Multimodal analysis reveals asynchronous aging dynamics between endometriosis-associated ovarian cancer and endometriosis patients

Background The malignant transformation of endometriosis into endometriosis-associated ovarian cancer (EAOC) is still of great interest among researchers. Despite the shared inflammatory and hormonal drivers, the cellular aging dynamics differentiating these two pathologies remain elusive. This study aimed to quantify biological aging using DNA methylation status and telomere length to identify distinct signatures associated with disease progression.

We conducted a comparative analysis of multiple aging-related biomarkers in blood, including biological age, telomere length, and DNA methylation status, as well as specific longevity markers, across cohorts of age-matched patients with endometriosis and EAOC, compared with unaffected controls. Precision assessment utilized machine learning models to determine biological age deviation and methylation levels at specific CpG sites. Finally, the study employs a Random Forest machine learning classifier to assess the discriminative power of gene signatures, complemented by Gene Ontology enrichment.

Our analysis revealed a profound divergence in biological aging trajectories. Patients with endometriosis exhibited significant age acceleration (+ 16.92%) and maintained telomeres that were significantly longer (13.18 ± 0.46 kbp). Conversely, EAOC patients displayed a biologically younger profile compared to their chronological age (− 14.64%). These patients also demonstrated significant telomeric attrition (12.45 ± 0.72 kbp; p < 0.001), reflecting their extensive mitotic history and the rapid turnover of malignant cells. Age-related methylation was the most robust marker, distinguishing the groups with high statistical significance.

This study provides novel evidence that the transition from endometriosis to EAOC involves a fundamental shift in biological age and in telomere dynamics. Identifying these divergent aging signatures offers a powerful tool for precision oncology, enabling the development of predictive models to identify patients at high risk of malignant transformation and facilitating personalized interceptive strategies based on biological, rather than chronological age. Similar content being viewed by others Abbreviations - Abbreviation: - Explanation - ANCOVA: - Analysis of Covariance - AUC: - Area Under the Receiver Operating Characteristic Curve - BP: - Biological Process (Gene Ontology category) - CA: - Chronological Age - CC: - Cellular Component (Gene Ontology category) - CI: - Confidence Interval - CV: - Coefficient of Variation - DAVID: - Database for Annotation, Visualization and Integrated Discovery - DES: - Deep Endometriosis - DNA: - Deoxyribonucleic Acid - EAOC: - Endometriosis-Associated Ovarian Cancer - FDR: - False Discovery Rate - FIGO: - International Federation of Gynecology and Obstetrics - GO: - Gene Ontology - IQR: - Interquartile Range - LINE-1: - Long Interspersed Nuclear Element-1 - MA: - Methylation Age - MF: - Molecular Function (Gene Ontology category) - MSD: - Meso Scale Discovery - MWU: - Mann–Whitney U Test - RNA: - Ribonucleic Acid - ROC: - Receiver Operating Characteristic - SD: - Standard Deviation

The Tumor Bank Ovarian Cancer at the Department of Gynecology with Centre of Oncological Surgery, Charité University Hospital, is gratefully acknowledged for providing the tissue samples. We thank Sandra Bock for her assistance during this study. The authors used Grammarly to enhance the accuracy of their English and improve the reading flow. Funding Open Access funding enabled and organized by Projekt DEAL. This work was also supported by the Nicolaus Copernicus University IDUB programme through funding awarded to the FemLife_OMICS Research Team for Women’s Health Across the Lifespan. The financial support of the German Federal Ministry of Research, Technology, and Space (BMFTR) is gratefully acknowledged (ENDO-PAIN grant, number 01EJ2402A). Author information Authors and Affiliations Corresponding author Ethics declarations Ethics approval and consent to participate The study was approved by the ethics commissions of Charité – Universitätsmedizin Berlin (EA2/266/22 and EA1/150/24). Blood samples from patients were obtained with informed consent, following local institutional review and the Declaration of Helsinki. Consent for publication All patients have consented to publish the generated results. Competing interests Jalid Sehouli reports research activities by Roche Pharma, AstraZeneca, Bayer, Clovis Oncology, GlaxoSmithKline, Lilly, Iqvia, Mural, and MSD; receiving honoraries by GlaxoSmithKline, PharmaMar, AstraZeneca, Clovis Oncology, Bayer, Roche Pharma, Vifor Pharma, Hexal AG, Novartis Pharma, Eisai, Esteve Pharmaceuticals, Incyte Biosciences, Phytolife Nutrition, JenaPharm, Kyowa Kirin, Oncoinvent AS, Daiichi, Medtronic Covidien, AMGEN, AbbVie, Corcept Therapeutics, Gilead Sciences, and Myriad; and consulting activities for Merck /Pfizer, PharmaMar, Clovis Oncology, AstraZeneca, Roche Pharma, GlaxoSmithKline, MSD, Eisai, Novocure, Oncoinvent, Intuitive Surgical, Seagen, Bayer Vital, Mundipharma, Sanofi‐Aventis Deutschland GmbH, Immunogen, Tubulis GmbH, Daiichi Sankyo, Bristol Myers Squibb, KaryopharmTherapeutics, and Corcept Therapeutics. The remaining authors declared no conflicts of interest. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Supplementary Information Below is the link to the electronic supplementary material. Rights and permissions Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, 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 changes were made. 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/4.0/. About this article Cite this article Kordowitzki, P., Ying, K., Beck, M.H. et al. Multimodal analysis reveals asynchronous aging dynamics between endometriosis-associated ovarian cancer and endometriosis patients. BMC Med (2026). https://doi.org/10.1186/s12916-026-05101-6 Received: Accepted: Published: DOI: https://doi.org/10.1186/s12916-026-05101-6