Construction and evaluation of endometriosis diagnostic prediction model and immune infiltration based on efferocytosis-related genes - Pei, Fang-Li; Jia, Jin-Jin; Lin, Shu-Hong; Chen, Xiao-Xin; Wu, Li-Zheng; Lin, Zeng-Xian; Sun, Bo-Wen; Zeng, Cheng (Frontiers in Molecular Biosciences, 2024)
Background: Endometriosis (EM) is a chronic inflammatory disease that is difficult to treat and prevent. Previous studies have shown that immune infiltration is crucial to the development of EM.Endocytosis has an important immunomodulatory function. However, research on the identification and clinical significance of efferocytosis-related genes (EFRGs)in EM is sparse.Methods: The differentially expressed EFRGs (EFRDEGs) associated with EM-related datasets were comprehensively analyzed based on the Gene Expression Omnibus (GEO) and GeneCards databases, and the PPI and TF regulatory network of EFRDEGs were constructed. Then, machine learning methods (Univariate logistic regression, LASSO, and SVM classification) were used to filter and identify diagnostic biomarkers. Furthermore, ROC, multivariate regression analysis, nomogram, and calibration curve were used to establish and evaluate the diagnostic model. Subsequently, the CIBERSORT algorithm and scRNA-seq were utilized to investigate the immune cell infiltration, and the CTD database was used to identify potential therapeutic drugs in endometriosis. Finally, IHC and RT-qPCR quantified the expression levels of biomarkers from EM clinical samples.Results: Our results identified 13 EFRDEGs associated with EM and Six hub genes (ARG2, GAS6, C3, PROS1, CLU, and FGL2) screened by the LASSO and SVM regression model. ARG2, GAS6, and C3 were identified as diagnostic biomarkers using multivariate logistic regression analysis finally.The ROC curve analysis of GSE37837 (AUC=0.627) and GSE6374 (AUC=0.635), calibration curve, and DCA curve indicated that the nomogram based on the three biomarkers had a good ability to predict disease. The ratio of nine immune cells was significantly different between the eutopic and ectopic endometrial samples, and scRNA-seq suggested that M0 Macrophages, Fibroblasts, and CD8 Tex cells were identified as the cell populations exhibiting the most significant changes in the three biomarkers. In total, seven potential drugs were predicted against endometriosis. Finally, the expression of the three biomarkers in clinical samples was validated by RT-qPCR and IHC, consistent with the result of the public database. Conclusion: we identified three biomarkers and constructed a diagnostic model for EM in this study, these findings are helpful for the follow-up mechanism research and clinical work of EM.