Identification of hub genes and key signaling pathways by weighted gene co-expression network analysis for human aortic stenosis and insufficiency
BackgroundHuman aortic valve stenosis (AS) and insufficiency (AI) are common diseases in aging population. Identifying the molecular regulatory networks of AS and AI is expected to offer novel perspectives for AS and AI treatment.MethodsHighly correlated modules with the progression of AS and AI wer...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-08-01
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| Series: | Frontiers in Cardiovascular Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2023.857578/full |
| _version_ | 1797749070668759040 |
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| author | Yang Yang Yang Yang Yang Yang Yang Yang Yang Yang Bing Xiao Xin Feng Yue Chen Qunhui Wang Jing Fang Ping Zhou Ping Zhou Ping Zhou Ping Zhou Xiang Wei Lin Cheng |
| author_facet | Yang Yang Yang Yang Yang Yang Yang Yang Yang Yang Bing Xiao Xin Feng Yue Chen Qunhui Wang Jing Fang Ping Zhou Ping Zhou Ping Zhou Ping Zhou Xiang Wei Lin Cheng |
| author_sort | Yang Yang |
| collection | DOAJ |
| description | BackgroundHuman aortic valve stenosis (AS) and insufficiency (AI) are common diseases in aging population. Identifying the molecular regulatory networks of AS and AI is expected to offer novel perspectives for AS and AI treatment.MethodsHighly correlated modules with the progression of AS and AI were identified by weighted genes co-expression network analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by the clusterProfiler program package. Differentially expressed genes (DEGs) were identified by the DESeqDataSetFromMatrix function of the DESeq2 program package. The protein-protein interaction (PPI) network analyses were implemented using the STRING online tool and visualized with Cytoscape software. The DEGs in AS and AI groups were overlapped with the top 30 genes with highest connectivity to screen out ten hub genes. The ten hub genes were verified by analyzing the data in high throughput RNA-sequencing dataset and real-time PCR assay using AS and AI aortic valve samples.ResultsBy WGCNA algorithm, 302 highly correlated genes with the degree of AS, degree of AI, and heart failure were identified from highly correlated modules. GO analyses showed that highly correlated genes had close relationship with collagen fibril organization, extracellular matrix organization and extracellular structure organization. KEGG analyses also manifested that protein digestion and absorption, and glutathione metabolism were probably involved in AS and AI pathological courses. Moreover, DEGs were picked out for 302 highly correlated genes in AS and AI groups relative to the normal control group. The PPI network analyses indicated the connectivity among these highly correlated genes. Finally, ten hub genes (CD74, COL1A1, TXNRD1, CCND1, COL5A1, SERPINH1, BCL6, ITGA10, FOS, and JUNB) in AS and AI were found out and verified.ConclusionOur study may provide the underlying molecular targets for the mechanism research, diagnosis, and treatment of AS and AI in the future. |
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| format | Article |
| id | doaj.art-acb854f408b24c72880d7ac0fe0e0b69 |
| institution | Directory Open Access Journal |
| issn | 2297-055X |
| language | English |
| last_indexed | 2024-03-12T16:14:43Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj.art-acb854f408b24c72880d7ac0fe0e0b692023-08-09T07:31:05ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2023-08-011010.3389/fcvm.2023.857578857578Identification of hub genes and key signaling pathways by weighted gene co-expression network analysis for human aortic stenosis and insufficiencyYang Yang0Yang Yang1Yang Yang2Yang Yang3Yang Yang4Bing Xiao5Xin Feng6Yue Chen7Qunhui Wang8Jing Fang9Ping Zhou10Ping Zhou11Ping Zhou12Ping Zhou13Xiang Wei14Lin Cheng15Division of Cardiothoracic and Vascular Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaInstitute of OrganTransplantation, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaKey Laboratory of Organ Transplantation, Ministry of Education, Wuhan, ChinaNHC Key Laboratory of Organ Transplantation, Chinese Academy of Medical Sciences, Wuhan, ChinaKey Laboratory of Organ Transplantation, Chinese Academy of Medical Sciences, Wuhan, ChinaDepartment of Obstetrics and Gynecology, Peking University People's Hospital, Peking University, Beijing, ChinaDivision of Cardiothoracic and Vascular Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDivision of Cardiothoracic and