Unknown

Dataset Information

0

Bioinformatics analysis of key genes and miRNAs associated with Stanford type A aortic dissection.


ABSTRACT: Background:Aortic dissection is one of the most detrimental cardiovascular diseases with a high risk of mortality and morbidity. This study aimed to examine the key genes and microRNAs associated with Stanford type A aortic dissection (AAD). Methods:The expression data of AAD and healthy samples were downloaded from two microarray datasets in the Gene Expression Omnibus (GEO) database to identify highly preserved modules by weighted gene co-expression network analysis (WGCNA). Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEmiRNAs) were selected and functionally annotated. The predicted interactions between the DEGs and DEmiRNAs were further illustrated. Results:In two highly preserved modules, 459 DEGs were identified. These DEGs were functionally enriched in the HIF1, Notch, and PI3K/Akt pathways. Furthermore, 6 DEmiRNAs that were enriched in the regulation of vasculature development and HIF1 pathway, were predicted to target 23 DEGs. Conclusions:Our study presented several promising modulators, both DEGs and DEmiRNAs, as well as possible pathological pathways for AAD, which narrows the scope for further fundamental research.

SUBMITTER: Bi S 

PROVIDER: S-EPMC7578500 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

altmetric image

Publications

Bioinformatics analysis of key genes and miRNAs associated with Stanford type A aortic dissection.

Bi Siwei S   Liu Ruiqi R   Shen Yinzhi Y   Gu Jun J  

Journal of thoracic disease 20200901 9


<h4>Background</h4>Aortic dissection is one of the most detrimental cardiovascular diseases with a high risk of mortality and morbidity. This study aimed to examine the key genes and microRNAs associated with Stanford type A aortic dissection (AAD).<h4>Methods</h4>The expression data of AAD and healthy samples were downloaded from two microarray datasets in the Gene Expression Omnibus (GEO) database to identify highly preserved modules by weighted gene co-expression network analysis (WGCNA). Dif  ...[more]

Similar Datasets

| S-EPMC8809966 | biostudies-literature
| S-EPMC5651857 | biostudies-other
| S-EPMC8284479 | biostudies-literature
| S-EPMC8262045 | biostudies-literature
| S-EPMC9036163 | biostudies-literature
| S-EPMC7650806 | biostudies-literature
| S-EPMC10251017 | biostudies-literature
| S-EPMC7711383 | biostudies-literature
| S-EPMC9252449 | biostudies-literature
| S-EPMC8339749 | biostudies-literature