UNRAVELING THE MOLECULAR LANDSCAPE OF ANGINA PECTORIS: A SYSTEMS BIOINFORMATICS APPROACH TO IMMUNE DYSREGULATION AND CARDIOVASCULAR PATHOPHYSIOLOGY

Abstract

Angina pectoris (AP), a prevalent clinical manifestation of coronary artery disease, continues to impose a significant global health burden. Despite advances in pharmacologic and interventional therapies, a substantial proportion of patients remain symptomatic, suggesting the involvement of molecular and immunological mechanisms beyond classical ischemia. This study, titled aims to systematically dissect the immune and metabolic dimensions of AP through a comprehensive systems bioinformatics framework, enabling the identification of novel biomarkers and therapeutic targets. We integrated multi-omics datasets including transcriptomics, proteomics, and genome-wide association studies from patients with stable and unstable angina. Differential expression analysis, functional enrichment, and network modeling were performed to define key regulatory modules. Protein-protein interactions and hub gene networks were visualized using Cytoscape. Immune infiltration profiling and single-cell RNA sequencing deconvolution uncovered cell-type-specific patterns and signaling trajectories. Diagnostic biomarkers were prioritized using LASSO regression and Random Forest algorithms. Our integrative analysis revealed immune-centric molecular networks enriched in interferon signaling, oxidative stress, and NLRP3-mediated inflammation. Network topology highlighted novel hub genes driving endothelial dysfunction and plaque instability. Machine learning models demonstrated high diagnostic performance (AUC > 0.90), and single-cell resolution unveiled distinct immune cell dynamics associated with unstable angina. Experimental validation confirmed the translational relevance of predicted targets.