The underlying molecular mechanisms and prognostic factors of RNA binding protein in colorectal cancer: a study based on multiple online databases

Abstract Background RNA binding protein (RBP) is an active factor involved in the occurrence and development of colorectal cancer (CRC). Therefore, the potential mechanism of RBP in CRC needs to be clarified by dry-lab analyses or wet-lab experiments. Methods The differential RBP gene obtained from the GEPIA 2 (Gene Expression Profiling Interactive Analysis 2) were performed functional enrichment analysis. Then, the alternative splicing (AS) events related to survival were acquired by univariate regression analysis, and the correlation between RBP and AS was analyzed by R software. The online databases were conducted to analyze the mutation and methylation of RBPs in CRC. Moreover, 5 key RBP signatures were obtained through univariate and multivariate Cox regression analysis and established as RBP prognosis model. Subsequently, the above model was verified through another randomized group of TCGA CRC cohorts. Finally, multiple online databases and qRT-PCR analysis were carried to further confirm the expression of the above 5 RBP signatures in CRC. Results Through a comprehensive bioinformatics analysis, it was revealed that RBPs had genetic and epigenetic changes in CRC. We obtained 300 differentially expressed RBPs in CRC samples. The functional analysis suggested that they mainly participated in spliceosome. Then, a regulatory network for RBP was established to participate in AS and DDX39B was detected to act as a potentially essential factor in the regulation of AS in CRC. Our analysis discovered that 11 differentially expressed RBPs with a mutation frequency higher than 5%. Furthermore, we found that 10 differentially expressed RBPs had methylation sites related to the prognosis of CRC, and a prognostic model was constructed by the 5 RBP signatures. In another randomized group of TCGA CRC cohorts, the prognostic performance of the 5 RBP signatures was verified. Conclusion The potential mechanisms that regulate the aberrant expression of RBPs in the development of CRC was explored, a network that regulated AS was established, and the RBP-related prognosis model was constructed and verified, which could improve the individualized prognosis prediction of CRC.

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The Underlying Molecular Mechanisms and Prognostic Factors of Rna Binding Protein in Colorectal Cancer: A Study Based on Multiple Online Databases

10.21203/rs.3.rs-366831/v1 ◽

2021 ◽

Author(s):

Qinglian He ◽

Ziqi Li ◽

Xue Lei ◽

Qian Zou ◽

Haibing Yu ◽

...

Keyword(s):

Colorectal Cancer ◽

Regression Analysis ◽

Rna Binding ◽

Binding Protein ◽

Rna Binding Protein ◽

Functional Enrichment ◽

Differentially Expressed ◽

Aberrant Expression ◽

Online Databases ◽

Prognosis Model

Abstract Background: RNA binding protein (RBP) is an active factor involved in the occurrence and development of colorectal cancer (CRC). Therefore, the potential mechanism of RBP in CRC needs to be clarified by dry-lab analyses or wet-lab experiments.Methods: The differential RBP gene obtained from the GEPIA 2 (Gene Expression Profiling Interactive Analysis 2) were performed functional enrichment analysis. Then, the alternative splicing (AS) events related to survival were acquired by univariate regression analysis, and the correlation between RBP and AS was analyzed by R software. The online databases were conducted to analyze the mutation and methylation of RBPs in CRC. Moreover, 5 key RBP signatures were obtained through univariate and multivariate Cox regression analysis and established as RBP prognosis model. Subsequently, the above model was verified through another randomized group of TCGA CRC cohorts. Finally, multiple online databases and qRT-PCR analysis were carried to further confirm the expression of the above 5 RBP signatures in CRC.Results: Through a comprehensive bioinformatics analysis, it was revealed that RBPs had genetic and epigenetic changes in CRC. We obtained 300 differentially expressed RBPs in CRC samples. The functional analysis suggested that they mainly participated in spliceosome. Then, a regulatory network for RBP was established to participate in AS and DDX39B was detected to act as a potentially essential factor in the regulation of AS in CRC. Our analysis discovered that 11 differentially expressed RBPs with a mutation frequency higher than 5%. Furthermore, we found that 10 differentially expressed RBPs had methylation sites related to the prognosis of CRC, and a prognostic model was constructed by the 5 RBP signatures. In another randomized group of TCGA CRC cohorts, the prognostic performance of the 5 RBP signatures was verified. Conclusion: The potential mechanisms that regulate the aberrant expression of RBPs in the development of CRC was explored, a network that regulated AS was established, and the RBP-related prognosis model was constructed and verified, which could improve the individualized prognosis prediction of CRC.

