Identification and Immune Characteristics Study of Pyroptosis‑Related Genes in Endometriosis

Zhe Su, Wenjing Su, Chenglong Li, Pei‐Hui Ding, Peihui Ding, Kaixue Lao, Yiqian Li, Yanlin Wang
Biochemical genetics · 2023 · vol. 62(4) , pp. 2810–2829 · doi:10.1007/s10528-023-10583-7 · PMID:38017285 · W4389108690
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⚙ AI-generated summary by claude@2026-06+body, 2026-06-07 ⓘ

This bioinformatics study identified differentially expressed pyroptosis-related genes in endometriosis and used machine learning to develop a diagnostic model, exploring the hub gene's immune microenvironment correlations.

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⚙ AI-generated deep summary by claude@2026-06, 2026-06-07 ⓘ

This paper used a comprehensive bioinformatics workflow to study pyroptosis-related genes in endometriosis, beginning with two GEO expression datasets and differential expression analyses to identify pyroptosis-related genes differentially expressed between endometriosis and non-endometriosis samples. Multiple machine learning approaches (LASSO regression, SVM-RFE, and random forest) were applied to derive a hub gene and build a diagnostic model, which was then assessed using ROC analysis, nomograms, calibration, and decision curve analysis; differential expression was also performed between high- and low-hub-gene groups to infer associated functions and signaling pathways, and immune-cell correlations were evaluated. The authors additionally constructed a pyroptosis-related competing endogenous RNA network to describe regulatory interactions involving the hub gene. A key limitation stated is that the work is based on in silico analysis of public datasets rather than experimental validation. This paper is centrally about endometriosis — it identifies pyroptosis-related genes and an immune-associated hub gene using GEO-based expression profiling and machine learning to support endometriosis diagnosis.

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Abstract

Endometriosis (EMT) is a prevalent gynecological disorder characterized by pain and infertility associated with the menstrual cycle. Pyroptosis, an emerging cell death mechanism, has been implicated in the pathogenesis of diverse diseases, highlighting its pivotal role in disease progression. Therefore, our study aimed to investigate the impact of pyroptosis in EMT using a comprehensive bioinformatics approach. We initially obtained two datasets from the Gene Expression Omnibus database and performed differential expression analysis to identify pyroptosis-related genes (PRGs) that were differentially expressed between EMT and non-EMT samples. Subsequently, several machine learning algorithms, namely least absolute shrinkage selection operator regression, support vector machine-recursive feature elimination, and random forest algorithms were used to identify a hub gene to construct an effective diagnostic model for EMT. Receiver operating characteristic curve analysis, nomogram, calibration curve, and decision curve analysis were applied to validate the performance of the model. Based on the selected hub gene, differential expression analysis between high- and low-expression groups was conducted to explore the functions and signaling pathways related to it. Additionally, the correlation between the hub gene and immune cells was investigated to gain insights into the immune microenvironment of EMT. Finally, a pyroptosis-related competing endogenous RNA network was constructed to elucidate the regulatory interactions of the hub gene. Our study revealed the potential contribution of a specific PRG to the pathogenesis of EMT, providing a novel perspective for clinical diagnosis and treatment of EMT.

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Condition tags

endometriosis

MeSH descriptors

Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Pyroptosis Pyroptosis Pyroptosis Pyroptosis Pyroptosis Pyroptosis Pyroptosis Pyroptosis Pyroptosis

Citation neighborhood

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

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