Identification of potential biomarkers in hepatocellular carcinoma: A network-based approach

Abstract

BackgroundHepatocellular carcinoma (HCC) is one of the leading causes of death worldwide. Identification of potential therapeutic and diagnostic biomarkers can be helpful to screen cancer progress. This study was implemented to discover potential biomarkers for HCC within a network-based approach integrated with microarray data. MethodsThrough downloading a gene expression profile GSE62232 differentially expressed genes (DEGs) were identified. Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for DEGs were performed utilizing Enrichr server. Following reconstruction of protein-protein interaction network of DEGs with STRING, network visualization, analyses, and clustering into structural modules were carried out using Cytoscape. Considering degree centrality, 15 hub genes were selected as early biomarker candidates for final validation. To validate hub genes, the GEPIA server was used to perform overall survival (OS) and disease-free survival (DFS). ResultsIn our approach, 1996 DEGs were identified including 995 up-regulated genes and 1001 down-regulated genes. KEGG pathway enrichment analysis shows that DEGs are associated with chemical carcinogenesis and cell cycle. GO term enrichment analysis indicated the relation of DEGs with epoxygenase P450 pathway, arachidonic acid monooxygenase activity, and secretory granule lumen. Following analysis of the protein-protein interaction network of DEGs, the top three structural modules and 15 early hub genes were selected. Validation of hub genes was performed using GEPIA. Consequently, CDK1, CCNB1, CCNA2, CDC20, AURKA, MAD2L1, TOP2A, KIF11, BUB1B, TYMS, EZH2, and BUB1 were considered as our final proposed biomarkers. Conclusionusing an integrated network-based approach with microarray data our results revealed 12 final candidates with the potential to be considered as biomarkers in hepatocellular carcinoma.