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Endocrinol."],"abstract":"<jats:p>Adrenocortical carcinoma (ACC) is a rare endocrine malignancy with poor prognosis. The disease originates from the cortex of adrenal gland and lacks effective treatment. Efforts have been made to elucidate the pathogenesis of ACC, but the molecular mechanisms remain elusive. To identify key genes and pathways in ACC, the expression profiles of GSE12368, GSE90713 and GSE143383 were downloaded from the Gene Expression Omnibus (GEO) database. After screening differentially expressed genes (DEGs) in each microarray dataset on the basis of cut-off, we identified 206 DEGs, consisting of 72 up-regulated and 134 down-regulated genes in three datasets. Function enrichment analyses of DEGs were performed by DAVID online database and the results revealed that the DEGs were mainly enriched in cell cycle, cell cycle process, mitotic cell cycle, response to oxygen-containing compound, progesterone-mediated oocyte maturation, p53 signaling pathway. The STRING database was used to construct the protein\u2013protein interaction (PPI) network, and modules analysis was performed using Cytoscape. Finally, we filtered out eight hub genes, including CDK1, CCNA2, CCNB1, TOP2A, MAD2L1, BIRC5, BUB1 and AURKA. Biological process analysis showed that these hub genes were significantly enriched in nuclear division, mitosis, M phase of mitotic cell cycle and cell cycle process. Violin plot, Kaplan-Meier curve and stage plot of these hub genes confirmed the reliability of the results. In conclusion, the results in this study provided reliable key genes and pathways for ACC, which will be useful for ACC mechanisms, diagnosis and candidate targeted treatment.<\/jats:p>","DOI":"10.3389\/fendo.2023.1250033","type":"journal-article","created":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T03:22:43Z","timestamp":1700450563000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Identification of key genes and pathways in adrenocortical carcinoma: evidence from bioinformatic 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