Integrating epigenetic and metabolic indicators for non-invasive endometrial cancer triage: a machine learning approach.

ABSTRACT:

Objective

Abnormal uterine bleeding (AUB) is a primary symptom indicative of endometrial cancer (EC), yet its diagnosis still primarily relies on invasive biopsy. This study aimed to develop and validate a non-invasive, interpretable machine learning (ML) model integrating local epigenetic (exfoliated cell CDO1 methylation) and systemic metabolic indicators for EC triage.

Methods

CDO1 was identified as a potential biomarker via bioinformatics. Its diagnostic performance was assessed using quantitative methylation-specific PCR (qMSP) in a clinical cohort of 267 women with AUB. Subsequently, a multimodal ML framework was developed that incorporated logistic regression (LR), support vector machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms to

SUBMITTER: Mao X 

PROVIDER: S-EPMC13640100 | biostudies-literature | 2026 Jul

REPOSITORIES: biostudies-literature