{"paper_id":"d276543d-e05e-46ce-91cb-20614ba12fc6","body_text":"Abstract\nEndometriosis (EM) is a heterogeneous gynecological disorder with incompletely understood molecular mechanisms. Disulfidptosis, a recently identified form of programmed cell death, has not been systematically explored in EM. Gene expression profiles from the GSE51981 dataset were analyzed to investigate disulfidptosis-related genes (DRGs) and immune characteristics in EM. Consensus clustering identified molecular subtypes, and weighted gene co-expression network analysis (WGCNA) was performed to screen subtype-associated genes. Four machine-learning algorithms were subsequently compared to establish an optimal diagnostic model, which was further evaluated in two independent external datasets and validated at the expression level using RT-qPCR experiments. EM tissues showed abnormal DRG expression patterns and immune infiltration. Two disulfidptosis-associated molecular subtypes with distinct immune features and biological pathways were identified. The Extreme Gradient Boosting (XGB) algorithm showed the best performance and generated a five-gene signature comprising BTBD2, SLC4A2, SSC5D, LPPR2, and SRGAP2C. External validation, calibration, decision curve analysis, and nomogram evaluation provided complementary evidence supporting the potential diagnostic utility of the signature. RT-qPCR provided partial expression-level validation of selected genes within the five-gene signature in EM patients. Disulfidptosis-related molecular heterogeneity is associated with immune remodeling in EM. The five-gene signature provides a potential diagnostic tool and new insights into EM mechanisms.\nSimilar content being viewed by others\nAcknowledgements\nThe authors would like to thank all participants who contributed to this study. We also acknowledge the GEO database for providing the public datasets used in this study.\nFunding\nThis work was supported by the Central Government Guidance Fund for Local Science and Technology Development of Guangxi Province (Grant No. Guike ZY24212050), and the Self-Funded Scientific Research Project of the National Health Commission of Guangxi Zhuang Autonomous Region, China (Grant No. Z-A20220064).\nAuthor information\nAuthors and Affiliations\nCorresponding authors\nEthics declarations\nCompeting interests\nThe authors declare no competing interests.\nAdditional information\nPublisher’s note\nSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\nSupplementary Information\nBelow is the link to the electronic supplementary material.\nRights and permissions\nOpen Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\nAbout this article\nCite this article\nLi, Q., Zhao, B., Yuan, Y. et al. Integrating disulfidptosis patterns and machine learning identifies a novel diagnostic signature for endometriosis. Sci Rep (2026). https://doi.org/10.1038/s41598-026-76837-7\nReceived:\nAccepted:\nPublished:\nDOI: https://doi.org/10.1038/s41598-026-76837-7","source_license":"CC0","license_restricted":false}