Identification and immuno-infiltration analysis of cuproptosis regulators in human atherosclerosis

Abstract

Abstract Introduction The development of atherosclerosis (AS) may be aided by cuproptosis. As a result, we examined the cuproptosis regulators in human AS, gauged the degree of immune cell infiltration, and developed a prediction model. Methods We obtained the GSE100927 gene expression dataset associated with AS from the Gene Expression Omnibus (GEO) database and used it to identify cuproptosis-related differentially expressed genes (CuDEGs). This was accomplished by comparing AS samples and control samples. We also examined the relationship between CuDEGs and immune cell infiltration status, and investigated the molecular groupings of both CuDEGs and immune cell infiltration status. To pinpoint cluster-specific differentially expressed genes, we employed weighted gene co-expression network analysis (WGCNA). Furthermore, gene set variation analysis (GSVA) was carried out to annotate the enriched genes. From four different machine-learning models, we selected the model with the best performance. Lastly, we validated the accuracy of our predictions using nomograms and ROC curves. Results Our study confirmed the presence of CuDEGs and activated immune responses among AS and control samples. We identified 12 CuDEGs through the dataset, and we also discovered two clusters in AS. Analysis of immune cell infiltration showed that there is heterogeneity in immunity between these two clusters. Cuproptosis-related molecular Cluster 2 was marked by enhanced expressions of NLRP3, SLC31A1, FDX1, LIPT2 and CDKN2A. And Cluster 1 exhibited a higher proportion of T cells CD4 memory resting、Monocytes、Macrophages M1 and Mast cells resting. And enriched KEGG pathways revealed the pathway of leukocyte transendothelial migration was up-regulated in Cluster 1. We subsequently developed a support vector machine (SVM) model based on five genes, which demonstrated good performance in predicting AS in the external validation dataset (AUC = 0.895). Our results indicate that this combined nomogram is highly accurate in predicting AS. Conclusion Our study sheds light on the relationship between AS and cuproptosis, as well as the association between CRGs and immune-infiltrated cells in AS. Additionally, we have established a good predictive model.