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      Immune Infiltration in Atherosclerosis is Mediated by Cuproptosis-Associated Ferroptosis Genes

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            Abstract

            Aims: In this study, we aimed to identify cuproptosis-associated ferroptosis genes in the atherosclerosis microarray of the Gene Expression Omnibus (GEO) database and to explore hub gene-mediated immune infiltration in atherosclerosis.

            Background: Immune infiltration plays a crucial role in atherosclerosis development. Ferroptosis is a mode of cell death caused by the iron-dependent accumulation of lipid peroxides. Cuproptosis is a recently discovered type of programmed cell death. No previous studies have examined the mechanism of cuproptosis-associated ferroptosis gene regulation in immune infiltration in atherosclerosis.

            Methods: We searched the qualified atherosclerosis gene microarray in the GEO database, integrated it with ferroptosis and cuproptosis genes, and calculated the correlation coefficients. We then obtained the cuproptosis-associated ferroptosis gene matrix and screened differentially expressed genes. Subsequently, we performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses and protein–protein interaction network analysis of differentially expressed genes. We also screened hub genes according to the Matthews correlation coefficient (MCC) algorithm. We conducted enrichment analysis of hub genes to explore their functions and predict related microRNAs (P<0.05). We also used the single-sample gene set enrichment analysis (ssGSEA) algorithm to analyze the relationships between hub genes and immune infiltration, and used immune-associated hub genes to construct a risk model. Finally, we used the drug prediction results and molecular docking technology to explore potential therapeutic drugs targeting the hub genes.

            Results: Seventy-eight cuproptosis-associated ferroptosis genes were found to be involved in the cellular response to oxidative and chemical stress, and to be enriched in multiple pathways, including ferroptosis, glutathione metabolism, and atherosclerosis. Ten hub genes were identified with the MCC algorithm; according to the ssGSEA algorithm, these genes were closely associated with immune infiltration, thus indicating that cuproptosis-associated ferroptosis genes may participate in atherosclerosis by mediating immune infiltration. The receiver operating characteristic curve indicated that the model had a good ability to predict atherosclerosis risk. The results of drug prediction (adjusted P<0.001) and molecular docking showed that glutathione may be a potential therapeutic drug that targets the hub genes.

            Conclusion: Cuproptosis-associated ferroptosis genes are associated with immune infiltration in atherosclerosis.

            Main article text

            Introduction

            Atherosclerosis is the leading cause of cardiovascular disease worldwide. The pathological lesions seen in atherosclerosis in large and moderately sized arteries demonstrate chronic inflammation, oxidative stress, and accumulation of low-density lipoprotein (LDL) particles and fibrous components. Fibrofatty lesions of the arterial walls gradually become unstable, break loose, and form thrombi, thus leading to myocardial infarction, stroke, and other severe cardiac ischemic syndromes [1]. In recent years, cardiovascular disease has a trend towards affecting younger people, that warrants further attention [2]. Current treatments for atherosclerosis focus on regulating conventional risk factors. These treatments include statins and antidiabetic drugs, such as metformin, which not only prevent and delay plaque formation, but also have anti-inflammatory effects. High-risk patients may particularly benefit from anti-inflammatory interventions, which may serve as useful new therapies [3]. Investigating the mechanisms of immune infiltration in atherosclerosis may provide new therapeutic modalities for atherosclerosis prevention and treatment.

            The immune system plays a major regulatory role in the appearance and progression of atherosclerosis [4]. In early stages of atherosclerosis, LDL becomes trapped in the arterial wall, and the levels of antioxidants in the arterial subintimal layer are much lower than those in the plasma. Hence, the immunogenicity of LDL is acquired only after oxidative modification by reactive oxygen species (ROS). Oxidized LDL (ox-LDL) behaves as an antigen, mobilizing innate and adaptive immune pathways, activating chemotactic innate immune cells and adaptive immune cells (mainly monocytes and T cells), and regulating the immune response [5]. During atherosclerosis progression, macrophages and monocytes promote foam cell formation and activate the inflammatory response by phagocytosing ox-LDL, thus resulting in deep ulcers, rupture of the injured intimal surface, hematoma or bleeding, and thrombus deposition [6].

            Inflammation plays important roles in the formation and development of plaques [7]. For example, C-reactive protein accumulates in atherosclerosis [8]. The levels of plasma IL-6 in patients with plaque rupture are significantly elevated [9]. P-selectin also plays an important role in atherosclerosis by participating in the activation, rolling, and attachment of leukocytes to endothelial cells through interaction with ligands [10]. In addition, Toll-like receptor 4 signaling [11], nuclear factor-κB (NF-κB) signaling [12], and other inflammatory signaling pathways are closely associated with atherosclerosis. Oxidative stress results from an imbalance between nitric oxide (NO) biosynthesis and ROS production, and is a cause of endothelial dysfunction [13]. Excessive production of ROS is also an important factor leading to the senescence of vascular endothelial cells [14]. Previous studies have found that Bruton’s tyrosine kinase (BTK) promotes atherosclerosis by regulating oxidative stress [15]. ZBTB20 is also a hub gene in the oxidative stress and inflammatory responses induced by ox-LDL in AS [16]. The main endogenous redox homeostasis mechanism involves the nucleophile glutathione system, which counteracts the deleterious effects of ROS [17]. The ratio of reduced glutathione to oxidized glutathione is an important indicator of oxidative stress [18]. Importantly, glutathione homeostasis is closely associated with cardiovascular diseases such as atherosclerosis [19].

