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Abstract
Breast cancer (BC) remains one of the most common malignancies among women worldwide, with persistently poor prognosis despite advancements in diagnostics and therapies. Long non-coding RNAs (lncRNAs) and coagulation-related genes (CRGs) are increasingly recognized for their roles in prognosis and immune modulation. Using transcriptomic data from 1,045 BC patients in TCGA, we identified CRG-associated lncRNAs via coexpression analysis (Pearson |R|> 0.4, p < 0.001) and constructed a prognostic model through univariate Cox analysis, LASSO regression with tenfold cross-validation (λ = 0.05), and multivariate Cox analysis. The model stratified patients into high- and low-risk groups with distinct overall survival (HR = 3.21, p < 0.001) and demonstrated robust predictive accuracy (AUC = 0.795 at 1 year). Functional enrichment revealed immune-related pathways (e.g., cytokine signaling, PD-L1 regulation), and high-risk patients exhibited elevated tumor mutational burden (TMB) and PD-L1 expression, suggesting enhanced immunotherapy responsiveness. Drug sensitivity analysis identified 5 targeted agents (e.g., BIBW2992) with differential efficacy between risk groups. This CRG-lncRNA signature provides a novel tool for prognosis prediction and personalized immunotherapy in BC, illuminating crosstalk between coagulation and immune pathways.