{"title":"TNFSF13 Drives Atherosclerosis Progression by Targeting Macrophage Senescence.","authors":"Xiao Lin, Mintong Liang, Shenglan Zeng, Kaiye Chen, Ying Yang, Fei Yu, Jiahui Zhou, Hui Tang, Hanqing Tang, Pengju Wen, Shulin Tang, Yueheng Wu","doi":"10.7150/ijms.130617","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Atherosclerosis (AS), a complex age-related disease characterized by arterial lipid plaque formation, remains poorly understood at the molecular level. Senescent cells, particularly macrophages, drive plaque progression, but key senescence-inducing genes and their mechanisms are unclear.</p><p><strong>Method: </strong>Senescence-related DEGs (SR-DEGs) were identified by intersecting differentially expressed genes (DEGs) from GSE28829 with senescence-related genes (SRGs) in CellAge genes. Machine learning algorithms prioritized hub genes, whose expression and diagnostic performance were subsequently visualized with box plots and receiver operating characteristic (ROC) curves in the training dataset GSE28829, and validation sets GSE163154 and GSE100927. Immune infiltration analysis compared early and advanced plaques. Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) mapped hub gene expression across cell types and plaque regions. TNFSF13 was further validated in clinical atherosclerosis samples using Quantitative Real-time PCR (RT-qPCR), immunohistochemistry (IHC) and multiplex Immunohistochemistry (mIHC).</p><p><strong>Results: </strong>Advanced plaques exhibited 74 SR-DEGs compared to early plaques. Machine learning identified six senescence-related hub genes. ScRNA-seq revealed macrophage enrichment of all six hub genes, aligning with immune infiltration results. Bayesian deconvolution confirmed macrophage compositional shifts. Cell-cell communication analysis implicated TNFSF13-associated pathways. Spatial transcriptomics localized TNFSF13 upregulation specifically to the plaque core region, rich in SPP1+ macrophages and foam macrophages. Critically, TNFSF13 upregulation was robustly validated in human coronary artery tissues.</p><p><strong>Conclusion: </strong>This study identifies TNFSF13 as a master regulator of macrophage senescence driving atherosclerosis progression. By leveraging multi-omics and machine learning, we pinpoint the spatial and cellular context of TNFSF13 within plaques and validate its clinical relevance. TNFSF13 represents a promising therapeutic target to disrupt senescence-mediated plaque advancement in AS.</p>","PeriodicalId":14031,"journal":{"name":"International Journal of Medical Sciences","volume":"23 9","pages":"2780-2795"},"PeriodicalIF":3.7000,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13539914/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Medical Sciences","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.7150/ijms.130617","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/1/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Atherosclerosis (AS), a complex age-related disease characterized by arterial lipid plaque formation, remains poorly understood at the molecular level. Senescent cells, particularly macrophages, drive plaque progression, but key senescence-inducing genes and their mechanisms are unclear.
Method: Senescence-related DEGs (SR-DEGs) were identified by intersecting differentially expressed genes (DEGs) from GSE28829 with senescence-related genes (SRGs) in CellAge genes. Machine learning algorithms prioritized hub genes, whose expression and diagnostic performance were subsequently visualized with box plots and receiver operating characteristic (ROC) curves in the training dataset GSE28829, and validation sets GSE163154 and GSE100927. Immune infiltration analysis compared early and advanced plaques. Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) mapped hub gene expression across cell types and plaque regions. TNFSF13 was further validated in clinical atherosclerosis samples using Quantitative Real-time PCR (RT-qPCR), immunohistochemistry (IHC) and multiplex Immunohistochemistry (mIHC).
Results: Advanced plaques exhibited 74 SR-DEGs compared to early plaques. Machine learning identified six senescence-related hub genes. ScRNA-seq revealed macrophage enrichment of all six hub genes, aligning with immune infiltration results. Bayesian deconvolution confirmed macrophage compositional shifts. Cell-cell communication analysis implicated TNFSF13-associated pathways. Spatial transcriptomics localized TNFSF13 upregulation specifically to the plaque core region, rich in SPP1+ macrophages and foam macrophages. Critically, TNFSF13 upregulation was robustly validated in human coronary artery tissues.
Conclusion: This study identifies TNFSF13 as a master regulator of macrophage senescence driving atherosclerosis progression. By leveraging multi-omics and machine learning, we pinpoint the spatial and cellular context of TNFSF13 within plaques and validate its clinical relevance. TNFSF13 represents a promising therapeutic target to disrupt senescence-mediated plaque advancement in AS.
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