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Purpose <p>To investigate whether cuproptosis-related genes contribute to coronary artery disease (CAD) pathogenesis and to develop a robust, blood-based diagnostic model.</p> Patients and methods <p>Whole-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 (<i>n</i> = 521) and controls (<i>n</i> = 191) were identified with FDR &lt; 0.05 and |log2FC|≥ 1. Consensus clustering (ConsensusClusterPlus, <i>k</i> = 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<i>,</i> 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.</p> Results <p>We 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.</p> Conclusion <p>We 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.</p>

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Identification of potential blood biomarkers of coronary artery disease using a cuproptosis gene set

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月7日

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  • Jia Li,
  • Kaibo Lei,
  • Ping Hu,
  • Zhanwei Zhu,
  • Lin Wang,
  • Can-E. Tang,
  • Fanyan Luo

摘要

Purpose

To investigate whether cuproptosis-related genes contribute to coronary artery disease (CAD) pathogenesis and to develop a robust, blood-based diagnostic model.

Patients and methods

Whole-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.

Results

We 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.

Conclusion

We 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.