摘要
目的多囊卵巢综合征(PCOS)常伴有肥胖表现,探索调控PCOS患者肥胖的关键基因,有益于肥胖型PCOS的诊断与防治。方法从GEO数据库中下载肥胖型PCOS患者和肥胖非PCOS患者转录组数据,运用加权基因共表达网络分析法(WGCNA)筛选出与PCOS发生发展相关的关键模块,用GO及KEGG对模块功能进行富集分析,进一步挖掘出与PCOS发生发展相关联的中枢基因(Hub gene)。结果WGCNA一共定义了13个基因共表达模块,从其中4个模块中筛选出与肥胖PCOS相关的189个差异表达基因,这些基因主要被富集到免疫相关的TNF-α和IL-17信号通路,从中最终筛选出PTGS2、IL6、CXCL8、CXCL2、IL1B、S100A9、S100A8、CCL2、ICAM1、LIF、TNFRSF1B和IRF112个Hub基因。结论本研究筛选出与肥胖PCOS相关的关键基因,并进一步聚类到免疫相关信号通路,提示肥胖型PCOS与免疫应答存在相关性。
Objective To explore the key modules and identify Hub genes related to the development of polycystic ovarian syndrome(PCOS).Methods The key modules for the development of PCOS were screened by weighted gene co-expression network analysis(WGCNA).The gene annotation database Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)were used to clarify the function of the modules,which aims to find out the Hub genes having the highest correlation with the development of PCOS.Results A total of 13 co-expression modules were defined by WGCNA,and 189 differentially expressed genes were screened from 4 PCOS-related modules.These genes were mainly enriched in immune-related signaling pathways,such as TNF-αand IL-17 signaling pathways.Among them,twelve members of Hub genes were finally mined from the key modules,which included PTGS2,IL6,CXCL8,CXCL2,IL1B,S100A9,S100A8,CCL2,ICAM1,LIF,TNFRSF1B and IRF1.Conclusion The key genes related to PCOS in obesity found in this study are mainly clustered in immune-related signaling pathways.These results suggest that obesity PCOS is correlated with immune response.
作者
陈莉
邹铸
徐靖原
沈梦溪
李俊
姚新生
Chen Li;Zhou Zhu;Xu Jingyuan;Shen Mengxi;Li Jun;Yao Xinsheng(Department of Immunology,Zunyi Medical University,Zunyi Guizhou 563099,China;Department of Medical Laboratory,theAffiliated Hospital of Zunyi Medical University,Zunyi Guizhou 563099,China)
出处
《合肥医科大学学报》
2022年第4期491-497,共7页
Journal of Zunyi Medical University
基金
国家自然科学基金资助项目(NO:82060272)
贵州省卫生健康委科学技术基金资助项目(NO:2019-1-043)。
关键词
多囊卵巢综合征
Hub基因
加权基因共表达网络分析
免疫信号通路
Polycystic ovarian syndrome
Hub genes
weighted gene co-expression network analysis
immune-related signaling pathways
