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Original Research ARTICLE

Front. Oncol., 08 November 2019 | https://doi.org/10.3389/fonc.2019.01214

The Tumor Suppressor Role of Zinc Finger Protein 671 (ZNF671) in Multiple Tumors Based on Cancer Single-Cell Sequencing

Jian Zhang1,2, Jianli Luo3, Huali Jiang4, Tao Xie1,2, Jieling Zheng5, Yunhong Tian1,2, Rong Li1,2, Baiyao Wang1,2, Jie Lin1,2, Anan Xu1,2, Xiaoting Huang1,2 and Yawei Yuan1,2*
  • 1Department of Radiation Oncology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
  • 2State Key Laboratory of Respiratory Diseases, Guangzhou Institute of Respiratory Disease, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
  • 3Department of General Disease, Health Center of Shuichun Town, Shanwei, China
  • 4Department of Cardiovascularology, the Affiliated Donghua Hospital of Sun Yat-sen University, Guangzhou, China
  • 5Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China

In humans, zinc finger protein 671 (ZNF671) is a type of transcription factor. However, the contribution of tumor heterogeneity to the functional role of ZNF671 remains unknown. The present study aimed to determine the functional states of ZNF671 in cancer single cells based on single-cell sequencing datasets (scRNA-seq). We collected cancer-related ZNF671 scRNA-seq datasets and analyzed ZNF671 in the datasets. We evaluated 14 functional states of ZNF671 in cancers and performed ZNF671 expression and function state correlation analysis. We further applied t-distributed stochastic neighbor embedding to describe the distribution of cancer cells and to explore the functional state of ZNF671 in cancer subgroups. We found that ZNF671 was downregulated in eight cancer-related ZNF671 scRNA-seq datasets. Functional analysis identified that ZNF671 might play a tumor suppressor role in cancer. The heterogeneous functional states of cell subgroups and correlation analysis showed that ZNF671 played tumor suppressor roles in heterogeneous cancer cell populations. Western blot and transwell assays identified that ZNF671 inhibited EMT, migration, and invasion of CNS cancers, lung cancer, melanoma, and breast carcinoma in vitro. These results from cancer single-cell sequencing indicated that ZNF671 played a tumor suppressor role in multiple tumors and may provide us with new insights into the role of ZNF671 for cancer treatment.

Introduction

Cancer is a complex ecosystem composed of cells with heterogeneous functional states, leading to both therapeutic resistance, and frequent cancer recurrence or metastasis, which poses a major obstacle to cancer diagnosis and treatment (13). Some tumor cells have high proliferative or apoptotic capacity, some have invasion and metastasis activities, some show stem-like properties, and some exhibit a quiescent state (4, 5). These functionally heterogeneous cancer cells act cooperatively or competitively during tumor progression or metastasis, leading to distinct tumor phenotypes (68). Therefore, it is essential to systematically and comprehensively identify the functional states of cancer cells.

Single-cell mRNA-sequencing (scRNA-seq) provides a powerful tool for characterizing the omic-scale features of heterogeneous cell populations (9, 10). ScRNA-seq technologies permit the dissection of primary tumor cells, metastatic tumor cells, cancer stem cells (CSC), circulating tumor cells (CTC), and disseminated tumor cells in a comprehensive and unbiased manner, with no need of any prior knowledge of the cell population. ScRNA-seq has become a reference tool for analyzing the composition of cancer tissues and for establishing the characteristics of the cellular microenvironment (11). Thus, understanding single cancer cells will advance our understanding of not only therapeutic resistance but all facets of cell biology. Furthermore, the application of scRNA-seq in the clinic has the potential to change our approach to cancer management fundamentally (12).

Zinc finger protein 671 (ZNF671) is a member of the KRAB-ZF (KRAB-ZFP) family of mammalian transcriptional repressors (1315). KRAB-ZFPs can regulate tumor cell differentiation, proliferation, apoptosis, invasion, metastasis, and transformation (1621). Previous studies showed that ZNF671 could act as a tumor suppressor in several solid tumors (2226). Our studies identified that ZNF671 played a tumor suppressor role in breast invasive carcinoma (BRCA), cervical squamous cell carcinoma, and endocervical adenocarcinoma (CESC), head and neck squamous cell carcinoma (HNSC), kidney renal papillary cell carcinoma (KIRP), lung adenocarcinoma (LUAD), pancreatic adenocarcinoma (PAAD), and uterine corpus endometrial carcinoma (UCEC) (26, 27). However, the roles of ZNF671 in the functional heterogeneity of cancer single cells remain unclear.

In this study, we analyzed the expression of ZNF671 in cancer scRNA-seq datasets systematically. We explored the functional role of ZNF671 in solid tumors and analyzed its expression and functional correlation in tumors. We further described the distribution of cancer single cells and explored their functional relevance in different tumor cell subgroups. Our results provide important insights into tumor heterogeneity and enhance knowledge of the tumor suppressor role of ZNF671 in solid tumors.

