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Development and Validation of a Prognostic Gene Signature in Clear Cell Renal Cell Carcinoma
Clear cell renal cell carcinoma (ccRCC), one of the most common urologic cancer types, has a relatively good prognosis. However, clinical diagnoses are mostly done during the medium or late stages, when mortality and recurrence rates are quite high. Therefore, it is important to perform real-time in...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8098777/ https://www.ncbi.nlm.nih.gov/pubmed/33968978 http://dx.doi.org/10.3389/fmolb.2021.609865 |
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author | Zhan, Chuanchuan Wang, Zichu Xu, Chao Huang, Xiao Su, Junzhou Chen, Bisheng Wang, Mingshan Qi, Zhihong Bai, Peiming |
author_facet | Zhan, Chuanchuan Wang, Zichu Xu, Chao Huang, Xiao Su, Junzhou Chen, Bisheng Wang, Mingshan Qi, Zhihong Bai, Peiming |
author_sort | Zhan, Chuanchuan |
collection | PubMed |
description | Clear cell renal cell carcinoma (ccRCC), one of the most common urologic cancer types, has a relatively good prognosis. However, clinical diagnoses are mostly done during the medium or late stages, when mortality and recurrence rates are quite high. Therefore, it is important to perform real-time information tracking and dynamic prognosis analysis for these patients. We downloaded the RNA-seq data and corresponding clinical information of ccRCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A total of 3,238 differentially expressed genes were identified between normal and ccRCC tissues. Through a series of Weighted Gene Co-expression Network, overall survival, immunohistochemical and the least absolute shrinkage selection operator (LASSO) analyses, seven prognosis-associated genes (AURKB, FOXM1, PTTG1, TOP2A, TACC3, CCNA2, and MELK) were screened. Their risk score signature was then constructed. Survival analysis showed that high-risk scores exhibited significantly worse overall survival outcomes than low-risk patients. Accuracy of this prognostic signature was confirmed by the receiver operating characteristic curve and was further validated using another cohort. Gene set enrichment analysis showed that some cancer-associated phenotypes were significantly prevalent in the high-risk group. Overall, these findings prove that this risk model can potentially improve individualized diagnostic and therapeutic strategies. |
format | Online Article Text |
id | pubmed-8098777 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-80987772021-05-06 Development and Validation of a Prognostic Gene Signature in Clear Cell Renal Cell Carcinoma Zhan, Chuanchuan Wang, Zichu Xu, Chao Huang, Xiao Su, Junzhou Chen, Bisheng Wang, Mingshan Qi, Zhihong Bai, Peiming Front Mol Biosci Molecular Biosciences Clear cell renal cell carcinoma (ccRCC), one of the most common urologic cancer types, has a relatively good prognosis. However, clinical diagnoses are mostly done during the medium or late stages, when mortality and recurrence rates are quite high. Therefore, it is important to perform real-time information tracking and dynamic prognosis analysis for these patients. We downloaded the RNA-seq data and corresponding clinical information of ccRCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A total of 3,238 differentially expressed genes were identified between normal and ccRCC tissues. Through a series of Weighted Gene Co-expression Network, overall survival, immunohistochemical and the least absolute shrinkage selection operator (LASSO) analyses, seven prognosis-associated genes (AURKB, FOXM1, PTTG1, TOP2A, TACC3, CCNA2, and MELK) were screened. Their risk score signature was then constructed. Survival analysis showed that high-risk scores exhibited significantly worse overall survival outcomes than low-risk patients. Accuracy of this prognostic signature was confirmed by the receiver operating characteristic curve and was further validated using another cohort. Gene set enrichment analysis showed that some cancer-associated phenotypes were significantly prevalent in the high-risk group. Overall, these findings prove that this risk model can potentially improve individualized diagnostic and therapeutic strategies. Frontiers Media S.A. 2021-04-08 /pmc/articles/PMC8098777/ /pubmed/33968978 http://dx.doi.org/10.3389/fmolb.2021.609865 Text en Copyright © 2021 Zhan, Wang, Xu, Huang, Su, Chen, Wang, Qi and Bai. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Molecular Biosciences Zhan, Chuanchuan Wang, Zichu Xu, Chao Huang, Xiao Su, Junzhou Chen, Bisheng Wang, Mingshan Qi, Zhihong Bai, Peiming Development and Validation of a Prognostic Gene Signature in Clear Cell Renal Cell Carcinoma |
title | Development and Validation of a Prognostic Gene Signature in Clear Cell Renal Cell Carcinoma |
title_full | Development and Validation of a Prognostic Gene Signature in Clear Cell Renal Cell Carcinoma |
title_fullStr | Development and Validation of a Prognostic Gene Signature in Clear Cell Renal Cell Carcinoma |
title_full_unstemmed | Development and Validation of a Prognostic Gene Signature in Clear Cell Renal Cell Carcinoma |
title_short | Development and Validation of a Prognostic Gene Signature in Clear Cell Renal Cell Carcinoma |
title_sort | development and validation of a prognostic gene signature in clear cell renal cell carcinoma |
topic | Molecular Biosciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8098777/ https://www.ncbi.nlm.nih.gov/pubmed/33968978 http://dx.doi.org/10.3389/fmolb.2021.609865 |
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