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

Hanke Zhang, Yuqing Fang, Dan Luo, Yan-Hui Li, Yanhui Li
Journal of inflammation research · 2025 · vol. Volume 18 , pp. 2783–2804 · doi:10.2147/jir.s497643 · PMID:40026309 · W4407922816
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⚙ AI-generated summary by claude@2026-06, 2026-06-07 ⓘ

This study integrated bulk and single-cell RNA sequencing to identify eight key genes and increased CD8+ T cells and monocytes in eutopic endometrium of endometriosis patients, constructing a predictive model and predicting potential drug targets.

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⚙ AI-generated deep summary by claude@2026-06, 2026-06-07 ⓘ

This study integrated bulk RNA-seq and single-cell RNA-seq datasets from proliferative-phase eutopic endometrium in endometriosis patients and healthy controls (sourced from GEO) to identify immune cell infiltration patterns and key molecular changes, then used LASSO regression to build a gene-based predictive model. The authors report that mesenchymal cells were major contributors to endometriosis pathogenesis, and they selected eight key genes (SYNE2, TXN, NUPR1, CTSK, GSN, MGP, IER2, CXCL12) that yielded strong diagnostic performance (AUC 1.00 in training and 0.8125 in validation). Immune infiltration analysis showed increased CD8+ T cells and monocytes, and gene expression trends were validated by RT-qPCR. A stated caveat is that inclusion criteria aimed to control for sample collection variables such as menstrual phase and prior hormonal treatments, implying results depend on those dataset selections, and the predictive and drug-target outputs are derived from in-silico analyses plus limited validation. This paper is centrally about endometriosis—specifically using single-cell and bulk transcriptomics of proliferative eutopic endometrium to construct an immune-infiltration informed predictive model and identify candidate drug targets.

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Abstract

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

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