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Integrated Bioinformatics Analysis the Function of RNA Binding Proteins (RBPs) and Their Prognostic Value in Breast Cancer.

ABSTRACT: Background and Purpose: Breast cancer is one of the leading causes of death among women. RNA binding proteins (RBPs) play a vital role in the progression of many cancers. Functional investigation of RBPs may contribute to elucidating the mechanisms underlying tumor initiation, progression, and invasion, therefore providing novel insights into future diagnosis, treatment, and prognosis. Methods: We downloaded RNA sequencing data from the cancer genome atlas (TCGA) by UCSC Xena and identified relevant RBPs through an integrated bioinformatics analysis. We then analyzed biological processes of differentially expressed genes (DEGs) by DAVID, and established their interaction networks and performed pathway analysis through the STRING database to uncover potential biological effects of these RBPs. We also explored the relationship between these RBPs and the prognosis of breast cancer patients. Results: In the present study, we obtained 1092 breast tumor samples and 113 normal controls. After data analysis, we identified 90 upregulated and 115 downregulated RBPs in breast cancer. GO and KEGG pathway analysis indicated that these significantly changed genes were mainly involved in RNA processing, splicing, localization and RNA silencing, DNA transposition regulation and methylation, alkylation, mitochondrial gene expression, and transcription regulation. In addition, some RBPs were related to histone H3K27 methylation, estrogen response, inflammatory mediators, and translation regulation. Our study also identified five RBPs associated with breast cancer prognosis. Survival analysis found that overexpression of DCAF13, EZR, and MRPL13 showed worse survival, but overexpression of APOBEC3C and EIF4E3 showed better survival. Conclusion: In conclusion, we identified key RBPs of breast cancer through comprehensive bioinformatics analysis. These RBPs were involved in a variety of biological and molecular pathways in breast cancer. Furthermore, we identified five RBPs as a potential prognostic biomarker of breast cancer. Our study provided novel insights to understand breast cancer at a molecular level.

SUBMITTER: Wang K

PROVIDER: S-EPMC6405693 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Integrated Bioinformatics Analysis the Function of RNA Binding Proteins (RBPs) and Their Prognostic Value in Breast Cancer.

Wang Ke K Li Ling L Fu Liang L Yuan Yongqiang Y Dai Hongying H Zhu Tianjin T Zhou Yuxi Y Yuan Fang F

Frontiers in pharmacology 20190301

<b>Background and Purpose:</b> Breast cancer is one of the leading causes of death among women. RNA binding proteins (RBPs) play a vital role in the progression of many cancers. Functional investigation of RBPs may contribute to elucidating the mechanisms underlying tumor initiation, progression, and invasion, therefore providing novel insights into future diagnosis, treatment, and prognosis. <b>Methods:</b> We downloaded RNA sequencing data from the cancer genome atlas (TCGA) by UCSC Xena and i ...[more]

PMID: 30881302

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Project description:BackgroundTo date, breast cancer remains the primary cause of tumor-related death among women, even though some leap-type developments of oncology have been done to slash the mortality. Considering the tumor heterogeneity and individual variation, the more reliable biomarkers are required to be identified for supporting the development of precision medicine in breast cancer.MethodsBased on the TCGA-BRCA and METABRIC databases, the differently expressed RNA binding proteins (RBPs) between tumor and normal tissues were investigated. In this study, we focused on the communal differently expressed RBPs in four subtypes of breast cancer. Lasso-penalized Cox analysis, Stepwise-multivariate Cox analysis and Kaplan-Meier survival curve were performed to identify the hub RBP-coding genes in predicting prognosis of breast cancer, and a prognostic model was established. The efficiency of this model was further validated in other independent GSE20685, GSE4922 and FUSCC-TNBC cohorts by calculating the risk score and performing survival analysis, ROC and nomogram. Moreover, pathologic functions of the candidate RBPs in breast cancer were explored using some routine experiments in vitro, and the potential compounds targeting these RBPs were predicted by reviewing the Comparative Toxicogenomics Database.ResultsHere, we identified 62 RBPs which were differently expressed between the tumor and normal tissues. Thereinto, three RBPs (MRPL12, MRPL13 and POP1) acted as independent risk factors, and their expression pattern also correlated with poor prognosis of patients. A prognostic model, built with these 3-RBPs, possessed statistical significance to predict the survival probability of patients with breast cancer. Furthermore, experimental validations showed that down-regulating the expression of endogenous MRPL12, MRPL13 or POP1 could dramatically suppress the cellular viability and migration of breast cancer cells in vitro. Besides, some compounds (such as the Acetaminophen, Urethane and Tunicamycin) were predicted for curing breast cancer via targeting MRPL12, MRPL13 and POP1 simultaneously.ConclusionThis study identified and established a 3-RBPs-based signature and nomogram for predicting the survival probability of patients with breast cancer. MRPL12, MRPL13 and POP1 might act as oncogenes in maintaining cellular viability and accelerating metastasis of breast cancer cells, implying the possibility of which to be designed as biomarkers and/or therapeutic targets for breast cancer.

Project description:BackgroundNeuroblastoma (NB) is the most common solid tumor in children. NB treatment has made significant progress; however, given the high degree of heterogeneity, basic research findings and their clinical application to NB still face challenges. Herein, we identify novel prognostic models for NB.MethodsWe obtained RNA expression data of NB and normal nervous tissue from TARGET and GTEx databases and determined the differential expression patterns of RNA binding protein (RBP) genes between normal and cancerous tissues. Lasso regression and Cox regression analyses identified the five most important differentially expressed genes and were used to construct a new prognostic model. The function and prognostic value of these RBPs were systematically studied and the predictive accuracy verified in an independent dataset.ResultsIn total, 348 differentially expressed RBPs were identified. Of these, 166 were up-regulated and 182 down-regulated RBPs. Two hubs RBPs (CPEB3 and CTU1) were identified as prognostic-related genes and were chosen to build the prognostic risk score models. Multivariate Cox analysis was performed on genes from univariate Cox regression and Lasso regression analysis using proportional hazards regression model. A five gene prognostic model: Risk score = (-0.60901*expCPEB3)+(0.851637*expCTU1) was built. Based on this model, the overall survival of patients in the high-risk subgroup was lower (P = 2.152e-04). The area under the curve (AUC) of the receiver-operator characteristic curve of the prognostic model was 0.720 in the TARGET cohort. There were significant differences in the survival rate of patients in the high and low-risk subgroups in the validation data set GSE85047 (P = 0.1237e-08), with the AUC 0.730. The risk model was also regarded as an independent predictor of prognosis (HR = 1.535, 95% CI = 1.368-1.722, P = 2.69E-13).ConclusionsThis study identified a potential risk model for prognosis in NB using Cox regression analysis. RNA binding proteins (CPEB3 and CTU1) can be used as molecular markers of NB.

Dataset Information

Integrated Bioinformatics Analysis the Function of RNA Binding Proteins (RBPs) and Their Prognostic Value in Breast Cancer.

Publications

Integrated Bioinformatics Analysis the Function of RNA Binding Proteins (RBPs) and Their Prognostic Value in Breast Cancer.

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