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Identifying an Eight-Gene Signature to Optimize Overall Survival Prediction of Esophageal Adenocarcinoma Using Bioinformatics Analysis of ceRNA Network
BACKGROUND AND AIMS: Esophageal adenocarcinoma (EAC) patients usually have a poor prognosis without early diagnosis. In this study, we aimed to identify a novel signature to improve the prediction of overall survival (OS) in EAC. METHODS: Eighty-one and 68 samples from The Cancer Genome Atlas (TCGA)...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Dove
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7764560/ https://www.ncbi.nlm.nih.gov/pubmed/33376353 http://dx.doi.org/10.2147/OTT.S287084 |
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author | Wang, Yuanyong Liang, Naixin Xue, Zhiqiang Xue, Xinying |
author_facet | Wang, Yuanyong Liang, Naixin Xue, Zhiqiang Xue, Xinying |
author_sort | Wang, Yuanyong |
collection | PubMed |
description | BACKGROUND AND AIMS: Esophageal adenocarcinoma (EAC) patients usually have a poor prognosis without early diagnosis. In this study, we aimed to identify a novel signature to improve the prediction of overall survival (OS) in EAC. METHODS: Eighty-one and 68 samples from The Cancer Genome Atlas (TCGA) and GSE19417 dataset were included for discovery and survival validation, respectively. In the TCGA cohort, a total of 1,811 DEmRNAs, 1,096 DElncRNAs, and 31 DEmiRNAs were identified between EAC and normal esophagus tissues. A mRNA–miRNA–lncRNA ceRNA network of EAC was established, which consisted of 94 DEmRNAs, 13 DEmiRNAs, and 46 DElncRNAs. RESULTS: In this study, we identified eight genes (UBE2B, LAMP2, B3GNT2, TAF9B, EFNA1, PHF8, PIGA, and NEURL1) which were related to survival in EAC. The independent external microarray data from the Gene Expression Omnibus (GEO) was used to validate these candidate genes. The prognostic ability of the signature was also validated in EAC patients in our hospital. Patients assigned to the high-risk group had a poor overall survival rate compared with the low-risk. CONCLUSION: The current study provides novel insights into the mRNA-related ceRNA network in EAC and the eight mRNA biomarkers may be independent prognostic signatures in predicting the survival of EAC patients. |
format | Online Article Text |
id | pubmed-7764560 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Dove |
record_format | MEDLINE/PubMed |
spelling | pubmed-77645602020-12-28 Identifying an Eight-Gene Signature to Optimize Overall Survival Prediction of Esophageal Adenocarcinoma Using Bioinformatics Analysis of ceRNA Network Wang, Yuanyong Liang, Naixin Xue, Zhiqiang Xue, Xinying Onco Targets Ther Original Research BACKGROUND AND AIMS: Esophageal adenocarcinoma (EAC) patients usually have a poor prognosis without early diagnosis. In this study, we aimed to identify a novel signature to improve the prediction of overall survival (OS) in EAC. METHODS: Eighty-one and 68 samples from The Cancer Genome Atlas (TCGA) and GSE19417 dataset were included for discovery and survival validation, respectively. In the TCGA cohort, a total of 1,811 DEmRNAs, 1,096 DElncRNAs, and 31 DEmiRNAs were identified between EAC and normal esophagus tissues. A mRNA–miRNA–lncRNA ceRNA network of EAC was established, which consisted of 94 DEmRNAs, 13 DEmiRNAs, and 46 DElncRNAs. RESULTS: In this study, we identified eight genes (UBE2B, LAMP2, B3GNT2, TAF9B, EFNA1, PHF8, PIGA, and NEURL1) which were related to survival in EAC. The independent external microarray data from the Gene Expression Omnibus (GEO) was used to validate these candidate genes. The prognostic ability of the signature was also validated in EAC patients in our hospital. Patients assigned to the high-risk group had a poor overall survival rate compared with the low-risk. CONCLUSION: The current study provides novel insights into the mRNA-related ceRNA network in EAC and the eight mRNA biomarkers may be independent prognostic signatures in predicting the survival of EAC patients. Dove 2020-12-22 /pmc/articles/PMC7764560/ /pubmed/33376353 http://dx.doi.org/10.2147/OTT.S287084 Text en © 2020 Wang et al. http://creativecommons.org/licenses/by-nc/3.0/ This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). |
spellingShingle | Original Research Wang, Yuanyong Liang, Naixin Xue, Zhiqiang Xue, Xinying Identifying an Eight-Gene Signature to Optimize Overall Survival Prediction of Esophageal Adenocarcinoma Using Bioinformatics Analysis of ceRNA Network |
title | Identifying an Eight-Gene Signature to Optimize Overall Survival Prediction of Esophageal Adenocarcinoma Using Bioinformatics Analysis of ceRNA Network |
title_full | Identifying an Eight-Gene Signature to Optimize Overall Survival Prediction of Esophageal Adenocarcinoma Using Bioinformatics Analysis of ceRNA Network |
title_fullStr | Identifying an Eight-Gene Signature to Optimize Overall Survival Prediction of Esophageal Adenocarcinoma Using Bioinformatics Analysis of ceRNA Network |
title_full_unstemmed | Identifying an Eight-Gene Signature to Optimize Overall Survival Prediction of Esophageal Adenocarcinoma Using Bioinformatics Analysis of ceRNA Network |
title_short | Identifying an Eight-Gene Signature to Optimize Overall Survival Prediction of Esophageal Adenocarcinoma Using Bioinformatics Analysis of ceRNA Network |
title_sort | identifying an eight-gene signature to optimize overall survival prediction of esophageal adenocarcinoma using bioinformatics analysis of cerna network |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7764560/ https://www.ncbi.nlm.nih.gov/pubmed/33376353 http://dx.doi.org/10.2147/OTT.S287084 |
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