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

Abstract

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.

Figure 1

An enrichment analysis in breast cancer. (a) Venn diagrams of RBPs-related genes in breast cancer. (b) Differentially expressed genes (DEGs) analysis. (c) GO enrichment analysis. (d) KEGG enrichment analysis, P < 0.05.

Figure 2

Identification of key prognostic genes. (a) PPI network for upregulated RBPs. (b) Top 30 RBPs with the most nodes. (c) The LASSO regression used to determine the independent prognostic pseudogenes. (d) LASSO coefficient profiles of 4 prognostic genes. (e) The forest map of multivariate Cox regression analysis.

Figure 3

Risk score analysis of the prognostic model in the ICGC-BRCA cohort. (a) Survival analysis according to risk score. (b) ROC analysis. (c) The relationship among the risk score. (d) Heat map. (e) Survival status of patients in different groups.

Figure 4

Risk score analysis of the prognostic model in the TCGA-BRCA cohort. (a) Survival analysis according to risk score. (b) ROC analysis. (c) The relationship among the risk score. (d) Heat map. (e) Survival status of patients in different groups.

Figure 5

Nomogram and calibration plots of 4 RBPs. (a) Nomogram to predict 1-, 3-, and 5-year OS in the TCGA cohort. (b–d) Calibration plots of the nomogram to predict OS at 1, 3, and 5 years.

Figure 6

Expression and genetic alterations of the four RBPs genes. (a) The expression profiles of the four genes in the TIMER database. (b) The representative protein expression of the four genes in BRCA and normal tissue. (c) The genetic alterations of the four genes in BRCA.

Figure 7

Framework for analyzing the RBPs in breast cancer.

Figure 8

Verification of the risk score model.

Figure 9

Univariate and multivariate Cox regression analyses of the risk score and other clinicopathological factors in the TCGA-BRCA dataset. (a) Univariate Cox regression analyses. (b) Multivariate Cox regression analyses.