Bioinformatics Insights: Diagnostic Markers in Atherosclerosis

A study conducted bioinformatics analysis on GSE28829 and GSE43292 datasets to identify diagnostic markers and molecular mechanisms in atherosclerosis. Glutamine metabolism-associated genes were analyzed, resulting in 308 differentially expressed genes (DEGs). Weighted Gene Co-expression Network Analysis (WGCNA) and Protein–protein Interaction (PPI) network analysis revealed 27 hub genes, demonstrating high diagnostic values. Enrichment analyses showed associations with muscle-related processes and pathways such as dilated and hypertrophic cardiomyopathy. Immune cell infiltration analysis indicated increased levels in atherosclerosis. Gene Set Enrichment Analysis highlighted dysregulation in multiple signaling pathways. This comprehensive analysis enhances our understanding of atherosclerosis pathogenesis, offering potential diagnostic biomarkers and insights for future therapeutic exploration.

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