The view of linear progression of AS driven by inflammation and lipids may underestimate the complexity of AS etiology. Although focused therapeutic strategies have shown some success, they remain insufficient (17). Recent studies have shown that programmed cell death regulates the inflammatory response, oxidative stress, and abnormalities in lipid metabolism in development of AS, which may allow for identification of novel therapeutic targets (18–19). The role of disulfidptosis and its related regulators in has not been evaluated in AS. In this study, several machine learning techniques were used to investigate the transcriptomes and immune infiltration of genes related to disulfidptosis. Furthermore, potential early AS biomarkers were identified and evaluated. An AS risk prediction model was built based on key biomarkers. According to the disulfidptosis-related DEGs, patients with advanced AS were separated into two clusters for analysis of differences in immune infiltration, which may help to determine immunotherapy targets.
We showed that most of the disulfidptosis-related DEGs were closely associated with either early or advanced plaques, which indicated that disulfidptosis may play a significant role in progression of AS. We then selected the most promising potential biomarkers from the 11 differentially expressed genes using a machine learning technique. We used random attribute selection in the decision tree training process. The RF algorithm, which is the most representative algorithm in Bagging ensemble learning, is a potent tool for screening the importance ranking of variables (20). The LASSO regression model can compress the variable coefficients for choosing the crucial variables to solve the regression problem in machine learning (21). Confounding factors were avoided by merging the two methods, and CAPZB, DSTN, MYL6, and PDLIM1 were selected as potential AS biomarkers. The findings of our investigation are consistent with those from previous studies. For example, DSTN, which is primarily expressed in vascular smooth muscle cells, has been reported as a therapeutic target for slowing the progression of AS by controlling the phenotypic differentiation and migration of smooth muscle cells (22). When compared with ApoE−/− mice, miR150 and ApoE double knockout mice showed more stable atherosclerotic plaques and lower inflammation levels through increased PDLIM1 expression, while PDLIM1 knockdown reversed these anti-atherosclerotic effects (23). Although the precise functions of CAPZB and MYL6 in AS have not been reported, the present study showed the intrinsic relationship between these genes and atherosclerotic disease. The cytoskeletal protein CAPZB is significantly differentially expressed before and after targeted drug treatment in Alzheimer's disease (24), and it has also been identified as a genetically sensitive gene that affects circulating thyroxine (25–26). In addition, a Mendelian randomization study combined with experimental validation showed that MYL6 is a hallmark gene for obesity and obesity-related disorders (27). Interestingly, anti-MYL6 antibody has been shown to alleviate polyangiitis-related multi-system damage (28). Our results provide a theoretical reference for further understanding the pathophysiological mechanism of disulfidptosis in AS and resulted in identification of four novel biomarkers.
The expression levels of DSTN, MYL6, and PDLIM1 were significantly downregulated, and the expression of CAPZB was upregulated, in advanced AS compared with those in early AS. Increased expression levels of DSTN and PDLIM1 may be associated with slowed progression of AS (23–23). In addition, ROC curves showed excellent diagnostic value for all four genes. To support this finding, we performed in vitro validation. The results agreed with our findings regarding the expression levels of these genes. Therefore, we identified CAPZB, DSTN, MYL6, and PDLIM1 as the signature genes for plaque stability, and we used the expression levels of these genes to generate a nomogram for predicting the risk of AS. Nomograms are preferred over conventional disease prediction models due to their convenience for clinical applications. Nomograms are frequently used in risk assessment, clinical judgment, and prognosis assessment for tumor and non-tumor illnesses (29). Based on the four key disulfidptosis-related genes, we built a risk prediction model that was calibrated for clinical decision-making. However, the nomogram's performance does not necessarily indicate the accuracy of illness risk prediction, and more testing is required to determine the nomogram's benefits in clinical practice (30). However, we developed the first practical AS risk prediction model for genes related to disulfidptosis, which may be beneficial for determination of a causal connection between disulfidptosis and AS.
Previous studies have shown that an imbalance in the immune response and promotion of chronic inflammation in plaque formation is mediated by synergistic interactions between various immune cells and crosstalk between immune cells and lipid metabolism (31–32). At different stages, the immune response either promotes or inhibits AS, and determination of the mechanisms of this functional dichotomy may aid in creation of new immunotherapies (33). In addition, it has become increasingly clear that cell death, and the synergistic immunological response it triggers, plays a role in AS progression (34). Although more evidence is needed, crosstalk between cell death and immunity will allow for better understanding of the mechanisms of development of AS. To further explore the potential role of disulfidptosis-related genes in AS, we divided AS samples into two subgroups based on differential expression of 11 genes, then thoroughly analyzed infiltration of 23 immune cells between the two clusters. The results showed that cluster A had significantly greater immune cell densities than cluster B, with the exception of T helper 2 cells. Furthermore, low levels of DSTN have been shown to increase inflammatory factors and increase immune cell aggregation (35). Therefore, we believe that atherosclerotic plaques with decreased DSTN expression may exhibit an enhanced immune response. Based on all DEGs, patients with AS were then divided into two subgroups, and patients with significantly worse immune responses exhibited relatively high levels of disulfidptosis-related gene expression. Therefore, our study was the first to assess the relationship between immune cell infiltration and disulfidptosis-related gene expression, which may inform development of immunotherapies for AS.
Bioinformatics analysis of immune cell infiltration showed that myeloid-derived suppressor cells (MDSC) and regulatory T cells (Tregs) differed significantly between the AS subtypes. Myeloid-derived suppressor cells are associated with immunosuppression, and studies have focused on biological functions of MDSC due to the lack of an established phenotype, particularly in the field of tumor immunotherapy (36). Recent studies of MDSCs have challenged MDSC cell types, whether they are anti-inflammatory or pro-inflammatory, and potential phenotypes. An interesting area of research has focused on the pathophysiological connection between MDSCs and non-tumor disorders (37). Atherosclerosis model mice were shown to have higher levels of polymorphonuclear myeloid-derived suppressor cells (PMN-MDSC) than control mice, which may be involved in development of neutrophil extracellular traps to exacerbate AS. In addition, Tregs buildup in atherosclerotic plaques can promote macrophage M2 polarization and produce inflammatory regulators to induce atherosclerotic plaque regression (38). In contrast, AS development and dyslipidemia have been shown to accelerate in response to Treg depletion (39). Examination of differential immune cell infiltration showed the immune heterogeneity of atherosclerotic plaques and showed the importance of disulfidptosis-related genes in immune control.
Our study was subject to the following limitations. First, the sample sizes of the AS datasets are relatively small, and it is challenging to maintain consistent plaque samples, which may have negatively impacted this study. Second, although the disulfidptosis-related genes included in the study were validated in vitro using multiple bioinformatics algorithms, future in vivo studies and clinical trials are required for verification. Therefore, more work is needed to precisely define the role of disulfidptosis in AS.