浆液性卵巢癌进展及预后关键分子的生物信息学筛选  

Screening of key molecules for predicting serous ovarian cancer progression and prognosis using bioinformatics

作　　者：饶玉梅[1] 张微微 张颖[1] 赵倩[1] 李留霞[1] 郭瑞霞[1] RAO Yumei;ZHANG Weiwei;ZHANG Ying;ZHAO Qian;LI Liuxia;GUO Ruixia(Department of Gynecology,the First Affiliated Hospital,Zhengzhou University,Zhengzhou 450052)

机构地区：[1]郑州大学第一附属医院妇科,郑州450052

出　　处：《郑州大学学报（医学版）》2021年第5期703-708,共6页Journal of Zhengzhou University(Medical Sciences)

基　　金：河南省高等学校重点科研项目(18A310031)。

摘　　要：目的:利用生物信息学方法探索浆液性卵巢癌的潜在生物学标志物和治疗靶点。方法:从GEO数据库下载浆液性卵巢癌数据集GSE54388、GSE105437,用GEO2R及Venn软件筛选差异表达基因(DEG),对DEG进行GO及KEGG富集分析,构建蛋白质-蛋白质相互作用网络,利用Cytoscape获取关键基因,利用Kaplan Meier plotter和GEPIA数据库对关键基因再次筛选。结果:筛选得到322个DEG,包括148个下调和174个上调基因。基因涉及染色体分离、RNA聚合酶Ⅱ启动子转录负调控、上皮细胞分化等细胞学过程,主要富集于细胞周期、癌症通路、干细胞多能性调节等信号通路。筛选出14个关键基因,其中7个高表达、1个低表达与卵巢癌患者的总生存率有关,且MCM10、TOP2A、ESPL1、CDCA3、NEK2、ESCO2等6个基因在卵巢癌组织中表达高于正常组织。结论:MCM10、TOP2A、ESPL1、CDCA3、NEK2、ESCO2等6个基因,可作为预测卵巢癌进展及预后的关键分子进一步研究。Aim:To screen potential biomarkers and therapeutic targets for serous ovarian cancer using bioinformatics.Methods:The serous ovarian cancer dataset GSE54388 and GSE105437 were downloaded from the GEO database.GEO2R tool and Venn diagram software were used to screen for differentially expressed genes(DEG),and DAVID database was used for GO and KEGG enrichment analysis.String database was used to construct protein-protein interaction network of these DEG,and Cytoscape was utilized to gain key genes.The critical genes were filtered by GEPIA and Kaplan Meier plotter database again.Results:There were 322 DEG,among which,148 downregulated and 174 upregulated genes.DEG involved in cytological processes such as chromosome segregation,negative regulation of transcription from RNA polymeraseⅡpromoter,epithelial cell differentiation,and mainly enriched in signaling pathways such as cell cycle,cancer pathway,and pluripotency regulation of stem cells.Fourteen key genes were obtained from DEG.Among them,7 higher and 1 lower expression genes were associated with the overall survival of patients with ovarian cancer,besides,6 genes such as MCM10,TOP2A,ESPL1,CDCA3,NEK2,ESCO2 were significantly higher in ovarian cancer tissue than in normal tissue.Conclusion:MCM10,TOP2A,ESPL1,CDCA3,NEK2,and ESCO2 genes could be further studied as the key molecules for predicting ovarian cancer progression and prognosis.

关 键 词：浆液性卵巢癌 差异表达基因 生物信息学 

分 类 号：R737.31[医药卫生—肿瘤]