Identification of Hub Genes Associated with Hepatocellular Carcinoma Prognosis by Bioinformatics Analysis

Abstract

Objective: This study aimed to identify hub genes that are associated with hepatocellular carcinoma (HCC) prognosis by bioinformatics analysis. Methods: Data were collected from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) liver HCC datasets. The robust rank aggregation algorithm was used in integrating the data on differentially expressed genes (DEGs). Online databases DAVID 6.8 and REACTOME were used for gene ontology and pathway enrichment analysis. R software version 3.5.1, Cytoscape, and Kaplan-Meier plotter were used to identify hub genes. Results: Six GEO datasets and the TCGA liver HCC dataset were included in this analysis. A total of 151 upregulated and 245 downregulated DEGs were identified. The upregulated DEGs most significantly enriched in the functional categories of cell division, chromosomes, centromeric regions, and protein binding, whereas the downregulated DEGs most significantly enriched in the epoxygenase P450 pathway, extracellular region, and heme binding, with respect to biological process, cellular component, and molecular function analysis, respectively. Upregulated DEGS most significantly enriched the cell cycle pathway, whereas downregulated DEGs most significantly enriched the metabolism pathway. Finally, 88 upregulated and 40 downregulated genes were identified as hub genes. The top 10 upregulated hub DEGs were CDK1, CCNB1, CCNB2, CDC20, CCNA2, AURKA, MAD2L1, TOP2A, BUB1B and BUB1. The top 10 downregulated hub DEGs were ESR1, IGF1, FTCD, CYP3A4, SPP2, C8A, CYP2E1, TAT, F9 and CYP2C9. Conclusions: This study identified several upregulated and downregulated hub genes that are associated with the prognosis of HCC patients. Verification of these results using in vitro and in vivo studies is warranted.