Integration of Single Cell and Bulk RNA-Sequencing Reveals Key Genes and Immune Cell Infiltration to Construct a Predictive Model and Identify Drug Targets in Endometriosis.

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 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.