ArticleHeliyon2024
Comprehensive analysis of gene signatures associated with aging in human aortic dissection.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- 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
- Identification of hub genes in aortic dissection based on weighted gene co-expression network analysis and Mendelian randomization study.Journal of cardiothoracic surgery · 2025Article
- IdentifyingFrontiers in cardiovascular medicine · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Aortic dissection (AD) is a lethal aortic disease with limited effective therapeutic strategies. Aging increases the risk of AD, yet the underlying mechanisms remain unclear. This study aims to analyze the association of aging-related genes (Args) and AD using bioinformatic analysis. This helps provide novel insights into AD pathogenesis and contributes to developing novel therapeutic strategies. Methods: mRNA (GSE52093, GSE153434), miRNA (GSE98770) and single-cell RNA-sequencing (scRNA-seq, GSE213740) datasets of AD were downloaded from GEO database. Args were downloaded from Aging Atlas database. Differentially-expressed Args were determined by intersecting Args and differentially-expressed mRNAs of two mRNA datasets. Cytoscape was used to identify hub genes and construct hub gene regulatory networks related to miRNAs. Seurat and clusterProfiler R package were used for investigating expression patterns of hub genes at single-cell level, and functional analysis, respectively. To validate the cellular expression pattern of hub genes, the same analysis was applied to our own scRNA-seq data. Drugs targeting hub Args were determined using the DGIdb database. Results: HGF, Conclusions: By sequencing transcriptomic data, this study revealed aging-related hub genes and regulatory network involved in AD formation. Additionally, this study proposed a noteworthy hypothesis that downregulated
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.