ArticleEuropean journal of medical research2025
Identification of potential blood biomarkers of coronary artery disease using a cuproptosis gene set.
Article in European journal of medical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeTo investigate whether cuproptosis-related genes contribute to coronary artery disease (CAD) pathogenesis and to develop a robust, blood-based diagnostic model. PATIENTS AND
methodsWhole-blood transcriptome profiles (GSE180081, GSE180082) were retrieved from the GEO database. After batch-effect correction (limma::removeBatchEffect) and quantile normalization, differentially expressed genes (DEGs) between CAD patients (n = 521) and controls (n = 191) were identified with FDR < 0.05 and |log2FC|≥ 1. Consensus clustering (ConsensusClusterPlus, k = 2) on 19 cuproptosis genes stratified patients into high- and low-cuproptosis activity groups. DEGs between these clusters were intersected with the CAD-DEG list to yield 818 cuproptosis-linked DEGs. A five-gene diagnostic signature (HIST1H4E, IL6ST, LST1, RN7SKP45, and SNORD50B) was selected by LASSO regression and modeled with logistic regression. Immune infiltration, ceRNA networks, and druggability were further analyzed. Local RT-qPCR in an independent cohort (12 CAD, 12 controls) confirmed expression trends.
resultsWe identified 818 differentially expressed genes that were common to the CAD and cuproptosis gene sets, and these principally represented the cell-substrate junction and the positive regulation of leukemia. Furthermore, HIST1H4E, IL6ST, RN7SKP45, LST1, and SNORD50B were found to be potentially useful for the diagnosis of CAD using our diagnostic model. These genes were also found to be closely associated with immune modification. Further validation revealed that HIST1H4E, IL6ST, and LST1 are very likely to be potential biomarkers.
conclusionWe constructed a diagnostic prediction model based on cuproptosis-related genes using whole-blood transcriptome data. Our results identify HIST1H4E, IL6ST, and LST1 as potential biomarkers for CAD risk assessment. These findings provide a novel basis for the prediction, prevention, and individualized treatment of CAD.
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