RNA-Binding Proteins as Novel Oncoproteins and Tumor Suppressors in Breast Cancer

2006 ◽  
Author(s):  
Gary Brewer
Keyword(s):  
Breast Cancer ◽  
Tumor Suppressors ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins

scholarly journals Prediction of breast cancer proteins using molecular descriptors and artificial neural networks: a focus on cancer immunotherapy proteins, metastasis driver proteins, and RNA-binding proteins

2019 ◽  
Author(s):  
Andrés López-Cortés ◽  
Alejandro Cabrera-Andrade ◽  
José M. Vázquez-Naya ◽  
Alejandro Pazos ◽  
Humberto Gonzáles-Díaz ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Cancer Immunotherapy ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Operating Characteristics ◽  
Associated Proteins ◽  
Artificial Neural ◽  
New Biomarkers

ABSTRACTBackgroundBreast cancer (BC) is a heterogeneous disease characterized by an intricate interplay between different biological aspects such as ethnicity, genomic alterations, gene expression deregulation, hormone disruption, signaling pathway alterations and environmental determinants. Due to the complexity of BC, the prediction of proteins involved in this disease is a trending topic in drug design.MethodsThis work is proposing accurate prediction classifier for BC proteins using six sets of protein sequence descriptors and 13 machine learning methods. After using a univariate feature selection for the mix of five descriptor families, the best classifier was obtained using multilayer perceptron method (artificial neural network) and 300 features.ResultsThe performance of the model is demonstrated by the area under the receiver operating characteristics (AUROC) of 0.980 ± 0.0037 and accuracy of 0.936 ± 0.0056 (3-fold cross-validation). Regarding the prediction of 4504 cancer-associated proteins using this model, the best ranked cancer immunotherapy proteins related to BC were RPS27, SUPT4H1, CLPSL2, POLR2K, RPL38, AKT3, CDK3, RPS20, RASL11A and UBTD1; the best ranked metastasis driver proteins related to BC were S100A9, DDA1, TXN, PRNP, RPS27, S100A14, S100A7, MAPK1, AGR3 and NDUFA13; and the best ranked RNA-binding proteins related to BC were S100A9, TXN, RPS27L, RPS27, RPS27A, RPL38, MRPL54, PPAN, RPS20 and CSRP1.ConclusionsThis powerful model predicts several BC-related proteins which should be deeply studied to find new biomarkers and better therapeutic targets. The script and the results are available as a free repository at https://github.com/muntisa/neural-networks-for-breast-cancer-proteins.

Abstract PD15-07: Myc regulates alternative splicing through a network of RNA binding proteins in breast cancer

2021 ◽  
Author(s):  
Laura Urbanski ◽  
Brittany Angarola ◽  
Mattia Brugiolo ◽  
Marina Yurieva ◽  
Sunghee Park ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Alternative Splicing ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins

scholarly journals Genetic polymorphisms in RNA binding proteins contribute to breast cancer survival

2012 ◽  
Vol 132 (3) ◽  
pp. E128-E138 ◽  
Author(s):  
Rohit Upadhyay ◽  
Sandhya Sanduja ◽  
Vimala Kaza ◽  
Dan A. Dixon
Keyword(s):  
Breast Cancer ◽  
Genetic Polymorphisms ◽  
Binding Proteins ◽  
Cancer Survival ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Breast Cancer Survival

scholarly journals Identification of a Novel AU-Rich Element in the 3′ Untranslated Region of Epidermal Growth Factor Receptor mRNA That Is the Target for Regulated RNA-Binding Proteins

2001 ◽  
Vol 21 (6) ◽  
pp. 2070-2084 ◽  
Author(s):  
L. A. Balmer ◽  
D. J. Beveridge ◽  
J. A. Jazayeri ◽  
A. M. Thomson ◽  
C. E. Walker ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Cells ◽  
Mrna Stability ◽  
Breast Cancer Cells ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Growth Factor Receptor ◽  
Human Breast ◽  
Shift Analysis

ABSTRACT The epidermal growth factor receptor (EGF-R) plays an important role in the growth and progression of estrogen receptor-negative human breast cancers. EGF binds with high affinity to the EGF-R and activates a variety of second messenger pathways that affect cellular proliferation. However, the underlying mechanisms involved in the regulation of EGF-R expression in breast cancer cells are yet to be described. Here we show that the EGF-induced upregulation of EGF-R mRNA in two human breast cancer cell lines that overexpress EGF-R (MDA-MB-468 and BT-20) is accompanied by stabilization (>2-fold) of EGF-R mRNA. Transient transfections using a luciferase reporter identified a novel EGF-regulated ∼260-nucleotide (nt)cis-acting element in the 3′ untranslated region (3′-UTR) of EGF-R mRNA. This cis element contains two distinct AU-rich sequences (∼75 nt), EGF-R1A with two AUUUA pentamers and EGF-R2A with two AUUUUUA extended pentamers. Each independently regulated the mRNA stability of the heterologous reporter. Analysis of mutants of the EGF-R2A AU-rich sequence demonstrated a role for the 3′ extended pentamer in regulating basal turnover. RNA gel shift analysis identified cytoplasmic proteins (∼55 to 80 kDa) from breast cancer cells that bound specifically to the EGF-R1A and EGF-R2A cis-acting elements and whose binding activity was rapidly downregulated by EGF and phorbol esters. RNA gel shift analysis of EGF-R2A mutants identified a role for the 3′ extended AU pentamer, but not the 5′ extended pentamer, in binding proteins. These EGF-R mRNA-binding proteins were present in multiple human breast and prostate cancer cell lines. In summary, these data demonstrate a central role for mRNA stabilization in the control of EGF-R gene expression in breast cancer cells. EGF-R mRNA contains a novel complex AU-rich 260-nt cis-acting destabilizing element in the 3′-UTR that is bound by specific and EGF-regulatedtrans-acting factors. Furthermore, the 3′ extended AU pentamer of EGF-R2A plays a central role in regulating EGF-R mRNA stability and the binding of specific RNA-binding proteins. These findings suggest that regulated RNA-protein interactions involving this novel cis-acting element will be a major determinant of EGF-R mRNA stability.

