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Construction and validation of a metabolic risk model predicting prognosis of colon cancer
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 metaboli...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7994414/ https://www.ncbi.nlm.nih.gov/pubmed/33767290 http://dx.doi.org/10.1038/s41598-021-86286-z |
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author | Zuo, Didi Li, Chao Liu, Tao Yue, Meng Zhang, Jiantao Ning, Guang |
author_facet | Zuo, Didi Li, Chao Liu, Tao Yue, Meng Zhang, Jiantao Ning, Guang |
author_sort | Zuo, Didi |
collection | PubMed |
description | 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 | Online Article Text |
id | pubmed-7994414 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-79944142021-03-29 Construction and validation of a metabolic risk model predicting prognosis of colon cancer Zuo, Didi Li, Chao Liu, Tao Yue, Meng Zhang, Jiantao Ning, Guang Sci Rep Article 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. Nature Publishing Group UK 2021-03-25 /pmc/articles/PMC7994414/ /pubmed/33767290 http://dx.doi.org/10.1038/s41598-021-86286-z Text en © The Author(s) 2021 Open Access This 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/. |
spellingShingle | Article Zuo, Didi Li, Chao Liu, Tao Yue, Meng Zhang, Jiantao Ning, Guang Construction and validation of a metabolic risk model predicting prognosis of colon cancer |
title | 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_short | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7994414/ https://www.ncbi.nlm.nih.gov/pubmed/33767290 http://dx.doi.org/10.1038/s41598-021-86286-z |
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