基于生物信息学分析筛选和鉴定肝细胞癌预后相关的代谢基因

目的:寻找与肝癌发病机制和预后相关的潜在代谢基因并构建预测肝细胞癌(hepatocellular carcino-ma,HCC)患者预后模型.方法:通过GSEA数据库获得所有与代谢途径相关的基因,从TCGA数据库下载肝癌和正常组织的基因表达数据.将二者的基因相映射,分析这些代谢差异表基因(DEGs)在肝癌标本中的表达情...

Screening and identification of metabolic genes related to prognosis of hepatocellular carcinoma by bioinformatics

Objective:To find the potential metabolic genes related to the pathogenesis and prognosis of HCC and to construct a model to predict the prognosis of HCC patients.Methods:All the genes related to metabolic pathway were obtained by GSEA database, and the gene expression data of HCC and normal tissues were downloaded from TCGA database. The differential expression of these metabolic genes(DEGs) in HCC tissue was analyzed.Then the metabolic genes related to prognosis were screened by univariate Cox regression analysis.On this basis, LASSO analysis was used to further screen prognostic genes to construct a prognostic risk model and analyze the prognosis of high and low risk groups.The genes in the prognostic model were analyzed by KEGG pathway enrichment analysis and GO analysis.Univariate Cox analysis and multivariate Cox regression analysis were used to analyze the independent prognosis of the prognostic model, and Nomogram diagram was drawn to evaluate the prognosis of the patients.Finally, the expression of prognostic genes in tumor tissues and normal tissues was compared by GEPIA2 database, and the effect of prognostic genes on survival was analyzed.Finally, the liver cancer samples(GSE14520) in GEO database were used for external verification.Results:A total of 959 metabolism-related genes were obtained.There were 156 genes with significant differences in expression(DEGs),of which 105 genes were up-regulated and 51 genes were down-regulated.Then, through univariate Cox analysis of these DEGs, 58 candidate genes related to prognosis were obtained.On this basis, the prognostic model of eleven-gene signature was constructed by LASSO analysis.In the model, the prognosis of the high risk group was worse than that of the low risk group(P<0.05).The model can be repeated in GSE14520.The main pathways involved in the prognostic model were pyrimidine metabolism, glutathione metabolism, drug metabolism, carbon metabolism, purine metabolism, amino sugar and nucleotide glucose metabolism.Among the 11 genes, survival analysis showed that the high expression of ATIC,ENO1,G6 PD,GNPDA1,HEXB,ME1,RRM1, RRM2 and UCK2 and the low expression of CYP2 C9 were significantly correlated with poor prognosis.Conclusion:The model of 11-gene signature and Nomogram map established in this study can help clinicians to evaluate the prognosis of patients with liver cancer.