Table_2_Transcriptome Analysis Revealed a Highly Connected Gene Module Associated With Cirrhosis to Hepatocellular Carcinoma Development.XLSX

IntroductionCirrhosis is one of the most important risk factors for development of hepatocellular carcinoma (HCC). Recent studies have shown that removal or well control of the underlying cause could reduce but not eliminate the risk of HCC. Therefore, it is important to elucidate the molecular mechanisms that drive the progression of cirrhosis to HCC.Materials and MethodsMicroarray datasets incorporating cirrhosis and HCC subjects were identified from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were determined by GEO2R software. Functional enrichment analysis was performed by the clusterProfiler package in R. Liver carcinogenesis-related networks and modules were established using STRING database and MCODE plug-in, respectively, which were visualized with Cytoscape software. The ability of modular gene signatures to discriminate cirrhosis from HCC was assessed by hierarchical clustering, principal component analysis (PCA), and receiver operating characteristic (ROC) curve. Association of top modular genes and HCC grades or prognosis was analyzed with the UALCAN web-tool. Protein expression and distribution of top modular genes were analyzed using the Human Protein Atlas database.ResultsFour microarray datasets were retrieved from GEO database. Compared with cirrhotic livers, 125 upregulated and 252 downregulated genes in HCC tissues were found. These DEGs constituted a liver carcinogenesis-related network with 272 nodes and 2954 edges, with 65 nodes being highly connected and formed a liver carcinogenesis-related module. The modular genes were significantly involved in several KEGG pathways, such as “cell cycle,” “DNA replication,” “p53 signaling pathway,” “mismatch repair,” “base excision repair,” etc. These identified modular gene signatures could robustly discriminate cirrhosis from HCC in the validation dataset. In contrast, the expression pattern of the modular genes was consistent between cirrhotic and normal livers. The top modular genes TOP2A, CDC20, PRC1, CCNB2, and NUSAP1 were associated with HCC onset, progression, and prognosis, and exhibited higher expression in HCC compared with normal livers in the HPA database.ConclusionOur study revealed a highly connected module associated with liver carcinogenesis on a cirrhotic background, which may provide deeper understanding of the genetic alterations involved in the transition from cirrhosis to HCC, and offer valuable variables for screening and surveillance of HCC in high-risk patients with cirrhosis.

肝硬化是肝细胞癌（HCC）发生发展的重要风险因素之一。近期研究表明，去除或有效控制潜在病因可降低但无法消除HCC的发生风险。因此，阐明驱动肝硬化向HCC进展的分子机制至关重要。材料与方法：从基因表达综合数据库（GEO）中确定了包含肝硬化与HCC受试者的微阵列数据集。通过GEO2R软件确定了差异表达基因（DEGs）。利用R语言的clusterProfiler包进行了功能富集分析。采用STRING数据库和MCODE插件分别建立了与肝细胞癌发生相关的网络和模块，并使用Cytoscape软件进行可视化。通过层次聚类、主成分分析（PCA）和接受者操作特征（ROC）曲线评估了模块基因特征在区分肝硬化与HCC方面的能力。使用UALCAN网络工具分析了顶级模块基因与HCC分级或预后的关联。利用人类蛋白质图谱数据库分析了顶级模块基因的蛋白表达和分布。结果：从GEO数据库中检索到四个微阵列数据集。与肝硬化肝脏相比，在HCC组织中发现了125个上调和252个下调基因。这些DEGs构成了一个包含272个节点和2954条边的肝细胞癌发生相关网络，其中65个节点高度连接，形成了一个肝细胞癌发生相关模块。这些模块基因在多个KEGG通路中显著参与，如“细胞周期”、“DNA复制”、“p53信号通路”、“错配修复”、“碱基切除修复”等。在验证数据集中，这些确定的模块基因特征能够稳健地区分肝硬化与HCC。相反，模块基因的表达模式在肝硬化肝脏与正常肝脏之间保持一致。顶级模块基因TOP2A、CDC20、PRC1、CCNB2和NUSAP1与HCC的发生、进展和预后相关，并在人类蛋白质图谱数据库中表现出比正常肝脏更高的表达。结论：本研究揭示了一个与肝硬化背景下的肝细胞癌发生高度相关的连接模块，这可能为深入了解从肝硬化向HCC转变过程中涉及的遗传改变提供更深刻的理解，并为筛查和监测肝硬化高风险患者的HCC提供有价值的变量。