Integrated Analysis of Ferroptosis- and Cellular Senescence-Related Biomarkers in Atherosclerosis based on Machine Learning and Single-Cell Sequencing Data

doi:10.21203/rs.3.rs-5239772/v1

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Research Article

Integrated Analysis of Ferroptosis- and Cellular Senescence-Related Biomarkers in Atherosclerosis based on Machine Learning and Single-Cell Sequencing Data

https://doi.org/10.21203/rs.3.rs-5239772/v1

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Background

Atherosclerosis (As) is a chronic inflammatory disease characterized by fat deposition on the inner wall of blood vessels, and the related cardiovascular disease has a huge health and economic burden in the world. At present, Ferroptosis and cellular senescence play an important role in the pathogenesis of As. This study combined machine learning and single-cell sequencing data to comprehensively analyze the biomarkers related to Ferroptosis and cellular senescence in the process of AS.

Methods

AS disease datasets were obtained from the GEO database for differential expression gene (DEG) analysis. Weighted correlation network analysis (WGCNA) was used to identify AS-related module genes. The intersection of DEGs, WGCNA module genes, and genes related to cellular senescence and ferroptosis was taken to obtain cellular senescence- and ferroptosis-related DEGs (CF-DEGs). Based on CF-DEGs, consensus clustering analysis was performed on the AS dataset, and differential genes between each clustering subtype were analyzed. Enrichment analysis and immune infiltration analysis were conducted on the differential genes. Eight machine learning methods, including Decision Tree (DT), Extreme Gradient Boosting (XGBoost), C5.0, Neural Network (NNET), K-Nearest Neighbors (KNN), Lasso Regression (LASSO), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM), were used to screen diagnostic genes and construct diagnostic models, which were then validated using an external dataset. Further correlation analysis was conducted to explore the association between Hub genes and AS immune phenotypes. Finally, "monocle3" and "CellChat" algorithms were applied to the single-cell RNA-seq dataset to explore the potential impact of these genes on intercellular communication and cell developmental trajectories.

Results

A total of 23 CF-DEGs were identified. Consensus clustering analysis based on these 23 genes resulted in two subtypes, and differential analysis between the subtypes yielded 421 differential genes. Immune infiltration analysis of the differential genes revealed differences in eight immune cells between the two subtypes, including activated dendritic cells, Macrophages M0, resting NK cells, plasma cells, naive CD4 T cells, follicular helper T cells, gamma delta T cells, and regulatory T cells (Tregs). Enrichment analysis indicated that the mechanisms of AS are closely related to biological processes such as fatty acid metabolism, inflammatory. Furthermore, IL1B and CCl4 were identified as Hub genes by machine learning method, and Hub genes were associated with T.cells. follicular. helper, T.cells. gamma. delta and T.cells. regulatory..Tregs was significantly correlated. Finally, by visualizing the communication between different types of cells, we found that the pathogenesis and progression of As are closely related to immune cells and stromal cells. We also found that the expression of Hub gene changed during the dynamic transformation of macrophages and monocytes by pseudo temporal analysis.

Conclusion

This study predicted the characteristic genes IL1B and CCL4 related to cellular senescence and ferroptosis in the progression of AS and validated their diagnostic value for AS. These findings are significant for understanding the mechanisms of AS and for exploring therapeutic and diagnostic strategies for the disease. Future research should validate the clinical applicability of these diagnostic biomarkers and further investigate the roles of IL1B and CCL4 in the development of AS, thoroughly assessing their potential as biomarkers and therapeutic targets for AS.

Atherosclerosis

Ferroptosis

Cell senescence

machine learning

Single-cell RNA-Seq

biomarkers

Atherosclerosis (AS) is a chronic inflammatory disease characterized by the deposition of fats within the walls of arteries. During the progression of the disease, plaques form within the arteries, restricting blood flow and leading to serious complications such as heart attacks and strokes[1]. AS is a life-threatening manifestation of cardiovascular disease, primarily affecting large and medium-sized arteries, including the coronary arteries, carotid arteries, aorta, and cerebral arteries. Epidemiological studies indicate that in 2019, over 17 million people worldwide died from cardiovascular diseases, with 85% of these deaths attributed to stroke and other conditions caused by AS[2]. As of 2020, nearly 2 billion people globally were estimated to have carotid atherosclerosis[3]. In summary, AS-related cardiovascular diseases impose a significant global health and economic burden[4]. It is widely recognized that the development of AS is associated with dyslipidemia and hypercholesterolemia, conditions that can be triggered by factors such as age, genetics, diet, lifestyle, and underlying health conditions like hypertension, diabetes, and obesity[5]. Currently, the primary pharmacological treatments for AS are statins or PCSK9 inhibitors, aimed at controlling low-density lipoprotein (LDL) cholesterol levels in the blood[6]. Although these therapies are effective in reducing LDL levels, the incidence of major adverse cardiovascular events (MACEs) remains unmitigated. Therefore, gaining a deeper understanding of the pathophysiological mechanisms of AS and developing more effective diagnostic and therapeutic approaches are critical areas of ongoing research.