Materials and Methods

Data Collection

Data were collected based on the following keywords: (“single cells” OR “single cell” OR “single-cell” OR “single-cells”) AND (“transcriptome” OR “transcriptomics” OR “scRNA-seq” OR “scRNA seq” OR “RNA-sequencing” OR “RNA-seq” OR”RNA sequencing”) AND (“carcinoma” OR “tumor” OR “tumor” OR “cancer” OR “neoplasm” OR “neoplastic”). According to the method used by Yuan et al. (28), three human data sets from Array Express, Sequence Read Archive (SRA), and Gene Expression Omnibus (GEO) datasets were collected and all single-cell data in these datasets were analyzed via expression quantification, quality control, and characterization of functional states.

Data Processing

Transcript expression quantification was performed using Salmon (version 0.9.1) with the optional parameter k (k = 31 for long reads and k = 15 for short reads). The GENCODE (Release 28, GRCh38) reference transcriptome was used to detect gcBias, seqBias, and other default parameters in the quasi-mapping-based mode. For scRNA-seq datasets with only an expression matrix, we directly converted the expression values to transcripts per million (TPM)/counts per million (CPM) values using a custom script. Expression values were log2 transformed with an offset of 1.

Characterizing Functional States of Cancer Single Cells

After reviewing cancer single-cell sequencing studies, 14 crucial functional states of cancer cells were selected, including angiogenesis, apoptosis, cell cycle, differentiation, DNA damage, DNA repair, epithelial–mesenchyme transition (EMT), hypoxia, inflammation, invasion, metastasis, proliferation, quiescence, and stemness using Gene Ontology, MSigDB, Cyclebase, HCMDB, and StemMapper (2933). According to the method used by Yuan et al. (28), the activities of the 14 functional states across cancer single cells in the datasets were assessed using the Gene Set Variation Analysis (GSVA) package downloaded from http://www.bioconductor.org (34).

Dimensionality Reduction Using t-distributed Stochastic Neighbor Embedding (t-SNE) Analysis

According to the method used by Li et al. (35), donor files were imported into R, and expression matrices containing measured intensities at the single-cell level were extracted from the flowCore package. A subset of cells was selected for each donor at random and merged into a single expression matrix before t-SNE analysis. The beads, viability, center, offset, residual, event length, intercalator, and time channels were removed from the expression matrix. The ZNF671 protein marker was the only factor included in the t-SNE analysis, and ZNF671 intensities were transformed using the inverse hyperbolic sine (arcsinh) function.

T-SNE calculations were performed with 1,000 iterations, a perplexity parameter of 30, and a trade-off θ of 0.5, which was used to visualize similarities and the proximity of cells in a two-dimensional plot. T-SNE maps were generated by plotting each event of the t-SNE dimensions in a dot-plot. ZNF671 intensities were overlaid on the dot-plot to show the expression in different cell islands and to facilitate the assignment of cell subsets to these islands. The t-SNE dimensions were characterized by t-SNE1 and t-SNE2 in the given graphs. The software is available at https://github.com/KlugerLab/FIt-SNE.

ZNF671 Expression and Functional State Correlation Analysis

The expression level statistics of ZNF671 in each cell were converted to normalized ranks and Next, the Kolmogorov–Smirnov liker random walk statistic, similar to the GSEA method, was used to summarize the ZNF671 expression-level rank statistics of a given signature gene set into a final enrichment score, which was used to characterize the signature activity. The enrichments of 14 signatures across cells in the scRNA-seq data were calculated, and only cells with detectable expression of ZNF671 were used. Correlations between ZNF671 expression and functional state activities were assessed using correlation analysis with false discovery rate (FDR) corrections for multiple comparisons (FDR < 0.05 and P < 0.05).

Cell Culture

Human GBM cell lines (U87 and U251), the A375 melanoma cell line, and triple-negative breast cancer cell lines (MDA-MB-231 and BT-549) were obtained from the American Type Culture Collection (ATCC, Manassas, VA, USA). Cells were maintained at 37°, 5% CO2 in 10% DMEM (Invitrogen, Carlsbad, CA, USA) supplemented with 10% fetal bovine serum.

Western Blot Analysis

After cells were transfected with the pEnter-ZNF671 or pEnter-vector plasmids (Vigene Biosciences, Shandong, China) for 48 h, RIPA lysis buffer (Beyotime, Shanghai, China) was used to isolate proteins. Proteins were separated by sodium dodecyl sulfate (SDS)-polyacrylamide gel electrophoresis (SDS-PAGE, Beyotime), transferred onto polyvinylidene fluoride (PVDF) membranes (Millipore, Billerica, MA, USA), and incubated with primary anti-ZNF671 (1:500; Proteintech, Chicago, IL, USA), E-cadherin (1:500, BD Biosciences), Vimtenin (1:500, BD Biosciences), and anti-GAPDH (1:1,000, Proteintech, Chicago, IL, USA).

Migration and Invasion Assays

Transwell plates (8-μm pores) (Costar/Corning, Lowell, MA) were used for Transwell migration or invasion assays. 5 × 104 (migration assay) or 1 × 105 (invasion assay) cells resuspended in serum-free medium were placed in the upper chamber of each insert, either uncoated or coated with Matrigel (BD Biosciences). The lower chamber contained culture medium with 10% FBS to act as a chemoattractant. The cells were incubated for 12 or 24 h and were then fixed and stained. Cells on the undersides of the filters were observed and counted under 200× magnification.

