ArticleAnnals of translational medicine2022
Exploration of the shared gene signatures and molecular mechanisms between atherosclerosis and rheumatoid arthritis via multi-microarray data analyses.
Article in Annals of translational medicine, 2022. 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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1 citing paper in PubMed, 1 citations in OpenAlex.
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Accumulating evidence indicates the inflammatory state of rheumatoid arthritis (RA) predisposes to the acceleration of atherosclerosis (AS). Nevertheless, the potential mechanisms of accelerating AS in RA have not been fully elucidated. Our current study was to probe the problem via multi-microarray data analyses. Methods: The transcriptional profiling of synovial tissues from RA (GSE55235 and GSE55457) and that of atherosclerotic plaques from AS (GSE28829 and GSE41571) were downloaded from the Gene Expression Omnibus database. Bioinformatics analyses procedures included identifying common differentially expressed genes (DEGs), constructing protein-protein interaction network, key modules analysis and identifying hub genes, validating hub genes by using external datasets (GSE77298 and GSE163154), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, constructing transcription factor (TF)-miRNA coregulatory network and exploiting candidate drugs targeting hub genes. Results: A total of 67 common DEGs were identified for downstream analyses. GO and KEGG analyses of these genes expounded a critical role of inflammatory mediators and reactions in the comorbidities. Sixteen hub genes were identified, and their functional analyses further highlighted a complicated inflammatory micro-environment and signaling pathways involving RA and AS. Six TFs and four miRNAs interacted with hub genes, and the candidate drugs targeting them were simvastatin, 5-azacytidine, bisindolylmaleimide, retinoic acid, and verteporfin, etc. Conclusions: Our comprehensive bioinformatics analyses provided a novel view regarding the potential pathogenesis of AS in RA. Furthermore, exploitation of candidate drugs might hold great promise in the future fight against the comorbidities.
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