Identification and validation of immune-related and inflammation-related genes in endometriosis.

AIM

Endometriosis is characterized by immune evasion and progressive inflammation. This study aimed to identify key genes related to immune and inflammation in endometriosis.

METHODS

Differentially expressed genes between patients with and without endometriosis were identified from the GEO database. Furthermore, immune- and inflammation-related genes (IRGs) were identified by intersecting the differentially expressed genes with known immune and inflammatory genes. Functional analyses of the GO and KEGG pathways of these genes were performed. Subsequently, three machine learning models-LASSO regression, SVM-RFE, and Boruta-were conducted to identify the potential key genes in endometriosis. Finally, the expressions of key genes in endometriosis were verified in two validation cohorts using an online database and qRT-PCR, and their immunoregulatory properties were verified.

RESULTS

A total of 13 differentially expressed IRGs were identified. Using machine learning algorithms, five key genes were selected in the endometriosis: BST2, IL4R, INHBA, PTGER2, and MET. Furthermore, the three hub genes exhibited consistent trends across both training and validation datasets. The three keys also correlated with infiltrated immune cells, checkpoint genes, and immune factors in various degrees. Finally, validation analysis using the online database and qRT-PCR confirmed that MET expression aligned with outcomes from both training and validation datasets.

CONCLUSION

Three immune- and inflammation-related genes were identified as potential biomarkers of endometriosis, providing new insights into the molecular mechanisms underlying immune function in endometriosis. The immune-related function of MET, particularly its correlation with NK cell activity in endometriosis, will be the focus of future studies.