ArticleBMC medical genomics2023
Identification of key genes and pathways in atherosclerosis using integrated bioinformatics analysis.
Article in BMC medical genomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 7 citations in OpenAlex.
- Integrated proteomics and metabolomics analyses reveal potential molecular signatures of rabbit atherosclerotic plaques.International journal of cardiology. Heart & vasculature · 2025Article
- Identifying common genes and immune infiltration characteristics between systemic sclerosis and atherosclerosis.Clinical rheumatology · 2025Article
- Article
- Identification and validation of hub genes involved in foam cell formation and atherosclerosis developmentPeerJ · 2023Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAtherosclerosis (AS) is a chronic inflammatory disease that might induce severe cardiovascular events, such as myocardial infarction and cerebral infarction. These risk factors in the pathogenesis of AS remain uncertain and further research is needed. This study aims to explore the potential molecular mechanisms of AS by bioinformatics analyses.
methodsGSE100927 gene expression profiles, including 69 AS samples and 35 healthy controls, were downloaded from Gene Expression Omnibus database and indenfied for key genes and pathways in AS.
resultsA total of 443 differentially expressed genes (DEGs) between control and AS were identified, including 323 down-regulated genes and 120 up-regulated genes. The Gene ontology terms enriched by the up-regulated DEGs were associated with the regulation of leukocyte activation, endocytic vesicle, and cytokine binding, while the down-regulated DEGs were associated with negative regulation of cell growth, extracellular matrix, and G protein-coupled receptor binding. KEGG pathway analysis showed that the up-regulated DEGs were enriched in Osteoclast differentiation and Phagosome, while the down-regulated DEGs were enriched in vascular smooth muscle contraction and cGMP-PKG signaling pathway. Using the modular analysis of Cytoscape, we identified 3 modules mainly involved in Leishmaniasis and Osteoclast differentiation. The GSEA analysis showed the up-regulated gene sets were enriched in the ribosome, ascorbated metabolism, and propanoate metabolism. The LASSO Cox regression analysis showed the top 3 genes were TNF, CX3CR1, and COL1R1. Finally, we found these immune cells were conferred significantly higher infiltrating density in the AS group.
conclusionsOur data showed the pathway of Osteoclast differentiation and Leishmaniasis was involved in the AS process and we developed a three-gene model base on the prognosis of AS. These findings clarified the gene regulatory network of AS and may provide a novel target for AS therapy.
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