Cellular senescence is a biological process associated with the deterioration of cellular structure and function, and it is a major cause of age-related diseases[7]. Currently, cellular senescence is widely recognized as an important target for the prevention of cardiovascular diseases. AS is an age-related disease, and its progression may involve various senescent cardiovascular cells, including cardiomyocytes, endothelial cells, vascular smooth muscle cells, fibroblasts/myofibroblasts, and T cells[8]. Additionally, multiple forms of cell death are involved in the progression of AS, including autophagic cell death, apoptosis, pyroptosis, necroptosis, and ferroptosis[9]. ferroptosis is an iron-dependent form of programmed cell death. The main mechanism of ferroptosis is the catalysis of highly expressed polyunsaturated fatty acids on the cell membrane by ferrous iron or lipoxygenase, leading to lipid peroxidation and cell death[10]. Numerous studies have found that ferroptosis can participate in the onset and progression of AS through different pathways[11]. In advanced AS, cell ferroptosis induced by functional defects can accelerate endothelial dysfunction and the formation of vulnerable plaques. Additionally, dysregulated intracellular iron ions can damage macrophages, vascular smooth muscle cells, and endothelial cells, affecting many AS risk factors or pathological processes, such as oxidative stress, inflammation, and lipid metabolism disorders[12]. Recent research has revealed that the activation of ferroptosis signaling pathways in VSMCs not only induces the loss of Nicotinamide adenine dinucleotide (NAD⁺) and senescence but also promotes the release of pro-senescent secretory factors. These findings are significant for exploring therapeutic strategies for age-related cardiovascular diseases from the perspective of ferroptosis[13]. Peripheral blood biomarkers are increasingly important non-invasive methods for monitoring the progression of coronary artery disease (CAD). Their advantages in early detection, prognosis evaluation, and classification diagnosis are becoming irreplaceable[14]. Some studies also suggest that cellular senescence and death can directly or indirectly induce immunosuppression by recruiting myeloid cells, such as immunosuppressive macrophage subsets[15]. Given the close link between AS and immune therapy response, optimizing immunotherapy strategies for AS has been a research focus[16].

In recent years, bioinformatics has provided new approaches for the treatment of atherosclerosis. Machine learning has been widely applied across various research fields as a powerful tool for identifying important diagnostic markers[17]. Single-cell technology is an ideal tool for the comprehensive and unbiased analysis of cellular heterogeneity. High-throughput, high-resolution single-cell sequencing technology can be used to analyze the complex cellular and molecular composition of atherosclerotic plaques. It can also be used to examine the state, origin, dynamic transformation trajectories, intercellular communication, and molecular regulatory mechanisms of cells within plaques[18]. The combination of these technologies facilitates a comprehensive and in-depth understanding of the mechanisms of AS onset and progression. This approach provides new directions for the diagnosis and treatment of AS and the study of its immunological mechanisms.

Data acquisition and processing

The research data were obtained from the GEO database (http://www.ncbi.nlm.nih.gov/geo). The datasets include GSE10097, based on the GPL96 platform, and GSE132651, also based on the GPL96 platform. These datasets contain 82 AS cases and 41 control cases. The GSE10097 dataset was used as the training set, while the GSE132651 dataset served as the validation set. Single-cell sequencing data were downloaded from the GPL18573 platform's GSE159677 dataset, which includes carotid tissue samples from 6 AS patients. Ferroptosis-related genes were obtained from the FerrDb V2 database (http://www.zhounan.org/ferrdb/current/), yielding a total of 905 genes. Cellular senescence-related genes were acquired from the Gen Age database (https://genomics.senescence.info/genes/), with a total of 866 genes retrieved.

Screening of Differentially Expressed Genes

Differentially expressed genes (DEGs) in the GSE10097 dataset were identified using the "Limma" package in R software. Background correction and data normalization were performed using the NormExp and Normalize Between Arrays functions of the "Limma" package. The screening parameters for DEGs were set with an absolute value of log FC ≥ 0.5 and a P-value < 0.05. After identifying the DEGs, heatmaps and volcano plots of the DEGs were created using the "pheatmap" and "ggplot2" packages in R software.

