基于生物信息学筛选乙型肝炎差异表达基因及其在预测肝癌预后中的作用

Screening differentially expressed genes of hepatitis B based on bioinformatics and its role in prognosis of liver cancer

目的:应用生物数据库及生物信息分析技术探索乙型肝炎病毒(HBV)感染肝细胞的差异表达基因,分析其在肝癌预后中的预测价值。方法:登录基因芯片公共数据库(GEO)下载数据并检测基因芯片品质,应用R软件甄选差异基因,富集分析对差异基因进行分类注释,构建蛋白质相互作用网络筛选核心基因,运用基因表达谱数据动态分析(GEPIA)验证核心差异表达基因,分析5年生存率。结果:合计1041个基因在乙型肝炎病毒感染肝细胞中存在表达差异,基因本体(GO)富集分析显示差异基因主要与细胞外间隙、胞外区、胞外外泌体、环氧化酶P450通路、受体结合相关。京都基因和基因组百科全书(KEGG)通路分析显示差异基因主要参与补体及凝血级联反应、糖酵解/糖异生、视黄醇代谢、过氧化物酶体增殖物激活受体(PPAR)信号通路、碳代谢、代谢途径、初级胆汁酸生物合成、癌症中的蛋白聚糖、胆汁分泌等信号通路。蛋白互作网络筛选出10个关键基因。GEPIA数据库验证结果显示其6个基因高表达于肝癌组织,而生存分析验证激肽原基因1(KNG1)高表达组肝癌患者5年生存率更高。结论:生物信息学技术能有效筛选HBV感染肝细胞中的核心差异基因,并具有预测肝癌预后的作用。

Objective:To explore the differentially expressed genes of hepatitis B virus(HBV)infected hepatocytes and analyze their role in predicting the prognosis of liver cancer by using biological database and Bioinformation Analysis Technology.Methods:Log in the gene chip public database(GEO)to download the data and test the quality,select the differential gene by R software,classify and annotate the differential gene by enrichment analysis,construct the protein interaction network to screen the core gene,verify the differential expression gene by gene expression profiling interactive analysis(GEPIA)database and analyze the five-year survival rate.Results:A total of 1041 genes were differentially expressed in hepatocytes infected with hepatitis B virus.Gene ontology(GO)enrichment analysis showed that the differentially expressed genes were mainly related to extracellular space,extracellular region,extracellular exosomes,epoxygenase P450 pathway and receptor binding.Kyoto encyclopedia of senes and genomes(KEGG)pathway analysis showed that the differential genes were mainly involved in complement and coagulation cascade,glycolysis/gluconeogenesis,retinol metabolism,peroxisome proliferators-activated receptors(PPAR)signaling pathway,carbon metabolism,metabolic pathway,primary bile acid biosynthesis,proteoglycan in cancer,bile secretion and other signaling pathways.Ten key genes were screened by protein interaction network.The results of GEPIA database showed that the six genes were highly expressed in the liver cancer tissues,and the survival analysis showed that the five-year survival rate of the liver cancer patients in recombinant kininogen 1(KNG1)high expression group was higher.Conclusion:Bioinformatics can effectively screen the core differential genes in HBV infected hepatocytes and predict the prognosis of liver cancer.