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

Abstract

Background Coronary artery disease (CAD) is a multifactorial cardiovascular disease that causes high mortality worldwide. Cuproptosis is a newly discovered method of programmed cell death, but it is unclear whether it is involved in the development of CAD. Methods GSE180081 was downloaded from the GEO database and genes that were differentially expressed in controls and patients with CAD were identified. These were clustered according to the cuproptosis gene set, to identify differentially expressed cuproptosis related genes. The intersection of the two sets of differentially expressed genes was used to identify genes relevant to the diagnosis of CAD using LASSO regression. A diagnostic model was created using the selected genes and logistic regression. Enriched immune genes were identified, the associated ceRNA network was characterized, and drugs that may target the identified genes were searched for. Results We identified 818 differentially expressed genes that were common to the CAD and cuproptosis gene sets, which principally represented the cell-substrate junction and the positive regulation of leukemia. Furthermore, HIST1H4E, IL6ST, RN7SKP45, LST1, and SNORD50B were found be potentially useful for the diagnosis of CAD using the diagnostic model. These genes were found to be closely associated with immune modification. Conclusion We have constructed a diagnostic prediction model based on a cuproptosis gene set using whole-blood transcriptome data. Using this, we have identified HIST1H4E, IL6ST, and LST1 as potential biomarkers of the risk of CAD. These findings provide a novel approach to the prediction, prevention, and individualized treatment of CAD.