ORIGINAL RESEARCH article
Front. Cell Dev. Biol.
Sec. Cellular Biochemistry
Volume 13 - 2025 | doi: 10.3389/fcell.2025.1672521
Identification and Validation of Lactate-related gene signatures in endometriosis for clinical valuation and immune characterization by WGCNA and machine learning
Provisionally accepted
- Shengjing Hospital of China Medical University, Shenyang, China
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Background: Endometriosis is a common benign gynecologic disease in women of reproductive age and its manifestations remarkably decrease females' quality of life. Lactate, as a metabolite, exerts prominent actions in a wide range of biological processes. The objective of this research is to explore the clinical values and immune features of lactate-related genes in endometriosis, and to contribute novel strategies for guiding the clinical management of patients with endometriosis. Methods: To begin with, we conducted a differential expression analysis to identify differentially expressed genes (DEGs) in the training set. By integrating critical module genes from WGCNA and lactate-related genes (LRGs), we preliminarily screened lactate-related differentially expressed genes (LR-DEGs). Machine learning algorithms and single cell datasets and clinical samples were used to further identify and validate core LR-DEGs. Subsequently, we evaluated the diagnostic value of the model constructed from core LR-DEGs for endometriosis and explored the biological functions of these genes. Additionally, we conducted immune related analysis in endometriosis and searched for the small molecule compounds targeting core LR-DEGs. Results: In this study, 22 candidate genes were identified by intersecting 2318 DEGs and 2177 key module genes with 357 LRGs. Using three machine learning algorithms, this list was further refined, resulting in three primary lactate-related biomarkers: BPGM,DHFR and SLC25A13. A nomogram model constructed on core LR-DEGs demonstrated outstanding diagnostic performance in identifying patients with endometriosis. Immune related analysis revealed significant associations between the hub LR-DEGs and cellular immune dysregulation in endometriosis. Additionally, a gene-small molecule compound regulatory network was established to guide treatment for clinical patients. Conclusion: Taken together, our study has established a robust relationship between the lactate metabolism related genes and endometriosis, with the model promising to realize the early diagnosis of endometriosis, contributing to the excavation of the immune molecular mechanisms of endometriosis and seeking to discover potential targets for therapy in the metabolism of endometriosis.
Keywords: Endometriosis, Lactate-related gene, Diagnostic model, immune diversity, Clinical Management
Received: 24 Jul 2025; Accepted: 16 Sep 2025.
Copyright: © 2025 Li, Hu, Pan, Liu, Zhang, Yuan and Zhou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xin Zhou, drzhouxin@163.com
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