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Establishment and validation of an RNA binding protein-associated prognostic model for ovarian cancer

Journal of Ovarian Research ◽

10.1186/s13048-021-00777-1 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Chaofan He ◽

Fuxin Huang ◽

Kejia Zhang ◽

Jun Wei ◽

Ke Hu ◽

...

Keyword(s):

Ovarian Cancer ◽

Target Genes ◽

Rna Binding ◽

Cox Regression ◽

Binding Protein ◽

Expression Profiles ◽

Rna Binding Protein ◽

Functional Enrichment ◽

Differentially Expressed ◽

Cox Regression Analysis

Abstract Background Ovarian cancer (OC) is one of the most common gynecological malignant tumors worldwide, with high mortality and a poor prognosis. As the early symptoms of malignant ovarian tumors are not obvious, the cause of the disease is still unclear, and the patients’ postoperative quality of life of decreases. Therefore, early diagnosis is a problem requiring an urgent solution. Methods We obtained the gene expression profiles of ovarian cancer and normal samples from TCGA and GTEx databases for differential expression analysis. From existing literature reports, we acquired the RNA-binding protein (RBP) list for the human species. Utilizing the online tool Starbase, we analyzed the interaction relationship between RBPs and their target genes and selected the modules of RBP target genes through Cytoscape. Finally, univariate and multivariate Cox regression analyses were used to determine the prognostic RBP signature. Results We obtained 527 differentially expressed RBPs, which were involved in many important cellular events, such as RNA splicing, the cell cycle, and so on. We predicted several target genes of RBPs, constructed the interaction network of RBPs and their target genes, and obtained many modules from the Cytoscape analysis. Functional enrichment of RBP target genes also includes these important biological processes. Through Cox regression analysis, OC prognostic RBPs were identified and a 10-RBP model constructed. Further analysis showed that the model has high accuracy and sensitivity in predicting the 3/5-year survival rate. Conclusions Our study identified differentially expressed RBPs and their target genes in OC, and the results promote our understanding of the molecular mechanism of ovarian cancer. The current study could develop novel biomarkers for the diagnosis, treatment, and prognosis of OC and provide new ideas and prospects for future clinical research.

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RNA-binding protein RALY reprogrammes mitochondrial metabolism via mediating miRNA processing in colorectal cancer

Gut ◽

10.1136/gutjnl-2020-320652 ◽

2020 ◽

pp. gutjnl-2020-320652

Author(s):

Lei Sun ◽

Arabella Wan ◽

Zhuolong Zhou ◽

Dongshi Chen ◽

Heng Liang ◽

...

Keyword(s):

