-PEG 20-based treatment. Gray bubbles indicate that autophagy has occurred. ns, no significance.

Discussion

To address the urgent need for new HCC therapeutic strategies, arginine deprivation therapy has been developed (e.g., ADI-PEG 20) in the past decades (12, 14, 15). However, unsatisfactory efficacies remained, with no significant difference in OS between groups of patients receiving placebo and ADI-PEG 20 treatment (15). However, ADI-PEG 20 prolonged OS for unknown reasons in a substantial proportion of enrolled patients. Herein, we discovered that the WWOX-rs13338697-GG genotype could predict the therapeutic efficacy of ADI-PEG 20 monotherapy in terms of prolonged OS (Figure 1). Additionally, another SNP, the rs6025211-TT genotype could predict unfavorable TTP in all ADI-PEG 20-treated HCC patients (Figures 2, 4). These findings provide helpful information for patient selection in the upcoming ADI-PEG 20 clinical trials for HCC.

WWOX protein has been demonstrated to be a tumor suppressor in many types of cancers, including HCC, due to its downregulated expression and functional links. It modulates various oncogenic signaling pathways and alters the expression levels of several growth-related molecules, such as HIF1A, in either animal models or patient-derived tissues/cells (28, 31). Additionally, WWOX expression has been reported to be sensitive to (and suppressed under) hypoxia (32). In contrast, ASS1 expression has been demonstrated to be altered by hypoxic conditions, where elevated HIF1A binds directly to the ASS1 promoter and leads to a significant decrease in ASS1 expression (29). Our results further confirmed these observations, as WWOX and ASS1 levels were markedly reduced under hypoxia (Figure 7A). Although the underlying molecular mechanisms by which the WWOX-rs13339697-GG genotype can lead to a lower WWOX expression remain unclear, there is a promising link between the WWOX-rs13339697-GG genotype and reduced WWOX expression. This correlation suggests that induction of hypoxia in HCC tissue during treatment with ADI-PEG 20 (for example, by TACE or TKI) may improve the therapeutic efficacy of ADI-PEG 20, and HCC patients harboring the WWOX-rs13339697-GG genotype should have further reduction of ASS1 levels and correspondingly further increase of tumor susceptibility to ADI-PEG 20 (Figure 8B).

A hypoxic microenvironment with elevated HIF1A that promotes cancer growth and creates anticancer drug resistance has been reported in patients treated with TACE and TKI treatments (33). However, we found that increased hypoxia in HCC may be more suitable for ADI-PEG 20 combination therapy. An increased hypoxic microenvironment correlates with higher HIF1A expression and represses the expression of ASS1, a key enzyme associated with therapeutic efficacy of ADI-PEG 20 in HCC, through transcriptional regulation of ASS1 promoter activity (29). The results of this study suggest that either TACE or TKI may be an excellent combinatory therapy for ADI-PEG 20. This is because both treatments promote a hypoxic microenvironment (34), generating favorable conditions for ADI-PEG 20 targeting.

In surgically resected liver tissue, we found lower levels of ASS1 in patients with the WWOX-rs13339697-GG genotype (Figures 6A–D). This suggests a lower efficacy of de novo arginine biosynthesis, resembling the arginine auxotrophic phenotype. Therefore, tumors with the WWOX-rs13339697-GG genotype have an increased demand for extracellular arginine. Additionally, only patients with higher plasma arginine levels can maintain the progressive growth of ASS1-deficient HCC. This may be the reason why we found higher plasma arginine levels in cohort-1 patients with the WWOX-rs13339697-GG genotype (Figure 6G), as their tumor growth is highly dependent on exogenous arginine. Hence, ADI-PEG 20 suppressed the tumor growth drastically in patients with the WWOX-rs13339697-GG genotype, probably because these tumors displayed an arginine auxotrophic-like phenotype and were highly dependent on plasma arginine supply. In the future, it would be more compelling to use other advanced research methods to better understand the mechanisms by which the WWOX-rs13338697-GG genotype or the WWOX protein controls HCC growth and susceptibility to ADI-PEG 20. If tumor biopsies of patients with advanced HCC are available, more advanced tools can be used, for example, the systems biology approach using tumor growth-dependent prognostic subnetworks and the single-cell multi-omics analysis (35, 36).

Emerging evidence has suggested that immunotherapy, including using inhibitors to target programmed death-1 (PD-1) on effector T-cells or programmed death ligand-1 (PD-L1) on the tumor cell surface, providing adoptive cell therapy, and applying cancer vaccines, could be promising strategies to treat HCC (37, 38). According to the current understanding, the goal of achieving better efficacy of immunotherapy is to reduce immune-tolerance, activate full-function effector T-cell, and avoid expansion of regulatory T-cells (39, 40). Previously, certain amino acids and their metabolites have been documented to regulate the effectiveness of the immune system. Arginine and its metabolites, citrulline and nitric oxide (NO), can enhance effector T-cell proliferation and cytotoxicity (41); histidine and its metabolite, histamine, can promote lymphocyte growth and prevent apoptosis (42); and the metabolite of tryptophan from the kynurenine pathway, kynurenine, can result in immunosuppressive effects on T-cell and immune-tolerance to tumor cells (43). Notably, the deprivation of arginine by rhArg1, which leads to the production of urea and ornithine, or tryptophan by indoleamine 2,3-dioxygenase-1 (IDO1) or tryptophan 2,3-dioxygenase (TDO), which promotes the generation of the downstream metabolite kynurenine, has been demonstrated to induce immunosuppressive effects (44, 45). However, no immunosuppressive effects were observed when ADI-PEG 20 was used to achieve arginine deprivation either in vitro, in vivo (46), or in clinical settings (47). This may be because the product of ADI-PEG 20 treatment is citrulline, which, like arginine, has been documented to be able to promote T-cell proliferation (48). In this study, hypoxia enhanced the effectiveness of ADI-PEG 20 when WWOX expression was reduced (for example, patients with the WWOX-rs13338697-GG genotype). Since kynurenine biosynthesis is thought to be an oxygen-dependent event (49), it is reasonable to propose a combination therapy of ADI-PEG 20 and immunotherapy for the treatment of patients with advanced HCC, especially when a hypoxic tumor environment is achieved.

This study has limitations. For example, the limited availability of patient samples may bias the analysis of results. Furthermore, the relatively small number of cases, the rare frequency of the WWOX-rs13338697-GG genotype present in HCC patients, and the unelucidated molecular mechanisms behind the SNP genotype and gene expression all suggest the need for further investigation. Although these limitations exist, this study provides novel insights into the field of arginine deprivation therapy.

In conclusion, this study identified the WWOX-rs13338697-GG genotype as an independent predictor of OS in patients receiving ADI-PEG 20 monotherapy. Furthermore, the rs6025211-TT genotype was shown to be a predictor of TTP in cohort-1 (ADI-PEG 20 monotherapy) or in all patients experienced ADI-PEG 20 treatments (cohort-1 plus 2). HCC patients with the WWOX-rs13338697-GG genotype were associated with lower WWOX and ASS1 levels and higher plasma arginine levels. Decreased ASS1 and elevated extracellular arginine demand, resembling the arginine auxotrophic phenotype, are believed to be an important indicator of the efficacy of ADI-PEG 20. Cell-based assays demonstrated that silencing WWOX to mimic low WWOX protein expression in HCC in patients with the WWOX-rs13338697-GG genotype improved the cellular hypoxia response through a further increase of HIF1A. Since HIF1A is a known repressor of ASS1, increasing HIF1A leads to further decrease of ASS1. Subsequently, decrease in ASS1 increased the sensitivity of HCC cells to ADI-PEG 20.

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 IRB in Chang Gung Memorial Hospital, Taiwan and complied with the Declaration of Helsinki. 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

Conceptualization, Y-DC, C-HC, and C-TY. Methodology, C-TY. Formal analysis, Y-DC, H-FL, Y-CC, and C-HC. Original draft writing, Y-DC and C-HC. Manuscript review and editing, C-TY. Supervision, C-TY. Project administration, C-TY. Funding acquisition, C-TY. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Polaris Pharmaceuticals, grant number SCRPD1L0171, and Chang Gung Memorial Hospital, Linkou branch, grant number CMRPG3M0891, to C-TY.

Acknowledgments

The authors would like to thank all members of the Liver Research Center of Chang Gung Memorial Hospital, especially Ms. Yu-Ru Liang, and colleagues at Polaris Pharmaceuticals Inc.

Conflict of interest

H-FL, Y-CC, C-HC were employed by Polaris Pharmaceuticals Inc. C-TY received funding from Polaris Pharmaceuticals Inc.

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.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2022.996820/full#supplementary-material

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ORIGINAL RESEARCH
published: 23 January 2023
doi: 10.3389/fonc.2022.1113785

GPBAR1 is associated with asynchronous bone metastasis and poor prognosis of hepatocellular carcinoma

Nan Chen ¹, Jieqing Wang ², Lei Zhou ¹, Baiqiang Hu ³, Yinzhong Chen ¹ and Zhuangchen Zhu ¹*

¹ Department of Orthopaedic Surgery, The Second Affiliated Hospital of Shandong First Medical University, Tai'an, Shandong, China, ² Department of Pharmacy, The Affiliated Taian City Central Hospital of Qingdao University, Tai'an, Shandong, China, ³ Department of Orthopaedic Surgery, Yantai Yuhuangding Hospital, Yantai, Shandong, China

Edited by:
Zongli Zhang, Qilu Hospital of Shandong University, China

Reviewed by:
Wentao Mu, First Affiliated Hospital of Jilin University, Jilin University, China
Zhaochen Liu, Zhengzhou University, China
Chaojie Liang, First Hospital of Shanxi Medical University, China

*Correspondence:
Zhuangchen Zhu
zzctaian2019@163.com

Specialty section:
This article was submitted to Cancer Molecular Targets and Therapeutics, a section of the journal Frontiers in Oncology

Received: 01 December 2022

Accepted: 30 December 2022

Published: 23 January 2023

Citation:
Chen N, Wang J, Zhou L, Hu B, Chen Y and Zhu Z (2023) GPBAR1 is associated with asynchronous bone metastasis and poor prognosis of hepatocellular carcinoma. Front. Oncol. 12:1113785. doi: 10.3389/fonc.2022.1113785

Background

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related death in China. Asynchronous metastasis is the main reason for HCC recurrence, but the current assessment of HCC metastasis and prognosis is far from clinically satisfactory.

Materials

In our study, we investigated the expression of G-protein-coupled bile acid receptor (GPBAR1) in HCC tissues and tumor-adjacent tissues by qRT-PCR and immunohistochemistry. The associations between GPBAR1 expression, clinicopathological factors, and asynchronous metastases were assessed by the Chi-square test. The overall survival curves of different variables were plotted with the Kaplan–Meier method, and the statistical significance between different subgroups was analyzed with the log-rank test. The independent prognostic factors were identified by the Cox regression hazard model.

Results

GPBAR1 was more highly expressed in HCC tissues than in tumor-adjacent tissues. GPBAR1 expression in HCC was significantly higher than that in liver cirrhosis, followed by normal liver tissues. GPBAR1 was significantly associated with poor prognosis in HCC and can be regarded as an independent prognostic biomarker. Interestingly, GPBAR1 expression in HCC was significantly correlated with asynchronous metastasis to the bone but not to the liver or lung.

Conclusions

GPBAR1 was found to be an independent, unfavorable prognostic factor of HCC, as well as an indicator of asynchronous bone metastasis but not liver or lung metastases. Our results could provide a new aspect for HCC metastasis studies and help identify high-risk HCC patients, which helps ameliorate the prognostic assessment of HCC.

Keywords: GPBAR1, asynchronous bone metastasis, HCC, prognosis, biomarker

Introduction

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death and the second leading cause of cancer death worldwide (1). China has the most severe hepatitis B virus (HBV) prevalence, and the incidence of HCC has doubled in the UK and tripled in the USA over the past three decades (2). More than one million deaths from HCC are predicted for 2030 by the WHO (2, 3). In addition to surgical resection, several new treatment approaches to HCC have been developed, such as trans-arterial chemoembolization, tyrosine kinase inhibitors, and immunotherapy (4). However, the prognosis for HCC is still very dismal. The current prognostic assessment of HCC is far from clinically satisfactory. Several proposals have been made for better predicting outcomes and guiding the treatment (5). New biomarkers and drug targets are still urgently needed to improve patient stratification for precision treatment.

Hepatocytes produce bile acids (BAs) to help the absorption of cholesterol, fat-soluble vitamins, and lipids. In addition, BAs participate in plenty of physiological processes such as glucose homeostasis, lipid and energy metabolism, and immune response (6). Deregulation of BA homeostasis is also reported to be involved in tumorigenesis and tumor progression (7). BAs bind with BA receptors with different affinities and mediate BA-specific signaling (8, 9). BA receptors consist of nuclear receptors and membrane-bound G protein-coupled receptors. The former contains farnesoid X receptors (FXR), pregnane X receptors (PXR), and vitamin D receptors (VDR) (10); the latter is composed of G-protein-coupled bile acid receptor (GPBAR1, also known as TGR5) and sphingosine-1-phosphate receptor 2 (S1PR2) (11).

Among all the BA receptors, GPBAR1 is widely expressed in human tissues (12). High GPBRA1 expression is reported in the gallbladder, placenta, spleen, lung, liver, intestine, kidney, adrenal glands, adipose tissue, etc. (13). GPABR1 plays an essential role in regulating energy homeostasis, bile acid homeostasis, and glucose metabolism (14, 15). Emerging evidence has shown the involvement of GPBAR1 in cancer progression. GPBAR1 is upregulated and promotes the progression of gallbladder cancer, cholangiocarcinoma, and lung cancer (16–18). The expression of GPBAR1 and its clinical significance, especially the prognostic significance in HCC, are still unknown.

In our study, we investigated the expression of GPBAR1 in HCC tissues and tumor-adjacent tissues. Moreover, we evaluated the clinical significance of GPBAR1 in HCC by analyzing its correlation with clinicopathological factors, overall survival (OS) rate, and asynchronous metastases to the liver, lung, and bone.

Materials and methods

HCC cohort and ethics

With the patients’ prior consent, 10 pairs of HCC tissues and tumor-adjacent tissues were obtained from surgery. The other cohort contains 201 HCC patients who underwent radical surgery from January 2014 to December 2019 in the Second Affiliated Hospital of Shandong First Medical University and The Affiliated Taian City Central Hospital of Qingdao University. Formalin-fixed and paraffin-embedded specimens were obtained after the approval of patients or patients’ relatives. The OS time was determined by the time interval from the surgery date to the date of the last follow-up or the patient’s death. HCC staging was according to the eighth TNM staging system. Asynchronous metastasis was determined by biopsy or a clinical diagnosis, including elevated AFP and classic imaging manifestations. Patients with asynchronous metastasis received standard treatments according to the HCC treatment guideline (19, 20). For patients with liver metastasis, topical treatments such as tumor ablation or transcatheter arterial chemoembolization (TACE) were performed, supplemented with systemic treatments such as target therapy or immune checkpoint inhibitor (ICI) treatment. Patients with bone and lung metastasis mainly received radiotherapy or systemic treatment.

All experiments were performed with the approval of the Ethics Committees of the Second Affiliated Hospital of Shandong First Medical University and The Affiliated Taian City Central Hospital of Qingdao University.

Immunohistochemistry and evaluation

GPBAR1 expression in HCC was detected by immunohistochemistry (IHC) with the streptavidin peroxidase complex method. In brief, the specimens were first soaked in boiled EDTA (pH = 9.0) buffer for antigen retrieval and then incubated in 3% hydrogen peroxide to inactivate the endogenous peroxidase. After that, 5% bovine serum albumin was applied to incubate specimens to block unspecific antigen binding. The primary antibody for GPBAR1 (Abcam, ab72608, Cambridge, UK) was administrated to incubate the tissues overnight at 1:100 concentrations, followed by the secondary antibody labeled with biotin (Zsbio, Beijing, China) treatment for 30 min at room temperature. The final antigen vision was performed with 3,3-diaminobenzidine (DAB) solution (Zsbio).

The slides were evaluated by two senior pathologists, and the IHC results were semiquantified by scores of the staining intensity and the positively stained cell percentage. The final IHC score was defined as the number of the score (staining intensity) multiplied by the score (positively stained cell percentage). Scores of staining intensity were classified as 0 (negative staining), 1 (weak staining), 2 (moderate staining), and 3 (strong staining). The scores of positively stained cell percentage were determined as 1 (<25% positive cells), 2 (25%–50% positive cells), 3 (50%–75% positive cells), and 4 (75%–100% positive cells). The final IHC score varied from 0 to 12. The cohort was divided into subsets with different GPBAR1 expressions by the cutoff of the IHC score, which was identified by the receiver operating characteristic (ROC) curve. In our study, the cutoff for the GPBAR1 IHC score in HCC was 2.5.

Quantitative real-time PCR

The total mRNA of HCC tissues and corresponding tumor-adjacent tissues was extracted by the TRIzol reagent (Thermo Fisher, Waltham, MA, USA). cDNA synthesis was accomplished by a reverse transcriptase kit (TOYOBO, Osaka, Japan), and real-time PCR was performed with SYBR Green Master Mix (Roche, Basel, Switzerland) and Light Cycler Roche 480 PCR instrument. The Ct value of GAPDH was used as an internal control, and the 2−ΔΔCt method was applied to compare the difference between HCC and tumor-adjacent tissues. The primer sequences for GPBAR1 and GAPDH were as follows: GPBAR1 sequence (5′–3′): forward: CCCAGGCTATCTTCCCAGC; reverse: GCCAGGACTGAGAGGAGCA. GAPDH sequence (5′–3′) forward: GCACCGTCAAGGCTGAGAAC; reverse: TGGTGAAGACGCCA GTGGA.

Statistical analysis

All data were analyzed with SPSS 22.0 software (SPSS, Chicago, IL, USA). The association between GPBAR1 expression, clinicopathological factors, and asynchronous metastases was assessed by the Chi-square test. OS curves were plotted with the Kaplan–Meier method, and the statistical significance between different subgroups was analyzed with the log-rank test. The independent prognostic factors were identified by the Cox regression hazard model. p-value <0.05 was considered statistically significant.

Results

Expression of GPBAR1 in HCC

The expressions of GPBAR1 in HCC and tumor-adjacent tissues were first assessed by mRNA. A total of 10 pairs of HCC tissues and corresponding tumor-adjacent tissues were detected. In HCC tissues, GPBAR1 expression was significantly higher than that in tumor-adjacent tissues, indicating a potential oncogenic role for GPBAR1 in HCC (Figure 1A). Moreover, we investigated the expression of GPBAR1 in the HCC tissues of our cohort, consisting of 201 HCC patients with IHC. GPBAR1 was mainly expressed in the cell membrane and cytosol of HCC (Figure 1B). GPBAR1 expression was evaluated by IHC score, which divided the cohort into GPBAR1low and GPBAR1high subtypes. The GPBAR1low and GPBAR1high patients accounted for 57.21% and 42.78%, respectively. Expression of GPBAR1 in normal liver tissues, liver cirrhosis and HCC was detected with IHC and qPCR (Figures 1C-1E), showing that GPBAR1 expression was increased in liver cirrhosis and HCC.

Figure 1 | Expression of GPBAR1 in HCC and adjacent tissues. (A) The mRNA level of GPBAR1 was assessed by qPCR in 10 pairs of HCC and tumor-adjacent tissues. The statistical significance is analyzed by a paired t-test. (B) GPBAR1 expression in HCC was detected by IHC, which divided the cohort into GPBAR1low and GPBAR1high subsets. (C, D) GPBAR1 expressions in normal liver tissues, liver cirrhosis, and HCC tissues were detected with IHC (C). The IHC scores of GPBAR1 in these tissues were evaluated and compared (D). (E) GPBAR1 expressions in normal liver tissues, liver cirrhosis, and HCC tissues were detected with qRT-PCR. The average level of GPBAR1 mRNA in normal liver tissues was set as a baseline for data normalization. ***p < 0.001 with a one-way ANOVA. The data were from at least three independent experiments.

Correlation between GPBAR1 and other clinicopathological factors

The Chi-square test was applied to investigate the correlation between GPBAR1 and other clinicopathological factors (Table 1). The clinicopathological factors included the gender and age of patients, tumor number, tumor size, histopathological grade, vascular invasion, liver cirrhosis, HBV/HCV infection, T stage, N stage, and TNM stage. There are no patients with distant metastasis, so the M stage was excluded. All factors were two-categorized for the Chi-square test. In the analysis, GPBAR1 had no significant correlations with the above factors.

Table 1 | Correlation between GPBAR1 and clinicopathological factors.

Prognostic significance of GPBAR1 and other clinicopathological factors

The prognostic significance of GPBAR1 and the enrolled clinicopathological factors were analyzed with survival analysis. The OS rates were analyzed with univariate and multivariate analyses. In the univariate analysis, GPBAR1 was an unfavorable prognostic biomarker of HCC, predicting a poor outcome (Table 2). The 5-year OS rates of GPBAR1low and GPBAR1high patients were 23.3% and 6.9%, respectively (Figure 2A). In addition to GPBAR1, multiple tumor numbers, positive vascular invasion, advanced T stage, and TNM stage were all significantly associated with a poor prognosis for HCC (Figures 2B–2E). Correlations between GPBAR1 and other clinicopathological factors were not observed.

Table 2 | The prognostic significance of GPBAR1 and other clinicopathological factors.

Figure 2 | The correlation between GPBAR1 and OS rates. The cohort was divided into subsets according to different parameters. High GPBAR1 (A), multiple tumor numbers (B), positive vascular invasion (C), advanced T (D), and TNM (E) stage were all significantly associated with poor prognosis. The statistical significance is analyzed by a log-rank test.

The independent prognostic significance of enrolled factors was further validated by multivariate analysis with the Cox regression hazard model. Prognostic parameters in the univariate analysis were further included in the multivariate analysis for independent prognostic significance verification. As a result, high GPBAR1 levels and an advanced T stage were identified as the independent prognostic factors for a poor prognosis. The hazard ratio of GPBAR1 was 2.08, suggesting that the odds of HCC-related death for GPBAR1high patients were 2.08 times those for GPBAR1low patients.

Correlation between asynchronous metastasis and GPBAR1

Asynchronous metastases of HCC, including intrahepatic metastasis and lung and bone metastases, were particularly focused on during HCC patient follow-up (Table 3). Asynchronous liver metastasis during the 5-year follow-up after surgery was 43.78%, while this number in the lung and bone was 19.90% and 12.44%, respectively. Moreover, we analyzed the correlation between GPBAR1 expression and asynchronous metastasis of different involved organs. Interestingly, high GPBAR1 expression in HCC was significantly associated with positive bone metastasis but not with liver or lung metastases. This result suggested that GPBAR1 expression may be involved in bone metastasis but not metastases to other organs.

Table 3 | Correlation between different metastasis, GPBAR1, and prognosis.

In addition, we analyzed the prognostic significance of asynchronous metastasis to the liver, lung, and bone. Lung and bone metastases were significantly associated with poor prognoses in HCC patients (Figures 3A, B). The 5-year OS rates of positive and negative lung and bone metastases were 7.7% vs. 19.9% and 0% vs. 20.6%, respectively. Interestingly, the asynchronous intrahepatic metastasis seemed to have an influence on prognosis, but the effect was statistically insignificant (p = 0.163) (Figure 3C).

Figure 3 | The correlation between asynchronous metastases and OS rates. The correlation between asynchronous metastases of the lung (A), bone (B), liver (C), and OS rates was analyzed with the log-rank test.

Discussion

BAs have important physiological functions such as evacuating cholesterol, bilirubin, and hormone derivatives. However, the deregulation of BAs can result in pathological progress such as cholestasis and cancer. The correlation between BAs and HCC has been raised for decades. Current evidence mainly focuses on the fact that BAs in the liver will result in cholestasis, and cholestasis is linked to an increased risk of liver cancer (21). Direct evidence linking BAs or BA receptors and HCC is rare. Moreover, the correlation between BA receptors and HCC is still poorly understood. For a long time, GPBAR1 was mainly considered a regulator of lipid and energy metabolism; here, we demonstrate that GPBAR1 is a prognostic biomarker of HCC for the first time. In previous studies, GPBAR1 expression in the liver was abundant in liver sinusoidal endothelial cells, stellate cells, Kupffer cells, and biliary epithelium cells. Its expression in hepatocytes is the lowest (6). However, its expression and function in HCC have not gained full consensus (7). For the first time, we showed that GPBAR1 expression in HCC is dependent on the time course. GPBAR1 was significantly upregulated in HCC than in liver cirrhosis, with normal liver having the lowest level. In addition, our results suggest that patients with high GPBAR1 are more likely to suffer bone metastasis and a poor outcome. This conclusion indicates that patients with high GPBAR1 expression should get more severe surveillance for tumor recurrence and more intensive precision treatment.

The functions of GPBAR1 in cancer are still very controversial (22). GPBAR1 has been reported to be a tumor suppressor in several cancer types, including gastric cancer and colon cancer (23, 24). However, GPBAR1 promotes progression or correlates with a poor prognosis in some other cancers, including lung cancer, cholangiocarcinoma, and gallbladder cancer (16–18). In HCC, the expression and function of GPBAR1 are poorly studied. Aberrant DNA methylation of GPBAR1 is identified as a potential biomarker for hepatitis B virus-associated hepatocellular carcinoma (25). The expression and molecular function of GPBAR1 in different cancer types may be context- and tissue-dependent. GPBAR1 couples Gαs and activates cAMP-PKA signaling in most cell types, but it is reported to couple both Gαs and Gαi in cholangiocytes (26). More interestingly, functions of GPBAR1 may be dependent on its subcellular localization, as it has been suggested to couple Gαi in the primary cilium of cholangiocytes and Gαs in the apical plasma membrane (26). Moreover, all BAs and many steroids, such as pregnanolone, can activate GPBAR1 (27). GPBAR1 is activated by these ligands and regulates different pathways including PKA, AKT, Src, Rho, mTORC1, NF-κB, and ERK (28). Moreover, GPBAR1 in cholangiocytes, hepatic stellate cells, and macrophages can stimulate different signaling and has various functions (29). These complex GPBAR1 signaling networks confer GPBAR1 more molecular functions in cells, but they also make elucidating GPBAR1 precision function in some special conditions difficult. Since GPBAR1 is involved in many intracellular processes and mediates different signaling pathways, determining how GPBAR1 is activated and what downstream signaling of GPBAR1 is responsible for GPBAR1-associated HCC prognosis remains a difficult task.

Metastasis is a complex, multistep process, and its underlying mechanism is still poorly understood. Tumor metastasis accounts for approximately 90% of all tumor-related deaths (30). HCC has high aggressivity, and most patients suffer from tumor recurrence or metastasis in the course of HCC (31). Intrahepatic metastasis is the most common metastatic site, followed by the lung and other organs. In our study, the prognostic value of metastasis to the liver was not as significant as bone or lung metastasis. Compared with distant metastasis, intrahepatic recurrence has more approaches for treatment, such as lesion ablation or TACE, which may achieve the effect of a radical cure. However, most patients with bone or lung metastasis only receive palliative treatments such as systemic treatments (mainly target therapy or ICI treatment). Different treatment strategies also affect the prognostic significance of metastasis. The metastasis of HCC is a severe threat to patients’ lives, but its molecular mechanism is less studied. Emerging evidence provided new perspectives and brought up the concept of the “pre-metastatic niche,” which remodels the pre-metastatic microenvironment and facilitates tumor metastasis. Obviously, the liver, lung, and bone had different pre-metastatic niches for HCC metastasis. By clinical analysis, we strikingly showed that HCC was more likely to metastasize to the bone instead of the liver or lung. This is an interesting result with great clinical guidance because HCC metastasis to the bone is a rarely studied topic to date. Our results suggest that GPBAR1 overexpression may confer on HCC cells the ability to suit the bone microenvironment but not those of the liver or lung. This is the first report about GPBAR1 and metastasis to a specific organ. This study may provide a new view and help elucidate the molecular mechanism of tumor metastasis.

In summary, we investigated GPBAR1 expression and evaluated its clinical significance by analyzing its association with clinicopathological factors, OS rates, and asynchronous metastases. As a result, we identified GPBAR1 as an independent prognostic factor for HCC and showed that GPBAR1 expression was associated with an increased risk of bone metastasis but not liver or lung metastasis. Our results could provide a new aspect for HCC metastasis studies and help identify high-risk HCC patients, which may ameliorate the prognostic assessment of 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 author.

Ethics statement

The studies involving human participants were reviewed and approved by Ethics Committee of the Second Affiliated Hospital of Shandong First Medical University and The Affiliated Taian City Central Hospital of Qingdao University. The patients/participants provided their written informed consent to participate in this study.

Author contributions

NC, JW, LZ, BH and YC collected the specimens and finished the follow-up. NC and JW performed the experiments. NC and ZZ designed the study. ZZ wrote the paper. All authors contributed to the article and approved the submitted version.

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.

