Lu Yan, Yuan Rong, Xin Qiqi, Chen Keji, Cong Weihong
Laboratory of Cardiovascular Diseases, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China.
National Clinical Research Center for Chinese Medicine Cardiology, Xiyuan Hospital of Chinese Academy of Chinese Medical Sciences, Beijing, China.
Front Immunol. 2025 Jun 19;16:1557266. doi: 10.3389/fimmu.2025.1557266. eCollection 2025.
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.
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.
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.
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.
动脉粥样硬化（AS）是一种慢性疾病，其风险随年龄增长而增加。识别可靠的生物标志物并了解免疫衰老与AS之间的相互作用可能为AS提供重要的治疗机会。
从基因表达综合数据库下载RNA测序和单细胞RNA测序（scRNA-seq）数据集，所有数据均来自人体组织。随后，进行差异表达分析、加权基因共表达网络分析，并结合LASSO、支持向量机（SVM）和随机森林（RF）这3种机器学习算法来识别诊断基因。使用列线图和受试者工作特征分析来评估诊断价值。通过CIBERSORT和单样本基因集富集分析（ssGSEA）评估诊断基因的免疫细胞浸润和生物学功能。接下来，我们使用scRNA-seq数据构建了AS斑块的细胞图谱。使用AUCell评分算法对细胞群体中的衰老特征进行量化。使用CellChat探索细胞间的串扰。应用Monocle2阐明巨噬细胞的发育轨迹，探索生物标志物与免疫细胞之间的关系。最后，使用免疫荧光评估ApoE AS小鼠主动脉斑块中生物标志物的表达和巨噬细胞浸润情况。
全面筛选确定了89个与衰老相关的关键基因。其中， 、 和 被确定为生物标志物，在AS诊断中显示出高准确性（AUC>0.7）。基于ssGSEA、CIBERSORT和Pearson分析，发现这些生物标志物与多种免疫细胞显著相关，表明它们可能参与免疫浸润过程。伪时间轨迹分析表明，PDLIM1、PARP14和SEL1L3在巨噬细胞分化过程中表现出阶段特异性表达模式。基于CellChat的分析表明，衰老的血管细胞主要与巨噬细胞通信，在不同的巨噬细胞群体中观察到这些生物标志物的差异表达。最后，AS小鼠主动脉根部PDLIM1表达下调，PARP14和SEL1L3表达上调。巨噬细胞在AS小鼠主动脉根部显著积聚，M1/M2巨噬细胞比例失调，这与生物信息学分析一致。
总之，衰老相关基因可能驱动巨噬细胞转化，它们在AS的监测和风险分层中显示出很高的潜力，这可能为AS的免疫治疗提供依据。
