Abstract
BACKGROUND: Bioinformatics analysis of hepatocellular carcinoma (HCC) expression profiles can aid in understanding its molecular mechanisms and identifying new targets for diagnosis and treatment. AIM: In this study, we analyzed expression profile datasets and miRNA expression profiles related to HCC from the GEO using R software to detect differentially expressed genes (DEGs) and differentially expressed miRNAs (DEmiRs). METHODS AND RESULTS: Common DEGs were identified, and a PPI network was constructed using the STRING database and Cytoscape software to identify hub genes. The reduced levels of tumor suppressor miRNAs or down regulated DEmiRs may be increased levels of oncogenes, the oncomirs or up regulated DEmiRs may be decreased levels of tumor suppressor genes in cancerous cells. According to this strategy, increased and decreased DEGs, also increased and decreased DEmiRs were selected. The multimir package was employed to predict target genes for DEmiRs then DEmiRs-hub gene network created. We identified approximately 1000 overlapping DEGs and 60 DEmiRs. Hub genes included RRM2, MELK, KIF11, KIF23, NCAPG, DLGAP5, BUB1B, AURKB, CCNB1, KIF20A, CCNA2, TTK, PBK, TOP2A, CDK1, MAD2L1, BIRC5, ASPM, CDCA8, and CENPF, all associated with significantly worse survival in HCC. miR-224, miR-24, miR-182, miRNA-1-3p, miR-30a, miR-27a, and miR-214 were identified as important DEmiRs with targeting more than six hub genes. CONCLUSION: Generally, our findings offer insight into the interaction of hub genes and miRNAs in the development of HCC by bioinformatics analysis, information that may prove useful in identifying biomarkers and therapeutic targets in HCC.