Abstract
Breast cancer patients with estrogen receptor-negative (ERneg) status, encompassing triple negative breast cancer (TNBC) and human epidermal growth factor receptor 2 positive breast cancer, are confronted with a heightened risk of drug resistance, often leading to early recurrence; the biomarkers and biological processes associated with recurrence is still unclear. In this study, we analyzed bulk RNA sequencing (RNA-seq) data from 285 cancer and paracancerous samples from 155 TNBC patients, along with transcriptome data from 11 independent public cohorts comprising 7,449 breast cancer patients and 26 single-cell RNA-seq datasets. Our results revealed differential enrichment of nerve-related pathways between TNBC patients with and without 10-year recurrence-free survival. We developed an early recurrence index (ERI) using a machine learning model and constructed a nomogram that accurately predicts the 10-year survival of ERneg patients (area under the curve [AUC](Training) = 0.79; AUC(Test) = 0.796). Further analysis linked ERI to enhanced neural function and immunosuppression. Additionally, we identified EN1, the most significant ERI gene, as a potential biomarker that may regulate the tumor microenvironment and sensitize patients to immunotherapy.