Key gene screening and diagnostic model establishment for acute type a aortic dissection.

Background: Aortic dissection, particularly acute type A aortic dissection (ATAAD), is a life-threatening cardiovascular emergency with alarmingly high mortality rates globally. Despite advancements in imaging techniques like computed tomography angiography (CTA), delayed diagnosis and incomplete understanding of molecular mechanisms persist, contributing to poor outcomes. Recent studies highlight the role of immune dysregulation, vascular smooth muscle cell (VSMC) apoptosis, and metabolic-epigenetic interactions in AD pathogenesis, underscoring the need for novel biomarkers and therapeutic targets. Objective: This study aims to identify critical genes and molecular pathways associated with ATAAD, develop a multi-omics diagnostic model, and evaluate potential therapeutic interventions to improve clinical outcomes. Methods: Transcriptome datasets from the Gene Expression Omnibus (GEO) database were analyzed using differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms (SVM, Random Forest, LASSO regression). Functional enrichment and immunoinfiltration analyses were performed to explore biological pathways and immune cell interactions. External dataset validation and PCR testing of clinical samples (n = 9) were conducted to confirm gene expression differences. A nomogram diagnostic model was constructed and evaluated for predictive accuracy. Results: Six core genes were identified: Conclusion: This study identifies key molecular markers and pathways in ATAAD pathogenesis, emphasizing the role of immune dysregulation and extracellular matrix remodeling. The multi-omics diagnostic model provides a novel tool for early screening, potentially reducing mortality through timely intervention. These findings advance the understanding of aortic dissection mechanisms and offer actionable targets for future research and clinical applications.