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Identification of hub genes in thyroid carcinoma to predict prognosis by integrated bioinformatics analysis

The aim of this study was to identify hub genes closely related to the pathogenesis and prognosis of thyroid carcinoma (THCA) by integrated bioinformatics analysis. In this study, through differential gene expression analysis, 1916 and 665 differentially expressed genes (DEGs) were obtained from The...

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Autores principales: Pan, Yangwang, Wu, Linjing, He, Shuai, Wu, Jun, Wang, Tong, Zang, Hongrui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8806580/
https://www.ncbi.nlm.nih.gov/pubmed/34167437
http://dx.doi.org/10.1080/21655979.2021.1940615
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author Pan, Yangwang
Wu, Linjing
He, Shuai
Wu, Jun
Wang, Tong
Zang, Hongrui
author_facet Pan, Yangwang
Wu, Linjing
He, Shuai
Wu, Jun
Wang, Tong
Zang, Hongrui
author_sort Pan, Yangwang
collection PubMed
description The aim of this study was to identify hub genes closely related to the pathogenesis and prognosis of thyroid carcinoma (THCA) by integrated bioinformatics analysis. In this study, through differential gene expression analysis, 1916 and 665 differentially expressed genes (DEGs) were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, and 7 and 11 co-expressed modules were identified from the TCGA-THCA and GSE153659 datasets, respectively, by weighted gene co-expression network analysis. We identified 162 overlapping genes between the DEGs and co-expression module genes as candidate hub genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the 162 overlapping DEGs identified significant functions and pathways of THCA, such as thyroid hormone generation and metabolic process. A protein–protein interaction (PPI) analysis detected the top 10 hub genes (QSOX1, WFS1, EVA1A, FSTL3, CHRDL1, FABP4, PRDM16, PPARGC1A, PPARG, COL23A1). Finally, survival analysis, clinical correlation analysis, and protein abundance validation confirmed that 3 of the 10 hub genes were associated with survival prognosis of patients with THCA, and 8 of them were associated with the clinical stages of THCA. In summary, we identified hub genes and key modules that were closely related to THCA, and validated these genes by survival analysis, clinical correlation analysis, and Human Protein Atlas image analysis. Our results provide important information that will help to elucidate the pathogenesis of THCA and identify novel candidate prognostic biomarkers and potential therapeutic targets.
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spelling pubmed-88065802022-02-02 Identification of hub genes in thyroid carcinoma to predict prognosis by integrated bioinformatics analysis Pan, Yangwang Wu, Linjing He, Shuai Wu, Jun Wang, Tong Zang, Hongrui Bioengineered Research Paper The aim of this study was to identify hub genes closely related to the pathogenesis and prognosis of thyroid carcinoma (THCA) by integrated bioinformatics analysis. In this study, through differential gene expression analysis, 1916 and 665 differentially expressed genes (DEGs) were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, and 7 and 11 co-expressed modules were identified from the TCGA-THCA and GSE153659 datasets, respectively, by weighted gene co-expression network analysis. We identified 162 overlapping genes between the DEGs and co-expression module genes as candidate hub genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the 162 overlapping DEGs identified significant functions and pathways of THCA, such as thyroid hormone generation and metabolic process. A protein–protein interaction (PPI) analysis detected the top 10 hub genes (QSOX1, WFS1, EVA1A, FSTL3, CHRDL1, FABP4, PRDM16, PPARGC1A, PPARG, COL23A1). Finally, survival analysis, clinical correlation analysis, and protein abundance validation confirmed that 3 of the 10 hub genes were associated with survival prognosis of patients with THCA, and 8 of them were associated with the clinical stages of THCA. In summary, we identified hub genes and key modules that were closely related to THCA, and validated these genes by survival analysis, clinical correlation analysis, and Human Protein Atlas image analysis. Our results provide important information that will help to elucidate the pathogenesis of THCA and identify novel candidate prognostic biomarkers and potential therapeutic targets. Taylor & Francis 2021-06-24 /pmc/articles/PMC8806580/ /pubmed/34167437 http://dx.doi.org/10.1080/21655979.2021.1940615 Text en © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Paper
Pan, Yangwang
Wu, Linjing
He, Shuai
Wu, Jun
Wang, Tong
Zang, Hongrui
Identification of hub genes in thyroid carcinoma to predict prognosis by integrated bioinformatics analysis
title Identification of hub genes in thyroid carcinoma to predict prognosis by integrated bioinformatics analysis
title_full Identification of hub genes in thyroid carcinoma to predict prognosis by integrated bioinformatics analysis
title_fullStr Identification of hub genes in thyroid carcinoma to predict prognosis by integrated bioinformatics analysis
title_full_unstemmed Identification of hub genes in thyroid carcinoma to predict prognosis by integrated bioinformatics analysis
title_short Identification of hub genes in thyroid carcinoma to predict prognosis by integrated bioinformatics analysis
title_sort identification of hub genes in thyroid carcinoma to predict prognosis by integrated bioinformatics analysis
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8806580/
https://www.ncbi.nlm.nih.gov/pubmed/34167437
http://dx.doi.org/10.1080/21655979.2021.1940615
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