Statistical Analysis

Statistical analysis was performed using SPSS version 17.0 (SPSS Inc., Chicago, IL, USA). Differences between two groups were analyzed using the two-tailed unpaired Student's t-test; P < 0.05 was considered statistically significant.

Results

scRNA-seq Dataset Features

As shown in Table 1, a total of eight cancer-related ZNF671 scRNA-seq datasets were included in the study. They contained 14 functional states of 13941 cancer single cells from glioblastoma (GBM; n = 623), glioma (brain; n = 2259), glioma (PDX; n = 167), astrocytoma (AST; n = 5097), oligodendroglioma (ODG; n = 4043), lung adenocarcinoma (LUAD; n = 126), melanoma (MEL; n = 1257), and breast cancer (BRCA; n = 369).

TABLE 1
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Table 1. Features of the scRNA-seq datasets searched with ZNF671.

ZNF671 Functional States in the scRNA-seq Datasets

Expression analysis showed that ZNF671 was obviously downregulated in GBM, glioma, AST, ODG, LUAD, MEL, and BRCA (Figure 1), which indicated that ZNF671 might play an important role in tumor progression. To further explore the functional role of ZNF671 in different cancers, 14 crucial functional states of cancer cells, including angiogenesis, apoptosis, cell cycle, differentiation, DNA damage, DNA repair, EMT, hypoxia, inflammation, invasion, metastasis, proliferation, quiescence, and stemness were summarized and analyzed. As shown in Figure 2, the expression of ZNF671 and the activity of each functional state across single-cell datasets in different cancers were explored using an interactive bubble chart. The upper bar plot shows a summary of the association between the functional state and the number of single-cell datasets. We found that the expression of ZNF671 had a significant negative regulation for angiogenesis, apoptosis, EMT, hypoxia, invasion, and quiescence, which was consistent with our previous research (26, 27). These results indicate that ZNF671 might play a suppressor role in tumor development.

FIGURE 1
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Figure 1. ZNF671 is downregulated in primary solid tumors. Box diagram indicates the expression distribution of ZNF671 in cells in the scRNA-seq datasets. Glioblastoma (GBM), Glioma (Brain), Glioma (PDX), Astrocytoma (AST), Oligodendroglioma (ODG), Lung adenocarcinoma (LUAD), Melanoma (MEL), and Breast cancer (BRCA). **p ≤ 0.01 compared with the control using Student's t-test.

FIGURE 2
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Figure 2. Relevance of ZNF671 across 14 functional states in distinct cancers. The upper bar chart shows the number of datasets in which ZNF671 is significantly related to the corresponding state. In the bubble chart in the second section, a results table is used to display the basic information of all single-cell datasets in the selected cancer type and the corresponding correlations with the 14 functional states. Glioblastoma (GBM), Glioma (Brain), Glioma (PDX), Astrocytoma (AST), Oligodendroglioma (ODG), Lung adenocarcinoma (LUAD), Melanoma (MEL), and Breast cancer (BRCA).

The Different Roles of ZNF671 in Cancers

We next explored the functional roles of ZNF671 in cancers, and analyzed the correlation between ZNF671 expression and functional state. We found that ZNF671 was positively associated with DNA damage (R = 0.18; ***P < 0.001), apoptosis (R = 0.13; *P < 0.05), DNA repair (R = 0.10; *P < 0.05) in GBM; with stemness (R = 0.11; *P < 0.05) and inflammation (R = 0.06; *P < 0.05) in AST; with proliferation (R = 0.29; **P < 0.01), quiescence (R = 0.23; *P < 0.05), and differentiation (R = 0.21; *P < 0.05) in MEL; with inflammation (R = 0.17; ***P < 0.001), metastasis (R = 0.16; ***P < 0.001), stemness (R = 0.15; **P < 0.01), hypoxia (R = 0.13; **P < 0.01), EMT (R = 0.13; *P < 0.05), and differentiation (R = 0.08; *P < 0.05) in ODG; with EMT (R = 0.12; *P < 0.05) and hypoxia (R = 0.12; *P < 0.05) in glioma (brain); and with stemness (R = 0.18; *P < 0.05), and hypoxia (R = 0.18; *P < 0.05) in BRCA (Figures 3, 4).

FIGURE 3
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Figure 3. Functional relevance of ZNF671 in primary solid tumors. ZNF671 plays different functional states in different single-cell datasets. Glioblastoma (GBM), Glioma (Brain), Glioma (PDX), Astrocytoma (AST), Oligodendroglioma (ODG), Lung adenocarcinoma (LUAD), Melanoma (MEL), and Breast cancer (BRCA). ***p ≤ 0.001; **p ≤ 0.01; *p ≤ 0.05 compared with the control using Student's t-test.

FIGURE 4
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Figure 4. Correlations between ZNF671 mRNA expression and functional states in primary solid tumors. The detailed functional relevance in each specific cell group of a specific scRNA-seq dataset is shown. Glioblastoma (GBM), Glioma (Brain), Glioma (PDX), Astrocytoma (AST), Oligodendroglioma (ODG), Lung adenocarcinoma (LUAD), Melanoma (MEL), and Breast cancer (BRCA).

