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

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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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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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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. Data Availability The datasets presented in this study can be found on the GEO website (https://www.ncbi.nlm.nih.gov/geo/).

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Acknowledgements

The authors thank the GEO database for providing valuable datasets. Funding No funding was received for conducting this study. Author information Authors and Affiliations Contributions ZS and WS designed the study and wrote the original manuscript. CL and PD searched relevant literature and prepared figures. KL and YL conducted data acquisition and analysis. YW helped revise the manuscript and supervised the study. All authors approved the manuscript. Corresponding author Ethics declarations Competing interests The authors declare no competing interests. Additional information Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Rights and permissions Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. About this article Cite this article Su, Z., Su, W., Li, C. et al. Identification and Immune Characteristics Study of Pyroptosis‑Related Genes in Endometriosis. Biochem Genet 62, 2810–2829 (2024). https://doi.org/10.1007/s10528-023-10583-7 Received: Accepted: Published: Version of record: Issue date: DOI: https://doi.org/10.1007/s10528-023-10583-7

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endometriosis

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Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis

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