Weighted Gene Co-Expression Network Analysis

Weighted gene co-expression network analysis (WGCNA) is used to identify gene modules with high correlation. In this study, WGCNA was performed using the "WGCNA" package in R software[19]. Hierarchical clustering of the study samples was conducted to detect outliers and remove abnormal samples. A soft threshold of β=6 was selected to construct a scale-free network and a topological overlap matrix. The correlation between each module and the disease group was then calculated. Modules with high correlation coefficients were considered key modules closely related to the development of AS. The intersection of key module genes with cellular senescence- and ferroptosis-related genes, as well as DEGs, was taken to obtain cellular senescence- and ferroptosis-related DEGs (CF-DEGs).

Consensus Clustering Analysis of Genes

Unsupervised clustering analysis of gene expression data from AS patients was performed based on the identified CF-DEGs using the "ConsensusClusterPlus" package in R. The optimal k value for the subtypes was determined using consensus matrices, consensus scores, and cumulative distribution function (CDF) analysis. To identify differentially expressed genes between different clusters, the "Limma" package in R was used for differential expression analysis, with parameters set to an absolute value of log FC ≥ 0.5 and P-value < 0.05. Differentially expressed genes between subtypes were obtained, define them as subtypes of DEGs related to ferroptosis and senescence，and volcano plots and heatmaps were generated using the "ggplot2" package. A protein-protein interaction (PPI) network of the differentially expressed genes between subtypes was then constructed based on the STRING database (https://www.stringdb.org), and Hub genes were calculated using the Cytohubba plugin in Cytoscape (3.8.2).

Immune Infiltration Analysis and Enrichment Analysis

The "CIBERSORT" package in R was used to perform immune infiltration analysis on differentially expressed genes between subtypes. The abundance of 22 immune cell types in different immune patterns was quantified. The Wilcoxon rank-sum test was used to assess differences in immune cell proportions, with P<0.05 considered statistically significant. The results were visualized using the "ggplot2" package. Additionally, gene enrichment analysis was performed using the "clusterProfiler" package to explore potential biological mechanisms.

Screening and Validation of Diagnostic Biomarkers

Eight machine learning methods, including Decision Tree (DT), Extreme Gradient Boosting (XGBoost), C5.0, Neural Network (NNET), K-Nearest Neighbors (KNN), Lasso Regression (LASSO), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM), were used to screen diagnostic genes for AS from ferroptosis- and senescence-related genes. A diagnostic model was constructed using the "rms" package based on the selected genes. The predictive ability of the nomogram model was evaluated using a calibration curve. Finally, decision curve analysis (DCA) was performed using the "ggDCA" package to assess the clinical utility of the model, and its diagnostic value was evaluated using the Receiver Operating Characteristic Curve (ROC). The accuracy of the diagnostic genes was validated using the GSE132651 dataset.

Correlation Analysis Between Hub Genes and Immune Cells

The intersection of Hub genes obtained from PPI analysis and diagnostic genes identified through machine learning was used to obtain key diagnostic genes for AS. The expression levels of these genes in disease and control groups were visualized. Finally, Pearson correlation analysis was performed to reveal the correlation between key diagnostic genes, immune cells, and CF-DEGs. The P-value filter for correlation tests was set to P < 0.01. The strong correlations between Hub genes and immune cells were visualized.

Single-Cell Analysis

The downloaded scRNA-seq dataset included calcified atherosclerotic core plaques and adjacent proximal carotid tissues from three patients who underwent carotid endarterectomy, totaling six tissue samples. The scRNA-seq data were converted into Seurat objects using the "Seurat" package in R software. Quality control was performed by removing cells with a mitochondrial gene proportion greater than 20% and those expressing fewer than 200 or more than 3000 genes. The "Find Variable Features" function was used to identify the top 2000 highly variable genes for principal component analysis (PCA), resulting in the identification of 15 principal components. The "RunUMAP" function was then used for dimensionality reduction and clustering analysis of the Seurat objects. The clustering results were annotated based on literature searchings[20]. Finally, pseudotime analysis and cell communication analysis were conducted on the annotated cells using the "monocle3" and "CellChat" packages.

Identification of Differentially Expressed Genes Related to Ferroptosis and Senescence

The study workflow is shown in Figure 1. First, the "limma" package was used to perform background correction and data normalization on the gene expression data from the GSE10097 dataset (Figure 2 a-b). Differential expression analysis was then conducted on the normalized data, resulting in the identification of 2288 DEGs. A volcano plot and heatmap were further generated to visualize these DEGs (Figure 2 c-d).

