Introduction
The major histological subtype of liver cancer is HCC which accounted for 80–90% of primary liver cancer and the third most common reason for tumor-related mortality worldwide (Pan et al.
2011; Yang et al.
2019). Both high recurrence and metastasis rates contribute to the poor prognosis of HCC (Siegel et al.
2021). In clinical, transplantation of liver is the only treatment for terminal cancer, 5-year survival was gained by about 60%, but due to the limited donor, surgical removal of the lesions and transcatheter hepatic artery embolization with conventional chemotherapy treatment is the main choice of clinical treatment (Alqahtani, et al.
2019). It is necessary to establish a scoring model for the prognosis of HCC patients to provide the theoretical basis for the treatment selection and prognostic evaluation of clinicians.m
6A RNA modification, the most dominant RNA modification, has been shown to play a crucial role in regulating RNA metabolism (Yang et al.
2018). m
6A regulatory factors consists of “writers” that generate the m
6A markers, “erasers” that can remove the m
6A modification that has occurred, and “readers” that identify the m
6A modification and further promote the transcription of downstream genes. m
6A modification affects RNA metabolism in many ways, such as processing, nuclear output, translation, and decay of RNA (Yang et al.
2019; Wang et al.
2021; Shi et al.
2017; Roignant and Soller
2017). m
6A RNA methylation plays many roles in cancer through a variety of mechanisms and offers more possibilities for early diagnosis and treatment of cancer. In addition, m
6A modification also plays a critical role in CSCs (Wang et al.
2021). m
6A RNA modification has been certified to be involved in the production and maintenance of CSCs, control of tumor progression and treatment resistance (Yang et al.
2018; Roignant and Soller
2017; Corbett
2018). In recent years, genetic signatures of m
6A regulatory factors have been constructed to predict the prognosis of a variety of tumors, including gastric cancer (Guan et al.
2020), adrenocortical carcinoma (Jin et al.
2021), and liver cancer (Li, et al.
2021a). However, it has not been clarified the exact association between m
6A and CSCs of HCC.
Giving the RNA-seq data available online, we tried to analyze whether the expression patterns of m
6A regulatory factors can predict prognosis in HCC patients. Tathiane M. Malta et al. used an innovative single-class logistic regression (OCLR) machine learning algorithm to generate mRNAsi index that reflects the gene expression characteristics of stem cells through multi-platform analysis of stem cell transcriptome, methylation and transcription factor binding sites (Malta et al.
2018). The stemness index based on mRNA expression (mRNAsi index) is a measure of how similar tumor cells to stem cells, the higher the mRNAsi index, the stronger the stemness of tissues is. For understanding the association between m
6A regulatory factors expression and tissue stemness, we accorded to the median data of mRNAsi index and split HCC patients into two groups with high-mRNAsi and low-mRNAsi, respectively, using TCGA (The Cancer Genome Atlas) dataset. In addition, we collected 23 m
6A regulatory factors in this study and selected m
6A differential regulatory factors (DEGs) from patients with high-mRNAsi and low-mRNAsi for further study in TCGA dataset. Furthermore, we selected m
6A DEGs that had prognostic value to be candidate genes in both TCGA and ICGC (The International Cancer Genome Consortium). We found that these hub m
6A regulatory factors were highly expressed in liver cancer stem cells (LCSCs) using scRNA-seq data (GSE149614) and RT-qPCR. GSVA analysis also showed that the hub m
6A regulatory factors’ expression was associated with the CSCs related pathways. Moreover, we performed the analyses of correlation, function, Consensus, PCA, survival, and ROC to investigate the biological functions and mechanisms of these regulatory factors in HCC patients, highlighting the importance of these candidate hub regulatory factors. In addition, we observed the prognostic role of m
6A regulatory genes in HCC, and build a prognostic model in ICGC, and verified it in TCGA. Finally, the overall survival of HCC patients could be better estimated using the prognostic model. In summary, our work suggests that the m
6A regulatory factors are a potential prognostic indicator for HCC patients.
Materials and methods
HCC data source and preprocessing
The data of gene expression data and clinical annotations were gained from ICGC and TCGA datasets. Two cohorts were enrolled in our work: TCGA-LIHC (371 samples) and ICGC-LIRI-JP (243 samples). For mRNA expression data, FPKM-normalized, log2-transformed data were gained, respectively, from web sites Genomic Data Commons Data Portal (GDC Data Portal) (RRID: SCR_014514), ICGC Data Portal (RRID: SCR_021722). Clinical annotations in ICGC with 232 HCC patients and in TCGA with 240 HCC patients were also downloaded for further analysis. The data of Copy number variation (CNV) were gained from TCGA. In addition, the scRNA-seq (GSE149614) of HCC was downloaded from Gene Expression Omnibus (GEO) (RRID: SCR_005012) (Zheng et al.
2018).
23 m
6A regulatory factors were identified from PubMed (RRID: SCR_004846), including 8 writers (METTL3, METTL14, WTAP, METTL16, RBM15B, ZC3H13 and VIRMA) (Roundtree et al.
2017; Chen et al.
2019), 3 erasers (ALKBH5, ALKBH7 and FTO) (Jia et al.
2011; Zheng et al.
2013), and 12 readers (IGF2BP1, IGF2BP2, IGF2BP3, YTHDF1, YTHDF2, YTHDF3, HNRNPA2B1, YTHDC1, YTHDC2, HNRNPC, LRPPRC and RBMX) (Chen et al.
2019; Du et al.
2021; Casella et al.
2019). We gained the gene expression matrix of corresponding m
6A regulatory factors from TCGA and ICGC data for further investigation.
Identification of m6A regulatory factors CNV and expression difference between high-mRNAsi and low-mRNAsi groups
The m6A regulatory factors expression matrix and CNV data were applicated to the differentially CNV genes and the DEGs between high-mRNAsi and low-mRNAsi patients. According to the median data, we first classified HCC patients into high-mRNAsi and low-mRNAsi group to identify among the 371 samples in TCGA. Then R package “limma” was used to determine m6A DEGs by comparing high-mRNAsi specimens with low-mRNAsi specimens. 20 m6A DEGs (adjusted P < 0.05) in TCGA were identified for further study.
The Kaplan–Meier curve based on m6A DEGs in HCC patients
Among 17 m6A differential regulatory factors in TCGA and 13 m6A regulatory factors DEGs in ICGC, we figured out the potential prognostic m6A differential regulatory factors by Survival analysis and Kaplan–Meier curves using the “survival” R package (adjusted p < 0.05). The R package “survminer” and a two-sided log-rank test were used to identify the optimal cutoff value. To gain more accurate results, a Venn plot was used to obtain the overlapped prognostic m6A different regulatory factors in either TCGA or ICGC. Finally, ten shared hub regulatory factors were left for further analysis.
scRNA-seq data analyses
scRNA sequence data of different sites of HCC was downloaded, including ten HCC patients from four relevant sites: primary tumor, metastatic lymph node, portal vein tumor thrombus (PVTT) and non-tumor liver tissue. After aggregation of the primary tumor data of the 10 patients (Du, et al.
2021), R package “Seurat” was used for cell filtration, normalization, then we found the top-2000 highly variable genes for dimensional reduction and 6 clusters: carcinoma-associated fibroblasts (CAFs), endothelial cells (ECs), bursa dependent lymphocyte (B cells), macrophages, thymus-dependent lymphocyte (T cells) and tumors.
Moreover, we classified the cluster of tumors into three groups: 3_Positive LCSCs, 3_Negative liver cancer cells, and other cells. The Seurat functions “DotPlot” were applicated to show the expression of ten hub m6A regulatory factors in 3_Positive LCSCs and 3_Negative liver cancer cells.
Human primary HCC cells, LCSCs and HCC cell lines
The methods of LCSCs isolation and culture were adopted in this study: tumor stem cells derived from liver cancer cell lines were cultured by low adsorption suspension into pellets, and Triple
+ (CD133
+CD24
+EPCAM
+) flow separation method (Zheng et al.
2018). Human primary tumor stem cells derived from clinical liver cancer tumor tissues were cultured on trophoblast by our research group (Park et al.
2015).
The primary HCC and LCSCs from HCC samples were gained from liver HCC patients who underwent surgery at Guangzhou First People’s Hospital. The method and human samples used in our study were approved by the ethics committee at Guangzhou First People’s Hospital of South China University of Technology and strict adherence to the Declaration of Helsinki. Separation of the primary HCC cells has been established as previously described (Xie et al.
2021), and the isolation of LCSCs was followed after the separation of HCC cells which were washed with Dulbecco’s modified Eagle medium Nutrient Mixture F-12 (HAM) for 3 times and resuspended in HAM added with 0.5% bovine serum albumin (BSA), 20 ng/mL Epidermal Growth Factor (EGF), 4 ng/mL Basic Fibroblast Growth Factor (bFGF) and 1% penicillin/streptomycin. We seeded these cells onto mouse embryonic fibroblasts (MEF) and incubated in a 5% CO2 and 37 °C incubator. The cells grew clonally on MEF and the medium was refreshed every day.
