Identification of central genes for endometriosis through integration of single-cell RNA sequencing and bulk RNA sequencing analysis

Abstract

This study aimed to identify the key genes involved in the development of endometriosis and construct an accurate predictive model to provide new directions for the diagnosis and treatment of endometriosis. Using bioinformatics analysis, we employed the single-cell cell communication method to identify the key cell subtypes. By combining chip data and integrating differential analysis, WGCNA analysis, and the least absolute shrinkage and selection operator (LASSO) model, key genes were identified for immune infiltration and functional enrichment analyses. Cell communication analysis identified tissue stem cells as the key subtype. Differential analysis revealed 1879 differentially expressed genes, whereas WGCNA identified 357 module genes. The LASSO model further selects 4 key genes: Adipocyte Enhancer Binding Protein 1(AEBP1), MBNL1, GREM1, and DES. All 4 key genes showed significant correlations with immune cell content. Moreover, these genes were significantly expressed in single cells. The predictive model demonstrated good diagnostic performance. Through scRNA-seq, WGCNA, and LASSO methodologies, DES, GREM1, MBNL1, and AEBP1 emerged as crucial core genes linked to tissue stem cell markers in endometriosis. These genes have promising applications as diagnostic markers and therapeutic targets for endometriosis.