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

Wang, Ke; Li, Ling; Fu, Liang; Yuan, Yongqiang; Dai, Hongying; Tianjin, Zhu; Zhou, Yue; Fang, Yuan

doi:10.3389/fphar.2019.00140

Cited by 64 publications

(53 citation statements)

References 58 publications

(58 reference statements)

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“…To date, the dysregulation of RBPs has been in the limelight in numerous cancer researches. However, the relationship between most of RBPs and gastric cancer are still not well characterized [19]. In this study, 261 differently expressed RBPs were RBPs play a vital role in practically all aspects of this post-transcriptional regulatory network, affecting the function of each transcript and maintaining cellular homeostasis [32].…”

Section: Discussionmentioning

confidence: 81%

“…In short, these studies have revealed that the RBPs are involved in the initiation and progression of tumors. Besides, there are also reports using high-throughput bioinformatic profiling in The Cancer Genome Atlas (TCGA) database to reveal a similar pattern of dysregulations in RBPs expression across multiple cancer types [18][19][20]. However, the majority of RBPs have not been studied comprehensively in gastric cancer via bioinformatic method.…”

Section: Introductionmentioning

confidence: 99%

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Exploring an RNA binding proteins-associated prognostic model for Gastric Cancer

Liu

Xie

Luo

et al. 2020

Preprint

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Background: Gastric cancer (GC) is one of the most prevalent malignant cancers around the world. Given that abnormal RNA binding proteins (RBPs) are involved in the tumorigenesis, we aimed to explore the potential value of RBPs-associated genes in gastric cancer.Methods: RNA-seq and clinical data were retrieved from The Cancer Genome Atlas (TCGA) database and differentially expressed RBPs genes were screened. GO and KEGG pathway enrichment analyses were implemented to elucidate the roles of RBPs in GC. The protein-protein interaction (PPI) networks of RBPs were carried out, and the hub genes were determined by MCODE built in Cytoscape. The TCGA-STAD dataset was randomly divided into training and testing groups. A prognostic signature including five RBPs was developed within the training cohort after Cox regression and Lasso regression analyses. We used Kaplan–Meier (KM) and receiver operating characteristic (ROC) curves to evaluate the capacity of the model in both groups. Then, a nomogram based on hub RBPs expression was established. Gene Set Enrichment Analysis was performed between the high-risk and low-risk group.Results: A total of 166 up-regulated RBPs and 130 down-regulated RBPs were identified. Via Cox regression and Lasso regression analysis within the training group, five hub RBPs (RNASE1, SETD7, BOLL, PPARGC1B, MSI2) were screened and the prognostic model was constructed. The risk score was calculated and gastric cancer patients were divided into high-risk and low-risk groups. In multivariate analysis, risk score was still an independent prognostic indicator (HR = 1.80, 95% CI = 1.45-2.22, P < 0.01). Patients with low risk had favorable survival rate in both training and testing group compared to those at high risk (P < 0.001). The areas under the ROC curves (AUC) of the prognostic model are 0.718 in the training cohort and 0.651 in the testing cohort. The hub RBPs-based nomogram model exhibited excellent ability to predict the OS of GC. GSEA illustrated that several cancer-related signaling pathways were enriched in patients with a high-risk score.Conclusions: This study discovered a five RBPs signature which might provide a potential prognostic value to GC patients.

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Section: Discussionmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Exploring an RNA binding proteins-associated prognostic model for Gastric Cancer

Liu

Xie

Luo

et al. 2020

Preprint

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“…Previous study has reported that the expression IGF2BP3 or IGF2BP1 can predict poor overall survival in lung cancer patients (Shi et al, 2017), which were consistent with our results. Two other studies also showed that DCAF13 was increased in hepatocellular carcinoma or breast cancer, and obviously associated with poor overall survival (Cao et al, 2017;Wang et al, 2019). The ROC curve analysis revealed that these 8 RBPs with better diagnostic accuracy for distinguishing LUAD patients from healthy people, which could be used as potential diagnostic molecular markers in the future.…”

Section: Discussionmentioning

confidence: 87%

Integrated analysis of the roles and prognostic value of RNA binding proteins in lung adenocarcinoma

Gao

et al. 2020

PeerJ

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Lung cancer is the top cause of carcinoma-associated deaths worldwide. RNA binding proteins (RBPs) dysregulation has been reported in various malignant tumors, and that dysregulation is closely associated with tumorigenesis and tumor progression. However, little is known about the roles of RBPs in lung adenocarcinoma (LUAD). In this study, we downloaded the RNA sequencing data of LUAD from The Cancer Genome Atlas (TCGA) database and determined the differently expressed RBPs between normal and cancer tissues. We then performed an integrative analysis to explore the expression and prognostic significance of these RBPs. A total of 164 differently expressed RBPs were identified, including 40 down-regulated and 124 up-regulated RBPs. Pathway and Gene ontology (GO) analysis indicated that the differently expressed RBPs were mainly related to RNA processing, RNA metabolic process, RNA degradation, RNA transport, splicing, localization, regulation of translation, RNA binding, TGF-beta signaling pathway, mRNA surveillance pathway, and aminoacyl-tRNA biosynthesis. Survival analysis revealed that the high expression of BOP1 or GNL3 or WDR12 or DCAF13 or IGF2BP3 or IGF2BP1 were associated with poor overall survival (OS). Conversely, overexpression of KHDRBS2/SMAD predicted high OS in these patients. ROC curve analysis showed that the eight hub genes with a better diagnostic accuracy to distinguish lung adenocarcinoma. The results provided novel insights into the pathogenesis of LUAD and the development of treatment targets and prognostic molecular markers.

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“…To date, 1,393 RBPs have been experimentally identified from human RNA interactomes 27 . Despite efforts to understand their role in cancer 28,29 , an integrated analysis of the aforementioned databases along with other in silico approaches is still missing in BC. To shed light on this matter, we analyzed and integrated RBPs genomic alterations, protein–protein interaction (PPI) networks, immunohistochemical profiles, and loss-of-function experiments to unravel new putative BC RBPs.…”

Section: Introductionmentioning

confidence: 99%

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

Guerrero

López‐Cortés

García-Cárdenas

et al. 2020

Preprint

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

Cited by 64 publications

References 58 publications

Exploring an RNA binding proteins-associated prognostic model for Gastric Cancer

Exploring an RNA binding proteins-associated prognostic model for Gastric Cancer

Integrated analysis of the roles and prognostic value of RNA binding proteins in lung adenocarcinoma

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

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