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DNA methylation patterns differ significantly between acute coronary syndrome (ACS) and stable coronary syndrome (SCS). These distinct epigenetic profiles highlight potential molecular markers for coronary artery disease acuity and progression.

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Area of Science:

  • Cardiovascular Epigenetics
  • Molecular Biology
  • Genomics

Background:

  • Coronary artery disease (CAD) manifests as stable (SCS) and acute (ACS) syndromes.
  • Epigenetic factors, like DNA methylation, are implicated in CAD progression and plaque destabilization.
  • Comprehensive genome-wide methylation differences between ACS, SCS, and controls are not fully understood.

Purpose of the Study:

  • To investigate genome-wide DNA methylation differences across ACS, SCS, and healthy individuals.
  • To identify distinct epigenetic profiles associated with acute versus stable coronary syndromes.
  • To explore the role of DNA methylation in CAD pathogenesis and disease acuity.

Main Methods:

  • Genome-wide DNA methylation analysis was conducted on patients with ACS, SCS, and healthy controls.
  • Pairwise comparisons (ACS vs. control, SCS vs. control, ACS vs. SCS) were performed.
  • Differentially methylated regions were identified using logistic regression (methylKit package, R) with stringent statistical criteria (q-value < 0.05, |Δβ| > 20%).

Main Results:

  • Unsupervised hierarchical clustering revealed distinct epigenetic profiles separating ACS, SCS, and control samples.
  • ACS exhibited more significant methylation alterations than SCS compared to controls.
  • Pathway analysis indicated enrichment of stress response, apoptosis, and cell adhesion pathways in ACS, and intercellular communication/vascular signaling in SCS.

Conclusions:

  • Acute and stable coronary syndromes possess distinct DNA methylation landscapes and associated pathway signatures.
  • Epigenetic regulation of stress, adhesion, and signaling pathways may influence CAD acuity and progression.
  • DNA methylation represents a potential molecular marker for differentiating CAD states and predicting disease trajectory.