Vascular Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDivision of Cardiothoracic and Vascular Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDivision of Cardiothoracic and Vascular Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaInstitute of OrganTransplantation, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaKey Laboratory of Organ Transplantation, Ministry of Education, Wuhan, ChinaNHC Key Laboratory of Organ Transplantation, Chinese Academy of Medical Sciences, Wuhan, ChinaKey Laboratory of Organ Transplantation, Chinese Academy of Medical Sciences, Wuhan, ChinaDivision of Cardiothoracic and Vascular Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDivision of Cardiothoracic and Vascular Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaBackgroundHuman aortic valve stenosis (AS) and insufficiency (AI) are common diseases in aging population. Identifying the molecular regulatory networks of AS and AI is expected to offer novel perspectives for AS and AI treatment.MethodsHighly correlated modules with the progression of AS and AI were identified by weighted genes co-expression network analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by the clusterProfiler program package. Differentially expressed genes (DEGs) were identified by the DESeqDataSetFromMatrix function of the DESeq2 program package. The protein-protein interaction (PPI) network analyses were implemented using the STRING online tool and visualized with Cytoscape software. The DEGs in AS and AI groups were overlapped with the top 30 genes with highest connectivity to screen out ten hub genes. The ten hub genes were verified by analyzing the data in high throughput RNA-sequencing dataset and real-time PCR assay using AS and AI aortic valve samples.ResultsBy WGCNA algorithm, 302 highly correlated genes with the degree of AS, degree of AI, and heart failure were identified from highly correlated modules. GO analyses showed that highly correlated genes had close relationship with collagen fibril organization, extracellular matrix organization and extracellular structure organization. KEGG analyses also manifested that protein digestion and absorption, and glutathione metabolism were probably involved in AS and AI pathological courses. Moreover, DEGs were picked out for 302 highly correlated genes in AS and AI groups relative to the normal control group. The PPI network analyses indicated the connectivity among these highly correlated genes. Finally, ten hub genes (CD74, COL1A1, TXNRD1, CCND1, COL5A1, SERPINH1, BCL6, ITGA10, FOS, and JUNB) in AS and AI were found out and verified.ConclusionOur study may provide the underlying molecular targets for the mechanism research, diagnosis, and treatment of AS and AI in the future.https://www.frontiersin.org/articles/10.3389/fcvm.2023.857578/fullaortic valve stenosisaortic valve insufficiencyheart failureWGCNAco-expression moduleshub genes |
| spellingShingle | Yang Yang Yang Yang Yang Yang Yang Yang Yang Yang Bing Xiao Xin Feng Yue Chen Qunhui Wang Jing Fang Ping Zhou Ping Zhou Ping Zhou Ping Zhou Xiang Wei Lin Cheng Identification of hub genes and key signaling pathways by weighted gene co-expression network analysis for human aortic stenosis and insufficiency Frontiers in Cardiovascular Medicine aortic valve stenosis aortic valve insufficiency heart failure WGCNA co-expression modules hub genes |
| title | Identification of hub genes and key signaling pathways by weighted gene co-expression network analysis for human aortic stenosis and insufficiency |
| title_full | Identification of hub genes and key signaling pathways by weighted gene co-expression network analysis for human aortic stenosis and insufficiency |
| title_fullStr | Identification of hub genes and key signaling pathways by weighted gene co-expression network analysis for human aortic stenosis and insufficiency |
| title_full_unstemmed | Identification of hub genes and key signaling pathways by weighted gene co-expression network analysis for human aortic stenosis and insufficiency |
| title_short | Identification of hub genes and key signaling pathways by weighted gene co-expression network analysis for human aortic stenosis and insufficiency |
| title_sort | identification of hub genes and key signaling pathways by weighted gene co expression network analysis for human aortic stenosis and insufficiency |
| topic | aortic valve stenosis aortic valve insufficiency heart failure WGCNA co-expression modules hub genes |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2023.857578/full |
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