Chen Meihong, Wang Liqun, Chen Yuanting, Wang Ting, Jiang Guanqun, Chen Qi
Department of Obstetrics and Gynecology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
Department of Gynecology, Maternal and Child Health Hospital of Jiangxi, Nanchang, China.
Front Med (Lausanne). 2025 Jul 18;12:1641982. doi: 10.3389/fmed.2025.1641982. eCollection 2025.
Endometriosis is often diagnosed late and presents significant challenges in clinical treatment. A comprehensive investigation of the cellular classification and composition of endometriosis is essential for studying its diagnosis and treatment.
This study utilized the Gene Expression Omnibus (GEO) public database and referenced single-cell RNA sequencing (scRNA-seq) atlases. The CIBERSORTx algorithm was applied to perform deconvolution on the samples and estimate the proportions of endometrial cell subtypes. A random forest model was constructed to predict the diagnosis of endometriosis. Additionally, immunohistochemical validation was performed on the marker genes of MUC5B+ epithelial cells and dStromal late mesenchymal cells, which showed high diagnostic contribution.
Endometriosis consists of 5 major cell types, further classified into 52 distinct cell subtypes. Compared to healthy controls, these subtypes exhibited varying degrees of alterations, with MUC5B+ epithelial cells, dStromal late mesenchymal cells, and M2 macrophages showing an increasing trend. Enriched signaling pathways were primarily associated with epithelial-mesenchymal transition (EMT), cell migration, and inflammatory responses. A random forest model, based on cell-type proportions, has been shown to achieve excellent diagnostic performance (AUC = 0.932), with MUC5B+ epithelial cells identified as the top predictive feature. Immunohistochemical validation confirmed high expression of the marker genes and .
By integrating single-cell and bulk transcriptomics, we identified MUC5B+ epithelial cells and dStromal-late mesenchymal cells as dual drivers of fibrosis and inflammation in endometriosis. Our findings revealed that MUC5B+ epithelial cells may serve as the top factor for the diagnosis of endometriosis.
子宫内膜异位症常被误诊，给临床治疗带来重大挑战。全面研究子宫内膜异位症的细胞分类和组成对于其诊断和治疗研究至关重要。
本研究利用基因表达综合数据库（GEO）公共数据库并参考单细胞RNA测序（scRNA-seq）图谱。应用CIBERSORTx算法对样本进行反卷积分析，估计子宫内膜细胞亚型的比例。构建随机森林模型以预测子宫内膜异位症的诊断。此外，对具有高诊断贡献的MUC5B +上皮细胞和dStromal晚期间充质细胞的标记基因进行免疫组化验证。
子宫内膜异位症由5种主要细胞类型组成，进一步细分为52种不同的细胞亚型。与健康对照相比，这些亚型表现出不同程度的改变，MUC5B +上皮细胞、dStromal晚期间充质细胞和M2巨噬细胞呈增加趋势。富集的信号通路主要与上皮-间质转化（EMT）、细胞迁移和炎症反应相关。基于细胞类型比例的随机森林模型已显示出优异的诊断性能（AUC = 0.932），其中MUC5B +上皮细胞被确定为最重要的预测特征。免疫组化验证证实了标记基因的高表达。
通过整合单细胞和大量转录组学，我们确定MUC5B +上皮细胞和dStromal晚期间充质细胞是子宫内膜异位症纤维化和炎症的双重驱动因素。我们的研究结果表明，MUC5B +上皮细胞可能是子宫内膜异位症诊断的首要因素。
