Hanke Zhang, Yuqing Fang, Dan Luo, Yan-Hui Li

Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430022, People’s Republic of China

Correspondence: Yan-Hui Li, Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430022, People’s Republic of China, Tel +86-13407159190, Email liyanhui251@163.com

Purpose: Endometriosis is a common chronic neuroinflammatory disease with a poorly understood pathogenesis. Molecular changes and specific immune cell infiltration in the eutopic endometrium are critical to disease progression. This study aims to explore immune mechanisms and molecular differences in the proliferative eutopic endometrium of endometriosis by integrating bulk RNA-seq and single-cell RNA sequencing (scRNA-seq) data, and to develop diagnostic and predictive models for the disease.
Methods: Gene expression profiles from the proliferative endometrium of endometriosis patients and healthy controls were obtained from the Gene Expression Omnibus. Single-cell RNA-seq data were processed using R packages, and cell clusters’ contributions to endometriosis were calculated. Differentially expressed genes (DEGs) from bulk RNA-seq were intersected with significant mesenchymal cell genes from scRNA-seq, and a predictive model was constructed using LASSO analysis. Key gene mechanisms were explored through Gene Set Enrichment and Variation Analyses. miRNA networks and transcriptional regulation analyses were conducted, and potential drugs were predicted using the Connectivity Map database. RT-qPCR validated key gene expression.
Results: Mesenchymal cells in the proliferative eutopic endometrium were identified as major contributors to endometriosis pathogenesis. LASSO regression identified eight key genes: SYNE2, TXN, NUPR1, CTSK, GSN, MGP, IER2, and CXCL12. The predictive model based on these genes achieved AUC values of 1.00 and 0.8125 in training and validation cohorts. Immune infiltration analysis showed increased CD8+ T cells and monocytes in the eutopic endometrium of endometriosis patients. Drug target prediction indicated that drugs like Retinol, Orantinib, Piperacillin, and NECA were negatively correlated with the expression profiles of endometriosis. RT-qPCR validated gene expression in patients aligned with bioinformatics analysis.
Conclusion: Significant transcriptomic changes and altered immune cell infiltration in the proliferative eutopic endometrium potentially contribute to endometriosis pathogenesis. Our predictive model based on the key genes demonstrates high diagnostic accuracy, offering insights for diagnosis and potential treatment strategies.

Keywords: endometriosis, eutopic endometrium, transcriptomics, single-cell sequencing, predictive model, immune cell infiltration