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Development and validation of a survival model for esophageal adenocarcinoma based on autophagy-associated genes

Autophagy is a highly conserved catabolic process which has been implicated in esophageal adenocarcinoma (EAC). We sought to investigate the biological functions and prognostic value of autophagy-related genes (ARGs) in EAC. A total of 21 differentially expressed ARGs were identified between EAC and...

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Autores principales: Duan, Lili, Cao, Lu, Zhang, Rui, Niu, Liaoran, Yang, Wanli, Feng, Weibo, Zhou, Wei, Chen, Junfeng, Wang, Xiaoqian, Li, Yiding, Zhang, Yujie, Liu, Jinqiang, Zhao, Qingchuan, Fan, Daiming, Hong, Liu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8806464/
https://www.ncbi.nlm.nih.gov/pubmed/34252349
http://dx.doi.org/10.1080/21655979.2021.1946235
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author Duan, Lili
Cao, Lu
Zhang, Rui
Niu, Liaoran
Yang, Wanli
Feng, Weibo
Zhou, Wei
Chen, Junfeng
Wang, Xiaoqian
Li, Yiding
Zhang, Yujie
Liu, Jinqiang
Zhao, Qingchuan
Fan, Daiming
Hong, Liu
author_facet Duan, Lili
Cao, Lu
Zhang, Rui
Niu, Liaoran
Yang, Wanli
Feng, Weibo
Zhou, Wei
Chen, Junfeng
Wang, Xiaoqian
Li, Yiding
Zhang, Yujie
Liu, Jinqiang
Zhao, Qingchuan
Fan, Daiming
Hong, Liu
author_sort Duan, Lili
collection PubMed
description Autophagy is a highly conserved catabolic process which has been implicated in esophageal adenocarcinoma (EAC). We sought to investigate the biological functions and prognostic value of autophagy-related genes (ARGs) in EAC. A total of 21 differentially expressed ARGs were identified between EAC and normal samples. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were then applied for the differentially expressed ARGs in EAC, and the protein–protein interaction (PPI) network was established. Cox survival analysis and Lasso regression analysis were performed to establish a prognostic prediction model based on nine overall survival (OS)-related ARGs (CAPN1, GOPC, TBK1, SIRT1, ARSA, BNIP1, ERBB2, NRG2, PINK1). The 9-gene prognostic signature significantly stratified patient outcomes in The Cancer Genome of Atlas (TCGA)-EAC cohort and was considered as an independently prognostic predictor for EAC patients. Moreover, Gene set enrichment analysis (GSEA) analyses revealed several important cellular processes and signaling pathways correlated with the high-risk group in EAC. This prognostic prediction model was confirmed in an independent validation cohort (GSE13898) from The Gene Expression Omnibus (GEO) database. We also developed a nomogram with a concordance index of 0.78 to predict the survival possibility of EAC patients by integrating the risk signature and clinicopathological features. The calibration curves substantiated favorable concordance between actual observation and nomogram prediction. Last but not least, Erb-B2 Receptor Tyrosine Kinase 2 (ERBB2), a member of the prognostic gene signature, was identified as a potential therapeutic target for EAC patients. To sum up, we established and verified a novel prognostic prediction model based on ARGs which could optimize the individualized survival prediction in EAC.
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spelling pubmed-88064642022-02-02 Development and validation of a survival model for esophageal adenocarcinoma based on autophagy-associated genes Duan, Lili Cao, Lu Zhang, Rui Niu, Liaoran Yang, Wanli Feng, Weibo Zhou, Wei Chen, Junfeng Wang, Xiaoqian Li, Yiding Zhang, Yujie Liu, Jinqiang Zhao, Qingchuan Fan, Daiming Hong, Liu Bioengineered Research Paper Autophagy is a highly conserved catabolic process which has been implicated in esophageal adenocarcinoma (EAC). We sought to investigate the biological functions and prognostic value of autophagy-related genes (ARGs) in EAC. A total of 21 differentially expressed ARGs were identified between EAC and normal samples. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were then applied for the differentially expressed ARGs in EAC, and the protein–protein interaction (PPI) network was established. Cox survival analysis and Lasso regression analysis were performed to establish a prognostic prediction model based on nine overall survival (OS)-related ARGs (CAPN1, GOPC, TBK1, SIRT1, ARSA, BNIP1, ERBB2, NRG2, PINK1). The 9-gene prognostic signature significantly stratified patient outcomes in The Cancer Genome of Atlas (TCGA)-EAC cohort and was considered as an independently prognostic predictor for EAC patients. Moreover, Gene set enrichment analysis (GSEA) analyses revealed several important cellular processes and signaling pathways correlated with the high-risk group in EAC. This prognostic prediction model was confirmed in an independent validation cohort (GSE13898) from The Gene Expression Omnibus (GEO) database. We also developed a nomogram with a concordance index of 0.78 to predict the survival possibility of EAC patients by integrating the risk signature and clinicopathological features. The calibration curves substantiated favorable concordance between actual observation and nomogram prediction. Last but not least, Erb-B2 Receptor Tyrosine Kinase 2 (ERBB2), a member of the prognostic gene signature, was identified as a potential therapeutic target for EAC patients. To sum up, we established and verified a novel prognostic prediction model based on ARGs which could optimize the individualized survival prediction in EAC. Taylor & Francis 2021-07-12 /pmc/articles/PMC8806464/ /pubmed/34252349 http://dx.doi.org/10.1080/21655979.2021.1946235 Text en © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Paper
Duan, Lili
Cao, Lu
Zhang, Rui
Niu, Liaoran
Yang, Wanli
Feng, Weibo
Zhou, Wei
Chen, Junfeng
Wang, Xiaoqian
Li, Yiding
Zhang, Yujie
Liu, Jinqiang
Zhao, Qingchuan
Fan, Daiming
Hong, Liu
Development and validation of a survival model for esophageal adenocarcinoma based on autophagy-associated genes
title Development and validation of a survival model for esophageal adenocarcinoma based on autophagy-associated genes
title_full Development and validation of a survival model for esophageal adenocarcinoma based on autophagy-associated genes
title_fullStr Development and validation of a survival model for esophageal adenocarcinoma based on autophagy-associated genes
title_full_unstemmed Development and validation of a survival model for esophageal adenocarcinoma based on autophagy-associated genes
title_short Development and validation of a survival model for esophageal adenocarcinoma based on autophagy-associated genes
title_sort development and validation of a survival model for esophageal adenocarcinoma based on autophagy-associated genes
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8806464/
https://www.ncbi.nlm.nih.gov/pubmed/34252349
http://dx.doi.org/10.1080/21655979.2021.1946235
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