Table_3_Construction of a Comprehensive Multiomics Map of Hepatocellular Carcinoma and Screening of Possible Driver Genes.XLSX

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.

研究目标：肝细胞癌（hepatocellular carcinoma, HCC）的发生是一个涉及基因突变、表观遗传变异及基因表达异常的复杂过程。然而，目前尚缺乏针对肝细胞癌的全面多组学研究，且已有的多组学证据尚未能推动临床实践的优化。为此，本研究通过整合多水平获取的多组学数据，解析肝细胞癌发生发展的分子机制，以期为肝细胞癌的早期诊断、个性化治疗及临床诊疗指导提供创新视角与全新理论基础。

研究方法：本研究从癌症基因组图谱（The Cancer Genome Atlas, TCGA）中获取全外显子组测序数据、RNA（mRNA及miRNA）测序数据、DNA甲基化芯片数据以及单核苷酸多态性（single nucleotide polymorphism, SNP）阵列数据。采用GISTIC2工具分析肝细胞癌的拷贝数变异（copy number variation, CNV）；运用MutSigCV算法识别显著突变基因（significantly mutated genes, SMGs）；借助R软件中的clusterProfiler包开展功能富集分析；通过Kaplan–Meier生存曲线评估离散变量的预后价值。

研究结果：通过分析TCGA数据库中的肝细胞癌数据，本研究构建了肝细胞癌的全面多组学图谱。经拷贝数分析，共识别出29个显著扩增位点及33个显著缺失位点；确定了13个显著突变基因。此外，还识别出3个与肝细胞癌相关的突变特征，其中特征22与马兜铃酸暴露密切相关。随后，本研究分析了肝细胞癌样本的甲基化水平，鉴定出51个与甲基化显著相关的表观遗传沉默基因；通过差异表达分析，筛选出肝细胞癌样本中差异表达的mRNA及miRNA。基于上述结果，本研究共鉴定出93个潜在肝细胞癌驱动基因，这些基因受突变、甲基化及拷贝数变异调控，且具有预后价值。

研究结论：本研究揭示了肝细胞癌多维度的分子变异特征。我们整合分析了肝细胞癌的基因组特征、单核苷酸变异（single nucleotide variants, SNVs）、拷贝数变异、甲基化及基因表达数据。基于研究结果，本研究鉴定出潜在肝细胞癌驱动基因，可为预后预测及治疗方案选择提供决策支持。