An integrative bioinformatics analysis for identifying hub genes associated with infection of lung samples in patients infected with SARS-CoV-2
Abstract Background At the end of 2019, the world witnessed the emergence and ravages of a viral infection induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Also known as the coronavirus disease 2019 (COVID-19), it has been identified as a public health emergency of internatio...
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| Format: | Article |
| Language: | English |
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BMC
2021-12-01
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| Series: | European Journal of Medical Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40001-021-00609-4 |
| _version_ | 1819008817108615168 |
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| author | Tian-Ao Xie Zhi-Jian He Chuan Liang Hao-Neng Dong Jie Zhou Shu-Jin Fan Xu-Guang Guo |
| author_facet | Tian-Ao Xie Zhi-Jian He Chuan Liang Hao-Neng Dong Jie Zhou Shu-Jin Fan Xu-Guang Guo |
| author_sort | Tian-Ao Xie |
| collection | DOAJ |
| description | Abstract Background At the end of 2019, the world witnessed the emergence and ravages of a viral infection induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Also known as the coronavirus disease 2019 (COVID-19), it has been identified as a public health emergency of international concern (PHEIC) by the World Health Organization (WHO) because of its severity. Methods The gene data of 51 samples were extracted from the GSE150316 and GSE147507 data set and then processed by means of the programming language R, through which the differentially expressed genes (DEGs) that meet the standards were screened. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on the selected DEGs to understand the functions and approaches of DEGs. The online tool STRING was employed to construct a protein–protein interaction (PPI) network of DEGs and, in turn, to identify hub genes. Results A total of 52 intersection genes were obtained through DEG identification. Through the GO analysis, we realized that the biological processes (BPs) that have the deepest impact on the human body after SARS-CoV-2 infection are various immune responses. By using STRING to construct a PPI network, 10 hub genes were identified, including IFIH1, DDX58, ISG15, EGR1, OASL, SAMD9, SAMD9L, XAF1, IFITM1, and TNFSF10. Conclusion The results of this study will hopefully provide guidance for future studies on the pathophysiological mechanism of SARS-CoV-2 infection. |
| first_indexed | 2024-12-21T00:46:30Z |
| format | Article |
| id | doaj.art-48e6c557a065462eb8b63c962dc545e9 |
| institution | Directory Open Access Journal |
| issn | 2047-783X |
| language | English |
| last_indexed | 2024-12-21T00:46:30Z |
| publishDate | 2021-12-01 |
| publisher | BMC |
| record_format | Article |
| series | European Journal of Medical Research |
| spelling | doaj.art-48e6c557a065462eb8b63c962dc545e92022-12-21T19:21:30ZengBMCEuropean Journal of Medical Research2047-783X2021-12-0126111310.1186/s40001-021-00609-4An integrative bioinformatics analysis for identifying hub genes associated with infection of lung samples in patients infected with SARS-CoV-2Tian-Ao Xie0Zhi-Jian He1Chuan Liang2Hao-Neng Dong3Jie Zhou4Shu-Jin Fan5Xu-Guang Guo6Department of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical UniversityDepartment of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical UniversityDepartment of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical UniversityDepartment of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical UniversityDepartment of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical UniversityDepartment of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical UniversityDepartment of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical UniversityAbstract Background At the end of 2019, the world witnessed the emergence and ravages of a viral infection induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Also known as the coronavirus disease 2019 (COVID-19), it has been identified as a public health emergency of international concern (PHEIC) by the World Health Organization (WHO) because of its severity. Methods The gene data of 51 samples were extracted from the GSE150316 and GSE147507 data set and then processed by means of the programming language R, through which the differentially expressed genes (DEGs) that meet the standards were screened. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on the selected DEGs to understand the functions and approaches of DEGs. The online tool STRING was employed to construct a protein–protein interaction (PPI) network of DEGs and, in turn, to identify hub genes. Results A total of 52 intersection genes were obtained through DEG identification. Through the GO analysis, we realized that the biological processes (BPs) that have the deepest impact on the human body after SARS-CoV-2 infection are various immune responses. By using STRING to construct a PPI network, 10 hub genes were identified, including IFIH1, DDX58, ISG15, EGR1, OASL, SAMD9, SAMD9L, XAF1, IFITM1, and TNFSF10. Conclusion The results of this study will hopefully provide guidance for future studies on the pathophysiological mechanism of SARS-CoV-2 infection.https://doi.org/10.1186/s40001-021-00609-4SARS-CoV-2Hub genesProtein–protein interactions networkDifferentially expressed genes |
| spellingShingle | Tian-Ao Xie Zhi-Jian He Chuan Liang Hao-Neng Dong Jie Zhou Shu-Jin Fan Xu-Guang Guo An integrative bioinformatics analysis for identifying hub genes associated with infection of lung samples in patients infected with SARS-CoV-2 European Journal of Medical Research SARS-CoV-2 Hub genes Protein–protein interactions network Differentially expressed genes |
| title | An integrative bioinformatics analysis for identifying hub genes associated with infection of lung samples in patients infected with SARS-CoV-2 |
| title_full | An integrative bioinformatics analysis for identifying hub genes associated with infection of lung samples in patients infected with SARS-CoV-2 |
| title_fullStr | An integrative bioinformatics analysis for identifying hub genes associated with infection of lung samples in patients infected with SARS-CoV-2 |
| title_full_unstemmed | An integrative bioinformatics analysis for identifying hub genes associated with infection of lung samples in patients infected with SARS-CoV-2 |
| title_short | An integrative bioinformatics analysis for identifying hub genes associated with infection of lung samples in patients infected with SARS-CoV-2 |
| title_sort | integrative bioinformatics analysis for identifying hub genes associated with infection of lung samples in patients infected with sars cov 2 |
| topic | SARS-CoV-2 Hub genes Protein–protein interactions network Differentially expressed genes |
| url | https://doi.org/10.1186/s40001-021-00609-4 |
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