Machine learning-based identification of glycolytic-related genes highlights BPGM as a potential therapeutic target in endometriosis

Linjuan Liu, Jian Zhao, Yu Xi, Liu Q, Shixing Wu, Jiena Li, Can Chen, Ming He
In: European Journal of Medical Research · 2026 · vol. 31(1) · doi:10.1186/s40001-026-04545-z · W7163898185
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This study utilized bioinformatics and experimental validation to identify BPGM as a key glycolysis-related gene dysregulated in endometriosis, suggesting its potential as a therapeutic target.

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⚙ AI-generated deep summary by claude@2026-06, 2026-06-12 · read from full text ⓘ

This paper analyzed transcriptomic data from three Gene Expression Omnibus studies to identify glycolysis-related genes dysregulated in endometriosis, using differential expression and machine learning methods (LASSO, random forest, and SVM-RFE), and used single-cell RNA sequencing to localize expression patterns across cell types. Integrated analyses highlighted ALDH9A1, BPGM, and ALDH3A2 as key candidates, with single-cell data showing BPGM upregulation mainly in epithelial cells within ectopic lesions. Experimental validation in human tissue sections found elevated BPGM expression in endometriotic tissues, and in 12Z cells BPGM knockdown reduced proliferation, migration, invasion, and lactate production, linking it to glycolytic reprogramming, though the study used archived/retrospective materials rather than prospective intervention. This paper is centrally about endometriosis — it identifies BPGM as a glycolysis-associated candidate biomarker and therapeutic target in endometriosis.

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Abstract

Endometriosis 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. This study aimed to identify glycolysis-related genes involved in endometriosis using integrated bioinformatics and experimental validation, and explore their potential diagnostic and biological significance. Transcriptomic 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. Integrated 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. Our 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. Not applicable. This is a bioinformatics study with validation using archived human tissue sections, no prospective intervention or participant assignment was performed.
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Abstract

Background Endometriosis 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.

Objective

This study aimed to identify glycolysis-related genes involved in endometriosis using integrated bioinformatics and experimental validation, and explore their potential diagnostic and biological significance.

Methods

Transcriptomic 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.

Results

Integrated 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.

Conclusions

Our 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. Trial registration Not applicable. This is a bioinformatics study with validation using archived human tissue sections, no prospective intervention or participant assignment was performed. Similar content being viewed by others Abbreviations - ALDH3A2: - Aldehyde dehydrogenase 3 family member A2 - ALDH9A1: - Aldehyde dehydrogenase 9 family member A1 - AUC: - Area under the receiver operating characteristic curve - BPGM: - Bisphosphoglycerate mutase - DCA: - Decision curve analysis - DEGs: - Differentially expressed genes - FC: - Fold change - GEO: - Gene expression omnibus - HEECs: - Human endometrial epithelial cells - IHC: - Immunohistochemistry - LASSO: - Least absolute shrinkage and selection operator - PCA: - Principal component analysis - qRT-PCR: - Quantitative real-time PCR - RF: - Random forest - ROC: - Receiver operating characteristic - scRNA-seq: - Single-cell RNA sequencing - SVM-RFE: - Support vector machine recursive feature elimination - UMAP: - Uniform manifold approximation and projection Funding This 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). Author information Authors and Affiliations Corresponding authors 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. Supplementary Information Rights and permissions Open 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/. About this article Cite this article Liu, 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 Received: Accepted: Published: DOI: https://doi.org/10.1186/s40001-026-04545-z

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