Construction of Weighted Gene Co-Expression Network

Clustering analysis was performed on the GSE10097 dataset from GEO, and outlier samples were identified and excluded (Figure 3a). The soft threshold β was set to 6 when R² > 0.9 and the mean connectivity was relatively high (Figure 3b). Using the dynamic tree cut method, 11 target modules were identified (Figure 3c-d). Correlation analysis between the target modules and the AS group revealed that the "turquoise" and "yellow" modules were highly correlated with the disease group. A total of 3695 key genes were obtained from these modules (Figure 3e). The intersection of DEGs, WGCNA, and genes related to cellular senescence and ferroptosis yielded 23 CF-DEGs (Figure 3f).

Using the expression profiles of 23 CF-DEGs, unsupervised clustering analysis was performed with a consensus clustering algorithm to identify subtypes associated with AS. The value of k was set from 1 to 9, and the results indicated that k=2 was the optimal parameter, dividing the 69 AS samples into Subtype A and Subtype B (Figure 4 a-d). Differential analysis between the A and B subtypes was conducted, resulting in the identification of 421 ferroptosis- and senescence-related subtype DEGs. Volcano plots and heatmaps were generated using the "ggplot2" package to visualize these findings (Figure 4 e-f).

Immune Infiltration and Enrichment Analysis of DEGs

Immune infiltration analysis of the 421 key genes was performed using the "CIBERSORT" package. The results showed that dendritic cells had the highest proportion in all samples. Eight immune cell types showed significant differences between the two subtypes (P<0.05): activated dendritic cells (p=0.008), macrophages M0 (p=0.0007), resting NK cells (p=0.0088), plasma cells (p=0.04), naive CD4 T cells (p=0.0004), follicular helper T cells (p=0.036), gamma delta T cells (p=0.0028), and regulatory T cells (Tregs) (p=0.0028). These findings suggest that these eight immune cell types play a role in the pathogenesis of AS (Figure 5). GO enrichment analysis of the 421 genes indicated that AS lesions involve processes such as myeloid leukocyte activation, phagocytosis, leukocyte-mediated immunity, leukocyte migration, positive regulation of cytokine production, positive regulation of tumor necrosis factor superfamily cytokine production, secretory granule membrane, endocytic vesicle, specific granule, lysosomal lumen, secretory granule lumen, low-density lipoprotein particle, pattern recognition receptor activity, integrin binding, immune receptor activity, proteoglycan binding, glycosaminoglycan binding, and structural constituent of muscle (Figure 6). KEGG enrichment analysis identified pathways such as lysosome, Toll-like receptor signaling pathway, chemokine signaling pathway, leukocyte transendothelial migration, lipid and atherosclerosis, neutrophil extracellular trap formation, PPAR signaling pathway, Type I diabetes mellitus, efferocytosis, apoptosis, and NF-kappa B signaling pathway (Figure 6). GSEA analysis revealed that these gene sets are associated with the upregulation of the cellular senescence pathway and the NOD-like receptor signaling pathway, and the downregulation of cholesterol metabolism and ferroptosis pathways.

Construction of PPI Network and Development and Validation of Machine Learning Models

To identify potential interactions among the 421 ferroptosis- and senescence-related subtype DEGs, the genes were imported into STRING for PPI analysis, and the data were further analyzed using Cytoscape software. Using the Cytohubba plugin, 20 key genes were identified: IL1B, CCL3, TLR1, ITGAX, TLR7, CXCR4, IRF8, FCGR3A, MMP9, CCR5, CCL4, ITGB2, CCR1, CD68, ITGAM, CD86, TYROBP, IL18, SPI1, and TLR2 (Figure 7 d-e).Next, eight machine learning algorithms were applied to the 421 ferroptosis- and senescence-related subtype DEGs to identify diagnostic genes. LASSO regression analysis was found to be the most accurate in predicting the onset of AS (Figure 7 a-c). A VENN diagram was created by intersecting LASSO and PPI genes, resulting in the identification of two diagnostic biomarkers: IL1B and CCL4 (Figure 7 g). An AS diagnostic nomogram was then constructed using the "rms" package (Figure 8 a). The predictive ability of the model was assessed using a calibration curve, which showed minimal difference between the predicted and actual risk of disease, indicating that the nomogram model accurately predicts AS onset (Figure 8 b). ROC analysis revealed that both IL1B (AUC=0.987) and CCL4 (AUC=0.952) had AUC values above 0.6 (Figure 8 e-f). The validation set results were consistent with those of the training set (Figure 8 g-h), indicating a high predictive accuracy of these genes for AS onset. DCA analysis showed that the IL1B and CCL4 curves provided significant clinical benefit for patients when the risk threshold was between 0 and 0.75 (Figure 8 c), and the validation set analysis supported these findings (Figure 8 d). This further demonstrates that the nomogram model has good diagnostic capability for early-stage AS patients.

