Comprehensive Analysis of Epigenetic Associated Genes on Differential Gene Expression and Prognosis in Hepatocellular Carcinoma.

Abstract

Early detection of hepatocellular carcinoma (HCC) is significantly effective in clinical management. This study aimed to identify potential HCC biomarkers. Analysis of expression profiles in HCC clinical samples downloaded from the cancer genome atlas (TCGA) and the gene expression omnibus (GEO) datasets was performed to identify differentially expressed genes (DEGs) using R packages. The epigenetic differentially expressed genes (epiDEGs) were obtained after intersections of genes between DEGs and epigenetic factors (EFs). The biological functions of epiDEGs were annotated by gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis. Protein-protein interaction and expression correlation were performed to investigate the interactions among epiDEGs by the STRING online tool and R packages. The epiDEGs associated with overall survival (OS) were identified as patient prognosis using the Cox regression analysis. The levels of gene expression were validated by RT-qPCR and Western blot between HCC cell lines, (HepG2, and Huh-7) and normal cell lines (THLE-2). Thirty-five epiDEGs were obtained, including 25 upregulated genes and 10 downregulated genes. Functional enrichment and PPI analysis indicated the development of HCC is a complicated process involving various genes and proteins. Survival analysis showed nine epiDEGs associated with the OS of patients and these might be the independent prognostic biomarkers for HCC. The expressions of most epiDEGs were significantly higher in HCC patients with stage II and III compared with stage I. Furthermore, the expression of these epiDEGs between HCC cell lines with normal cell lines was shown to be consistent with the TCGA and GEO datasets except PBK. Eight hub epiDEGs, including EZH2, CDK1, CENPA, RAD54L, HELLS, HJURP, AURKA, and AURKB, were associated with the overall survival of HCC patients and could be potential biomarkers to predict prognosis.