{"paper_id":"d0ef5e10-e5c8-428b-b221-28054f123990","body_text":"Abstract\nBackground\nEndometriosis is a prevalent gynecological disorder characterized by the ectopic growth of endometrial-like tissue, often leading to chronic pelvic pain, infertility, and reduced quality of life. Despite available treatments, therapeutic outcomes remain unsatisfactory with frequent recurrence, underscoring the need for reliable biomarkers and novel therapeutic targets.\nObjective\nThis study aimed to identify glycolysis-related genes involved in endometriosis using integrated bioinformatics and experimental validation, and explore their potential diagnostic and biological significance.\nMethods\nTranscriptomic datasets from three studies in the Gene Expression Omnibus were analyzed using differential expression analysis and machine learning algorithms, including LASSO, Random Forest, and SVM-RFE. Cellular heterogeneity and gene expression patterns were further delineated using single-cell RNA sequencing. Key computational findings were validated through molecular and cellular experiments.\nResults\nIntegrated bioinformatics analysis identified ALDH9A1, BPGM, and ALDH3A2 as key glycolysis-related candidate genes significantly dysregulated in endometriosis. Single-cell resolution revealed BPGM was predominantly upregulated in epithelial cells within ectopic lesions. Experimental validation confirmed elevated BPGM expression in endometriotic tissues. In 12Z cells, BPGM knockdown reduced proliferation, migration, and invasion, while also significantly decreasing lactate production, suggesting its involvement in glycolytic reprogramming.\nConclusions\nOur findings identify BPGM as a glycolysis-associated factor involved in the progression of endometriosis and suggest that it may serve as a potential biomarker and therapeutic candidate for this disease.\nTrial registration\nNot applicable. This is a bioinformatics study with validation using archived human tissue sections, no prospective intervention or participant assignment was performed.\nSimilar content being viewed by others\nAbbreviations\n- ALDH3A2:\n-\nAldehyde dehydrogenase 3 family member A2\n- ALDH9A1:\n-\nAldehyde dehydrogenase 9 family member A1\n- AUC:\n-\nArea under the receiver operating characteristic curve\n- BPGM:\n-\nBisphosphoglycerate mutase\n- DCA:\n-\nDecision curve analysis\n- DEGs:\n-\nDifferentially expressed genes\n- FC:\n-\nFold change\n- GEO:\n-\nGene expression omnibus\n- HEECs:\n-\nHuman endometrial epithelial cells\n- IHC:\n-\nImmunohistochemistry\n- LASSO:\n-\nLeast absolute shrinkage and selection operator\n- PCA:\n-\nPrincipal component analysis\n- qRT-PCR:\n-\nQuantitative real-time PCR\n- RF:\n-\nRandom forest\n- ROC:\n-\nReceiver operating characteristic\n- scRNA-seq:\n-\nSingle-cell RNA sequencing\n- SVM-RFE:\n-\nSupport vector machine recursive feature elimination\n- UMAP:\n-\nUniform manifold approximation and projection\nFunding\nThis work was supported by National Natural Science Foundation of China (No. 82074247), S&T Program of Hebei (No. 21377771D), Hebei Natural Science Foundation (No. H2023423056), Postgraduates Innovation Funding Program of Hebei University of Chinese Medicine (No. XCXZZBS2025004).\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\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\nLiu, L., Zhao, J., Xi, Y. et al. Machine learning-based identification of glycolytic-related genes highlights BPGM as a potential therapeutic target in endometriosis. Eur J Med Res (2026). https://doi.org/10.1186/s40001-026-04545-z\nReceived:\nAccepted:\nPublished:\nDOI: https://doi.org/10.1186/s40001-026-04545-z","source_license":"CC0","license_restricted":false}