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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doaj-1bd6039980d647f3aa0b3a280b19da612021-07-06T12:16:11ZengTaylor & Francis GroupBioengineered2165-59792165-59872021-01-011212928294010.1080/21655979.2021.19406151940615Identification of hub genes in thyroid carcinoma to predict prognosis by integrated bioinformatics analysisYangwang Pan0Linjing Wu1Shuai He2Jun Wu3Tong Wang4Hongrui Zang5Beijing Tongren Hospital, Capital Medical UniversityBeijing Tongren Hospital, Capital Medical UniversityBeijing Tongren Hospital, Capital Medical UniversityBeijing Tongren Hospital, Capital Medical UniversityBeijing Tongren Hospital, Capital Medical UniversityBeijing Tongren Hospital, Capital Medical UniversityThe 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.http://dx.doi.org/10.1080/21655979.2021.1940615thyroid carcinomahub geneskey modulesprognosisintegrated bioinformatics analysis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yangwang Pan Linjing Wu Shuai He Jun Wu Tong Wang Hongrui Zang |
spellingShingle |
Yangwang Pan Linjing Wu Shuai He Jun Wu Tong Wang Hongrui Zang Identification of hub genes in thyroid carcinoma to predict prognosis by integrated bioinformatics analysis Bioengineered thyroid carcinoma hub genes key modules prognosis integrated bioinformatics analysis |
author_facet |
Yangwang Pan Linjing Wu Shuai He Jun Wu Tong Wang Hongrui Zang |
author_sort |
Yangwang Pan |
title |
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_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_sort |
identification of hub genes in thyroid carcinoma to predict prognosis by integrated bioinformatics analysis |
publisher |
Taylor & Francis Group |
series |
Bioengineered |
issn |
2165-5979 2165-5987 |
publishDate |
2021-01-01 |
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. |
topic |
thyroid carcinoma hub genes key modules prognosis integrated bioinformatics analysis |
url |
http://dx.doi.org/10.1080/21655979.2021.1940615 |
work_keys_str_mv |
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1721317393929601024 |