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ORIGINAL RESEARCH
published: 26 January 2023
doi: 10.3389/fonc.2023.1118268

SLC-0111, an inhibitor of carbonic anhydrase IX, attenuates hepatoblastoma cell viability and migration

Katja Eloranta ¹, Marjut Pihlajoki ¹*, Emmi Liljeström ¹, Ruth Nousiainen ¹, Tea Soini ¹, Jouko Lohi ², Stefano Cairo ³,⁴,⁵, David B. Wilson ⁶,⁷, Seppo Parkkila ⁸,⁹,¹⁰ and Markku Heikinheimo ¹,⁶,¹¹

¹ Pediatric Research Center, Children’s Hospital, Helsinki University Hospital, University of Helsinki, Helsinki, Finland, ² Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland, ³ Xentech, Evry, Evry, France, ⁴ Istituto di Ricerca Pediatrica, Padova, Italy, ⁵ Champions Oncology, Hackensack, NJ, United States, ⁶ Department of Pediatrics, Washington University School of Medicine, St. Louis Children’s Hospital, St. Louis, MO, United States, ⁷ Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, United States, ⁸ Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland, ⁹ FICAN Mid, Tampere University, Tampere, Finland, ¹⁰ Fimlab Ltd, Tampere University Hospital, Tampere, Finland, ¹¹ Faculty of Medicine and Health Technology, Center for Child, Adolescent, and Maternal Health Research, Tampere University, Tampere, Finland

Edited by:
Zongli Zhang, Department of General Surgery, Qilu Hospital of Shandong University, China

Reviewed by:
Andrea Angeli, University of Florence, Italy
Baowen Yuan, Chinese Academy of Medical Sciences and Peking Union Medical College, China

*Correspondence:
Marjut Pihlajoki
marjut.pihlajoki@helsinki.fi

Specialty section:
This article was submitted to Cancer Molecular Targets and Therapeutics, a section of the journal Frontiers in Oncology

Received: 07 December 2022

Accepted: 13 January 2023

Published: 26 January 2023

Citation:
Eloranta K, Pihlajoki M, Liljeström E, Nousiainen R, Soini T, Lohi J, Cairo S, Wilson DB, Parkkila S and Heikinheimo M (2023) SLC-0111, an inhibitor of carbonic anhydrase IX, attenuates hepatoblastoma cell viability and migration. Front. Oncol. 13:1118268. doi: 10.3389/fonc.2023.1118268

Background

In response to hypoxia, tumor cells undergo transcriptional reprogramming including upregulation of carbonic anhydrase (CA) IX, a metalloenzyme that maintains acid-base balance. CAIX overexpression has been shown to correlate with poor prognosis in various cancers, but the role of this CA isoform in hepatoblastoma (HB) has not been examined.

Methods

We surveyed the expression of CAIX in HB specimens and assessed the impact of SLC-0111, a CAIX inhibitor, on cultured HB cells in normoxic and hypoxic conditions.

Results

CAIX immunoreactivity was detected in 15 out of 21 archival pathology HB specimens. The CAIX-positive cells clustered in the middle of viable tumor tissue or next to necrotic areas. Tissue expression of CAIX mRNA was associated with metastasis and poor clinical outcome of HB. Hypoxia induced a striking upregulation of CAIX mRNA and protein in three HB cell models: the immortalized human HB cell line HUH6 and patient xenograft-derived lines HB-295 and HB-303. Administration of SLC-0111 abrogated the hypoxia-induced upregulation of CAIX and decreased HB cell viability, both in monolayer and spheroid cultures. In addition, SLC-0111 reduced HB cell motility in a wound healing assay. Transcriptomic changes triggered by SLC-0111 administration differed under normoxic vs. hypoxic conditions, although SLC-0111 elicited upregulation of several tumor suppressor genes under both conditions.

Conclusion

Hypoxia induces CAIX expression in HB cells, and the CAIX inhibitor SLC-0111 has in vitro activity against these malignant cells.

Keywords: carbonic anhydrase IX, hepatoblastoma, pediatric oncology, tumor hypoxia, targeted therapy

1 Introduction

Hepatoblastoma (HB) is a rare pediatric liver malignancy with an incidence of 2.16 per million person-years (1). Approximately 80% of cases are diagnosed before the age of three years (2). Although the etiology of HB is not well understood, its morphology and molecular landscape suggest an embryonal origin (3–5). Most HB cases are sporadic, but certain congenital disorders such as Beckwith-Wiedemann syndrome and familial adenomatous polyposis are risk factors for HB development (3). HB treatment entails surgical resection or liver transplantation combined with pre- and post-operative administration of cisplatin, carboplatin, and doxorubicin (6). This approach has improved the 5-year overall survival (OS) rate to greater than 80% (7), though patients with metastases or relapsed/refractory disease have significantly lower survival rates, emphasizing the need for novel treatment strategies (8).

Solid tumors often contain hypoxic regions due to an imbalance between microvascularization and rapid growth (9). The hypoxic microenvironment is associated with cancer progression and treatment failure (10). Cancer cells adapt to low oxygen tension via hypoxia inducible factor 1α (HIF1α) mediated responses including upregulation of carbonic anhydrase (CA) IX (11). CAs are evolutionary conserved metalloenzymes catalyzing reversible hydration of CO₂ to and H⁺ (12). In humans, fifteen CA isoforms have been identified, three of which lack catalytic activity (13). Transmembrane CAIX is a tumor associated CA isoform with restricted expression in healthy tissue (14). In fetal liver, scattered CAIX-positive hepatocytes have been reported, but postnatal CAIX immunoreactivity in liver is limited to bile duct cells (15–17).

High CAIX expression has been linked to enhanced cell survival, high proliferation rate, increased motility/invasion, and chemoresistance in a wide range of tumors including breast, lung, and oral cancers (18–20). CAIX plays a pivotal role in maintaining acid-base balance in tumors (21). While intracellular acidification reduces tumor cell survival and can be utilized to kill cancer cells, an acidic extracellular milieu supports tumor progression (22). CAIX drives both neutralization of the intracellular compartment and acidification of the tumor microenvironment, promoting an aggressive cancer phenotype (23). Consequently, CAIX is an attractive therapeutic target. A small molecule inhibitor of CAIX, SLC-0111, has completed a phase 1 clinical trial for treatment of advanced solid tumors; no significant dose-limiting toxicities were encountered (24).

Herein, we characterize CAIX expression in archival HB specimens and use cell culture models to study the impact of SLC-0111 on this malignancy. We show that hypoxia induces CAIX expression in HB cells and that SLC-0111 has considerable in vitro activity against this cancer.

2 Materials and methods

2.1 Patient samples

Samples were acquired from the Helsinki Biobank at Helsinki University Hospital. Informed written consent was collected at the time of sample deposit. The study was approved by an ethics committee at Helsinki University Hospital (HUS/3319/2018) and was performed in accordance with Finnish bylaws. Tumor samples (n=21) were obtained from HB patients treated at Children’s Hospital, Helsinki University Hospital between January 1, 1990 and December 31, 2017. Sampling was performed during surgical resection or liver transplantation (after pre-operative chemotherapy). Normal liver (NL) control samples were collected from organ donors (n=3).

2.2 Immunohistochemistry

Five µm sections of formalin-fixed paraffin embedded tumor specimens were deparaffinized and immunostained with a monoclonal anti-human CAIX antibody (M75) (25). Immunoperoxidase staining was performed using an automated Lab Vision Autostainer 480 (LabVision Corporation, Fremont, CA, USA) and Power Vision+ Poly-HRP Immunohistochemistry kit reagents (ImmunoVision Technologies Co). The staining protocol included the following steps: (a) rinsing in wash buffer; (b) treatment in 3% H₂O₂ in ddH₂O for five minutes and rinsing with wash buffer; (c) blocking with cow colostrum diluted 1:2 in Tris-buffered saline (TBS) containing 0.05% Tween-20 for 30 minutes and rinsing in wash buffer; (d) incubation with 1:100 diluted M75 for 30 minutes; (e) rinsing in wash buffer three times for five minutes each; (f) incubation with poly-HRP-conjugated anti-mouse IgG for 30 minutes and rinsing in wash buffer three times for five minutes each; (g) incubation in DAB (3,3`-diaminobenzidine tetrahydrochloride) solution (one drop of DAB solution A and one drop of DAB solution B in 1 ml of ddH₂O) for six minutes and rinsing in ddH₂O; (h) CuSO₄ treatment for five minutes to enhance the signal and rinsing in ddH₂O; (i) treatment with hematoxylin for one minute; (j) rinsing with ddH₂O. All steps were performed at room temperature (RT). Imaging was performed using 3DHISTECH Panoramic 250 FLASH II digital slide scanner at Genome Biology Unit (Research Programs Unit, Faculty of Medicine, University of Helsinki Biocenter, Helsinki, Finland).

2.3 Clinical data

Raw microarray data of gene expression and clinical data from 53 HB tissue samples and 14 noncancerous liver tissue samples were acquired from the Gene Expression Omnibus (GEO) database of the National Center for Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov/geo/), accession number GSE131329. Microarray data were analyzed with Chipster software (https://chipster.rahtiapp.fi/) (26) using the normalization tool for Affymetrix gene arrays (27, 28). Statistical tests were conducted using the “Two group tests” tool (empirical Bayes as test and Benjamini-Hochberg as p-value adjustment method) (29). Differences in the distribution of continuous variables were assessed using the Mann-Whitney U test. Statistical significance was set to p-value < 0.05. Analyses were conducted with IBM SPSS Statistics version 28.0 (IBM Corp., Armonk, NY, USA).

2.4 Cell lines and maintenance

The human HB cell line HUH6 was purchased from the Japanese Collection of Research Bioresources Cell Bank (Osaka, Japan). HB cell lines established from patient-derived xenografts (PDX; HB-303 and HB-295) were obtained from XenTech (Evry, France). HUH6 cells were maintained in Dulbecco’s modified Eagle medium (DMEM)-GlutaMAX (glucose: 1 g/l) supplemented with 10% fetal bovine serum (FBS), 100 U/ml penicillin, and 100 µg/ml streptomycin (all from Gibco, Stockholm, Sweden). HB-303 and HB-295 cells were cultured in Advanced DMEM/F12 medium (Gibco) supplemented with 8% FBS, 100 U/ml penicillin, and 100 µg/ml streptomycin, and 20 µM of Y-27632 (SelleckChem, Houston, TX, USA). Cells were routinely maintained at +37°C in a humidified incubator with 5% CO₂. In this study, these conditions represent normoxia (21% O₂) (30). Hypoxic conditions were generated utilizing the XVivo incubation system (partial pressures: 94% N₂, 1% O₂, 5% CO₂) (BioSpherix, Parish, NY, USA). All cell lines were authenticated through short tandem repeat profiling.

2.5 SLC-0111 and cisplatin treatments

Carbonic anhydrase IX/XII inhibitor SLC-0111 (alias: U-104) was purchased from SelleckChem (catalog no. S2866) and dissolved in sterile DMSO as a 10 mM stock solution. Further dilutions were prepared in adequate cell culture medium. Normal cell culture medium supplemented with DMSO was utilized as a control treatment. Incubations were performed for 48 h if not otherwise stated, and the medium was not changed during the incubations.

2.6 RNA and protein extraction

RNA and protein extraction was performed with Nucleospin RNA/Protein Mini extraction kit (Macherey-Nagel, Düren, Germany) following the manufacturer’s instructions.

2.7 RNA sequencing and data processing

HUH6 cells were cultured under normoxic or hypoxic conditions and treated with 100 µM of SLC-0111 or vehicle. RNA was extracted after 48 h incubation. Prior to sequencing, RNA concentration, quality, and integrity were assessed by the Biomedicum Functional Genomics Unit (Helsinki Institute of Life Science and Biocenter Finland, University of Helsinki, Finland) using the TapeStation system (Agilent, Glostrup, Denmark). RNA libraries were prepared applying polyA selection, and Illumina compatible cDNA libraries were constructed by GENEWIZ (Leipzig, Germany). Subsequently, samples were sequenced on Illumina NovaSeq 6000 yielding 2x150bp paired end reads (GENEWIZ). An RNA sequencing dataset containing 11 HB patient samples and 11 NL samples was obtained from the GEO database of NCBI (accession number: GSE151347) (31, 32). FastQC tool was utilized to control quality of the reads (33). Sequenced reads were mapped to human reference genome hg38 using HISAT2 aligner (34). Reads per gene were counted with HTseq (35). To analyze differentially expressed genes (DEGs), edgeR2 tool was employed (36). Cut-off values were set to fold change lg₂ +/-0.8 and Benjamini-Hochberg adjusted p-value <0.05. Preprocessing of data and DEG analysis was carried out with Chipster (26). Gene Ontology (GO) analysis was performed with Enrichr (37, 38). R packages tidyverse and ggplot2 were used for data visualization in R (v. 4.0.3).

2.8 Real-time quantitative PCR (RT-qPCR)

RNA was reverse transcribed using the iScript cDNA Synthesis Kit (Bio-Rad, Hercules, CA, USA) following the manufacturer’s instructions. RT-qPCR was carried out with a CFX384 thermocycler instrument (Bio-Rad), and PowerUP SYBR Green Master Mix (Thermo Fisher Scientific) was used for gene amplification. Relative gene expression was assessed using the 2-ΔΔCT method (39). Geometric mean of ACTB and PPIG expression served as a reference. Primer sequences were: ACTB; GCGTGACATCAAAGAGAAGC (forward), AGGATTCCATACCCAAGAAGG (reverse); CAIX GCCTTTGCCAGAGTTGACGA (forward), TCTGAGCCTTCCTCAGCGAT (reverse); PPIG CAATGGCCAACAGAGGGAAG (forward), CCAAAAACAACATGATGCCCA (reverse).

2.9 Western blotting

Proteins (10 µg) were separated by electrophoresis using Mini-Protean TGX Stain-Free Gels (Bio-Rad, Hercules, CA, USA) Next, proteins were transferred onto polyvinyl fluoride membrane. Blocking was performed with 5% non-fat milk in TBS. Primary antibody incubations were performed at +4°C for overnight (anti-human CAIX rabbit IgG at a of dilution 1:1500; NB100-417, Novus Biologicals, Littleton, CO, USA). Secondary antibody incubation was carried out at RT for 1 h (1:10,000; #111-035-144, Jackson ImmunoResearch, West Grove, PA, USA). Protein bands were illuminated using the Enhanced Chemiluminescence detection kit (Amersham ECL reagent; GE Healthcare, Barrington, IL, USA). Quantification was performed with Image Lab Software 6.0 (Bio-Rad). CAIX band intensities were normalized to the amount of total protein in the corresponding lane using stain-free technology (37).

2.10 Wound healing assay

Ibidi-treated cell culture inserts (3-well in µ-dish; Ibidi, Munich, Germany) were used to generate wounds. Cells were seeded into inserts in high density 24 h prior to the experiment. Before treatment initiation with vehicle or 100 µM of SLC-0111, inserts were removed, and cells were washed with phosphate buffered saline (PBS) to eliminate debris. Wounds were imaged at treatment outset (0 h) and after 20 h with an Eclipse TS100 microscope supplemented with the DS-Fi1 digital imaging system (Nikon, Tokyo, Japan). Wounds (16 images/insert) were analyzed with ImageJ software to calculate the percentage of wound closure. The following formula was used: Wound closure (%) = ((W₀ – Wt)/W₀) x 100 (W₀ = Wound area at 0 h and Wt = Wound area at 20 h).

2.11 Spheroid cultures

Cells were seeded at a density of 2000 cells/well into 96-well ultra-low attachment plates (PerkinElmer, Waltham, MA, USA) and cultured without disturbance for 48 h at +37°C in a humified incubator with 5% CO₂. After the establishment period, cells were incubated with vehicle or increasing concentrations of SLC-0111 for 48 h.

2.12 Viability measurements

Cell viability (ATP concentration) was assessed with the ATPLite™ 2D or 3D monitoring system (PerkinElmer) following manufacturer’s instructions. Luminescence was measured with a GloMax microplate reader (Promega, Madison, WI, USA).

2.13 Immunofluorescence

HUH6 and HB-303 cells (100 000 cells/well) were grown in 4-well chamber slides coated with collagen I for 24 h. Cells were fixed with 4% paraformaldehyde. Non-specific binding was blocked with UltraVision Protein Block solution (Thermo Scientific, Fremont, CA, USA). Next, cells were incubated with primary antibody at room temperature for 1 h (NB100-417 human anti-rabbit CAIX at 1:1000 dilution, Novus Biologicals, Littleton, CO, USA). Secondary antibody incubation was performed with goat anti-rabbit IgG (H+L) AlexaFluor 647 (1 h, room temperature) at 1:800 dilution (A32733, Invitrogen, Carlsbad, CA, USA). Images were captured with a Zeiss Axio Imager M2 (objective: EC Plan Neofluar 40 X/0.75 Ph2 M27) (Carl-Zeiss, Oberkochen, Germany).

Spheroids were fixed with chilled 100% methanol for 20 minutes at RT. Following washes with PBS, 0.1% Triton-X was utilized to permeabilize the cells. Nonspecific binding was blocked with UltraVision Protein Block Solution (Thermo Fisher). Primary antibody incubation (anti-human CAIX rabbit IgG, at a dilution of 1:100; NB100-417, Novus Biologicals) was performed at RT for 1.5 h. Subsequently, spheroids were incubated with secondary antibody (anti-rabbit IgG (H+L) AlexaFluor 647, at dilution 1:200; A32733, Thermo Fisher) at RT for 1 h. Hoechst (at dilution 1:2000; #62249, Thermo Fisher) was used for nuclear staining. Opera Phenix High Content Screening System was employed to capture images (Perkin Elmer). Imaging was performed in the High Content Imaging and Analysis unit (FIMM, University of Helsinki).

2.14 Target prediction analyses

Potential bioactive targets of SLC-0111 were assessed with SwissTargetPrediction (http://www.swisstargetprediction.ch/) (40) and SUPERpred (https://prediction.charite.de) (41) online tools.

2.15 Statistical analysis

Cell experiments were conducted in triplicate. Statistical analyses were carried out with GraphPad Prism (v. 8.4.2; San Diego, CA, USA). Student’s t-test or one-way ANOVA followed with Tukey’s test were utilized to assess statistical significance depending on the experimental setting. p-value < 0.05 was considered significant.

3 Results

3.1 CAIX protein expression in clinical HB samples

We analyzed 21 specimens of HB (11 male, 10 female) in the Helsinki Biobank. The median patient age at surgery was 3.18 years (0.23-10.83 years). Patient characteristics, treatments, and CAIX expression status are summarized in Table 1. Three pediatric donor liver samples (age 2.0-8.2 years) were used as normal controls. Consistent with previous studies, in healthy liver CAIX immunostaining was restricted to bile duct cells (Figures 1A, B) (16, 17). Over 70% of the HB specimens demonstrated CAIX immunoreactivity; 9/21 had intermediate (Figures 1C, D) expression, and 6/21 had high CAIX expression (Figures 1E, F). CAIX staining was predominantly membranous in both the HB and healthy liver samples (Figures 1A–F). Within HB specimens, CAIX-positive cells were grouped in small clusters in the middle of viable tissue (Figures 1C, D) or adjacent to necrotic areas (Figures 1E, F), regions presumed to be hypoxic due to limited blood supply.

Table 1 | HB patient characteristics and CAIX expression status.

Figure 1 | CAIX expression in HB patient samples and normal liver. CAIX expression was restricted to bile duct cells in normal liver tissue (A, B). 9/21 HB tumor samples demonstrated intermediate (C, D) and 6/21 high CAIX immunoreactivity (E, F). CAIX expression localized to small clusters in the middle of viable HB tissue (arrow, C, D) or adjacent to necrotic areas (arrowhead, E, F). Scale bars: 50 µm (A) and 20 µm (B).

3.2 CAIX mRNA expression in HB correlates with poor clinical outcome

In the Helsinki cohort, all 5 cases of metastatic HB demonstrated CAIX immunoreactivity (Table 1), suggesting that CAIX expression correlates with advanced disease. We used a larger patient cohort [GSE131329, a dataset containing 53 HB and 14 normal liver samples] to compare CAIX mRNA expression with three clinical variables – occurrence of an unfavorable event, metastasis, and overall survival. Total CAIX expression was higher in normal liver (median 7.485, [IQR 7.308–7.613]) than in HB samples (median 7.260, [IQR 7.115–7.470]) (Figure 2A), likely a reflection of CAIX expression in the biliary epithelium of normal tissue. The occurrence of any event was associated with higher CAIX expression (median 7.360 [IQR 7.245-7.640]) than an event-free disease course (median 7.145 [IQR 7.075-7.376]) (Figure 2B). Patients with distant metastases had higher CAIX expression (median 7.470 [IQR 7.223-7.638]) than those without metastases (median 7.170 [IQR 7.110-7.380]) (Figure 2C). Poor overall survival was associated with elevated CAIX expression (HB median 7.230 [IQR 7.162-7.360] vs. normal liver median 7.105 [IQR 6.999-7.170]) (Figure 2D).

Figure 2 | High CAIX expression associates with events, distant metastases, poor overall survival in HB. Total CAIX expression was higher in normal liver compared to HB samples (A). Occurrence of events associated with higher CAIX expression (B). Patients with distant metastasis demonstrated higher CAIX expression compared to those with no metastasis (C). Poor overall survival was linked to elevated CAIX expression (D). Dots represent individual samples, the box represents the interquartile range, the whiskers represent the 1st and 4th quartile and the line inside the box is the median. *p-value < 0.05, **p-value < 0.01.

3.3 Hypoxia induces CAIX expression in cell models of HB

We used cell culture models to investigate whether low oxygen tension induces CAIX expression in HB. The immortalized human HB cell line HUH6 or the PDX-derived cell lines HB-295 and HB-303 were cultured in a hypoxic chamber or normoxic incubator for 48 h. All three HB cell lines demonstrated little or no baseline CAIX mRNA expression when cultured under normoxia (Figures 3A–C). CAIX mRNA expression was markedly upregulated in hypoxic cells compared to normoxic controls, with increases of 740-fold in HUH6 (Figure 3A), 165-fold in HB-295 (Figure 3B), and 6.7-fold in HB-303 cells (Figure 3C). Similarly, hypoxia induced 6- to 50-fold increases in CAIX protein levels (Figures 3D–I).

Figure 3 | Hypoxia-induced expression of CAIX in HB cell lines is attenuated by SLC-0111. CAIX mRNA expression was negligible under normoxic condition in HUH6 (A), HB-295 (B), and HB-303 (C) cells. In response to hypoxia, CAIX mRNA expression drastically increased in all cell lines (A–C). SLC-0111 treatment decreased CAIX mRNA expression 40-60% under hypoxia (A–C). CAIX protein levels were significantly higher in cells grown under hypoxic compared to normoxic conditions in all cell models (E–I), and in hypoxic HUH6 cells CAIX expression significantly decreased following SLC-0111 treatment (D, G). Bar plots reflect the mean of three independent experiments ± RSD. Band intensity was normalized to total protein expression in each lane. Normalization factor (NF) describing the amount of total protein in lane relative other lanes is given beneath the bands. *p-value < 0.05, **p-value < 0.01. SLC-0111 = 100 µM.

Next, we assessed the impact of 100 µM SLC-0111 on CAIX mRNA and protein expression under normoxic and hypoxic conditions. In normoxia, CAIX mRNA expression remained invariant after SLC-0111 treatment in all three cell models (Figures 3A–C). HB cells cultured under hypoxia and treated with SLC-0111 demonstrated a 40-60% reduction in CAIX mRNA expression compared to vehicle treated control cells (Figures 3A–C). Following SLC-0111 treatment, levels of CAIX protein decreased significantly in hypoxic HUH6 cells but not in HB-295 or HB-303 cells (Figures 3D–I).

3.4 SLC-0111 treatment attenuates HB cell viability in monolayer and spheroid cultures

To explore the effects of CAIX inhibition on HB cell survival in monolayer and spheroid cultures, we measured ATP concentrations, a surrogate for cell viability, following exposure of cells to increasing amounts of SLC-0111. In monolayer cultures, HUH6 cell viability decreased in a dose-dependent manner both in normoxia and hypoxia (Figure 4A). As with HUH6 cells, the impact of SLC-0111 on the viability of HB-295 monolayer cultures was more pronounced in normoxic than hypoxic conditions (Figure 4C). HB-303 had a dissimilar response to SLC-0111 than the other two models; cell viability increased with doses of 50-100 µM and with doses of 125-175 µM a modest decrease in viability was observed (Figure 4E).

Figure 4 | SLC-0111 decreases cell viability of HB monolayers and spheroids. To assess cell viability, ATP levels were measured after SLC-0111 treatment. HUH6 cell viability decreased dose-dependently both in normoxia and hypoxia (A). HUH6 spheroids showed significantly decreased cell viability at SLC-0111 concentrations of 125 and 175 µM (B). In HB-295 cells the reduction in cell viability was significant at concentrations between 75-175 µM under normoxia and between 100-175 µM under hypoxia (C). In HB-295 spheroids the viability was decreased at concentrations of 100-175 µM (D). The response of HB-303 to SLC-0111 differed from other models. Cell viability increased at concentrations of 50-100 µM and decreased at concentrations of 125-175 µM (E). In HB-303 spheroids the viability was decreased with all concentrations (F). *and §p-value < 0.05, **p-value < 0.01 (compared to corresponding control).

The spatiotemporal distribution of oxygen in solid cancers cannot fully be mimicked in monolayer cell cultures. Instead, spheroids more closely resemble the 3-dimensional architecture of solid tumors, as oxygen levels differ for cells exposed directly to growth medium vs. those located in the inner parts of spheroids (42). To further assess the effects of SLC-0111 on HB cells, we used spheroids cultured in normoxia. SLC-0111 elicited a decrease in viability in all three HB spheroid models (Figures 4B, D, F). We also noticed spontaneous expression of CAIX in HB spheroids under normoxic conditions, whereas the cells grown in 2D showed negligible CAIX expression (Supplementary Figure 1).

3.5 HB cell motility is impaired by SLC-0111 treatment

Several studies have reported decreased cell motility after pharmacological inhibition of CAIX or silencing of the CAIX gene (43–45). In our HB cell models, migration rates decreased significantly after 20 h treatment with 100 µM SLC-0111 compared to control cells both under normoxic and hypoxic conditions (Figures 5A–C). SLC-0111 had the most drastic effect on migration in HUH6 cells, wherein motility decreased approximately 70% in normoxia and 40% in hypoxia after 20 h of SLC-0111 treatment (Figure 5A). Hypoxia increased the migratory capacity of HB-295 cells compared to normoxic control cells, and SLC-0111 reduced motility in both normoxia and hypoxia (Figure 5B). A modest reduction in migration was observed in HB-303 cells treated with SLC-0111. In these cells the decrease was approximately 30% in normoxia and 35% in hypoxia compared to corresponding vehicle treated controls (Figure 5C).

Figure 5 | SLC-0111 treatment decreases cell motility in HB cell models. After 20 h of SLC-0111 treatment, migration rate was significantly reduced in HUH6 (A), HB-295 (B), and HB-303 (C) cells in both normoxic and hypoxic conditions. Histograms show the percentage of wound closure relative to DMSO treated control. Bar plots are presented as relative values of mean of three independent experiments ± RSD. *p-value < 0.05. SLC-0111 = 100 µM. N, normoxia; H, hypoxia.

3.6 Transcriptomic changes induced by SLC-0111 diverge in normoxic and hypoxic conditions

As noted above, SLC-0111 decreased viability and motility in HB cells even under normoxic conditions when CAIX expression was undetectable or extremely low, suggesting that the drug may have CAIX-independent effects. To explore transcriptomic changes induced by SLC-0111 treatment, we performed RNA sequencing analysis for HUH6 cells treated with 100 µM SLC-0111 for 48 h under either normoxia or hypoxia. First, we assessed global gene expression alterations triggered by hypoxia compared to baseline expression in normoxia. A total of 2876 DEGs were observed of which 2155 genes were upregulated and 721 were downregulated (Supplementary Figure 2; Supplementary Table 1). The three most upregulated protein coding genes were gamma-aminobutyric acid receptor subunit alpha-2 (GABRA2), CAIX, and aquaporin 10 (AQP10) (Figure 6; Supplementary Table 2). Regulatory factor X6 (RFX6), acyl-CoA thioesterase 12 (ACOT12), and adrenoceptor alpha 2A (ADRA2A) were the most downregulated protein coding genes under hypoxia in comparison to normoxia (Figure 6; Supplementary Table 2).

Figure 6 | Hypoxia-induced transcriptomic alterations in HUH6 cells. Heatmap of the 25 most downregulated and the 25 most upregulated differentially expressed genes in RNA sequencing analysis performed for cells grown under normoxic or hypoxic conditions sorted by logFC. The three most up- and downregulated protein coding genes are highlighted in red color.

