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

ABSTRACT:

Introduction

Endometrial 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.

Methods

We 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.

Results

Microbial diversity was significantly higher in endometrial canc

SUBMITTER: Dodani D 

PROVIDER: S-EPMC12719438 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature