Identification of vital modules and genes associated with heart failure based on weighted gene coexpression network analysis
Abstract Aims Heart failure (HF) is a chronic heart disease with a high incidence and mortality. Due to the regulatory complexity of gene coexpression networks, the underlying hub genes regulation in HF remain incompletely appreciated. We aimed to explore potential key modules and genes for HF using...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-04-01
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| Series: | ESC Heart Failure |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/ehf2.13827 |
| _version_ | 1818306216311390208 |
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| author | Weikang Bian Zhicheng Wang Xiaobo Li Xiao‐Xin Jiang Hongsong Zhang Zhizhong Liu Dai‐Min Zhang |
| author_facet | Weikang Bian Zhicheng Wang Xiaobo Li Xiao‐Xin Jiang Hongsong Zhang Zhizhong Liu Dai‐Min Zhang |
| author_sort | Weikang Bian |
| collection | DOAJ |
| description | Abstract Aims Heart failure (HF) is a chronic heart disease with a high incidence and mortality. Due to the regulatory complexity of gene coexpression networks, the underlying hub genes regulation in HF remain incompletely appreciated. We aimed to explore potential key modules and genes for HF using weighted gene coexpression network analysis (WGCNA). Methods and results The expression profiles by high throughput sequencing of heart tissues samples from HF and non‐HF samples were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between HF and non‐HF samples were firstly identified. Then, a coexpression network was constructed to identify key modules and potential hub genes. The biological functions of potential hub genes were analysed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. Finally, a protein–protein interaction (PPI) network was constructed using the STRING online tool. A total of 135 DEGs (133 up‐regulated and 2 down‐regulated DEGs) between HF and non‐HF samples were identified in the GSE135055 and GSE123976 datasets. Moreover, a total of 38 modules were screened based on WGCNA in the GSE135055 dataset, and six potential hub genes (UCK2, ASB1, CCNI, CUX1, IRX6, and STX16) were screened from the key module by setting the gene significance over 0.2 and the module membership over 0.8. Furthermore, 78 potential hub genes were obtained by taking the intersection of the 135 DEGs and all genes in the key module, and enrichment analysis revealed that they were mainly involved in the MAPK and PI3K‐AKT signalling pathways. Finally, in a PPI network constructed with the 78 potential hub genes, CUX1 and ASB1 were identified as hub genes in HF because they were also identified as potential hub genes in the WGCNA. Conclusions To the best of our knowledge, our study is the first to employ WGCNA to identify the key module and hub genes for HF. Our study identified a module and two genes that might play important roles in HF, which may provide potential biomarkers for the diagnosis of HF and improve our knowledge of the molecular mechanisms underlying HF. |
| first_indexed | 2024-12-13T06:38:57Z |
| format | Article |
| id | doaj.art-b11ce06904a9462781bb9be83b5e9bcd |
| institution | Directory Open Access Journal |
| issn | 2055-5822 |
| language | English |
| last_indexed | 2024-12-13T06:38:57Z |
| publishDate | 2022-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | ESC Heart Failure |
| spelling | doaj.art-b11ce06904a9462781bb9be83b5e9bcd2022-12-21T23:56:28ZengWileyESC Heart Failure2055-58222022-04-01921370137910.1002/ehf2.13827Identification of vital modules and genes associated with heart failure based on weighted gene coexpression network analysisWeikang Bian0Zhicheng Wang1Xiaobo Li2Xiao‐Xin Jiang3Hongsong Zhang4Zhizhong Liu5Dai‐Min Zhang6Department of Cardiology Nanjing First Hospital, Nanjing Medical University 68 Changle Road Nanjing 210006 ChinaDepartment of Cardiology Nanjing First Hospital, Nanjing Medical University 68 Changle Road Nanjing 210006 ChinaDepartment of Cardiology Nanjing First Hospital, Nanjing Medical University 68 Changle Road Nanjing 210006 ChinaDepartment of Cardiology Nanjing First Hospital, Nanjing Medical University 68 Changle Road Nanjing 210006 ChinaDepartment of Cardiology Nanjing First Hospital, Nanjing Medical University 68 Changle Road Nanjing 210006 ChinaDepartment of Cardiology Nanjing First Hospital, Nanjing Medical University 68 Changle Road Nanjing 210006 ChinaDepartment of Cardiology Nanjing First Hospital, Nanjing Medical University 68 Changle Road Nanjing 210006 ChinaAbstract Aims Heart failure (HF) is a chronic heart disease with a high incidence and mortality. Due to the regulatory complexity of gene coexpression networks, the underlying hub genes regulation in HF remain incompletely appreciated. We aimed to explore potential key modules and genes for HF using weighted gene coexpression network analysis (WGCNA). Methods and results The expression profiles by high throughput sequencing of heart tissues samples from HF and non‐HF samples were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between HF and non‐HF samples were firstly identified. Then, a coexpression network was constructed to identify key modules and potential hub genes. The biological functions of potential hub genes were analysed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. Finally, a protein–protein interaction (PPI) network was constructed using the STRING online tool. A total of 135 DEGs (133 up‐regulated and 2 down‐regulated DEGs) between HF and non‐HF samples were identified in the GSE135055 and GSE123976 datasets. Moreover, a total of 38 modules were screened based on WGCNA in the GSE135055 dataset, and six potential hub genes (UCK2, ASB1, CCNI, CUX1, IRX6, and STX16) were screened from the key module by setting the gene significance over 0.2 and the module membership over 0.8. Furthermore, 78 potential hub genes were obtained by taking the intersection of the 135 DEGs and all genes in the key module, and enrichment analysis revealed that they were mainly involved in the MAPK and PI3K‐AKT signalling pathways. Finally, in a PPI network constructed with the 78 potential hub genes, CUX1 and ASB1 were identified as hub genes in HF because they were also identified as potential hub genes in the WGCNA. Conclusions To the best of our knowledge, our study is the first to employ WGCNA to identify the key module and hub genes for HF. Our study identified a module and two genes that might play important roles in HF, which may provide potential biomarkers for the diagnosis of HF and improve our knowledge of the molecular mechanisms underlying HF.https://doi.org/10.1002/ehf2.13827Heart failureBiomarkerGene expression omnibusWeighted gene coexpression network analysis |
| spellingShingle | Weikang Bian Zhicheng Wang Xiaobo Li Xiao‐Xin Jiang Hongsong Zhang Zhizhong Liu Dai‐Min Zhang Identification of vital modules and genes associated with heart failure based on weighted gene coexpression network analysis ESC Heart Failure Heart failure Biomarker Gene expression omnibus Weighted gene coexpression network analysis |
| title | Identification of vital modules and genes associated with heart failure based on weighted gene coexpression network analysis |
| title_full | Identification of vital modules and genes associated with heart failure based on weighted gene coexpression network analysis |
| title_fullStr | Identification of vital modules and genes associated with heart failure based on weighted gene coexpression network analysis |
| title_full_unstemmed | Identification of vital modules and genes associated with heart failure based on weighted gene coexpression network analysis |
| title_short | Identification of vital modules and genes associated with heart failure based on weighted gene coexpression network analysis |
| title_sort | identification of vital modules and genes associated with heart failure based on weighted gene coexpression network analysis |
| topic | Heart failure Biomarker Gene expression omnibus Weighted gene coexpression network analysis |
| url | https://doi.org/10.1002/ehf2.13827 |
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