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Identification of biomarkers associated with diagnosis and prognosis of gastroesophageal junction adenocarcinoma–a study based on integrated bioinformatics analysis in GEO and TCGA database

Gastroesophageal junction adenocarcinoma (GEJAC) is a malignant tumor with high mortality. Its incidence has increased sharply all over the world in recent years. The study aims to search for potential biomarkers for the diagnosis and prognosis of GEJAC based on the Gene Expression Omnibus database...

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Autores principales: Song, Danlei, Tian, Jiming, Hu, Yuping, Wei, Yongjian, Lu, Hong, Wang, Yuping, Guan, Quanlin, Zhou, Yongning
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7748358/
https://www.ncbi.nlm.nih.gov/pubmed/33371094
http://dx.doi.org/10.1097/MD.0000000000023605
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author Song, Danlei
Tian, Jiming
Hu, Yuping
Wei, Yongjian
Lu, Hong
Wang, Yuping
Guan, Quanlin
Zhou, Yongning
author_facet Song, Danlei
Tian, Jiming
Hu, Yuping
Wei, Yongjian
Lu, Hong
Wang, Yuping
Guan, Quanlin
Zhou, Yongning
author_sort Song, Danlei
collection PubMed
description Gastroesophageal junction adenocarcinoma (GEJAC) is a malignant tumor with high mortality. Its incidence has increased sharply all over the world in recent years. The study aims to search for potential biomarkers for the diagnosis and prognosis of GEJAC based on the Gene Expression Omnibus database (GEO) database and The Cancer Genome Atlas (TCGA) database. Microarray dataset (GSE96668 and GSE74553) of GEJAC was downloaded from the GEO. After screening overlapping differentially expressed genes (DEGs) by GEO2R and Wayne map, functional enrichment analysis of the DEGs was performed by the DAVID database. Then, a protein–protein interaction (PPI) network was constructed, and the hub gene was identified by using STRING and Cytoscape, as well as the diagnostic value of hub genes was evaluated by the receiver operating characteristic (ROC) curves. Finally, the gene transcriptome profiles of gastric cancer named TCGA-STAD were downloaded from TCGA database to screen the potential prognostic genes and construct the prognostic risk model using Cox proportional hazards regression. Meanwhile, the Kaplan–Meier curve and time-dependent ROC curve were adopted to test the prognostic value of the prognostic gene signature. In this study, we identified 10 hub genes that might have high diagnostic value for GEJAC, and inferred that they might be involved in the occurrence and development of GEJAC. Moreover, we conducted a survival prediction model consisting of 6 genes and proved that they have value to some extent in predicting prognosis for GEJAC patients.
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spelling pubmed-77483582020-12-21 Identification of biomarkers associated with diagnosis and prognosis of gastroesophageal junction adenocarcinoma–a study based on integrated bioinformatics analysis in GEO and TCGA database Song, Danlei Tian, Jiming Hu, Yuping Wei, Yongjian Lu, Hong Wang, Yuping Guan, Quanlin Zhou, Yongning Medicine (Baltimore) 4500 Gastroesophageal junction adenocarcinoma (GEJAC) is a malignant tumor with high mortality. Its incidence has increased sharply all over the world in recent years. The study aims to search for potential biomarkers for the diagnosis and prognosis of GEJAC based on the Gene Expression Omnibus database (GEO) database and The Cancer Genome Atlas (TCGA) database. Microarray dataset (GSE96668 and GSE74553) of GEJAC was downloaded from the GEO. After screening overlapping differentially expressed genes (DEGs) by GEO2R and Wayne map, functional enrichment analysis of the DEGs was performed by the DAVID database. Then, a protein–protein interaction (PPI) network was constructed, and the hub gene was identified by using STRING and Cytoscape, as well as the diagnostic value of hub genes was evaluated by the receiver operating characteristic (ROC) curves. Finally, the gene transcriptome profiles of gastric cancer named TCGA-STAD were downloaded from TCGA database to screen the potential prognostic genes and construct the prognostic risk model using Cox proportional hazards regression. Meanwhile, the Kaplan–Meier curve and time-dependent ROC curve were adopted to test the prognostic value of the prognostic gene signature. In this study, we identified 10 hub genes that might have high diagnostic value for GEJAC, and inferred that they might be involved in the occurrence and development of GEJAC. Moreover, we conducted a survival prediction model consisting of 6 genes and proved that they have value to some extent in predicting prognosis for GEJAC patients. Lippincott Williams & Wilkins 2020-12-18 /pmc/articles/PMC7748358/ /pubmed/33371094 http://dx.doi.org/10.1097/MD.0000000000023605 Text en Copyright © 2020 the Author(s). Published by Wolters Kluwer Health, Inc. http://creativecommons.org/licenses/by-nc/4.0 This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc/4.0
spellingShingle 4500
Song, Danlei
Tian, Jiming
Hu, Yuping
Wei, Yongjian
Lu, Hong
Wang, Yuping
Guan, Quanlin
Zhou, Yongning
Identification of biomarkers associated with diagnosis and prognosis of gastroesophageal junction adenocarcinoma–a study based on integrated bioinformatics analysis in GEO and TCGA database
title Identification of biomarkers associated with diagnosis and prognosis of gastroesophageal junction adenocarcinoma–a study based on integrated bioinformatics analysis in GEO and TCGA database
title_full Identification of biomarkers associated with diagnosis and prognosis of gastroesophageal junction adenocarcinoma–a study based on integrated bioinformatics analysis in GEO and TCGA database
title_fullStr Identification of biomarkers associated with diagnosis and prognosis of gastroesophageal junction adenocarcinoma–a study based on integrated bioinformatics analysis in GEO and TCGA database
title_full_unstemmed Identification of biomarkers associated with diagnosis and prognosis of gastroesophageal junction adenocarcinoma–a study based on integrated bioinformatics analysis in GEO and TCGA database
title_short Identification of biomarkers associated with diagnosis and prognosis of gastroesophageal junction adenocarcinoma–a study based on integrated bioinformatics analysis in GEO and TCGA database
title_sort identification of biomarkers associated with diagnosis and prognosis of gastroesophageal junction adenocarcinoma–a study based on integrated bioinformatics analysis in geo and tcga database
topic 4500
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7748358/
https://www.ncbi.nlm.nih.gov/pubmed/33371094
http://dx.doi.org/10.1097/MD.0000000000023605
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