However, ZNF671 was negatively associated with DNA damage in ODG (R = −0.11; **P < 0.01); with hypoxia (R = −0.55; *P < 0.05), EMT (R = −0.50; *P < 0.05), apoptosis (R = −0.49; *P < 0.05), angiogenesis (R = −0.48; *P < 0.05), and quiescence (R = −0.43; *P < 0.05) in glioma (PDX), and with inflammation (R = −0.20; *P < 0.05) and differentiation (R = −0.18; *P < 0.05) in BRCA (Figures 3, 4). These results indicated that ZNF671 plays a different functional role in cancers and that the functional difference could be associated with the functional populations of cancer cells.

The Different Roles of ZNF671 in Different Cell Groups

To determine the functionally heterogeneous roles of ZNF671 in cancer cells, we inferred that single cells exhibited widespread heterogeneity in terms of their functional states in cancer. We applied t-SNE to reduce the non-linear dimensionality of the cancer cell data and placed different cell clusters on a t-SNE map (Figure 5), which indicated that the cell groups might be associated with the functional heterogeneity of cancer.

FIGURE 5
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Figure 5. The distribution of heterogeneous cell groups. Tridimensional t-SNE representation of cell clusters in primary tumors after classic multidimensional scaling and dimensionality reduction. Every point represents a single cell, and the color of the point represents the expression level of the gene (gene list) in the cell. Glioblastoma (GBM), Glioma (Brain), Glioma (PDX), Astrocytoma (AST), Oligodendroglioma (ODG), Lung adenocarcinoma (LUAD), Melanoma (MEL), and Breast cancer (BRCA).

To reveal the roles of ZNF671 in different cell groups, we further the explored functional roles and correlations of ZNF671 in different cancer subgroups. As shown in Figure 6, ZNF671 expression was positively associated with DNA repair, DNA damage, and apoptosis but negatively associated with angiogenesis, differentiation, and proliferation in MGH30 cell groups of GBM, while ZNF671 expression was positively associated with proliferation in MGH31 cell groups of GBM. In glioma (brain), ZNF671 expression was negatively correlated with angiogenesis in MUV1, with DNA repair, DNA damage, and cell cycle in MUV5, with DNA repair in BCH836, and with apoptosis in BCH869. ZNF671 expression was positively correlated with hypoxia in MUV10, BCH836, and BCH869 in glioma (brain). In glioma (PDX), ZNF671 expression in BCH869 correlated negatively not only with hypoxia but also with EMT, apoptosis, angiogenesis, and quiescence. In AST, ZNF671 expression was positively correlated with stemness in MGH45 and MGH56, with invasion in MGH61, and with inflammation in MGH64, and it was negatively correlated with cell cycle and invasion in MGH45, with angiogenesis in MGH57, and with invasion in MGH64. In ODG, ZNF671 expression was positively correlated with metastasis, hypoxia, inflammation, and apoptosis in MGH36 and with inflammation in MGH60 but negatively correlated with apoptosis in MGH54 and with quiescence in MGH93. Similarly, in MEL, ZNF671 expression was positively correlated with stemness in tumor78, with proliferation and stemness in tumor79, with proliferation and differentiation in tumor88, and with inflammation in tumor89. However, ZNF671 expression was negatively correlated with DNA repair in tumor78, DNA damage and angiogenesis in tumor80, and cell cycle in tumor89. In LUAD, ZNF671 expression was positively correlated with DNA repair in MBT15 but negatively correlated with metastasis and invasion in PT45. In BRCA, ZNF671 expression was only positively correlated with DNA damage in CSL KO xenograft tumor (Figure 6, all *P < 0.05; **P < 0.01).

FIGURE 6
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Figure 6. Detailed functional relevance of ZNF671 in different specific cell groups. The detailed functional relevance of ZNF671 in each specific cell group of a specific dataset. Glioblastoma (GBM), Glioma (Brain), Glioma (PDX), Astrocytoma (AST), Oligodendroglioma (ODG), Lung adenocarcinoma (LUAD), Melanoma (MEL), and Breast cancer (BRCA). **p ≤ 0.01; *p ≤ 0.05 compared with the control using Student's t-test.

ZNF671 Inhibits Cell EMT, Migration, and Invasion in vitro

To determine the functional roles of ZNF671 in cancer cells, we performed Western blot assay and migration and invasion assays using U87, U251, A375, MDA-MB-231, and BT-549 cell lines transfected with ZNF671 or vector plasmids. As shown in Figure 7A, Western blot analysis validated that ZNF671 protein was obviously upregulated after transfection of ZNF671 plasmid. Furthermore, the overexpression of ZNF671 was associated with increased expression of the epithelial marker E-cadherin and decreased expression of the mesenchymal marker Vimentin. Transwell assays showed that overexpression of ZNF671 inhibited cancer cell migration and invasion in vitro (Figures 7B–D). These findings indicate that ZNF671 inhibits the EMT, migration, and invasion of U87, U251, A375, MDA-MB-231, and BT-549 cells in vitro.

