Development and validation of novel prognostic models based on RNA-binding proteins in breast cancer

OBJECTIVES: We aimed to construct novel prognostic models based on RNA-binding proteins (RBPs) in breast cancer (BRCA) and explore their roles in this disease and their effects on tumor-infiltrating immune cells (TIICs). METHODS: Datasets were downloaded from the Gene Expression Omnibus (GEO) database. Functions and prognostic values of RBPs were systematically investigated using a series of bioinformatics analysis methods. TIICs were assessed using CIBERSORT. RESULTS: Overall, 138 differentially expressed RBPs were identified, of which 86 were upregulated and 52 were downregulated. Of these, 13 RBPs were identified as prognosis-related and adopted to construct an overall survival (OS) model, while 12 RBPs were used for the relapse-free survival (RFS) model. High-risk patients had poorer OS and RFS rates than low-risk patients. The results indicate that the OS and RFS models are good prognostic models with reliable predictive abilities. In addition, the proportions of CD8, CD4 naïve, and CD4 memory resting T cells, as well as resting dendritic cells, were significantly different between the low-risk and high-risk groups in the OS model. CONCLUSIONS: OS and RFS signatures can be used as reliable BRCA prognostic biomarkers. This work will help understand the prognostic roles and functions of RBPs in BRCA.