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The prediction of survival in Gastric Cancer based on a Robust 13-Gene Signature

Gastric cancer represents a major public health problem. Owing to the great heterogeneity of GC, conventional clinical characteristics are limited in the accurate prediction of individual outcomes and survival. This study aimed to establish a robust gene signature to predict the prognosis of GC base...

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Autores principales: Wang, Guoguang, Zhan, Tian, Li, Fan, Shen, Jian, Gao, Xiang, Xu, Lei, Li, Yuan, Zhang, Jianping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Ivyspring International Publisher 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8100809/
https://www.ncbi.nlm.nih.gov/pubmed/33976744
http://dx.doi.org/10.7150/jca.49658
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author Wang, Guoguang
Zhan, Tian
Li, Fan
Shen, Jian
Gao, Xiang
Xu, Lei
Li, Yuan
Zhang, Jianping
author_facet Wang, Guoguang
Zhan, Tian
Li, Fan
Shen, Jian
Gao, Xiang
Xu, Lei
Li, Yuan
Zhang, Jianping
author_sort Wang, Guoguang
collection PubMed
description Gastric cancer represents a major public health problem. Owing to the great heterogeneity of GC, conventional clinical characteristics are limited in the accurate prediction of individual outcomes and survival. This study aimed to establish a robust gene signature to predict the prognosis of GC based on multiple datasets. Initially, we downloaded raw data from four independent datasets of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), and performed univariate Cox proportional hazards regression analysis to identify prognostic genes associated with overall survival (OS) from each dataset. Thirteen common genes from four datasets were screened as candidate prognostic signatures. Then, a risk score model was developed based on this 13‑gene signature and validated by four independent datasets and the entire cohort. Patients with a high-risk score had poorer OS and recurrence-free survival (RFS). Multivariate regression and stratified analysis revealed that the 13-gene signature was not only an independent predictive factor but also associated with recurrence when adjusting for other clinical factors. Furthermore, in the high-risk group, gene set enrichment analysis (GSEA) showed that the mTOR signaling pathway and MAPK signaling pathway were significantly enriched. The present study provided a robust and reliable gene signature for prognostic prediction of both OS and RFS of patients with GC, which may be useful for delivering individualized management of patients.
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spelling pubmed-81008092021-05-10 The prediction of survival in Gastric Cancer based on a Robust 13-Gene Signature Wang, Guoguang Zhan, Tian Li, Fan Shen, Jian Gao, Xiang Xu, Lei Li, Yuan Zhang, Jianping J Cancer Research Paper Gastric cancer represents a major public health problem. Owing to the great heterogeneity of GC, conventional clinical characteristics are limited in the accurate prediction of individual outcomes and survival. This study aimed to establish a robust gene signature to predict the prognosis of GC based on multiple datasets. Initially, we downloaded raw data from four independent datasets of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), and performed univariate Cox proportional hazards regression analysis to identify prognostic genes associated with overall survival (OS) from each dataset. Thirteen common genes from four datasets were screened as candidate prognostic signatures. Then, a risk score model was developed based on this 13‑gene signature and validated by four independent datasets and the entire cohort. Patients with a high-risk score had poorer OS and recurrence-free survival (RFS). Multivariate regression and stratified analysis revealed that the 13-gene signature was not only an independent predictive factor but also associated with recurrence when adjusting for other clinical factors. Furthermore, in the high-risk group, gene set enrichment analysis (GSEA) showed that the mTOR signaling pathway and MAPK signaling pathway were significantly enriched. The present study provided a robust and reliable gene signature for prognostic prediction of both OS and RFS of patients with GC, which may be useful for delivering individualized management of patients. Ivyspring International Publisher 2021-04-12 /pmc/articles/PMC8100809/ /pubmed/33976744 http://dx.doi.org/10.7150/jca.49658 Text en © The author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.
spellingShingle Research Paper
Wang, Guoguang
Zhan, Tian
Li, Fan
Shen, Jian
Gao, Xiang
Xu, Lei
Li, Yuan
Zhang, Jianping
The prediction of survival in Gastric Cancer based on a Robust 13-Gene Signature
title The prediction of survival in Gastric Cancer based on a Robust 13-Gene Signature
title_full The prediction of survival in Gastric Cancer based on a Robust 13-Gene Signature
title_fullStr The prediction of survival in Gastric Cancer based on a Robust 13-Gene Signature
title_full_unstemmed The prediction of survival in Gastric Cancer based on a Robust 13-Gene Signature
title_short The prediction of survival in Gastric Cancer based on a Robust 13-Gene Signature
title_sort prediction of survival in gastric cancer based on a robust 13-gene signature
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8100809/
https://www.ncbi.nlm.nih.gov/pubmed/33976744
http://dx.doi.org/10.7150/jca.49658
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