Novel insights of disulfidptosis-mediated immune microenvironment regulation in atherosclerosis based on bioinformatics analyses

Abstract

Atherosclerosis (AS) is the leading cause of coronary heart disease, which is the primary cause of death worldwide. Recent studies have identified disulfidptosis as a new type of cell death that may be involved in onset and development of many diseases. However, the role of disulfidptosis in AS is not clear. In this study, bioinformatics analysis and experiments in vivo and in vitro were performed to evaluate the potential relationship between disulfidptosis and AS. AS-related sequencing data were obtained from the Gene Expression Omnibus (GEO). Bioinformatics techniques were used to evaluate differentially expressed genes (DEGs) associated with disulfidptosis-related AS. Hub genes were screened using least absolute shrinkage and selection operator (LASSO) and random forests (RF) methods. In addition, we established a foam cell model in vitro and an AS mouse model in vivo to verify the expressions of hub genes. In addition, we constructed a diagnostic nomogram with hub genes to predict progression of AS. Finally, the consensus clustering method was used to establish two different subtypes, and associations between subtypes and immunity were explored. As the results, 9 disulfidptosis-related AS DEGs were identified from GSE28829 and GSE43292 datasets. Evaluation of DEGs using LASSO and RF methods resulted in identification of 4 hub genes (CAPZB, DSTN, MYL6, PDLIM1), which were analyzed for diagnostic value using ROC curve analysis and verified in vitro and in vivo. Furthermore, a nomogram including hub genes was established that accurately predicted the occurrence of AS. The consensus clustering algorithm was used to separate patients with early atherosclerotic plaques and patients with advanced atherosclerotic plaques into two disulfidptosis subtypes. Cluster B displayed higher levels of infiltrating immune cells, which indicated that patients in cluster B may have a positive immune response for progression of AS. In summary, disulfidptosis-related genes including CAPZB, DSTN, MYL6, and PDLIM1 may be diagnostic markers and therapeutic targets for AS. In addition, these genes are closely related to immune cells, which may inform immunotherapy for AS.

Keywords: Atherosclerosis; Bioinformatics; Consensus cluster; Disulfidptosis; Immune cell infiltration.

Fig. 1

Workflow of the study.

Fig. 2

Disulfidptosis-related DEGs between patients with early atherosclerotic plaques and patients with advanced atherosclerotic plaques in GSE28829 and GSE43292 databases. (A) Differential expression analysis of the 14 disulfidptosis-related genes. (B) Heatmap of 11 disulfidptosis-related DEGs. (C) The correlation among the 11 disulfidptosis-related DEGs.

Fig. 3

Screening for hub disulfidptosis-related genes using multiple strategies. (A) The influence of the number of decision trees on the error rate. (B) The importance of the 11 disulfidptosis-related DEGs based on the RF model. (C) Ten-fold cross-verification for tuning parameter selection in the LASSO model. Each curve corresponds to a single gene. (D) LASSO coefficient profiling. The dotted vertical line is drawn at the optimal lambda. (E) Venn diagram of LASSO and RF algorithms.

Fig. 4

Hub genes verification. (A) The expression levels of CAPZB, DSTN, MYL6, and PDLIM1 in GSE28829 and GSE43292 datasets. (B) Receiver operating characteristic curves for CAPZB, DSTN, MYL6, and PDLIM1 in GSE28829 and GSE43292 datasets. (C) H&E staining of mouse aorta. (D) Oil red O staining of mouse aorta. (E) Tc, TG, LDL-c and HDL-c levels in ApoE^−/− mice. (F) The expression levels of CAPZB, DSTN, MYL6, and PDLIM1 in ApoE^−/− mice. (G) The expression levels of CAPZB, DSTN, MYL6, and PDLIM1 in a foam cell model derived from THP-1 cells. (H) The expression levels of CAPZB, DSTN, MYL6, and PDLIM1 in a foam cell model derived from RAW264.7 cells.

Fig. 5

Nomogram construction (A) Nomogram for prediction of MI. (B) Calibration curve analysis of the nomogram. The dashed line represents perfect prediction. The dotted line represents apparent estimates of predicted vs. observed values, and the solid line (bias) shows the corrected estimates. (C) The DCA of the proposed nomogram for predicting MI in the training datasets. (D) The CIC curve of the proposed nomogram. The red curve indicates the number of people classified as positive (high risk) by the prediction model at each threshold probability. The blue curve is the number of true positives at each threshold probability. (E) ROC curves showing the accuracy of the nomogram.

Fig. 6

Consensus clustering of the 11 significant disulfidptosis-related genes in GSE28829 and GSE43292 datasets. (A) Consensus matrix of the 11 significant disulfidptosis-related genes when k = 2. (B) Consensus CDF when k = 2–9. (C) Relative alterations in the area under the CDF curve. (D) Expression of the 11 significant disulfidptosis-related genes in disulfidptosis cluster A and cluster B via heatmap. (E )Differential expression of the 11 significant disulfidptosis-related genes in disulfidptosis cluster A and cluster B. (F) Principal component analysis of the expression data of the 11 significant disulfidptosis-related genes showed differences between the transcriptomes of the two disulfidptosis subtypes.

Fig. 7

Immune cell infiltration analysis. (A) Heatmap of correlations between immune cells and the 11 significant disulfidptosis-related genes. (B) Difference in the abundance of infiltrating immune cells between the high and low expression groups, including CAPZB, DSTN, MYL6, and PDLIM1. (C) Differential immune cell infiltration between disulfidptosis cluster A and cluster B.

Fig. 8

Consensus clustering of the 47 disulfidptosis-related DEGs in GSE28829 and GSE43292 datasets. (A) Consensus matrix of the 47 m⁶A-related DEGs when k = 2. (B) Consensus CDF when k = 2–9. (C) Relative alterations in the area under the CDF curve. (D) Expression of the 47 disulfidptosis-related DEGs in gene cluster A and cluster B using a heatmap. (E) Differential expression of the 11 significant disulfidptosis-related genes in gene cluster A and cluster B. (F) Differential enrichment of immune cells between gene cluster A and cluster B.

Fig. 9

Correlation between disulfidptosis cluster, gene cluster, and disulfidptosis score. (A) Differences in disulfidptosis scores between disulfidptosis cluster A and cluster B. (B) Differences in disulfidptosis score between gene cluster A and cluster B. (C) Sankey diagram of the relationship between disulfidptosis cluster, gene cluster, and disulfidptosis score.