ArticleJournal of cardiothoracic surgery2025
Integrative bioinformatics and machine learning approach unveils potential biomarkers linking coronary atherosclerosis and fatty acid metabolism-associated gene.
Article in Journal of cardiothoracic surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Leveraging machine learning models to evaluate immune infiltration in the ovarian cancer microenvironment: a single-cell analysis approach.Discover oncology · 2025Article
- Identification and functional analysis ofTranslational cancer research · 2025Article
- Identifying and Diagnosing Lytic Cell Death Genes in Atherosclerosis Using Machine Learning and Bioinformatics.Journal of inflammation research · 2025Article
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6 authors.
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Abstract
backgroundAtherosclerosis (AS) is increasingly recognized as a chronic inflammatory disease that significantly compromises vascular health and acts as a major contributor to cardiovascular diseases. Advancements in lipidomics and metabolomics have unveiled the complex role of fatty acid metabolism (FAM) in both healthy and pathological states. However, the specific roles of fatty acid metabolism-related genes (FAMGs) in shaping therapeutic approaches, especially in AS, remain largely unexplored and are a subject of ongoing research.
methodsThis study employed advanced bioinformatics techniques to identify and validate FAMGs associated with AS. We conducted differential expression analysis on a select list of 49 candidate FAMGs. GSEA and GSVA were utilized to elucidate the potential biological roles and pathways of these FAMGs. Subsequently, Lasso regression and SVM-RFE were applied to identify key hub genes and assess the diagnostic efficacy of seven FAMGs in distinguishing AS. The study also explored the correlation between these hub FAMGs and clinical features of AS. Validation of the expression levels of the seven FAMGs was performed using datasets GSE43292 and GSE9820.
resultsThe study pinpointed seven FAMGs with a close association to AS: ACSBG2, ELOVL4, ACSL3, CPT2, ALDH2, HSD17B10, and CPT1B. Analysis of their biological functions underscored their significant involvement in critical processes such as fatty acid metabolism, small molecule catabolism, and nucleoside bisphosphate metabolism. The diagnostic potential of these seven FAMGs in AS differentiation showed promising results.
conclusionsThis research has successfully identified seven key FAMGs implicated in AS, offering novel insights into the pathophysiology of the disease. These findings not only contribute to our understanding of AS but also present potential biomarkers for the disease, opening avenues for more effective monitoring and progression tracking of AS.
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