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pmid:34167437

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

Yangwang Pan · Linjing Wu · Shuai He · Jun Wu · Tong Wang · Hongrui Zang

Bioengineered · 2021 doi:10.1080/21655979.2021.1940615

Paper

complete

Experiment

complete

Data

partial

Analysis

partial

Execution

absent

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title
Identification of hub genes in thyroid carcinoma to predict prognosis by integrated bioinformatics analysis.
doi
10.1080/21655979.2021.1940615
pmid
34167437
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Yangwang Pan
[1]
Linjing Wu
[2]
Shuai He
[3]
Jun Wu
[4]
Tong Wang
[5]
Hongrui Zang
journal
Bioengineered
year
2021
abstract
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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To confirm the reliability of the identified network hub genes and verify their prognostic value, we performed an overall survival (OS) analysis using the Kaplan Meier method [29] in the R packages survival and survminer [30] based on the clinical data from the TCGA-THCA dataset.
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To confirm the reliability of the identified network hub genes and verify their prognostic value, we performed an overall survival (OS) analysis using the Kaplan Meier method [29] in the R packages survival and survminer [30] based on the clinical data from the TCGA-THCA dataset.
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title
Next Generation Sequencing Facilitates Quantitative Analysis of Papillary Thyroid Carcinoma
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Complete file
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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. 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