Next, we assessed the effects of SLC-0111 on the HUH6 cell transcriptome. In normoxia, we observed 304 upregulated genes and 96 downregulated genes after SLC-0111 treatment (Supplementary Table 1). Under hypoxic conditions, SLC-0111 induced upregulation of 175 genes and downregulation of 312 genes (Supplementary Table 1). Altogether, 76 genes were differentially expressed in both normoxic and hypoxic HUH6 cells treated with SLC-0111 (Figure 7D). Of these 76 genes, 15 genes were downregulated both in normoxia and hypoxia, 60 genes were upregulated in both conditions, and one gene was differentially regulated in hypoxia and normoxia (Figure 7A; Supplementary Table 3). Molecular functions associated with these overlapping genes included semaphorin binding, protein-arginine deaminase activity, and protease binding (Figure 7B). Metal ion related biological processes were highly overrepresented in SLC-0111 treated cells (Figure 7C). We also characterized overlaps in genes dysregulated in HB patient samples and expression alterations caused by SLC-0111 in HUH6 cells. A total of 7411 DEGs (Supplementary Table 1) were noted in HB vs NL, and 35 of these genes were also differentially expressed in HUH6 cells following SLC-0111 administration (Supplementary Table 1). SLC-0111 treatment of HUH6 cells caused upregulation of 20 genes that were downregulated in HB tumor tissue (Table 2), including the tumor suppressor genes MT1G, MT1X, MT2A, OTC, PCK2, PGLYRP2, SERPINC1, and NR1I3. Three genes (FOXJ1, PRRT1, and TSSK5P) were downregulated in SLC-0111 treated HUH6 cells and upregulated in HB tumor tissue (Table 2).

Figure 7 | Impact of SLC-0111 on gene expression in normoxic and hypoxic HUH6 cells. RNA sequencing analysis identified 243 upregulated and 81 downregulated genes after SLC-0111 treatment in normoxia (A). In hypoxia the corresponding numbers were 115 upregulated and 296 downregulated (A). 60 genes were upregulated and 15 downregulated both in normoxia and hypoxia (A). One gene was upregulated in normoxia but downregulated in hypoxia (A). Enriched molecular functions in overlapping genes (B). Enriched biological processes in overlapping genes (C). Heatmap of overlapping differentially regulated genes in hypoxia and normoxia (D).

Table 2 | Overlaps in genes dysregulated in HB patient samples and expression alterations caused by SLC-0111 in HUH6 cells.

3.7 Target prediction analysis for SLC-0111

To identify other potential targets for SLC-0111, we performed in silico target prediction analysis with two online tools (SwissTargetPrediction and SUPERpred). We combined the common predicted targets from both tools (Table 3). In addition to CAIX and CAXII, SLC-0111 had high expected probability of binding CAII (Table 3). Other identified targets included histone deacetylase (HDAC) 3, thymidylate synthase (TYSY), nuclear factor NF kappa-B inhibitor kinase alpha (CHUK), mammalian target of rapamycin (mTOR), cyclin dependent kinases (CDKs) 1/2/4/5, and phosphatidylinositol 3-kinases PK3CA, PK3CB, and PK3CG (Table 3).

Table 3 | Predicted bioactive targets of SLC-0111.

4 Discussion

Hypoxia triggers metabolic reprogramming in tumor cells, resulting in decreased intracellular pH levels (46). To counter this acidic stress, cancer cells induce the expression of CAIX (11). In various malignancies, CAIX expression associates with advanced disease and treatment failure, underscoring its potential as a biomarker and treatment target (37–41). We found that CAIX is expressed in most HB samples and is associated with unfavorable clinical outcome.

Our findings echo studies of adult liver cancer, wherein high CAIX expression has been linked to treatment resistance, recurrence, and unfavorable outcome (47, 48). Huang et al. reported a diffuse perinecrotic localization of CAIX in hepatocellular carcinoma (HCC) tissues (48). Similarly, we observed CAIX immunoreactivity in perinecrotic regions and in small clusters in the middle of viable tumor tissue in HB specimens. Cancer stem cells (CSCs) facilitating tumorigenicity and metastasis are thought to reside in specific niches within tumors, including perinecrotic regions (49). Of note, CAIX expression has been suggested to support CSC survival in PDX-models of cervical and breast cancer (50, 51).

Upregulation of CAIX expression has been observed in numerous cancer cell lines in response to hypoxia (52–56). In keeping with these studies, we found that CAIX expression was strongly upregulated at the mRNA and protein levels in HB cell models exposed to hypoxic conditions, while its baseline expression in normoxia was extremely low. Treatment with SLC-0111 abrogated hypoxia-induced CAIX mRNA expression in all three HB cell lines studied but caused a notable decrease in CAIX protein level in only one cell line. This may be explained by the fact that CAIX protein and mRNA expression were measured at the same timepoint. Owing to protein turnover rates, it may take longer to see a decrease in protein levels compared to RNA levels.

SLC-0111 is a ureido-sulfonamide inhibitor of CA that has been reported to target hypoxia-induced CAIX and CAXII with a high selectivity (57–59). A multitude of novel SLC-0111 analogues have recently been developed to inhibit these cancer-associated enzymes with even better selectivity compared to the classical compound (60). Since SLC-0111 acts mechanistically as an inhibitor of CAIX enzymatic activity (61), it was surprising to observe a drastic impact on CAIX mRNA levels in HB cells with low basal CAIX expression in the present study. A similar reduction in hypoxia-induced CAIX mRNA expression after SLC-0111 treatment was observed in breast cancer cells (45). Based on these findings it is possible that inhibition of CAIX activity has a negative regulatory effect on its transcription.

Tumor cell motility is a prerequisite for metastasis. Multiple studies have demonstrated an association between increased migratory or invasive capability and high CAIX expression in cancer (19, 62–65). Consistent with those reports, the motility of HB cells was reduced when CAIX function was inhibited with SLC-0111. Mechanistically, CAIX has been shown to interact with cell adhesion proteins, matrix metalloproteinases, integrins, and ion exchangers to facilitate migration and invasion (63, 66–68). Interactome studies are required to clarify which proteins are co-operating with CAIX in HB cells.

Interestingly, we noticed that SLC-0111 attenuated HB cell viability and motility when there was no observable CAIX expression, suggesting that there may be alternative targets for this drug. SLC-0111 is an efficient nanomolar inhibitor of CAIX and CAXII (69). At micromolar concentrations, SLC-0111 also inhibits CAI and CAII, consistent with our target prediction analysis (57). In metastatic lung, colorectal, and breast cancer models, combination therapy with SLC-0111 and the HDAC inhibitor SAHA has demonstrated higher potency than these agents as monotherapy (70). Moreover, this multi-drug treatment associated with increased p53 and histone H4 acetylation (70). Our target prediction analyses suggested that SLC-0111 may interact with HDAC3. SLC-0111 has potential to act as an epigenetic modifier and may potentiate HDAC inhibitors partially by targeting the very same proteins. We also observed enrichment of cell cycle regulation related proteins (CDK1/2/4/5) in predicted targets of SLC-0111. This may be one of the mechanisms how SLC-0111 reduces cell viability in normoxia and should be validated in the future. Further investigations are needed to understand SLC-0111 mechanisms of action in the absence of CAIX expression in HB as well as other tumor types.

SLC-0111 treatment triggered distinct patterns of gene expression in normoxic vs. hypoxic HUH6 cells. This suggests that the mechanism of action of SLC-0111 may be environment-dependent. Notably, we found that SLC-0111 enhanced expression of eight genes (MT1G, MT1X, MT2A, OTC, PCK2, PGLYRP2, SERPINC1, and NR1I3) under both normoxic and hypoxic conditions. Each of these genes has been shown to be epigenetically silenced or deactivated in HB or other liver malignancies, and restoring expression was associated with improved prognosis, reduced cell viability, and/or decreased metastatic capacity (71–77).

Conversely, SLC-0111 attenuated expression of FOXJ1 in HUH6 cells under normoxia and hypoxia. Overexpression of FOXJ1 has been linked with poor prognosis and increased proliferation rate in HCC (78). Based on these transcriptomic changes and earlier studies, we propose that SLC-0111 may act as an epigenetic modifier activating tumor suppressor genes and downregulating oncogenes in addition to functioning as a CAIX inhibitor. This mechanism could explain the drastic impact of SLC-0111 on HB cell viability and motility in the absence of observable CAIX expression. More investigations are needed to delineate the exact effectors.

To date, one clinical trial of SLC-0111 has been reported. In that Phase 1 study, no objective responses were observed in adults with advanced solid tumors, but 2 out of 17 heavily pre-treated patients had stable disease for up to 24 weeks (24). It must be emphasized that confirmed CAIX tissue expression was not used as an inclusion criterion for that study. There is also a Phase Ib clinical trial on the efficacy of SLC-0111 in combination with gemcitabine in CAIX-positive pancreatic cancer patients (79). These and future trials will hopefully identify patients who may benefit from SLC-0111 treatment. We suggest that the role of SLC-0111 as a potential epigenetic regulator of tumor suppressor genes should be considered when planning future clinical trials. Pediatric clinical trials are needed to confirm the safety of SLC-0111 in this population.

One limitation of our study is that the impact of SLC-0111 on HB was not examined in vivo. Another shortcoming is that several of the in vitro experiments were conducted in monolayer cultures which do not fully recapitulate oxygen gradients in tumor tissue.

The key findings of this study are summarized in Figure 8. All in all, CAIX is expressed in the majority of HBs and may have potential as a prognostic marker. In HB cell culture models, hypoxia induces CAIX expression, and the CAIX inhibitor SLC-0111 reduces HB cell survival and motility. Our results also suggest that SLC-0111 may have CAIX-independent effects. We speculate that SLC-0111 administration may restore expression of tumor suppressor genes in HB via epigenetic mechanisms.

Figure 8 | Schematic illustration of the findings.

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/. GSE185937.

Ethics statement

The studies involving human participants were reviewed and approved by Helsinki University Hospital institutional ethics committee. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

Author contributions

KE, MP, SP, and MH: conceptualization and research design. KE, MP, EL, RN, TS, JL, DW, SP and MH: acquisition, analysis, or interpretation of data. SC: establishing and providing PDX cell models. MP: Preparing the final Figures. KE: writing the first draft. KE, MP, EL, RN, TS, JL, SC, DW, SP, and MH: reviewing and editing. KE, MP, EL, RN, TS, JL, SC, DW, SP, and MH: final approval of the manuscript version to be published. All authors contributed to the article and approved the submitted version.

Funding

This study was supported by Doctoral Program in Clinical Research at University of Helsinki Funds, Finska Läkaresällskapet, Helsinki University Central Hospital Research Grants, Päivikki and Sakari Sohlberg Foundation, and Sigrid Jusélius Foundation and Lasten Syöpäsäätiö Väre (Väre Foundation).

Acknowledgments

We thank Professor Silvia Pastorekova for providing the M75 antibody, Docent Ras Trokovic and MSc Joonas Sokka for helping us with the XVivo incubation system, and Dr. Antti Hassinen for assisting with the Opera Phenix High Content Screening System. Institute for Molecular Medicine Finland FIMM Technology Centre Genotyping lab and (University of Helsinki) is thanked for the cell line authentication.

Conflict of interest

Author Stefano Cairo has formerly been employed by the company XenTech and is currently employed by the company Champions Oncology.

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

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2023.1118268/full#supplementary-material

Abbreviations

ACOT12, acyl-CoA thioesterase 12; ACTB, actin beta; ADRA2A, adrenoceptor alpha 2A; AQP10, aquaporin 10; CA, carbonic anhydrase; CAIX, carbonic anhydrase IX; CDK, cyclin dependent kinase; CHUK, nuclear factor NF kappa-B inhibitor kinase alpha; CSC, cancer stem cell; ddH2O, double distilled water; DMSO, dimethyl sulfoxide; DEG, differential gene expression; GABRA2, gamma-aminobutyric acid receptor subunit alpha-2; HB, hepatoblastoma; HDAC, histone deacetylase; IKBKB, nuclear factor NF-kappa-B inhibitor kinase beta; MT, metallothionein PDX, patient-derived xenograft; mTOR, mammalian target of rapamycin; PI3K, phosphatidylinositol 3-kinases; PPIG, peptidyl-prolyl cis-trans isomerase G; RFX6, regulatory factor X6; TYSY, thymidylate synthase

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CASE REPORT
published: 30 March 2023
doi: 10.3389/fonc.2023.1165979

Case report: Obstructive jaundice caused by biliary cystadenoma

Yun-cheng Li †, An-da Shi † and Kang-shuai Li *

Department of General Surgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China

Edited by:
Xuesong Gu, Beth Israel Deaconess Medical Center and Harvard Medical School, United States

Reviewed by:
Binghai Zhou, Nanchang University, China
Yun Zhang, Chinese Academy of Medical Sciences and Peking Union Medical College, China

*Correspondence:
Kang-shuai Li
likangshuai_sdu@163.com

†These authors have contributed equally to this work

Specialty section:
This article was submitted to Cancer Molecular Targets and Therapeutics, a section of the journal Frontiers in Oncology

Received: 14 February 2023

Accepted: 15 March 2023

Published: 30 March 2023

Citation:
Li Y-c, Shi A-d and Li K-s (2023) Case report: Obstructive jaundice caused by biliary cystadenoma. Front. Oncol. 13:1165979. doi: 10.3389/fonc.2023.1165979

Biliary cystadenoma (also called mucinous cystic neoplasm with low-grade intraepithelial neoplasia) is a rare cystic tumor that arises from the biliary epithelium. The cause of biliary cystadenoma is still unclear. Jaundice is a rare presentation of intrahepatic biliary cystadenoma, which can lead to a diagnostic dilemma. Herein, we present a case of intrahepatic biliary cystadenoma that primarily exhibited as jaundice. A 56-year-old woman has suffered from yellow staining of her skin and sclera for more than 1 month. She had a poor appetite and mild epigastric pain. Laboratory examination showed elevated levels of total bilirubin and elevated carbohydrate antigen 19-9 (CA19-9). A contrast-enhanced computed tomography of the abdomen showed a 7.4 * 5.3-cm, oval, low-density lesion in the left liver parenchyma with a clear boundary and visible septa. The common bile duct was obviously dilated with wall thickening. On magnetic resonance imaging, the lesion in the liver showed a multilocular cystic, unenhanced long T2 signal. There was local thickening of the common bile duct wall with short T2-like filling defects and high signal intensity on diffusion-weighted imaging (DWI). The patient had no history of other malignant tumors and adjuvant therapy such as radiotherapy and chemotherapy. She was clinically suspected of having either biliary cystadenoma or a malignancy; hence, resection was performed. Macroscopically, the excised tissue specimen showed a polypoid mass in the common bile duct, which extended along the bile duct to the intrahepatic bile duct. There was a cystic and solid mass in the left liver with yellow turbid fluid, which was associated with the polypoid mass in the common bile duct. Histopathology suggests mucinous cystadenoma of the liver and hilar bile duct. The differential diagnosis of biliary cystadenoma and treatment selection have been discussed.

Keywords: hepatic cyst, biliary cystadenoma, obstructive jaundice, endoscopic nasobiliary drainage, common bile duct (CBD)

Introduction

Obstructive jaundice is a heterogeneous disease characterized by bile duct obstruction and cholestasis. Patients may have typical clinical manifestations such as jaundice of the skin and sclera, dark yellow urine, and lighter or even clay-white stool. It is vital to determine the nature of the lesion for the formulation of the treatment plan. However, the differential diagnosis of obstructive jaundice is usually difficult.

Biliary cystadenoma is a rare neoplasm arising from intrahepatic or extrahepatic bile ducts. The majority of biliary cystadenoma arises from the intrahepatic ducts and is usually asymptomatic. The presenting symptoms are usually right hypochondrium pain or abdominal mass. Extrahepatic biliary cystadenoma usually presents with obstructive jaundice either directly by tumor blockage or by mucin secretion. In this paper, we present a patient with biliary cystadenoma involving the left intrahepatic and left main hepatic ducts presenting as obstructive jaundice. We also discussed the differential diagnosis and treatment selection of biliary cystadenoma.

Case presentation

A 56-year-old woman was admitted to our hospital because of yellow staining of her skin and sclera for more than 1 month. She had received endoscopic nasobiliary drainage at a local hospital 1 day prior. She had a poor appetite and mild epigastric pain and denied a history of fever, cough, malaise, vomiting, diarrhea, or weight loss. She had no history of hepatitis B/C infection and no history of alcohol intake. Physical examination revealed jaundice, scleral icterus, and mild subxiphoid tenderness without rebound or muscle tension, and there were no signs of hepatomegaly, splenomegaly, or ascites. Relevant laboratory examination showed abnormal liver function test results, including elevated levels of total bilirubin (149.1 μmol/L; normal range, 5.0–21.0 μmol/L), direct bilirubin (118.0 μmol/L; normal range, <6.0 μmol/L), indirect bilirubin (31.1 μmol/L; normal range, 2.0–15.0 μmol/L), alanine aminotransferase (56 U/L; normal range, 7–40 U/L), aspartate aminotransferase (36 U/L; reference range, 13–35 U/L), gamma-glutamyl transferase (324 U/L; reference range, 7–45 U/L), and alkaline phosphatase (529 U/L; reference range, 50–135 U/L). A blood cell routine showed a decreased red blood cell count (2.9 * 10¹²/L; reference range, 3.8 * 10¹²/L to 5.1 * 10¹²/L) and a decreased hemoglobin (92.0 g/L; reference range, 115–150 g/L). Autoimmunological test and IgG4 were normal. Hepatitis B serologies demonstrated negative hepatitis B surface antigens and hepatitis C virus antibodies. Serum tumor marker examination showed elevated carbohydrate antigen 19-9 (CA19-9) (189.0 U/ml; reference range, 0.0–34.0 U/ml), while CA125 and carcinoembryonic antigen were within the normal range. A contrast-enhanced computed tomography of the abdomen showed a 7.4 * 5.3-cm, oval, low-density lesion in the left liver parenchyma with a clear boundary and visible septa. The septation was slightly enhanced, and there was no enhancement of its internal fluid components. The intrahepatic bile ducts were mildly dilated. The common bile duct was obviously dilated with wall thickening. The gallbladder was normal in size, but the wall was thickened. In addition, slightly larger retroperitoneal lymph nodes and cystic shadows in the duodenum could be seen. No obvious discontinuity or filling defect was found in the portal vein branch and trunk, and the presence of a cancer thrombus was not considered (Figure 1). On magnetic resonance imaging, the lesion in the liver showed a multilocular cystic, unenhanced long T2 signal. There was local thickening of the common bile duct wall with short T2-like filling defects and high signal intensity on diffusion-weighted imaging (DWI). The wall of the gallbladder was thickened and markedly enhanced, and several slightly larger lymph nodes were seen in the hilar region (Figure 2). The tube placed due to endoscopic nasobiliary drainage was visible on both CT and MRI.

Figure 1 | Typical images of contrast-enhanced computed tomography of the liver lesions. (A) The arterial phase image of the liver lesions. (B) The venous phase image of the liver lesions.

Figure 2 | Typical images of magnetic resonance imaging of the liver lesions. (A) The arterial phase image of the liver lesions. (B) The T2 phase image of the liver lesions. (C) The DWI phase image of the liver lesions. DWI, diffusion-weighted imaging.

One year ago, she underwent inferior vena cava filter implantation and received thrombolytic drugs because of a pulmonary embolism. At present, the filter has been removed, and thrombolytic drugs have not been taken again. Lower extremity vascular ultrasound examination showed bilateral varicose great saphenous veins and old thrombosis in the right popliteal vein and posterior tibial vein.

Preoperative ultrasound, CT, MRI, and biopsy results should be combined to determine the surgical plan; lobectomy is the preferred treatment; cystectomy is recommended only when complete resection is likely to injure the vascular structure of the liver. The patient received surgical resection after adequate preoperative preparation. During the operation, multiple cystic solid masses were palpable in the left inner lobe of the liver, cord masses were palpable in the common bile duct, and multiple enlarged lymph nodes were observed in the hepatoduodenal ligament. Considering the possibility of a left intrahepatic cystic tumor and common bile duct tumor, left hemihepatectomy, extrahepatic bile duct resection, cholecystectomy, and choledochojejunostomy were performed. The excised tissue specimen showed a polypoid mass in the common bile duct, which extended along the bile duct to the intrahepatic bile duct, and part of it was suspected to be necrotic (Figure 3). There was a cystic and solid mass in the left liver with yellow turbid fluid, which was associated with the polypoid mass in the common bile duct. Histopathology suggests mucinous cystadenoma of the liver and hilar bile duct (Figure 4). Finally, we reached a pathological diagnosis of mucinous cystic neoplasm with low-grade intraepithelial neoplasia (Biliary cystadenoma). Moreover, after surgical resection, the patient’s jaundice symptoms improved significantly, and the levels of total bilirubin decreased. The patient was discharged after stable condition.

Figure 3 | Macroscopic image of the specimen with the tumor within the common bile duct.

Figure 4 | Microscopic examination of the resected specimen showing mucinous cystic neoplasm with low-grade intraepithelial neoplasia.

Discussion

In our case, the patient presented increasing jaundice with decreased appetite and mild abdominal pain. Abnormal liver function with mainly elevated direct bilirubin suggested biliary obstruction. He had a history of pulmonary embolism, and venous ultrasound of the lower extremities showed no active thrombus. Physical examination revealed mild tenderness in the upper abdomen and no rebound pain or muscle tension. CT revealed the presence of a multilocular hypodense mass in the liver with a mild enhancement of the internal septation and no significant enhancement of the fluid component. There was mild dilatation of the intrahepatic bile ducts and marked dilatation of the hilar common bile duct with thickening of the bile duct wall. Magnetic resonance imaging showed local thickening of the common bile duct and high signal on DWI. From the patient’s clinical presentation, physical examination, and imaging findings, combined with the patient’s age and sex, we considered the lesion as the following possibilities:

Cyst of the liver: large hepatic cysts may present with non-specific symptoms such as upper abdominal discomfort and loss of appetite. A small number of patients may have jaundice due to the compression of the bile duct by the cyst. CT plain scan showed single or multiple low-density masses in the liver, and a contrast-enhanced scan showed low-density masses without obvious enhancement. On MRI, the cyst showed a low signal on T1WI and a high signal on T2WI. Therefore, the possibility of a hepatic cyst could not be completely excluded.

Cholangiocarcinoma: the patient presented with progressive jaundice, CT scan showed mild dilatation of the intrahepatic bile duct and thickening and obvious dilatation of the common bile duct wall. MRI showed thickening of the common bile duct wall in the hilar region with a high signal on DWI, and the patient’s CA199 increased. It is necessary to obtain pathological results to exclude this disease.

Intraductal papillary neoplasm of the bile duct (IPNB): this kind of patient can present with non-specific clinical manifestations such as abdominal pain and jaundice. This disease can be manifested as hepatic cystic lesion and dilatation of the intrahepatic bile duct and common bile duct on CT; its clinical manifestations, laboratory examination, and imaging are not specific. The diagnosis of this disease mainly depends on postoperative pathological examination.

Biliary cystadenoma: the patient was a middle-aged woman with progressive jaundice. CT showed a well-defined, low-density intrahepatic mass with internal septa, which showed mild enhancement on contrast-enhanced CT. MRI showed a well-defined multilocular high signal occupation with internal septa on T2WI. Mucinous cystic neoplasm often occurs in the liver. According to the principle of monism, we considered that the mass of the hilar may be caused by partial detachment of the intrahepatic mass, thus causing obstructive jaundice.

Cyst of the liver is a common benign disease and generally will not exhibit clinical symptoms, and very few people will be jaundiced due to cyst compression of the bile duct. The most common clinical type is a congenital hepatic cyst, which can be divided into two subtypes: single and multiple. Multiple hepatic cysts can be seen in the liver with multiple dark areas of fluid, and several cysts may be fused to communicate or appear as several septations within a large cyst. In addition, the congenital polycystic liver is often accompanied by cysts in other organs such as the kidneys and lungs. This patient does not exhibit the typical clinical features of a cyst of the liver.

Cholangiocarcinoma is a diverse group of malignancies emerging from the biliary epithelium. According to its anatomical site, cholangiocarcinoma can be divided into intrahepatic cholangiocarcinoma (iCCA), perihilar cholangiocarcinoma (pCCA), and distal cholangiocarcinoma (dCCA). They have different clinical features. The most common symptoms of iCCA patients are right upper abdominal pain and weight loss. Jaundice occurs in approximately 25% of patients, and CA199 levels can be elevated in some patients. iCCA appears as a focal hepatic mass on CT and MRI, and the bile duct surrounding the mass may be dilated. The typical finding on an enhanced scan is that the mass has peripheral or central enhancement. Patients with pCCA may have obvious jaundice. The lesion shows delayed enhancement on CT. The characteristic is helpful for the differential diagnosis of pCCA. Histopathology is the gold standard to determine the diagnosis of cholangiocarcinoma.

Intraductal papilloma neoplasm of the bile duct is divided into intraductal papillary mucinous neoplasm of the bile duct (IPMN-B) and IPNB-without mucin secretion (IPNB-NM) according to the presence or absence of mucous secretion. While IPMN-B has similar histological features to the corresponding pancreatic lesions, IPNB-NM may represent a distinct type. IPMN-B communicates with the bile duct; mucus or tumor debris can cause intermittent bile duct obstruction, thereby causing jaundice. As the bile duct pressure rises, the mucus can be discharged, and the obstruction is relieved. Thus, jaundice is often fluctuating. Secondary dilatation of the duodenal papilla has been observed in some patients. The presence of extensive bile duct dilatation with or without tumor on imaging and mucus outflow from the opening of the duodenal papilla under endoscopic retrograde cholangiopancreatography (ERCP) are highly suggestive of this disease. Compared with IPMN-B, IPNB-NM has no mucus secretion, the downstream biliary obstruction usually does not dilate, and the degree of biliary dilation is not as obvious as that of IPMN-B. Compared with IPMN-B, IPNB-NM is more aggressive and has a worse prognosis. IPNB may have different imaging findings according to the location of the tumor, the size and morphology of the mass, and the degree of mucin secretion (1).

Biliary cystadenoma is a rare cystic tumor that tends to be more common in middle-aged women. This study reports an intrahepatic biliary cystadenoma partially shedding into the common bile duct, causing obstructive jaundice. Biliary cystadenoma mostly occurs in the liver and is often found due to non-specific abdominal pain or jaundice. On CT, it can appear as a low-density mass in the liver with internal septa and wall nodules, and enhancement can be seen along its wall and internal septum. MRI usually shows multilocular hyperintensity on T2WI with a well-defined internal septum and variable signal intensity on T1WI. Increased thickening and enhancement of the septa, nodules, and capsule with coarse calcifications have been cited as determinants of malignancy (2). The differential diagnosis of biliary cystadenoma and IPMN is difficult. Biliary cystadenoma does not communicate with the bile duct, and the mucus does not enter the bile duct. Therefore, the bile duct does not dilate, and there is no mucous outflow from the duodenal papilla. In this case, the patient had bile duct dilatation due to the obstruction of the common bile duct caused by partial tumor detachment. Pathology is the gold standard for diagnosis. In a pathological examination, the presence of ovarium-like subepithelial stroma is a tumor-specific indicator and suggests a good prognosis.

Biliary cystadenoma is a mucinous cystic tumor with malignant transformation potential. In the past, its treatment methods included aspiration, marsupialization, internal drainage, and partial excision (3). Accumulated evidence indicates that complete excision of the mass is the ideal treatment for this disease. After adequate preoperative preparation, we performed surgical treatment on the patient. Rapid pathological examination was performed during the operation to ensure complete resection of the tumor, and supportive treatment was given after the operation, such as fluid replenishment, nutritional support, hepatoprotective drugs, and anti-infective drugs. The patient recovered well after the operation, and no recurrence has been observed up to now.

Conclusion

Intrahepatic biliary cystadenoma is a rare cause of obstructive jaundice, and it has no specificity in clinical manifestations, laboratory examinations, and imaging examinations. The combined application of CT, MRI, ERCP, and other examinations is helpful for the diagnosis of the disease. Radical excision is the ideal treatment for intrahepatic biliary cystadenoma.

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 Qilu Hospital of Shandong 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

K-SL evaluated the patient. Y-CL and A-DS initiated the case report, reviewed the literature, and drafted the manuscript. K-SL: concept and design, study supervision, and critical revision of the manuscript for important intellectual content. All authors contributed to the article and approved the submitted version.

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.

References

1. Park, HJ, Kim, SY, Kim, HJ, Lee, SS, Hong, GS, Byun, JH, et al. Intraductal papillary neoplasm of the bile duct: Clinical, imaging, and pathologic features. AJR Am J Roentgenol (2018) 211(1):67–75. doi: 10.2214/AJR.17.19261

2. Korobkin, M, Stephens, DH, Lee, JK, Stanley, RJ, Fishman, EK, Francis, IR, et al. Biliary cystadenoma and cystadenocarcinoma: CT and sonographic findings. AJR Am J Roentgenol (1989) 153(3):507–11. doi: 10.2214/ajr.153.3.507

3. Preetha, M, Chung, AYF, Lim-Tan, SK, Lim, DTH, and Thng, CH. Intrahepatic biliary cystadenoma presenting with obstructive jaundice. Asian J Surg (2004) 27(3):243–5. doi: 10.1016/S1015-9584(09)60043-9

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.

