Co-expression network analysis identifies potential candidate hub genes in severe influenza patients needing invasive mechanical ventilation
Abstract Background Influenza is a contagious disease that affects people of all ages and is linked to considerable mortality during epidemics and occasional outbreaks. Moreover, effective immunological biomarkers are needed for elucidating aetiology and preventing and treating severe influenza. Her...
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| Format: | Article |
| Language: | English |
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BMC
2022-10-01
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| Series: | BMC Genomics |
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| Online Access: | https://doi.org/10.1186/s12864-022-08915-9 |
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| author | Liang Chen Jie Hua Xiaopu He |
| author_facet | Liang Chen Jie Hua Xiaopu He |
| author_sort | Liang Chen |
| collection | DOAJ |
| description | Abstract Background Influenza is a contagious disease that affects people of all ages and is linked to considerable mortality during epidemics and occasional outbreaks. Moreover, effective immunological biomarkers are needed for elucidating aetiology and preventing and treating severe influenza. Herein, we aimed to evaluate the key genes linked with the disease severity in influenza patients needing invasive mechanical ventilation (IMV). Three gene microarray data sets (GSE101702, GSE21802, and GSE111368) from blood samples of influenza patients were made available by the Gene Expression Omnibus (GEO) database. The GSE101702 and GSE21802 data sets were combined to create the training set. Hub indicators for IMV patients with severe influenza were determined using differential expression analysis and Weighted correlation network analysis (WGCNA) from the training set. The receiver operating characteristic curve (ROC) was also used to evaluate the hub genes from the test set's diagnostic accuracy. Different immune cells' infiltration levels in the expression profile and their correlation with hub gene markers were examined using single-sample gene set enrichment analysis (ssGSEA). Results In the present study, we evaluated a total of 447 differential genes. WGCNA identified eight co-expression modules, with the red module having the strongest correlation with IMV patients. Differential genes were combined to obtain 3 hub genes (HLA-DPA1, HLA-DRB3, and CECR1). The identified genes were investigated as potential indicators for patients with severe influenza who required IMV using the least absolute shrinkage and selection operator (LASSO) approach. The ROC showed the diagnostic value of the three hub genes in determining the severity of influenza. Using ssGSEA, it has been revealed that the expression of key genes was negatively correlated with neutrophil activation and positively associated with adaptive cellular immune response. Conclusion We evaluated three novel hub genes that could be linked to the immunopathological mechanism of severe influenza patients who require IMV treatment and could be used as potential biomarkers for severe influenza prevention and treatment. |
| first_indexed | 2024-04-11T19:33:35Z |
| format | Article |
| id | doaj.art-edcdf6fe8707416bb0cfdd7f3ffc77a2 |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-04-11T19:33:35Z |
| publishDate | 2022-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-edcdf6fe8707416bb0cfdd7f3ffc77a22022-12-22T04:06:56ZengBMCBMC Genomics1471-21642022-10-012311910.1186/s12864-022-08915-9Co-expression network analysis identifies potential candidate hub genes in severe influenza patients needing invasive mechanical ventilationLiang Chen0Jie Hua1Xiaopu He2Department of Infectious Diseases, Nanjing Lishui People’s Hospital, Zhongda Hospital Lishui Branch, Southeast UniversityDepartment of Gastroenterology, Liyang People’s Hospital, Liyang Branch Hospital of Jiangsu Province HospitalDepartment of Geriatric Gastroenterology, The First Affiliated Hospital With Nanjing Medical UniversityAbstract Background Influenza is a contagious disease that affects people of all ages and is linked to considerable mortality during epidemics and occasional outbreaks. Moreover, effective immunological biomarkers are needed for elucidating aetiology and preventing and treating severe influenza. Herein, we aimed to evaluate the key genes linked with the disease severity in influenza patients needing invasive mechanical ventilation (IMV). Three gene microarray data sets (GSE101702, GSE21802, and GSE111368) from blood samples of influenza patients were made available by the Gene Expression Omnibus (GEO) database. The GSE101702 and GSE21802 data sets were combined to create the training set. Hub indicators for IMV patients with severe influenza were determined using differential expression analysis and Weighted correlation network analysis (WGCNA) from the training set. The receiver operating characteristic curve (ROC) was also used to evaluate the hub genes from the test set's diagnostic accuracy. Different immune cells' infiltration levels in the expression profile and their correlation with hub gene markers were examined using single-sample gene set enrichment analysis (ssGSEA). Results In the present study, we evaluated a total of 447 differential genes. WGCNA identified eight co-expression modules, with the red module having the strongest correlation with IMV patients. Differential genes were combined to obtain 3 hub genes (HLA-DPA1, HLA-DRB3, and CECR1). The identified genes were investigated as potential indicators for patients with severe influenza who required IMV using the least absolute shrinkage and selection operator (LASSO) approach. The ROC showed the diagnostic value of the three hub genes in determining the severity of influenza. Using ssGSEA, it has been revealed that the expression of key genes was negatively correlated with neutrophil activation and positively associated with adaptive cellular immune response. Conclusion We evaluated three novel hub genes that could be linked to the immunopathological mechanism of severe influenza patients who require IMV treatment and could be used as potential biomarkers for severe influenza prevention and treatment.https://doi.org/10.1186/s12864-022-08915-9Severe influenzaInvasive mechanical ventilationCo-expression network analysisHub gene |
| spellingShingle | Liang Chen Jie Hua Xiaopu He Co-expression network analysis identifies potential candidate hub genes in severe influenza patients needing invasive mechanical ventilation BMC Genomics Severe influenza Invasive mechanical ventilation Co-expression network analysis Hub gene |
| title | Co-expression network analysis identifies potential candidate hub genes in severe influenza patients needing invasive mechanical ventilation |
| title_full | Co-expression network analysis identifies potential candidate hub genes in severe influenza patients needing invasive mechanical ventilation |
| title_fullStr | Co-expression network analysis identifies potential candidate hub genes in severe influenza patients needing invasive mechanical ventilation |
| title_full_unstemmed | Co-expression network analysis identifies potential candidate hub genes in severe influenza patients needing invasive mechanical ventilation |
| title_short | Co-expression network analysis identifies potential candidate hub genes in severe influenza patients needing invasive mechanical ventilation |
| title_sort | co expression network analysis identifies potential candidate hub genes in severe influenza patients needing invasive mechanical ventilation |
| topic | Severe influenza Invasive mechanical ventilation Co-expression network analysis Hub gene |
| url | https://doi.org/10.1186/s12864-022-08915-9 |
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