A signature based on 11 autophagy genes for prognosis prediction of colorectal cancer.

<h4>Aim</h4>To develop an autophagy-gene-based signature that could help to anticipate the therapeutic effects of Colorectal Cancer (CRC).<h4>Methods</h4>We downloaded the gene expression profiles of CRC samples from the Cancer Genome Atlas (TCGA) and the Gene Expression Omni...

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Autores principales:	Shuo Chen, Yan Wang, Boxue Wang, Lin Zhang, Yinan Su, Mingyue Xu, Mingqing Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/da16a03477054c7baaf0d6a6aa9e835f
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spelling	oai:doaj.org-article:da16a03477054c7baaf0d6a6aa9e835f2021-12-02T20:16:34ZA signature based on 11 autophagy genes for prognosis prediction of colorectal cancer.1932-620310.1371/journal.pone.0258741https://doaj.org/article/da16a03477054c7baaf0d6a6aa9e835f2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0258741https://doaj.org/toc/1932-6203<h4>Aim</h4>To develop an autophagy-gene-based signature that could help to anticipate the therapeutic effects of Colorectal Cancer (CRC).<h4>Methods</h4>We downloaded the gene expression profiles of CRC samples from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) datasets. Genes with significant prognostic value in CRC were screened through univariate Cox regression analysis, while the LASSO Cox regression method was applied to screen optimal genes to construct the autophagy-related prognostic signature.<h4>Results</h4>11 autophagy genes were identified and selected for the establishment of prognosis prediction model for CRC patients. The CRC patients were classified into the low- and high-risk groups according to the optimal cutoff value. The time-dependent ROC curves indicated the good performance of this model in prognosis prediction, with AUC values of 0.66, 0.66, and 0.67 at 1, 3 and 5 years for TCGA samples, as well as AUC values of 0.63, 0.65 and 0.64 for GEO samples, respectively. The multivariate Cox regression analysis results confirmed risk score as the independent marker for prognosis prediction in CRC. Besides, the constructed nomogram also had high predictive value. The results analysis on the tumor infiltrating immune cells (TIICs) relative ratios and mRNA levels of key immune checkpoint receptors indicated the signature was closely related to immune microenvironment of CRC in the context of TIICs and immune checkpoint receptors' mRNA level. The proportion of MSI-L + MSI-H in the high-risk group was higher than that in the low-risk group. Moreover, the tumor purity was evaluated by estimate function package suggested that lower tumor purity in CRC might lead to a poorer prognosis.<h4>Conclusion</h4>The autophagy-related features obtained in this study were able to divide the CRC patients into low- and high-risk groups, which should be contribute to the decision-making of CRC treatment.Shuo ChenYan WangBoxue WangLin ZhangYinan SuMingyue XuMingqing ZhangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0258741 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shuo Chen Yan Wang Boxue Wang Lin Zhang Yinan Su Mingyue Xu Mingqing Zhang A signature based on 11 autophagy genes for prognosis prediction of colorectal cancer.
description	<h4>Aim</h4>To develop an autophagy-gene-based signature that could help to anticipate the therapeutic effects of Colorectal Cancer (CRC).<h4>Methods</h4>We downloaded the gene expression profiles of CRC samples from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) datasets. Genes with significant prognostic value in CRC were screened through univariate Cox regression analysis, while the LASSO Cox regression method was applied to screen optimal genes to construct the autophagy-related prognostic signature.<h4>Results</h4>11 autophagy genes were identified and selected for the establishment of prognosis prediction model for CRC patients. The CRC patients were classified into the low- and high-risk groups according to the optimal cutoff value. The time-dependent ROC curves indicated the good performance of this model in prognosis prediction, with AUC values of 0.66, 0.66, and 0.67 at 1, 3 and 5 years for TCGA samples, as well as AUC values of 0.63, 0.65 and 0.64 for GEO samples, respectively. The multivariate Cox regression analysis results confirmed risk score as the independent marker for prognosis prediction in CRC. Besides, the constructed nomogram also had high predictive value. The results analysis on the tumor infiltrating immune cells (TIICs) relative ratios and mRNA levels of key immune checkpoint receptors indicated the signature was closely related to immune microenvironment of CRC in the context of TIICs and immune checkpoint receptors' mRNA level. The proportion of MSI-L + MSI-H in the high-risk group was higher than that in the low-risk group. Moreover, the tumor purity was evaluated by estimate function package suggested that lower tumor purity in CRC might lead to a poorer prognosis.<h4>Conclusion</h4>The autophagy-related features obtained in this study were able to divide the CRC patients into low- and high-risk groups, which should be contribute to the decision-making of CRC treatment.
format	article
author	Shuo Chen Yan Wang Boxue Wang Lin Zhang Yinan Su Mingyue Xu Mingqing Zhang
author_facet	Shuo Chen Yan Wang Boxue Wang Lin Zhang Yinan Su Mingyue Xu Mingqing Zhang
author_sort	Shuo Chen
title	A signature based on 11 autophagy genes for prognosis prediction of colorectal cancer.
title_short	A signature based on 11 autophagy genes for prognosis prediction of colorectal cancer.
title_full	A signature based on 11 autophagy genes for prognosis prediction of colorectal cancer.
title_fullStr	A signature based on 11 autophagy genes for prognosis prediction of colorectal cancer.
title_full_unstemmed	A signature based on 11 autophagy genes for prognosis prediction of colorectal cancer.
title_sort	signature based on 11 autophagy genes for prognosis prediction of colorectal cancer.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/da16a03477054c7baaf0d6a6aa9e835f
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_version_	1718374495501156352

A signature based on 11 autophagy genes for prognosis prediction of colorectal cancer.

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