Copyright © 2023 Li, Shi 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: 18 May 2023
doi: 10.3389/fonc.2023.1156177

CEP55 as a promising biomarker and therapeutic target on gallbladder cancer

Maotuan Huang ¹,²,³,⁴†, Fuxiu Zhong ¹,²,⁵†, Mingyuan Chen ¹,²,³,⁴, Lingju Hong ¹,²,³,⁴, Weihong Chen ¹,²,³,⁴, Xiahenazi Abudukeremu ¹,²,³,⁴, Feifei She ³,⁴* and Yanling Chen ¹,²,³,⁴*

¹ Department of Hepatobiliary Surgery and Fujian Institute of Hepatobiliary Surgery, Fujian Medical University Union Hospital, Fujian Medical University, Fuzhou, China, ² Fujian Medical University Cancer Center, Fujian Medical University, Fuzhou, China, ³ Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China, ⁴ Fujian Key Laboratory of Tumor Microbiology, Department of Medical Microbiology, Fujian Medical University, Fuzhou, China, ⁵ Department of Nursing, Fujian Medical University Union Hospital, Fujian Medical University, Fuzhou, China

Edited by:
Yunfei Xu, Shandong University, China

Reviewed by:
Vinay Kumar Sharma, Eunice Kennedy Shriver National Institute of Child Health and Human Development (NIH), United States
Dalong Sun, Fudan University, China

*Correspondence:
Yanling Chen
chenyanling@fjmu.edu.cn
Feifei She
shefeifei@yeah.net

†These authors have contributed equally to this work

Received: 01 February 2023

Accepted: 05 May 2023

Published: 18 May 2023

Citation:
Huang M, Zhong F, Chen M, Hong L, Chen W, Abudukeremu X, She F and Chen Y (2023) CEP55 as a promising biomarker and therapeutic target on gallbladder cancer. Front. Oncol. 13:1156177. doi: 10.3389/fonc.2023.1156177

Introduction

Gallbladder cancer (GBC) is a highly malignant biliary tumor with a poor prognosis. As existing therapies for advanced metastatic GBC are rarely effective, there is an urgent need to identify more effective targets for treatment.

Methods

Hub genes of GBC were identified by bioinformatics analysis and their expression in GBC was analyzed by tissue validation. The biological role of CEP55 in GBC cell and the underlying mechanism of the anticancer effect of CEP55 knockdown were evaluated via CCK8, colony formation assay, EDU staining, flow cytometry, western blot, immunofluorescence, and an alkaline comet assay.

Results

We screened out five hub genes of GBC, namely PLK1, CEP55, FANCI, NEK2 and PTTG1. CEP55 is not only overexpressed in the GBC but also correlated with advanced TNM stage, differentiation grade and poorer survival. After CEP55 knockdown, the proliferation of GBC cells was inhibited with cell cycle arrest in G2/M phase and DNA damage. There was a marked increase in the apoptosis of GBC cells in the siCEP55 group. Besides, in vivo, CEP55 inhibition attenuated the growth and promoted apoptosis of GBC cells. Mechanically, the tumor suppressor effect of CEP55 knockdown is associated with dysregulation of the AKT and ERK signaling networks.

Discussion

These data not only demonstrate that CEP55 is identified as a potential independent predictor crucial to the diagnosis and prognosis of gallbladder cancer but also reveal the possibility for CEP55 to be used as a promising target in the treatment of GBC.

Keywords: gallbladder cancer, CEP55, DNA damage, apoptosis, proliferation, therapeutic target

1 Introduction

Gallbladder carcinoma (GBC) is a rare but highly malignant biliary tumor originating from gallbladder mucosal epithelial cells, accounting for 1.2% of all malignant tumors (1). Gallbladder stones and gallbladder polyps with chronic inflammation are important risk factors for GBC. It is one of the few cancers that exhibit a gender bias, with female patients having a three- to six-times higher incidence rate compared to males (2). Meanwhile, GBC is showing increasing morbidity and mortality in some countries and regions, such as China (3). However, GBC cannot be diagnosed until the later stages of the disease or even after metastasis due to the lack of early clinical manifestations. Therefore, patients often miss the best opportunity for surgical treatment, making GBC one of the cancers with an extremely poor prognosis (4). Additionally, there are almost no ideal therapeutic strategies for advanced GBC. With the development of targeted drug therapy, there is hope for the treatment of gallbladder cancer (5–7). Necessarily, it is urgent to find molecular markers and susceptibility genes that can aid in the diagnosis and treatment of gallbladder cancer.

Recently, advanced gene sequencing technology not only provides favorable conditions for tumor research, including gallbladder cancer, but also deepens researchers’ understanding of the precision medicine concept for tumor treatment (8, 9). The pivotal link of precision medicine is to screen therapeutic targets through high-throughput sequencing technology (10), in which bioinformatics analysis is an indispensable step. The screening of tumor-related susceptibility genes based on bioinformatics is helpful to reveal the mechanism of tumorigenesis at the molecular level. Moreover, these screened genes have been identified and successfully used to assist and guide clinical diagnosis and treatment (11). This gives us a hint that it is possible to uncover molecules associated with GBC progression and poor prognosis with the help of bioinformatics analysis. Nevertheless, few studies on gallbladder cancer based on bioinformatics analysis have been reported.

CEP55 (Centrosomal Protein 55), also known as c10orf3 and FLJ10540, is a recently discovered member of the centrosome-associated protein family and plays a key role in cytoplasmic division via association with centrosomes and intermediates (12). It has been reported that CEP55 interacts with ESCRT family members and promotes the contraction of intracellular bridges leading to cell division (12). Numerous studies have confirmed that CEP55 influences the performance of midbody-dependent cellular functions, such as centrosome replication (13, 14). Besides, CEP55 shows great potential for enhancing the pluripotent stemness of cancer cells by participating in the autophagic degradation of midbody derivatives (15, 16). The AKT and ERK signaling pathways are demonstrated to exert diverse roles in tumor progression, including apoptosis, angiogenesis, and epithelial-mesenchymal transition (17–20). However, so far, it has remained unknown whether CEP55 is involved in the occurrence and development of GBC, and the role of the AKT and ERK signaling networks in the effect of CEP55 on gallbladder cancer remains to be elucidated.

Here, the hub genes, namely PLK1, CEP55, FANCI, NEK2, and PTTG1, were screened out by analyzing the GBC datasets GSE74048 and GSE76633 in the public database using bioinformatics technology. Subsequently, based on gallbladder cancer tissue validation, we finally determined that CEP55 is a vital gene involved in the progression of gallbladder cancer. Then, by using an in vitro CEP55 knockdown system performed by targeted siRNA and an in vivo subcutaneous tumorigenesis model in nude mice, we observed that the knockdown of CEP55 markedly restrained the proliferation of gallbladder cancer via impeding AKT and ERK pathway activation, resulting inG2/M phase arrest, DNA damage, and apoptosis. Our work not only reveals the previously unrecognized mechanism of CEP55 in the pathogenesis of gallbladder cancer but also shows the possibility for CEP55 to be a promising marker and target to be used in the treatment of GBC and improve the survival rate of patients with GBC, which provides an experimental basis for designing effective targeted therapy drugs for gallbladder cancer.

2 Materials and methods

2.1 Microarray data

Two Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo/) datasets were used in the present study, including GSE74048 (three pairs of human GBC tissues and the matched peri-carcinomatous tissues were included) and GSE76633 (nine pairs of GBC tissues and paired normal gallbladder tissues were included) (21). The database was accessed and data extracted on 23 January 2022. Detailed information on the datasets is presented in Table S1.

2.2 Identification and enrichment analysis of DEGs

DEGs between GBC tissues and peri-carcinomatous tissues were identified through the GEO2R program (http://www.ncbi.nlm.nih.gov/geo/geo2r) (22). A P-value < 0.05 and a |log(2) fold change (FC)| ≥ 1 was defined as the thresholds for DEG screening. Probe sets without corresponding gene symbols or genes with more than one probe were removed or averaged, respectively. The DEGs that overlapped among the two datasets were analyzed using the “ggplot2 (3.3.3)” package in R (3.6.3). GO and KEGG databases were utilized as references (23), and the “clusterProfiler (3.14.3)” R package carried out an analysis of enrichment.

2.3 PPI network construction and module analysis

The protein–protein interaction network analysis among DEGs was conducted using the STRING online database (https://string-db.org/) (24). An interaction with a combined score >0.4 was set as the cut-off value. Then the network was visualized using Cytoscape software (25). The Molecular Complex Detection (MCODE) plugin was used to identify the most significant module in PPI networks with MCODE scores ≥10, degree cut-off = 2, node score cut-off = 0.2, max depth = 100, and k-score = 2.

2.4 Hub gene validation and clinical significance

Further GO and KEGG analyses of genes in the top 2 modules were performed, while the DEGs in cluster 1 with the highest score were analyzed using the cytoHubba plugin (26). Specifically, 12 sets of scoring results were obtained using 11 different algorithms. The top 40 genes in various scoring results were selected for intersection by Venn analysis, and the overlapping genes were finally identified as hub genes. The Biological Networks Gene Oncology (BiNGO) plug-in of Cytoscape (27) was applied for the biological process analysis of hub genes. The UCSC Cancer Genomics Browser (28) (https://genome-cancer.ucsc.edu/) was used to construct the hierarchical clustering of hub genes in bile duct cancer samples from the TCGA database. The expression of hub genes in pan-cancer was verified by the GEPIA database (http://gepia.cancer-pku.cn/), and the relationship between hub genes and the survival of patients with pan-cancer was analyzed.

2.5 Tissue samples and cell lines

A total of 15 samples of fresh GBC tissues and 174 samples of GBC paraffin specimens and control samples were obtained from patients who were admitted to Fujian Medical University Union Hospital in China and provided written informed consent prior to surgical resection. The samples were collected based on a protocol approved by the Ethics Committee of Fujian Medical University Union Hospital in accordance with the Declaration of Helsinki (application number: 2021QH031). All tumors were confirmed as GBC by the Clinicopathology Department. The human GBC cell lines, NOZ (obtained from the Health Science Research Resources Bank in Japan) and SGC-996 (provided by the Tumor Cytology Research Unit, Medical College, Tongji University, China) were maintained in DMEM (Hyclone, USA) supplemented with 10% FBS (Hyclone, USA). All cells were incubated at 37°C in a humidified atmosphere with 5% CO₂.

2.6 Quantitative real-time PCR analysis

Total RNA was extracted from the GBC tissues or cells with TRIzol reagent (Invitrogen, USA) and reverse transcribed using the All-In-One 5× RT MasterMix (Abm, Canada). PCR was performed with BlasTaq 2× qPCR MasterMix (ABM, Canada), and fluorescence was measured using an Mx3000P QPCR System (Agilent, USA) following the manufacturer’s instructions, with GAPDH as an internal control. The data were analyzed using the −ΔCt or 2−ΔΔCt method. Detailed information on the primer sequences is presented in Table S2.

2.7 Immunohistochemical analysis and evaluation of CEP55 expression

Immunohistochemistry was performed as previously described (29). Primary antibodies specific for CEP55 (1:400, Abcam) were incubated overnight at 4°C. Signals from samples were developed with DAB reagents (Vector Laboratories, SK-4100) and hematoxylin counterstaining. Images of five random fields per section were obtained under a light microscope (Olympus, Tokyo, Japan). The IHC score was calculated by multiplying the percentage score and intensity score of positively stained cells. The percentage of positively stained cells (0 = 0–10%; 1 = 11–25%; 2 = 26–50%; 3 = 51–75%; 4 = 76–100%) was scored on a scale from 0 to 4. The staining intensity (0 = negative staining; 1 = weak; 2 = moderate; 3 = strong) was scored on a scale from 0 to 3. The IHC score ranged from 0 to 12.

2.8 Small interference RNA, short hairpin RNA, and transfection

Two siRNAs targeting the human CEP55 sequence as references (19, 30) were produced by GenePharma (Shanghai, China). Transfection of siRNA was performed with Lipofectamine 3000 (Life Technologies). Short hairpin RNA oligonucleotides targeting CEP55 (shCEP55) and negative control short hairpin RNA oligonucleotides (shNC) were annealed and ligated into the lentiviral vector GV112 (Genechem, Shanghai, China). After 2 weeks, puromycin treatment was used to select for stably infected cells. CEP55 expression in the infected cells was validated by qRT-PCR analysis and Western blot assays.

2.9 Cell proliferation assays

Cells were seeded in 96-well plates at a concentration of 2,500 cells/well and incubated with 10% CCK8 reagent (Dojindo Laboratories, Kumamoto, Japan) at 37 °C for 1 h. Cell viability was evaluated using the absorbance values determined at 450 nm by a microplate reader (BioTek, Winooski, VT, USA). Cells were seeded in 96-well plates at 500 cells/well and, after 14 days, fixed in 4% paraformaldehyde for 20 min. Cell colonies were counted after staining with crystal violet (Solarbio, Beijing, China) to assess anchorage-independent growth. DNA replication ability was evaluated by EDU assay (RiboBio, Guangzhou, China). Cells were seeded in 96-well plates at 10⁵ cells/well, labeled with the EDU working fluid for 2 h, washed in PBS, fixed in 4% PFA for 30 min, and stained with Apollo. The images were acquired with Operetta CLS High Content Analysis System (PerkinElmer, USA).

2.10 Flow cytometric analysis of cell cycle and apoptosis

Cells were collected and stained with PI/RNase staining buffer (BD Pharmingen™) according to the manufacturer’s instructions to measure cell cycle activity by FACSCanto™ II (BD Biosciences, USA). ModFit LT 5.0 (Verity Software House, USA) was used to analyze the percentage of cell cycle and generate a histogram. Apoptosis was measured with the PE Annexin V Apoptosis Detection Kit I (BD Pharmingen™) according to the manufacturer′s instructions. Cells were collected, washed with cold PBS, gently resuspended in binding buffer containing PE-conjugated annexin-V and 7-AAD, and then incubated at room temperature in the dark for 15 min. The analysis was performed using FACSCanto™ II.

2.11 Western blot and antibodies

Cell lysates were extracted using RIPA lysis buffer containing PMSF (Beyotime, Shanghai, China). The protein concentration was measured with a BCA assay kit (Solarbio, Beijing, China). The samples were heated at 95 °C in the loading buffer, separated on SDS-polyacrylamide gels, and transferred to PVDF membranes (Millipore, USA). Then blotted with 5% BSA and incubated with the primary antibody at 4°C overnight. Before incubation with the secondary antibodies, the membranes were washed in TBST. The following primary antibodies were used: Rabbit anti-CEP55 (#81693), anti-γ-H2AX (#9718), anti-p21 (#2947), anti-p53 (#2527), anti-cleaved caspase-3 (#9664), anti-cleaved PARP (#5625), anti-cleaved caspase-9 (#9505), anti-AKT (#9272), anti-p-AKT (Ser473) (#4060), anti-Erk1/2 (#4695), and anti-p-ERK1/2 (Thr202/Tyr204) (#4370) were obtained from CST (Danvers, USA). Rabbit anti-Cyclin B1 (ab181593), anti-Cyclin D1 (ab134175), anti-CDK6 (ab124821), anti-GSK3β (ab32391), and anti-p-GSK3β (Ser9) (ab75814) were purchased from Abcam (Cambridge, UK). Rabbit anti-Bax (A19684), anti-Bcl-2 (A19693), and anti-FoxO3a (A9270) were acquired from Abclonal (Wuhan, China). GAPDH (Servicebio, Wuhan, China) was used as a protein marker for cytosolic proteins. The antibody–antigen complexes were visualized with WesternBrightTM ECL (Advansta, USA).

2.12 Alkaline comet assay

The comet assay was performed using the Oxiselect Comet Assay Kit (Cellbiolabs, USA) according to the manufacturer’s instructions under a fluorescence microscope (ZEISS, Germany) with minor modifications. The prepared gel slides containing cells were put into the cell lysate and cracked overnight at 4 °C. Then the gel slides were soaked in the electrophoresis solution for 20 min before electrophoresis. After electrophoresis, the single cells in low melting point agarose were stained in the dark room for 20 min with PI (5 μg/ml). The images were analyzed quantitatively by the software CASP.

2.13 Immunofluorescence analysis

After cells were plated on coverslips and cultured overnight, they were washed, fixed, permeabilized, blocked, and stained with anti-γ-H2AX, anti-p-AKT, and anti-p-ERK. Images were observed under a laser scanning confocal microscope (Leica TCS SP5, Germany). Actin-Tracker Red-555, Hoechst 33342, and DAPI staining solution were obtained from the Beyotime Institute of Biotechnology (Shanghai, China).

2.14 Subcutaneous xenograft models

BALB/C nude mice (6-week-old, weighing an average of 18–22 g, male) were purchased from Gempharmatech Co., Ltd. (Shanghai, China). All mice were housed in specific pathogen-free conditions following the guidelines of the Ethics Committee of the Medical Faculty of the Fujian Medical University (application number: FJMU IACUC 2021-0435). The nude mice were randomly and equally assigned to two groups and labeled with ear labels. To explore the effects of CEP55 on tumor growth in vivo, 1 × 10⁵ NOZ cells (shNC and shCEP55) were suspended in 200 μl serum-free medium and subcutaneously injected into the left axilla of the mice (six mice/group). Tumor growth was monitored every three days and measured in two dimensions. The tumor volume was calculated by the formula: tumor volume = 0.5 × width² × length, where the width and length were the shortest and longest diameters, respectively. The mice were sacrificed after approximately 4 weeks when the maximum tumor volume reached 2,000 mm³. Then the tumors were dissected and weighed. Ki-67, cleaved caspase-3, anti-p-AKT, and anti-p-ERK were evaluated in xenograft tumors by immunohistochemical staining (IHC).

2.15 TUNEL assay

Cell apoptosis was measured in vivo using a TUNEL assay (Roche, Switzerland). After dewaxing, the paraffin sections were repaired with protease K and incubated for 25 min at 37 °C. Then the samples were incubated with 0.1% TritonX-100 at room temperature for 10 min. The samples were incubated in a TUNEL working solution prepared according to the instructions at 37 °C for 2 h. The samples were stained with DAPI for 10 min at room temperature and then observed by a fluorescence microscope (ZEISS, Germany). To calculate the apoptosis rate, the number of positive cells was calculated by randomly selecting three fields under a high-power lens (×400).

2.16 Statistical analysis

All data are expressed as the mean ± standard deviation (SD) of independent replicates (n ≥3). SPSS 21.0 software and GraphPad Prism 8.0 software were used for statistical analysis. A t-test statistical analysis was used to compare the mean between the two groups of samples, and a one-way ANOVA statistical analysis was used to compare the mean between multiple groups of samples. The relationship between CEP55 expression in gallbladder carcinoma and adjacent tissues and clinicopathological features was tested by the chi-square test. The difference was statistically significant with a p <0.05.

3 Results

3.1 Identification, GO enrichment, and KEGG pathway analyses of DEGs

To investigate DEGs between GBC tissues and paracarcinoma tissues, we employed the GEO2R online tool to analyze the GSE74048 dataset and the GSE76633 dataset from the GEO database. We identified 3,907 DEGs in the GSE74048 dataset, of which 975 DEGs were elevated and 2,932 DEGs were depressed in GBC tissues compared to paracarcinoma tissues (Figure 1A and Figure S1A). In the GSE76633 dataset, there were 6,144 DEGs between GBC tissues and paracarcinoma tissues, among which 3,934 DEGs were upregulated and 2,210 DEGs were downregulated (Figure 1B and Figure S1B). The heatmaps showed the top 20 DEGs of the two datasets (Figures 1C, D). To further explore the correlation of DEGs in the two datasets (Figure S1C), a Venn diagram was applied and found 386 common upregulated genes and 355 common downregulated genes in GBC tissues as compared to paracarcinoma tissues (Figures 1E, F, Table S3). In addition, we used the “clusterProfiler” package in R for GO and KEGG pathway enrichment analyses to examine the biological roles of the identified DEGs. The GO and KEGG pathway analysis showed that upregulated DEGs were related to biological processes (including mitotic nuclear division and organelle fission), cellular components (including the spindle and chromosomal region), molecular functions (including ATPase activity and microtubule motor activity), and signaling pathways (connecting the cell cycle and the p53 signaling pathway) (Figure 1G). Besides, the enrichment analyses of downregulated DEGs indicated that this group contains genes linked to biological processes (including small molecule catabolic processes and organic acid catabolic processes), cellular components (including the apical part of the cell and the apical plasma membrane), molecular functions (including monooxygenase activity and iron ion binding), and signaling pathways (connecting the PPAR signaling pathway and complement and coagulation cascades) (Figure 1H).

Figure 1 | Differentially expressed genes (DEGs) between GBC and paracarcinoma tissues identified in GSE74048 and GSE76633 datasets. (A, B) The volcano map visually shows the total DEGs in GSE74048 and GSE76633. (C, D) Heat map visualization shows the top 20 DEGs in GSE74048 and GSE76633. (E, F) Identification of common upregulated and downregulated genes from DEGs in the GSE74048. (G, H) Functional enrichment analysis of common up- and down-regulated DEGs genes in GBC.

3.2 Construction of the PPI network and discovery of hub genes

Based on the data above, we investigated the interaction among the 741 DEGs using a PPI network constructed from STRING with interaction scores >0.4. It was displayed by Cytoscape to elucidate protein interactions, obtaining a total of 614 nodes and 4,911 edges (Figure 2A). The MCODE plug-in was used to analyze the PPI network and identify clusters. Cluster 1 consisted of 72 nodes and 2,317 edges, which exhibited the highest score (Figure 2B). Furthermore, cluster 2, containing 18 nodes and 134 edges, also possessed strong connections (Figure 2C). Based on the GO function and KEGG pathway analyses, these two clusters were principally associated with cell cycle, systemic lupus erythematosus, microtubule binding, nucleosomal DNA binding, spindle, nucleosome, mitotic nuclear division, and nucleosome assembly (Figures S1D, S1E). These findings suggested that the mentioned genes showed higher hub degrees and could serve critical roles in GBC.

Figure 2 | Identification, biological process and differential expression of hub genes from DEGs. (A) The PPI network was constructed using common 741 DEGs. Red nodes indicated upregulated genes, and green nodes represented downregulated genes. (B) Cluster 1 consisted of 72 nodes and 2317 edges and exhibited the highest score using MCODE plug-in. (C) Cluster 2 containing 18 nodes and 134 edges possessed the second highest score. (D) PPI network of hub genes in STRING database. (E) Biological process of hub genes was analyzed by BiNGO plug-in. (F, G) Hierarchical clustering and expression analysis of hub genes in TCGA biliary tract tumors (***P < 0.001).

Subsequently, we employed the cytoHubba plug-in to evaluate the 72 DEGs in cluster 1 with the highest tumor-related closeness through 11 different algorithms and acquired 12 sets of scoring results (Table S4). Further Venn intersection analysis of the top 40 genes in various scoring results confirmed the five overlapped genes, namely, PLK1, CEP55, FANCI, NEK2, and PTTG1, as potential hub genes (Figures S1F–I and Figure 2D). To further clarify the role of the five hub genes, biological process analysis was performed using the BiNGO plug-in. The alterations were mainly enriched in organelle fission, nuclear division, the M phase of the mitotic cell cycle, mitosis, and the cell cycle (Figure 2E). Meanwhile, we also observed that the five hub genes were obviously upregulated in bile duct cancer samples (Figures 2F, G) and highly expressed in almost all human tumors from TCGA (Figure S2) after hierarchical clustering through the UCSC Cancer Genomics Browser. Moreover, pan-cancer patients with upregulated PLK1, CEP55, FANCI, NEK2, and PTTG1 exhibited a poorer overall survival rate and disease-free survival (Figure S1J). Taken together, our results demonstrated that the five hub genes have great potential to be markers that distinguish cancer from normal tissues.

3.3 CEP55 serves as an underlying promoter and promising predictor of the prognosis in GBC

To further determine whether the five hub genes can be used as detectors of GBC, qRT-PCR was used to measure the mRNA expression of these hub genes in 15 human GBC tissue samples. The results showed that the mRNA levels of PLK1 and CEP55 in GBC tissues were significantly higher than those in paracarcinoma tissues, respectively, by 1.67 times (P = 0.006) and 2.97 times (P <0.001) (Figure 3A and Tables S5, S6). While FANCI, NEK2, and PTTG1 showed no statistically significant differences (Figure S3). It has been reported that PLK1 is upregulated in gallbladder cancer and is associated with poor prognosis in gallbladder cancer patients (31, 32), while the role of CEP55 in gallbladder cancer has not been reported. In combined analysis, CEP55 was markedly positively correlated with PLK1 in gallbladder cancer, R = 0.557 (Figure 3B), confirming that CEP55 may play a vital role in the progression of gallbladder cancer as PLK1. Consistently, immunohistochemistry analysis showed that CEP55 protein was mainly localized in the cytoplasm and increased pronouncedly in GBC tissues compared with adjacent tissues (Figures 3C–E). Of note, we found that there was a prominent positive correlation between the expression of CEP55 and Ki67 in immunohistochemical staining of the same batch (Figure 3F). Besides, the analysis of two datasets in GEO (GSE76633 and GSE74048) revealed that CEP55 was also positively related to the expression of MKI67 (encoding Ki67 protein, a cell proliferation marker) (R = 1.000, P <0.001), indicating that CEP55 may play a promoter role in the malignant proliferation of gallbladder cancer (Figure 3G).

Figure 3 | Expression of hub genes in GBC tissues and upregulated expression of CEP55 indicates poor prognosis. (A) The mRNA expression levels of PLK1 and CEP55 in GBC tissues. The data are shown as the ΔCt values. (B) Analysis of the correlative expression be-tween CEP55 and PLK1. (C, D) Protein expressions of CEP55 in GBC and matched paracarcinoma tissues were measured by immunohistochemistry (scale bar, 100 μm). (E) Results of immunohistochemical score. (F) Analysis of the correlation between CEP55 expression and Ki67 expression in GBC tissues (scale bar, 100 μm). (G) Correlation between CEP55 and MKI67 in GSE76633 and GSE74048. (H) The relationship between the expression of CEP55 and the overall survival rate (**P < 0.01; ***P < 0.001).

Our above results suggested CEP55’s potential to be used as a marker to help GBC diagnose. Thus, we set out to explore the correlation between CEP55 expression and the clinicopathological characteristics of GBC patients. Notably, it showed that the expression of CEP55 was positively correlated with TNM stage (X2 = 6.345, P = 0.012) and negatively correlated with the degree of differentiation of gallbladder carcinoma (X2 = 6.702, P = 0.010) (Table 1). Additionally, Kaplan–Meier curves manifested a sharp reduction of the overall survival rate in patients with elevated CEP55 expression compared with the patients with lower CEP55 (P = 0.002) (Figure 3H). Consistently, compared to the low-expression group, the 3-year overall survival rate of the high-expression group was profoundly lower (11.17 vs 23.32%, respectively) (Table S7). Meanwhile, the risk ratio of CEP55 overexpression was HR = 1.76 (CI: 1.22–2.55) (Figure 3H), revealing that CEP55 may be a promising predictor of the prognosis of patients with gallbladder cancer.

Table 1 | The relationship between the expression of CEP55 protein and clinical features in gallbladder carcinoma.

3.4 CEP55 augments the growth of GBC cells in vitro and in vivo

To clarify whether CEP55 promotes GBC cell proliferation, we first transfected NOZ and SGC-996 cells in vitro with siRNA to induce the downregulation of CEP55, with siNC and untreated cells used as controls. The reduction efficiency was determined by siRNA knockdown of CEP55, followed by RT-qPCR and Western blotting (Figures S4A, B). The CCK-8 assay demonstrated that the proliferative rate of NOZ and SGC-996 cells was impeded by CEP55 knockdown (Figure 4A). Consistent with CCK-8 data, the colony formation revealed that the number of colonies in the siCEP55 group was significantly decreased compared with the siNC group (Figure 4B). Similarly, as shown, the EDU staining indicated that the proportion of EDU-positive cells was prominently reduced following CEP55 knockdown in NOZ and SGC-996 cells (Figure 4C).

Figure 4 | CEP55 knockdown inhibits the growth of gallbladder cancer cells in vitro and in vivo. (A) The proliferative activity of cells was detected by CCK8. (B) Images and count analysis of colony formation assay. (C) Detection of DNA replication by EDU staining and its statistical analysis (scale bar, 50 μm). (D) The volume change and weight of gallbladder cancer xenograft tumor. (E) Immunohistochemistry to detect the protein expression of CEP55 and Ki67 of gallbladder carcinoma xenograft tumor (scale bar, 50 μm). The experimental group in vitro consisted of three groups, namely, Ctrl (un-treated) group, siNC (negative control) group and siCEP55 (siRNA targeting CEP55) group. The experimental group in vivo consisted of two groups, namely, shNC (negative control) group and shCEP55 (shRNA targeting CEP55) group. The statistical results are based on three and more independent experiments (*P < 0.05; **P < 0.01; ***P < 0.001). NOZ and SCG-996are two cell lines of GBC.

In addition, we established tumor xenograft models of GBC in nude mice inoculated with stably transduced cells to investigate the effect of CEP55 on gallbladder cancer growth in vivo. A lentiviral vector expressing the siCEP55 construct (shCEP55) as well as a control vector (shNC) were used to establish cells that stably expressed shRNA for CEP55 knockdown or a control shRNA. The protein levels of CEP55 in the shCEP55 cells were sharply decreased relative to those in the wild-type and shNC cells (Figure S4C). As expected, there was a striking decline in the xenograft tumor volume and weight in the shCEP55 group compared with the shNC group (Figure 4D). Besides, immunohistochemical staining showed that the expression of Ki67 was decreased in the shCEP55 group as compared with the shNC group, which was consistent with the results of the in vitro experiment and bioinformatics analysis (Figure 4E). Collectively, these results demonstrated the notion that CEP55 augments the proliferation of GBC cells in vitro and promotes the growth of GBC in tumor xenograft models of GBC.

