A lactylation-related transcriptional signature as candidate biomarkers for the early detection of polycystic ovary syndrome: an integrated genomic analysis.
4 affiliations
- 1Translational Medical Center, The Second Clinical Medical College of Henan University, Kaifeng, 475000, China.
- 2Department of General Surgery, The Second Clinical Medical College of Henan University, Kaifeng, 475000, China.
- 3Quality Control Office, The People's Hospital of Kaifeng, Kaifeng, 475000, China.
- 4Department of General Surgery, The Second Clinical Medical College of Henan University, Kaifeng, 475000, China. [email protected].
Abstract
Background
Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder that negatively impacts women's reproductive and metabolic health, leading to complications such as irregular menstruation, infertility, obesity, and metabolic syndrome. Current diagnostic methods, which primarily rely on clinical symptoms and hormone levels, lack specificity in early detection. This study aimed to identify lactylation-related transcriptional signatures as candidate biomarkers for the early detection of PCOS.Methods
We conducted a comprehensive bioinformatic analysis using transcriptomic data from granulosa cells of PCOS patients in the GEO database. Patients were categorized into two molecular subtypes via consensus clustering based on the expression of lactylation-related genes. Subsequently, diagnostic models were constructed and validated to evaluate the potential of these transcriptional signatures as early diagnostic markers. External validation was performed using an independent dataset, and selected candidate genes were further examined in DHEA-treated KGN cells.Results
We identified nine key genes (HK3, SDC3, TGFBI, ZYX, LSP1, HMGA1, LCP1, B3GAT1, and ZNF280C) that were significantly differentially expressed in PCOS granulosa cell transcriptomic datasets. Pathway enrichment analyses revealed their involvement in energy metabolism, cell proliferation and apoptosis, which highlights their potential roles in PCOS pathogenesis. A random forest-based diagnostic model showed high internal predictive performance, with an ROC AUC of 0.993; however, external validation yielded a lower AUC of 0.628, indicating limited generalizability and the need for further validation.Conclusion
These findings suggest that lactylation-related transcriptional signatures may serve as potential candidate biomarkers for the early detection of PCOS. However, because this study was based primarily on transcriptomic inference and did not directly measure lactylation modifications, the results should be interpreted as hypothesis-generating. Future studies should validate these biomarkers in larger clinical cohorts, primary granulosa cells, and through direct lactylation or proteomic assays.
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BibTeX
@article{wei2026lactylationrelated,
title = {{A lactylation-related transcriptional signature as candidate biomarkers for the early detection of polycystic ovary syndrome: an integrated genomic analysis.}},
author = {Rui Wei and Hao Zhang and Wenting Xu and Changjiang Qin},
journal = {Journal of ovarian research},
year = {2026},
month = jul,
doi = {10.1186/s13048-026-02208-5},
url = {https://europepmc.org/article/MED/42902322},
note = {CC BY},
}
RIS
TY - JOUR
TI - A lactylation-related transcriptional signature as candidate biomarkers for the early detection of polycystic ovary syndrome: an integrated genomic analysis.
AU - Rui Wei
AU - Hao Zhang
AU - Wenting Xu
AU - Changjiang Qin
PY - 2026
DA - 2026/07/01
JO - Journal of ovarian research
DO - 10.1186/s13048-026-02208-5
UR - https://europepmc.org/article/MED/42902322
AB - Background Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder that negatively impacts women's reproductive and metabolic health, leading to complications such as irregular menstruation, infertility, obesity, and metabolic syndrome. Current diagnostic methods, which primarily rely on clinical symptoms and hormone levels, lack specificity in early detection. This study aimed to identify lactylation-related transcriptional signatures as candidate biomarkers for the early detection of PCOS.Methods We conducted a comprehensive bioinformatic analysis using transcriptomic data from granulosa cells of PCOS patients in the GEO database. Patients were categorized into two molecular subtypes via consensus clustering based on the expression of lactylation-related genes. Subsequently, diagnostic models were constructed and validated to evaluate the potential of these transcriptional signatures as early diagnostic markers. External validation was performed using an independent dataset, and selected candidate genes were further examined in DHEA-treated KGN cells.Results We identified nine key genes (HK3, SDC3, TGFBI, ZYX, LSP1, HMGA1, LCP1, B3GAT1, and ZNF280C) that were significantly differentially expressed in PCOS granulosa cell transcriptomic datasets. Pathway enrichment analyses revealed their involvement in energy metabolism, cell proliferation and apoptosis, which highlights their potential roles in PCOS pathogenesis. A random forest-based diagnostic model showed high internal predictive performance, with an ROC AUC of 0.993; however, external validation yielded a lower AUC of 0.628, indicating limited generalizability and the need for further validation.Conclusion These findings suggest that lactylation-related transcriptional signatures may serve as potential candidate biomarkers for the early detection of PCOS. However, because this study was based primarily on transcriptomic inference and did not directly measure lactylation modifications, the results should be interpreted as hypothesis-generating. Future studies should validate these biomarkers in larger clinical cohorts, primary granulosa cells, and through direct lactylation or proteomic assays.
SN - 1757-2215
ER -