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dc.contributor.authorZhang, H.
dc.contributor.authorChen, T.
dc.contributor.authorZhang, Y.
dc.contributor.authorLin, J.
dc.contributor.authorZhao, W.
dc.contributor.authorShi, Y.
dc.contributor.authorLau, H.
dc.contributor.authorZhang, Y.
dc.contributor.authorYang, M.
dc.contributor.authorXu, C.
dc.contributor.authorTang, L.
dc.contributor.authorXu, B.
dc.contributor.authorJiang, J.
dc.contributor.authorChen, X.
dc.date.accessioned2022-03-18T00:03:54Z
dc.date.available2022-03-18T00:03:54Z
dc.date.issued2022
dc.identifier.citationZhang, H., Chen, T., Zhang, Y., Lin, J., Zhao, W., Shi, Y., Lau, H., Zhang, Y., Yang, M., Xu, C., Tang, L., Xu, B., Jiang, J., & Chen, X. (2022). Crucial Genes in Aortic Dissection Identified by Weighted Gene Coexpression Network Analysis. Journal of Immunology Research.
dc.identifier.issn2314-8861
dc.identifier.pmid35178459
dc.identifier.doi10.1155/2022/7585149
dc.identifier.urihttp://hdl.handle.net/10150/663657
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. © 2022 Hongliang Zhang et al.
dc.language.isoen
dc.publisherHindawi Limited
dc.rightsCopyright © 2022 Hongliang Zhang et al. This is an open access article distributed under the Creative Commons Attribution License.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleCrucial Genes in Aortic Dissection Identified by Weighted Gene Coexpression Network Analysis
dc.typeArticle
dc.typetext
dc.contributor.departmentDepartment of Radiation Oncology, University of Arizona
dc.identifier.journalJournal of Immunology Research
dc.description.noteOpen access journal
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
dc.eprint.versionFinal published version
dc.source.journaltitleJournal of Immunology Research
refterms.dateFOA2022-03-18T00:03:55Z


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Copyright © 2022 Hongliang Zhang et al. This is an open access article distributed under the Creative Commons Attribution License.
Except where otherwise noted, this item's license is described as Copyright © 2022 Hongliang Zhang et al. This is an open access article distributed under the Creative Commons Attribution License.