3.5 Cell cycle arrest at G2/M checkpoint occurs in GBC cells of CEP55 knockdown

To further reveal the mechanism behind CEP55 affecting the growth of gallbladder cancer cells, flow cytometry was employed to test the changes in cell cycle after CEP55 knockdown in NOZ and SGC-996 cells. The results showed that the proportion of cells in G0/G1 phase was significantly reduced in the CEP55 knockdown group compared with the control group. In contrast, the proportion of cells in G2/M phase was significantly increased, while the proportion of S phase cells did not change much (Figure 5A). Western blot was used to assess the expression of cell cycle-related proteins in NOZ and SGC-996 cells. Consistently, we found that the expression of CyclinD1 and CDK6 was significantly downregulated, but the expression of CyclinB1 and P21 was sharply upregulated in the CEP55 knockdown group (Figure 5B), which suggested that G1 phase regulatory proteins were inhibited and G2/M phase regulatory proteins were activated after CEP55 knockdown. Based on the data above, we speculate that cell cycle arrest at the G2/M checkpoint occurs in the GBC of CEP55 knockdown, which results in impeding cell proliferation mediated by mitotic failure.

Figure 5 | CEP55 knockdown induces gallbladder cancer cells cycle arrest in G2/M phase. (A) The cell cycle was detected by flow cytometry. (B) Detection of cell cycle related protein expression by western blot. The statistical results are based on three independent experiments (*P < 0.05; ***P < 0.001).

3.6 CEP55 knockdown promotes DNA damage even apoptosis in GBC cells

Growing evidence indicates that severe cell cycle arrest induces DNA damage (33, 34). To further investigate whether the cell cycle arrest after CEP55 knockdown led to DNA damage in NOZ and SGC-996 cells, an alkaline comet assay was performed to evaluate the change in DNA damage. There was an obvious trailing phenomenon in the cells of the silenced group compared with the control group, suggesting that the CEP55 knockdown cells underwent DNA damage (Figure 6A). Meanwhile, immunofluorescence showed that the staining intensity of γ-H2AX was significantly enhanced in the silenced group of cells (Figure 6B). Furthermore, Western blot results were consistent with the immunofluorescence results (Figure 6C). Compared with the control group, the expression of γ-H2AX, p53, and Bax in the siCEP55 group was increased, but the expression of Bcl-2 was decreased (Figure 6B). It was indicated that severe cell cycle arrest in the GBC cells of CEP55 knockdown leads to DNA damage. Previous studies have revealed that cell cycle arrest and DNA damage can promote cell apoptosis (35, 36). We employed Hoechst33258 staining to determine the effects of CEP55 knockdown on nuclear morphological changes in GBC cells. The results showed that after knocking down CEP55, NOZ, and SGC-996 cells, they appeared binuclear or even multinuclear, and their nuclear morphology was fragmented and condensed. Quantitative analysis showed that the proportion of multinucleation and nuclear fragmentation in gallbladder cancer cells increased after knocking down CEP55 (Figure 7A). Consistently, flow cytometry with Annexin-V-PE and PI staining exhibited that the proportion of cells with early apoptosis (Q3) and late apoptosis (Q2) was significantly higher in the siCEP55 group, but there was no difference between the Ctrl group and the siNC group (Figure 7B). Additionally, the apoptosis-related proteins Cleaved-PARP, Cleaved caspase9 and Cleaved caspase3 were upregulated in the silenced group (Figure 7C). Furthermore, the TUNEL assay and immunohistochemistry and applied to verify the effect of CEP55 on the apoptosis of GBC xenografts. In the TUNEL assay, there were more TUNEL-positive cells were observed in the CEP55 knockdown tumor (Figure 7D). Furthermore, the results of cleaved caspase-3 immunohistochemistry were consistent with those of TUNEL (Figure 7E). Overall, it was suggested that apoptosis occurs after silencing the expression of CEP55 in gallbladder cancer cells.

Figure 6 | CEP55 knockdown induces DNA damage gallbladder cancer cells. (A) Alkaline comet assay to detect DNA damage in gallbladder cancer cells (scale bar, 50 μm). (B) Immunofluorescence detection of γ-H2AX expression in gallbladder cancer cells (scale bar, 25 μm). (C) Detection of DNA damage-related protein expression in gallbladder cancer cells by western blot. The statistical results are based on three independent experiments (***P < 0.001).

Figure 7 | CEP55 knockdown promotes apoptosis of gallbladder cancer cells. (A) Hoechst33258 staining to observe the morphology and statistical analysis of gallbladder cancer cell nuclei (scale bar, 50 μm). The red arrows represent binuclear or even multinucleated cells, and the yellow arrows represent nuclear fragmentation and nuclear condensation. (B) Detection of apoptosis in gallbladder cancer cells by flow cytometry. (C) The ex-pression of apoptosis protein in gallbladder cancer cells was detected by western blot. (D) The apoptosis of gallbladder cancer cells was detected by TUNEL assay (scale bar, 50 μm). (E) The expression of cleaved caspase-3 protein in xenograft tumors were de-tected by immunohistochemistry (scale bar, 50 μm). The statistical results are based on three and more independent experiments (***P < 0.001).

3.7 The tumor suppressor effect of CEP55 knockdown is associated with dysregulation of AKT and ERK signaling pathways

Studies have shown that CEP55 upregulates the phosphorylation of AKT by directly combining with the subunit P110 of PI3K, thus promoting cell proliferation in vitro (37, 38). To further decipher the molecular signaling network involved in CEP55 accelerating the proliferation and survival of gallbladder cancer cells, we employed immunofluorescence assays to detect the effect of downregulation of CEP55 on AKT and ERK-dependent signaling pathways. The results showed that the fluorescence intensity of p-AKT and p-ERK1/2 expression in gallbladder cancer cells decreased significantly after CEP55 knockdown (Figure 8A). In addition, the results of western blot showed that the phosphorylation levels of AKT (Ser473), GSK-3 β (Ser9), and ERK1/2 (Thr202/Tyr204) decreased in gallbladder cancer cells after CEP55 knockdown, but the protein expression level of FOXO3a increased in the siCEP55 group (Figure 8B). Furthermore, the immunohistochemical results of transplanted tumor sections in nude mice were consistent with the above results, showing that the expression of p-AKT and p-ERK1/2 decreased significantly in the shCEP55 group compared with the shNC group (Figure 8C). The above results suggest that the inhibition of gallbladder cancer cell growth after knocking down CEP55 is related to the cascade inactivation of the AKT and ERK signaling networks.

Figure 8 | The tumor suppressor effect of CEP55 knockdown is associated with dysregulation of AKT and ERK signaling pathways. (A) Immunofluorescence detection of p-AKT and p-ERK expression in gallbladder cancer cells (scale bar, 25 μm). (B) Detection of AKT and ERK pathways related protein expression by western blot. The statistical results are based on three independent experiments (***P < 0.001). (C) The expression of p-AKT and p-ERK1/2 protein in xenograft tumors were detected by immunohistochemistry (scale bar, 50 μm).

4 Discussion

Due to our limited understanding of gallbladder cancer etiology and pathogenesis, there is a huge obstacle to its prevention and treatment. It is of great significance to discover susceptible genes related to GBC and further reveal its molecular pathogenesis. Excitingly, the application of bioinformatics analysis presented a new insight into exploring molecularly targeted drugs for gallbladder cancer. In our study, it was found that the GO analysis revealed that upregulated DEGs were mainly participated in cell division and cell cycle regulation, while the downregulated DEGs were related to small molecule catabolism and organic acid catabolism. In addition, it displayed cell cycle and P53 signaling pathways obtained by the KEGG analysis of upregulated DEGs, and the downregulated DEGs were mainly enriched in the complement pathway and PPAR signaling pathways. Notably, we noticed that five screened hub genes, including PLK1, CEP55, FANCI, NEK2, and PTTG1, are also closely associated with cell division and proliferation. Meanwhile, a database search found that these hub genes were highly expressed in TCGA bile duct cancer tissue. Therefore, it is speculated that these five hub genes may be important markers for the diagnosis and therapy of gallbladder cancer. It was worth noting that the mRNA levels of PLK1 and CEP55 were significantly upregulated in gallbladder cancer tissues than in adjacent tissues. However, there was no significant difference in the expression of FANCI, NEK2, or PTTG1, which may be due to the insufficient sample size. Strikingly, previous studies demonstrated that PLK1 is highly expressed in GBC tissues, advances GBC progression, and exacerbates the prognosis of gallbladder cancer (31, 32). But the expression of CEP55 in gallbladder cancer and its role in the progression of gallbladder cancer remains to be elucidated. Correlation studies suggested that the expression of CEP55 in gallbladder cancer was significantly positively correlated with that of PLK1, indicating that CEP55 has potential as a molecular target for gallbladder cancer therapy like PLK1. Accumulating evidence indicates that CEP55 expression was elevated in some other tumors, such as liver cancer (38), cervical cancer (39), and lung cancer (40). Likewise, the protein expressions of CEP55 were obviously increased in 174 gallbladder cancer tissues. Moreover, we observed that CEP55 overexpression in gallbladder cancer was positively correlated with TNM stage and negatively correlated with the degree of differentiation of gallbladder cancer, which implies that the detection of CEP55 in gallbladder cancer tissues may help to assess the progressive status of gallbladder cancer patients more accurately. Expectedly, subsequent Kaplan–Meier analysis and Cox regression analysis suggested that CEP55 was suitable as an independent predictor to help determine the prognosis of patients with gallbladder cancer.

CEP55 was identified as a coiled-coil centrosome protein that plays a critical role in many physiological and pathological processes. Furthermore, a few studies have shown that CEP55 is involved in the proliferation process of kidney cancer cells (41), NSCLC cells (42), and neuroblastoma cells (43). Consistent with previous studies, we found that the proliferation ability of gallbladder cancer cell lines NOZ and SGC-996 was obviously reversed after CEP55 knockdown. Moreover, the subcutaneously transplanted tumor in nude mice in the CEP55 silent group was significantly smaller than that in the control group. Besides, immunohistochemical staining showed that Ki-67, a marker representing cell proliferation, was downregulated in the silent group. Overall, we demonstrated that high expression of CEP55 promotes the proliferation of gallbladder cancer cells in vitro and in vivo. An abnormal cell cycle is one of the fundamental mechanisms of tumorigenesis. To date, growing evidence indicates that overexpression of Aurora A/B, cyclin D/E, Cdc20, Skp2, and PLK1 has been frequently detected in all kinds of cancers, and these oncoproteins serve as effective therapeutic targets in the clinic (44). In our study, it is noteworthy that the mitosis failure of GBC cells leads to cell cycle arrest after knocking down CEP55, which triggers the inhibition of G1 phase regulatory proteins like CyclinD1 and CDK6, and the activation of G2/M phase regulatory proteins like CyclinB1. Furthermore, it is noteworthy that the cycle inhibitory protein P21 was also elevated.

Disruption of the cell cycle results in polynucleated polyploid cells that are prone to genomic instability and induced DNA damage, which is one of the reasons why failure of mitosis activates cell death (45, 46). CEP55 is an important regulator involved in cytoplasmic division, and its abnormal expression is clearly associated with genomic instability (47, 48), which is an important sign of malignant tumors (49). Here, we observed that gallbladder cancer cells after CEP55 knockout showed obvious tailing and appeared binucleate or even multinucleate, indicating the instability of the gallbladder cancer cell genome and DNA damage. The level of γ-H2AX effectively reflects the degree of DNA damage and repair (50, 51). Expectedly, the expression of γ-H2AX was significantly increased in the CEP55 silent group. Therefore, gallbladder cancer cells show DNA damage because of cell division failure after knocking down CEP55. Recently, it has been shown that DNA damage is an important factor in the induction of apoptosis (52). Additionally, the tumor suppressor protein p53 prevents cells with damaged DNA from dividing and inhibits tumor formation (53, 54). In the event of severe DNA damage that is difficult to repair, p53 induces apoptosis (55, 56). Notably, after knockdown of CEP55, the expression of p53 and Bax in GBC cells was dramatically upregulated while the expression of Bcl-2 declined. It has been reported that p53 can upregulate the expression of p21, which leads to cell cycle arrest by binding to Cyclin E/CDK2 and Cyclin D/CDK4 (57). This provides a reasonable explanation for the simultaneous increase of P53 and P21 in gallbladder cancer cells after knockdown of CEP55 in this study. Moreover, it was found that the nuclei of GBCs were fragmented and condensed, and the expression of apoptosis proteins in GBCs was also obviously raised after CEP55 knockdown. Consistently, it showed an elevated apoptosis rate. In addition, the fluorescence intensity of TUNEL staining and the positive ratio of cleaved caspase-3 in the silent group were also increased. Combined with the above results, it suggested that the severe DNA damage that occurred after CEP55 knockdown induced apoptosis. Apoptosis has been shown to have a sophisticated relationship with the abnormal proliferation of tumor cells (58). Apoptosis and cell cycle alterations promoted by DNA damage have been demonstrated to regulate cancer cell proliferation in colorectal cancer (59), liver cancer (60), and lung cancer (61). Overall, the failure of mitosis in gallbladder cancer cells after knockdown of CEP55 causes severe DNA damage and even apoptosis, which in turn inhibits cell proliferation.

Increasing evidence suggests that activation of the AKT and ERK pathways plays an important role in CEP55’s promotion of tumor progression. It has been reported that the interaction between CEP55 and SPAG5 triggered the phosphorylation of AKT at Ser473 and activated the PI3K/AKT pathway to exhibit pro-hepatocellular carcinoma activities (18). Meanwhile, Liu suggested that miR-34a/c induced the apoptosis of caprine endometrial epithelial cells by binding CEP55 via the ERK and AKT pathways (17). Additionally, the researchers implied that CEP55 positively influenced the tumorigenesis of esophageal squamous cell carcinoma by enhancing the colony formation and migration of ESCC cells through the phosphorylation of Src, FAK, and ERK (40). Consistent with previous studies, the results in our study demonstrated that the knockdown of CEP55 downregulated the phosphorylation level of AKT, GSK-3β, and ERK1/2 to inhibit AKT and ERK signaling pathways in gallbladder cancer cells, causing the cascade inactivation of the AKT and ERK signaling networks.

Collectively, our studies led us to conclude that CEP55, highly expressed in gallbladder carcinoma, can be used as a potential independent predictor for disease diagnosis and prognostic prediction. Moreover, knockdown of CEP55 in gallbladder cancer cells ultimately affects the proliferative activity of the cells through AKT and ERK pathways mediated by cell cycle arrest, DNA damage, and even apoptosis. Our findings provide further insight into why CEP55 is a target for precision medicine therapy for gallbladder cancer.

Data availability statement

Publicly available datasets were analyzed in this study. This data can be found here: GSE74048 and GSE76633 from GEO database.

Ethics statement

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

Conceptualization, YC, FS, and MH. Methodology, FZ, MC, and LH. Validation, MH, FZ, and WC. Formal analysis, FZ, WC, and LH. Investigation, MH and XA. Data curation, FZ and MC. Writing—original draft preparation, MH. Writing—review and editing, FS. Visualization, MH and FZ. Resources, supervision, and project administration, FS and YC. Funding acquisition, YC. All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

Funding

This research was funded by the Startup Fund for Scientific Research, Fujian Medical University (No. 2020QH20), the Science and Technology Innovation Joint Foundation of Fujian Province (No. 2020Y9075), and the High-Level Medical Care Construction Foundation of Fujian province (No. [2021]76).

Acknowledgments

Thank Dr. Wenxiu Yuan for providing technical guidance and fluorescent secondary antibody in immunofluorescence experiment. Will you marry me?

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.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2023.1156177/full#supplementary-material

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ORIGINAL RESEARCH
published: 13 September 2023
doi: 10.3389/fonc.2023.1221498

System analysis based on the lysosome-related genes identifies HPS4 as a novel therapy target for liver hepatocellular carcinoma

Ke‐Jie He ¹*† and Zhiqiang Nie ²†

¹The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People’s Hospital, Quzhou, Zhejiang, China, ²Global Health Research Center, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China

Edited by:
Zongli Zhang, Qilu Hospital of Shandong University, China

Reviewed by:
Xiaoting Huang, Guangzhou Medical University Cancer Hospital, China
Surendra Kumar Shukla, University of Oklahoma, United States
Sudhir Varma, Hithru Analytics LLC, United States

*Correspondence:
Ke‐Jie He
hekejie@stu.xmu.edu.cn

†These authors share first authorship

Received: 12 May 2023

Accepted: 21 August 2023

Published: 13 September 2023

Citation:
He K and Nie Z (2023) System analysis based on the lysosome-related genes identifies HPS4 as a novel therapy target for liver hepatocellular carcinoma. Front. Oncol. 13:1221498. doi: 10.3389/fonc.2023.1221498

Background

Liver cancer is a leading cause of cancer-related deaths worldwide. Lysosomal dysfunction is implicated in cancer progression; however, prognostic prediction models based on lysosome-related genes (LRGs) are lacking in liver cancer. This study aimed to establish an LRG-based model to improve prognosis prediction and explore potential therapeutic targets in liver cancer.

Methods

Expression profiles of 61 LRGs were analyzed in The Cancer Genome Atlas liver cancer cohorts. There were 14 LRGs identified, and their association with clinical outcomes was evaluated. Unsupervised clustering, Cox regression, and functional assays were performed.

Results

Patients were classified into high-risk and low-risk subgroups based on the 14 LRGs. The high-risk group had significantly worse overall survival. Aberrant immune infiltration and checkpoint expression were observed in the high-risk group. Furthermore, HPS4 was identified as an independent prognostic indicator. Knockdown of HPS4 suppressed liver cancer cell proliferation and induced apoptosis.

Conclusion

This study developed an LRG-based prognostic model to improve risk stratification in liver cancer. The potential value of HPS4 as a therapeutic target and biomarker was demonstrated. Regulation of HPS4 may offer novel strategies for precision treatment in liver cancer patients.

Keywords: LIHC, HPS4, gene mutation, copy number variation, methylation, immune, prognostic signature, chemotherapy response

1 Introduction

Globally, LIHC ranks sixth among the most common cancers (1, 2). There are projected to be more than one million cases of liver cancer by 2030 (3). The number of patients who die from LIHC each year exceeds half a million (4). In the early stages, LIHC has no symptoms but rapidly progresses (5). At present, LIHC is mainly treated with liver transplantation, hepatic resection, and medication (6). LIHC can be surgically excised in only 10%–20% of patients, but recurrences are common (7). The current drug therapy has limited efficacy for LIHC because it is chemoresistant (8, 9). Despite the application of new treatment strategies for LIHC, efficacy remains unsatisfactory (10). Thus, identification of specific prognostic markers is essential for guiding LIHC therapy and improving OS of patients.

In all types of cells, lysosomes are membrane-bound phospholipid bilayers that catabolize protein degradation and recycle it through phagocytosis, endocytosis, and autophagy (11–13). It has been reported that lysosome functions are drastically altered during cancer progression, including alterations in lysosome volume, localization, and composition within the cell (14, 15). In addition, lysosomes have been found to be routed to the periphery of the cell under multiple stimuli that can result in a variety of pathological conditions (11). In malignant transformations, lysosomes are juxtaposed to the plasma membrane, which contributes to cell invasion and migration (16, 17). A number of cancers show significant overexpression of lysosomal hydrolases, which increases invasion of tumor. TMEM106B has been reported to increase lysosomal hydrolase synthesis in lung cancer cells, which is packaged into lysosomes and enlarge lysosomes (11). A hyperinvasive microenvironment is created as lysosomes secrete their protease cargo into the extracellular matrix under conditions of calcium flux (18–20).

We identified 14 genes out of 61 lysosome-related genes (LRGs) as significant predictors in LIHC. The 14 genes are ANKRD27, AP3M1, BCL10, CD63, CTSC, GLA, HPS1, HPS4, NPC2, PPT1, RAB7A, TPP1, VPS45, and RAMP3. The 14 lysosome-related genes were screened to classify molecular subgroups by NMF consensus clustering. The patients of LIHC from TCGA data were divided into C1 and C2 clusters. In addition, we explored that the most common CNVs are heterozygous amplifications and deletions of genes based on the LRGs. Further analysis of the pathway prediction and methylation was performed. An OS prognostic signature was constructed using LASSO regression analysis based on the LRGs. As an external verification database, we used the ICGC-LIHC cohorts for training the prognosis model.

We further explored the association between LRGs, immune checkpoints, and immune cells. Moreover, the TIDE scores of group G1 were significantly higher than those of group G2. The chemotherapy response in the two subtypes was also explored by comparing the IC50s of doxorubicin and cisplatin. We developed the nomogram by the univariate Cox regression model. Our result demonstrated that HPS4 and a worse prognosis are strongly associated with LIHC. We further explored the relation between HPS4 and immune cells based on eight single-cell datasets. We found that HPS4 could reduce proliferation and apoptosis significantly in liver cancer cells; it may be a promising therapeutic target and biomarker for LIHC, and regulating its activity may be an effective treatment strategy.

2 Materials and methods

2.1 Differential expression analysis of LRGs

We obtained 61 LRGs from the MSigDB (Supplementary Table 1). Expression profiles and clinical information for LIHC are provided via TCGA dataset (Supplementary Table 2). The raw read counts of genes were converted to transcripts per million (TPM) using STAR. Further analysis of LRG expression in LIHC was carried out using version R 3.6.3 and ggplot2. Our statistical significance criterion of log2(fold change) > 2 and adjusted p-value <0.05 indicated a significant difference.

2.2 Nomogram construction

Our goal was to identify the right terms for the nomogram by using univariate Cox regression analysis. For each variable, we calculated p values, HRs, and 95% confidence intervals using the “forestplot” R package. In order to predict 1-, 3-, and 5-year overall recurrence, a nomogram was developed using univariate Cox proportional hazards analysis. An individual’s recurrence risk can be calculated using nomograms by the “rms” R package.

2.3 The association between LRGs and gene mutation, CNV

Data about somatic mutations were provided by the UCSC Xena server for the GDC TCGA-LIHC project. According to the mutation order, the result was generated using the R package “maftools”. Data from TCGA database were downloaded and processed using GISTIC2.0, which can identify regions significantly altered including amplification and deletion.

2.4 The analysis of correlation between LRGs and immune infiltration

A TCGA dataset was downloaded to obtain LIHC clinical information and RNA sequencing profiles (level 3). LRG gene expression was correlated with immune scores using R’s ggstatsplot package, and multigene correlations were analyzed using R’s pheatmap package. To describe correlations between quantitative variables that do not follow a normal distribution, Spearman’s correlation analysis was used. R was used to implement all the analysis methods, and a statistical significance level below 0.05 was considered statistically significant.

2.5 Construct prognostic signature of LRGs

A TCGA dataset was downloaded to obtain LIHC clinical information and RNA sequencing profiles (level 3). In addition to converting count data to TPM, log2 (TPM+1) is normalized; it is important to keep samples together that contain information about the patient’s clinical status. A log-rank test was used to compare survival rates between the two groups. TimeROC (v0.4) was used to compare predictive accuracy between LRGs and risk scores. The features were selected using LASSO regression, cross-validated 10 times, and analyzed with the R package glmnet. With the survival package and Cox regression analysis, a prognostic model was constructed. We generated Kaplan–Meier curves with log-rank p-values and 95% confidence intervals (CIs) using log-rank tests. R was used to implement all the analysis methods, and a statistical significance level below 0.05 was considered statistically significant.

2.6 Identification of potential subtypes

80% of the samples were analyzed 100 times by ConsensusClusterPlus R package (v1.54.0). Using the R software package pheatmap (v1.0.12), we generated cluster heatmaps. Heatmaps showing gene expression were retained for genes with SD over 0.1. When the input gene number exceeded 1,000 after sorting the SD, the top 25% were extracted. All analysis methods and R packages were implemented in R version 4.0.3. 80% of TCGA LIHC samples (n = 371) were used as a discovery cohort to identify molecular subgroups. The remaining 20% of samples (n = 93) were held out as an internal validation set, to evaluate the reproducibility of the subgroups. Consensus clustering was run on the 80% discovery cohort, identifying two stable subgroups. These two subgroups were confirmed by testing their ability to classify the held-out 20% of samples.

2.7 The correlation between subtypes and immune

Evaluation of immune scores was conducted using immunedeconv. We extracted the expression of immune checkpoints including HAVCR2, PDCD1, CTLA4, LAG3 SIGLEC15, TIGIT, CD274, and PDCD1LG2. We predicted potential ICB responses using the TIDE algorithm and calculated mRNAsi based on the OCLR algorithm (21). Based on the mRNA expression signature, 11,774 genes are present in the gene expression profile. In order to map the dryness index to the range, the minimum value was subtracted and divided by the maximum value. R was used to implement all the analysis methods, and a statistical significance level below 0.05 was considered statistically significant.

2.8 The association of LRGs and drug sensitivity

With the help of the Genomics of Drug Sensitivity in Cancer (GDSC), we were able to predict chemotherapeutic response based on each sample. A prediction process was implemented using R package “pRRophetic”. The half-maximal inhibitory concentration (IC50) of samples was calculated using ridge regression. Using the batch effect of battle and tissue type, the duplicate gene expression values were summed up as a mean. R was used to implement all the analysis methods, and a statistical significance level below 0.05 was considered statistically significant.

2.9 Cell culture and transfection

From Procell (Wuhan, China), SK-hep-1 and Huh7 cells were obtained without mycoplasma infection. At 37°C with 5% CO₂, the SK-hep-1 and Huh7 cell lines were cultured in MEM (Gibco, Grand Island, NY) and DMEM (Gibco), respectively. The medium includes 10% fetal bovine serum (FBS; Gibco), 100 U/mL penicillin, and 100 mg/mL streptomycin (Invitrogen, Waltham, MA). We transfected si-HPS4 and si-NC using Lipofectamine 2000 (Invitrogen). The cells were harvested 24 h after transfection.

2.10 Cell proliferation and apoptosis

Cells transfected with si-HPS4 were seeded in cell culture plates, 5,000 cells per well. A colony formation assay was performed by inoculating 500 cells into six-well plates and growing them at 37°C for 15 days. Following fixing the colonies with methanol and staining them with hematoxylin, TRIzol (Invitrogen) was used to extract RNA from liver cancer cells. All of the primer sequences and small interfering RNA sequences are listed in Supplementary Table 2. A 48- h transfection of si-HPS4 or si-NC was followed by harvesting of SK-hep-1 and Huh7 cells. The cells were then stained with FITC Annexin V Apoptosis Detection Kit (Meilunbio, Dalian, China) and detected using flow cytometry (Beckman Coulter, Brea, CA). We analyzed the results using the FCAP Array software. A triplicate of each trial was conducted.

2.11 Single-cell RNA sequencing-based analysis

We further explored the correlation between HPS4 and immune cells based on eight single-cell datasets by the TISCH2 resource (22). We next analyzed the expression of HPS4 in the hepatocyte population and hepatocellular carcinoma cells by single-cell and HCL resources (23). We further explored the gene expression levels in all cancer single-cell samples by the CancerSCEM resource (24).

3 Results

3.1 Identification of prognostic molecular subgroups

There were 14 genes out of 61 lysosome-related genes (LRGs) identified as significant predictors in LIHC. Among them, 13 are risk factors, namely, ANKRD27, AP3M1, BCL10, CD63, CTSC, GLA, HPS1, HPS4, NPC2, PPT1, RAB7A, TPP1, and VPS45, whereas RAMP3 is a favorable factor (Figure 1A). According to their cumulative distribution function and function delta area, k = 2 appeared to be the best clustering value for the 14 genes (Figure 1B). Initially, the 14 lysosome-related genes were screened to classify molecular subgroups by NMF consensus clustering. The patients of LIHC from TCGA data were divided into C1 and C2 clusters according to the consensus map (Figure 1C). Heat maps of differentially expressed genes show red for high expression and blue for low expression (Figure 1D). An additional confirmation was obtained by performing a principal component analysis (PCA) (Figure 1E). The log-rank test was used to examine the survival of the different groups by utilizing Kaplan–Meier curves (p < 0.0001) (Figure 1F). We found that cluster 2 had a worse clinical prognosis compared with cluster 1. Our result demonstrated that LRGs were associated with drug sensitivity based on the CTRP and GDSC resources (Supplementary Figure 1). The differential genes between cluster G1 and G2 were further explored (Supplementary Figure 3).

Figure 1 | Clustering of molecular subgroup based on the lysosome-related genes (LRGs) in liver hepatocellular carcinoma (LIHC). (A) There were 14 genes out of 61 LRGs identified as significant predictors in LIHC. (B) K = 2 appeared to be the best clustering value for 14 genes by cumulative distribution function and function delta area. (C) There were 14 lysosome-related genes screened to classify molecular subgroups by NMF consensus clustering. (D) Heat maps of differentially expressed genes between the C1 and C2 clusters. (E) Principal component analysis (PCA). (F) Cluster 2 had a worse clinical prognosis compared with cluster 1. Statistical analyses were conducted by one-way ANOVA, principal component analysis, and log-rank test. p < 0.05 was considered statistically significant.

3.2 Analysis of LRG copy number variations and immune correlations

A comparison of LRG expression between normal and tumor tissues was performed in LIHC. The LRG expression in tumors was obviously higher than in normal tissues, whereas RAMP3 expression was significantly lower in tumors (Figures 2A, C). A strong correlation was observed according to LRG expression, such as RAB7A and AP3M1 which are most correlated (r = 0.72) (Figure 2B). There was a significant positive correlation between CNV and mRNA expression in most LRGs (Figure 2D). The copy number variation (CNV) is prevalent in human cancers, contributing to tumor development. We found that the most common CNVs are heterozygous amplifications and deletions of genes, whereas homozygous amplifications and deletions are much less common (Figures 2E, G, H). The impact of tumor CNV levels on immune evasion is particularly intriguing. Analyzing the differences in immune infiltration between gene-set CNV groups, we estimate the relationship between immune and gene- set CNV levels. In the amplification group, NK cells, CD4 T cells, Tfh cells, Th2 cells, CD4 naïve cells, and macrophage, their infiltration scores all showed a negative correlation with CNV, whereas that of Treg cells was positively correlated with CNV in the group of amplification. The macrophage, Tfh cell, and CD4 naïve cell infiltration scores were negatively correlated with CNV in the group of deletion; however, the B- cell score showed the opposite trend (Figure 2F).

Figure 2 | The copy number variation (CNV) of LRGs was correlated with immune cells. (A, C) A comparison of LRG expression between normal and tumor tissues was performed in LIHC. (B) A strong correlation was observed according to the expression of LRGs. (D) There was a significant positive correlation between CNV and most LRGs. (E, G, H) The most common CNVs are heterozygous amplifications and deletions of genes, whereas homozygous amplifications and deletions are much less common based on the LRGs’ (F) The relationship between immune and gene set CNV level was estimated. Statistical analyses were performed using t-test, Pearson correlation, and Fisher’s exact test. p < 0.05 was considered statistically significant. **p < 0.01; ***p < 0.001.

3.3 Pathway enrichment analysis and DNA methylation of LRGs

Further analysis of the CRGs’ GSVA (gene set variation analysis) scores was performed. The GSVA is an unsupervised method for measuring variations in gene- set activity (represented by GSVA scores) across cancer populations. By GSVA, the activity of differential gene sets between tumor and normal samples is analyzed. We found that the GSVA score was significantly increased in LIHC (Figure 3A). There was a worse prognosis in LIHC for patients with a high GSVA score (Figure 3B). In addition to the GSVA score, we studied the correlation between tumor-related pathways in the LIHC and the GSVA score. Our results demonstrated that GSVA score was positively associated with apoptosis and EMT pathway, and GSVA score was negatively associated with hormone ER and RTK pathway (Figure 3C). Additionally, LRG expression was associated with immune cell infiltrates using Spearman’s correlation analysis. We found that LRGs were negatively associated with Th17 cell, neutrophil, and monocyte, whereas LRGs were positively associated with cytotoxic cell, NK cell, Tfh cell, Treg cell, macrophage, CD8 T cell, Th1 cell, NKT cell, and DC cell (Figure 3D). In addition, we found that PPT1, HPS1, and RAMP3 were hypermethylated and CTSC, CD63, and GLA have a hypomethylation level (Figure 3E). Methylation and mRNA expressions were also investigated. We explored that most of LRGs had a negative association with methylation including ANKRD27, RAB7A, AP3M1, BCL10, NPC2, CTSC, HPS4, VPS45, HPS1, PPT1, and CD63 whereas GLA had a positive association with methylation (Figure 3F). In the LIHC cohorts, hypomethylation of VPS45 had an association with a worse prognosis whereas hypermethylation of GLA showed the opposite situation (Figure 3G). Our results demonstrated that LRGs had potential impact on pathways including the TSC/mTOR, RTK, RAS/MAPK, PI3K/AKT, hormone ER, hormone AR, EMT, DNA damage response, cell cycle, and apoptosis pathways. Most LRGs could activate the apoptotic cell cycle and the EMT pathway, inhibiting the RTK and TSC/mTOR pathways (Figures 3I, H). Immunohistochemistry staining images from the HPA were analyzed to further explore the LRG protein expression in LIHC. Our results demonstrated that the expression of most LRG proteins was higher in tumor tissues (Figure 4).

Figure 3 | The analysis of pathway prediction and methylation. (A) The activity of differential gene sets between tumor and normal samples is analyzed based on the GSVA (gene set variation analysis) scores. (B) There was a worse prognosis in LIHC for patients with a high GSVA score. (C) The correlation between tumor-related pathways in LIHC and GSVA score was analyzed. (D) LRGs were associated with immune cell infiltrates using Spearman’s correlation analysis. (E) The hypomethylation level was explored based on the LRGs. (F) The association between methylation and mRNA expression was investigated. (G) The hypomethylation level of LRGs was associated with clinical prognosis. (H, I) The LRGs had a potential impact on pathway. Statistical analyses included t-test, Pearson correlation, log-rank test, and enrichment analyses. p < 0.05 was considered statistically significant. *p < 0.05; #FDR ≤ 0.05.

Figure 4 | Immunohistochemistry staining images from the HPA resource were analyzed to further explore the LRGs protein expression in LIHC.

3.4 Development of an LRG-based prognostic signature model

As part of this research, we evaluated the relationship between LRGs and the prognosis of LIHC patients. Based on univariate Cox regression, LRGs were selected for LASSO regression (Figures 5A, C). An OS prognostic signature was constructed using LASSO regression analysis for 10 genes of LRGs: risk score = (0.0817)*BCL10+(0.0043)*CD63+(0.0601)*CTSC+(0.0502)*GLA+(−0.2469)*HPS4+(0.1112)*PPT1+(0.3027)*RAB7A+(-0.2508)*RAMP3+(0.1498)*TPP1+(0.1685)*VPS45 (lambda.min = 0.0173). There was a significant association between LRGs’ signature risk score and poor overall survival in the study (HR = 2.312 (1.619–3.301), log-rank p = 3.99e−06). In terms of AUCs, the 1-, 3-, and 5-year ROC curves were accurate by 0.774, 0.737, and 0.723, respectively. Survival of LIHC patients was significantly associated with LRG-related risk signatures at the individual level (Figure 5E). As an external verification database, we used the ICGC-LIHC cohorts for training the prognosis model. Based on univariate Cox regression, LRGs were selected for LASSO regression in the ICGC-LIHC cohorts (Figures 5B, D). There was a significant association between LRGs’ signature risk score and poor overall survival in the ICGC-LIHC cohorts (HR = 3.696 (1.821–7.501), log-rank p = 0.000296, Figure 5F). The association between subtypes and ferroptosis and m6A was further performed (Supplementary Figure 2). We also found that the relative expression levels of macrophages were lower in the high-risk group (Supplementary Figure 4).

Figure 5 | Prognostic signature construction for LRGs. (A, C) Based on univariate Cox regression, LRGs were selected for LASSO regression. (B, D) As an external verification database, we selected the ICGC-LIHC cohorts for training the prognosis model. (E) Survival of LIHC patients was significantly associated with LRG-related risk signatures at the individual level in the TCGA-LIHC cohorts. (F) There was a significant association between LRGs’ signature risk score and poor overall survival in the ICGC-LIHC cohorts. Statistical analyses utilized LASSO regression, log-rank test, and time-dependent ROC analysis. p < 0.05 was considered statistically significant.

3.5 Evaluation of subtype-specific immune landscape and drug response

A comparison of LRG expression in the two subtypes was also carried out. We found that most LRGs increased significantly in group G1 whereas gene RAMP3 showed the opposite trend (Figure 6A). In tumors with higher mRNAsi, the tumor has dedifferentiated more and its cancer stem cells are more active. With a better clinical prognosis, the G2 group had lower mRNAsi scores whereas the G1 group had higher mRNAsi scores (Figure 6B). Our results demonstrated that ANKRD27, AP3M1, BCL10, CTSC, HPS1, HPS4, PPT1, RAB7A, TPP1, and RAMP3 had a negative relation to immune infiltration of CD4 T cells using the EPIC algorithm. Moreover, ANKRD27, AP3M1, BCL10, CD63, CTSC, GLA, HPS1, HPS4, NPC2, PPT1, RAB7A, TPP1, and VPS45 had a positive correlation with macrophage (Figure 6C). Furthermore, the TIDE algorithm showed low efficacy of immune checkpoint blockade therapy (ICB) based on high TIDE scores. As a result, the TIDE scores of group G1 were significantly higher than those of group G2 (Figure 6D). We found that subtypes had a significant association with immune cells including B cell, CD4 T cell, CD8 T cell, endothelial cell, macrophage, and NK cell. Macrophages in the G1 group had lower immune scores than those in the G2 group (Figure 6E). Furthermore, the subtypes were significantly related to immune checkpoints, with the G1 group having a greater abundance of immune checkpoints than the G2 group (Figure 6G). The chemotherapy response in the two subtypes was further explored by comparing the IC₅₀s of doxorubicin and cisplatin. A significant decrease in chemotherapy response was observed in G1 compared with G2 based on IC50 of doxorubicin and cisplatin (Figures 6F, H).

Figure 6 | The subtypes had a correlation with immune and immune response. (A) A comparison of LRG expression in the two subtypes was also carried out. (B) With a better clinical prognosis, the G2 group had lower mRNAsi scores. (C) LRGs were associated with immune infiltration by the EPIC algorithm. (D) TIDE scores of group G1 were significantly higher than those of group G2. (E) The subtypes had a significant association with immune cell. (G) The G1 group having a greater abundance of immune checkpoints than the G2 group. (F, H) The chemotherapy response in the two subtypes was further explored by comparing the IC₅₀s of doxorubicin and cisplatin. Statistical analyses were conducted by t-test, Pearson correlation, log-rank test, and ANOVA. p < 0.05 was considered statistically significant. *p < 0.05; **p < 0.01; ***p < 0.001.

3.6 HPS4 as a potential therapeutic target in LIHC

A univariate Cox regression model was used to develop the nomogram (Figure 7A). On the basis of its c-index of 0.71, it displayed relatively good predictive ability (Figure 7B). According to the calibration plots, the predicted OS and observed OS at 1, 3, and 5 years were well concordant (Figure 7C). As HPS4 and a worse prognosis are strongly associated with LIHC, we further explored whether HPS4 contributes to liver cancer proliferation. SK-hep-1 and Huh7 cells were treated with si-HPS4 to interfere with HPS4 expression (Figure 7D). Moreover, apoptosis could contribute to tumor cell growth; the level of apoptosis in liver cancer cells was also detected using flow cytometry. A significant increase in apoptotic cells was observed when HPS4 was knocked down by si-HPS4 (Figure 7E). The interference of HPS4 with SK-hep-1 and Huh7 cells significantly decreased proliferation by the colony formation assay (Figures 7F). Furthermore, we analyzed the association between HPS4 and a proliferation marker (MKI67). We found that HPS4 was positively related to MKI67 (Figures 7G). Collectively, HPS4 may be a promising therapeutic target and biomarker for LIHC, and regulating its activity may be an effective treatment strategy.

Figure 7 | Identifying HPS4 as novel therapy target for LIHC. (A) A univariate Cox regression model was used to develop the nomogram. (B) The nomogram displayed relatively good predictive ability. (C) The predicted OS and observed OS at 1, 3, and 5 years. (D) SK-hep-1 and Huh7 cells were treated with si-HPS4 to interfere with HPS4 expression. (E) A significant increase in apoptotic cells was observed when HPS4 was knocked down by si-HPS4. (F) The interference of HPS4 with SK-hep-1 and Huh7 cells significantly decreased proliferation by the colony formation assay. (G) The association between HPS4 and a proliferation marker (MKI67). Statistical analyses included Cox regression, log-rank test, t-test, and Pearson correlation. p < 0.05 was considered statistically significant. ***p < 0.001.

3.7 Analysis of single-cell RNA sequencing data

We further explored the relation between HPS4 and immune cells based on eight single-cell datasets. Our results demonstrated that HPS4 had a significant correlation with CD8 T cell, B cell, and macrophage (Figure 8A). We next analyzed the expression of HPS4 in the hepatocyte population. We found that HPS4 was expressed in cluster 1, cluster 3, cluster 6, cluster 8, cluster 10, cluster 11, and cluster 17. Their corresponding cell populations are activated T cell, sinusoidal endothelial cell, myeloid cell, dendritic cell, smooth muscle cell, hepatocyte_APOA2 high, and epithelial cell_SCGB3A1 high (Figures 8B–D). Similarly, we found that HPS4 was also expressed in hepatocellular carcinoma cells including HEP3B217, HUH6, JHH6, JHH7, LI7, SNU423, and SNU449 (Figures 8E–G). The gene expression level was explored in all cancer single-cell samples. HPS4 was highly expressed in GBM, LUAD, PDAC, and AML relative to other tumors (Figure 8H).

Figure 8 | The analysis of single-cell RNA sequencing. (A) HPS4 had a significant correlation with CD8 T, B cell, and macrophage based on eight single-cell datasets. (B–D) HPS4 was expressed in cluster 1, cluster 3, cluster 6, cluster 8, cluster 10, cluster 11, and cluster 17. Their corresponding cell populations are activated T cell, sinusoidal endothelial cell, myeloid cell, dendritic cell, smooth muscle cell, hepatocyte_APOA2 high, and epithelial cell_SCGB3A1 high. (E–G) HPS4 was also expressed in hepatocellular carcinoma cells including HEP3B217, HUH6, JHH6, JHH7, LI7, SNU423, and SNU449. (H) The HPS4 expression level was explored in all cancer single-cell samples. Correlation, clustering, and differential expression analyses were performed. p < 0.05 was considered statistically significant.

4 Discussion

Globally, LIHC ranks sixth among the most common cancers. Despite the application of new treatment strategies for LIHC, efficacy remains unsatisfactory. Thus, identification of specific prognostic markers is essential for guiding LIHC therapy and improving OS of patients. It has been reported that lysosome functions are drastically altered during cancer progression, including alterations in lysosome volume, localization, and composition within the cell. A number of cancers show significant overexpression of lysosomal hydrolases, which increases invasion of the tumor (11). It is therefore imperative to understand how LRGs affect LIHC and determine if they serve as prognostic indicators. There has not been any reporting on a prognostic model based on LRGs.

To verify the efficacy of lysosome-related genes on the prognosis of hepatocellular carcinoma, we identified 14 genes out of 61 lysosome-related genes (LRGs) as significant predictors in LIHC. The 14 genes are ANKRD27, AP3M1, BCL10, CD63, CTSC, GLA, HPS1, HPS4, NPC2, PPT1, RAB7A, TPP1, VPS45, and RAMP3. The 14 lysosome-related genes were screened to classify molecular subgroups by NMF consensus clustering. The patients of LIHC from TCGA data were divided into C1 and C2 clusters. We found that cluster 2 had a worse clinical prognosis compared with cluster 1. In addition, we explored that most common CNVs are heterozygous amplifications and deletions of genes based on the LRGs. The impact of tumor CNV levels on immune evasion is particularly intriguing. We also analyzed the differences in immune infiltration between gene-set CNV groups. Further analysis of the pathway prediction and methylation was performed. An OS prognostic signature was constructed using LASSO regression analysis based on the LRGs. As an external verification database, we used the ICGC-LIHC cohorts for training the prognosis model.

Tumor-associated immune responses are regulated and determined by immune cells in the tumor microenvironment (25–27). We also explored the association between LRGs and immune cells. According to some studies, declining CD4+ T cell counts can contribute to liver cancer development (28, 29), whereas our results demonstrated that ANKRD27, AP3M1, BCL10, CTSC, HPS1, HPS4, PPT1, RAB7A, TPP1, and RAMP3 had a negative relation to immune infiltration of CD4 T cells using the EPIC algorithm. We found that subtypes had a significant association with immune cells including B cell, CD4 T cell, CD8 T cell, endothelial cell, macrophage, and NK cell. Several cancer types show a correlation between macrophage density and poor prognosis, suggesting that macrophages play a key role in tumor progression (30–32). Macrophages in the G1 group had lower immune scores than those in the G2 group. Furthermore, the TIDE algorithm showed low efficacy of immune checkpoint blockade therapy (ICB) based on high TIDE scores (21, 33). As a result, the TIDE scores of group G1 were significantly higher than those of group G2. A profound advance in cancer therapy has come from the discovery that immunocheckpoint molecules overexpress in the tumor microenvironment and contribute to antitumor immunity evasion (34–36). Furthermore, the subtypes were significantly related to immune checkpoints, with the G1 group having a greater abundance of immune checkpoints than the G2 group. The resistance to chemotherapy contributes significantly to cancer mortality (37, 38). Thus, the chemotherapy response in the two subtypes was further explored by comparing the IC₅₀s of doxorubicin and cisplatin. A significant decrease in chemotherapy response was observed in G1 compared with G2 based on the IC₅₀ of doxorubicin and cisplatin.

We developed the nomogram by the univariate Cox regression model. Our result demonstrated that HPS4 and a worse prognosis are strongly associated with LIHC. SK-hep-1 and Huh7 cells were treated with si-HPS4 to interfere with HPS4 expression. The interference of HPS4 with SK-hep-1 and Huh7 cells significantly decreased proliferation and colony formation. Moreover, apoptosis could contribute to tumor cell growth; the level of apoptosis in liver cancer cells was also detected using flow cytometry. A significant increase in apoptotic cells was observed when HPS4 was knocked down by si-HPS4. We found that HPS4 was positively related to MKI67 whereas MKI67 is an important marker of proliferation (39). As a tool for studying cell types and states, single-cell RNA sequencing (scRNA-seq) is becoming increasingly important. We further explored the relation between HPS4 and immune cells based on eight single-cell datasets. Our results demonstrated that HPS4 had a significant correlation with CD8 T, B cell, and macrophage. We found that the genes were mainly expressed in activated T cell, sinusoidal endothelial cell, myeloid cell, dendritic cell, smooth muscle cell, hepatocyte_APOA2 high, and epithelial cell_SCGB3A1 high. Collectively, it turns out that HPS4 may be a promising therapeutic target and biomarker for LIHC, and regulating its activity may be an effective treatment strategy.

The study has some limitations. There is still a lack of understanding of how HPS4 impacts proliferation or apoptosis of liver cancer cells. This is our next endeavor to further explore how HPS4 regulates LIHC in vivo and in vitro.

5 Conclusion

A prognostic model based on LRGs was developed in this study to predict the prognosis of patients with LIHC. HPS4, which could affect proliferation and apoptosis of liver cancer cells, may be a promising therapeutic target and biomarker for LIHC. HPS4 may be a promising therapeutic target and biomarker for LIHC, and regulating its activity may be an effective treatment strategy.

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

K-JH designed this work. K-JH performed bioinformatic analyses. K-JH wrote the manuscript. K-JH and ZN revised the manuscript and supervised the whole experiment. All authors have read and approved the final submitted manuscript.

Funding

This project was funded by Quzhou Municipal Science and Technology Bureau (2023K117) and Quzhou Medical and Health personnel scientific research fund project (KYQD2022-18).

Conflict of interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2023.1221498/full#supplementary-material

Supplementary Figure 1 | LRGs were associated with drug sensitivity based on the CTRP (a) and GDSC resource (b). Statistical analysis was performed using Pearson correlation. P < 0.05 was considered statistically significant. *P < 0.05; **P < 0.01; *** P < 0.001.

Supplementary Figure 2 | The association between subtypes and ferroptosis and m6A. Enrichment analysis and correlation tests were conducted. P < 0.05 was considered statistically significant. *P < 0.05; **P < 0.01; *** P < 0.001.

Supplementary Figure 3 | The differential genes between cluster G1 and G2; (a) The volcano plot; (b) heat map of differential genes; (c) Pathway enrichment analysis. P < 0.05 was considered statistically significant. *P < 0.05; **P < 0.01; *** P < 0.001.

Supplementary Figure 4 | Correlation between high and low risk groups and immune cells. Correlation analysis was conducted using Pearson correlation. P < 0.05 was considered statistically significant. *P < 0.05; **P < 0.01; *** P < 0.001.

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ORIGINAL RESEARCH
published: 07 December 2023
doi: 10.3389/fonc.2023.1184900

Preclinical evidence for anaplastic lymphoma kinase inhibitors as novel therapeutic treatments for cholangiocarcinoma

Kyaw Zwar Myint ¹, Mireia Sueca-Comes ², Pamela Collier ², Brinda Balasubramanian ¹, Simran Venkatraman ¹, John Gordan ³, Abed M. Zaitoun ⁴, Abhik Mukherjee ²,⁴, Arvind Arora ²,⁵, Noppadol Larbcharoensub ⁶, Chinnawut Suriyonplengsaeng ⁷, Kanokpan Wongprasert ⁷, Tavan Janvilisri ¹,⁸, Dhanny Gomez ⁹, Anna M. Grabowska ², Rutaiwan Tohtong ⁸*, David O. Bates ²* and Kiren Yacqub-Usman ²*

¹ Graduate Program in Molecular Medicine, Faculty of Science, Mahidol University, Bangkok, Thailand, ² Division of Cancer and Stem Cells, Biodiscovery Institute, School of Medicine, Biodiscovery Institute, University of Nottingham, Nottingham, United Kingdom, ³ Department of Medicine, University of California, San Francisco, San Francisco, CA, United States, ⁴ Department of Pathology, Nottingham Universities National Health Service (NHS) Hospital Trust, Queens Medical Centre, Nottingham, United Kingdom, ⁵ Department of Medical Oncology, Nottingham Universities National Health Service (NHS) Hospital Trust, Queens Medical Centre, Nottingham, United Kingdom, ⁶ Department of Pathology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand, ⁷ Department of Anatomy, Faculty of Science, Mahidol University, Bangkok, Thailand, ⁸ Department of Biochemistry, Faculty of Science, Mahidol University, Bangkok, Thailand, ⁹ Department of Hepatobiliary and Pancreatic Surgery, and National Institute of Health Care Research (NIHR) Nottingham Digestive Disease Biomedical Research Unit, University of Nottingham, Nottingham, United Kingdom

Edited by:
Zongli Zhang, Qilu Hospital of Shandong University, China

Reviewed by:
Veronika Lukacs-Kornek, University of Bonn, GermanyBruno Köhler, University of Heidelberg, Germany

*Correspondence:
Rutaiwan Tohtong
Rutaiwan.toh@mahidol.ac.th
David O. Bates
david.bates@nottingham.ac.uk
Kiren Yacqub-Usman
kiren_yacqub@hotmail.co.uk

Received: 12 March 2023

Accepted: 06 November 2023

Published: 07 December 2023

Citation:
Myint KZ, Sueca-Comes M, Collier P, Balasubramanian B, Venkatraman S, Gordan J, Zaitoun AM, Mukherjee A, Arora A, Larbcharoensub N, Suriyonplengsaeng C, Wongprasert K, Janvilisri T, Gomez D, Grabowska AM, Tohtong R, Bates DO and Yacqub-Usman K (2023) Preclinical evidence for anaplastic lymphoma kinase inhibitors as novel therapeutic treatments for cholangiocarcinoma. Front. Oncol. 13:1184900. doi: 10.3389/fonc.2023.1184900

Introduction

Bile duct cancer (cholangiocarcinoma, CCA) has a poor prognosis for patients, and despite recent advances in targeted therapies for other cancer types, it is still treated with standard chemotherapy. Anaplastic lymphoma kinase (ALK) has been shown to be a primary driver of disease progression in lung cancer, and ALK inhibitors are effective therapeutics in aberrant ALK-expressing tumors. Aberrant ALK expression has been documented in CCA, but the use of ALK inhibitors has not been investigated. Using CCA cell lines and close-to-patient primary cholangiocarcinoma cells, we investigated the potential for ALK inhibitors in CCA.

Methods

ALK, cMET, and ROS1 expression was determined in CCA patient tissue by immunohistochemistry and digital droplet polymerase chain reaction, and that in cell lines was determined by immunoblot and immunofluorescence. The effect on cell viability and mechanism of action of ALK, cMet, and ROS1 inhibitors was determined in CCA cell lines. To determine whether ceritinib could affect primary CCA cells, tissue was taken from four patients with biliary tract cancer, without ALK rearrangement, mutation, or overexpression, and grown in three-dimensional tumor growth assays in the presence or absence of humanized mesenchymal cells.

Results

ALK and cMet but not ROS were both upregulated in CCA tissues and cell lines. Cell survival was inhibited by crizotinib, a c-met/ALK/ROS inhibitor. To determine the mechanism of this effect, we tested c-Met-specific and ALK/ROS-specific inhibitors, capmatinib and ceritinib, respectively. Whereas capmatinib did not affect cell survival, ceritinib dose-dependently inhibited survival in all cell lines, with IC₅₀ ranging from 1 to 9 µM and co-treatments with gemcitabine and cisplatin further sensitized cells, with IC₅₀ ranging from IC₅₀ 0.60 to 2.32 µM. Ceritinib did not inhibit cMet phosphorylation but did inhibit ALK phosphorylation. ALK was not mutated in any of these cell lines. Only ceritinib inhibited 3D growth of all four patient samples below mean peak serum concentration, in the presence and absence of mesenchymal cells, whereas crizotinib and capmatinib failed to do this. Ceritinib appeared to exert its effect more through autophagy than apoptosis.

Discussion

These results indicate that ceritinib or other ALK/ROS inhibitors could be therapeutically useful in cholangiocarcinoma even in the absence of aberrant ALK/ROS1 expression.

Keywords: cholangiocarcinoma, Opisthorchis viverrini, anaplastic lymphoma kinase 1, novel therapeutics, cytotoxicity and autophagy

Introduction

Our understanding of the pathogenic mechanisms responsible for the evolution and outgrowth of cholangiocarcinomas (CCA) is far from complete. Moreover, this heterogenous cancer consists of a milieu of transcription factors, growth factors, and their cognate receptors that are molecularly heterogenous and subtype-specific and differ dependent on their geographical location. The incidence and mortality rates are increased in the Mekong basin areas of Southeast Asia (Thailand, Loas, and Vietnam) due to infection with the liver fluke Opisthorchis viverrini (Ov-CCA) compared with CCA not associated with Ov (Non-Ov CCA). Current treatments include surgical resection with an overall median survival rate of 2 years and standard systemic treatment of combination of gemcitabine-based therapies in combination with cisplatin, oxaliplatin, capecitabine, or 5-FU (1), resulting in very small increases in overall survival. Recent characterization studies have identified recurrent genetic alterations in CCA, which may be amenable to therapeutic targeting.

Studies from several groups have explored the potential of overexpression of cMet and preclinical characterization of novel inhibitors of the cMet pathway as treatment options for cholangiocarcinomas. In CCA cell lines and in vivo studies, the selective cMet inhibitor capmatinib inhibited cMet phosphorylation and activation of key downstream effectors of cMet, and as a result inhibits cell proliferation and migration and induces apoptosis (2). MET-amplified (MKN45) and MET-overexpressed (H441) xenograft models of CCA were used to further the preclinical development of an orally bioavailable small-molecule inhibitor LY2801653 targeting MET kinase leading to in vivo anti-tumor effects and in vivo vessel normalization effects (3). LY2801653 is currently in phase 1 clinical trials in patients with advanced disease (NCT01285037). Indeed, several subsequent clinical trials are currently being performed in CCA, but all have limitations due to underpowered studies, mixed cohorts, and design without intent to enrich for markers, required to optimize success for targeted therapies (4, 5).

Anaplastic lymphoma kinase (ALK) (6), hepatocyte growth factor receptor (HGFR/c-Met) (7), and ROS proto-oncogene 1 receptor tyrosine kinase (ROS1) (8) are tyrosine receptor kinases, implicated to be aberrantly expressed in human cancers (9–11). ALK, c-Met, and ROS1 were reported to have high expression in advanced biliary tract carcinoma and patients with high expression of all 3 proteins by IHC staining had a significantly inferior median overall survival than patients with low-expressing tumors (12). CCA tissues were reported to have high c-Met expression; however, c-Met expression in CCA was reported with contradicting findings on disease-free survival and treatment response to standard gemcitabine and cisplatin therapy (12–14).

In addition, subsequent investigations into multi-targeted agents to treat CCA have been employed. Crizotinib is a multi-targeted tyrosine kinase inhibitor, targeting ALK, c-Met, and ROS1, and FDA approved for the treatment of non-small cell lung cancer (NSCLC) patients with ALK rearranged mutations and melanoma patients (15–17). These studies showed an objective response rate of 72% with a median progression-free survival of 19.2 months of NSCLC patients treated with crizotinib with high ROS1 levels and resulted in further therapeutic options of multi-targeted treatment for CCA (18). Furthermore, ceritinib (LDK378), a more potent second-generation tyrosine kinase inhibitor with higher anaplastic lymphoma kinase (ALK) selectivity than crizotinib (19), has been investigated and shown to be active against NSCLC. ALK overexpression is found in several cancers included but not limited to ovarian, progressive neuroblastoma, lung cancer, and cholangiocarcinoma.

The purpose of this study was to gain further insight into the relationship and interplay between the expression of ALK, cMet, and ROS1 and the cytotoxicity of crizotinib, ceritinib, and capmatinib in cholangiocarcinoma cells. We therefore determined the expression of ALK, cMet, and ROS in surgically resected patient tissue, primary cell lines derived from surgically resected patient tissue, and immortalized cell lines. In addition, we determined the cytotoxicity of crizotinib, ceritinib, and capmatinib. Furthermore, we also used a knockdown strategy towards ALK, cMet, and ROS1 to determine the effect on cell proliferation. We investigated the possible mechanism of action of these inhibitors in CCA. The efficacy of each inhibitor was further confirmed using primary cells derived from surgically resected patient tissue in a 3D tumor growth assay and compared to serum plasma concentrations. This work shows for the first time that ceritinib is a potential therapeutic option for the treatment of cholangiocarcinoma.

