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Integrated analysis of RNA-binding proteins in human colorectal cancer

BACKGROUND: Although RNA-binding proteins play an essential role in a variety of different tumours, there are still limited efforts made to systematically analyse the role of RNA-binding proteins (RBPs) in the survival of colorectal cancer (CRC) patients. METHODS: Analysis of CRC transcriptome data...

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Autores principales: Fan, Xuehui, Liu, Lili, Shi, Yue, Guo, Fanghan, Wang, Haining, Zhao, Xiuli, Zhong, Di, Li, Guozhong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7443297/
https://www.ncbi.nlm.nih.gov/pubmed/32828126
http://dx.doi.org/10.1186/s12957-020-01995-5
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author Fan, Xuehui
Liu, Lili
Shi, Yue
Guo, Fanghan
Wang, Haining
Zhao, Xiuli
Zhong, Di
Li, Guozhong
author_facet Fan, Xuehui
Liu, Lili
Shi, Yue
Guo, Fanghan
Wang, Haining
Zhao, Xiuli
Zhong, Di
Li, Guozhong
author_sort Fan, Xuehui
collection PubMed
description BACKGROUND: Although RNA-binding proteins play an essential role in a variety of different tumours, there are still limited efforts made to systematically analyse the role of RNA-binding proteins (RBPs) in the survival of colorectal cancer (CRC) patients. METHODS: Analysis of CRC transcriptome data collected from the TCGA database was conducted, and RBPs were extracted from CRC. R software was applied to analyse the differentially expressed genes (DEGs) of RBPs. To identify related pathways and perform functional annotation of RBP DEGs, Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out using the database for annotation, visualization and integrated discovery. Protein-protein interactions (PPIs) of these DEGs were analysed based on the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized by Cytoscape software. Based on the Cox regression analysis of the prognostic value of RBPs (from the PPI network) with survival time, the RBPs related to survival were identified, and a prognostic model was constructed. To verify the model, the data stored in the TCGA database were designated as the training set, while the chip data obtained from the GEO database were treated as the test set. Then, both survival analysis and ROC curve verification were conducted. Finally, the risk curves and nomograms of the two groups were generated to predict the survival period. RESULTS: Among RBP DEGs, 314 genes were upregulated while 155 were downregulated, of which twelve RBPs (NOP14, MRPS23, MAK16, TDRD6, POP1, TDRD5, TDRD7, PPARGC1A, LIN28B, CELF4, LRRFIP2, MSI2) with prognostic value were obtained. CONCLUSIONS: The twelve identified genes may be promising predictors of CRC and play an essential role in the pathogenesis of CRC. However, further investigation of the underlying mechanism is needed.
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spelling pubmed-74432972020-08-25 Integrated analysis of RNA-binding proteins in human colorectal cancer Fan, Xuehui Liu, Lili Shi, Yue Guo, Fanghan Wang, Haining Zhao, Xiuli Zhong, Di Li, Guozhong World J Surg Oncol Research BACKGROUND: Although RNA-binding proteins play an essential role in a variety of different tumours, there are still limited efforts made to systematically analyse the role of RNA-binding proteins (RBPs) in the survival of colorectal cancer (CRC) patients. METHODS: Analysis of CRC transcriptome data collected from the TCGA database was conducted, and RBPs were extracted from CRC. R software was applied to analyse the differentially expressed genes (DEGs) of RBPs. To identify related pathways and perform functional annotation of RBP DEGs, Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out using the database for annotation, visualization and integrated discovery. Protein-protein interactions (PPIs) of these DEGs were analysed based on the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized by Cytoscape software. Based on the Cox regression analysis of the prognostic value of RBPs (from the PPI network) with survival time, the RBPs related to survival were identified, and a prognostic model was constructed. To verify the model, the data stored in the TCGA database were designated as the training set, while the chip data obtained from the GEO database were treated as the test set. Then, both survival analysis and ROC curve verification were conducted. Finally, the risk curves and nomograms of the two groups were generated to predict the survival period. RESULTS: Among RBP DEGs, 314 genes were upregulated while 155 were downregulated, of which twelve RBPs (NOP14, MRPS23, MAK16, TDRD6, POP1, TDRD5, TDRD7, PPARGC1A, LIN28B, CELF4, LRRFIP2, MSI2) with prognostic value were obtained. CONCLUSIONS: The twelve identified genes may be promising predictors of CRC and play an essential role in the pathogenesis of CRC. However, further investigation of the underlying mechanism is needed. BioMed Central 2020-08-22 /pmc/articles/PMC7443297/ /pubmed/32828126 http://dx.doi.org/10.1186/s12957-020-01995-5 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Fan, Xuehui
Liu, Lili
Shi, Yue
Guo, Fanghan
Wang, Haining
Zhao, Xiuli
Zhong, Di
Li, Guozhong
Integrated analysis of RNA-binding proteins in human colorectal cancer
title Integrated analysis of RNA-binding proteins in human colorectal cancer
title_full Integrated analysis of RNA-binding proteins in human colorectal cancer
title_fullStr Integrated analysis of RNA-binding proteins in human colorectal cancer
title_full_unstemmed Integrated analysis of RNA-binding proteins in human colorectal cancer
title_short Integrated analysis of RNA-binding proteins in human colorectal cancer
title_sort integrated analysis of rna-binding proteins in human colorectal cancer
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7443297/
https://www.ncbi.nlm.nih.gov/pubmed/32828126
http://dx.doi.org/10.1186/s12957-020-01995-5
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