            Ferroptosis is a mode of cell death activated by iron-dependent phospholipid peroxidation. It is regulated by redox homeostasis; iron treatment; mitochondrial activity; metabolism of amino acids, lipids, and sugars; and many disease-associated signaling pathways [20]. Ferroptosis can cause organ damage and degenerative pathological changes. Hence, pharmacological induction or inhibition of ferroptosis has excellent potential for the treatment of drug-resistant cancer, ischemic organ damage, and other degenerative diseases associated with excessive lipid peroxidation [20]. A previous study has indicated that ferroptosis may promote atherosclerosis by accelerating endothelial cell dysfunction during lipid peroxidation [21]. Furthermore, intracellular iron disorders can damage macrophages, vascular smooth muscle cells, and vascular endothelial cells, and can influence many pathological processes involved in atherosclerosis, such as lipid peroxidation, oxidative stress, inflammation, and dyslipidemia [21].

            Previous studies have shown that ferroptosis interacts with innate immune cells, such as macrophages and neutrophils, and with adaptive immune cells, including T and B lymphocytes [22, 23]. Ferroptosis not only affects the number and function of immune cells in the body but also is recognized by them, thereby triggering the immune response. Under certain circumstances, ferroptosis serves as a type of autophagy-dependent cell death. Nuclear receptor coactivator 4-associated iron autophagy, Ras-associated protein-associated lipid autophagy, sequestosome 1-associated clockwise autophagy, beclin 1-dependent autophagy, and insufficient autophagy can all lead to ferroptosis dominated by iron accumulation and lipid peroxidation [24]. When induced, ferroptosis results in anti-tumor and anti-immune reactions in ischemia–reperfusion injury [25].

            Cuproptosis is another type of regulatory cell death induced by metal ions. Copper directly binds and induces the oligomerization of lipoylated DLAT, and when protein lipoylation was abrogated, DLAT no longer bound copper, and induces cell death independent of apoptosis [26]. The role of cuproptosis in atherosclerosis has not been reported to date; however, some studies have found that high serum copper levels accelerate atherosclerotic plaque formation by affecting lipid metabolism, LDL oxidation, and inflammation, thus increasing the risk of atherosclerotic heart disease [27]. Other studies have shown that copper tetrathiomolybdate inhibits vascular inflammation and atherosclerosis in apolipoprotein E-deficient mice [28].

            Gao et al. [29] have found that combined treatment of colon cancer cells with elesclomol and copper leads to copper retention in the mitochondria, thereby causing ROS accumulation and degradation of solute carrier family 7 member 11 (SLC7A11). SLC7A11 is closely associated with ferroptosis; thus, ferroptosis inhibition decreases the cell death induced by elesclomol. Consequently, cuproptosis and ferroptosis have been speculated to be related.

            The roles and mechanisms of cuproptosis in the immune system have not been extensively investigated. A previous study has reported that imbalanced copper homeostasis can affect tumor growth and induce tumor-cell death. Moreover, copper plays an essential role in tumor immunity and anti-tumor therapy [30]. Cuproptosis may not only regulate the tumor microenvironment – particularly CD8+ T cells, which contribute to tumor growth and progression – but also promote the appearance and progression of viral diseases [31].

            Elucidating the mechanisms of cuproptosis-associated ferroptosis genes in immune infiltration is critical. However, no studies have linked cuproptosis-associated ferroptosis genes mediating immune infiltration to atherosclerosis progression. To our knowledge, this study is the first to describe the specific mechanism of immune infiltration mediated by cuproptosis-associated ferroptosis genes in atherosclerosis by combining atherosclerotic microarray data and cuproptosis-associated ferroptosis genes. In this study, we used the GSE43292 dataset of the Gene Expression Omnibus (GEO) database, combined with the ferroptosis genes from the Ferroptosis database (FerrDb, (http://www.zhounan.org/ferrdb/legacy/operations/download.html)) and the cuproptosis genes published by Tsvetkov et al. [26] We performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to explore the functions of cuproptosis-associated ferroptosis genes, screened hub genes with the MCC algorithm, and analyzed the associations between hub genes and immune infiltration. We also constructed a risk model, performed enrichment analysis, and predicted drugs and microRNAs (miRNAs) on the basis of the hub genes most associated with immunity. In this study, we provide theoretical support for the involvement of cuproptosis-associated ferroptosis gene-mediated immune infiltration in atherosclerosis.

            Materials and Methods

            Acquisition of Chip Data and Data Preprocessing

            We used the GEO database (https://www.ncbi.nlm.nih.gov/geo) to search for “atherosclerosis.” We limited the species to human and obtained the gene chip dataset GSE43292 (platform ID GPL6244), which contains 64 samples, including 32 cases of atherosclerotic plaques (stage IV and above, according to the Stary classification) and 32 cases in the control group (i.e., distal macroscopic intact tissue, stages I and II). We transformed the probe expression matrix into a gene expression matrix in R software. We also derived 13 cuproptosis genes from the research results of Tsvetkov et al. [26] and 259 ferroptosis genes from the FerrDb database by combining driver, suppressor, and marker genes. We combined ferroptosis-associated genes, cuproptosis-associated genes, and atherosclerosis datasets to obtain a ferroptosis-associated atherosclerosis and cuproptosis-associated atherosclerosis expression matrix. We studied the correlations between ferroptosis-associated genes and cuproptosis-associated genes in R software (cor>0.45, P<0.05, positive correlation; cor<−0.45, P<0.05, negative correlation), and identified 180 ferroptosis genes associated with cuproptosis in atherosclerosis.

