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

BACKGROUND: 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...

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Autores principales: Zhang, Hongliang, Chen, Tingting, Zhang, Yunyan, Lin, Jiangbo, Zhao, Wenjun, Shi, Yangyang, Lau, Huichong, Zhang, Yang, Yang, Minjun, Xu, Cheng, Tang, Lijiang, Xu, Baohui, Jiang, Jianjun, Chen, Xiaofeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8844153/
https://www.ncbi.nlm.nih.gov/pubmed/35178459
http://dx.doi.org/10.1155/2022/7585149
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author Zhang, Hongliang
Chen, Tingting
Zhang, Yunyan
Lin, Jiangbo
Zhao, Wenjun
Shi, Yangyang
Lau, Huichong
Zhang, Yang
Yang, Minjun
Xu, Cheng
Tang, Lijiang
Xu, Baohui
Jiang, Jianjun
Chen, Xiaofeng
author_facet Zhang, Hongliang
Chen, Tingting
Zhang, Yunyan
Lin, Jiangbo
Zhao, Wenjun
Shi, Yangyang
Lau, Huichong
Zhang, Yang
Yang, Minjun
Xu, Cheng
Tang, Lijiang
Xu, Baohui
Jiang, Jianjun
Chen, Xiaofeng
author_sort Zhang, Hongliang
collection PubMed
description BACKGROUND: 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.
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spelling pubmed-88441532022-02-16 Crucial Genes in Aortic Dissection Identified by Weighted Gene Coexpression Network Analysis Zhang, Hongliang Chen, Tingting Zhang, Yunyan Lin, Jiangbo Zhao, Wenjun Shi, Yangyang Lau, Huichong Zhang, Yang Yang, Minjun Xu, Cheng Tang, Lijiang Xu, Baohui Jiang, Jianjun Chen, Xiaofeng J Immunol Res Research Article BACKGROUND: 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. Hindawi 2022-02-07 /pmc/articles/PMC8844153/ /pubmed/35178459 http://dx.doi.org/10.1155/2022/7585149 Text en Copyright © 2022 Hongliang Zhang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Zhang, Hongliang
Chen, Tingting
Zhang, Yunyan
Lin, Jiangbo
Zhao, Wenjun
Shi, Yangyang
Lau, Huichong
Zhang, Yang
Yang, Minjun
Xu, Cheng
Tang, Lijiang
Xu, Baohui
Jiang, Jianjun
Chen, Xiaofeng
Crucial Genes in Aortic Dissection Identified by Weighted Gene Coexpression Network Analysis
title Crucial Genes in Aortic Dissection Identified by Weighted Gene Coexpression Network Analysis
title_full Crucial Genes in Aortic Dissection Identified by Weighted Gene Coexpression Network Analysis
title_fullStr Crucial Genes in Aortic Dissection Identified by Weighted Gene Coexpression Network Analysis
title_full_unstemmed Crucial Genes in Aortic Dissection Identified by Weighted Gene Coexpression Network Analysis
title_short Crucial Genes in Aortic Dissection Identified by Weighted Gene Coexpression Network Analysis
title_sort crucial genes in aortic dissection identified by weighted gene coexpression network analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8844153/
https://www.ncbi.nlm.nih.gov/pubmed/35178459
http://dx.doi.org/10.1155/2022/7585149
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