Correlation Analysis Between Core Genes and Immune Cells

Further analysis of the Hub genes revealed that both IL1B and CCL4 were highly expressed in the disease group (Figure 9 a-b). Correlation analysis between significantly different immune infiltrating cells and Hub genes showed that IL1B was significantly positively correlated with follicular helper T cells and significantly negatively correlated with gamma delta T cells. CCL4 was significantly positively correlated with follicular helper T cells and significantly negatively correlated with regulatory T cells (Tregs) (Figure 9 c-d). Correlation analysis between Hub genes and CF-DEGs showed that IL1B was highly positively correlated with PTPN6 (r=0.72) and highly negatively correlated with ZEB1 (r=-0.64). Similarly, CCL4 was highly positively correlated with PTPN6 (r=0.89) and highly negatively correlated with ZEB1 (r=-0.74) (Figure 9 e-i).

Single-Cell Analysis Results

Since the previous analyses were based on transcriptomics and did not describe the cellular diversity of the AS vascular microenvironment at the single-cell level, we analyzed the importance of Hub genes using published single-cell transcriptome data (GSE159677). After constructing the Seurat object from the dataset, the normalized expression profiles were displayed (Figure 10 a), showing a positive correlation between nCountRNA and nFeatureRNA (Figure 10 b). Principal component analysis and clustering analysis of the dataset identified 15 clusters (Figure 10 d). After annotation using the "SingleR" package, six different cell types were identified: Endothelial Cells (EC), Fibroblasts, Macrophages, Monocytes, T cells, and Vascular Smooth Muscle Cells (VSMC) (Figure 10 e). The study also found that IL1B and CCL4 were highly expressed in the disease group, consistent with the transcriptome sequencing data analysis (Figure 10 f). Further analysis of IL1B and CCL4 expression across the 12 cell types showed that IL1B was highly expressed in Macrophages and Monocytes, while CCL4 was significantly expressed in Macrophages, Monocytes, and T cells (Figure 10 g-h). Cell communication analysis revealed important interactions between immune cells and stromal cells during AS development, suggesting mutual regulation between these cells and the AS vascular microenvironment (Figure 11 a-c). Next, pseudotime analysis was conducted using the "monocle3" package to map the developmental trajectories of single cells (Figure 11 d-e). Arrows indicate the starting points of the timelines for each cell group. We observed changes in IL1B and CCL4 expression during the dynamic transition of macrophages and monocytes, suggesting that these genes may be involved in the fate determination of these cell groups.

Currently, the number of AS patients worldwide is increasing annually, with a trend toward younger age groups, and the complications it causes place a significant burden on patients[21]. The pathogenesis of atherosclerosis is complex and involves multiple cell types, including endothelial cells (ECs), vascular smooth muscle cells (SMCs), adventitial fibroblasts, macrophages, and other immune cells. Key factors in the development of atherosclerosis include endothelial dysfunction, leukocyte adhesion, foam cell formation, and SMC phenotypic transformation[22–23]. Notably, the pathological progression of AS is closely linked to cellular senescence and ferroptosis. Cellular senescence is a cell state triggered by endogenous or exogenous stimuli, characterized by stable cell cycle arrest and a complex senescence-associated secretory phenotype (SASP). Once senescent cells accumulate in tissues, they can accelerate the progression of age-related diseases such as atherosclerosis, osteoarthritis, and cancer[24]. senescent endothelial cells reduce NO production and impair the endothelial barrier function due to disruptions in intercellular adhesion and tight junctions in AS, [25]. The extent of endothelial coverage in AS lesions is a key protective factor for plaque stability, and the erosion of endothelial cells can lead to atherosclerotic thrombosis on the lesion surface. Additionally, increased endothelial inflammation is considered a harmful process in the chronic inflammatory environment of arteries. Senescent endothelial cells can exacerbate the inflammatory response by increasing the expression of adhesion molecules such as VCAM-1 and ICAM-1, which enhances the recruitment of leukocytes[26–27]. Ferroptosis is a distinct form of iron-dependent, lipid peroxidation-driven programmed cell death. In recent years, it has increasingly been recognized as an important process mediating the onset and progression of various cardiovascular diseases, including atherosclerosis, myocardial ischemia-reperfusion injury, and septic cardiomyopathy[28]. Furthermore, AS patients often exhibit changes in peripheral blood immune components during disease onset, reflecting the disease's severity. Therefore, exploring immune-related biomarkers and potential therapeutic targets is a research focus in this field[14].

