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Background   Breast cancer (BRCA) is one of the most common cancers and the leading cause of cancer-related death in women. RNA-binding proteins (RBPs) play an important role in the emergence and pathogenesis of tumors. The target RNAs of RBPs are very diverse; in addition to binding to mRNA, RBPs also bind to noncoding RNA. Noncoding RNA can cause secondary structures that can bind to RBPs and regulate multiple processes such as splicing, RNA modification, protein localization, and chromosomes remodeling, which can lead to tumor initiation, progression, and invasion.Methods   (1) BRCA data were downloaded from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases and were used as training and testing datasets, respectively. (2) The prognostic RBPs-related genes were screened according to the overlapping differentially expressed genes (DEGs) from the TCGA database. (3) Univariate Cox proportional hazard regression was performed to identify the genes with significant prognostic value. (4) Further, we used the LASSO regression to construct a prognostic signature and validated the signature in the TCGA and ICGC cohort. (5) Besides, we also performed prognostic analysis, expression level verification, immune cell correlation analysis, and drug correlation analysis of the genes in the model.Results   Four genes (MRPL13, IGF2BP1, BRCA1, and MAEL) were identified as prognostic gene signatures. The prognostic model has been validated in the TCGA and ICGC cohorts. The risk score calculated with four genes signatures could largely predict overall survival for 1, 3, and 5 years in patients with BRCA. The calibration plot demonstrated outstanding consistency between the prediction and actual observation. The findings of online database verification revealed that these four genes were significantly highly expressed in tumors. Also, we observed their significant correlations with some immune cells and also potential correlations with some drugs.Conclusion   We constructed a 4-RBPs-based prognostic signature to predict the prognosis of BRCA patients, and it has the potential for treating and diagnosing BRCA.

Analysing a Novel RNA-Binding-Protein-Related Prognostic Signature Highly Expressed in Breast Cancer