Department of Obstetrics and Gynecology, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China.
Department of Obstetrics and Gynecology, David Geffen School of Medicine, University of California, Los Angeles, California 90095, United States.
Int J Med Sci. 2021 Aug 3;18(15):3425-3436. doi: 10.7150/ijms.63541. eCollection 2021.
Endometriosis is a common gynecological disorder with high rates of infertility and pelvic pain. However, its pathogenesis and diagnostic biomarkers remain unclear. This study aimed to elucidate potential hub genes and key pathways associated with endometriosis in ectopic endometrium (EC) and eutopic endometrium (EU). EC and EU-associated microarray datasets were obtained from the gene expression omnibus (GEO) database. Gene set enrichment analysis was performed to obtain further biological insight into the EU and EC-associated genes. Weighted gene co-expression network analysis (WGCNA) was performed to find clinically significant modules of highly-correlated genes. The hub genes that belong to both the weighted gene co-expression network and protein-protein interaction (PPI) network were identified using a Venn diagram. We obtained EC and EU-associated microarray datasets GSE7305 and GSE120103. Genes in the EC were mainly enriched in the immune response and immune cell trafficking, and genes in the EU were mainly enriched in stress response and steroid hormone biosynthesis. PPI networks and weighted gene co-expression networks were constructed. An EC-associated blue module and an EU-associated magenta module were identified, and their function annotations revealed that hormone receptor signaling or inflammatory microenvironments may promote EU passing through the oviducts and migrating to the ovarian surfaces, and adhesion and immune correlated genes may induce the successful ectopic implantation of the endometrium (EC). Twelve hub genes in the EC and sixteen hub genes in the EU were recognized and further validated in independent datasets. Our study identified, for the first time, the hub genes and enrichment pathways in the EC and EU using WGCNA, which may provide a comprehensive understanding of the pathogenesis of endometriosis and have important clinical implications for the treatment and diagnosis of endometriosis.
子宫内膜异位症是一种常见的妇科疾病,其不孕和盆腔疼痛的发生率较高。然而,其发病机制和诊断生物标志物仍不清楚。本研究旨在阐明与异位内膜(EC)和在位内膜(EU)中子宫内膜异位症相关的潜在枢纽基因和关键途径。从基因表达综合数据库(GEO)数据库中获得与 EC 和 EU 相关的微阵列数据集。进行基因集富集分析以获得对 EU 和 EC 相关基因的进一步生物学见解。进行加权基因共表达网络分析(WGCNA)以找到具有高度相关性的临床显着模块。使用 Venn 图鉴定属于加权基因共表达网络和蛋白质-蛋白质相互作用(PPI)网络的枢纽基因。我们获得了与 EC 和 EU 相关的微阵列数据集 GSE7305 和 GSE120103。EC 中的基因主要富集在免疫反应和免疫细胞运输中,而 EU 中的基因主要富集在应激反应和甾体激素生物合成中。构建了 PPI 网络和加权基因共表达网络。确定了与 EC 相关的蓝色模块和与 EU 相关的洋红色模块,它们的功能注释表明激素受体信号或炎症微环境可能促进 EU 通过输卵管迁移到卵巢表面,以及粘附和免疫相关基因可能诱导子宫内膜(EC)的成功异位植入。在独立数据集进一步验证了在 EC 中的 12 个枢纽基因和在 EU 中的 16 个枢纽基因。我们首次使用 WGCNA 鉴定了 EC 和 EU 中的枢纽基因和富集途径,这可能为子宫内膜异位症的发病机制提供全面的了解,并对子宫内膜异位症的治疗和诊断具有重要的临床意义。