Integrated analysis of single-cell and bulk transcriptomic data reveals altered cellular composition and predictive cell types in ectopic endometriosis.

BACKGROUND

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.

METHODS

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.

RESULTS

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 .

CONCLUSION

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.