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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Ivyspring International Publisher
2021
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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. |
format | Online Article Text |
id | pubmed-8100809 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Ivyspring International Publisher |
record_format | MEDLINE/PubMed |
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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