The cells of Hep3B and Huh7 were gained from ATCC and grew in a 5% CO2 and 37 °C incubator. The culture media were Dulbecco’s modified Eagle medium (DMEM) with 10% fetal bovine serum and 1% penicillin/streptomycin. Hep3B and Huh7 suspension cells (Hep3B SP and Huh7 SP) were cultured in HAM with 2% B27, 20 ng/ml EGF, 4 ng/ml bFGF, and 1% penicillin/streptomycin. Cells were cultured in a 5% CO2 and 37 °C incubator.
Flow cytometry
Single-cell suspensions (Hep3B SP and Huh7 SP) were prepared and incubated with CD24 antibody (BioLegend Cat#311,106, RRID: AB_314855), EPCAM antibody (BioLegend Cat#324,212, RRID: AB_756086), and CD133 antibody (Biolegend, #S16016B, RRID: AB_2734479) for 30 min in 4 °C, followed by flow cytometry analysis. LCSCs (LCSC1 and LCSC2) were prepared and incubated with ALDH antibody (STEMCELL, Cat#01700) for 30 min in 37 °C, followed by flow cytometry analysis.
RNA extraction and quantitative real-time PCR (RT-qPCR)
Total RNA was extracted using TRIzol reagent (TakaRa, Cat#9109) based on the manufacturer’s protocol. cDNA synthesis was operated using the PrimeScript™ Reverse Transcriptase kit (TakaRa, Cat#9767). The primers in Table S1 and Table S2 were used to perform qPCR.
Consensus cluster analyses and PCA analyses
R package “ConsensusClusterPlus” and “PCA” was used to determine the function of ten hub m6A regulatory factors to divide HCC patients into various subgroups and study the gene expression patterns in the corresponding subgroups. After performing consensus clustering cumulative distribution function (CDF) for k = 2 to 9 in both TCGA and ICGC, k = 2 was shown and chosen for further study. To verify the significance of the cluster results of ten hub m6A regulators, we performed survival analysis and clinical information in TCGA and ICGC datasets.
GSVA and functional annotation
R package “GSVA” was used to perform GSVA enrichment analysis, getting different biological processes between stratified subgroups. GSVA, using a non-parametric and unsupervised method, is a common way to estimate the variation in biological process activities and pathways in the samples of gene expression dataset. We obtained function sets of “c2.cp.kegg.v7.4.symbols.gmt” and “h.all.v7.4.symbols.gmt” from MSigDB dataset. Adjusted p < 0.05 and logFC > 0.1 was considered as statistically significant, and we got the top 100 different pathways between 2 models in KEGG and top 30 different pathways between 2 models in HALLMARK with “limma” R package.
Establishment and verification of the prognostic model
UNICOX Analysis of ten candidate m6A regulatory factors (adjusted p < 0.05) was performed to confirm prognostic genes in ICGC. The LASSO analysis (adjusted p < 0.05) was applicated to construct a prognostic mode at the least and best risk factors. Then using the MULTICOX Analysis, we chose four regulators to build a prognostic risk signature. Finally, survival analysis, ROC analyses and risk score distribution in both TCGA and ICGC datasets were performed to verify the prognostic model.
Establishment of a novel nomogram for individuals
We performed nomogram analyses with R “rms” to combinate this prognostic signature with relevant clinical information.
Discussion
Hepatocellular carcinoma (HCC) ranking third in cancer deaths worldwide is a serious disease (Siegel et al.
2021; Sung et al.
2021). The recurrence rate of HCC remains high, although many patients can be early diagnosis and treatment (Siegel et al.
2021). For patients whose disease has entered the middle and late stages, the prognosis is much less optimistic. Moreover, CSCs are known to work in the development of cancer, relapse and resistance after treatment. The characterization of CSCs and its molecular basis may provide many benefits for cancer treatment and therapy. Furthermore, m
6A RNA modifications can be modified by “writes” and removed by “erases” (Fu et al.
2014). Differential expression of specific m
6A regulatory factors was correlated with abnormally regulated RNAs in cancers; however, the same m
6A regulatory factors may play different roles in different cancers (Chen et al.
2019; Huang et al.
2020). It is also well known that m
6A RNA modification has a crucial function in regulating the generation, maintenance, and drug resistance with CSCs (Cui et al.
2017). The biological basis and functional characteristics of m
6A regulatory factors may be innovative indicators and targets.
