ArticleFrontiers in cardiovascular medicine2023
Bioinformatics analysis of aging-related genes in thoracic aortic aneurysm and dissection.
Article in Frontiers in cardiovascular medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 10 citations in OpenAlex.
- Loss of CASQ2 promotes vascular smooth muscle cell phenotypic switching in aortic dissection uncovered by integrated single-cell transcriptomics.BMC medical genomics · 2026Article
- Aortic dissection as a disease of vascular wall homeostasis: integrating vasa vasorum-inflammation-metabolism axis for mechanistic insight and clinical translation.Frontiers in immunology · 2026Review
- The role of cellular senescence in cardiovascular disease.Cell death discovery · 2025Review
- Targeting farnesoid X receptor as aging intervention therapy.Acta pharmaceutica Sinica. B · 2025Article
- Establishment of a nomogram model based on immune-related genes using machine learning for aortic dissection diagnosis and immunomodulation assessment.International journal of medical sciences · 2025Article
- Comprehensive analysis of gene signatures associated with aging in human aortic dissection.Heliyon · 2024Article
- OxLDL/LOX-1 mediated sex, age, stiffness, and endothelial dependent alterations in mouse thoracic aortic vascular reactivity.Frontiers in physiology · 2024Article
- IdentifyingFrontiers in cardiovascular medicine · 2024Article
- Independent and Interactive Roles of Immunity and Metabolism in Aortic Dissection.International journal of molecular sciences · 2023Review
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Thoracic aortic aneurysm and dissection (TAAD) is a cardiovascular disease with a high mortality rate. Aging is an important risk factor for TAAD. This study explored the relationship between aging and TAAD and investigated the underlying mechanisms, which may contribute to the diagnosis and treatment of TAAD. Methods: Human aging genes were obtained from the Aging Atlas official website. Various datasets were downloaded from the GEO database:the human TAAD dataset GSE52093 were used for screening differentially expressed genes (DEGs); GSE137869, GSE102397 and GSE153434 were used as validation sets, and GSE9106 was used for diagnostic prediction of receiver operating characteristic (ROC) curves. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and protein-protein interaction (PPI) network analysis were used to screen differentially co-expressed genes from human aging genes and TAAD. Using five methods of the cytoHubba plugin in Cytoscape software (Degree, Closeness, EPC, MNC, Radiality), hub genes were identified from the differentially co-expressed genes. Single-cell RNA sequencing was used to verify the expression levels of hubgenes in different cell types of aortic tissue. ROC curves were used to further screen for diagnostic genes. Results: A total of 70 differentially co-expressed genes were screened from human aging genes and DEGs in human TAAD dataset GSE52093. GO enrichment analysis revealed that the DEGs played a major role in regulating DNA metabolism and damaged DNA binding. KEGG enrichment analysis revealed enrichment in the longevity regulating pathway, cellular senescence, and HIF-1 signaling pathway. GSEA indicated that the DEGs were concentrated in the cell cycle and aging-related p53 signaling pathway. The five identified hubgenes were Conclusion: The HIF-1 signaling pathway may play an important role in TAAD and aging.
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