LGALS2 and EGR1: markers of endometriosis for predictive, preventive and personalized medicine

Abstract

Abstract Endometriosis (EM) is a chronic gynecological disorder that causes infertility and chronic pelvic pain. The aim of the current study was to identify markers of efferocytosis with utility for EM diagnosis.RNA sequencing profile and single-cell sequencing (scRNA-seq) data were collated from the Gene Expression Omnibus (GEO) database and 46 efferocytosis-related genes (ERGs) from Genecards. Results of single-cell, differential expression and Weighted Gene Co-expression Network Analysis (WGCNA) were combined into a Venn diagram to identify 41 intersecting genes. LGALS2, EGR1 and CLINT1 were shown to be key EM markers by least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms. Area under the curve (AUC) values were 0.9 for LGALS2, 0.81 for EGR1 and 0.76 for CLINT1, indicating good diagnostic efficacy. Functional annotation analysis revealed the markers to be enriched in cell cycle, DNA repair, neuroactive ligand-receptor interactions, cell cycle, chromosomal segregation and other pathways. Drug-gene interaction network indicated that beta-D-glucose, pseudoephedrine and fostamatinib were potential therapeutic agents, exposing the possibility of personalized medicine for EM. RT-qPCR showed LGALS2 and EGR1 to be more highly expressed in ectopic than in eutopic endometrium. LGALS2 and EGR1 are introduced as potential novel targets for risk prediction, non-invasive diagnosis and health care personalization in EM. The potential for personalized medicine (PPPM) to treat EM patients is illuminated.