Practical Geriatrics ›› 2026, Vol. 40 ›› Issue (3): 241-246.doi: 10.3969/j.issn.1003-9198.2026.03.005

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Mechanism of macrophage senescence-driven atherosclerosis based on single-cell multi-omics analysis

CHEN Xuguan, XIA Yudong, WANG Yingying, XU Yunfan, WU Jun   

  1. Department of Geriatric Cardiology,the First Affiliated Hospital with Nanjing Medical University,Nanjing 210029, China
  • Received:2025-09-05 Published:2026-03-26
  • Contact: WU Jun,Email:wujun9989@njmu.edu.cn

Abstract: Objective To investigate the role of macrophage senescence in atherosclerosis(AS) and identify the key genes. Methods The data were obtained from the Gene Expression Omnibus (GEO) database. Cell clusters were analyzed using Seurat, aging scores were calculated via the UCell method, co-expression networks were constructed through high-dimensional weighted gene co-expression network analysis (hdWGCNA), differential expression was analyzed with limma, core genes were screened using Lasso regression and random forest algorithms, gene significance was assessed by Shapley additive explanations(SHAP), and immune infiltration was evaluated by CIBERSORT. Results The senescence score of macrophages in the AS core plaques (AC group) was higher than that in the patient-matched proximal adjacent portions (PA group) of carotid artery tissue (P<0.001). hdWGCNA identified 11 functional modules, and Macrophages_NEW6 showed the strongest correlation with senescence scores. Four core genes (MAF、FILIP1L、PEBP1、SPRED1) were screened out, and the random forest model had high predictive efficacy (AUC=0.889). SHAP analysis revealed MAF as the most important gene in terms of feature importance. M2/M0 macrophages were the predominant infiltrating cells in atherosclerotic plaque tissues and showed a significant correlation with the core genes. Conclusions Macrophages regulate AS development through four genes, which has potential value for clinical application.

Key words: macrophage senescence, atherosclerosis, single-cell multi-omics analysis, four-gene diagnostic model

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