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. Methods 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. Results 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. Conclusions 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.