ArticleBMC cardiovascular disorders2022
Role of ferroptosis-related genes in coronary atherosclerosis and identification of key genes: integration of bioinformatics analysis and experimental validation.
Article in BMC cardiovascular disorders, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Mapping current research and identifying hotspots of ferroptosis in cardiovascular diseases.Frontiers in cardiovascular medicine · 2022Pooled it
- Insights Into Macrophage Ferroptosis: Implications for Atherosclerosis.Cell proliferation · 2026Review
- Integrative bioinformatics and machine learning approach unveils potential biomarkers linking coronary atherosclerosis and glutamine metabolism-associated gene.Journal of cardiothoracic surgery · 2025Article
- Utilizing integrated bioinformatics and machine learning approaches to elucidate biomarkers linking sepsis to purine metabolism-associated genes.Scientific reports · 2025Article
- Utilizing integrated bioinformatics and machine learning approaches to elucidate biomarkers linking sepsis to fatty acid metabolism-associated genes.Scientific reports · 2024Article
- Identification of plasma miR-4505, miR-4743-5p and miR-4750-3p as novel diagnostic biomarkers for coronary artery disease in patients with type 2 diabetes mellitus: a case-control study.Cardiovascular diabetology · 2024Article
- IL‑37 suppresses macrophage ferroptosis to attenuate diabetic atherosclerosis via the NRF2 pathway.Experimental and therapeutic medicine · 2023Article
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCoronary atherosclerosis (CA) is the most common type of atherosclerosis. However, the inherent pathogenesis and mechanisms of CA are unclear, and the relationship with ferroptosis-related genes (FRGs) has not been reported. The purpose of this study was to use bioinformatics techniques to evaluate potential therapeutic targets for CA.Please provide the given name for author "Dingshun".Please provide the given name for author "Dingshun".
methodsFirst, the GSE132651 dataset was acquired from the Gene Expression Omnibus database. Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and Protein-Protein interaction network were successively conducted. Next, overlapping genes between hub genes and CA genes were found. FRGs were found when comparing the CA group with the normal group. The correlation between overlapping genes and FRGs was further analyzed. At last, we performed Elisa to validate the expression of these genes in human blood specimens. Mice aortic tissues were used for western blot to detect the expression of proteins.
resultsBased on the GSE132651 dataset, 102 differentially expressed genes were identified. Five overlapping genes between hub genes and CA genes were found (CCNA2, RRM2, PBK, PCNA, CDK1). TFRC and GPX4 were found to be FRGs. TFRC was positively correlated with CCNA2, PBK, PCNA, CDK1, RRM2, with CDK1 being the strongest correlation. GPX4 was negatively correlated with these genes, among which CCNA2 was the strongest correlation. The ELISA results showed that CCNA2, CDK1, and TFRC expression were markedly increased in serum of the CA samples compared with controls, while GPX4 expression was markedly decreased in the CA samples. The western blot results show that GPX4 expression was lower in the model group, TFRC, CDK1, and CCNA2 protein expression were high in the model group.
conclusionsFerroptosis-related genes GPX4 and TFRC were closely correlated with the identified overlapping genes CCNA2 and CDK1, which may serve as targeted therapies for the treatment of CA.
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