Primer sequences for RT-qPCR.

Background Endometriosis (EMs) is a chronic inflammatory disease characterized by ectopic endometrial growth. This study aimed to identify and analyze potential signatures of pyroptosis-related genes in EMs. Methods We conducted a comprehensive bioinformatics analysis using transcriptomic datasets from the GEO database to identify pyroptosis-related differentially expressed genes (PRDEGs) in endometriosis. Gene Set Enrichment Analysis (GSEA), Gene Set Variation Analysis (GSVA), Weighted Gene Co-expression Network Analysis (WGCNA), and Protein-Protein Interaction (PPI) network construction were applied to explore the functional relevance of PRDEGs. A candidate gene signature was constructed using Least Absolute Shrinkage and Selection Operator (LASSO) regression based on pyroptosis scores, and its predictive performance was evaluated in an independent dataset. The expression of key PRDEGs was validated by RT-qPCR in eutopic and ectopic endometrial tissue samples from patients (n = 10 each). Results Based on the pyroptosis score, endometriosis samples were divided into high- and low-score groups, with a significant difference in score distribution between the two groups. This score was primarily used to characterize the pyroptosis-related stratification features within the samples. Further screening of differentially expressed genes identified five candidate diagnostic-related genes (KIF13B, BAG6, MYO5A, HEATR2, and AK055981). The model constructed using these genes showed moderate discriminatory ability in an independent dataset. RT-qPCR results confirmed differential expression of KIF13B, BAG6, MYO5A, and HEATR2 between ectopic and normal endometrial tissues, and several IL-17 pathway‑related genes exhibited consistent trends. Conclusions This study suggests a potential role for pyroptosis in endometriosis and identifies a candidate gene signature. These findings may provide new clues for understanding inflammation- and cell death-related mechanisms in endometriosis and serve as a reference for future studies conducted in larger cohorts and under more rigorous validation frameworks.