Abstract
Objectives: The occurrence of hepatocellular carcinoma (HCC) is a complex process involving genetic mutations, epigenetic variation, and abnormal gene expression. However, a comprehensive multiomics investigation of HCC is lacking, and the available multiomics evidence has not led to improvements in clinical practice. Therefore, we explored the molecular mechanism underlying the development of HCC through an integrative analysis of multiomics data obtained at multiple levels to provide innovative perspectives and a new theoretical basis for the early diagnosis, personalized treatment and medical guidance of HCC.Methods: In this study, we collected whole-exome sequencing data, RNA (mRNA and miRNA) sequencing data, DNA methylation array data, and single nucleotide polymorphism (SNP) array data from The Cancer Genome Atlas (TCGA). We analyzed the copy number variation (CNV) in HCC using GISTIC2. MutSigCV was applied to identify significantly mutated genes (SMGs). Functional enrichment analyses were performed using the clusterProfiler package in R software. The prognostic values of discrete variables were estimated using Kaplan–Meier survival curves.Results: By analyzing the HCC data in TCGA, we constructed a comprehensive multiomics map of HCC. Through copy number analysis, we identified significant amplification at 29 loci and significant deletions at 33 loci. A total of 13 significant mutant genes were identified. In addition, we also identified three HCC-related mutant signatures, and among these, signature 22 was closely related to exposure to aristolochic acids. Subsequently, we analyzed the methylation level of HCC samples and identified 51 epigenetically silenced genes that were significantly associated with methylation. The differential expression analysis identified differentially expressed mRNAs and miRNAs in HCC samples. Based on the above-described results, we identified a total of 93 possible HCC driver genes, which are driven by mutations, methylation, and CNVs and have prognostic value.Conclusion: Our study reveals variations in different dimensions of HCC. We performed an integrative analysis of genomic signatures, single nucleotide variants (SNVs), CNVs, methylation, and gene expression in HCC. Based on the results, we identified HCC possible driver genes that might facilitate prognostic prediction and support decision making with regard to the choice of therapy.
Highlights
Hepatocellular carcinoma (HCC) is a leading cause of cancerrelated death in many parts of the world, and its surveillance and early detection increase the possibility of potentially curative treatment (Llovet et al, 2018)
The results showed that these three mutation signatures can be used to classify HCC samples (Figure 4E)
Based on methylation data in The Cancer Genome Atlas (TCGA), we identified 51 significantly silenced genes (|Cor| > 0.7) (Table S2), and interestingly, 10 genes from the zinc finger protein (ZNF) family were included in this sett (Figure 5C)
Summary
Hepatocellular carcinoma (HCC) is a leading cause of cancerrelated death in many parts of the world, and its surveillance and early detection increase the possibility of potentially curative treatment (Llovet et al, 2018). Pan et al sequenced the transcriptome of HCC patients and identified 755 differentially expressed genes (DEGs). Cheng et al identified a group of patients with a CpG island methylator phenotype (CIMP) and found that the overall survival (OS) rate of CIMP patients was poorer than that of non-CIMP patients These researches identified promising biomarkers for the diagnosis of HCC (Cheng et al, 2018). These above-mentioned studies focused on single genes or individual omics, and conducting an in-depth systematic study of the molecular mechanism of HCC from a comprehensive, multidimensional perspective is difficult
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