Molecular characterization of breast cancer: a potential novel immune-related lncRNAs signature.

Background: Accumulating evidence has demonstrated that immune-related lncRNAs (IRLs) are commonly aberrantly expressed in breast cancer (BC). Thus, we aimed to establish an IRL-based tool to improve prognosis prediction in BC patients.

Methods: We obtained IRL expression profiles in large BC cohorts (N = 911) from The Cancer Genome Atlas (TCGA) database. Then, in light of the correlation between each IRL and recurrence-free survival (RFS), we screened prognostic IRL signatures to construct a novel RFS nomogram via a Cox regression model. Subsequently, the performance of the IRL-based model was evaluated through discrimination, calibration ability, risk stratification ability and decision curve analysis (DCA).

Results: A total of 52 IRLs were obtained from TCGA. Based on multivariate Cox regression analyses, four IRLs (A1BG-AS1, AC004477.3, AC004585.1 and AC004854.2) and two risk parameters (tumor subtype and TNM stage) were utilized as independent indicators to develop a novel prognostic model. In terms of predictive accuracy, the IRL-based model was distinctly superior to the TNM staging system (AUC: 0.728 VS 0.673, P = 0.010). DCA indicated that our nomogram had favorable clinical practicability. In addition, risk stratification analysis showed that the IRL-based tool efficiently divided BC patients into high- and low-risk groups (P < 0.001).

Conclusions: A novel IRL-based model was constructed to predict the risk of 5-year RFS in BC. Our model can improve the predictive power of the TNM staging system and identify high-risk patients with tumor recurrence to implement more appropriate treatment strategies.