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

ABSTRACT:

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 diagnosti

SUBMITTER: Chen M 

PROVIDER: S-EPMC12313728 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature