ArticleExperimental and therapeutic medicine2021
Identification of key genes in osteoarthritis using bioinformatics, principal component analysis and meta-analysis.
Article in Experimental and therapeutic medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed, 2 citations in OpenAlex.
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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