Crucial Genes in Aortic Dissection Identified by Weighted Gene Coexpression Network Analysis

dc.contributor.authorZhang, Hongliang
dc.contributor.authorChen, Tingting
dc.contributor.authorZhang, Yunyan
dc.contributor.authorLin, Jiangbo
dc.contributor.authorZhao, Wenjun
dc.contributor.authorShi, Yangyang
dc.contributor.authorLau, Huichong
dc.contributor.authorZhang, Yang
dc.contributor.authorYang, Minjun
dc.contributor.authorXu, Cheng
dc.contributor.authorTang, Lijiang
dc.contributor.authorXu, Baohui
dc.contributor.authorJiang, Jianjun
dc.contributor.authorChen, Xiaofeng
dc.contributor.departmentRadiation Oncology, School of Medicineen_US
dc.date.accessioned2023-05-04T12:10:30Z
dc.date.available2023-05-04T12:10:30Z
dc.date.issued2022-02-07
dc.description.abstractBackground: Aortic dissection (AD) is a lethal vascular disease with high mortality and morbidity. Though AD clinical pathology is well understood, its molecular mechanisms remain unclear. Specifically, gene expression profiling helps illustrate the potential mechanism of aortic dissection in terms of gene regulation and its modification by risk factors. This study was aimed at identifying the genes and molecular mechanisms in aortic dissection through bioinformatics analysis. Method: Nine patients with AD and 10 healthy controls were enrolled. The gene expression in peripheral mononuclear cells was profiled through next-generation RNA sequencing. Analyses including differential expressed gene (DEG) via DEGseq, weighted gene coexpression network (WGCNA), and VisANT were performed to identify crucial genes associated with AD. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was also utilized to analyze Gene Ontology (GO). Results: DEG analysis revealed that 1,113 genes were associated with AD. Of these, 812 genes were markedly reduced, whereas 301 genes were highly expressed, in AD patients. DEGs were rich in certain categories such as MHC class II receptor activity, MHC class II protein complex, and immune response genes. Gene coexpression networks via WGCNA identified 3 gene hub modules, with one positively and 2 negatively correlated with AD, respectively. Specifically, module 37 was the most strongly positively correlated with AD with a correlation coefficient of 0.72. Within module 37, five hub genes (AGFG1, MCEMP1, IRAK3, KCNE1, and CLEC4D) displayed high connectivity and may have clinical significance in the pathogenesis of AD. Conclusion: Our analysis provides the possible association of specific genes and gene modules for the involvement of the immune system in aortic dissection. AGFG1, MCEMP1, IRAK3, KCNE1, and CLEC4D in module M37 were highly connected and strongly linked with AD, suggesting that these genes may help understand the pathogenesis of aortic dissection.en_US
dc.eprint.versionFinal published versionen_US
dc.identifier.citationZhang H, Chen T, Zhang Y, et al. Crucial Genes in Aortic Dissection Identified by Weighted Gene Coexpression Network Analysis. J Immunol Res. 2022;2022:7585149. Published 2022 Feb 7. doi:10.1155/2022/7585149en_US
dc.identifier.urihttps://hdl.handle.net/1805/32792
dc.language.isoen_USen_US
dc.publisherHindawien_US
dc.relation.isversionof10.1155/2022/7585149en_US
dc.relation.journalJournal of Immunology Researchen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.sourcePMCen_US
dc.subjectAortic dissectionen_US
dc.subjectComputational biologyen_US
dc.subjectGene expression profilingen_US
dc.subjectGene expression regulationen_US
dc.subjectGene ontologyen_US
dc.subjectGene regulatory networksen_US
dc.subjectMicroarray analysisen_US
dc.titleCrucial Genes in Aortic Dissection Identified by Weighted Gene Coexpression Network Analysisen_US
dc.typeArticleen_US
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
JIR2022-7585149.pdf
Size:
3.52 MB
Format:
Adobe Portable Document Format
Description:
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.99 KB
Format:
Item-specific license agreed upon to submission
Description: