Bioinformatics analysis for hepatocellular carcinoma genes based on the data of expression profile chip

OBJECTIVESHepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide, especially in Asia and Africa. However, the underlying mechanism is still unclear. Consequently, it is important to explore its key genes and prognosis-related genes via bioinformatics. This study aimed to explore the molecular mechanism of HCC by using bioinformatics analysis for HCC gene chip data. METHODSMicroarray data of HCC genes were downloaded from public GEO database and screened for differentially expressed genes (DEGs) by GEO2R analysis. Then DAVID online tool was used for GO annotation and KEGG pathway enrichment analysis. STRING-DB online database and Cytoscape software were used for protein interaction network analysis.GEPIA and Ualcan were applied to evaluate prognosis and promoter methylation level. RESULTSA total of 87 DEGs of HCC were screened, of which 15 genes were up-regulated and 72 genes were down-regulated. GO annotation indicated that most of the genes were involved in oxidation reduction,cellular amino acid derivative metabolic process, carboxylic acid catabolic process, and response to wounding. KEGG pathways were enriched in linoleic acid metabolism, retinol metabolism, complement and coagulation cascades,steroid hormone biosynthesis, drug metabolism, and other pathways. Two key modules and key genes AURKA and SPP2 were obtained by protein interaction network analysis. Prognostic analysis showed that the 2 genes were significantly correlated with the total survival time of patients with HCC. There was no significant difference in the methylation level of AURKA promoter between the primary tumor group and the normal group (P=0.296) and the methylation level of SPP2 promoter was significantly lower in the primary tumor group than that in the normal group (P