{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:56:07Z","timestamp":1778201767147,"version":"3.51.4"},"reference-count":62,"publisher":"Public Library of Science (PLoS)","issue":"6","license":[{"start":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T00:00:00Z","timestamp":1750809600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec021\">\n<jats:title>Background<\/jats:title>\n<jats:p>Stanford type A aortic dissection (AAD) is a fatal disease that confers extremely high morbidity and mortality. Cellular senescence, characterized by a permanent cell cycle arrest, has been implicated in the onset and progression of cardiovascular disease and immune cell infiltration has been observed in the aortic walls of dissected specimens. However, the precise mechanisms through which senescent cells interact with immune infiltration to contribute to the development and progression of AAD remain unclear.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec022\">\n<jats:title>Methods<\/jats:title>\n<jats:p>Cellular senescence-related genes (SRGs) were identified via the CellAge database. Patient and normal control datasets (GSE52093 and GSE190635) were retrieved from the Gene Expression Omnibus (GEO) database, with GSE190635 serving as the validation set. Differentially expressed genes (DEGs) linked to AAD were determined from the GSE52093 dataset. We intersected SRGs with DEGs to identify differentially expressed senescence-related genes (DESRGs), which were subsequently analyzed for Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein-protein interactions (PPI). Hub DESRGs were selected based on their connectivity degree and diagnostic genes were further refined via gene expression level evaluation and receiver operating characteristic (ROC) curve analysis. Additionally, a miRNA-gene network involving hub DESRGs was constructed. Finally, CIBERSORT was employed to analyze the compositional patterns of the 22 types of immune cell fractions in AAD.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec023\">\n<jats:title>Results<\/jats:title>\n<jats:p>A total of 700 DEGs were identified from the GSE52093 dataset, and 279 SRGs were obtained from the CellAge database. 20 DESRGs, comprising 9 senescence suppressor genes and 11 senescence inducible genes, were identified eventually by overlapping DEGs and SRGs. The top 8 hub DESRGs, including CHEK1, CENPA, FOXM1, BRCA1, AURKA, MAD2L1, PTTG1 and EZH2 were identified. Moreover, three diagnostic genes with high Area Under the Curve (AUC\u2009&gt;\u20090.9) were identified: CHEK1, FOXM1, BRCA1. Additionally, immune cell infiltration analysis revealed correlations between hub DESRGs and CD8 T cells, NK cells, and macrophages. Compared with normal tissues, AAD tissues exhibited a significant decrease in CD8 T cells and an increase in NK cells and macrophages.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec024\">\n<jats:title>Conclusion<\/jats:title>\n<jats:p>Cellular SRGs, such as CHEK1, CENPA, FOXM1, BRCA1, AURKA, MAD2L1,PTTG1 and EZH2 might hold significance in AAD, among which CHEK1, FOXM1, BRCA1 could potentially serve as molecular biomarkers for the diagnosis and treatment of AAD. These genes might contribute significantly to the occurrence and advancement of AAD by modulating the inflammatory response or immune regulation.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0326939","type":"journal-article","created":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T18:03:06Z","timestamp":1750874586000},"page":"e0326939","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":2,"title":["The role of senescence-related hub genes correlating with immune infiltration in type A aortic dissection: Novel insights based on bioinformatic 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