Objectives: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide, especially in Asia and Africa. However, the underlying mechanism is still unclear. Consequently, it is important to explore its key genes and prognosis-related genes via bioinformatics. This study aimed to explore the molecular mechanism of HCC by using bioinformatics analysis for HCC gene chip data.
Methods: Microarray data of HCC genes were downloaded from public GEO database and screened for differentially expressed genes (DEGs) by GEO2R analysis. Then DAVID online tool was used for GO annotation and KEGG pathway enrichment analysis. STRING-DB online database and Cytoscape software were used for protein interaction network analysis.GEPIA and Ualcan were applied to evaluate prognosis and promoter methylation level.
Results: A total of 87 DEGs of HCC were screened, of which 15 genes were up-regulated and 72 genes were down-regulated. GO annotation indicated that most of the genes were involved in oxidation reduction,cellular amino acid derivative metabolic process, carboxylic acid catabolic process, and response to wounding. KEGG pathways were enriched in linoleic acid metabolism, retinol metabolism, complement and coagulation cascades,steroid hormone biosynthesis, drug metabolism, and other pathways. Two key modules and key genes AURKA and SPP2 were obtained by protein interaction network analysis. Prognostic analysis showed that the 2 genes were significantly correlated with the total survival time of patients with HCC. There was no significant difference in the methylation level of AURKA promoter between the primary tumor group and the normal group (P=0.296) and the methylation level of SPP2 promoter was significantly lower in the primary tumor group than that in the normal group (P<0.001).
Conclusions: HCC-relevant AURKA and SPP2 are obtained via bioinformatics analysis, which are closely related to the prognosis of patients with HCC. Gene promoter methylation is not the main factor for AURKA and SPP2 expression levels.
目的: 肝细胞癌是全球范围最常见的恶性肿瘤之一，尤其是在亚洲和非洲。然而，肝细胞癌的分子机制仍不清楚。因而，利用生物信息学这一手段寻找参与肝细胞癌的关键基因，以及与肝细胞癌预后相关的基因至关重要。本研究通过对肝细胞癌基因芯片数据进行生物信息学分析，为探索肝细胞癌的分子机制提供理论依据。方法: 从基因芯片公共数据库(GEO)下载肝细胞癌基因的芯片数据，经过GEO2R分析筛选差异表达基因后，分别通过DAVID在线分析、STRING-DB数据库、Cytoscape软件、GEPIA和Ualcan在线分析工具，进行差异基因的功能注释、通路分析、蛋白质互作网络分析、预后分析和启动子甲基化水平研究。结果: 共筛选出87个肝细胞癌差异表达基因，其中上调基因15个，下调基因72个。功能注释和通路分析结果显示：这些差异基因主要影响氧化还原、细胞氨基酸衍生物代谢、羧酸代谢分解、损伤反应等生物过程，影响亚油酸和维生素A代谢、补体系统及级联反应、甾体激素生物合成和药物代谢等通路。经蛋白质互作网络分析获得2个关键模块区和2个关键基因AURKA和SPP2。预后分析结果显示这两个基因与肝细胞癌患者总生存时间显著相关。AURKA启动子甲基化水平在Normal组和Primary tumor组间差异无统计学意义(P=0.296)，在Primary tumor组中SPP2启动子甲基化水平显著下降(P<0.001)。结论: 通过生物信息学分析获得2个(AURKA和SPP2)与肝细胞癌相关的关键基因，它们与肝细胞癌患者预后密切相关；基因启动子甲基化不是AURKA和SPP2表达水平的主要因素。.
Keywords: bioinformatics; differentially expressed genes; gene chip; hepatocellular carcinoma.