Identification and diagnostic potential of pyroptosis-related genes in endometriosis: A novel bioinformatics analysis and validation

Li Wang, P.S. Teng, Jiawen Chen, Caiyun Ding, Xianchen Luo, Chang Su, Guantai Ni, Yuanyuan Lyu, Jin Ding
In: PLOS One · 2026 · vol. 21(6) , pp. e0350751 · doi:10.1371/journal.pone.0350751 · PMID:42263089 · W7164018494
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⚙ AI-generated summary by claude@2026-06, 2026-06-12 ⓘ

This bioinformatics analysis identified five pyroptosis-related genes (KIF13B, BAG6, MYO5A, HEATR2, AK055981) that could potentially serve as diagnostic markers for endometriosis.

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⚙ AI-generated deep summary by claude@2026-06, 2026-06-12 ⓘ

This study used transcriptomic datasets from GEO (37 endometriosis vs 42 normal samples) to identify pyroptosis-related differentially expressed genes (PRDEGs) via batch-corrected differential expression analysis and enrichment/coexpression approaches including GSEA, GSVA, WGCNA, and STRING-based PPI networking, then constructed and assessed a pyroptosis score stratification and an LASSO-derived gene signature for diagnostic performance in an independent dataset. The authors report that pyroptosis scores differed between high- and low-score endometriosis groups and that five genes (KIF13B, BAG6, MYO5A, HEATR2, AK055981) formed a candidate diagnostic signature with moderate discrimination in validation; they also found pathway trends including IL-17-related genes and validated differential expression of KIF13B, BAG6, MYO5A, and HEATR2 by RT-qPCR in ectopic versus eutopic/normal tissues (n = 10 per group). A key limitation explicitly noted is the need for larger cohorts and more rigorous validation frameworks. This paper is centrally about endometriosis — it identifies and validates pyroptosis-related gene signatures and diagnostic candidates in endometriosis.

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Abstract

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.

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