Identification of Susceptibility Modules and Genes for Cardiovascular Disease in Diabetic Patients Using WGCNA Analysis
Objective. To identify susceptibility modules and genes for cardiovascular disease in diabetic patients using weighted gene coexpression network analysis (WGCNA). Methods. The raw data of GSE13760 were downloaded from the Gene Expression Omnibus (GEO) website. Genes with a false discovery rate<0....
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Diabetes Research |
| Online Access: | http://dx.doi.org/10.1155/2020/4178639 |
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| author | Weiwei Liang Fangfang Sun Yiming Zhao Lizhen Shan Hanyu Lou |
| author_facet | Weiwei Liang Fangfang Sun Yiming Zhao Lizhen Shan Hanyu Lou |
| author_sort | Weiwei Liang |
| collection | DOAJ |
| description | Objective. To identify susceptibility modules and genes for cardiovascular disease in diabetic patients using weighted gene coexpression network analysis (WGCNA). Methods. The raw data of GSE13760 were downloaded from the Gene Expression Omnibus (GEO) website. Genes with a false discovery rate<0.05 and a log2 fold change≥0.5 were included in the analysis. WGCNA was used to build a gene coexpression network, screen important modules, and filter the hub genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for the genes in modules with clinical interest. Genes with a significance over 0.2 and a module membership over 0.8 were used as hub genes. Subsequently, we screened these hub genes in the published genome-wide SNP data of cardiovascular disease. The overlapped genes were defined as key genes. Results. Fourteen gene coexpression modules were constructed via WGCNA analysis. Module greenyellow was mostly significantly correlated with diabetes. The GO analysis showed that genes in the module greenyellow were mainly enriched in extracellular matrix organization, extracellular exosome, and calcium ion binding. The KEGG analysis showed that the genes in the module greenyellow were mainly enriched in antigen processing and presentation, phagosome. Fifteen genes were identified as hub genes. Finally, HLA-DRB1, LRP1, and MMP2 were identified as key genes. Conclusion. This was the first study that used the WGCNA method to construct a coexpression network to explore diabetes-associated susceptibility modules and genes for cardiovascular disease. Our study identified a module and several key genes that acted as essential components in the etiology of diabetes-associated cardiovascular disease, which may enhance our fundamental knowledge of the molecular mechanisms underlying this disease. |
| format | Article |
| id | doaj-art-7b02b9f1fe8540cba5a1a2e58dda6e49 |
| institution | Kabale University |
| issn | 2314-6745 2314-6753 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Diabetes Research |
| spelling | doaj-art-7b02b9f1fe8540cba5a1a2e58dda6e492025-08-20T03:35:03ZengWileyJournal of Diabetes Research2314-67452314-67532020-01-01202010.1155/2020/41786394178639Identification of Susceptibility Modules and Genes for Cardiovascular Disease in Diabetic Patients Using WGCNA AnalysisWeiwei Liang0Fangfang Sun1Yiming Zhao2Lizhen Shan3Hanyu Lou4Department of Endocrinology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaDepartment of Colorectal Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, ChinaDepartment of Endocrinology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaDepartment of Endocrinology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaDepartment of Endocrinology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaObjective. To identify susceptibility modules and genes for cardiovascular disease in diabetic patients using weighted gene coexpression network analysis (WGCNA). Methods. The raw data of GSE13760 were downloaded from the Gene Expression Omnibus (GEO) website. Genes with a false discovery rate<0.05 and a log2 fold change≥0.5 were included in the analysis. WGCNA was used to build a gene coexpression network, screen important modules, and filter the hub genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for the genes in modules with clinical interest. Genes with a significance over 0.2 and a module membership over 0.8 were used as hub genes. Subsequently, we screened these hub genes in the published genome-wide SNP data of cardiovascular disease. The overlapped genes were defined as key genes. Results. Fourteen gene coexpression modules were constructed via WGCNA analysis. Module greenyellow was mostly significantly correlated with diabetes. The GO analysis showed that genes in the module greenyellow were mainly enriched in extracellular matrix organization, extracellular exosome, and calcium ion binding. The KEGG analysis showed that the genes in the module greenyellow were mainly enriched in antigen processing and presentation, phagosome. Fifteen genes were identified as hub genes. Finally, HLA-DRB1, LRP1, and MMP2 were identified as key genes. Conclusion. This was the first study that used the WGCNA method to construct a coexpression network to explore diabetes-associated susceptibility modules and genes for cardiovascular disease. Our study identified a module and several key genes that acted as essential components in the etiology of diabetes-associated cardiovascular disease, which may enhance our fundamental knowledge of the molecular mechanisms underlying this disease.http://dx.doi.org/10.1155/2020/4178639 |
| spellingShingle | Weiwei Liang Fangfang Sun Yiming Zhao Lizhen Shan Hanyu Lou Identification of Susceptibility Modules and Genes for Cardiovascular Disease in Diabetic Patients Using WGCNA Analysis Journal of Diabetes Research |
| title | Identification of Susceptibility Modules and Genes for Cardiovascular Disease in Diabetic Patients Using WGCNA Analysis |
| title_full | Identification of Susceptibility Modules and Genes for Cardiovascular Disease in Diabetic Patients Using WGCNA Analysis |
| title_fullStr | Identification of Susceptibility Modules and Genes for Cardiovascular Disease in Diabetic Patients Using WGCNA Analysis |
| title_full_unstemmed | Identification of Susceptibility Modules and Genes for Cardiovascular Disease in Diabetic Patients Using WGCNA Analysis |
| title_short | Identification of Susceptibility Modules and Genes for Cardiovascular Disease in Diabetic Patients Using WGCNA Analysis |
| title_sort | identification of susceptibility modules and genes for cardiovascular disease in diabetic patients using wgcna analysis |
| url | http://dx.doi.org/10.1155/2020/4178639 |
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