Previous study has shown that m
6A regulatory factors could exert effectively in various tumors and m
6A methylation is highlighted in relation to CSCs and cancer biology. For example, Jeremy et al. found that m
6A methylation was generally upregulated in glioblastoma and the “writers” YTHDF2 specifically stabilizes MYC mRNA in CSCs of glioblastoma (Dixit et al.
2021). Likewise, Zhang CZ et al. suggested that YTHDF2 regulated the expression of OCT4 through m
6A methylation, promoting the phenotype LCSCs and tumor metastasis (Zhang et al.
2020). Studies have shown that FTO plays a key role in the self-renewal and immune evasion of CSCs and indicates the great prospects of cancer therapy by targeting FTO (Su et al.
2020). Giving the study of m
6A in CSCs, we found that m
6A mechanism might serve as effective strategies and new target for elimination of CSCs. In this study, we focused on the comprehensive analyses of TCGA and ICGC databases. The analyses of the m
6A regulatory DEGs in high-mRNAsi and low-mRNAsi were conducted in TCGA, and relevant OS analyses of hub regulators were performed in ICGC and were almost confirmed in TCGA. The data of scRNA-seq with HCC were also performed to verify the positive association between m
6A regulatory factors and LCSCs. Furthermore, besides the consensus analysis and nomogram, we also successfully build a prognostic model of m
6A regulators for HCC which was established in ICGC verified in TCGA databases, offering a prospective m
6A-related target for patients with HCC.
Ten overlapped m
6A regulatory DEGs had been gained in both TCGA and ICGC in our study, these ten hub m
6A regulatory factors were upregulated in high-mRNAsi groups and showed worse prognosis in HCC patients. As a result, we figured out that m
6A regulatory factors might be a prospective target for CSCs in HCC, then by analyzing the scRNA-seq data of HCC and followed by RT-qPCR validation, we found that the ten hub m
6A regulatory factors were positively related to the CSCs markers CD24, EPCAM, and CD133 and highly expressed in LCSCs. Besides the positive correlation with CSCs markers, the results of GSVA function analysis of KEGG and HALLMARK enrichment also showed that the enriched terms were not only related to cell cycle, immune modifications and tumor malignancy, but also to the developmental signaling pathways of CSCs. The results of function analysis showed that these ten hub regulatory factors might affect the survival and development of CSCs in HCC patients. Moreover, we used UNICOX, LASSO, and MULTICOX analysis to select four risk factors (RBM15B, LRPPRC, IGF2BP1 and IGF2BP3) for establishing prognostic model. RBM15B, RNA-binding protein, performs as a key regulator of m
6A methylation of RNA. Jaffrey SR’s team demonstrated that RBM15 and related RBM15B interacted with WTAP to form the complex to target mRNA and also were critical for XIST-mediated gene silencing (Patil et al.
2016). Leucine-rich PPR motif protein (LRPPRC), a member of the PPR family, was a known genetic mutation. The team from Liu et al. found that SNHG17 could inhibit C-MYC ubiquitination by interacting with LRPPRC in HCC and promote HCC proliferation through snHG17-LRPPRC-C-MYC regulatory axis, which provides a potential target for cancer therapy (Liu et al.
2021). As the member of the insulin-like growth factor2 mRNA binding protein family, IGF2BP1 can promote the stability of MGAT5 mRNA by upregulating m
6A modification of MGAT5, thus maintaining the LCSCs phenotype (Yang et al.
2021). IGF2BP3, the protein encoded by this gene was primarily found in the nucleolus, and also known as that HBV-pgRNA (pregenomic RNA) could be a potential biomarker to predict clinical outcome and recurrence of HCC (Ding et al.
2021), thus the pgRNA-IGF2BP3 axis had a crucial function in HBV-associated liver cancer. In addition, IFN-α-2A could augment the level of m
6A RNA modification to reduce pgRNA stability, thereby controlling the occurrence of HBV-associated HCC. The built RiskScore with those four factors RBM15B, LRPPRC, IGF2BP1, and IGF2BP3 were likely to divide HCC patients into two prognosis-related clusters and further verified by survival, ROC, UNICOX and MULTICOX analysis in this study. In summary, we analyzed RNA-seq data of m
6A regulatory factors employing ICGC and TCGA datasets. In addition, the RiskScore feature and prognostic graph were inferred to comprehensively forecast the clinical outcome for patients with HCC. These consequences suggest that ten hub regulators are promising targets for potential therapeutic strategies, and clinical and experimental validation are needed to further verify the clinical function and more potential applications of this model.
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