This study identified 23 ferroptosis- and senescence-related DEGs. Based on these genes, unsupervised clustering analysis was performed using a consensus clustering algorithm to identify AS-related subtypes, resulting in 421 subtype DEGs. Immune infiltration analysis of these 421 DEGs revealed differences in eight immune cell types between the two subtypes: activated dendritic cells, Macrophages M0, resting NK cells, plasma cells, naive CD4 T cells, follicular helper T cells, gamma delta T cells, and regulatory T cells (Tregs). Previous studies have shown that both the innate and adaptive immune systems play key roles in driving AS-related chronic inflammation, with significant heterogeneity among leukocyte subtypes in the arterial wall, which regulate inflammation in atherosclerosis formation[29]. This study also identified two diagnostic biomarkers through machine learning methods based on the 421 ferroptosis- and senescence-related subtype DEGs, providing insights into AS-related immune genes. Currently, many immune infiltration algorithms have revealed differences in immune cell infiltration between disease groups and controls in AS onset. Jing Wang[30] using bioinformatics methods, studied the role of immune cells in the formation of unstable plaques and explored diagnostic biomarkers. Their findings indicated that M1 macrophages are an important factor in unstable plaque formation, and CD68, PAM, and IGFBP6 can serve as diagnostic markers for identifying unstable plaques. In future research, predictive biomarkers should not only involve the composition of immune infiltration and the characteristics of the inflammatory response but also reveal the heterogeneity of immune composition, spatial distribution, and function. GO analysis of the differentially expressed genes indicated that AS lesions involve biological processes such as myeloid leukocyte activation, phagocytosis, leukocyte-mediated immunity, leukocyte migration, positive regulation of cytokine production, positive regulation of tumor necrosis factor superfamily cytokine production, pattern recognition receptor activity, and immune receptor activity. KEGG enrichment analysis showed that pathways such as the Toll-like receptor signaling pathway, chemokine signaling pathway, leukocyte transendothelial migration, lipid and atherosclerosis, PPAR signaling pathway, apoptosis, and NF-κB signaling pathway are associated with AS onset. This suggests that the mechanisms of AS are closely related to biological processes such as fatty acid metabolism, inflammatory response, and immune regulation. For example, PPARs are a class of ligand-activated transcription factors that regulate genes related to lipid metabolism. PPARα, one of its subtypes, is highly expressed in the heart and vascular walls and regulates target genes closely related to lipid metabolism and inflammation. Activation of the PPARα pathway can reduce autophagy-dependent ferroptosis caused by mitochondrial DNA damage and alleviate hyperlipidemia-induced vascular calcification[31]. Studies have also shown that senescent immune cells in AS undergo cell cycle arrest, morphological changes, and phenotypic alterations in their abundance and secretory profiles, including cytokines, chemokines, matrix metalloproteinases, and Toll-like receptor expression. Currently, the clearance of senescent cells is considered a key target for preventing or treating AS[32]. GSEA analysis showed that these gene sets are associated with the upregulation of the cellular senescence pathway and the NOD-like receptor signaling pathway, and the downregulation of cholesterol metabolism and ferroptosis pathways. Since the production of free radicals, increased fatty acid supply, and enhanced lipid peroxidation are key inducers of ferroptosis, they may influence ferroptosis by regulating lipid metabolism and fatty acid metabolism, thereby mediating the onset of AS[10, 33]. Senescence is associated with elevated levels of inflammatory cytokines in the aorta, and impaired vascular mitochondrial function accelerates the formation of AS. Of course, this requires further study[34]. Research has found that macrophages, vascular endothelial cells, and vascular smooth muscle cells are in a senescent state at atherosclerotic lesion sites. Additionally, bacterial infections and the accumulation of lipopolysaccharides have been observed in AS patients, along with a SASP. Therefore, some studies suggest that LPS derived from Gram-negative bacteria may exacerbate AS lesions by inducing and enhancing the senescence of specific vascular cells and SASP-associated inflammatory characteristics in AS lesions[35].

