Identification of key genes underlying clinical features of hepatocellular carcinoma based on weighted gene co‑expression network analysis and bioinformatics analysis

Objective: To identify module genes that are closely related to clinical features of hepatocellular carcinoma (HCC) by weighted gene co‑expression network analysis, and to provide a reference for early clinical diagnosis and treatment. Methods: GSE84598 chip data were downloaded from the GEO database, and module genes closely related to the clinical features of HCC were extracted by comprehensive weighted gene co‑expression network analysis. Hub genes were identified through protein interaction network analysis by the maximum clique centrality (MCC) algorithm;Finally, the expression of hub genes was validated by TCGA database and the Kaplan Meier plotter online database was used to evaluate the prognostic relationship between hub genes and HCC patients. Results: By comparing the gene expression data between HCC tissue samples and normal liver tissue samples, a total of 6 262 differentially expressed genes were obtained, of which 2 207 were upregulated and 4 055 were downregulated. Weighted gene co‑expression network analysis was applied to identify 120 genes of key modules. By intersecting with the differentially expressed genes, 115 candidate hub genes were obtained. The results of enrichment analysis showed that the candidate hub genes were closely related to cell mitosis, p53 signaling pathway and so on. Further application of the MCC algorithm to the protein interaction network of 115 candidate hub genes identified five hub genes, namely NUF2, RRM2, UBE2C, CDC20 and MAD2L1. Validation of hub genes by TCGA database revealed that all five hub genes were significantly upregulated in HCC tissues compared to normal liver tissues;Moreover, survival analysis revealed that high expression of hub genes was closely associated with poor prognosis in HCC patients. Conclusions: This study identifies five hub genes by combining multiple databases, which may provide directions for the clinical diagnosis and treatment of HCC.

基于加权基因共表达网络分析及生物信息学分析鉴定肝细胞癌临床特征关键基因

目的:通过加权基因共表达网络分析鉴定肝细胞癌(hepatocellular carcinoma,HCC)临床特征密切相关模块基因,为临床早期诊断及治疗提供参考。方法:从GEO数据库下载GSE84598芯片数据,综合加权基因共表达网络分析提取与HCC临床特征密切相关的模块基因。通过最大集团中心性(maximal clique centrality,MCC)算法对蛋白相互作用网络分析,鉴定出枢纽基因;最后通过TCGA数据库验证枢纽基因的表达情况、Kaplan-Meier Plotter在线数据库评估枢纽基因与HCC患者的预后关系。结果:通过对比HCC组织样本与正常肝组织样本的基因表达数据,共获得6262个差异表达基因,其中上调2207个,下调4055个。运用加权基因共表达网络分析鉴定出关键模块的120个基因;通过与差异表达基因取交集,得到候选枢纽基因115个。富集分析结果显示,候选枢纽基因与细胞有丝分裂、p53信号通路等密切相关。进一步运用MCC算法对115个候选枢纽基因的蛋白相互作用网络进行分析,鉴定出5个枢纽基因,即NUF2、RRM2、UBE2C、CDC20和MAD2L1。通过TCGA数据库对枢纽基因的验证,结果发现,与正常肝组织相比,在HCC组织中5个枢纽基因均明显上调;而且生存分析显示枢纽基因的高表达与HCC患者的不良预后密切相关。结论:本研究通过结合多个数据库鉴定出了5个枢纽基因,为HCC的临床诊断及治疗提供方向。