基于GEO数据库生物信息学方法分析子宫内膜癌相关基因和候选通路

doi:10.13481/j.1671-587x.20200422

吉林大学学报(医学版) ›› 2020, Vol. 46 ›› Issue (04): 804-809.doi: 10.13481/j.1671-587x.20200422

• 临床研究 • 上一篇

基于GEO数据库生物信息学方法分析子宫内膜癌相关基因和候选通路

王治, 洪莉, 李素廷, 曾婉玲

武汉大学人民医院妇产科, 湖北武汉 430060

收稿日期:2019-10-30 发布日期:2020-08-20
通讯作者: 洪莉,教授,主任医师,博士研究生导师(Tel:027-88041911,E-mail:dr_hongli@whu.edu.cn) E-mail:dr_hongli@whu.edu.cn
作者简介:王治(1996-),男,河南省驻马店市人,在读医学硕士,主要从事妇科肿瘤基础方面的研究。
基金资助:
科技部国家重点研发计划项目资助课题（2018YFC1311300）；中央高校基本科研业务费专项资金项目资助课题（2042018gf0039）；湖北省第二届医学领军人才工程第二层次基金资助课题（（2019）47号）

Analysis on endometrial cancer-related genes and candidate pathways based on GEO database bioinformatics methods

WANG Zhi, HONG Li, LI Suting, ZENG Wanling

Department of Gynecology and Obstetrics, People's Hospital, Wuhan University, Wuhan 430060, China

Received:2019-10-30 Published:2020-08-20

摘要/Abstract

摘要： 目的：通过生物信息学方法分析与子宫内膜癌（EC）发生发展相关的关键基因和候选通路，探讨EC的发病机制和治疗靶点。方法：自公共基因芯片数据库（GEO）下载EC芯片数据集GSE17025和GSE63678，使用GEO2R在线分析工具和R软件筛选EC癌组织与癌旁组织的差异表达基因（DEGs），并对DEGs进行基因本体论（GO）富集分析和京都基因与基因组百科全书（KEGG）信号通路分析，采用String数据库进行蛋白质-蛋白质互作网络（PPI）分析，最后采用Cytoscape软件对PPI网络进行可视化并进行模块分析。结果：对芯片数据集GSE17025和GSE63678进行DEGs分析后共获取100个共同上调基因和106个共同下调基因。GO富集分析DEGs主要富集于有丝分裂染色体分离、核分裂和细胞器分裂等生物学过程；KEGG信号通路分析DEGs主要富集于细胞周期、miRNA、p53信号通路和2型糖尿病等信号通路。通过Cytoscape软件分析，PPI网络中细胞分裂周期基因20（CDC20）、极光激酶A（AURKA）、细胞周期蛋白B1（CCNB1）、泛素E3连接酶（DTL）、中心体相关蛋白55（CEP55）、细胞周期蛋白依赖性激酶1（CDK1）、驱动蛋白家族成员11（KIF11）、母体胚胎亮氨酸拉链激酶（MELK）、细胞周期蛋白B2（CCNB2）和苯并咪唑出芽抑制解除同源物蛋白1（BUB1）被筛选为关键基因。结论：细胞周期相关基因与通路调控网络的失调可能是EC发病的主要机制。

关键词: 生物信息学, 子宫内膜癌, 差异基因, 基因表达汇编芯片数据集

Abstract: Objective: To analyze the key genes and candidate pathways related to the occurrence and development of endometrial cancer(EC) with the bioinformatics methods, and to explore the pathogenesis and the therapeutic targets of EC. Methods: The EC datasets (GSE17025 and GSE63678) were downloaded from the Gene Expression Omnibus (GEO), and the GEO2R online analysis tools and R software were used to screen for the differential expression genes (DEGs) in the EC tissue and the adjacent normal tissue. The GO enrichment analysis and KEGG pathway analysis of DEGs were performed with the String database for protein-protein interaction network (PPI) analysis. Finally, the PPI network was analyzed and visualized by Cytoscape software. Results: After the DEGs analysis of the datasets GSE17025 and GSE63678, 100 co-upregulated genes and 106 co-downregulated genes were obtained. The results of GO enrichment analysis indicated that DEGs were mainly enriched in mitotic chromosome segregation, nuclear division, organelle division and other biological processes. The result of KEGG signaling pathway analysis showed that DEGs were mainly enriched in cell cycle, miRNA, p53 signaling pathway, type Ⅱ diabetes signal pathway. Through Cytoscape software analysis,CDC20, AURKA, CCNB1, DTL, CEP55, CDK1, KIF11, MELK, CCNB2, and BUB1 in the PPI network were screened as the key genes. Conclusion: The imbalance of cell cycle-related genes and pathway regulatory networks may be involved in the occurrence of EC.

Key words: bioinformatics, endometrial cancer, differential gene, Gene Expression Omnibus dataset

中图分类号:

R737.33

王治, 洪莉, 李素廷, 曾婉玲. 基于GEO数据库生物信息学方法分析子宫内膜癌相关基因和候选通路[J]. 吉林大学学报(医学版), 2020, 46(04): 804-809.

WANG Zhi, HONG Li, LI Suting, ZENG Wanling. Analysis on endometrial cancer-related genes and candidate pathways based on GEO database bioinformatics methods[J]. Journal of Jilin University(Medicine Edition), 2020, 46(04): 804-809.

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基于GEO数据库生物信息学方法分析子宫内膜癌相关基因和候选通路

Analysis on endometrial cancer-related genes and candidate pathways based on GEO database bioinformatics methods

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