ArticleLipids in health and disease2019
Identification of biomarkers in macrophages of atherosclerosis by microarray analysis.
Article in Lipids in health and disease, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
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- Genetic Polymorphism of NQO1 Gene is Associated with Susceptibility of Ischemic Stroke in Chinese Han Nationality.Neuropsychiatric disease and treatment · 2022Article
- Autophagy-Related Genes in Atherosclerosis.Journal of healthcare engineering · 2021Article
- Identification of Molecular Subtypes and Key Genes of Atherosclerosis Through Gene Expression Profiles.Frontiers in molecular biosciences · 2021Article
- Identification of Seven-Gene Hypoxia Signature for Predicting Overall Survival of Hepatocellular Carcinoma.Frontiers in genetics · 2021Article
- Bioinformatics analysis identified CDC20 as a potential drug target for cholangiocarcinoma.PeerJ · 2021Article
- Identification of potential crucial genes in monocytes for atherosclerosis using bioinformatics analysis.The Journal of international medical research · 2020Article
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- Identification and Verification of Biomarker in Clear Cell Renal Cell Carcinoma via Bioinformatics and Neural Network Model.BioMed research international · 2020Article
- Underlying Genes Involved in Atherosclerotic Macrophages: Insights from Microarray Data Mining.Medical science monitor : international medical journal of experimental and clinical research · 2019Article
- Identification of differentially expressed genes between primary lung cancer and lymph node metastasis via bioinformatic analysis.Oncology letters · 2019Article
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10 authors.
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Abstract
backgroundAtherosclerotic cardiovascular disease (ASCVD) refers to a series of diseases caused by atherosclerosis (AS). It is one of the most important causes of death worldwide. According to the inflammatory response theory, macrophages play a critical role in AS. However, the potential targets associated with macrophages in the development of AS are still obscure. This study aimed to use bioinformatics tools for screening and identifying molecular targets in AS macrophages.
methodsTwo expression profiling datasets (GSE7074 and GSE9874) were obtained from the Gene Expression Omnibus dataset, and differentially expressed genes (DEGs) between non-AS macrophages and AS macrophages were identified. Functional annotation of the DEGs was performed by analyzing the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. STRING and Cytoscape were employed for constructing a protein-protein interaction network and analyzing hub genes.
resultsA total of 98 DEGs were distinguished between non-AS macrophages and AS macrophages. The functional variations in DEGs were mainly enriched in response to hypoxia, respiratory gaseous exchange, protein binding, and intracellular, ciliary tip, early endosome membrane, and Lys63-specific deubiquitinase activities. Three genes were identified as hub genes, including KDELR3, CD55, and DYNC2H1.
conclusionHub genes and DEGs identified by using microarray techniques can be used as diagnostic and therapeutic biomarkers for AS.
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