Aberrantly Methylated-Differentially Expressed Genes Identify Novel Atherosclerosis Risk Subtypes

Area of Science:

• Epigenetics
• Molecular Biology
• Cardiovascular Research

Background:

• Epigenetic mechanisms, particularly DNA methylation and long non-coding RNA (lncRNA) regulation, are increasingly implicated in atherosclerosis.
• Subgrouping patients based on epigenetic profiles can improve understanding of atherosclerotic progression.
• Unsupervised clustering analysis has been underutilized for identifying epigenetic-driven atherosclerosis subtypes.

Purpose of the Study:

• To identify epigenetics-related differentially expressed genes associated with atherosclerosis subtypes.
• To characterize the clinical utility of these genes in atherosclerosis.
• To develop a classifier for stratifying atherosclerotic patients based on epigenetic profiles.

Main Methods:

• Downloaded and analyzed methylation (GSE46394) and expression (GSE40231) data from large artery plaques.
• Identified aberrantly methylated-differentially expressed (AMDE) genes.
• Utilized Weighted Gene Correlation Network Analysis (WGCNA) and co-expression analysis to find core AMDE genes.
• Applied K-means clustering to define atherosclerosis subtypes and validated findings in independent cohorts (GSE28829, GSE90074, GSE34822).

Main Results:

• Identified 1,569 AMDE mRNAs and 8 AMDE lncRNAs; 32 mRNAs and 7 lncRNAs were core genes.
• Functional enrichment analysis linked AMDE genes to inflammation and axon guidance pathways.
• Atherosclerotic subtypes correlated with coronary artery disease severity and adverse event risk.
• Eight signature AMDE genes (PARP15, SERGEF, PDGFD, MRPL45, UBR1, STAU1, WIZ, LSM4) differentiated subtypes with high AUC values (0.858 and 0.812).

Conclusions:

• Aberrantly methylated-differentially expressed genes are crucial in atherosclerosis development and can define distinct patient subtypes.
• The identified signature AMDE genes demonstrate potential as biomarkers for stratifying atherosclerotic patients.
• This epigenetic-based approach offers a novel strategy for identifying high-risk individuals in clinical settings.