A comprehensive analysis of immune features and construction of an immune gene diagnostic model for sepsis
Abstract Sepsis is a life-threatening syndrome resulting from immune system dysfunction that is caused by infection. It is of great importance to analyze the immune characteristics of sepsis, identify the key immune system related genes, and construct diagnostic models for sepsis. In this study, the...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-12-01
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| Series: | BMC Genomics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12864-023-09896-z |
| _version_ | 1797377203769442304 |
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| author | Haiyan Xue Ziyan Xiao Xiujuan Zhao Shu Li Zhenzhou Wang Jie Zhao Fengxue Zhu |
| author_facet | Haiyan Xue Ziyan Xiao Xiujuan Zhao Shu Li Zhenzhou Wang Jie Zhao Fengxue Zhu |
| author_sort | Haiyan Xue |
| collection | DOAJ |
| description | Abstract Sepsis is a life-threatening syndrome resulting from immune system dysfunction that is caused by infection. It is of great importance to analyze the immune characteristics of sepsis, identify the key immune system related genes, and construct diagnostic models for sepsis. In this study, the sepsis transcriptome and expression profiling data were merged into an integrated dataset containing 277 sepsis samples and 117 non-sepsis control samples. Single-sample gene set enrichment analysis (ssGSEA) was used to assess the immune cell infiltration. Two sepsis immune subtypes were identified based on the 22 differential immune cells between the sepsis and the healthy control groups. Weighted gene co-expression network analysis (WCGNA) was used to identify the key module genes. Then, 36 differentially expressed immune-related genes were identified, based on which a robust diagnostic model was constructed with 11 diagnostic genes. The expression of 11 diagnostic genes was finally assessed in the training and validation datasets respectively. In this study, we provide comprehensive insight into the immune features of sepsis and establish a robust diagnostic model for sepsis. These findings may provide new strategies for the early diagnosis of sepsis in the future. |
| first_indexed | 2024-03-08T19:49:24Z |
| format | Article |
| id | doaj.art-7dd2888aadd343729bfef7241fde7a5f |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-03-08T19:49:24Z |
| publishDate | 2023-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-7dd2888aadd343729bfef7241fde7a5f2023-12-24T12:10:31ZengBMCBMC Genomics1471-21642023-12-0124111410.1186/s12864-023-09896-zA comprehensive analysis of immune features and construction of an immune gene diagnostic model for sepsisHaiyan Xue0Ziyan Xiao1Xiujuan Zhao2Shu Li3Zhenzhou Wang4Jie Zhao5Fengxue Zhu6Department of Critical Care Medicine, Peking University People’s HospitalDepartment of Critical Care Medicine, Peking University People’s HospitalDepartment of Critical Care Medicine, Peking University People’s HospitalDepartment of Critical Care Medicine, Peking University People’s HospitalDepartment of Critical Care Medicine, Peking University People’s HospitalDepartment of Critical Care Medicine, Peking University People’s HospitalDepartment of Critical Care Medicine, Peking University People’s HospitalAbstract Sepsis is a life-threatening syndrome resulting from immune system dysfunction that is caused by infection. It is of great importance to analyze the immune characteristics of sepsis, identify the key immune system related genes, and construct diagnostic models for sepsis. In this study, the sepsis transcriptome and expression profiling data were merged into an integrated dataset containing 277 sepsis samples and 117 non-sepsis control samples. Single-sample gene set enrichment analysis (ssGSEA) was used to assess the immune cell infiltration. Two sepsis immune subtypes were identified based on the 22 differential immune cells between the sepsis and the healthy control groups. Weighted gene co-expression network analysis (WCGNA) was used to identify the key module genes. Then, 36 differentially expressed immune-related genes were identified, based on which a robust diagnostic model was constructed with 11 diagnostic genes. The expression of 11 diagnostic genes was finally assessed in the training and validation datasets respectively. In this study, we provide comprehensive insight into the immune features of sepsis and establish a robust diagnostic model for sepsis. These findings may provide new strategies for the early diagnosis of sepsis in the future.https://doi.org/10.1186/s12864-023-09896-zSepsisInfectionImmune disorderImmune subtypeDiagnostic model |
| spellingShingle | Haiyan Xue Ziyan Xiao Xiujuan Zhao Shu Li Zhenzhou Wang Jie Zhao Fengxue Zhu A comprehensive analysis of immune features and construction of an immune gene diagnostic model for sepsis BMC Genomics Sepsis Infection Immune disorder Immune subtype Diagnostic model |
| title | A comprehensive analysis of immune features and construction of an immune gene diagnostic model for sepsis |
| title_full | A comprehensive analysis of immune features and construction of an immune gene diagnostic model for sepsis |
| title_fullStr | A comprehensive analysis of immune features and construction of an immune gene diagnostic model for sepsis |
| title_full_unstemmed | A comprehensive analysis of immune features and construction of an immune gene diagnostic model for sepsis |
| title_short | A comprehensive analysis of immune features and construction of an immune gene diagnostic model for sepsis |
| title_sort | comprehensive analysis of immune features and construction of an immune gene diagnostic model for sepsis |
| topic | Sepsis Infection Immune disorder Immune subtype Diagnostic model |
| url | https://doi.org/10.1186/s12864-023-09896-z |
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