AUTHOR=Dodani Dollina , Talhouk Aline 

TITLE=Multi-cohort ensemble learning framework for vaginal microbiome-based endometrial cancer detection

JOURNAL=Frontiers in Cellular and Infection Microbiology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1641413

DOI=10.3389/fcimb.2025.1641413

ISSN=2235-2988

ABSTRACT=IntroductionEndometrial cancer is the most common gynecological malignancy in high-income countries and lacks an established strategy for early detection. Prior studies suggest that the vaginal microbiome may hold diagnostic potential, but inconsistent findings have limited clinical translation.MethodsWe conducted a systematic review to collect and analyze vaginal 16S rRNA sequencing data from five independent cohorts (n = 265). These studies included women with histologically confirmed endometrial cancer and controls with benign gynecologic conditions. We used these datasets to identify microbial signatures associated with endometrial cancer and to develop a predictive machine learning model.ResultsMicrobial diversity was significantly higher in endometrial cancer samples, and host characteristics influenced community composition. Peptoniphilus was reproducibly enriched in cancer samples across cohorts. An ensemble classifier accurately identified endometrial cancer in a held-out test set, achieving an area under the receiver operating characteristic curve of 0.93 (95% CI: 0.71–0.93), sensitivity of 1.0 (95% CI: 0.74–1.0), and a negative predictive value of 1.0 (95% CI: 0.59–1.0).DiscussionThese findings support the potential of vaginal microbiome profiling as a minimally invasive approach for early detection of endometrial cancer.