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Integrative Transcriptomic and Epigenomic Profiling for Signature Identification in Coronary Artery Disease: A Pilot
Mario Zanfardino1, Anna D'Agostino1, Ilaria Leone1
1IRCCS SYNLAB SDN, 80143 Naples, Italy.
Insights
This study used multi-omics to find new molecular markers for coronary artery disease (CAD). Researchers identified a gene signature and regulatory patterns for better CAD risk stratification.
Area of Science:
- Cardiovascular Research
- Genomics and Epigenomics
- Molecular Biology
Background:
- Coronary Artery Disease (CAD) is a leading cause of death globally, driven by atherosclerotic plaques.
- Despite advances, ~30% of initial CAD events remain fatal, highlighting the need for early detection.
- Effective risk stratification is crucial for managing CAD patients.
Purpose of the Study:
- To identify novel molecular markers for Coronary Artery Disease (CAD) using a multi-omics approach.
- To uncover potential biomarkers for improved clinical risk stratification in CAD patients.
- To investigate gene expression and chromatin accessibility patterns in CAD.
Main Methods:
- Integrated transcriptomic (RNA-seq) and epigenomic (ATAC-seq) profiling of peripheral blood mononuclear cells (PBMCs).
- Analysis of samples from individuals undergoing cardiac computed tomography angiography (CCTA).
- Validation of key findings in an independent patient cohort.
Main Results:
- Identified 39 genes consistently dysregulated across all CAD subtypes.
- Revealed distinct chromatin accessibility patterns at CAD-associated loci.
- Confirmed expression patterns of key Differentially Expressed Genes (DEGs), including Claudin 18 (CLDN18).
Conclusions:
- Multi-omics data integration identified a core gene signature associated with CAD severity.
- Distinct regulatory patterns were uncovered, offering potential biomarkers for clinical risk stratification.
- Findings support the use of molecular markers for enhanced CAD management.
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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