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Construction and validation of a prognostic model of RNA binding proteins in clear cell renal carcinoma

BACKGROUND: The dysfunction of RNA binding proteins (RBPs) is associated with various inflammation and cancer. The occurrence and progression of tumors are closely related to the abnormal expression of RBPs. There are few studies on RBPs in clear cell renal carcinoma (ccRCC), which allows us to expl...

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Autores principales: Han, Wenkai, Fan, Bohao, Huang, Yongshen, Wang, Xiongbao, Zhang, Zhao, Gu, Gangli, Liu, Zhao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9069774/
https://www.ncbi.nlm.nih.gov/pubmed/35513791
http://dx.doi.org/10.1186/s12882-022-02801-y
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author Han, Wenkai
Fan, Bohao
Huang, Yongshen
Wang, Xiongbao
Zhang, Zhao
Gu, Gangli
Liu, Zhao
author_facet Han, Wenkai
Fan, Bohao
Huang, Yongshen
Wang, Xiongbao
Zhang, Zhao
Gu, Gangli
Liu, Zhao
author_sort Han, Wenkai
collection PubMed
description BACKGROUND: The dysfunction of RNA binding proteins (RBPs) is associated with various inflammation and cancer. The occurrence and progression of tumors are closely related to the abnormal expression of RBPs. There are few studies on RBPs in clear cell renal carcinoma (ccRCC), which allows us to explore the role of RBPs in ccRCC. METHODS: We obtained the gene expression data and clinical data of ccRCC from the Cancer Genome Atlas (TCGA) database and extracted all the information of RBPs. We performed differential expression analysis of RBPs. Risk model were constructed based on the differentially expressed RBPs (DERBPs). The expression levels of model markers were examined by reverse transcription-quantitative PCR (RT-qPCR) and analyzed for model-clinical relevance. Finally, we mapped the model's nomograms to predict the 1, 3 and 5-year survival rates for ccRCC patients. RESULTS: The results showed that the five-year survival rate for the high-risk group was 40.2% (95% CI = 0.313 ~ 0.518), while the five-year survival rate for the low-risk group was 84.3% (95% CI = 0.767 ~ 0.926). The ROC curves (AUC = 0.748) also showed that our model had stable predictive power. Further RT-qPCR results were in accordance with our analysis (p < 0.05). The results of the independent prognostic analysis showed that the model could be an independent prognostic factor for ccRCC. The results of the correlation analysis also demonstrated the good predictive ability of the model. CONCLUSION: In summary, the 4-RBPs (EZH2, RPL22L1, RNASE2, U2AF1L4) risk model could be used as a prognostic indicator of ccRCC. Our study provides a possibility for predicting the survival of ccRCC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12882-022-02801-y.
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spelling pubmed-90697742022-05-05 Construction and validation of a prognostic model of RNA binding proteins in clear cell renal carcinoma Han, Wenkai Fan, Bohao Huang, Yongshen Wang, Xiongbao Zhang, Zhao Gu, Gangli Liu, Zhao BMC Nephrol Research BACKGROUND: The dysfunction of RNA binding proteins (RBPs) is associated with various inflammation and cancer. The occurrence and progression of tumors are closely related to the abnormal expression of RBPs. There are few studies on RBPs in clear cell renal carcinoma (ccRCC), which allows us to explore the role of RBPs in ccRCC. METHODS: We obtained the gene expression data and clinical data of ccRCC from the Cancer Genome Atlas (TCGA) database and extracted all the information of RBPs. We performed differential expression analysis of RBPs. Risk model were constructed based on the differentially expressed RBPs (DERBPs). The expression levels of model markers were examined by reverse transcription-quantitative PCR (RT-qPCR) and analyzed for model-clinical relevance. Finally, we mapped the model's nomograms to predict the 1, 3 and 5-year survival rates for ccRCC patients. RESULTS: The results showed that the five-year survival rate for the high-risk group was 40.2% (95% CI = 0.313 ~ 0.518), while the five-year survival rate for the low-risk group was 84.3% (95% CI = 0.767 ~ 0.926). The ROC curves (AUC = 0.748) also showed that our model had stable predictive power. Further RT-qPCR results were in accordance with our analysis (p < 0.05). The results of the independent prognostic analysis showed that the model could be an independent prognostic factor for ccRCC. The results of the correlation analysis also demonstrated the good predictive ability of the model. CONCLUSION: In summary, the 4-RBPs (EZH2, RPL22L1, RNASE2, U2AF1L4) risk model could be used as a prognostic indicator of ccRCC. Our study provides a possibility for predicting the survival of ccRCC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12882-022-02801-y. BioMed Central 2022-05-05 /pmc/articles/PMC9069774/ /pubmed/35513791 http://dx.doi.org/10.1186/s12882-022-02801-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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
Han, Wenkai
Fan, Bohao
Huang, Yongshen
Wang, Xiongbao
Zhang, Zhao
Gu, Gangli
Liu, Zhao
Construction and validation of a prognostic model of RNA binding proteins in clear cell renal carcinoma
title Construction and validation of a prognostic model of RNA binding proteins in clear cell renal carcinoma
title_full Construction and validation of a prognostic model of RNA binding proteins in clear cell renal carcinoma
title_fullStr Construction and validation of a prognostic model of RNA binding proteins in clear cell renal carcinoma
title_full_unstemmed Construction and validation of a prognostic model of RNA binding proteins in clear cell renal carcinoma
title_short Construction and validation of a prognostic model of RNA binding proteins in clear cell renal carcinoma
title_sort construction and validation of a prognostic model of rna binding proteins in clear cell renal carcinoma
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9069774/
https://www.ncbi.nlm.nih.gov/pubmed/35513791
http://dx.doi.org/10.1186/s12882-022-02801-y
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