            Acquisition of Differentially Expressed Genes and Heatmap/Volcano Plot Construction

            Using the limma package [32] in R, we screened for differentially expressed genes on the basis of |log FC| ≥0.2 and adjusted P value of <0.05. We constructed a heatmap with the pheatmap package [33] and a volcano plot with the ggplot2 package [34].

            Enrichment Analysis of Differentially Expressed Genes

            We used the clusterProfiler package [35, 36] in R to perform the GO enrichment analysis and KEGG enrichment analysis of the above differentially expressed genes to explore their potential functions. We set the screening condition to an adjusted P value cutoff of <0.05 to draw the column charts. We also used the pathview package [37] to visualize the KEGG pathways.

            Construction of the PPI Network and Screening of Hub Genes

            We uploaded the differentially expressed genes to the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and set the highest confidence score to 0.7. We then constructed the PPI network. We imported the PPI information into Cytoscape 3.7.1 software and visualized the PPI network. We used the Matthews correlation coefficient (MCC) algorithm to analyze the PPI network with the cytoHubba plug-in and derived the top ten hub genes with the highest scores.

            Evaluation of Immune Cell Infiltration and Immune Function Infiltration

            We used the gene set variation analysis (GSVA) package [38] and the single-sample gene set enrichment analysis (ssGSEA) algorithm in R software to extract and quantify the immune cell infiltration and assess immune function of the transformed expression matrix. After obtaining the ssGSEA expression matrix, we constructed a heatmap of immune function with the pheatmap package. Using corrplot package [39], we visualized the correlations among 16 types of infiltrating immune cells and 13 types of infiltrating immune functions. We utilized the ggplot2 package to construct a box plot to visualize the differences in immune cell infiltration and immune function between atherosclerotic plaques and healthy tissues.

            Immune Correlation Analysis and Model Establishment with Hub Genes

            We used the psych package [40] in R to analyze the correlations of hub genes with immune cells and immune functions, as well as the ggcorrplot package [41] to draw the correlation heatmap to screen the hub genes most associated with immunity. We constructed the logistic regression risk model with the hub genes most associated with immunity. High expression was defined by a value above the median, whereas low expression was defined by a value below the median. We used the rms packages [42] in R to construct the nomogram and calibration map, and used the ROCR packages [43] in R to construct the receiver operating characteristic (ROC) curve, then calculated the risk score of each hub gene. We measured model recognition ability by calculating the C-index and drawing a calibration curve and a ROC curve.

            Functional Analysis and Prediction of Related Drugs and miRNAs on the Basis of Immune Hub Genes

            We used clusterProfiler package of R to perform the GO enrichment analysis and the KEGG enrichment analysis of the immune hub genes to explore their possible functions. We uploaded the immune hub genes to the Drug Signatures Database (DSigDB) of the Enrichr platform (https://maayanlab.cloud/Enrichr/) to predict potential therapeutic drugs for atherosclerosis based on targeting of cuproptosis-related ferroptosis genes. We then screened drugs according to a standard with an adjusted P value of <0.001. To explore the mechanism regulating the expression of ferroptosis genes related to cuproptosis, we obtained predictive miRNAs from the TargetScan database and screened miRNAs with significant differences according to a P value of <0.05. We then introduced the miRNA–gene relationship into Cytoscape 3.7.1 to visualize the regulatory network.

            To explore the effects of drugs on target genes, we explored the intensity of action using molecular docking. Hypoxia-inducible factor-1α (HIF-1α) and ferritin heavy chain 1 (FTH1) were selected, and the molecular structures of the targets and glutathione were downloaded in the PDB website to conduct molecular docking using AutoDock software after PyMOL processing. After exportation and format conversion, the results were visualized in PyMOL to obtain two groups of docking results.

            Experimental Validation

            We selected 21 patients with carotid plaques and 27 healthy adults from the physical examination population. We obtained triglyceride (TG), total cholesterol, high-density lipoprotein cholesterol, and LDL-cholesterol values from these individuals. Homocysteine values were obtained from seven healthy people and 11 patients with atherosclerosis. This study was approved by the ethics committee of Chengde Medical University (approval number: 2022013), and all individuals provided written informed consent for participation.

            We performed all statistical analyses with R, version 4.2.1.

            Results

            Screening Results of Differentially Expressed Genes

            We used R software to analyze the cuproptosis-associated ferroptosis gene expression matrix in atherosclerosis (64 samples: 32 in the atherosclerosis group and 32 in the control group). We obtained 78 differentially expressed genes, including 57 upregulated and 21 downregulated genes. We mapped these differentially expressed genes in a heatmap and volcano plot (Figure 1). We also constructed a Sankey diagram of the differentially expressed genes most closely associated with cuproptosis genes (|cor|>0.8) (Figure 2).

            Figure 1

            Differentially Expressed Genes in a Heatmap and Volcano Plot. (A) Heatmap of differentially expressed genes between the atherosclerosis group and the control group. Each column represents a tissue sample and each row represents a differently expressed gene. (B) Volcano plot of differentially expressed genes between the atherosclerosis group and the control group. Red indicates upregulated genes, whereas green indicates downregulated genes.

            Figure 2

            Differentially Expressed Ferroptosis-Associated Genes (FRG) Most Closely Associated with Cuproptosis-Associated Genes (CRG).

            GO and KEGG Enrichment Analyses of Differentially Expressed Genes

            Figure 3 depicts the conclusion of the GO enrichment analysis. In terms of biological processes, differentially expressed genes were involved primarily in the response of cells to chemical and oxidative stress, nutrient levels, and extracellular stimulation. In contrast, the main significantly enriched cell component terms were secondary lysosomes, autolysosomes, tertiary particles, the nicotinamide adenine dinucleotide phosphate (NADPH) oxidase complex, perinuclear endoplasmic reticulum, and other cellular components. Regarding molecular functions, the differentially expressed genes were involved primarily in oxidoreductase, glucose transmembrane transporter, and hexose transmembrane transporter activities. KEGG pathway enrichment analysis (Figure 4) showed that the differentially expressed genes were enriched in ferroptosis, HIF-1α signaling, glutathione metabolism, atherosclerosis, chemical carcinogenesis induced by ROS, and p53 signaling.

            Figure 3

            Gene Ontology (GO) Enrichment Analysis of Differentially Expressed Genes. A: Bar chart of GO analysis; B, C: biological processes; D: cellular components; E: molecular functions.

            Figure 4

            A, B: Kyoto Encyclopedia of Genes and Genomes (KEGG) Enrichment Analysis of Differentially Expressed Genes.

            Construction of the PPI Network and Screening of Immune Hub Genes

            We input the differentially expressed genes into the STRING database to determine the relationships among the 78 selected differentially expressed genes, with the highest confidence score set to 0.7. We acquired a PPI network with 77 nodes and 63 edges, with an average node degree of 1.64. The enrichment P value of the PPI network was <2.36−12. We then imported the PPI network into Cytoscape 3.7.1 software for visualization with the cytoHubba plug-in and the MCC algorithm and screened the top ten key genes. These genes were HIF1A, EGFR (encoding the epidermal growth factor receptor), TLR4 (encoding Toll-like receptor 4), FTH1, CYBB (encoding cytochrome B-245 beta chain), SLC3A2 (encoding solute carrier family 3 member 2), ATM (encoding ataxia-telangiectasia mutated), IDH1 (encoding isocitrate dehydrogenase 1), CDKN1A (encoding cyclin-dependent kinase inhibitor 1A), and MAP3K5 (encoding mitogen-activated protein kinase 5) (Figure 5).

            Figure 5

            PPI network of core differentially expressed genes (A) and the hub gene network (B).

            Degree of Immune Cell Infiltration and Immune Function

            We used the ssGSEA algorithm to examine the degree of infiltration of 16 types of immune cell and 13 immune functions in the GSE43292 dataset (Figure 6). The degree of infiltration of macrophages and helper T cells in atherosclerotic plaques was significantly elevated for the 16 immune cell types. The degree of parainflammation in atherosclerotic plaques was significantly elevated for the 13 immune functions.

            Figure 6

            Heatmap of Immune Cell Infiltration in the Atherosclerosis Group and the Control Group.

            Correlations and Differences in Immune Cell Infiltration

            The immune cell correlation thermogram demonstrates strong positive correlations between activated dendritic cells and tumor-infiltrating lymphocytes (r=0.93), regulatory T cells and tumor-infiltrating lymphocytes (r=0.93), neutrophils and tumor-infiltrating lymphocytes (r=0.92), and neutrophils and regulatory T cells (r=0.91). There was a strong positive correlation between activated dendritic cells and plasma cell-like dendritic cells (r=0.9) and between macrophages and helper T cells (r=0.9). There was also a strong positive correlation between neutrophils and helper T cells (r=0.9), plasma cell-like dendritic cells and tumor-infiltrating lymphocytes (r=0.89), and helper T cells and tumor-infiltrating lymphocytes (r=0.89). Moreover, there was a strong positive correlation between activated dendritic cells and regulatory T cells (r=0.89) and between activated dendritic cells and neutrophils (r=0.88). The heatmap of immune function correlations showed a strong positive correlation between antigen presentation co-stimulation, and checkpoint (r=0.97), between T cell co-inhibition and immune checkpoint (r=0.97), between the chemokine C-C-motif receptor and immune checkpoint (r=0.96), and between the chemokine C-C-motif receptor and T-cell co-inhibition (r=0.95). There was a strong positive correlation between the immune checkpoint and T-cell co-stimulation (r=0.94), between antigen presentation co-stimulation, and the chemokine C-C-motif receptor (r=0.93), between antigen presentation co-stimulation, and T-cell co-inhibition (r=0.93), and between immune checkpoints and parainflammation (r=0.93). There was also a strong positive correlation between parainflammation and T-cell co-inhibition (r=0.93) (Figure 7).

            Figure 7

            Heatmap of the correlation analysis of immune cells (A) and immune functions (B). Note: red and positive values represent positive correlations, whereas blue and negative values represent negative correlations. The darker the red and blue colors, the greater the absolute value, and the more significant the correlation. Abbreviaions: APC, antigen-presenting cell; CCR, chemokine C-C-motif receptor; DCs, dendritic cells; HLA, human leucocyte antigen; IFN, interferon; MHC, major histocompatibility complex; NK, natural killer; Tfh, follicular helper T cell; TIL, tumor infiltrating lymphocyte; Treg, regulatory T cell.

            We used a box plot to evaluate the differences in immune cell infiltration and immune function infiltration between atherosclerotic plaques and healthy tissues (Figure 8). We observed significant differences between atherosclerotic plaques and healthy tissues. In atherosclerotic plaques, immune cells, except for follicular helper T cells and T helper 2 cells, were elevated to different degrees (P<0.05). Ten types of immune cell were significantly elevated (P<0.001), 13 immune functions were elevated to different degrees (P<0.01), and 11 immune functions were significantly elevated in atherosclerotic plaques compared with healthy tissues (P<0.001).

            Figure 8

            Analysis of differences in infiltrating immune cells (A) and immune functions (B) between the atherosclerosis and control groups.Note: red represents the atherosclerosis group, and green and blue represent the control group. * represents 0.01<P<0.05, ** represents 0.001<P<0.01, and *** represents P<0.001.

            Analysis of the Relationships of Cuproptosis-Associated Ferroptosis Genes with Immune Cell Infiltration and Immune Function Infiltration

            We derived the correlation score map of the associations of cuproptosis-associated ferroptosis gene with immune cell infiltration and immune function infiltration by analyzing the expression matrix of ssGSEA and cuproptosis-associated ferroptosis genes in R software (Figure 9). HIF1A, EGFR, TLR4, FTH1, CYBB, SLC3A2, ATM, IDH1, CDKN1A, and MAP3K5 were significantly correlated with multiple immune cell types and immune functions. Therefore, these genes were considered the ten hub genes most associated with immunity.

            Figure 9

            Analysis of the Relationships between Cuproptosis-Associated Ferroptosis Genes and Immune Infiltration.Note: red and positive values represent positive correlations, whereas blue and negative values represent negative correlations. The darker the red and blue colors, the greater the absolute values and the more significant the correlations.

            Risk Model Construction and Verification

            We included the 10 hub genes most related to immunity in the risk study. We constructed a line graph (nomogram) based on the logistic regression analysis to calculate the total score and analyze the risk probability of atherosclerosis. Figure 10-A shows that the cuproptosis-related ferroptosis genes TLR4 and ATM may be risk factors for atherosclerosis. The receiver operating characteristic curve (area under the curve: 0.969) shows that the line chart model had good predictive ability for assessing atherosclerosis risk factors. In addition, the calibration curve shows that the results of the line chart model were in high agreement with the actual results. We also listed the cuproptosis genes associated with the immune hub genes in the Sankey diagram (Figure 10).

            Figure 10

            Line graph (nomogram) of patients with atherosclerosis (A), receiver operating characteristic curve (B), calibration curve (C), and cuproptosis genes associated with immune hub genes (D, |cor| >0.5). Abbreviation: AUC, area under the curve.

            Functional Correlation Analysis, and Drug and miRNA Prediction Results

            We next explored the functions of hub genes by the GO and KEGG enrichment analysis. The GO results showed that the above 10 genes were mainly related to the response to oxidative stress, regulation of B-cell proliferation, and regulation of fibroblast proliferation. They were primarily enriched with cellular components, such as tertiary granules, peroxisomal matrix, and the protein kinase complex, and had molecular functions, such as MAP kinase activity and cadherin binding (Figure 11A). The KEGG analysis results showed that the hub genes were mainly enriched in HIF-1α signaling, ferroptosis, forkhead box O (FOXO) signaling, atherosclerosis, and other related pathways (Figure 11B).

            Figure 11

            (A) Gene Ontology (biological processes, cellular components, and molecular functions) enrichment analysis of hub genes related to immunity (adjusted P<0.05). (B) Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of hub genes related to immunity (adjusted P<0.05).

            We used the Enrichr platform and the DSigDB database to predict potential therapeutic drugs for atherosclerosis that target cuproptosis-associated ferroptosis genes. Using an adjusted P value of <0.001 as the screening criterion, we acquired a total of 120 related drugs; 14 of these drugs and their target genes are shown in Table 1.

            Table 1

            Prediction of Drugs Targeting Cuproptosis-Associated Ferroptosis Genes.

            MedicineAdjusted P valueCuproptosis-associated ferroptosis genes
            Glutathione0.0000000278 CDKN1A; FTH1; SLC3A2; HIF1A; TLR4; EGFR
            N-Acetyl-L-cysteine0.0000000726 CYBB; ATM; HIF1A; TLR4; EGFR; MAP3K5
            Curcumin0.0000120635 CDKN1A; SLC3A2; ATM; HIF1A; TLR4; EGFR
            Simvastatin0.0000284014 CDKN1A; IDH1; ATM; HIF1A; TLR4
            Andrographolide0.0000763407 CDKN1A; HIF1A; MAP3K5
            Lovastatin0.0001223794 FTH1; IDH1; EGFR
            Resveratrol0.0001282534 ATM; HIF1A; TLR4; EGFR
            Berberine0.0002132155 FTH1; TLR4; EGFR
            Chrysin0.0002871271 CDKN1A; CYBB; HIF1A
            Neocuproine0.0003932005 IDH1; EGFR
            Apigenin0.0004414637 CDKN1A; CYBB; HIF1A
            Luteolin0.0005130788 CDKN1A; CYBB; EGFR
            Cinnamaldehyde0.0008971081 CDKN1A; FTH1; TLR4
            Hesperetin0.0009532851 CDKN1A; HIF1A

            Molecular docking indicated good binding efficiency of glutathione with the targets. In Figure 12, glutathione is in orange, the amino acid residues bound to glutathione are in blue, and the hydrogen bonds are in yellow. The lengths of the marked hydrogen bond and amino acid residues are also shown. Table 2 lists the lowest binding energy of the two docking analyses.

            Figure 12

            Glutathione is Presented in Orange, the Amino Acid Residues Bound to Glutathione are Presented in Blue, and the Hydrogen Bonds are Presented in Yellow. Molecular docking between hypoxia-inducible factor-1α and glutathione (A), and between ferritin heavy chain 1 and glutathione (B).

            Table 2

            Lowest Binding Energies between the Target and Glutathione.

            NameLowest binding energy
            HIF-1α–glutathione−2.17
            FTH1–glutathione−0.85

            FTH1, ferritin heavy chain 1; HIF-1α, hypoxia-inducible factor-1α.

            Using the TargetScan database, we screened the ferroptosis genes related to cuproptosis in atherosclerosis to predict miRNAs with P values of <0.05. We identified 11 miRNAs, including hsa-miR-572, hsa-miR-3144-3p, and has-miR-4670-5p. We imported the relationship between the genes and miRNAs into Cytoscape 3.7.1 software for visual processing (Figure 13).

            Figure 13

            MiRNA Gene Network Diagram of Cuproptosis-Associated Ferroptosis Genes Associated with Atherosclerosis.Note: The circles represent cuproptosis-associated ferroptosis genes, and the diamonds represent miRNAs.

            Comparison of Lipid and Homocysteine Concentrations between Patients with Atherosclerosis and Healthy Adults

            The TG and homocysteine concentrations were significantly higher in patients with atherosclerosis than healthy adults (P<0.01 and P<0.05, respectively) (Figure 14). The results of this experiment were consistent with the results of the KEGG analysis. The enriched pathways included atherosclerosis and glutathione metabolism. In the glutathione pathway, homocysteine was involved in a series of reactions that contributed to atherosclerosis development.

            Figure 14

            Comparison of Lipid and Homocysteine Concentrations between Patients with Atherosclerosis and Healthy Adults.**P<0.01, *P<0.05.

            Discussion

            Ferroptosis is a mode of cell death driven by iron-dependent peroxidation of phospholipids, and cuproptosis is a newly discovered form of cell death characterized by the accumulation of free copper in cells and protein lipidation, thus leading to cytotoxic stress and inducing cell death [20, 26]. Research on the mechanism of cuproptosis-associated ferroptosis in atherosclerosis is scant. To bridge this gap, we obtained relevant chip data through the GEO database and analyzed the correlations and differences in immune cell infiltration and immune function infiltration. We combined the cuproptosis-associated ferroptosis genes, explored the relationship between these gene and immune infiltration, constructed a risk model, and finally obtained ten cuproptosis-associated ferroptosis hub genes (HIF1A, EGFR, TLR4, FTH1, CYBB, SLC3A2, ATM, IDH1, CDKN1A, and MAP3K5). The risk model indicated that TLR4 and ATM might be risk factors for atherosclerosis.

            Previous studies have shown that ATM participates in insulin-associated pathways and decreases the risk of insulin resistance. Insulin resistance increases the risk of atherosclerosis by promoting the decomposition of adipose tissue and the secretion of very low-density lipoprotein, thus inhibiting the decline in lipoprotein concentration and the synthesis of high-density lipoprotein, and leading to abnormal lipid metabolism. Therefore, ATM has an anti-atherosclerotic effect. The specific mechanism of action is associated with Jun N-terminal kinase (JNK), a protein kinase that induces the expression of early growth response genes and lipoprotein lipases, thus promoting atherosclerotic plaque formation. When ATM is defective, JNK activity is inhibited, and the gene plays an anti-atherosclerotic role [44].

            Previous studies have reported that TLRs have crucial roles in the pathogenesis of coronary artery disease. TLR4 is expressed primarily in atherosclerotic plaques. Ox-LDL in plaques upregulates TLR4 and promotes lipid accumulation in macrophages, thus causing them to differentiate into foam cells. The TLR4 ligand also triggers the activation of NF-κB signaling, which plays an essential role in inflammation and in the stress response in atherosclerosis, and induces pro-inflammatory cytokine expression. In addition, recent literature has indicated that TLR4 activates matrix metalloproteinase-9 (MMP9), degrades the collagen matrix, participates in the remodeling of external arteries, and aggravates atherosclerosis [45, 46].

            EGFR also plays an essential role in atherosclerosis development. EGFR activation consequently activates NF-κB and other signaling pathways, thus promoting p65 expression, thereby upregulating the expression of the gene encoding pyruvate kinase. Some studies have shown that ox-LDL upregulates NF-κB expression and promotes the interaction between pyruvate kinase M2 type and tyrosine phosphorylated EGFR, thus enhancing the activity of the latter, promoting the proliferation of vascular smooth muscle cells, and aggravating atherosclerotic plaque formation [47]. Other genes, such as CDKN1A, a cell-cycle inhibitor, demonstrate significant differences in expression between healthy tissues and atherosclerotic plaques, and are associated with the risk of atherosclerosis development [48]. FTH1 not only participates in ferroptosis but also is involved in the macrophage response triggered by LDL [49]. HIF1A, together with AS2, inhibits inflammation by decreasing pro-inflammatory factors, such as interleukin (IL)-1β, IL-6, and vascular cell adhesion molecule-1 [50]. IDH1 is positively correlated with adipogenesis, a process associated with obesity as well as cancer [51]. MAP3K5 promotes vascular injury through ten-eleven translocation 2-mediated differentiation of vascular smooth muscle cells [52].

            In addition, through KEGG pathway analysis, we identified the HIF-1α signaling pathway, ferroptosis, platinum drug resistance, the FOXO signaling pathway, necroptosis, proteoglycans in cancer, and atherosclerosis. Thus, the identified hub genes are likely to act on atherosclerosis via these pathways.

            We analyzed the degree of infiltration of 16 immune cell types in the GSE43292 dataset with the ssGSEA algorithm. Among them, macrophages and helper T cells were significantly elevated in atherosclerotic plaques. In addition to follicular helper T cells and T helper 2 cells, other immune cells showed different degrees of elevation. Adaptive immune cells, such as regulatory T cells and B cells; innate immune cells, such as dendritic cells and natural killer cells; and neutrophils, showed the most significant elevation, thus indicating that these cells play an indispensable regulatory role in the manifestation and progression of atherosclerosis.

            Some studies have shown that, in early stages of atherosclerosis, LDL oxidization by ROS in vivo can result in immunogenicity. Subsequently, leukocytes (mainly monocytes) are recruited to the plaques and facilitate the inflammatory response. In the progression of atherosclerosis, monocytes further differentiate into macrophages, which form foam cells through phagocytosis of ox-LDL, thereby promoting inflammation, initiating the formation of necrotic cores in plaques, and aggravating the degree of plaque lesions [6]. However, macrophages appear to polarize when they sense signals from the microenvironment (such as ox-LDL) in atherosclerotic plaques. macrophage differentiation follows two main directions: M1 and M2. M1 macrophages are distributed primarily in the lipoprotein core area and play pro-inflammatory roles in atherosclerosis, whereas M2 macrophages are distributed primarily at the edges of the plaque area, have a stronger phagocytic capacity, and promote the regression of inflammation [53].

            Experimental evidence has suggested that T cells play a crucial role in the immune response associated with atherosclerosis, particularly T helper 1 cells and regulatory cells, which show extremely different degrees of infiltration [4], as also observed in the present study. T helper 1 cells produce interferon-γ and increase the extent of vascular endothelial injury, thereby enhancing the activation and polarization of macrophages, inhibiting the stability of plaques, and increasing lesion size. T helper 1 cells also release IL-2 and tumor necrosis factor-α, which aggravate inflammation. Therefore, T helper 1 cells promote the initiation and progression of atherosclerosis. Regulatory T cells produce IL-10 and transforming growth factor (TGF)-β. They inhibit and block atherosclerosis-associated signaling pathways and play anti-atherosclerotic roles [6].

            B cells are divided into B1 cells, which are involved in the innate immune response; B2 cells, which are involved in the adaptive immune response; and regulatory B cells. B1 cells exert an anti-atherosclerotic effect by secreting natural immunoglobulin (Ig)M antibodies and competitively binding ox-LDL and apoptotic cells, thus blocking inflammatory cytokine production and foam cell formation in the atherosclerotic inflammatory cycle. B2 cells rely on the assistance of T follicular helper cells to proliferate and differentiate into plasma cells and produce IgG antibodies, which not only promote the proliferation of vascular smooth muscle cells but also affect plaque size and stability. IgG antibodies can also advance the inflammatory response by promoting innate immune cell phagocytosis, enhancing their ability to present antigens, and inducing cytokine production. Thus, B2 cells promote atherosclerosis. Finally, regulatory B cells play an anti-atherosclerotic role similar to that of regulatory T cells by producing the cytokine IL-10 [6, 53].

            Previous studies have shown that dendritic cells are activated and proliferate rapidly in plaques in late stages of atherosclerosis. Dendritic cells not only effectively accumulate lipids but also produce a variety of cytokines that support inflammation and the initiation and atherosclerosis progression [6]. Natural killer cells are activated by lipid antigens presented by antigen-presenting cells. Animal experiments have shown that natural killer cells promote the pathogenesis and progression of atherosclerosis through perforin and granzyme pathways. However, another study has indicated that diminished natural killer cells in late plaques may play different roles [54]. Because of their strong chemotactic and phagocytic abilities, neutrophils have an essential role in atherosclerosis development.

            The results of this study demonstrated that all ten hub genes were significantly associated with the infiltration of multiple immune cell types and immune functions, and therefore may play indispensable roles in the immune response. For example, ATM not only is essential in the growth and development of healthy lymphocytes, but also participates in the development of dendritic cells, via the release of granulocyte-macrophage colony-stimulating factor [55]. Phosphorylated ATM is also a potential driver of cytotoxic T-cell infiltration [56]. A previous study has indicated that ATM directly or indirectly affects tumor growth. For example, when ATM is silenced, tumor development is inhibited (Xu Peng, Masters Thesis, Tianjin Medical University).

            TLR4 encodes TLR4, the main site for recognizing pathogens and infectious microorganisms through transcription and translation. The primary mechanism involves binding of lipopolysaccharide to TLR4, thereby inducing innate and acquired immunity. In addition, TLR4 recognizes pathogens that invade from the outside and pathogens that are captured within cells [57].

            As a cell-cycle inhibitor, cyclin-dependent kinase inhibitor 1, which is encoded by CDKN1A, not only participates in the regulation of cell metabolism but also is converted into oncogenes and tumor suppressors. In addition, it regulates the survival of Langerhans cells and promotes the generation of regulatory T cells under specific conditions [58].

            FTH1 is positively correlated with macrophage infiltration in some tumors, thus demonstrating that it plays a critical role in tumor immunity [59]. In addition, IDH1, MAP3K5, and SLC3A2 all have essential roles in the TME, immune response, and immunotherapy [60, 61, 62].

            Using DSigDB, we identified potential therapeutic drugs for atherosclerosis, including glutathione, curcumin, and apigenin, that target cuproptosis-associated ferroptosis genes. Among them, glutathione correlated strongly with cuproptosis-associated ferroptosis genes. A recent study has indicated that glycine induces glutathione biosynthesis and has antioxidant function, thus providing a potential new treatment concept to alleviate atherosclerosis without a lipid-lowering effect [63]. N-acetylcysteine is an effective substance used to supplement glutathione. In an animal model of diabetes mellitus, N-acetylcysteine has been found to promote the clearance of methylglyoxal. The aortic and systemic responses can also be modulated by correcting glutathione-dependent methylglyoxal elimination, thereby decreasing oxidative stress and restoring the p-Akt/p-endothelial nitric oxide synthase pathway in the aorta, and modulating atherosclerosis [64].

            Curcumin and resveratrol are phenolic compounds. Curcumin is a bisphenol compound that hinders the TLR4/NF-κB pathway, thus decreasing macrophage infiltration in plaques and the levels of inflammatory factors [65]. Resveratrol, a polyphenol that inhibits the phosphorylation of NF-κB/MAPK signaling, plays an anti-inflammatory role in ameliorating arterial plaques in rabbits (Li Wanqiu, Masters Thesis, Kunming Medical University). In a mouse model, resveratrol has been found to decrease the expression of TLR4 in the aorta, as well as the plasma level of MMP9 in mice, thereby correcting the arterial vascular structure [66].

            Apigenin, luteolin, and populin are all flavonoids. A previous study has demonstrated that they induce apoptosis of macrophage-derived foam cells and decrease inflammatory factor expression in atherosclerosis (Wang Qun, Masters Thesis, Southern Medical University). Luteolin inhibits the secretion of vascular endothelial cell adhesion molecule-1 from microvascular endothelial cells by inhibiting the expression of the MAPK and NF-κB pathway proteins [67]. Chrysin, a flavonoid found in plants such as Oroxylum indicum, inhibits the activation of NF-κB signaling, cell adhesion, and the endothelial inflammatory response [68]. Berberine is an isoquinoline alkaloid that inhibits the activation of MAPK and NF-κB signaling, decreases the plaque area in mice with atherosclerosis, suppresses the expression of pro-inflammatory factors in the serum in mice, and decreases the accumulation of total cholesterol in the liver [69]. Simvastatin and lovastatin are conventional drugs used for the treatment of atherosclerosis. Herein, we found that their mechanism of action may involve the regulation of cuproptosis-associated ferroptosis genes. Neopromazine is a selective copper iodide chelator, but its mechanism of action against cuproptosis-associated ferroptosis genes requires further study.

            Through the TargetScan database, we also predicted the upstream miRNAs that might potentially regulate the expression of cuproptosis-associated ferroptosis genes. A total of 11 related miRNAs were identified. MiRNAs are highly conserved non-coding RNAs with a length of only 22 nucleotides. They play crucial roles in the regulation of gene expression at the post-transcriptional level. MiRNAs in the endothelium are involved in cardiovascular disease development [70]. Among them, miR-28 has been found to regulate the expression of nuclear factor erythroid 2-associated factor 2, an important transcription factor involved in the cellular antioxidant response [71]. Moreover, miR28–5p regulates the cellular response to oxidative stress by targeting the p53 deacetylase sirtuin 3 [72]. In addition, another study has found that miR-331 regulates TGF-β signaling, decreases levels of fibrosis-associated proteins in cardiac fibroblast culture, and regulates the fibrosis of cardiac fibroblasts [73]. The prediction of related miRNAs may provide a new understanding to guide gene therapy targeting cuproptosis-associated ferroptosis genes in the context of atherosclerosis.

            This study has several limitations that should be noted. First, although the atherosclerosis chip dataset met the sample size requirement for the study, the results might have been biased because of the small sample size. Second, although cuproptosis-associated ferroptosis genes involved in atherosclerosis were screened in this study, their specific mechanisms of action have not been experimentally verified, an aspect requiring further investigation.

            In conclusion, the results of this study may serve as a reference for future experimental research on the mechanism of cuproptosis-associated ferroptosis gene-mediated immune infiltration in atherosclerosis.

            Conflict of interest

            The authors declare no conflicts of interest.

            Citation Information

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            Author and article information

            Journal
            CVIA
            Cardiovascular Innovations and Applications
            CVIA
            Compuscript (Ireland )
            2009-8782
            2009-8618
            08 March 2023
            : 7
            : 1
            : e978
            Affiliations
            [1] 1Basic Medical College of Chengde Medical University, Chengde 067000, China
            [2] 2Shandong First Medical University, Jinan, Shandong 250000, China
            Author notes
            Correspondence: Qian Xu, Basic Medical College of Chengde Medical University, Chengde 067000, China, E-mail: qianxu@ 123456cdmc.edu.cn

            aBoyu Zhang and Shuhan Li contributed equally to this work.

            Article
            cvia.2023.0003
            10.15212/CVIA.2023.0003
            370e25e3-547d-4330-a7e6-15c8d8461f95
            Copyright © 2023 Cardiovascular Innovations and Applications

            This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 Unported License (CC BY-NC 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See https://creativecommons.org/licenses/by-nc/4.0/.

            History
            : 04 November 2022
            : 27 December 2022
            : 02 January 2023
            Page count
            Figures: 14, Tables: 2, References: 73, Pages: 21
            Funding
            Funded by: Education Department of Hebei Province
            Award ID: QN2016145
            Funded by: Chengde Medical University
            Award ID: 2021101
            Funded by: Chengde Medical University
            Award ID: 2022086
            Funded by: university-level scientific research project in CDMC
            Award ID: 202118
            Funded by: Fundamental Research Funds for Chengde Medical University
            Award ID: KY202220
            This study was funded by the Education Department of Hebei Province (project No. QN2016145), a student research project at Chengde Medical University (project No. 2021101, 2022086), the university-level scientific research project in CDMC (project No. 202118), and Fundamental Research Funds for Chengde Medical University (project No. KY202220).
            Categories
            Research Paper

            General medicine,Medicine,Geriatric medicine,Transplantation,Cardiovascular Medicine,Anesthesiology & Pain management
            immune infiltration,atherosclerosis,cuproptosis-associated ferroptosis

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