ArticleFrontiers in pharmacology2020
Regulatory Master Genes Identification and Drug Repositioning by Integrative mRNA-miRNA Network Analysis for Acute Type A Aortic Dissection.
Article in Frontiers in pharmacology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- miR-485-3p targets SIRT1 in vascular smooth muscle cells mediating the occurrence of aortic dissection.Journal of cellular and molecular medicine · 2024Article
- Artificial intelligence, medications, pharmacogenomics, and ethics.Pharmacogenomics · 2024Article
- Analysis of mRNA-miRNA interaction network reveals the role of CAFs-derived exosomes in the immune regulation of oral squamous cell carcinoma.BMC cancer · 2023Article
- Investigation of the Pathogenic Mechanism of Ciprofloxacin in Aortic Aneurysm and Dissection by an Integrated Proteomics and Network Pharmacology Strategy.Journal of clinical medicine · 2023Article
- Bioinformatics Analysis Reveals Cell Cycle-Related Gene Upregulation in Ascending Aortic Tissues From Murine Models.Frontiers in genetics · 2022Article
- Construction and Integrated Analysis of Competitive Endogenous Long Non-Coding RNA Network in Thoracic Aortic Dissection.International journal of general medicine · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acute type A aortic dissection (ATAAD) is a life-threatening disease. The understanding of its pathogenesis and treatment approaches remains unclear. In the present work, differentially expressed genes (DEGs) from two ATAAD datasets GSE52093 and GSE98770 were filtered. Transcription factor TEAD4 was predicted as a key modulator in protein-protein interaction (PPI) network. Weighted correlation network analysis (WGCNA) identified five modules in GSE52093 and four modules in GSE98770 were highly correlated with ATAAD. 71 consensus DEGs of highly correlated modules were defined and functionally annotated. L1000CDS
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Registered trials
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