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A Correlation Study of Prognostic Risk Prediction for Colorectal Cancer Based on Autophagy Signature Genes

Autophagy plays a complex role in tumors, sometimes promoting cancer cell survival and sometimes inducing apoptosis, and its role in the colorectal tumor microenvironment is controversial. The purpose of this study was to investigate the prognostic value of autophagy-related genes (ARGs) in colorect...

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Autores principales: Zhao, Haibi, Huang, Chengzhi, Luo, Yuwen, Yao, Xiaoya, Hu, Yong, Wang, Muqing, Chen, Xin, Zeng, Jun, Hu, Weixian, Wang, Junjiang, Li, Rongjiang, Yao, Xueqing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8218632/
https://www.ncbi.nlm.nih.gov/pubmed/34168974
http://dx.doi.org/10.3389/fonc.2021.595099
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author Zhao, Haibi
Huang, Chengzhi
Luo, Yuwen
Yao, Xiaoya
Hu, Yong
Wang, Muqing
Chen, Xin
Zeng, Jun
Hu, Weixian
Wang, Junjiang
Li, Rongjiang
Yao, Xueqing
author_facet Zhao, Haibi
Huang, Chengzhi
Luo, Yuwen
Yao, Xiaoya
Hu, Yong
Wang, Muqing
Chen, Xin
Zeng, Jun
Hu, Weixian
Wang, Junjiang
Li, Rongjiang
Yao, Xueqing
author_sort Zhao, Haibi
collection PubMed
description Autophagy plays a complex role in tumors, sometimes promoting cancer cell survival and sometimes inducing apoptosis, and its role in the colorectal tumor microenvironment is controversial. The purpose of this study was to investigate the prognostic value of autophagy-related genes (ARGs) in colorectal cancer. We identified 37 differentially expressed autophagy-related genes by collecting TCGA colorectal tumor transcriptome data. A single-factor COX regression equation was used to identify 11 key prognostic genes, and a prognostic risk prediction model was constructed based on multifactor COX analysis. We classified patients into high and low risk groups according to prognostic risk parameters (p <0.001) and determined the prognostic value they possessed by survival analysis and the receiver operating characteristic (ROC) curve in the training and test sets of internal tests. In a multifactorial independent prognostic analysis, this risk value could be used as an independent prognostic indicator (HR=1.167, 95% CI=1.078-1.264, P<0.001) and was a robust predictor without any staging interference. To make it more applicable to clinical procedures, we constructed nomogram based on risk parameters and parameters of key clinical characteristics. The area under ROC curve for 3-year and 5-year survival rates were 0.735 and 0.718, respectively. These will better enable us to monitor patient prognosis, thus improve patient outcomes.
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spelling pubmed-82186322021-06-23 A Correlation Study of Prognostic Risk Prediction for Colorectal Cancer Based on Autophagy Signature Genes Zhao, Haibi Huang, Chengzhi Luo, Yuwen Yao, Xiaoya Hu, Yong Wang, Muqing Chen, Xin Zeng, Jun Hu, Weixian Wang, Junjiang Li, Rongjiang Yao, Xueqing Front Oncol Oncology Autophagy plays a complex role in tumors, sometimes promoting cancer cell survival and sometimes inducing apoptosis, and its role in the colorectal tumor microenvironment is controversial. The purpose of this study was to investigate the prognostic value of autophagy-related genes (ARGs) in colorectal cancer. We identified 37 differentially expressed autophagy-related genes by collecting TCGA colorectal tumor transcriptome data. A single-factor COX regression equation was used to identify 11 key prognostic genes, and a prognostic risk prediction model was constructed based on multifactor COX analysis. We classified patients into high and low risk groups according to prognostic risk parameters (p <0.001) and determined the prognostic value they possessed by survival analysis and the receiver operating characteristic (ROC) curve in the training and test sets of internal tests. In a multifactorial independent prognostic analysis, this risk value could be used as an independent prognostic indicator (HR=1.167, 95% CI=1.078-1.264, P<0.001) and was a robust predictor without any staging interference. To make it more applicable to clinical procedures, we constructed nomogram based on risk parameters and parameters of key clinical characteristics. The area under ROC curve for 3-year and 5-year survival rates were 0.735 and 0.718, respectively. These will better enable us to monitor patient prognosis, thus improve patient outcomes. Frontiers Media S.A. 2021-05-25 /pmc/articles/PMC8218632/ /pubmed/34168974 http://dx.doi.org/10.3389/fonc.2021.595099 Text en Copyright © 2021 Zhao, Huang, Luo, Yao, Hu, Wang, Chen, Zeng, Hu, Wang, Li and Yao 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 Oncology
Zhao, Haibi
Huang, Chengzhi
Luo, Yuwen
Yao, Xiaoya
Hu, Yong
Wang, Muqing
Chen, Xin
Zeng, Jun
Hu, Weixian
Wang, Junjiang
Li, Rongjiang
Yao, Xueqing
A Correlation Study of Prognostic Risk Prediction for Colorectal Cancer Based on Autophagy Signature Genes
title A Correlation Study of Prognostic Risk Prediction for Colorectal Cancer Based on Autophagy Signature Genes
title_full A Correlation Study of Prognostic Risk Prediction for Colorectal Cancer Based on Autophagy Signature Genes
title_fullStr A Correlation Study of Prognostic Risk Prediction for Colorectal Cancer Based on Autophagy Signature Genes
title_full_unstemmed A Correlation Study of Prognostic Risk Prediction for Colorectal Cancer Based on Autophagy Signature Genes
title_short A Correlation Study of Prognostic Risk Prediction for Colorectal Cancer Based on Autophagy Signature Genes
title_sort correlation study of prognostic risk prediction for colorectal cancer based on autophagy signature genes
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8218632/
https://www.ncbi.nlm.nih.gov/pubmed/34168974
http://dx.doi.org/10.3389/fonc.2021.595099
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