Identification of programmed cell death-related genes and diagnostic biomarkers in endometriosis using a machine learning and Mendelian randomization approach - Xie, Zi-Wei; He, Yue; Feng, Yu-Xin; Wang, Xiao-Hong (Frontiers in Endocrinology, 2024)
The pathophysiology of endometriosis (EM) is significantly influenced by programmed cell death (PCD). Therefore, identifying PCD-related biomarkers is crucial for improving the diagnosis and treatment of EM. In this study, we used the Gene Expression Omnibus (GEO) database to obtain gene expression data (GSE7305 and GSE23339 for validation and GSE51981 for the training set). Overall, 269 differentially expressed PCD-related genes (DPGs) were identified by crossing differentially expressed genes (DEGs) with PCD-related genes (PCDs). These DPGs were then subjected to pathway enrichment analysis using Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). Using Mendelian randomization and machine learning methods, three biomarkers that were highly associated with EM (TNFSF12, AP3M1, and PDK2) were identified. The diagnostic efficacy of these biomarkers was validated in the sets GSE7305 and GSE23339 by evaluating their area under the receiver operating characteristic curve (AUC) and expression levels. This validation was further reinforced using single-cell analysis. Three associated subclusters were identified based on the expression profiles of these biomarkers. The different immunological microenvironments of these subclusters were evaluated using single-sample gene set enrichment analysis (ssGSEA). Finally, molecular docking analysis validated the potential binding abilities of clinical drugs with these biomarkers.