Integrative multi-omics profiling and machine learning reveal enhancer RNA signatures for early detection and prognostic stratification in breast cancer

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.