基于生物信息学分析肝细胞癌潜在的关键基因及miRNA

Bioinformatics analysis of potential hub genes and miRNAs for hepatocellular carcinoma

目的利用生物信息学分析差异表达基因,结合临床生存分析,探讨肝细胞癌(HCC)中的关键基因、miRNA及免疫浸润。方法通过GEO数据库获取5张芯片(GSE14520、GSE84598和GSE84402 3张基因芯片和GSE98269、GSE57555 2张miRNA数据芯片),筛选差异表达基因(DEGs)和差异miRNA(DEMs);Cytoscape3.7.1软件搭建DEGs的PPI网络并筛选关键基因;对关键基因进行GO功能分析、KEGG通路富集分析、表达水平验证和免疫组化分析、病理分期关系和患者生存分析;并评估关键基因在肝癌组织中的免疫浸润表达情况,预测miRNA并与miRNA芯片取交集。结果筛选出164个DEGs, GO富集于细胞周期、视黄酸受体信号通路的调控等生物学过程,KEGG信号通路涉及卵母细胞减数分裂、p53信号通路等,10个关键基因在HCC组织中表达存在差异,并与肝癌总体生存期、无病生存期、临床分期和免疫浸润相关,经miRDB数据库预测得到132个miRNA。结论 CDK1、CCNB1、NDC80、TOP2A、KPNA2、BIRC5、AURKA、CYP3A4、TAT和ESR1可作为HCC的潜在生物学标记物,HCC的关键分子机制可能是p53信号通路,hsa-miR-224-5p、hsa-miR-144-3p、hsa-miR-148a-3p、hsa-miR-130a-3p及hsa-miR-22-3p是HCC关键的miRNA。

Objective Using bioinformatics to analyze differential gene expression data, combined with clinical survival analysis, toexplore the hub genes, miRNA and immune infiltration in HCC.Methods Obtain 5 chips from the GEO database(GSE14520,GSE84598 and GSE84402 for gene chips and GSE98269,GSE57555 for miRNA data chips).Screen differentially expressed genes(DEGs) and differentially expressed miRNAs(DEMs).Cytoscape 3.7.1 software was used to build the PPI network of DEGs and to screen key genes.The GO function analysis KEGG pathway enrichment analysis, expression level verification and immunohistochemical analysis of key genes, and pathological stage relationship and patient survival analysis were performed.The immune infiltration and expression of key genes in HCC tissues were evaluated.Predict miRNAs and their intersection with miRNA chip was analyzed.Results We screened 164 DEGs.GO is enriched in biological processes such as the cell cycle and the regulation of the retinoic acid receptor signaling pathway.KEGG signaling pathway involves oocyte meiosis, p53 signaling pathway, etc.There were differences in the expression of 10 key genes in HCC tissues, which were related to the overall survival of HCC,disease-free survival, clinical stage and immune infiltration.A total of 132 miRNAs were predicted by miRDB database.Conclusion CDK1,CCNB1,NDC80,TOP2A,KPNA2,BIRC5,AURKA,CYP3A4,TAT and ESR1 can be used as potential biomarkers of HCC.The key molecular mechanism of HCC may be the p53 signaling pathway;hsa-miR-224-5p, hsa-miR-144-3p, hsa-miR-148 a-3p, hsa-miR-130 a-3p and hsa-miR-22-3p are important miRNAs for HCC.