FIGURE 7
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Figure 7. Effects of ZNF671 overexpression on EMT, migration, and invasion in vitro. (A) Representative western blot analysis of ZNF671 overexpression in U87, U251, A375, MDA-MB-231, and BT-549 cell lines. (B,C) Representative images of the effects of ZNF671 overexpression on the migratory and invasive abilities of cells as determined by Transwell migration (B) and invasion (C) assays. (D) Quantification of the effects of ZNF671 overexpression on migratory and invasive abilities. All of the experiments were performed at least three times. Data presented are the mean ± SD; **P < 0.01. All of the experiments were performed at least three test.

Discussion

ZNF671, which contains C2H2-type zinc fingers (ZFs) and a Krüppel-associated box (KRAB) domain, is a member of the KRAB-ZF (KRAB-ZFP) transcriptional family. KRAB-ZFPs are involved in regulating angiogenesis (36), apoptosis (3739), the cell cycle (40, 41), inflammation (42), invasion and metastasis (43, 44), and stemness (45). Our previous studies demonstrated that ZNF671 is a tumor suppressor that is epigenetically silenced by DNA methylation in nasopharyngeal carcinoma, BRCA, CESC, HNSC, KIRP, LUAD, PAAD, and UCEC (26, 27). However, there is limited information regarding the role of ZNF671 in cancer progression and development, and there have been no systematic studies of the role of ZNF671 in cancer's heterogeneous functional states.

In this study, we found a total of eight solid tumor-related ZNF671 scRNA-seq datasets, including GBM, glioma, AST, ODG, LUAD, MEL, and BRCA. ScRNA-seq functional state analysis showed that ZNF671 played a tumor suppressor role and/or an oncogenic role in angiogenesis, apoptosis, cell cycle, differentiation, DNA damage, DNA repair, EMT, hypoxia, inflammation, invasion, metastasis, proliferation, quiescence, and stemness. The different functional states in tumors may be associated with the inherent heterogeneity of the tumor. However, the synthetic analysis of eight solid tumors showed that ZNF671 was negatively associated with angiogenesis, apoptosis, EMT, hypoxia, invasion, and quiescence. Western blot and transwell assays showed that ZNF671 inhibited EMT, migration, and invasion of CNS cancers, lung cancer, melanoma, and breast carcinoma in vitro. These results suggested a crucial tumor suppressor role for ZNF671 in the progression of these cancers, which was consistent with our previous studies (26, 27).

To further explore the heterogeneous functional state of ZNF671 in cancers, we applied t-SNE to describe the distribution of cells. We found different cell clusters on a t-SNE map and proposed that these cell subgroups might lead to cancer functional heterogeneity. Functional analysis of the cancer cell subgroups validated that the heterogeneous cell populations had different roles in cancer progression and development, which provided us with a fine level of resolution for cancer treatment. However, there were still several limitations. First, this study was based on current scRNA datasets, and several scRNA datasets only contain data for hundreds of single cells, so more cells should be considered for analysis. Second, we found the ZNF671 inhibits angiogenesis, apoptosis, EMT, hypoxia, invasion, and quiescence in CNS cancers, lung cancer, melanoma, and breast carcinoma. Moreover, we only identified that ZNF671 suppresses cell EMT, migration, and invasion in vitro. The angiogenesis, apoptosis, hypoxia, and quiescence functional states need be identified further, and the suppressor role of ZNF671 in vivo needs to be explored further.

In conclusion, this study systematically evaluated the tumor suppressor role of ZNF671 based on scRNA-seq datasets. Our findings revealed that ZNF671 is a tumor suppressor in LUAD, BRCA, GBM, glioma, AST, ODG, and MEL. However, the mechanism of ZNF671's tumor suppressor role remains unknown, and further studies are needed to clarify this issue. Our results provide new insights into the role of ZNF671 in multiple tumors and identifies ZNF671 as a novel target for cancer treatment.

Data Availability Statement

Publicly available datasets were analyzed in this study. This data can be found here: http://www.bioconductor.org.

Author Contributions

JZha, JLu, and HJ designed the research. TX, JZhe, YT, RL, BW, JLi, AX, and XH acquired and analyzed the data. JZha, HJ, and YY wrote the manuscript.

Funding

This work was supported by grants from the Social Science and Technology Development Key Project of Dongguan (201750715046462); Guangzhou Key Medical Discipline Construction Project Fund (B195002004042); Open Funds of State Key Laboratory of Oncology in South China (KY013711).

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.

Acknowledgments

We thank professors Ying Sun, Jun Ma, and Na Liu (State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy; Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China) for supporting this work. We would like to thank the native English-speaking scientists of Elixigen Company (Huntington Beach, California) for editing our manuscript.

