Identification of the molecular mechanisms associated with acute type A aortic dissection through bioinformatics methods

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Jiang, Tao;Si, Liangyi

Description

Aortic dissection is characterized by the redirection of blood flow, which flows through an intimal tear into the aortic media. The purpose of this study was to find potential acute type A aortic dissection (AAAD)-related genes and molecular mechanisms by bioinformatics. The gene expression profiles of GSE52093 were obtained from Gene Expression Omnibus (GEO) database, including 7 AAAD samples and 5 normal samples. The differentially expressed genes (DEGs) were detected between AAAD and normal samples. The functional annotation and pathway enrichment analysis were conducted through the Database for Annotation, Visualization and Integration Discovery (DAVID). A protein-protein interaction network was established by the Search Tool for the Retrieval of Interacting Genes (STRING) software. The microRNAs (miRNAs) of these differentially expressed genes were predicted using database. Moreover, DEGs were analyzed in the comparative toxicogenomics (CTD) database to screen out the potential therapeutic small molecules. As a result, there were 172 DEGs identified in patients with AAAD. These DEGs were significantly enriched in 6 pathways, including cell cycle, oocyte meiosis, DNA replication, extracellular matrix-receptor interaction, and mineral absorption pathway. Notably, CDC20, CDK1, CHEK1, KIF20A, MCM10, PBK, PTTG1, RACGAP, and TOP2A were crucial genes with a high degree in the protein-protein interaction network. Furthermore, potential miRNAs (miR-301, miR-302 family, and miR-130 family) were identified. In addition, small molecules like azathioprine and zoledronic acid were identified to be potential drugs for AAAD.

Citations (1)

Mentions (0)

Metrics

Dataset Index

0.5

FAIR Score

85%

Citations

1

Mentions

0

Metrics Over Time

Publication Details

DOI

Publisher

SciELO journals

Assigned Domain

Subfield

Molecular Biology

Field

Biochemistry, Genetics and Molecular Biology

Domain

Life Sciences

Confidence Score

55%

Source

Scholar Data Model

Keywords

MedicineMicrobiology not elsewhere classified

Normalization Factors

FT

30.77

CTw

1.00

MTw

1.00