Identification and Analysis of Potential Key Genes Associated With Hepatocellular Carcinoma Based on Integrated Bioinformatics Methods - Li, Zhuolin; Lin, Yao; Cheng, Bizhen; Zhang, Qiaoxin; Cai, Yingmu (Frontiers in Genetics, 2021)
Background: Hepatocellular carcinoma (HCC) is a malignant tumor with high mortality, and its molecular mechanism remains incompletely understood. This study aimed to identify significant differentially expressed genes (DEGs) in HCC pathogenesis and to provide a bioinformatics basis for the occurrence, prevention, and treatment of HCC. Methods: The Gene Expression Omnibus (GEO) dataset GSE121248 and The Cancer Genome Atlas (TCGA) dataset were used for bioinformatics analysis. We screened DEGs between tumor and normal tissues using the limma package and edgeR package of R. GO (Gene Ontology) functional analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis of the DEGs were conducted using the DAVID database. Next, the protein-protein interaction (PPI) network of the DEGs was constructed using the STRING database, visualized by Cytoscape. Hub genes were screened by applying the cytoHubba plug-in, followed by verification using the GEPIA and Oncomine databases. We demonstrated differences in the protein levels of hub genes using the HPA database. The GEPIA database was employed to analyze the prognostic values of hub genes. Additionally, the drug-gene interaction network was constructed using the Comparative Toxicogenomics Database (CTD). Results: A total of 763 DEGs (247 upregulated and 516 downregulated DEGs) were screened out that were mainly enriched in the oxidation-reduction process, epoxygenase P450 pathway, and metabolism-related pathways. A PPI network was constructed, and the most significantly enriched pathways were the cell cycle and P53 signaling pathway in module 1. We filtered out eight hub genes in the PPI, and they were significantly upregulated in HCC samples, findings consistent with the expression validation results. Additionally, survival analysis showed that, among these eight genes, except CCNB2, high expression levels of CDC20, CDK1, MAD2L1, BUB1, BUB1B CCNB1, and CCNA2 were associated with the poor overall survival (OS) of HCC patients. CTD analysis showed that only topotecan, oxaliplatin, and azathioprine could reduce the expression levels of all seven hub genes. Conclusion: The present study identified the crucial genes and pathways that may be involved in HCC pathogenesis. These findings provide some directive significance for the future prognosis prediction and molecular targeting therapy of HCC.