References

1. Burrell RA, McGranahan N, Bartek J, Swanton C. The causes and consequences of genetic heterogeneity in cancer evolution. Nature. (2013) 501:338–45. doi: 10.1038/nature12625

PubMed Abstract | CrossRef Full Text | Google Scholar

2. Meacham CE, Morrison SJ. Tumour heterogeneity and cancer cell plasticity. Nature. (2013) 501:328–37. doi: 10.1038/nature12624

PubMed Abstract | CrossRef Full Text | Google Scholar

3. Kreso A, Dick JE. Evolution of the cancer stem cell model. Cell Stem Cell. (2014) 14:275–91. doi: 10.1016/j.stem.2014.02.006

PubMed Abstract | CrossRef Full Text | Google Scholar

4. Lawson DA, Bhakta NR, Kessenbrock K, Prummel KD, Yu Y, Takai K, et al. Single-cell analysis reveals a stem-cell program in human metastatic breast cancer cells. Nature. (2015) 526:131–5. doi: 10.1038/nature15260

PubMed Abstract | CrossRef Full Text | Google Scholar

5. Sancho P, Barneda D, Heeschen C. Hallmarks of cancer stem cell metabolism. Br J Cancer. (2016) 114:1305–12. doi: 10.1038/bjc.2016.152

PubMed Abstract | CrossRef Full Text | Google Scholar

6. Visvader JE, Lindeman GJ. Cancer stem cells in solid tumours: accumulating evidence and unresolved questions. Nat Rev Cancer. (2008) 8:755–68. doi: 10.1038/nrc2499

PubMed Abstract | CrossRef Full Text | Google Scholar

7. Bao S, Wu Q, McLendon RE, Hao Y, Shi Q, Hjelmeland AB, et al. Glioma stem cells promote radioresistance by preferential activation of the DNA damage response. Nature. (2006) 444:756–60. doi: 10.1038/nature05236

PubMed Abstract | CrossRef Full Text | Google Scholar

8. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. (2011) 144:646–74. doi: 10.1016/j.cell.2011.02.013

PubMed Abstract | CrossRef Full Text | Google Scholar

9. Wen L, Tang F. Single-cell sequencing in stem cell biology. Genome Biol. (2016) 17:71. doi: 10.1186/s13059-016-0941-0

PubMed Abstract | CrossRef Full Text | Google Scholar

10. Zhang X, Marjani SL, Hu Z, Weissman SM, Pan X, Wu S. Single-cell sequencing for precise cancer research: progress and prospects. Cancer Res. (2016) 76:1305–12. doi: 10.1158/0008-5472.CAN-15-1907

PubMed Abstract | CrossRef Full Text | Google Scholar

11. Regev A, Teichmann SA, Lander ES, Amit I, Benoist C, Birney E, et al. The human cell atlas. eLife. (2017) 6:e27041. doi: 10.7554/eLife.27041

PubMed Abstract | CrossRef Full Text | Google Scholar

12. Pizzolato G, Kaminski H, Tosolini M, Franchini DM, Pont F, Martins F, et al. Single-cell RNA sequencing unveils the shared and the distinct cytotoxic hallmarks of human TCRVdelta1 and TCRVdelta2 gammadelta T lymphocytes. Proc Natl Acad Sci USA. (2019) 116:11906–15. doi: 10.1073/pnas.1818488116

PubMed Abstract | CrossRef Full Text | Google Scholar

13. Margolin JF, Friedman JR, Meyer WK, Vissing H, Thiesen HJ, Rauscher FJ III. Kruppel-associated boxes are potent transcriptional repression domains. Proc Natl Acad Sci USA. (1994) 91:4509–13. doi: 10.1073/pnas.91.10.4509

PubMed Abstract | CrossRef Full Text | Google Scholar

14. Witzgall R, O'Leary E, Leaf A, Onaldi D, Bonventre JV. The Kruppel-associated box-A (KRAB-A) domain of zinc finger proteins mediates transcriptional repression. Pro Natl Acad Sci USA. (1994) 91:4514–8. doi: 10.1073/pnas.91.10.4514

PubMed Abstract | CrossRef Full Text | Google Scholar

15. Urrutia R. KRAB-containing zinc-finger repressor proteins. Genome Biol. (2003) 4:231. doi: 10.1186/gb-2003-4-10-231

PubMed Abstract | CrossRef Full Text | Google Scholar

16. Friedman JR, Fredericks WJ, Jensen DE, Speicher DW, Huang XP, Neilson EG, et al. KAP-1, a novel corepressor for the highly conserved KRAB repression domain. Genes Dev. (1996) 10:2067–78. doi: 10.1101/gad.10.16.2067

PubMed Abstract | CrossRef Full Text | Google Scholar

17. Moosmann P, Georgiev O, Le Douarin B, Bourquin JP, Schaffner W. Transcriptional repression by RING finger protein TIF1 beta that interacts with the KRAB repressor domain of KOX1. Nucleic Acids Res. (1996) 24:4859–67. doi: 10.1093/nar/24.24.4859

PubMed Abstract | CrossRef Full Text | Google Scholar

18. Cheng Y, Geng H, Cheng SH, Liang P, Bai Y, Li J, et al. KRAB zinc finger protein ZNF382 is a proapoptotic tumor suppressor that represses multiple oncogenes and is commonly silenced in multiple carcinomas. Cancer Res. (2010) 70:6516–26. doi: 10.1158/0008-5472.CAN-09-4566

PubMed Abstract | CrossRef Full Text | Google Scholar

19. Zheng L, Pan H, Li S, Flesken-Nikitin A, Chen PL, Boyer TG, et al. Sequence-specific transcriptional corepressor function for BRCA1 through a novel zinc finger protein, ZBRK1. Mol Cell. (2000) 6:757–68. doi: 10.1016/S1097-2765(00)00075-7

PubMed Abstract | CrossRef Full Text | Google Scholar

20. White DE, Negorev D, Peng H, Ivanov AV, Maul GG, Rauscher FJ III. KAP1, a novel substrate for PIKK family members, colocalizes with numerous damage response factors at DNA lesions. Cancer Res. (2006) 66:11594–9. doi: 10.1158/0008-5472.CAN-06-4138

PubMed Abstract | CrossRef Full Text | Google Scholar

21. Peng H, Zheng L, Lee WH, Rux JJ, Rauscher FJ III. A common DNA-binding site for SZF1 and the BRCA1-associated zinc finger protein, ZBRK1. Cancer Res. (2002) 62:3773–81.

PubMed Abstract | Google Scholar

22. Mase S, Shinjo K, Totani H, Katsushima K, Arakawa A, Takahashi S, et al. ZNF671 DNA methylation as a molecular predictor for the early recurrence of serous ovarian cancer. Cancer Sci. (2019) 110:1105–16. doi: 10.1111/cas.13936

PubMed Abstract | CrossRef Full Text | Google Scholar

23. Schmitz M, Eichelkraut K, Schmidt D, Zeiser I, Hilal Z, Tettenborn Z, et al. Performance of a DNA methylation marker panel using liquid-based cervical scrapes to detect cervical cancer and its precancerous stages. BMC Cancer. (2018) 18:1197. doi: 10.1186/s12885-018-5125-8

PubMed Abstract | CrossRef Full Text | Google Scholar

24. Yeh CM, Chen PC, Hsieh HY, Jou YC, Lin CT, Tsai MH, et al. Methylomics analysis identifies ZNF671 as an epigenetically repressed novel tumor suppressor and a potential non-invasive biomarker for the detection of urothelial carcinoma. Oncotarget. (2015) 6:29555–72. doi: 10.18632/oncotarget.4986

PubMed Abstract | CrossRef Full Text | Google Scholar

25. Tian Y, Arai E, Gotoh M, Komiyama M, Fujimoto H, Kanai Y. Prognostication of patients with clear cell renal cell carcinomas based on quantification of DNA methylation levels of CpG island methylator phenotype marker genes. BMC Cancer. (2014) 14:772. doi: 10.1186/1471-2407-14-772

PubMed Abstract | CrossRef Full Text | Google Scholar

26. Zhang J, Wen X, Liu N, Li YQ, Tang XR, Wang YQ, et al. Epigenetic mediated zinc finger protein 671 downregulation promotes cell proliferation and tumorigenicity in nasopharyngeal carcinoma by inhibiting cell cycle arrest. J Exp Clin Cancer Res. (2017) 36:147. doi: 10.1186/s13046-017-0621-2

PubMed Abstract | CrossRef Full Text | Google Scholar

27. Zhang J, Zheng Z, Zheng J, Xie T, Tian Y, Li R, et al. Epigenetic-mediated downregulation of Zinc finger protein 671 (ZNF671) predicts poor prognosis in multiple solid tumors. Front Oncol. (2019) 9:342. doi: 10.3389/fonc.2019.00342

PubMed Abstract | CrossRef Full Text | Google Scholar

28. Yuan H, Yan M, Zhang G, Liu W, Deng C, Liao G, et al. CancerSEA: a cancer single-cell state atlas. Nucleic Acids Res. (2019) 47:D900–8. doi: 10.1093/nar/gky939

PubMed Abstract | CrossRef Full Text | Google Scholar

29. The Gene Ontology C. Expansion of the Gene Ontology knowledgebase and resources. Nucleic Acids Res. (2017) 45:D331–8. doi: 10.1093/nar/gkw1108

CrossRef Full Text | Google Scholar

30. Liberzon A, Birger C, Thorvaldsdottir H, Ghandi M, Mesirov JP, Tamayo P. The molecular signatures database (MSigDB) hallmark gene set collection. Cell Syst. (2015) 1:417–25. doi: 10.1016/j.cels.2015.12.004

PubMed Abstract | CrossRef Full Text | Google Scholar

31. Santos A, Wernersson R, Jensen LJ. Cyclebase 3.0: a multi-organism database on cell-cycle regulation and phenotypes. Nucleic Acids Res. (2015) 43:D1140–4. doi: 10.1093/nar/gku1092

PubMed Abstract | CrossRef Full Text | Google Scholar

32. Zheng G, Ma Y, Zou Y, Yin A, Li W, Dong D. HCMDB: the human cancer metastasis database. Nucleic Acids Res. (2018) 46:D950–5. doi: 10.1093/nar/gkx1008

PubMed Abstract | CrossRef Full Text | Google Scholar

33. Pinto JP, Machado RSR, Magno R, Oliveira DV, Machado S, Andrade RP, et al. StemMapper: a curated gene expression database for stem cell lineage analysis. Nucleic Acids Res. (2018) 46:D788–93. doi: 10.1093/nar/gkx921

