Early detection, clinicopathological subtyping, and prognosis prediction for endometrial cancer patients using fragmentomics liquid biopsy assay.

ABSTRACT:

Background

Endometrial cancer (EC) is among the most prevalent gynecological malignancies worldwide. This study explores the use of cell-free DNA (cfDNA) fragmentomics to develop a non-invasive liquid biopsy assay, aiming to improve early detection, subtyping, and prognostication of EC, thereby enhancing therapeutic outcomes and reducing associated mortality.

Materials and methods

A cohort of 120 patients with diagnosed EC and 120 healthy volunteers was used to develop a novel non-invasive liquid biopsy assay for EC. Five distinct fragmentomic features were analyzed from preoperative plasma samples using low-pass whole-genome sequencing. Ensemble models were created by integrating base models that utilized four different machine learning algorithms for early cancer detection

SUBMITTER: Rao Q 

PROVIDER: S-EPMC12495095 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature