Construction and validation of a metabolic risk model predicting prognosis of colon cancer

Abstract Metabolic genes have played a significant role in tumor development and prognosis. In this study, we constructed a metabolic risk model to predict the prognosis of colon cancer based on The Cancer Genome Atlas (TCGA) and validated the model by Gene Expression Omnibus (GEO). We extracted 753...

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Autores principales:	Didi Zuo, Chao Li, Tao Liu, Meng Yue, Jiantao Zhang, Guang Ning
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9294aff2485249d1a6e4a153ece90ea0
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spelling	oai:doaj.org-article:9294aff2485249d1a6e4a153ece90ea02021-12-02T11:45:03ZConstruction and validation of a metabolic risk model predicting prognosis of colon cancer10.1038/s41598-021-86286-z2045-2322https://doaj.org/article/9294aff2485249d1a6e4a153ece90ea02021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86286-zhttps://doaj.org/toc/2045-2322Abstract Metabolic genes have played a significant role in tumor development and prognosis. In this study, we constructed a metabolic risk model to predict the prognosis of colon cancer based on The Cancer Genome Atlas (TCGA) and validated the model by Gene Expression Omnibus (GEO). We extracted 753 metabolic genes and identified 139 differentially expressed genes (DEGs) from TCGA database. Then we conducted univariate cox regression analysis and Least Absolute Shrinkage and Selection Operator Cox regression analysis to identify prognosis-related genes and construct the metabolic risk model. An eleven-gene prognostic model was constructed after 1000 resamples. The gene signature has been proved to have an excellent ability to predict prognosis by Kaplan–Meier analysis, time-dependent receiver operating characteristic, risk score, univariate and multivariate cox regression analysis based on TCGA. Then we validated the model by Kaplan–Meier analysis and risk score based on GEO database. Finally, we performed a weighted gene co-expression network analysis and protein–protein interaction network on DEGs, and Kyoto Encyclopedia of Genes and Genomes pathways and Gene Ontology enrichment analyses were conducted. The results of functional analyses showed that most significantly enriched pathways focused on metabolism, especially glucose and lipid metabolism pathways.Didi ZuoChao LiTao LiuMeng YueJiantao ZhangGuang NingNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Didi Zuo Chao Li Tao Liu Meng Yue Jiantao Zhang Guang Ning Construction and validation of a metabolic risk model predicting prognosis of colon cancer
description	Abstract Metabolic genes have played a significant role in tumor development and prognosis. In this study, we constructed a metabolic risk model to predict the prognosis of colon cancer based on The Cancer Genome Atlas (TCGA) and validated the model by Gene Expression Omnibus (GEO). We extracted 753 metabolic genes and identified 139 differentially expressed genes (DEGs) from TCGA database. Then we conducted univariate cox regression analysis and Least Absolute Shrinkage and Selection Operator Cox regression analysis to identify prognosis-related genes and construct the metabolic risk model. An eleven-gene prognostic model was constructed after 1000 resamples. The gene signature has been proved to have an excellent ability to predict prognosis by Kaplan–Meier analysis, time-dependent receiver operating characteristic, risk score, univariate and multivariate cox regression analysis based on TCGA. Then we validated the model by Kaplan–Meier analysis and risk score based on GEO database. Finally, we performed a weighted gene co-expression network analysis and protein–protein interaction network on DEGs, and Kyoto Encyclopedia of Genes and Genomes pathways and Gene Ontology enrichment analyses were conducted. The results of functional analyses showed that most significantly enriched pathways focused on metabolism, especially glucose and lipid metabolism pathways.
format	article
author	Didi Zuo Chao Li Tao Liu Meng Yue Jiantao Zhang Guang Ning
author_facet	Didi Zuo Chao Li Tao Liu Meng Yue Jiantao Zhang Guang Ning
author_sort	Didi Zuo
title	Construction and validation of a metabolic risk model predicting prognosis of colon cancer
title_short	Construction and validation of a metabolic risk model predicting prognosis of colon cancer
title_full	Construction and validation of a metabolic risk model predicting prognosis of colon cancer
title_fullStr	Construction and validation of a metabolic risk model predicting prognosis of colon cancer
title_full_unstemmed	Construction and validation of a metabolic risk model predicting prognosis of colon cancer
title_sort	construction and validation of a metabolic risk model predicting prognosis of colon cancer
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/9294aff2485249d1a6e4a153ece90ea0
work_keys_str_mv	AT didizuo constructionandvalidationofametabolicriskmodelpredictingprognosisofcoloncancer AT chaoli constructionandvalidationofametabolicriskmodelpredictingprognosisofcoloncancer AT taoliu constructionandvalidationofametabolicriskmodelpredictingprognosisofcoloncancer AT mengyue constructionandvalidationofametabolicriskmodelpredictingprognosisofcoloncancer AT jiantaozhang constructionandvalidationofametabolicriskmodelpredictingprognosisofcoloncancer AT guangning constructionandvalidationofametabolicriskmodelpredictingprognosisofcoloncancer
_version_	1718395294866997248

Construction and validation of a metabolic risk model predicting prognosis of colon cancer

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