PubMed Abstract | CrossRef Full Text | Google Scholar

34. Hanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinform. (2013) 14:7. doi: 10.1186/1471-2105-14-7

PubMed Abstract | CrossRef Full Text | Google Scholar

35. Li W, Cerise JE, Yang Y, Han H. Application of t-SNE to human genetic data. J Bioinform Comput Biol. (2017) 15:1750017. doi: 10.1142/S0219720017500172

PubMed Abstract | CrossRef Full Text | Google Scholar

36. Sun HY, Wei SP, Xu RC, Xu PX, Zhang WC. Sphingosine-1-phosphate induces human endothelial VEGF and MMP-2 production via transcription factor ZNF580: novel insights into angiogenesis. Biochem Biophys Res Commun. (2010) 395:361–6. doi: 10.1016/j.bbrc.2010.04.019

PubMed Abstract | CrossRef Full Text | Google Scholar

37. Li K, Gao B, Li J, Chen H, Li Y, Wei Y, et al. ZNF32 protects against oxidative stress-induced apoptosis by modulating C1QBP transcription. Oncotarget. (2015) 6:38107–26. doi: 10.18632/oncotarget.5646

PubMed Abstract | CrossRef Full Text | Google Scholar

38. Huang J, Teng L, Li L, Liu T, Li L, Chen D, et al. ZNF216 Is an A20-like and IkappaB kinase gamma-interacting inhibitor of NFkappaB activation. J Biol Chem. (2004) 279:16847–53. doi: 10.1074/jbc.M309491200

PubMed Abstract | CrossRef Full Text | Google Scholar

39. Kuramoto K, Uesaka T, Kimura A, Kobayashi M, Watanabe H, Katoh O. ZK7, a novel zinc finger gene, is induced by vascular endothelial growth factor and inhibits apoptotic death in hematopoietic cells. Cancer Res. (2000) 60:425–30.

PubMed Abstract | Google Scholar

40. Pozner A, Terooatea TW, Buck-Koehntop BA. Cell-specific Kaiso (ZBTB33) Regulation of cell cycle through Cyclin D1 and Cyclin E1. J Biol Chem. (2016) 291:24538–50. doi: 10.1074/jbc.M116.746370

PubMed Abstract | CrossRef Full Text | Google Scholar

41. Huang C, Jia Y, Yang S, Chen B, Sun H, Shen F, et al. Characterization of ZNF23, a KRAB-containing protein that is downregulated in human cancers and inhibits cell cycle progression. Exp Cell Res. (2007) 313:254–63. doi: 10.1016/j.yexcr.2006.10.009

PubMed Abstract | CrossRef Full Text | Google Scholar

42. Kim GD, Das R, Goduni L, McClellan S, Hazlett LD, Mahabeleshwar GH. Kruppel-like factor 6 promotes macrophage-mediated inflammation by suppressing B cell leukemia/lymphoma 6 expression. J Biol Chem. (2016) 291:21271–82. doi: 10.1074/jbc.M116.738617

PubMed Abstract | CrossRef Full Text | Google Scholar

43. Wang T, Wang XG, Xu JH, Wu XP, Qiu HL, Yi H, et al. Overexpression of the human ZNF300 gene enhances growth and metastasis of cancer cells through activating NF-kB pathway. J Cell Mol Med. (2012) 16:1134–45. doi: 10.1111/j.1582-4934.2011.01388.x

PubMed Abstract | CrossRef Full Text | Google Scholar

44. Liu Y, Huang W, Gao X, Kuang F. Regulation between two alternative splicing isoforms ZNF148(FL) and ZNF148(DeltaN), and their roles in the apoptosis and invasion of colorectal cancer. Pathol Res Pract. (2019) 215:272–7. doi: 10.1016/j.prp.2018.10.036

CrossRef Full Text | Google Scholar

45. Oleksiewicz U, Gladych M, Raman AT, Heyn H, Mereu E, Chlebanowska P, et al. TRIM28 and interacting KRAB-ZNFs control self-renewal of human pluripotent stem cells through epigenetic repression of pro-differentiation genes. Stem Cell Rep. (2017) 9:2065–80. doi: 10.1016/j.stemcr.2017.10.031

PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: ZNF671, tumor suppressor, solid tumor, single-cell sequencing, data mining

Citation: Zhang J, Luo J, Jiang H, Xie T, Zheng J, Tian Y, Li R, Wang B, Lin J, Xu A, Huang X and Yuan Y (2019) The Tumor Suppressor Role of Zinc Finger Protein 671 (ZNF671) in Multiple Tumors Based on Cancer Single-Cell Sequencing. Front. Oncol. 9:1214. doi: 10.3389/fonc.2019.01214

Received: 10 June 2019; Accepted: 23 October 2019;
Published: 08 November 2019.

Edited by:

Chang Zou, Shenzhen People's Hospital, China

Reviewed by:

Fahd Al-Mulla, Genatak, Kuwait
Zi-Qi Zheng, Sun Yat-sen University Cancer Center (SYSUCC), China

Copyright © 2019 Zhang, Luo, Jiang, Xie, Zheng, Tian, Li, Wang, Lin, Xu, Huang and Yuan. 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.

*Correspondence: Yawei Yuan, yuanyawei@gzhmu.edu.cn

These authors have contributed equally to this work