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Construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) comprises the majority of kidney cancer death worldwide, whose incidence and mortality are not promising. Identifying ideal biomarkers to construct a more accurate prognostic model than conventional clinical parameters is crucial. METHODS: Raw coun...

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Autores principales: Zhang, Zedan, Lin, Enyu, Zhuang, Hongkai, Xie, Lu, Feng, Xiaoqiang, Liu, Jiumin, Yu, Yuming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6986036/
https://www.ncbi.nlm.nih.gov/pubmed/32002016
http://dx.doi.org/10.1186/s12935-020-1113-6
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author Zhang, Zedan
Lin, Enyu
Zhuang, Hongkai
Xie, Lu
Feng, Xiaoqiang
Liu, Jiumin
Yu, Yuming
author_facet Zhang, Zedan
Lin, Enyu
Zhuang, Hongkai
Xie, Lu
Feng, Xiaoqiang
Liu, Jiumin
Yu, Yuming
author_sort Zhang, Zedan
collection PubMed
description BACKGROUND: Clear cell renal cell carcinoma (ccRCC) comprises the majority of kidney cancer death worldwide, whose incidence and mortality are not promising. Identifying ideal biomarkers to construct a more accurate prognostic model than conventional clinical parameters is crucial. METHODS: Raw count of RNA-sequencing data and clinicopathological data were acquired from The Cancer Genome Atlas (TCGA). Tumor samples were divided into two sets. Differentially expressed genes (DEGs) were screened in the whole set and prognosis-related genes were identified from the training set. Their common genes were used in LASSO and best subset regression which were performed to identify the best prognostic 5 genes. The gene-based risk score was developed based on the Cox coefficient of the individual gene. Time-dependent receiver operating characteristic (ROC) and Kaplan–Meier (KM) survival analysis were used to assess its prognostic power. GSE29609 dataset from GEO (Gene Expression Omnibus) database was used to validate the signature. Univariate and multivariate Cox regression were performed to screen independent prognostic parameters to construct a nomogram. The predictive power of the nomogram was revealed by time-dependent ROC curves and the calibration plot and verified in the validation set. Finally, Functional enrichment analysis of DEGs and 5 novel genes were performed to suggest the potential biological pathways. RESULTS: PADI1, ATP6V0D2, DPP6, C9orf135 and PLG were screened to be significantly related to the prognosis of ccRCC patients. The risk score effectively stratified the patients into high-risk group with poor overall survival (OS) based on survival analysis. AJCC-stage, age, recurrence and risk score were regarded as independent prognostic parameters by Cox regression analysis and were used to construct a nomogram. Time-dependent ROC curves showed the nomogram performed best in 1-, 3- and 5-year survival predictions compared with AJCC-stage and risk score in validation sets. The calibration plot showed good agreement of the nomogram between predicted and observed outcomes. Functional enrichment analysis suggested several enriched biological pathways related to cancer. CONCLUSIONS: In our study, we constructed a gene-based model integrating clinical prognostic parameters to predict prognosis of ccRCC well, which might provide a reliable prognosis assessment tool for clinician and aid treatment decision-making in the clinic.
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spelling pubmed-69860362020-01-30 Construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma Zhang, Zedan Lin, Enyu Zhuang, Hongkai Xie, Lu Feng, Xiaoqiang Liu, Jiumin Yu, Yuming Cancer Cell Int Primary Research BACKGROUND: Clear cell renal cell carcinoma (ccRCC) comprises the majority of kidney cancer death worldwide, whose incidence and mortality are not promising. Identifying ideal biomarkers to construct a more accurate prognostic model than conventional clinical parameters is crucial. METHODS: Raw count of RNA-sequencing data and clinicopathological data were acquired from The Cancer Genome Atlas (TCGA). Tumor samples were divided into two sets. Differentially expressed genes (DEGs) were screened in the whole set and prognosis-related genes were identified from the training set. Their common genes were used in LASSO and best subset regression which were performed to identify the best prognostic 5 genes. The gene-based risk score was developed based on the Cox coefficient of the individual gene. Time-dependent receiver operating characteristic (ROC) and Kaplan–Meier (KM) survival analysis were used to assess its prognostic power. GSE29609 dataset from GEO (Gene Expression Omnibus) database was used to validate the signature. Univariate and multivariate Cox regression were performed to screen independent prognostic parameters to construct a nomogram. The predictive power of the nomogram was revealed by time-dependent ROC curves and the calibration plot and verified in the validation set. Finally, Functional enrichment analysis of DEGs and 5 novel genes were performed to suggest the potential biological pathways. RESULTS: PADI1, ATP6V0D2, DPP6, C9orf135 and PLG were screened to be significantly related to the prognosis of ccRCC patients. The risk score effectively stratified the patients into high-risk group with poor overall survival (OS) based on survival analysis. AJCC-stage, age, recurrence and risk score were regarded as independent prognostic parameters by Cox regression analysis and were used to construct a nomogram. Time-dependent ROC curves showed the nomogram performed best in 1-, 3- and 5-year survival predictions compared with AJCC-stage and risk score in validation sets. The calibration plot showed good agreement of the nomogram between predicted and observed outcomes. Functional enrichment analysis suggested several enriched biological pathways related to cancer. CONCLUSIONS: In our study, we constructed a gene-based model integrating clinical prognostic parameters to predict prognosis of ccRCC well, which might provide a reliable prognosis assessment tool for clinician and aid treatment decision-making in the clinic. BioMed Central 2020-01-28 /pmc/articles/PMC6986036/ /pubmed/32002016 http://dx.doi.org/10.1186/s12935-020-1113-6 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 Primary Research
Zhang, Zedan
Lin, Enyu
Zhuang, Hongkai
Xie, Lu
Feng, Xiaoqiang
Liu, Jiumin
Yu, Yuming
Construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma
title Construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma
title_full Construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma
title_fullStr Construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma
title_full_unstemmed Construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma
title_short Construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma
title_sort construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma
topic Primary Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6986036/
https://www.ncbi.nlm.nih.gov/pubmed/32002016
http://dx.doi.org/10.1186/s12935-020-1113-6
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