Integrative Transcriptomic and Epigenomic Profiling for Signature Identification in Coronary Artery Disease: A Pilot Study

TL;DR

This pilot study uses transcriptomic and epigenomic data from blood cells to identify molecular markers for coronary artery disease, aiming to improve early detection and risk assessment.

Contribution

The study introduces a novel multi-omics approach combining RNA-seq and ATAC-seq to identify CAD-associated gene and chromatin signatures.

Findings

39 genes were consistently dysregulated across all CAD subtypes.

ATAC-seq revealed distinct chromatin accessibility patterns at CAD-associated loci.

Key DEGs like CLDN18 were validated in an independent cohort.

Abstract

Coronary Artery Disease (CAD), mainly due to the progressive development of atherosclerotic plaques, is one of the world’s leading causes of mortality and morbidity. A significant percentage of initial events (around 30%) remain fatal to this day despite significant advances in the diagnosis and treatment of cardiovascular diseases (CVDs). Early detection and risk stratification are therefore essential. In this study, we adopted a multi-omics approach integrating transcriptomic (RNA-seq) and epigenomic (ATAC-seq) profiling of peripheral blood mononuclear cells (PBMCs) from a cohort of individuals undergoing clinically indicated cardiac computed tomography angiography (CCTA) to uncover potential novel molecular markers of CAD. We identified 39 genes consistently dysregulated across all CAD subtypes. ATAC-seq analysis revealed distinct chromatin accessibility patterns at CAD-associated loci, with a predominance of quiescent and transcriptionally active states. Validation in an independent cohort confirmed the expression patterns of key Differentially Expressed Genes (DEGs), such as Claudin 18 (CLDN18), supporting the robustness of our findings. Consequently, the integration of multi-omics data allowed us to identify a core gene signature and regulatory patterns associated with disease severity, offering potential biomarkers for clinical risk stratification in patients with CAD.

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