The identification and verification of hub genes associated with pulmonary arterial hypertension using weighted gene co-expression network analysis
Abstract Background Pulmonary arterial hypertension (PAH) is characterized by a progressive increase in pulmonary vascular resistance and pulmonary arterial pressure, with complex etiology, difficult treatment and poor prognosis. The objective of this study was to investigate the potential biomarker...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-12-01
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| Series: | BMC Pulmonary Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12890-022-02275-6 |
| _version_ | 1811292023841357824 |
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| author | Weibin Wu Ai Chen Siming Lin Qiuran Wang Guili Lian Li Luo Liangdi Xie |
| author_facet | Weibin Wu Ai Chen Siming Lin Qiuran Wang Guili Lian Li Luo Liangdi Xie |
| author_sort | Weibin Wu |
| collection | DOAJ |
| description | Abstract Background Pulmonary arterial hypertension (PAH) is characterized by a progressive increase in pulmonary vascular resistance and pulmonary arterial pressure, with complex etiology, difficult treatment and poor prognosis. The objective of this study was to investigate the potential biomarkers for PAH based on bioinformatics analysis. Methods The GSE117261 datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified by screening PAH patients and controls. Then the DEGs were analyzed using a Weighted Gene Co-expression Network Analysis (WGCNA) and the key modules were determined, and to further explore their potential biological functions via Gene Ontology analysis (GO), Kyoto Encyclopedia of Genes and Genomes Pathway analysis (KEGG), and Gene Set Enrichment Analysis (GSEA). Moreover, Protein–protein interaction (PPI) networks were constructed to identify hub gene candidates in the key modules. Finally, real-time quantitative polymerase chain reaction was supplied to detect the expressions of hub genes in human pulmonary arterial smooth cells treated with cobalt chloride (COCl2) which was used to mimic hypoxia. Results There were 2299 DEGs identified. WGCNA indicated that yellow module was the key one correlated with PAH. GO and KEGG analysis demonstrated that genes in the yellow module were mainly enriched in ‘Pathways in cancer’. GSEA revealed that ‘HALLMARK_MYC_TARGETS_V1’ was remarkably enriched in PAH. Based on the PPI network, vascular endothelial growth factor A, proto-oncogene receptor tyrosine kinase (KIT), PNN interacting serine and arginine rich protein (PNISR) and heterogeneous nuclear ribonucleoprotein H1 (HNRNPH1) were identified as the hub genes. Additionally, the PCR indicated that the elevated expressions of PNISR and HNRNPH1 were in line with the bioinformatics analysis. ROC analysis determined that PNISR and HNRNPH1 may be potential biomarkers to provide better diagnosis of PAH. Conclusion PNISR and HNRNPH1 were potential biomarkers to diagnosis PAH. In summary, the identified DEGs, modules, pathways, and hub genes provide clues and shed light on the potential molecular mechanisms of PAH. |
| first_indexed | 2024-04-13T04:38:16Z |
| format | Article |
| id | doaj.art-d6a802864b4247fb9d4722f71ed4d549 |
| institution | Directory Open Access Journal |
| issn | 1471-2466 |
| language | English |
| last_indexed | 2024-04-13T04:38:16Z |
| publishDate | 2022-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Pulmonary Medicine |
| spelling | doaj.art-d6a802864b4247fb9d4722f71ed4d5492022-12-22T03:02:06ZengBMCBMC Pulmonary Medicine1471-24662022-12-0122111110.1186/s12890-022-02275-6The identification and verification of hub genes associated with pulmonary arterial hypertension using weighted gene co-expression network analysisWeibin Wu0Ai Chen1Siming Lin2Qiuran Wang3Guili Lian4Li Luo5Liangdi Xie6Department of Geriatrics, The First Affiliated Hospital of Fujian Medical UniversityDepartment of Geriatrics, The First Affiliated Hospital of Fujian Medical UniversityFujian Hypertension Research Institute, The First Affiliated Hospital of Fujian Medical