Chen Jianhui, Li Qun, Liu Xiaofang, Lin Fang, Jing Yaling, Yang Jiayan, Zhao Lianfang
Prenatal Diagnosis Center, Center of Reproductive Medicine, Suining Central Hospital, Suining, Sichuan, China.
Department of Radiology, Suining Central Hospital, Suining, Sichuan, China.
Front Mol Biosci. 2025 Feb 3;12:1529507. doi: 10.3389/fmolb.2025.1529507. eCollection 2025.
Endometriosis (EMs) is a chronic inflammatory disease characterized by the presence of endometrial tissue in the non-uterine cavity, resulting in dysmenorrhea, pelvic pain, and infertility. Epidemiologic data have suggested the correlation between EMs and recurrent pregnancy loss (RPL), but the pathological mechanism is unclear. This study aims to investigate the potential biomarkers and immune infiltration in EMs and RPL, providing a basis for early detection and treatment of the two diseases.
Two RPL and six EMs transcriptomic datasets from the Gene Expression Omnibus (GEO) database were used for differential analysis via limma package, followed by weighted gene co-expression network analysis (WGCNA) for key modules screening. Protein-protein interaction (PPI) network and two machine learning algorithms were applied to identify the common core genes in both diseases. The diagnostic capabilities of the core genes were assessed by receiver operating characteristic (ROC) curves. Moreover, immune cell infiltration was estimated using CIBERSORTx, and the Cancer Genome Atlas (TCGA) database was employed to elucidate the role of key genes in endometrial carcinoma (EC).
26 common differentially expressed genes (DEGs) were screened in both diseases, three of which were identified as common core genes (MAN2A1, PAPSS1, RIBC2) through the combination of WGCNA, PPI network, and machine learning-based feature selection. The area under the curve (AUC) values generated by the ROC indicates excellent diagnostic powers in both EMs and RPL. The key genes were found to be significantly associated with the infiltration of several immune cells. Interestingly, MAN2A1 and RIBC2 may play a predominant role in the development and prognostic stratification of EC.
We identified three key genes linking EMs and RPL, emphasizing the heterogeneity of immune infiltration in the occurrence of both diseases. These findings may provide new mechanistic insights or therapeutic targets for further research of EMs and RPL.
子宫内膜异位症（EMs）是一种慢性炎症性疾病，其特征是子宫腔外存在子宫内膜组织，导致痛经、盆腔疼痛和不孕。流行病学数据表明EMs与复发性流产（RPL）之间存在关联，但其病理机制尚不清楚。本研究旨在探讨EMs和RPL中的潜在生物标志物及免疫浸润情况，为这两种疾病的早期检测和治疗提供依据。
从基因表达综合数据库（GEO）中获取两个RPL和六个EMs转录组数据集，通过limma软件包进行差异分析，随后采用加权基因共表达网络分析（WGCNA）筛选关键模块。应用蛋白质-蛋白质相互作用（PPI）网络和两种机器学习算法识别两种疾病中的共同核心基因。通过受试者工作特征（ROC）曲线评估核心基因的诊断能力。此外，使用CIBERSORTx估计免疫细胞浸润情况，并利用癌症基因组图谱（TCGA）数据库阐明关键基因在子宫内膜癌（EC）中的作用。
在两种疾病中筛选出26个共同的差异表达基因（DEG），其中三个通过WGCNA、PPI网络和基于机器学习的特征选择被确定为共同核心基因（MAN2A1、PAPSS1、RIBC2）。ROC生成的曲线下面积（AUC）值表明其在EMs和RPL中均具有出色的诊断能力。发现关键基因与多种免疫细胞的浸润显著相关。有趣的是，MAN2A1和RIBC2可能在EC的发生和预后分层中起主要作用。
我们鉴定出三个连接EMs和RPL的关键基因，强调了两种疾病发生过程中免疫浸润的异质性。这些发现可能为EMs和RPL的进一步研究提供新的机制见解或治疗靶点。
