运用生物信息学方法探究肾上腺皮质癌的关键基因

Exploring hub genes of adrenal cortical carcinoma by bioinformatics

摘要:

目的： 运用生物信息学方法筛选肾上腺皮质癌（adrenal cortical carcinoma，ACC）的差异表达基因，为ACC诊疗提供新的生物标志物。方法： 从GEO数据库中选择基因数据集GSE14922、GSE19776、GSE12368，通过GEO2R工具在线分析，筛选出肾上腺皮质癌组织与正常肾上腺组织的差异表达基因。利用DAVID和STRING在线分析差异表达基因并构建蛋白-蛋白相互作用网络。运用Cytoscape软件筛选关键基因，使用GEPIA在线分析关键基因与ACC预后的关系。结果： 共筛选出229个差异表达基因，其中上调基因51个，下调基因178个。分析显示其显著富集于细胞周期、P53信号通路，分子功能主要涉及细胞骨架蛋白组合、生长因子结合功能。MCC筛选出8个关键基因，分别是cyclinB1（CCNB1）、cyclinA2（CCNA2）、cyclin-dependent kinases（CDK1）、threonine-protein kinase BUB1 beta（BUB1B）、mitotic arrest deficient 2 like 1（MAD2L1）、ribonucleotide reductase M2（RRM2）、targeting protein for xenopus kinesin-like protein2（TPX2）、aurora kinase A（AURKA），通过GEPIA在线验证其在ACC中高表达且与ACC患者总体生存率及无病生存率密切相关。结论： CCNB1、CCNA2、CDK1、BUB1B、MAD2L1、RRM2、TPX2、AURKA这8个基因可能作为协助ACC诊疗的新生物标志物。

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.