Background: 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). Material and Method: 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. Results: 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. Conclusion: 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.

中文翻译：

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通过加权基因共表达网络分析鉴定与子宫内膜异位症相关的关键基因和途径。

背景：子宫内膜异位症是一种常见的妇科疾病，不孕和盆腔疼痛的发生率很高。然而，其发病机制和诊断生物标志物仍不清楚。本研究旨在阐明异位子宫内膜（EC）和在位子宫内膜（EU）中与子宫内膜异位症相关的潜在枢纽基因和关键通路。材料和方法：EC 和 EU 相关的微阵列数据集是从基因表达综合 (GEO) 数据库中获得的。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）来寻找高度相关基因的临床显着模块。使用维恩图识别属于加权基因共表达网络和蛋白质-蛋白质相互作用（PPI）网络的中心基因。结果：我们获得了 EC 和 EU 相关的微阵列数据集 GSE7305 和 GSE120103。EC中的基因主要富集于免疫反应和免疫细胞运输，EU中的基因主要富集于应激反应和类固醇激素生物合成。构建了PPI网络和加权基因共表达网络。鉴定出 EC 相关的蓝色模块和 EU 相关的洋红色模块，其功能注释表明激素受体信号传导或炎症微环境可能促进 EU 通过输卵管并迁移至卵巢表面，粘附和免疫相关基因可能诱导子宫内膜（EC）成功异位着床。欧盟的 12 个中心基因和欧盟的 16 个中心基因在独立数据集中得到了识别和进一步验证。结论：我们的研究首次利用WGCNA鉴定了EC和EU的枢纽基因和富集通路，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。