Colorectal Cancer ◽

Rna Binding ◽

Tumour Progression ◽

Binding Protein ◽

Rna Binding Protein ◽

Cancer Therapeutics ◽

Mitochondrial Metabolism ◽

The Cancer Genome Atlas ◽

Colorectal Tumour ◽

Specific Subset

ObjectiveDysregulated cellular metabolism is a distinct hallmark of human colorectal cancer (CRC). However, metabolic programme rewiring during tumour progression has yet to be fully understood.DesignWe analysed altered gene signatures during colorectal tumour progression, and used a complex of molecular and metabolic assays to study the regulation of metabolism in CRC cell lines, human patient-derived xenograft mouse models and tumour organoid models.ResultsWe identified a novel RNA-binding protein, RALY (also known as hnRNPCL2), that is highly associated with colorectal tumour aggressiveness. RALY acts as a key regulatory component in the Drosha complex, and promotes the post-transcriptional processing of a specific subset of miRNAs (miR-483, miR-676 and miR-877). These miRNAs systematically downregulate the expression of the metabolism-associated genes (ATP5I, ATP5G1, ATP5G3 and CYC1) and thereby reprogramme mitochondrial metabolism in the cancer cell. Analysis of The Cancer Genome Atlas (TCGA) reveals that increased levels of RALY are associated with poor prognosis in the patients with CRC expressing low levels of mitochondrion-associated genes. Mechanistically, induced processing of these miRNAs is facilitated by their N6-methyladenosine switch under reactive oxygen species (ROS) stress. Inhibition of the m6A methylation abolishes the RALY recognition of the terminal loop of the pri-miRNAs. Knockdown of RALY inhibits colorectal tumour growth and progression in vivo and in organoid models.ConclusionsCollectively, our results reveal a critical metabolism-centric role of RALY in tumour progression, which may lead to cancer therapeutics targeting RALY for treating CRC.

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RNA-binding protein HUR regulates VEGF expression in human colorectal cancer cells

Journal of Surgical Research ◽

10.1016/j.jss.2005.11.163 ◽

2006 ◽

Vol 130 (2) ◽

pp. 219-220 ◽

Cited By ~ 2

Author(s):

P.S. Yoo ◽

A.L. Mulkeen ◽

T. Silva ◽

J. Schmitz ◽

N. Tai ◽

...

Keyword(s):

Colorectal Cancer ◽

Cancer Cells ◽

Rna Binding ◽

Binding Protein ◽

Rna Binding Protein ◽

Human Colorectal Cancer ◽

Vegf Expression ◽

Colorectal Cancer Cells ◽

Human Colorectal Cancer Cells

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TIA-1 Cytotoxic Granule-Associated RNA Binding Protein Improves the Prognostic Performance of CD8 in Mismatch Repair-Proficient Colorectal Cancer

PLoS ONE ◽

10.1371/journal.pone.0014282 ◽

2010 ◽

Vol 5 (12) ◽

pp. e14282 ◽

Cited By ~ 38

Author(s):

Inti Zlobec ◽

Eva Karamitopoulou ◽

Luigi Terracciano ◽

Salvatore Piscuoglio ◽

Giandomenica Iezzi ◽

...

Keyword(s):

Colorectal Cancer ◽

Mismatch Repair ◽

Rna Binding ◽

Binding Protein ◽

Rna Binding Protein ◽

Prognostic Performance ◽

Cytotoxic Granule

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The Lyme disease spirochete's BpuR DNA/RNA‐binding protein is differentially expressed during the mammal–tick infectious cycle, which affects translation of the SodA superoxide dismutase

Molecular Microbiology ◽

10.1111/mmi.14336 ◽

2019 ◽

Vol 112 (3) ◽

pp. 973-991 ◽

Cited By ~ 1

Author(s):

Brandon L. Jutras ◽

Christina R. Savage ◽

William K. Arnold ◽

Kathryn G. Lethbridge ◽

Dustin W. Carroll ◽

...

Keyword(s):

Superoxide Dismutase ◽

Lyme Disease ◽

Rna Binding ◽

Binding Protein ◽

Rna Binding Protein ◽

Differentially Expressed ◽

Infectious Cycle

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RNA-binding protein recognition based on multi-view deep feature and multi-label learning

Briefings in Bioinformatics ◽

10.1093/bib/bbaa174 ◽

2020 ◽

Author(s):

Haitao Yang ◽

Zhaohong Deng ◽

Xiaoyong Pan ◽

Hong-Bin Shen ◽

Kup-Sze Choi ◽

...

