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Identification of key genes in osteoarthritis using bioinformatics, principal component analysis and meta-analysis

The present study aimed to identify key genes involved in osteoarthritis (OA). Based on a bioinformatics analysis of five gene expression profiling datasets (GSE55457, GSE55235, GSE82107, GSE12021 and GSE1919), differentially expressed genes (DEGs) in OA were identified. Subsequently, a protein-prot...

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Autores principales: Sun, Xiangxiang, Duan, Honghao, Xiao, Lin, Yao, Shuxin, He, Qiang, Chen, Xinlin, Zhang, Weijie, Ma, Jianbing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: D.A. Spandidos 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7678638/
https://www.ncbi.nlm.nih.gov/pubmed/33235627
http://dx.doi.org/10.3892/etm.2020.9450
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author Sun, Xiangxiang
Duan, Honghao
Xiao, Lin
Yao, Shuxin
He, Qiang
Chen, Xinlin
Zhang, Weijie
Ma, Jianbing
author_facet Sun, Xiangxiang
Duan, Honghao
Xiao, Lin
Yao, Shuxin
He, Qiang
Chen, Xinlin
Zhang, Weijie
Ma, Jianbing
author_sort Sun, Xiangxiang
collection PubMed
description The present study aimed to identify key genes involved in osteoarthritis (OA). Based on a bioinformatics analysis of five gene expression profiling datasets (GSE55457, GSE55235, GSE82107, GSE12021 and GSE1919), differentially expressed genes (DEGs) in OA were identified. Subsequently, a protein-protein interaction (PPI) network was constructed and its topological structure was analyzed. In addition, key genes in OA were identified following a principal component analysis (PCA) based on the DEGs in the PPI network. Finally, the functions and pathways enriched by these key genes were also analyzed. The PPI network consisted of 241 nodes and 576 interactives, including a total of 171 upregulated DEGs [e.g., aspartylglucosaminidase (AGA), CD58 and CD86] and a total of 70 downregulated DEGs (e.g., acetyl-CoA carboxylase β and dihydropyrimidine dehydrogenase). The PPI network complied with an attribute of scale-free small-world network. After PCA, 47 key genes were identified, including β-1,4-galactosyltransferase-1 (B4GALT1), AGA, CD58, CD86, ezrin, and eukaryotic translation initiation factor 4 γ 1 (EIF4G1). Subsequently, the 47 key genes were identified to be enriched in 13 Gene Ontology (GO) terms and 2 Kyoto Encyclopedia of Genes and Genomes pathways, with the GO terms involving B4GALT1 including positive regulation of developmental processes, protein amino acid terminal glycosylation and protein amino acid terminal N-glycosylation. In addition, B4GALT1 and EIF4G1 were confirmed to be downregulated in OA samples compared with healthy controls, but only EIF4G1 was determined to be significantly downregulated in OA samples, as determined via a meta-analysis of the 5 abovementioned datasets. In conclusion, B4GALT1 and EIF4G1 were indicated to have significant roles in OA, and B4GALT1 may be involved in positive regulation of developmental processes, protein amino acid terminal glycosylation and protein amino acid terminal N-glycosylation. The present study may enhance the current understanding of the molecular mechanisms of OA and provide novel therapeutic targets.
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spelling pubmed-76786382020-11-23 Identification of key genes in osteoarthritis using bioinformatics, principal component analysis and meta-analysis Sun, Xiangxiang Duan, Honghao Xiao, Lin Yao, Shuxin He, Qiang Chen, Xinlin Zhang, Weijie Ma, Jianbing Exp Ther Med Articles The present study aimed to identify key genes involved in osteoarthritis (OA). Based on a bioinformatics analysis of five gene expression profiling datasets (GSE55457, GSE55235, GSE82107, GSE12021 and GSE1919), differentially expressed genes (DEGs) in OA were identified. Subsequently, a protein-protein interaction (PPI) network was constructed and its topological structure was analyzed. In addition, key genes in OA were identified following a principal component analysis (PCA) based on the DEGs in the PPI network. Finally, the functions and pathways enriched by these key genes were also analyzed. The PPI network consisted of 241 nodes and 576 interactives, including a total of 171 upregulated DEGs [e.g., aspartylglucosaminidase (AGA), CD58 and CD86] and a total of 70 downregulated DEGs (e.g., acetyl-CoA carboxylase β and dihydropyrimidine dehydrogenase). The PPI network complied with an attribute of scale-free small-world network. After PCA, 47 key genes were identified, including β-1,4-galactosyltransferase-1 (B4GALT1), AGA, CD58, CD86, ezrin, and eukaryotic translation initiation factor 4 γ 1 (EIF4G1). Subsequently, the 47 key genes were identified to be enriched in 13 Gene Ontology (GO) terms and 2 Kyoto Encyclopedia of Genes and Genomes pathways, with the GO terms involving B4GALT1 including positive regulation of developmental processes, protein amino acid terminal glycosylation and protein amino acid terminal N-glycosylation. In addition, B4GALT1 and EIF4G1 were confirmed to be downregulated in OA samples compared with healthy controls, but only EIF4G1 was determined to be significantly downregulated in OA samples, as determined via a meta-analysis of the 5 abovementioned datasets. In conclusion, B4GALT1 and EIF4G1 were indicated to have significant roles in OA, and B4GALT1 may be involved in positive regulation of developmental processes, protein amino acid terminal glycosylation and protein amino acid terminal N-glycosylation. The present study may enhance the current understanding of the molecular mechanisms of OA and provide novel therapeutic targets. D.A. Spandidos 2021-01 2020-11-05 /pmc/articles/PMC7678638/ /pubmed/33235627 http://dx.doi.org/10.3892/etm.2020.9450 Text en Copyright: © Sun et al. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
spellingShingle Articles
Sun, Xiangxiang
Duan, Honghao
Xiao, Lin
Yao, Shuxin
He, Qiang
Chen, Xinlin
Zhang, Weijie
Ma, Jianbing
Identification of key genes in osteoarthritis using bioinformatics, principal component analysis and meta-analysis
title Identification of key genes in osteoarthritis using bioinformatics, principal component analysis and meta-analysis
title_full Identification of key genes in osteoarthritis using bioinformatics, principal component analysis and meta-analysis
title_fullStr Identification of key genes in osteoarthritis using bioinformatics, principal component analysis and meta-analysis
title_full_unstemmed Identification of key genes in osteoarthritis using bioinformatics, principal component analysis and meta-analysis
title_short Identification of key genes in osteoarthritis using bioinformatics, principal component analysis and meta-analysis
title_sort identification of key genes in osteoarthritis using bioinformatics, principal component analysis and meta-analysis
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7678638/
https://www.ncbi.nlm.nih.gov/pubmed/33235627
http://dx.doi.org/10.3892/etm.2020.9450
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