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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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Immunology Research |
| Online Access: | http://dx.doi.org/10.1155/2022/7585149 |
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| author | Hongliang Zhang Tingting Chen Yunyan Zhang Jiangbo Lin Wenjun Zhao Yangyang Shi Huichong Lau Yang Zhang Minjun Yang Cheng Xu Lijiang Tang Baohui Xu Jianjun Jiang Xiaofeng Chen |
| author_facet | Hongliang Zhang Tingting Chen Yunyan Zhang Jiangbo Lin Wenjun Zhao Yangyang Shi Huichong Lau Yang Zhang Minjun Yang Cheng Xu Lijiang Tang Baohui Xu Jianjun Jiang Xiaofeng Chen |
| author_sort | Hongliang Zhang |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-59cace39eace48e69bd102f96d62fc3f |
| institution | Kabale University |
| issn | 2314-7156 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Immunology Research |
| spelling | doaj-art-59cace39eace48e69bd102f96d62fc3f2025-02-03T01:07:11ZengWileyJournal of Immunology Research2314-71562022-01-01202210.1155/2022/7585149Crucial Genes in Aortic Dissection Identified by Weighted Gene Coexpression Network AnalysisHongliang Zhang0Tingting Chen1Yunyan Zhang2Jiangbo Lin3Wenjun Zhao4Yangyang Shi5Huichong Lau6Yang Zhang7Minjun Yang8Cheng Xu9Lijiang Tang10Baohui Xu11Jianjun Jiang12Xiaofeng Chen13Department of CardiologyDepartment of CardiologyDepartment of CardiologyDepartment of CardiologyDepartment of Vascular SurgeryDepartment of Radiation OncologyDepartment of MedicineDepartment of CardiologyDepartment of CardiologyDepartment of CardiologyDepartment of CardiologyDivision of Vascular SurgeryDepartment of CardiologyDepartment of CardiologyBackground. 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.http://dx.doi.org/10.1155/2022/7585149 |
| spellingShingle | Hongliang Zhang Tingting Chen Yunyan Zhang Jiangbo Lin Wenjun Zhao Yangyang Shi Huichong Lau Yang Zhang Minjun Yang Cheng Xu Lijiang Tang Baohui Xu Jianjun Jiang Xiaofeng Chen Crucial Genes in Aortic Dissection Identified by Weighted Gene Coexpression Network Analysis Journal of Immunology Research |
| 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 |
| url | http://dx.doi.org/10.1155/2022/7585149 |
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