Integrative multi-omics profiling and machine learning reveal enhancer RNA signatures for early detection and prognostic stratification in breast cancer
TL;DR
These findings establish eRNAs as a complementary noncoding layer to conventional messenger RNA classifiers, with clinical potential for breast cancer risk stratification, therapeutic guidance, and early detection.
Abstract
Abstract Current breast cancer biomarkers rely predominantly on protein-coding transcriptomes, leaving the regulatory information encoded by enhancer RNAs (eRNAs) largely unexploited. Here, we profiled eRNA expression across 1073 The Cancer Genome Atlas Breast Cancer (TCGA-BRCA) samples integrated with The Cancer eRNA Atlas annotations, using GTEx healthy breast tissue (n = 179) as a curated normal reference. For prognostic stratification, least absolute shrinkage and selection operator (LASSO)-Cox regression identified a 10-eRNA signature that stratified patients into high- and low-risk groups in both training (P <.0001) and independent testing cohorts (P = 0.0022), with a 3-year time-dependent area under the curve (AUC) of 0.743. Multivariate Cox analysis confirmed the signature as an independent predictor [hazard ratio = 3.176, 95% confidence interval (CI): 2.075–4.864, P <.001]. Multi-omics network integration linked these prognostic eRNAs to regulatory hubs centered on the ESR1/FOXA1/GATA3/AR axis. For early-stage detection, we developed a 19-eRNA panel via bootstrap LASSO feature selection and benchmarked ten classifiers; a Multilayer Perceptron achieved the best generalization (AUC = 0.959, 95% CI: 0.925–0.986) on an independent cohort (GSE225846). Both modules were deployed in eRNACare, a publicly accessible web application for individualized survival and tumor probability scoring. These findings establish eRNAs as a complementary noncoding layer to conventional messenger RNA classifiers, with clinical potential for breast cancer risk stratification, therapeutic guidance, and early detection.