ReviewCurrent cardiology reviews2025
Identifying Biomarkers for Atherosclerosis via Gene Expression and Biological Networking.
Review in Current cardiology reviews, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Identification of multimorbidity hub genes for knee osteoarthritis-atherosclerosis and potential clinical applications.Molecular medicine reports · 2026Article
- Long noncoding RNAs in familial hypercholesterolemia: biomarkers, therapeutics, and AI in precision medicine.Lipids in health and disease · 2025Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionAtherosclerosis is a chronic disease caused by the accumulation of lipids, inflammatory cells, and fibrous elements in arterial walls, leading to plaque formation and cardiovascular conditions like coronary artery disease, stroke, and peripheral arterial disease. Factors like hyperlipidemia, hypertension, smoking, and diabetes contribute to its development. Diagnosis relies on imaging and biomarkers, while management includes lifestyle modifications, pharmacotherapy, and surgical interventions. Computational biology is transforming biological knowledge into clinical practice by identifying biomarkers that can predict clinical outcomes. This involves omics data, predictive modeling, and data integration. Statistical analysis-based methods are also being developed to develop and integrate methods for screening, diagnosing, and prognosing atherosclerosis. METHODOLOGY: The present work aimed to uncover critical genes and pathways to enhance the understanding of the mechanism of atherosclerosis. GSE23746 was analyzed to find differentially expressed genes (DEGs) using 19 control samples and 76 atherosclerotic samples.
resultsA total of 76 DEGs were identified. Analysed DEGs using Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) to generate enrichment datasets. A Protein- protein Interaction (PPI) network of DEGs was created utilizing the Search Tool for the Retrieval of Interacting Genes (STRING).
conclusionTen hub genes, namely EGR1, PTGS2, TNF, NFKBIA, CXCL8, TNFAIP3, CCL3, IL1B, PTPRC, and CD83, were found to be significantly linked to atherosclerosis. Furthermore, the metabolic pathway analysis through KEGG and STRING provides potential targets for therapeutic interventions through HUB genes to diagnose the illness at an early stage, which aids in the reduction of cardiovascular risk. From risk factor profiling to the discovery of novel biomarkers, several components such as phospholipids, ANGPTL3, LCAT, and the proteinencoded OCT-1 gene, play a vital role in crucial processes. These compounds are potential therapeutic targets for early diagnosis of atherosclerotic lesions and future novel biomarkers.
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