Abstract:Objective: To explore differentially expressed genes of adrenal cortical carcinoma (ACC) through bioinformatics, so as to provide new biomarkers for diagnosing ACC. Methods: Gene data sets of GSE14922, GSE19776 and GSE12368 were selected from the GEO database, and the differentially expressed genes of adrenocortical carcinoma tissues and normal adrenal tissues were screened by GEO2R tool online analysis. DAVID and STRING online databases were used to online analyze differentially expressed genes and construct a protein-protein interaction network. Cytoscape software was used to screen for hub genes. GEPIA was used to analyze the relationship between hub genes and ACC prognosis. Results: A total of 229 differentially expressed genes were screened out, of which 51 genes were up-regulated and 178 genes were down-regulated. Analysis showed that they were significantly enriched in the cell cycle and P53 signaling pathway and its molecular functions were mainly related to cytoskeletal protein combination and growth factor binding function. MCC selected eight key genes, namely cyclinB1 (CCNB1), cyclinA2 (CCNA2), cyclin-dependent kinases (CDK1), three-protein kinase BUB1 beta (BUB1B), mitotic arrest deficient 2 like 1 (MAD2L1), ribonucleotide reductase M2 (RRM2), targeting protein for xenopus kinesin-like protein 2 (TPX2) and aurora kinase A (AURKA); GEPIA online database was used to verify they were highly expressed in ACC and were closely related to the overall survival rate and disease-free survival rate of ACC patients. Conclusion: Eight genes, including CCNB1, CCNA2, CDK1, BUB1B, MAD2L1, RRM2, TPX2 and AURKA, may be used as new biomarkers to assist the diagnosis and treatment of ACC.