In recent years, machine learning has been widely applied in the clinical field and is considered an important tool in healthcare[36]. This study identified IL1B and CCL4 as ferroptosis- and senescence-related biomarkers for AS, and validation showed that these biomarkers have good diagnostic value for AS. Previous studies have confirmed that IL-1β is associated with acute and chronic inflammation and plays an immunoregulatory role in the development of AS. Anti-inflammatory treatment with canakinumab, targeting the IL-1β innate immune pathway, has been shown to significantly reduce the recurrence rate of cardiovascular events compared to placebo[37]. In APOE^−/− mice, upregulation of IL-1β expression increased the area of atherosclerotic lesions in the ascending aorta and aortic root[38]. However, it is important to note that most previous studies on IL-1β have focused on its regulation of immune or inflammatory cells. Recent research has found that IL-1β can regulate iron-sulfur cluster homeostasis by inducing acetylation of the mitochondrial inner membrane protein NNT in tumor cells, thereby inhibiting ferroptosis and mediating resistance to immunotherapy[39]. This finding provides insights into potential AS treatments. Additionally, studies have shown that inhibiting the secretion of IL-1β can improve doxorubicin-induced senescence in cardiac fibroblasts and improve poor prognosis in heart disease[40].IL-1β, as a key biomarker of vascular calcification, may also be involved in the senescence of VSMCs. Linzi Han and colleagues found that IL-1β induces VSMC senescence and promotes VSMC calcification by activating the NF-κB/p53/p21 signaling pathway, which may be associated with AS plaque formation[41]. CCL4, also known as macrophage inflammatory protein MIP-1β, belongs to the CC chemokine family and can be secreted by monocytes, B cells, T cells, NK cells, dendritic cells, neutrophils, fibroblasts, endothelial cells, and epithelial cells[42]. Elevated levels of CCL4 have been reported in the peripheral blood of AS patients. Inhibiting CCL4 levels can reduce the activity of metalloproteinases-2 and − 9 in macrophages, suppress the production of TNF-α and IL-6, and decrease the activation of endothelial cells and macrophages, suggesting that CCL4 has potential as a new therapeutic target for AS[43]. Some studies hypothesize that CCL4 may also play a crucial role in aging-related vascular dysfunction. CCL4 promotes inflammation and cellular senescence by stimulating ROS production, leading to impaired cell function, and may be a potential therapeutic target for vascular protection during aging[44]. Currently, there is limited research on the relationship between CCL4 and ferroptosis. Future studies could explore the relationship between ferroptosis and immune cells during AS onset to develop new therapeutic strategies for AS.

Correlation analysis between Hub genes and immune cells revealed that the IL1B gene is significantly associated with T cells.follicular.helper and T cells.gamma.delta, while the CCL4 gene is significantly associated with T cells.follicular.helper and T cells.regulatory (Tregs). T cells.follicular.helper (Tfh) have recently been defined as a new subset of CD4 + T cells, primarily expressing surface molecules such as CXC chemokine receptor 5 and inducible costimulatory molecule, and secreting the cytokine interleukin-21. The key transcription factor for Tfh cells is B-cell lymphoma 6. Tfh cells are found in the germinal centers of lymphoid tissues and peripheral blood, where they promote B-cell maturation and differentiation, germinal center formation, and antibody production, playing an important role in the pathogenesis of various autoimmune diseases[45]. T cells.gamma.delta (γδT cells) are lymphocytes with multiple functions in innate and adaptive immune responses, pathogen defense, antigen presentation, and inflammation regulation, participating in the early development of atherosclerosis[46]. Duc M Vu revealed the pathogenic role of γδT cells in early AS formation through animal experiments, with a mechanism that may involve IL-17 production and neutrophil induction. Therefore, γδT cells are promising targets for AS intervention, potentially reducing plaque accumulation and inhibiting inflammatory responses[47]. Studies have shown that T cells.regulatory (Tregs) account for 5–10% of the total CD4 + T lymphocytes in peripheral blood and have been proven to have a protective effect against atherosclerosis, making them a new target for cardiovascular disease and atherosclerosis[48]. Monika Sharma[49] also found that in several mouse models of AS regression, an increase in Tregs was a common feature of plaque regression. In summary, the Hub genes are closely associated with these immune cells, leading us to speculate that CCL4 and IL1B may influence the onset and progression of AS by regulating these cells. Correlation analysis between CCL4 and IL1B with CF-DEGs revealed that these genes are highly correlated with PTPN6 and ZEB1.

The emergence of single-cell biology has opened a new chapter in understanding the biological processes of disease development and in diagnostics, monitoring, and treatment. This technology can identify novel cell populations that play critical roles in the progression of AS and assist in exploring new therapeutic strategies[50]. The infiltration of immune cells is closely related to the progression of AS and its response to immunotherapy. Studies using single-cell sequencing technology have explored the immune heterogeneity of atherosclerosis and identified interferon-induced CD8 + T cells and macrophage subpopulations as cells that influence AS progression and poor prognosis, which is of significant value for developing new AS immunotherapies[51]. In this study, 6 different cell types were identified through single-cell analysis, including ECs, Fibroblasts, Macrophages, Monocytes, T cells, and VSMC, providing a deeper understanding of the cellular diversity within atherosclerotic plaques and surrounding tissues. It was also found that the Hub gene IL1B is highly expressed in Macrophages and Monocytes, while CCL4 is significantly expressed in Macrophages, Monocytes, and T cells. Additionally, this study visualized the communication between different types of cells, confirming a close relationship between immune cells and stromal cells in the onset and progression of AS. Using the "Monocle3" package, the pseudotime algorithm was applied to single-cell RNA-Seq data to order and arrange individual cells based on their states during differentiation and other biological processes, revealing the connection between gene expression changes and cell fate determination[52]. Through this method, it was observed that IL1B and CCL4 expression levels change during the dynamic transition of macrophages and monocytes. These results suggest that Hub genes may influence the onset and progression of AS by regulating immune cell function.

This study has several limitations. First, the available AS-related RNA-seq and scRNA-seq datasets are limited, with small sample sizes. In the future, larger AS datasets will need to be analyzed to validate the findings derived from GSE10097, GSE132651, and GSE159677. Second, it should be noted that the key effectors involved in ferroptosis and cellular senescence pathways are mostly regulated at the post-translational level. Therefore, the post-translational levels of these molecules are crucial for maintaining cellular function and should be considered. This study is limited by the lack of corresponding proteomics data, as we only screened ferroptosis- and cellular senescence-related genes based on RNA-seq datasets. Additionally, at the beginning of the study, important diagnostic genes were screened solely from candidate genes related to ferroptosis and senescence. Consequently, only genes with significant diagnostic value were retained to establish the predictive model, while genes closely related to ferroptosis and senescence but with lower diagnostic value were excluded.

This study provides new diagnostic targets for predicting the onset of AS and confirms the diagnostic value of IL1B and CCL4 for AS. The study also predicts characteristic genes related to cellular senescence and ferroptosis in the development of AS, which holds significance for understanding the mechanisms of AS and exploring therapeutic strategies. Future research should validate the clinical applicability of these diagnostic biomarkers and investigate the roles of IL1B and CCL4 in the development of AS, assessing their potential as biomarkers and therapeutic targets for AS.

Atherosclerosis	AS
low-density lipoprotein	LDL
major adverse cardiovascular events	MACEs
coronary artery disease	CAD
Differentially expressed genes	DEGs
Weighted gene co-expression network analysis	WGCNA
cellular senescence-and ferroptosis-related DEGs	CF-DEGs
cumulative distribution function	CDF
protein-protein interaction	PPI
Decision Tree	DT
Extreme Gradient Boosting	XGBoost
Neural Network	NNET
K-Nearest Neighbors	KNN
Lasso Regression	LASSO
Support Vector Machine	SVM
Gradient Boosting Machine	GBM
decision curve analysis	DCA
Receiver Operating Characteristic Curve	ROC
principal component analysis	PCA
Vascular Smooth Muscle Cells	VSMC
endothelial cells	ECs
vascular smooth muscle cells	SMCs
senescence-associated secretory phenotype	SASP

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The authors declare that they have no competing interests

Authors' information

¹Henan University of Chinese Medicine, Zhengzhou, Henan, China

Funding

This present work was supported by the Natural Science Foundation of Henan Province (Grant Nos. 242300421295), Henan Provincial Science and Technology Research Project (Grant Nos. 232102310434), Construction Project of the National Inheritance Studio for Famous Traditional Chinese Medicine Experts – Cui Yingmin (Issued by National Administration of Traditional Chinese Medicine [2022] No. 75), Major Special Project of Henan Provincial Traditional Chinese Medicine Research (Grant Nos. 2022ZYZD20), Key Research Project of Henan Provincial Traditional Chinese Medicine (Grant Nos. 2023ZY1031).

Author Contribution

XQ analyzed the data and wrote the manuscript, JC and XY were responsible for data collection, SC conceived and designed the article. All authors read and approved the final manuscript.

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No competing interests reported.

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Integrated Analysis of Ferroptosis- and Cellular Senescence-Related Biomarkers in Atherosclerosis based on Machine Learning and Single-Cell Sequencing Data

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