Dual-Function RNA Biomarkers: Integrating Relapse Prediction and Immune Profiling in Triple-Negative Breast Cancer.

Abstract

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with a high risk of recurrence and poor clinical outcomes. However, the factors contributing to its relapse remain inadequately understood. In this study, we utilized transcriptomic data from The Cancer Genome Atlas (TCGA) to identify lncRNA pairs associated with both recurrence and immune response. A risk prediction model was constructed through the integration of LASSO regression, Cox proportional hazards analysis, and random forest algorithms. To validate its predictive capability, we employed an external validation cohort along with a backpropagation neural network (BPNN) to assess the model's performance. Our findings indicate that the proposed risk model correlates strongly with multiple clinical features, including immune cell infiltration, response to immunotherapy, tumor mutational burden (TMB), and chemotherapy sensitivity. Additionally, a nomogram integrating risk scores with clinical parameters demonstrated superior predictive accuracy compared to models based solely on risk scores. Experimental validation confirmed that silencing LINC01605 significantly impaired TNBC cell proliferation, migration, and invasion. Overall, this risk model provides a novel approach for predicting tumor recurrence and prognosis in TNBC patients. The study also highlights the potential of LINC01605 as a therapeutic target, offering new perspectives for personalized treatment strategies.