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使用加权共表达网络分析鉴定与不孕相关的拓扑重要基因

Identification of Infertility-Associated Topologically Important Genes Using Weighted Co-expression Network Analysis.

作者信息

Wu Jingni, Xia Xiaomeng, Hu Ye, Fang Xiaoling, Orsulic Sandra

机构信息

Department of Obstetrics and Gynecology, The Second Xiangya Hospital, Central South University, Changsha, China.

Department of Obstetrics and Gynecology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.

出版信息

Front Genet. 2021 Feb 3;12:580190. doi: 10.3389/fgene.2021.580190. eCollection 2021.

DOI:10.3389/fgene.2021.580190
PMID:33613630
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7887323/
Abstract

Endometriosis has been associated with a high risk of infertility. However, the underlying molecular mechanism of infertility in endometriosis remains poorly understood. In our study, we aimed to discover topologically important genes related to infertility in endometriosis, based on the structure network mining. We used microarray data from the Gene Expression Omnibus (GEO) database to construct a weighted gene co-expression network for fertile and infertile women with endometriosis and to identify gene modules highly correlated with clinical features of infertility in endometriosis. Additionally, the protein-protein interaction network analysis was used to identify the potential 20 hub messenger RNAs (mRNAs) while the network topological analysis was used to identify nine candidate long non-coding RNAs (lncRNAs). Functional annotations of clinically significant modules and lncRNAs revealed that hub genes might be involved in infertility in endometriosis by regulating G protein-coupled receptor signaling (GPCR) activity. Gene Set Enrichment Analysis showed that the phospholipase C-activating GPCR signaling pathway is correlated with infertility in patients with endometriosis. Taken together, our analysis has identified 29 hub genes which might lead to infertility in endometriosis through the regulation of the GPCR network.

摘要

子宫内膜异位症与高不孕风险相关。然而,子宫内膜异位症导致不孕的潜在分子机制仍知之甚少。在我们的研究中,我们旨在基于结构网络挖掘发现与子宫内膜异位症不孕相关的拓扑重要基因。我们使用来自基因表达综合数据库(GEO)的微阵列数据,为患有子宫内膜异位症的可育和不育女性构建加权基因共表达网络,并识别与子宫内膜异位症不孕临床特征高度相关的基因模块。此外,蛋白质-蛋白质相互作用网络分析用于识别潜在的20个枢纽信使核糖核酸(mRNA),而网络拓扑分析用于识别9个候选长链非编码核糖核酸(lncRNA)。对具有临床意义的模块和lncRNA的功能注释表明,枢纽基因可能通过调节G蛋白偶联受体信号(GPCR)活性参与子宫内膜异位症的不孕过程。基因集富集分析表明,磷脂酶C激活的GPCR信号通路与子宫内膜异位症患者的不孕相关。综上所述,我们的分析确定了29个枢纽基因,这些基因可能通过调节GPCR网络导致子宫内膜异位症患者不孕。

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