UniversityDepartment of Geriatrics, The First Affiliated Hospital of Fujian Medical UniversityDepartment of Geriatrics, The First Affiliated Hospital of Fujian Medical UniversityDepartment of Geriatrics, The First Affiliated Hospital of Fujian Medical UniversityDepartment of Geriatrics, The First Affiliated Hospital of Fujian Medical UniversityAbstract Background Pulmonary arterial hypertension (PAH) is characterized by a progressive increase in pulmonary vascular resistance and pulmonary arterial pressure, with complex etiology, difficult treatment and poor prognosis. The objective of this study was to investigate the potential biomarkers for PAH based on bioinformatics analysis. Methods The GSE117261 datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified by screening PAH patients and controls. Then the DEGs were analyzed using a Weighted Gene Co-expression Network Analysis (WGCNA) and the key modules were determined, and to further explore their potential biological functions via Gene Ontology analysis (GO), Kyoto Encyclopedia of Genes and Genomes Pathway analysis (KEGG), and Gene Set Enrichment Analysis (GSEA). Moreover, Protein–protein interaction (PPI) networks were constructed to identify hub gene candidates in the key modules. Finally, real-time quantitative polymerase chain reaction was supplied to detect the expressions of hub genes in human pulmonary arterial smooth cells treated with cobalt chloride (COCl2) which was used to mimic hypoxia. Results There were 2299 DEGs identified. WGCNA indicated that yellow module was the key one correlated with PAH. GO and KEGG analysis demonstrated that genes in the yellow module were mainly enriched in ‘Pathways in cancer’. GSEA revealed that ‘HALLMARK_MYC_TARGETS_V1’ was remarkably enriched in PAH. Based on the PPI network, vascular endothelial growth factor A, proto-oncogene receptor tyrosine kinase (KIT), PNN interacting serine and arginine rich protein (PNISR) and heterogeneous nuclear ribonucleoprotein H1 (HNRNPH1) were identified as the hub genes. Additionally, the PCR indicated that the elevated expressions of PNISR and HNRNPH1 were in line with the bioinformatics analysis. ROC analysis determined that PNISR and HNRNPH1 may be potential biomarkers to provide better diagnosis of PAH. Conclusion PNISR and HNRNPH1 were potential biomarkers to diagnosis PAH. In summary, the identified DEGs, modules, pathways, and hub genes provide clues and shed light on the potential molecular mechanisms of PAH.https://doi.org/10.1186/s12890-022-02275-6Hub genesPulmonary arterial hypertensionWGCNAGSEABioinformatics |
| spellingShingle | Weibin Wu Ai Chen Siming Lin Qiuran Wang Guili Lian Li Luo Liangdi Xie The identification and verification of hub genes associated with pulmonary arterial hypertension using weighted gene co-expression network analysis BMC Pulmonary Medicine Hub genes Pulmonary arterial hypertension WGCNA GSEA Bioinformatics |
| title | The identification and verification of hub genes associated with pulmonary arterial hypertension using weighted gene co-expression network analysis |
| title_full | The identification and verification of hub genes associated with pulmonary arterial hypertension using weighted gene co-expression network analysis |
| title_fullStr | The identification and verification of hub genes associated with pulmonary arterial hypertension using weighted gene co-expression network analysis |
| title_full_unstemmed | The identification and verification of hub genes associated with pulmonary arterial hypertension using weighted gene co-expression network analysis |
| title_short | The identification and verification of hub genes associated with pulmonary arterial hypertension using weighted gene co-expression network analysis |
| title_sort | identification and verification of hub genes associated with pulmonary arterial hypertension using weighted gene co expression network analysis |
| topic | Hub genes Pulmonary arterial hypertension WGCNA GSEA Bioinformatics |
| url | https://doi.org/10.1186/s12890-022-02275-6 |
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