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Shared Genetic Features of Psoriasis and Myocardial Infarction: Insights From a Weighted Gene Coexpression Network Analysis.
Journal of the American Heart Association 2024 March 28
BACKGROUND: Increasing evidence suggests a higher propensity for acute myocardial infarction (MI) in patients with psoriasis. However, the shared mechanisms underlying this comorbidity in these patients remain unclear. This study aimed to explore the shared genetic features of psoriasis and MI and to identify potential biomarkers indicating their coexistence.
METHODS AND RESULTS: Data sets obtained from the gene expression omnibus were examined using a weighted gene coexpression network analysis approach. Hub genes were identified using coexpression modules and validated in other data sets and through in vitro cellular experiments. Bioinformatics tools, including the Human microRNA Disease Database, StarBase, and miRNet databases, were used to construct a ceRNA network and predict potential regulatory mechanisms. By applying weighted gene coexpression network analysis, we identified 2 distinct modules that were significant for both MI and psoriasis. Inflammatory and immune pathways were highlighted by gene ontology enrichment analysis of the overlapping genes. Three pivotal genes-Src homology and collagen 1, disruptor of telomeric silencing 1-like, and feline leukemia virus subgroup C cellular receptor family member 2-were identified as potential biomarkers. We constructed a ceRNA network that suggested the upstream regulatory roles of these genes in the coexistence of psoriasis and MI.
CONCLUSIONS: As potential therapeutic targets, Src homology and collagen 1, feline leukemia virus subgroup C cellular receptor family member 2, and disruptor of telomeric silencing 1-like provide novel insights into the shared genetic features between psoriasis and MI. This study paves the way for future studies focusing on the prevention of MI in patients with psoriasis.
METHODS AND RESULTS: Data sets obtained from the gene expression omnibus were examined using a weighted gene coexpression network analysis approach. Hub genes were identified using coexpression modules and validated in other data sets and through in vitro cellular experiments. Bioinformatics tools, including the Human microRNA Disease Database, StarBase, and miRNet databases, were used to construct a ceRNA network and predict potential regulatory mechanisms. By applying weighted gene coexpression network analysis, we identified 2 distinct modules that were significant for both MI and psoriasis. Inflammatory and immune pathways were highlighted by gene ontology enrichment analysis of the overlapping genes. Three pivotal genes-Src homology and collagen 1, disruptor of telomeric silencing 1-like, and feline leukemia virus subgroup C cellular receptor family member 2-were identified as potential biomarkers. We constructed a ceRNA network that suggested the upstream regulatory roles of these genes in the coexistence of psoriasis and MI.
CONCLUSIONS: As potential therapeutic targets, Src homology and collagen 1, feline leukemia virus subgroup C cellular receptor family member 2, and disruptor of telomeric silencing 1-like provide novel insights into the shared genetic features between psoriasis and MI. This study paves the way for future studies focusing on the prevention of MI in patients with psoriasis.
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