Keyword(s):

Rna Binding ◽

Binding Protein ◽

Rna Binding Protein ◽

Feature Learning ◽

Classification Model ◽

Rna Sequences ◽

Aberrant Expression ◽

Interaction Information ◽

Deep Feature ◽

Deep Feature Learning

Abstract RNA-binding protein (RBP) is a class of proteins that bind to and accompany RNAs in regulating biological processes. An RBP may have multiple target RNAs, and its aberrant expression can cause multiple diseases. Methods have been designed to predict whether a specific RBP can bind to an RNA and the position of the binding site using binary classification model. However, most of the existing methods do not take into account the binding similarity and correlation between different RBPs. While methods employing multiple labels and Long Short Term Memory Network (LSTM) are proposed to consider binding similarity between different RBPs, the accuracy remains low due to insufficient feature learning and multi-label learning on RNA sequences. In response to this challenge, the concept of RNA-RBP Binding Network (RRBN) is proposed in this paper to provide theoretical support for multi-label learning to identify RBPs that can bind to RNAs. It is experimentally shown that the RRBN information can significantly improve the prediction of unknown RNA−RBP interactions. To further improve the prediction accuracy, we present the novel computational method iDeepMV which integrates multi-view deep learning technology under the multi-label learning framework. iDeepMV first extracts data from the views of amino acid sequence and dipeptide component based on the RNA sequences as the original view. Deep neural network models are then designed for the respective views to perform deep feature learning. The extracted deep features are fed into multi-label classifiers which are trained with the RNA−RBP interaction information for the three views. Finally, a voting mechanism is designed to make comprehensive decision on the results of the multi-label classifiers. Our experimental results show that the prediction performance of iDeepMV, which combines multi-view deep feature learning models with RNA−RBP interaction information, is significantly better than that of the state-of-the-art methods. iDeepMV is freely available at http://www.csbio.sjtu.edu.cn/bioinf/iDeepMV for academic use. The code is freely available at http://github.com/uchihayht/iDeepMV.

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RNA-binding protein CELF1 enhances cell migration, invasion, and chemoresistance by targeting ETS2 in colorectal cancer

Clinical Science ◽

10.1042/cs20191174 ◽

2020 ◽

Vol 134 (14) ◽

pp. 1973-1990

Author(s):

Huaiming Wang ◽

Rongkang Huang ◽

Wentai Guo ◽

Xiusen Qin ◽

Zifeng Yang ◽

...

Keyword(s):

Colorectal Cancer ◽

Rna Binding ◽

Malignant Tumors ◽

Binding Protein ◽

Rna Binding Protein ◽

Luciferase Reporter ◽

Migration And Invasion ◽

Tumor Growth Model

Abstract Colorectal cancer (CRC) is often diagnosed at later stages after it has metastasized to other organs. The development of chemoresistance also contributes to a poor prognosis. Therefore, an increased understanding of the metastatic properties of CRC and chemoresistance could improve patient survival. CUGBP elav-like family member 1 (CELF1) is an RNA-binding protein, which is overexpressed in many human malignant tumors. However, the influence of CELF1 in CRC is unclear. V-ets erythroblastosis virus E26 oncogene homologue 2 (ETS2) is an evolutionarily conserved proto-oncogene known to be overexpressed in a variety of human cancers including CRC. In thespresent tudy, we investigated the association between CELF1 and ETS2 in CRC tumorigenesis and oxaliplatin (L-OHP) resistance. We found a positive correlation between the elevated expression of CELF1 and ETS2 in human CRC tissues. Overexpression of CELF1 increased CRC cell proliferation, migration, and invasion in vitro and in a xenograft tumor growth model in vivo, and induced resistance to L-OHP. In contrast, CELF1 knockdown improved the response of CRC cells to L-OHP. Overexpression of ETS2 increased the malignant behavior of CRC cells (growth, migration, and invasion) and L-OHP resistance in vitro. Moreover, L-OHP resistance induced by CELF1 overexpression was reversed by ETS2 knockdown. The results of luciferase reporter and ribonucleoprotein immunoprecipitation assays indicated that CELF1 up-regulates ETS2 by binding to its 3′-UTR. Taken together, our findings have identified that CELF1 regulates ETS2 in a mechanism that results in CRC tumorigenesis and L-OHP resistance, and CELF1 may be a promising target for overcoming chemoresistance in CRC.

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