BACKGROUND: Atherosclerosis (AS) is a chronic disease whose risk increases with age. Identifying reliable biomarkers and understanding the interactions between immune senescence and AS may provide important therapeutic opportunities for AS. METHODS: The RNA sequencing and the single-cell RNA sequencing (scRNA-seq) dataset were downloaded from the Gene Expression Omnibus datasets, with all data derived from human tissues. Subsequently, differential expression analysis, weighted gene co-expression network analysis, accompanied by 3 machine learning algorithms, LASSO, SVM and RF, were performed to identify diagnostic genes. A nomogram and receiver operating characteristic analysis were used to assess diagnostic value. The immune cell infiltration and biological functions of the diagnostic genes were assessed by CIBERSORT and single-sample gene set enrichment analysis (ssGSEA). Next, we constructed a cellular map of AS plaques using scRNA-seq data. Senescence signatures in cell populations were quantified using the AUCell scoring algorithm. Intercellular crosstalk was explored using CellChat. Monocle2 was applied to elucidate macrophage developmental trajectories, exploring the relationship between biomarkers and immune cells. Finally, the expression of biomarkers and macrophage infiltration in aortic plaques of ApoE AS mice were evaluated using immunofluorescence. RESULTS: A comprehensive screening identified 89 key senescence-related genes. Among these, , and were identified as biomarkers and showed high accuracy (AUC>0.7) in AS diagnosis. Based on ssGSEA, CIBERSORT and Pearson analyses, these biomarkers were found to correlate significantly with multiple immune cells, suggesting their potential involvement in immune infiltration processes. Pseudotime trajectory analysis revealed that PDLIM1, PARP14, and SEL1L3 exhibited stage-specific expression patterns during macrophage differentiation. Analyses based on CellChat indicated that senescent vascular cells predominantly communicate with macrophages, with differential expression of these biomarkers observed across distinct macrophage populations. Finally, PDLIM1 expression was downregulated and PARP14 and SEL1L3 expression was upregulated in the aortic root of AS mice. Macrophages showed significant accumulation in the aortic root of AS mice with a dysregulated M1/M2 macrophage ratio, which is consistent with the bioinformatics analysis. CONCLUSIONS: In conclusion, senescence-associated genes may drive macrophage transformation, and they show high potential for surveillance and risk stratification in AS, which may inform immunotherapy for AS.