scholarly journals Identification of long non-coding RNAs and RNA binding proteins in breast cancer subtypes

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Claudia Cava ◽  
Alexandros Armaos ◽  
Benjamin Lang ◽  
Gian G. Tartaglia ◽  
Isabella Castiglioni
Keyword(s):  
Breast Cancer ◽  
Binding Proteins ◽  
Rna Binding ◽  
Molecular Subtypes ◽  
Rna Binding Proteins ◽  
Breast Cancer Subtypes ◽  
Luminal A ◽  
Cancer Subtypes ◽  
Luminal B ◽  
Non Coding Rnas

AbstractBreast cancer is a heterogeneous disease classified into four main subtypes with different clinical outcomes, such as patient survival, prognosis, and relapse. Current genetic tests for the differential diagnosis of BC subtypes showed a poor reproducibility. Therefore, an early and correct diagnosis of molecular subtypes is one of the challenges in the clinic. In the present study, we identified differentially expressed genes, long non-coding RNAs and RNA binding proteins for each BC subtype from a public dataset applying bioinformatics algorithms. In addition, we investigated their interactions and we proposed interacting biomarkers as potential signature specific for each BC subtype. We found a network of only 2 RBPs (RBM20 and PCDH20) and 2 genes (HOXB3 and RASSF7) for luminal A, a network of 21 RBPs and 53 genes for luminal B, a HER2-specific network of 14 RBPs and 30 genes, and a network of 54 RBPs and 302 genes for basal BC. We validated the signature considering their expression levels on an independent dataset evaluating their ability to classify the different molecular subtypes with a machine learning approach. Overall, we achieved good performances of classification with an accuracy >0.80. In addition, we found some interesting novel prognostic biomarkers such as RASSF7 for luminal A, DCTPP1 for luminal B, DHRS11, KLC3, NAGS, and TMEM98 for HER2, and ABHD14A and ADSSL1 for basal. The findings could provide preliminary evidence to identify putative new prognostic biomarkers and therapeutic targets for individual breast cancer subtypes.

scholarly journals In silico analyses reveal new putative Breast Cancer RNA-binding proteins

2020 ◽  
Author(s):  
Santiago Guerrero ◽  
Andres Lopez-Cortes ◽  
Jennyfer M. Garcia-Cardenas ◽  
Isaac Armendariz-Castillo ◽  
Ana Karina Zambrano ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Drug Development ◽  
In Silico ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Tumor Biology ◽  
The Cancer Genome Atlas ◽  
Cancer Drug ◽  
In Silico Analyses

Breast cancer (BC) is the leading cause of cancer-associated death among women worldwide. Despite treatment efforts, advanced BC with distant organ metastases is considered incurable. A better understanding of BC molecular processes is therefore of great interest to identify new therapeutic targets. Although large-scale efforts, such as The Cancer Genome Atlas (TCGA), have completely redefined cancer drug development, diagnosis, and treatment, additional key aspects of tumor biology remain to be discovered. In that respect, post-transcriptional regulation of tumorigenesis represents an understudied aspect of cancer research. As key regulators of this process, RNA-binding proteins (RBPs) are emerging as critical modulators of tumorigenesis but only few have defined roles in BC. To unravel new putative BC RBPs, we have performed in silico analyses of all human RBPs in three major cancer databases (TCGA-Breast Invasive Carcinoma, the Human Protein Atlas, and the Cancer Dependency Map project) along with complementary bioinformatics resources (STRING protein-protein interactions and the Network of Cancer Genes 6.0). Thus, we have identified six putative BC progressors (MRPL13, SCAMP3, CDC5L, DARS2, PUF60, and PLEC), and five BC suppressors RBPs (SUPT6H, MEX3C, UPF1, CNOT1, and TNKS1BP1). These proteins have never been studied in BC but show similar cancer-associated features than well-known BC proteins. Further research should focus on the mechanisms by which these proteins promote or suppress breast tumorigenesis, holding the promise of new therapeutic pathways along with novel drug development strategies.

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