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Identification of crucial genes in abdominal aortic aneurysm by WGCNA
BACKGROUND: Abdominal aortic aneurysm (AAA) is the full thickness dilation of the abdominal aorta. However, few effective medical therapies are available. Thus, elucidating the molecular mechanism of AAA pathogenesis and exploring the potential molecular target of medical therapies for AAA is of vit...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6788446/ https://www.ncbi.nlm.nih.gov/pubmed/31608184 http://dx.doi.org/10.7717/peerj.7873 |
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author | Chen, Siliang Yang, Dan Lei, Chuxiang Li, Yuan Sun, Xiaoning Chen, Mengyin Wu, Xiao Zheng, Yuehong |
author_facet | Chen, Siliang Yang, Dan Lei, Chuxiang Li, Yuan Sun, Xiaoning Chen, Mengyin Wu, Xiao Zheng, Yuehong |
author_sort | Chen, Siliang |
collection | PubMed |
description | BACKGROUND: Abdominal aortic aneurysm (AAA) is the full thickness dilation of the abdominal aorta. However, few effective medical therapies are available. Thus, elucidating the molecular mechanism of AAA pathogenesis and exploring the potential molecular target of medical therapies for AAA is of vital importance. METHODS: Three expression datasets (GSE7084, GSE47472 and GSE57691) were downloaded from the Gene Expression Omnibus (GEO). These datasets were merged and then normalized using the “sva” R package. Differential expressed gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA) were conducted. We compared the co-expression patterns between AAA and normal conditions, and hub genes of each functional module were identified. DEGs were mapped to co-expression network under AAA condition and a DEG co-expression network was generated. Crucial genes were identified using molecular complex detection (MCODE) (a plugin in Cytoscape). RESULTS: In our study, 6 and 10 gene modules were detected for the AAA and normal conditions, respectively, while 143 DEGs were screened. Compared to the normal condition, genes associated with immune response, inflammation and muscle contraction were clustered in three gene modules respectively under the AAA condition; the hub genes of the three modules were MAP4K1, NFIB and HPK1, respectively. A DEG co-expression network with 102 nodes and 303 edges was identified, and a hub gene cluster with 10 genes from the DEG co-expression network was detected. YIPF6, RABGAP1, ANKRD6, GPD1L, PGRMC2, HIGD1A, GMDS, MGP, SLC25A4 and FAM129A were in the cluster. The expression levels of these 10 genes showed potential diagnostic value. CONCLUSION: Based on WGCNA, we detected 6 modules under the AAA condition and 10 modules in the normal condition. Hub genes of each module and hub gene clusters of the DEG co-expression network were identified. These genes may act as potential targets for medical therapy and diagnostic biomarkers. Further studies are needed to elucidate the detailed biological function of these genes in the pathogenesis of AAA. |
format | Online Article Text |
id | pubmed-6788446 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-67884462019-10-11 Identification of crucial genes in abdominal aortic aneurysm by WGCNA Chen, Siliang Yang, Dan Lei, Chuxiang Li, Yuan Sun, Xiaoning Chen, Mengyin Wu, Xiao Zheng, Yuehong PeerJ Bioinformatics BACKGROUND: Abdominal aortic aneurysm (AAA) is the full thickness dilation of the abdominal aorta. However, few effective medical therapies are available. Thus, elucidating the molecular mechanism of AAA pathogenesis and exploring the potential molecular target of medical therapies for AAA is of vital importance. METHODS: Three expression datasets (GSE7084, GSE47472 and GSE57691) were downloaded from the Gene Expression Omnibus (GEO). These datasets were merged and then normalized using the “sva” R package. Differential expressed gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA) were conducted. We compared the co-expression patterns between AAA and normal conditions, and hub genes of each functional module were identified. DEGs were mapped to co-expression network under AAA condition and a DEG co-expression network was generated. Crucial genes were identified using molecular complex detection (MCODE) (a plugin in Cytoscape). RESULTS: In our study, 6 and 10 gene modules were detected for the AAA and normal conditions, respectively, while 143 DEGs were screened. Compared to the normal condition, genes associated with immune response, inflammation and muscle contraction were clustered in three gene modules respectively under the AAA condition; the hub genes of the three modules were MAP4K1, NFIB and HPK1, respectively. A DEG co-expression network with 102 nodes and 303 edges was identified, and a hub gene cluster with 10 genes from the DEG co-expression network was detected. YIPF6, RABGAP1, ANKRD6, GPD1L, PGRMC2, HIGD1A, GMDS, MGP, SLC25A4 and FAM129A were in the cluster. The expression levels of these 10 genes showed potential diagnostic value. CONCLUSION: Based on WGCNA, we detected 6 modules under the AAA condition and 10 modules in the normal condition. Hub genes of each module and hub gene clusters of the DEG co-expression network were identified. These genes may act as potential targets for medical therapy and diagnostic biomarkers. Further studies are needed to elucidate the detailed biological function of these genes in the pathogenesis of AAA. PeerJ Inc. 2019-10-08 /pmc/articles/PMC6788446/ /pubmed/31608184 http://dx.doi.org/10.7717/peerj.7873 Text en © 2019 Chen et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Chen, Siliang Yang, Dan Lei, Chuxiang Li, Yuan Sun, Xiaoning Chen, Mengyin Wu, Xiao Zheng, Yuehong Identification of crucial genes in abdominal aortic aneurysm by WGCNA |
title | Identification of crucial genes in abdominal aortic aneurysm by WGCNA |
title_full | Identification of crucial genes in abdominal aortic aneurysm by WGCNA |
title_fullStr | Identification of crucial genes in abdominal aortic aneurysm by WGCNA |
title_full_unstemmed | Identification of crucial genes in abdominal aortic aneurysm by WGCNA |
title_short | Identification of crucial genes in abdominal aortic aneurysm by WGCNA |
title_sort | identification of crucial genes in abdominal aortic aneurysm by wgcna |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6788446/ https://www.ncbi.nlm.nih.gov/pubmed/31608184 http://dx.doi.org/10.7717/peerj.7873 |
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