Materials and methods

Ethics approval and patient consent statement

Surgical material from tumor resections at Nottingham University NHS trust were collected with informed patient consent and National Research Ethics Service approval (NRES REC 10/H0405/6). Samples were used in accordance with NRES approval (NRES REC 08/H0403/37). The study protocol in Thailand was approved by the ethical clearance committee on human rights related to research involving human subjects, Faculty of Medicine Ramathibodi Hospital, Mahidol University (protocol no. 12-58-41) and Rajavithi Hospital (protocol no. 61042).

Specimen collection

Fresh surgical material from tumor resections at Nottingham University NHS trust were collected with informed patient consent and National Research Ethics Service approval (NRES REC 10/H0405/6). Samples were used in accordance with NRES approval (NRES REC 08/H0403/37). Surgical material was collected and processed as previously described (20). In brief, surgical material was immediately placed into tissue transfer and transferred to the laboratory and processed within 4–6 h.

Samples were dissected upon arrival at the laboratory. The majority of tissue was used for live use, further portions were snap frozen for protein analysis, formalin-fixed and paraffin-embedded (FFPE) for immunohistochemistry, or stored in RNAlater (Ambion) for subsequent analysis. A small amount of finely minced tumor tissue was enzymatically disaggregated [as described below (20)].

Disaggregation of tissue

Finely minced tumor was disaggregated using type II collagenase (100 U/mL: Invitrogen) and dispase (2.4 U/mL; Invitrogen) in Hanks’ balanced salt solution (HBSS) without calcium or magnesium (Sigma, UK) at 37°C under constant rotation. Cells were removed at hourly intervals until the tumor was completely disaggregated. Cell number and viability were determined using trypan blue exclusion as previously described.

Establishing close-to-patient cells using a feeder layer method

In vitro tumor cell growth was established and expanded with a layer of supporting feeder layer cells according to the method of Liu et al. (21) and as previously described (22). In brief, from this material, the tumor epithelial cells were expanded and harvested separately from the cancer-associated fibroblasts using differential trypsinization. Tumor cell number and viability were determined using trypan blue exclusion. At less than passage 5, cell aliquots were cryopreserved, utilized for 3D-TGA and further experimentation. Primary cells used within this study were as follows: CCA-UK5: extrahepatic adenocarcinoma, CCA-UK6: well-differentiated intrahepatic carcinoma, CCA-UK7: multi-desmoplastic and poorly differentiated perihilar, and CCA-UK9: extrahepatic adenocarcinoma low in stroma and epithelial alignments.

The 3D-tumor growth assay

As previously described (20), in brief, cells were resuspended in ice-cold Cultrex basement membrane extract (BME) (9 mg/mL: Trevigen) diluted in modified RPMI-1640 (life technologies; phenol red free with 6 mmol/L D-Glucose and pH 6.8) and plated at 6,250 tumor cells ± mesenchymal cells (bone marrow derived) (ScienCell) into low adherent, black-walled, clear-bottom, 384- well plates. Drugs were serially diluted in modified RPMI-1640 and 13 μL of drug was added in six replicates on day 3. Drugs used in combination were premixed and serially diluted together before adding to the assay. Drug exposure was for 96 h before final endpoint readings. The AlamarBlue assay [Invitrogen; 10% (v/v), 37°C for 1 h] was used to monitor cell growth daily using fluorescent plate reader (FLUOstar Omega, BMG Labtech). Drug sensitivity was calculated as a percentage of matched untreated control and IC₅₀ curves were determined using GraphPad Prism 5 (GraphPad Software Inc, nonlinear curve fit of Y = 100/(1 + 10 ((Log₁₀IC₅₀-X)*HillSlope). Error bars represent one standard error of the mean. Drugs in combination were at constant ratios to make them amenable to synergy testing using Chou-Tala-Lay method and CalcuSyn software.

Cell cultures

RBE, a human intrahepatic cholangiocarcinoma cell line, TFK-1, a human extrahepatic cholangiocarcinoma cell line, KKU-M213 and KKU-M156, two human intrahepatic cholangiocarcinoma cell lines from a patient with Opisthorchis viverrinae infection, and KKU-M055 from a different patient with intrahepatic cholangiocarcinoma cell line and Opisthorchis viverrinae infection and HuCCA-1, a human intrahepatic cholangiocarcinoma, were all obtained from Professor John Gordan, University of California under a material transfer agreement. All cell lines were cultured in DMEM supplemented with 10% fetal bovine serum and 2 mM L-glutamine (Sigma-Aldrich, UK) in 5% CO2 atmosphere at 37°C. Cells in the current study had undergone 10 passages.

Inhibitors

Inhibitors employed within these studies were crizotinib, ceritinib, capmatinib, and bafilomycin. Inhibitors used at the University of Nottingham were commercially bought from Selleckchem (Munchen, Deutschland), and inhibitors used at Mahidol University, Thailand were kindly provided by Assistant Professor Dr. Pimtip Sanvarinda, Department of Pharmacology, Faculty of Science, Mahidol University, Thailand. The drugs were dissolved in cell culture grade dimethyl sulfoxide (DMSO) (AppliChem, Barcelona, Spain) to prepare 5–30 mM stocks. Frozen DMSO stocks were prepared together with the drug to be used as vehicle. All drug treatments were performed at 60%–80% cell confluence. Hepatocyte growth factor (HGF) was purchased from Immunotools, Friesoythe, Germany and 100 µg/µl stock were prepared in 0.1% BSA/PBS.

Expression analysis

Absolute expression analysis (digital droplet reverse transcriptase PCR)

RNA was extracted and purified from surgically resected tumors, five primary cell lines (CCA-UK5, CCA-UK6, CCA-UK7, CCA-UK9, and CCA-UK11), and patient matched, histologically tumor free resected margin as a control as previously described. Total RNA (1 μg) was reverse transcribed using 500 ng of Oligo -d(T) and 250 ng of random primers and M-MLV reverse transcriptase (Takara) in 20 µl total volume. cDNA (1 μL) was added to 1 μL of Taqman Probe and 8 μL of ddH₂O with10 μL of ddPCR supermix for probes (Bio-Rad, UK). Droplets were generated using QX100 droplet generator (Bio-Rad, UK). Amplification was carried out using standard Taqman protocols with probe found in Table 1.0 in Supplementary Information). Samples were analyzed using QX100 droplet reader (Bio-Rad, UK). Thresholding was manually performed, based on negative and positive control results only when automatic thresholding of values was not possible. Data are expressed as copies of RNA per µg of total RNA.

RT-qPCR

qRT-PCR was performed using Faststart universal SYBR green Master (Roche, Mannheim, Germany). Primer sequence to ALK gene (NM_004304.5): forward: 5’-GAGGGGGCGGCAAGATT-3’ and reverse: 5’-CTTGTGGCTCCTCCAAGCTC-3’ were employed. The target gene was normalized to an endogenous control, 18S mRNA, forward: 5’-CCATCCAATCGGTAGTAGCG-3’ and reverse: 5’ GTAACCCGTTGAACCCCATT-3’. Relative quantification was carried out using relative-standard curve and the 2−ΔΔCT method, where −ΔΔCT = cycle threshold (CT) (ALK of treated cell lines − 10S of treated cell line) − CT (ALK control − 18S control).

Protein expression analysis

Total protein was extracted using NP-40 lysis buffer containing 1% (vol/vol) Triton X (Calbiochem, Germany), 1 × Protease Inhibitor (Roche, Germany), 50 mM NaF (Sigma, UK), 2 mM sodium orthovanadate (Sigma, UK), and phenylmethylsulfonyl fluoride (PMSF) (Sigma, UK) and quantified using Pierce® BCA Assay Kit (Thermo Scientific, UK); 25–40 μg (UK and Thailand respectively) were separated via SDS-PAGE on 12% gels followed by dry transfer of the proteins to nitrocellulose membrane using the Trans-Blot Turbo Transfer System (Bio-Rad, UK) for 10 min. The membranes were blocked in 5% BSA for 1.5 h. Blots were then incubated with primary antibodies (Table 2.0 in Supplementary Information) overnight. The blots were then incubated with IRDye fluorescent secondary antibodies for 1 h at room temperature and imaged using the LiCOR Odyssey for imaging. All blots were analyzed and quantified using Image Studio™ (LI-COR®).

Immunohistochemistry

Immunohistochemistry was used to examine the expression of ALK, cMET, and ROS1 receptors in CCA patient tissues from tumors with different etiologies (OV related in the Thai group and non-Ov in the UK group) and previously described. FFPE tumor samples were retrieved from the Department of Pathology, Ramathibodi Hospital, Mahidol University and Department of Pathology, Rajavithi Hospital, Bangkok, Thailand and also patients from the Department of Pathology, Nottingham Universities Hospital Trust, for immunohistochemistry assessment. All tissues were confirmed as mass forming CCA by the two pathologists of each institute (NL and CS, and AZ and AM). OV-related CCA in Thai patients was diagnosed by the patients fulfilling one of the five criteria as previously described (20). Each case was tested using the following primary antibodies: anti-cMET (#3077, Cell Signalling, UK), anti-ALK (ab190934, abcam, UK), and anti-ROS1(#3287, Cell Signalling, UK) antibodies. Serial 5-μm sections of FFPE tissue were sectioned by microtome for immunohistochemical analysis. The immunoreactivity was scored based on membrane and or cytoplasmic staining compared to positive controls as follows: no staining or faint staining < 10% of tumor cells; 1+, weak perceptible membranous staining in >10% of tumors; 2+, moderate complete membranous staining in >10% of tumor cells; 3+, strong complete membranous staining in >10% tumor cells (23).

Cell viability and proliferation assay

Cell proliferation and viability were determined by WST-1 assay (Sigma, UK). The cells were seeded in 96-well plate at a density of 5,000 cells per well, and 24 h post seeding, the cells were treated with 0.1, 1, 10, and 100 μM of crizotinib, ceritinib, or capmatinib. The treatments and drug combinations are shown in figure legends. Cells were incubated with drugs for 48 h and cell proliferation was determined, WST-1 assay was added 10% (v/v) and incubated for 45 min, and absorbance was measured 450 nm by a plate reader. Cells in autophagy reversal experiments were pre-treated with bafilomycin (10 nM) and then subjected to the IC₅₀ dose of ceritinib, crizotinib, or capmatinib at 10 µM (IC₅₀ not reached).

Receptor stimulation assay

Receptor stimulation was performed by initially blocking the receptor with specific inhibitor prior to stimulation of the receptor with receptor specific ligand. Cholangiocarcinoma cell lines were seeded at a density of 200,000 cells per well in a six-well plate for 24 h. The cells were serum starved in 0% FBS medium for a further 24 h. Cells were treated with 5 µM of either crizotinib, ceritinib, or capmatinib for 2 h and then stimulated with HGF 40 ng/mL added to the inhibitor containing medium for 1 h and protein extraction was performed.

siRNA-mediated gene silencing

siRNA targeting ALK genes, siALK-1 5′-GAGUCUGGCAGUUGACUUCTT-3′ targeting Exon-1 of ALK mRNA (24) and siALK-2 5′-GUGCCAUGCUGCCAGUUAAUU-3′ targeting Exon-26 kinase region of ALK mRNA (25), cMET gene, ROS1 gene, and a non-targeting siRNA (siNT) were synthesized commercially (Eurofins Genomics, Ebersberg, Germany). The transfection complex siRNA and RNAiMAX (Life technologies, UK) were prepared. RBE, TFK-1, KKU-M055, KKU-M156, and KKU-M213 cell lines were reversed transfected with the transfection complex. Low passage cells at a density of 200,000 cells were transfected with 15 nM siRNA or the non-target control according to the manufacturer’s instructions (Life Technologies, Paisley UK). Cells were transfected for 48 h prior to RT-qPCR and apoptosis assays. Experiments were carried out thrice independently.

Apoptosis assay

Apoptosis was determined using the Caspase 3/7 Glo Assay (Promega, UK) as per the manufacturer’s instructions. The cells were seeded in a 96-well plate at a density of 5,000 cells per well, and 24 h post seeding, the cells were treated with 0.1, 1, 10, and 100 μM of crizotinib, ceritinib, or capmatinib. The treatments and drug combinations are shown in figure legends. Cells were incubated with drugs for 48 h and caspase activity was determined, Caspase 3/7 Glo assay was added 10% (v/v) and incubated for 45 min and luminescence was measured by a plate reader.

Statistical analysis

All experiments were repeated at least three times and presented as the mean ± standard error of the mean (SEM) of three independent experiments. Comparison of data between two groups was performed with Student’s t-test. Data were analyzed for significance among more than two groups by one-way ANOVA with Dunnett’s multiple comparison post-test. An associated probability (p) of ≤ 0.05 was considered significant.

Target prediction and enrichment analysis

Putative targets of crizotinib, ceritinib, and capmatinib were identified based on structural similarity using the SMILES ID as an input in SwissTargetPrediction: http://www.swisstargetprediction.ch/ (26). Gene expression of the common targets was analyzed in CCA tumors when compared with normal bile duct from The Cancer Genome Atlas (TCGA) and plotted using the web tool, GEPIAv2 (http://gepia2.cancer-pku.cn/). Pathway enrichment for the target genes was performed using Enrichr(https://amp.pharm.mssm.edu/Enrichr/) and KEGG database (27–30).

Results

Differential endogenous expression of ALK, cMet in cholangiocarcinoma cell lines, and patient tumor samples

We first determined the expression of cMet, ALK, and ROS1 in patient tumor tissue compared to tissue resection margin. mRNA expression of Met and ALK was significantly elevated in patient tissue (Figure 1A) and tumor cells isolated from primary tissue (Figure 1B) compared to their respective normal margins; however, ROS1 expression was barely detectable in both tumor samples and resected margins.

Figure 1 | Expression of tyrosine kinase receptors in cholangiocarcinoma (CCA). Absolute mRNA expression (copies per µg of RNA) of cMET, ALK, and ROS1 was quantified using Droplet Digital PCR in surgically resected CCA patient tumor samples of eight UK patients (A), and four primary lines derived from surgically resected tissue (B) compared to tissue resected margins. Immunohistochemistry staining for phosphorylated cMET (pcMET), cMET, phosphorylated ALK (pALK), and ALK protein levels in CCA tissues (C). Scale bars = 100 µM. Significant difference was determined by paired t-test. *p < 0.005, **p < 0.01, ***p < 0.001 compared to margin. Negative controls (IgG).

Immunohistochemistry of tumor tissue sections from patients both from Thailand (presumed OV positive) and from the UK (presumed OV negative) were positive for phosphorylated cMet, cMet receptor, phosphorylated ALK, and ALK receptor with similar intensities (Table 1¹, Figure 1C) compared with histologically normal tissues taken from resection margin of CCA. cMet and ALK receptors and activated pcMet and pALK receptors had expression that was more intense in tumor cells from CCA patients irrespective of their global origin.

Table 1 | Levels of staining in cholangiocarcinoma patient tissue samples.

We next confirmed that the high expression of cMet and ALK and low expression of ROS1 was preserved in immortalized CCA cell lines (Figure 2). cMet and ALK expression was investigated in six CCA cell lines; four cell lines were derived from patients with OV infection, namely, KKU-M055, KKU-M213, KKU-M156, and HuCCA-1, and two lines from OV-negative patients, RBE and TFK-1. Transcript expression of all cell lines showed significant cMet and ALK levels (Figures 2A, B), respectively. cMet and ALK protein was expressed in all cell lines. Activated forms of cMet and ALK receptors were increased in OV-induced CCA cell lines compared to non-OV-induced CCA cell lines, but this was not correlated with transcript expression levels (Figures 2C, D, respectively). The protein and transcript expression of ROS1 in CCA cell was not detected by ddPCR or Western blot (data not shown). Immunofluorescence staining confirmed ALK and cMet expression in cell lines, but barely detectable levels of ROS1 were shown in all cell lines (Figure 2E).

Figure 2 | Expression of cMET, ALK, and ROS1 in the cholangiocarcinoma cell line. Absolute expression of cMET (A) and ALK (B) was determined in six immortalized cell lines: derived from Opisthorchis viverrinae (OV)-infected patients: HuCCA1, KKU-M213, KKU-M156, and KKU-M055 (solid bars) and two Non-Ov lines (open bars); RBE and TFK-1. Protein expression of C-MET and p-cMET (C) and ALK and p-ALK (D) was determined by Western blot. Full Western blot images are in Supplementary Figure 3A. Protein localization of c-Met, ALK, and ROS1 was observed in CCA cell lines with specific primary antibodies followed by probing secondary antibodies conjugated with Alexa488 and counterstained with DAPI (E).

Receptor activation status in CCA

The CCLE database that has fusion data for multiple cancer lines including CCA lines show that KKU-213 and RBE do not present with ALK fusions. Hence, we sought to explore the HGF/cMet axis. We determined receptor stimulation status, and this was achieved by stimulating with HGF, a cMet ligand. Post-stimulation, Western blot analysis was employed to determine the activity of the receptor through phosphorylation. All cell lines were able to be stimulated with HGF ligand, shown by an increase in the level of phosphorylated cMet receptor. However, the levels of activation were higher in OV-induced CCA cell lines compared to non-OV-induced ones (Figure 3A).

Figure 3 | Activation and inhibition of cMET receptor in CCA cell lines. cMET receptor activation was induced by HGF-1 in HuCCA-1, RBE, and KKU-213 (A) compared with untreated (Un) and vehicle-treated (Veh) controls. Full Western blot images are in Supplementary Figure 3B. Inhibition of HGF-induced receptor activation of cMET in KKU-213 (B) and RBE (C, D). Full Western blot images are in Supplementary Figure 3C top and bottom panel. The percentage of cell survival of CCA cell lines treated with increasing doses of gemcitabine and cisplatin combinations (E), crizotinib, ceritinib, capmatinib and combinations of gemcitabine- and cisplatin-treated CCA cell lines (F), ceritinib co-incubations with gemcitabine and cisplatin treatment in CCA cell lines (G). Lines are dose–response curve fits. No lines are shown when data could not be fitted.

Sensitivity of CCA cells to treatment with tyrosine kinase and chemotherapeutic inhibitors in activated CCA cell lines

Four CCA cell lines were employed to determine the sensitivity to crizotinib—a cMet, ALK, and ROS1 inhibitor, ceritinib—an ALK inhibitor, and capmatinib (INCB28060)—a cMet-specific inhibitor. Receptor activity was blocked using inhibitors prior to stimulation using HGF ligand. Crizotinib treatment reduced phosphorylated cMet in both unstimulated and stimulated cells without affecting total cMet, phosphorylated ALK, and ALK receptor levels (Figures 3B, C). Ceritinib treatment showed no change in p-cMet post-stimulation compared to unstimulated and when stimulated by HGF in both cells lines and had no effect on total cMet. Capmatinib treatment inhibited both untreated and treated phospho-cMet. ALK was not phosphorylated by HGF, and its baseline phosphorylation was not affected by crizotinib, ceritinib, or capmatinib (Figure 3D).

We then investigated the effects of crizotinib, ceritinib, capmatinib, and gemcitabine–cisplatin combinations in five CCA cell lines. Gemcitabine and cisplatin as single treatments or in combination showed little effect on cell growth (Figure 3E; Supplementary Figure 2A) with IC₅₀ ranging from 16 to 150 μM (Supplementary Figure 2B, S2 Table 2²). Ceritinib was cytotoxic, in a dose-dependent manner (IC₅₀ ranging from 1 to 9 μM), as was crizotinib (IC₅₀ 3.7–11.6 μM) whereas capmatinib was not particularly cytotoxic (IC₅₀ 19.51–246 μM, Figure 3F). To determine whether ceritinib inhibitors in addition to existing therapies would be more advantageous, we examined the effect of ceritinib in the presence of current chemotherapeutic agents gemcitabine and cisplatin combinations (Figure 3G). We first confirmed the sensitivity of the cells to ceritinib to be more sensitive than gemcitabine and cisplatin treatment (ceritinib IC₅₀ 1–9 μM, gemcitabine and cisplatin IC₅₀ 16–148.9 μM). Combinations of ceritinib and gemcitabine and cisplatin proved to be even more cytotoxic in a dose-dependent manner (IC₅₀ 0.60–2.32 μM) (Figure 3G; S2 Table 2.0²) with CI value towards synergism (Supplementary Figure 2C). These results indicate that the growth inhibitory effects of gemcitabine and cisplatin can be enhanced by the addition of ceritinib in CCA.

Table 2 | Sensitivity of cholangiocarcinoma cell lines to treatment.

Sensitivity of inhibitors in primary CCA cells

We then assessed the response of crizotinib, ceritinib, and capmatinib in four primary CCA cell cultures isolated from patients, in a 3D tumor growth assay, in the presence of human mesenchymal cells (MSCs). Cells were grown in 3D and treated, and cell survival was measured (Figures 4A, B). While all four patient-derived cell cultures tested showed some sensitivity to gemcitabine and cisplatin treatment that was at the same level or below the mean peak serum concentration (mPSC) for patients undergoing treatment with these agents [intravenous infusion over 30 min gemcitabine (1,000 mg/m²) and 2 h for cisplatin (25 mg/m²)], the addition of MSCs increased the resistance to the combined treatments and in four cases above the mPSC, indicating that while cells from these two tumors appear to be sensitive without mesenchymal support, they would be insensitive to treatment clinically (Figures 4B–D).

Figure 4 | Sensitivity of inhibitors in primary cell derived from cholangiocarcinoma patients in 3D tumor growth assays (TGA). Four primary cell lines were derived from surgically resected CCA tissue samples: CCA-UK5, CCA-UK6, CCA-UK7, and CCA-UK9 (A). Sensitivity of primary cells to gemcitabine (B), cisplatin (C), and combinations of gemcitabine and cisplatin (D) on four primary cell lines. Sensitivity of primary cells to capmatinib (E), crizotinib (F), and ceritinib (G) with and without co-culturing with humanized mesenchymal cells (+Mscs). Paired t-tests. Sensitivity of primary cells to crizotinib, ceritinib, and capmatinib (H). All cell survival growth was measured by AlamarBlue. The mean ± 20% peak serum concentration for patients is given as the dotted and shaded lines. Heatmap of mean IC₅₀ relative to mPSC levels (I).

Cells were insensitive to capmatinib and crizotinib, with higher IC₅₀s than the mPSC, and addition of MSCs made them even less sensitive (i.e., more resistant) (Figures 4E, F). In contrast, all four primary cells were sensitive (IC₅₀ less than mPSC) to ceritinib treatment and borderline sensitive when co-cultured with MSCs (Figure 4G), suggesting the potential for some clinical response. An example of one primary cell culture is given in Figure 4H, and a heatmap of sensitivity is provided in Figure 4I. These results show that the majority of the primary CCA cells are sensitive to ceritinib, even in a more tumor-relevant setting.

Mechanistic insight into the role of ceritinib treatment in CCA

To determine the possible mechanism of action for ceritinib treatment, we measured the levels of apoptosis and induction of autophagy post manipulation of ALK, cMet, and ROS receptor.

Apoptosis was determined by measuring caspase 3/7 using caspase Glo assay (Figure 5A) and Annexin V staining (Figure 4B) in five cell lines treated with crizotinib, ceritinib, and capmatinib. Increased caspase activity was observed in one cell line (KKU-M055) post crizotinib, and was increased in RBE cells post-ceritinib treatment relative to vehicle-treated cells, and no caspase difference was observed in capmatinib-treated cells relative to vehicle-treated cells (Figures 5A, B; Supplementary Figure 3A).

Figure 5 | Levels of apoptosis post-drug treatment and receptor inhibition. Apoptosis was determined in crizotinib-, ceritinib-, and capmatinib-treated cells by cleave caspase 3/7 glo (A). Apoptotic cells were determined by Annexin V and PI in untreated control, vehicle, crizotinib, ceritinib, and capmatinib and the percentage of apoptotic cells were measured (B). Absolute expression of ALK and MET post-siRNA knockdown of cMET, ALK, and ROS1 (C). Caspase activity of single knockdown (D) and of multiple siRNA knockdowns as demonstrated in the panel (E). Data were measured for significance by one-way ANOVA with Dunnett’s multiple comparison post-test. *p < 0.05, **p < 0.01, and ***p < 0.001 compared to vehicle and NT controls.

The effect of apoptosis post-siRNA knockdown of ALK, Met, and ROS manipulations as single knockdowns was determined. ddPCR revealed that Non-Target and Sham treated cells had similar levels of expression of cMet and ALK (Figure 5C) to vehicle-treated cells. Transfected cells showed 90% knockdown for cMet and ALK in all cell lines by their respective siRNA. Combined cMet and ROS1 knockdown also induced a reduction in ALK expression (Figure 5C).

siRNA knockdown of cMet, ALK, and ROS1 did not increase caspase activity in any cell line, and reduced it in one cell line (Figure 5D; Supplementary Figure 3B). Combined knockdown of ALK and Met augmented a small increase in the levels of apoptosis. However, combined knockdown of cMet and ROS1 had no effect on the levels of apoptosis. Combined knockdown of ALK and ROS1 significantly increased levels of apoptosis in one cell line and so did triple knockdown (Figure 5E; Supplementary Figure 3C). These results suggest that apoptosis was not being induced as a general mechanism of cell death in the CCA lines.

To investigate alternative cell death pathways induced post-treatment with ceritinib, crizotinib, and capmatinib, using phase contrast microscopy, we observed ceritinib-induced changes in the cell morphology and while vehicle-treated control cells have the typical cuboidal cell morphology, ceritinib-treated cells lost their cuboidal shapes and cell–cell contact and developed an elongated shape with cytoplasmic extensions. Ceritinib treatment induced an accumulation of vacuoles and dilation of autolysosomes (Figure 6A), suggestive of a possible autophagy mechanism. The turnover of autophagosomal marker levels LC3B-1, II, and p62 was examined by immunoblotting post-treatment with ceritinib, crizotinib, and capmatinib. Ceritinib treatment induced the conversion of LC3B-I to its lapidated form, LC3B-II. LC3B-II accumulation was present in ceritinib-treated cells. Furthermore, p62 levels were unaffected (Figures 6B–F). These results suggest that autophagic activity could be a mechanism of cell death induced post-treatment with ceritinib. To test this, we determined whether the effect could be reversed using an autophagy flux specific inhibitor bafilomycin. KKU-M213 cells and RBE cells were pre-treated with bafilomycin and then subjected to ceritinib, crizotinib, and capmatinib treatments and cell survival was determined. Bafilomycin partially reversed the effect of ceritinib (Figure 6G). In contrast, bafilomycin enhanced the effect of crizotinib treatments in KKU-M213 and RBE cells and had no effect on capmatinib-treated cells (Supplementary Figures 4A–C).

Figure 6 | Autophagy induction by ceritinib treatment. Crizotinib-, ceritinib-, and capmatinib-treated KKU-M213 and RBE cells. Vacuolation, elongated cells with little cell–cell interaction demonstrated with black arrow (A). Western blot analysis of autophagy markers in CCA cells lines KKU-M213 and RBE after treatment with crizotinib, ceritinib, and capmatinib (B–F). KKU-M213 cell viability after ceritinib or ceritinib+bafilomycin treatment at 24 h (G). Full Western blot images are in Supplementary Figure 3D. Data were measured for significance with one-way ANOVA with Dunnett’s multiple comparison post-test. *p < 0.05, **p < 0.01, and ***p < 0.001 relative to vehicle-treated cells.

Predictive targets and pathways of ceritinib treatment

A Venn diagram of predicted targets of crizotinib, ceritinib, and capmatinib reveals 17 targets common to crizotinib- and ceritinib-treated cells (Figures 7A, B). An increased gene signature of these 17 targets in TCGA databases was involved compared to normal bile duct (Figure 7C). High enrichment for 10 pathways were found especially for PI3K, pathways in cancer, and RAS signalling pathway (Figure 7D).

Figure 7 | Predictive targets and pathways of crizotinib and ceritinib mechanism of action in CCA. Venn diagram of predicted targets of crizotinib, ceritinib, and capmatinib. Canonical SMILES structures of crizotinib, ceritinib, and capmatinib were acquired from PubChem database. Using the SMILES structures, target prediction was performed by SwissTargetPrediction by Swiss Institute of Bioinformatics (A). A total of 17 common predicted targets of crizotinib and ceritinib excluding predicted targets of capmatinib (B). Gene signature expression patterns of common 17 target genes in CCA (TCGA) database compared to normal bile duct, analyzed on GEPIA2.0. (D) KEGG_pathway enrichment of 17 target genes by Enrichr (C, D). *P < 0.05.

Discussion

Treatment of bile duct cancer still has a very poor prognosis. Demonstration of a statistically significant increase in survival with gemcitabine and cisplatin was the first identification of a chemotherapeutic approach in patients, and recent studies have shown that addition of immunotherapy (durvalumab) can also enhance survival, but still 80% of patients succumb even on triple treatment. A number of targeted therapies have failed in patients including sorafenib (VEGFR and other tyrosine kinases) (31), erlotinib (EGFR inhibitor) (32), afatinib, (ERB2 antagonist) (33), and cabozantinib (VEGFR/MET inhibitor) (34), but the only effective targeted kinase inhibitors are those targeting FGFR in those small proportion of patients with FGFR fusions, translocation, or activating mutations (35, 36). Therefore, the need for targeted treatment in patients with CCA is still high, and identification of targets for kinase inhibitors is still required.

Our studies show that cMet and ALK and their active phosphorylated receptors (pcMet and pALK) are overexpressed in Thai and UK CCA tissue, UK primary cells, and different Thai CCA cell lines irrespective of their OV status. ROS1 expression was barely detectable, but high levels of ROS1 expression have been shown to be an indicator of better disease-free survival, indicating that the patient samples may be aggressive cholangiocarcinomas (37). However, CCA primary cell treatment with crizotinib or the cMet inhibitor capmatinib in 3D culture showed insensitivity (i.e., resistance) and the addition of humanized mesenchymal cells showed further insensitivity to crizotinib treatment. This insufficient effect of crizotinib suggests a potential lack of efficacy in vivo. The use of close-to-patient models (primary derived cells) in combination with humanized mesenchymal cells provides a more clinically relevant test of drug efficacy than using immortalized cells grown in 2D on plastic, which over time will have resulted in a drift from the parental original clone (38). It has been shown that cancer cells derived from patient tissue are more phenotypically and genetically related than immortalized cells (22) and their behavior in the presence of mesenchymal cells more closely reflects the tumor microenvironment (39).

To determine the causative mechanism of this action, we employed the cMet-specific inhibitor, capmatinib, and an ALK/ROS inhibitor, ceritinib. Capmatinib had no effect on cell survival in CCA cell lines or primary cells derived from CCA patient tissue. Capmatinib proved to be less potent than current chemotherapy regimens of gemcitabine and cisplatin treatments, had no effect on apoptosis, and was excluded for further progression within the study.

Surprisingly, ceritinib, which is an ALK inhibitor, did have a significant inhibitory effect on CCA cells. ALK is a highly conserved receptor tyrosine kinase belonging to the insulin receptor superfamily (40). Aberrant ALK expression has been documented in CCA, but the use of an ALK specific inhibitor has not been investigated.

We show that treatment with ceritinib dose-dependently inhibited growth and survival response of a panel of CCA cell lines more potently than crizotinib, capmatinib, and combinations of gemcitabine and cisplatin, without inhibiting cMet phosphorylation. The sensitivity to ceritinib was associated with the expression of ALK. We then determined the efficacy on growth inhibition of the combined treatment with gemcitabine, cisplatin, and ceritinib. We found an additive effect of ceritinib to the combinations of gemcitabine and cisplatin. We show that all primary cell lines were highly sensitive to ceritinib treatment in 3D culture with IC₅₀ values lower than the mean peak serum concentration levels. Although co-culturing them with humanized mesenchymal cells increased the IC₅₀ levels, they were still at or lower than the mean peak serum concentration levels, suggesting a more likelihood of translation through to positive response clinically. The results here suggest that the ALK inhibitor ceritinib is a promising treatment in CCA alone or in combinations with gemcitabine and cisplatin.

The mechanism of effect for ceritinib in CCA is unknown. Our studies show that treatment with ceritinib did not induce apoptosis whereas crizotinib induced apoptosis only in one cell line. Apoptosis levels were measured after single knockdown of cMet, ALK, and ROS1 receptors inhibiting the levels of apoptosis. Dual knockdown of receptors showed no increase in levels of apoptosis except for one cell line. Crizotinib is a multitarget inhibitor (41) and the knockdown of all three receptors showed no induction of apoptosis. This could be because crizotinib exerts its effects through the modulation of the growth, migration, and invasion of malignant cells.

However, following treatment with ceritinib, the cell morphology changed to elongated cells with fewer cell–cell interactions, higher recruitment of vacuoles in the cytoplasm, and dilation of autolysosomes, consistent with an autophagic mechanism (42). Ceritinib treatment also indicated a conversion of LC3B-I to its lipidated form, LC3B-II, compared to vehicle-treated without any further changes in p62 levels, suggesting that ceritinib exerts its effects through autophagy, and this treatment highlighted 17 downstream targets that could prove to be interesting downstream targets.

ALK receptors have been shown to be the primary driver of several cancers including anaplastic large cell lymphoma, lung cancer, and neuroblastoma as a consequence of fusion with oncogenes, gene amplification, and protein overexpression (10, 16, 43). Ceritinib is FDA-approved for NSCLC patients with ALK and ROS1 rearrangements. These rearrangements occur in 1% of patients and are rare. However, our studies show for the first time that ceritinib is a potent inhibitor for CCA irrespective of the rearrangement status, subtype specificity, and OV infection status. This could be due to its structurally distinct second-generation confirmation or that anaplastic lymphoma kinase could be the primary driver of cholangiocarcinoma disease and progression. Despite rapid and dramatic effects of crizotinib in other cancer types and its ineffectiveness on cholangiocarcinoma, a common concern of target therapy is the development of resistance, and in the case of NSCLC, resistance to crizotinib inevitably occurs with years of treatment. Crizotinib resistance occurs due to mutations of ALK and ROS (6). Resistance to ceritinib is certainly possible but has not yet been shown.

Conclusions

In conclusion, we demonstrate for the first time the potential therapeutic benefits of ceritinib in combination with gemcitabine and cisplatin, which exert anti-tumor activity in cholangiocarcinoma.

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 humans were approved by UK National Research Ethics Service. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

KY-U, RT, KM, AG, and DB: concept and design. KY-U, KM, MS-C, PC, BB, and SV: methodology and data acquisition. KY-U, KM, MS-C, PC, BB, SV, and AG: analysis and interpretation of data (e.g., image analysis, statistical analysis etc.). KY-U, DB, AG, RT, and KM: writing, review and revision of the manuscript. KY-U, DB, and RT: study supervision. All authors contributed to the article and approved the submitted version.

Funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work is supported by the Thailand Research Funds project No. DBG 5980006, the Medical Research Council UK, Newton Fund MR/N01247X/1, Rajavithi Hospital Research Fund, “Young Research development program 2018” from the National Research Council of Thailand (NRCT), and Research Assistantship, Faculty of Graduate Studies, Mahidol University, and Nottingham University NHS trust.

Acknowledgments

We would like to thank Dr. John Gordan for sharing cell lines.

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.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2023.1184900/full#supplementary-material

Supplementary Figure 1 | sensitivity if CCA cells lines to treatment of MET, ALK and ROS1 inhibitors. The percentage of cell survival of CCA cell lines treated with increasing doses of Gemcitabine and cisplatin combinations in KKU-M055 and TFK-1 cells (A), Crizotinib, Ceritinib, capmatinib and combinations of Gemcitabine and Cisplatin treated KKU-M055, KKU-M213 and TFK-1 cell lines (B). confidence interval measured for Ceritinib and gemcitabine and cisplatin co incubations (C).

Supplementary Figure 2 | levels of apoptosis post drug treatment and receptor inhibition. Apoptosis was determined by cleave caspase 3/7 glo in HuCCA-1 and TFK-1 cell lines (A). Caspase activity of single knockdown (B) and of multiple siRNA knockdowns as demonstrated in the figure (C) in HuCCA1 and TFK-1 cell lines. Data was measured for significance by one-way ANOVA with Dunett’s multiple comparison post test. *p < 0.05, **p < 0.01 and ***p < 0.001 compared to vehicle and NT controls.

Supplementary Figure 3 | supporting info: whole blots for western blot analysis. Full western blot analysis for expression for protein expression of C-MET and p-cMET and ALK and p-ALK (A). Western blot analysis of inhibition of HGF-induced receptor activation of cMET in HuCCA1 (B) KKU-213 and RBE (C). Western blot analysis of autophagy markers in KKU-M213 and RBE cell lines after treatment with crizotinib, ceritinib and capmatinib (D).

Abbreviations

3D TGA, 3D tumor growth assay; 5-FU, 5-Fluorouracil; ALK, Anaplastic lymphoma kinase; BME, Basement membrane extract; CCA, Cholangiocarcinoma; cMet, Hepatocyte growth factor receptor; dd-RTPCR, Digital droplet reverse transcription polymerase chain reaction; EGFR, Epidermal growth factor receptor; FFPE, Formalin-fixed and paraffin-embedded; FGFR, Fibroblast growth factor receptor; IHC, Immunohistochemistry; LCB1, Microtubule-associated protein 1A/1B; NSCLC, Non-small cell lung cancer; Ov, Opisthorchis viverrini; PI3K, Phosophoinositide 3-kinase; RAS, Rat sarcoma virus; ROS1, ROS proto-oncogene 1 receptor tyrosine kinase; SEM, Standard error of the mean; siRNA, Small interfering ribonucleic acid; VEGFR, Vascular endothelial growth factor receptor.

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CASE REPORT
published: 24 January 2024
doi: 10.3389/fonc.2024.1292319

Treatment with palbociclib and tislelizumab for CDKN2A-mutated and PD-L1-positive advanced intrahepatic cholangiocarcinoma: a case report and literature review

Yajun Liang ¹, Liya Hu ², Huanlei Wu ², Tiejun Yin ² and Jun Zhang ²,³*

¹ IV Ward of Pulmonary and Critial Care Medicine, Wuhan Pulmonary Hospotal, Wuhan, Hubei, China, ² Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China, ³ Department of Internal Medicine, The Fifth People’s Hospital of Jingzhou, Jingzhou, Hubei, China

Edited by:
Zongli Zhang, Qilu Hospital of Shandong University, China

Reviewed by:
Oriol Mirallas, Vall d’Hebron University Hospital, Spain
Yongsheng Xiao, Fudan University, China

*Correspondence:
Jun Zhang
zhangjunlyj@163.com

Received: 11 September 2023

Accepted: 02 January 2024

Published: 24 January 2024

Citation:
Liang Y, Hu L, Wu H, Yin T and Zhang J (2024) Treatment with palbociclib and tislelizumab for CDKN2A-mutated and PD-L1-positive advanced intrahepatic cholangiocarcinoma: a case report and literature review. Front. Oncol. 14:1292319. doi: 10.3389/fonc.2024.1292319

Background

Intrahepatic cholangiocarcinoma (ICC) is the second most common primary liver malignancy with a steadily increasing incidence worldwide. ICC has insidious onset, rapid progression, and poor prognosis. More multidisciplinary clinical studies are needed to continuously explore safer and more efficient diagnosis and treatment modes for ICC.

Methods and results

A 66-year-old female patient with ICC rapidly developed systemic multiple metastases after surgery, and the first-line two-drug combination chemotherapy was not effective. Due to cyclin-dependent kinase inhibitor 2A mutation and programmed cell death-ligand 1-positive, a partial response and progression-free survival of 9.5 months were achieved after a second-line treatment with cyclin-dependent kinase 4/6 inhibitor (CDK4/6i) combined with immunotherapy. The patient developed thromboembolism 7 months after treatment and died due to disseminated intravascular coagulation.

Conclusion

The combination of targeted and immune therapy has revealed a potentially effective regimen for the effective treatment of patients with ICC, which needs to be observed in larger clinical studies. The thromboembolism rates in real-world patients treated with CDK4/6 inhibitors are higher than those reported in clinical trials, and the application of prophylactic anticoagulation in this patient population may be questionable.

Keywords: intrahepatic cholangiocarcinoma, targeted therapy, CDKN2A, immunotherapy, case report, literature review

Introduction

Intrahepatic cholangiocarcinoma (ICC) refers to tumors originating from the epithelium of the secondary bile duct and its branches and is a relatively rare malignant tumor in humans (1). The patients with cholangiocarcinoma in our country account for 55% of the cases worldwide. The peak incidence is from 55 to 75 years old, and the male-to-female ratio is about 2:3 (2). Histopathological types include adenocarcinoma, adenosquamous carcinoma, squamous cell carcinoma, mucinous carcinoma, and so on. Most of them are adenocarcinomas with varying levels of differentiation. Surgery is the preferred treatment to prolong the survival of ICC patients, but most patients have lost the opportunity to be operated on when they are found. The postoperative recurrence rate is as high as 70%, so the prognosis is very poor, and the 5-year overall survival rate is only 20% (3–5). Patients with advanced ICC have poor efficacy with a single treatment. The multi-disciplinary treatment (MDT)-based diagnosis and treatment mode combined with chemotherapy, targeted therapy, immunotherapy, and other treatment methods has become an important strategy to prolong the survival of ICC patients. Therefore, we report an ICC patient with CDKN2A mutation and PD-L1-positive expression who achieved partial response (PR) with a progression-free survival (PFS) of 9.5 months after a second-line therapy with cyclin-dependent kinase 4/6 (CDK4/6) inhibitor-targeted therapy (palbociclib) combined with immunotherapy (tislelizumab).

Case presentation

A 66-year-old female patient went to the hospital on September 1, 2020 for a medical checkup, and an upper abdominal computed tomography (CT) scan revealed solid nodules in the right lobe of her liver. The carcino-embryonic antigen (CEA) level was 18 ng/mL (normal value: ≤5 ng/mL), and the carbohydrate antigen 19-9 (CA19-9) level was 2,865 U/mL (normal value: ≤34 U/mL). The positron emission tomography/computed tomography (PET/CT) result showed that the solid mass in the right lobe of the liver had a high metabolism (SUVmax: 12.3), indicating malignant neoplastic lesions. No obvious signs of malignant tumors were found in the other areas examined. Segmental hepatectomy was performed on September 9, 2020, and the postoperative pathological examination suggested that the liver lesion was poorly differentiated adenocarcinoma (CCA). Immunohistochemical (IHC) staining was positive for CK7 and CK19. However, arginase 1, GS, glypican-3, hepatocyte, CD31, CD34, and ERG were negative. The positive predictive value of Ki-67 was approximately 70% (Figure 1). The postoperative stage of the patient was pT1bN0M0 (stage IB). Because the tumor was in its early stage, the patient did not receive adjuvant therapy after surgery.

Figure 1 | Postoperative pathological examination of the reported case. (A) Hematoxylin and eosin (H&E), ×200. (B) H&E, ×100.

The patient developed abdominal pain and distension on October 28, 2020, and systemic imaging revealed rapid tumor progression. The chest CT examination showed scattered solid and subsolid density nodules in both lungs, suggesting intrapulmonary metastasis. The mediastinal lymph nodes were increased and enlarged, and metastasis was considered according to the history. The interlobular septa of both lungs were thickened, and lymphatic invasion was suspected. Liver magnetic resonance imaging (MRI) suggested multiple metastatic tumors in the liver as well as metastatic nodules in the liver capsule and peritoneum. A bone scan revealed multiple bone metastases throughout the body, including the Th5, Th6, and Th10 vertebrae, the left ilium, sacrum, bilateral acetabulum, right femoral head, left femoral neck, and bilateral ischium. The CEA and CA19-9 values were 15.91 ng/mL and 3,074 U/mL, respectively. The patient started the first cycle of chemotherapy with AG regimen (abraxane: 130 mg/m² d1,8; gemcitabine: 1,000 mg/m² d1,8 q3w) on November 10 and developed grade IV neutropenia and fever which recovered after treatment with colony-stimulating factor.

On December 4, 2020, the patient developed limited tongue movement and numbness in the right jaw and facial area. The physical examination showed mild atrophy of the right tongue and poor movement, dull sensation in the right lower lip, and shallow sensation in the right peripheral trigeminal nerve and mandibular branch. The CEA and CA19-9 values were 14.98 ng/mL and 2,914 U/mL, respectively. She was diagnosed with right lingual paralysis (damage to the hypoglossal nerve) and numbness in her lower lip on the right side (damage to the mandibular branch of the trigeminal nerve). The patient was treated with vitamin B1 and B12 for two weeks, but there was no significant improvement in the numbness and discomfort. On December 21, the brain MRI re-examination revealed bilateral frontal lobe abnormal signal with enhancement. Abnormal bone signals were found around the sublingual nerve foramina of the right occipital condyle with irregular enhancement, unclear boundary, and limited diffusion. The size of the lesion was 25 × 9 mm, involving the sublingual nerve, and the potential for metastasis was taken into account. Based on the patient’s symptoms, signs, and imaging findings, the progression of the disease was assessed. Chemotherapy was ineffective, and the patient had a poor tolerance to previous treatments, so chemotherapy was discontinued. Surgical specimens were analyzed using next-generation sequencing techniques to identify possible molecular-targeted variants. The genomic profiling results were as follows: gene variation: CDKN2A p.W15, mutation frequency: 20.4%; and gene variation: BRIP1 p.A60Sfs, mutation frequency: 5.1%. In addition, the PD-L1 expression level of patient tumor tissue was detected, and the IHC staining results of tissue sections revealed a tumor proportion score (TPS) of 25% and a combined positive score (CPS) of 40% for PD-L1. The routine blood counts, coagulation function, liver function, and kidney function were normal. Based on the abovementioned results, the patient began to receive targeted therapy (palbociclib administered orally each day at a dose of 100 mg per day) and immunotherapy (tislelizumab administered 200 mg as an intravenous infusion over 30 min every 3 weeks). Brain MRI, liver MRI, and chest CT were reviewed every 2 months to evaluate changes in target lesions. RECIST criteria were used for the response evaluation. The patient provided consent, and the Institutional Review Board of Tongji Hospital approved this case study. On February 5, 2021, the patient reported a significantly reduced range of numbness in the right facial and mandibular region, and the tongue extension movement was free. A reexamination of the brain MRI showed no abnormal enhancement foci in the bilateral frontal lobes and no apparent diffusion-limited signal foci. The liver MRI suggested that the metastatic tumor in the right posterior lobe of the liver had significantly decreased in size compared to before. However, the presence of multiple nodules in the liver capsule and peritoneum was not clearly visible on the scan. The chest CT revealed that bilateral pulmonary nodules and enlarged lymph nodes were not clearly identified. The CEA and CA19-9 values were 3.25 ng/mL and 48.71 U/mL, significantly lower than before. The CEA level dropped to the normal range. The comprehensive assessment of the disease was PR, and immunotherapy and molecular targeted therapy were continued as planned. The re-examination in April and June 2021 showed that the brain enhancement focus was still gradually reduced, and the signal range of multiple bone abnormalities in the lumbar spine, sacral vertebra, and pelvis was slightly reduced than before (Figure 2).

Figure 2 | Radiological examination of the patient. (A) Liver MRI after one cycle of chemotherapy. (B) Liver MRI after combination therapy for 2 months. (C) Liver MRI after combination therapy for 4 months. (D) Liver MRI after combination therapy for 8 months. MRI, magnetic resonance imaging. The red arrow in the figure points to the metastatic tumor in the right posterior lobe of the liver.

By July 2, the patient complained of mild dysphagia, and a physical examination revealed swelling in the neck. The D-D dimer in blood coagulation function test was 11.52 ug/mL FEU, higher than the upper limit of normal value (0.5 ug/mL FEU) significantly. The patient’s routine blood counts, liver function, and kidney function were normal. A color ultrasound examination of the jugular vein indicated thrombus formation in the left internal jugular vein, subclavian vein, and innominate vein. The computed tomography angiography examination revealed small plaque-filled defects in grade 3–4 branches of the pulmonary artery in the upper and lower lobes of both lungs. Pulmonary embolism was diagnosed, and heparin anticoagulant therapy was given (Figure 3). The lesions were evaluated as stable disease by brain and liver MRI and chest CT in August 2021. The CEA and CA19-9 levels were normal. By September 18, the blood routine showed a platelet count of 60 * 10⁹/L↓, serum alanine aminotransferase was 821 U/L↑, total bilirubin was 30.0 umol/L↑, and fibrinogen was 0.63 g/L↓↓↓. The brain MRI revealed a strengthened nodule in the right occipital lobe with peripheral edema, which was considered a new metastatic tumor. The liver MRI showed a significant increase in retroperitoneal lymph nodes compared to the anterior. The pelvic MRI suggested increased retroperitoneal and bilateral para-iliac lymph nodes. The CEA level was normal, and the CA19-9 value was 44.19 U/mL. The disease was evaluated as progressive disease (PD). Considering that the patient had grade III bone marrow suppression, grade IV liver dysfunction, and efficacy was assessed as PD, the immune and targeted therapy, respectively, were discontinued, and symptomatic and supportive treatments such as liver protection were given. By September 24, the patient began to show symptoms of gastrointestinal, urinary, and vaginal bleeding successively and was diagnosed with disseminated intravascular coagulation (DIC). The patient’s condition gradually deteriorated, and she passed away by October 26 (Figure 4).

Figure 3 | The computed tomography angiography examination revealed plaque-filled defects in the branches of the pulmonary artery.

Figure 4 | Summary of the treatment timeline of this case, including the levels of carcinoembryonic antigen (CEA) and carbohydrate antigen 199 (CA19-9). SD, stable disease; PD, progressive disease; PR, partial response.

Discussion

Intrahepatic cholangiocarcinoma originates from intrahepatic bile duct epithelial cells. The incidence of ICC is only lower than that of hepatocellular carcinoma, accounting for 10% to 15% of primary liver cancer, and the incidence is significantly increasing worldwide (6). The onset of ICC is insidious, and there are often no special clinical symptoms in the early stage. ICC is often found incidentally in imaging examination as an isolated intrahepatic mass, and the disease progresses rapidly. The most common type was adenocarcinoma, and immunohistochemical staining of tumor cells was positive for EMA, CK7, and CK19 (7). CA19-9 and CEA are the most commonly used serum markers for the diagnosis of ICC. Although their specificity is not strong, they are still valuable for diagnosis and treatment. Retrospective study results showed that postoperative dynamic monitoring of CA19-9 had a reference value for evaluating tumor residual or recurrence and predicting the prognosis of patients (8).

Patients with advanced ICC are mainly treated with systemic therapy, and single therapy is not effective. Two-drug combined chemotherapy is the main first-line standard treatment (9). The results of several clinical trials showed that chemotherapy combined with immunotherapy is effective for patients with biliary tract tumors. Camrelizumab plus GEMOX demonstrated a promising antitumor activity and an acceptable safety profile as a first-line treatment in advanced biliary tract cancer patients (10). ICC is associated with a variety of genetic variants, including mutations or fusions, and the corresponding targeted therapy regimens for these variants have been demonstrated to be effective in patients with ICC in phase II and III trials. These targets include IDH, FGFR, NTRK, HER2, and others (11–14). In clinical practice, more attention should be given to genetic testing and molecular targeted testing. It is recommended to implement MDT in the entire treatment process, ensuring individualized and reasonable selection for patients. This includes the combined application of chemotherapy, targeted therapy, immunotherapy, and other treatment methods.

Tislelizumab is a PD-1 monoclonal antibody that was originally developed in China. Based on its unique molecular structure, it has shown excellent efficacy and good safety. By modifying the Fc segment, the binding ability of FcγR was removed, antibody-dependent cell-mediated phagocytosis (ADCP effect) was eliminated, the anti-tumor efficacy was avoided due to the reduction of the number of T cells, and the adverse reactions caused by macrophages were avoided. Because its Fab segment has a larger surface area containing the PD-1 binding site, it exhibits a higher affinity for PD-1 and effectively inhibits the binding of PD-1 and PD-L1. In addition, the half-life of tislelizumab was longer than that of similar drugs in the highest range. Furthermore, the IC50 and EC50 values of tislelizumab were in the lowest range among similar drugs, indicating its strong anti-tumor activity. Clinical studies have shown that tislelizumab combined with first-line chemotherapy in the treatment of potentially resectable locally advanced biliary tract malignant tumors has an objective response rate of up to 56%, a disease control rate of 92%, and a R0 resection rate of 52%, with controllable safety (15). In another study of tislelizumab in the treatment of advanced biliary tract cancers, the conversion rate was 23.1% and the 6-month overall survival rate was 90.9% (95% CI, 50.8%–98.7%) (16). Its potent tumor reduction, safety, and effectiveness are surprising.

The CDKN2A gene is a frequently mutated gene in malignant tumors, commonly experiencing deletions and mutations in various types of tumors, including breast cancer and lung cancer. When the CDKN2A gene is deleted or mutated, it can result in the abnormal activation of cyclin-dependent kinases. Therefore, cyclin-dependent kinase inhibitors like palbociclib may hold promise as a potential treatment option. The previous literature reported that the clinical trial of palbociclib monotherapy in patients with cholangiocarcinoma showed no response and poor efficacy, leading to the closure of the clinical trial (17). However, a successful case of PR has also been reported (18). Palbociclib can activate T cells, promote the immune system’s ability to kill tumor cells, and enhance the effectiveness of immunotherapy (19). It has been reported that the CDK4/6 pathway is feasible as a potential target for treatment of patients with bladder cancer, especially when used in combination with immunotherapy (20). A patient with advanced lung squamous cell carcinoma had the CDKN2A mutation and a tumor proportion score of 80%. The patient responded well to CDK4/6 inhibitor abemaciclib therapy after experiencing disease progression with multiple chemotherapies and immunotherapy. A subsequent rechallenge with combined immunotherapy using Opdivo and ipilimumab resulted in a durable partial response (21).

The main adverse effect of palbociclib is neutropenia, which can be effectively managed by reducing the dosage of palbociclib. Palbociclib-induced liver function abnormalities mostly manifest as asymptomatic elevation of aminotransferase levels and rarely result in grade 3 elevation of aminotransferase. Drugs used in clinical liver protection can be used to treat CDK4/6 inhibitor-induced liver function abnormalities. Venous thromboembolism (VTE) events include deep vein thrombosis, subclavian vein thrombosis, and pulmonary embolism. Thrombosis is the second leading cause of death in cancer patients. During treatment, it is necessary to pay attention to and prevent the occurrence of VTE, monitor the symptoms and indications of deep vein thrombosis and pulmonary embolism in patients, and timely consult the relevant departments and take drug treatment if there is any abnormality. If VTE is not life-threatening, anti-tumor therapy can be continued in combination with antithrombotic therapy. A retrospective cohort study included 424 patients, of whom palbociclib was the most commonly used CDK4/6 inhibitor (n = 390, 91.8%). Venous thromboembolism occurred during CDK4/6 inhibitor use in 38 of 390 patients, with a rate of 6.3% at the first year. DVT, PE, and VVT accounted for 52.6%, 18.5%, and 15.8%, respectively. In real-world studies, the rates of VTE in patients with metastatic breast cancer treated with CDK4/6 inhibitors were found to be two to five times higher than what was reported in registered trials (22). The other study included 266 patients, with 89% of them in the palbociclib group. Out of these, 26 women (9.8%) experienced thrombotic events. Of these, 72% were venous thromboembolism. The incidence of thrombosis after 1 year was 10.4% overall and 10.9% in the palbociclib group, which was higher than that reported in clinical trials. Arterial thrombosis accounted for more than one-third of events, with the highest rate in the palbociclib group (23).

In this case, a liver-space-occupying lesion was accidentally found during routine physical examination, and surgery was performed in time. The postoperative pathological examination confirmed the diagnosis of ICC. At 1 month after surgery, the disease recurred rapidly and multiple metastatic lesions occurred throughout the body. The patient had a poor tolerance to the first-line treatment, which consisted of a combination of two drugs along with AG chemotherapy. Additionally, the treatment showed poor efficacy. There were still multiple new metastatic lesions, indicating that the disease was extremely malignant and progressed rapidly. According to the NGS test results, the patient switched to immunotherapy (tislelizumab) in combination with targeted therapy (palbociclib), which resulted in a rapid response. The patient’s discomfort symptoms were gradually relieved, and the blood levels of CA19-9 and CEA gradually decreased to normal. The imaging evaluation showed a PR 2 months later, and the PFS was as long as 9.5 months. Unfortunately, the patient developed thrombosis in the left internal jugular vein, subclavian vein, and innominate vein and pulmonary embolism 7 months after treatment. Despite receiving standard anticoagulant treatment, the effect was still not satisfactory. Later, there was a grade IV liver function abnormality and uncorrectable DIC, which occurred successively, and unfortunately, the patient was deceased in the end.

Concluding remarks

ICC is a highly aggressive tumor of the digestive system, with a low rate of early diagnosis, rapid disease progression, and an improved prognosis. MDT is an ideal treatment mode to further improve the efficacy of ICC, and targeted therapy for ICC gene mutation combined with immunotherapy has become an alternative second-line treatment. This is the first report of palbociclib combined with tislelizumab for advanced metastatic intrahepatic cholangiocarcinoma. The identification of targetable molecular signatures and the combination of targeted therapy and immunotherapy reveal a potentially option for the effective treatment of patients with intrahepatic cholangiocarcinoma and should be considered after the failure of standard chemotherapy, but larger clinical studies are needed to make further observations. The thromboembolism rates in real-world patients treated with CDK4/6 inhibitors are higher than those reported in clinical trials, and the role of prophylactic anticoagulation in this patient population may be questionable.

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 humans were approved by the Institutional Research Ethics Committee of Tongji Hospital. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants’ legal guardians/next of kin. Written informed consent was obtained from the minor(s)’ legal guardian/next of kin for the publication of any potentially identifiable images or data included in this article.

Author contributions

YL: Writing – original draft. LH: Writing – original draft. HW: Writing – original draft. TY: Writing – review & editing. JZ: Writing – review & editing.

Funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was financially supported by the Natural Science Foundation of Hubei Province of China (Grant No. 2021CFB355).

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.

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