Integrative analysis of DNA methylation, RNA sequencing, and genomic variants in the cancer genome atlas (TCGA) to predict endometrial cancer recurrence.

ABSTRACT:

Introduction

The prognosis within each subtype varies due to histological and molecular factors. This study leverages omics datasets and machine learning to identify biomarkers associated with EC recurrence in different molecular subtypes.

Methods

Utilizing DNA methylation, RNA-sequencing, and common variant data from 116 EC samples in The Cancer Genome Atlas (TCGA), differentially expressed genes (DEGs) and differentially methylated regions (DMRs) were identified using t-tests between recurrence and non-recurrence groups. These were visualized through volcano plots and heat maps, while decision trees and random forests classified and stratified the samples.

Results

A machine learning analysis combined with box plots showed that in the copy number-high (CN-H) recurren

SUBMITTER: Hong JH 

PROVIDER: S-EPMC12066751 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature