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

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.