Deciphering the molecular classification of pediatric sepsis: integrating WGCNA and machine learning-based classification with immune signatures for the development of an advanced diagnostic model
Introduction: Pediatric sepsis (PS) is a life-threatening infection associated with high mortality rates, necessitating a deeper understanding of its underlying pathological mechanisms. Recently discovered programmed cell death induced by copper has been implicated in various medical conditions, but...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-01-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2024.1294381/full |
| _version_ | 1797341210650607616 |
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| author | Junming Huang Junming Huang Jinji Chen Chengbang Wang Lichuan Lai Hua Mi Shaohua Chen |
| author_facet | Junming Huang Junming Huang Jinji Chen Chengbang Wang Lichuan Lai Hua Mi Shaohua Chen |
| author_sort | Junming Huang |
| collection | DOAJ |
| description | Introduction: Pediatric sepsis (PS) is a life-threatening infection associated with high mortality rates, necessitating a deeper understanding of its underlying pathological mechanisms. Recently discovered programmed cell death induced by copper has been implicated in various medical conditions, but its potential involvement in PS remains largely unexplored.Methods: We first analyzed the expression patterns of cuproptosis-related genes (CRGs) and assessed the immune landscape of PS using the GSE66099 dataset. Subsequently, PS samples were isolated from the same dataset, and consensus clustering was performed based on differentially expressed CRGs. We applied weighted gene co-expression network analysis to identify hub genes associated with PS and cuproptosis.Results: We observed aberrant expression of 27 CRGs and a specific immune landscape in PS samples. Our findings revealed that patients in the GSE66099 dataset could be categorized into two cuproptosis clusters, each characterized by unique immune landscapes and varying functional classifications or enriched pathways. Among the machine learning approaches, Extreme Gradient Boosting demonstrated optimal performance as a diagnostic model for PS.Discussion: Our study provides valuable insights into the molecular mechanisms underlying PS, highlighting the involvement of cuproptosis-related genes and immune cell infiltration. |
| first_indexed | 2024-03-08T10:14:34Z |
| format | Article |
| id | doaj.art-59e48f94e0aa483b9ae5d6b79584460a |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-03-08T10:14:34Z |
| publishDate | 2024-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-59e48f94e0aa483b9ae5d6b79584460a2024-01-29T04:33:57ZengFrontiers Media S.A.Frontiers in Genetics1664-80212024-01-011510.3389/fgene.2024.12943811294381Deciphering the molecular classification of pediatric sepsis: integrating WGCNA and machine learning-based classification with immune signatures for the development of an advanced diagnostic modelJunming Huang0Junming Huang1Jinji Chen2Chengbang Wang3Lichuan Lai4Hua Mi5Shaohua Chen6Department of Urology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, ChinaDepartment of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, ChinaDepartment of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, ChinaDepartment of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, ChinaDepartment of Laboratory, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, ChinaDepartment of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, ChinaDepartment of Urology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, ChinaIntroduction: Pediatric sepsis (PS) is a life-threatening infection associated with high mortality rates, necessitating a deeper understanding of its underlying pathological mechanisms. Recently discovered programmed cell death induced by copper has been implicated in various medical conditions, but its potential involvement in PS remains largely unexplored.Methods: We first analyzed the expression patterns of cuproptosis-related genes (CRGs) and assessed the immune landscape of PS using the GSE66099 dataset. Subsequently, PS samples were isolated from the same dataset, and consensus clustering was performed based on differentially expressed CRGs. We applied weighted gene co-expression network analysis to identify hub genes associated with PS and cuproptosis.Results: We observed aberrant expression of 27 CRGs and a specific immune landscape in PS samples. Our findings revealed that patients in the GSE66099 dataset could be categorized into two cuproptosis clusters, each characterized by unique immune landscapes and varying functional classifications or enriched pathways. Among the machine learning approaches, Extreme Gradient Boosting demonstrated optimal performance as a diagnostic model for PS.Discussion: Our study provides valuable insights into the molecular mechanisms underlying PS, highlighting the involvement of cuproptosis-related genes and immune cell infiltration.https://www.frontiersin.org/articles/10.3389/fgene.2024.1294381/fullpediatric sepsiscuproptosis-related genes (CRGs)cluster analysisgene regulatory networksimmunology |
| spellingShingle | Junming Huang Junming Huang Jinji Chen Chengbang Wang Lichuan Lai Hua Mi Shaohua Chen Deciphering the molecular classification of pediatric sepsis: integrating WGCNA and machine learning-based classification with immune signatures for the development of an advanced diagnostic model Frontiers in Genetics pediatric sepsis cuproptosis-related genes (CRGs) cluster analysis gene regulatory networks immunology |
| title | Deciphering the molecular classification of pediatric sepsis: integrating WGCNA and machine learning-based classification with immune signatures for the development of an advanced diagnostic model |
| title_full | Deciphering the molecular classification of pediatric sepsis: integrating WGCNA and machine learning-based classification with immune signatures for the development of an advanced diagnostic model |
| title_fullStr | Deciphering the molecular classification of pediatric sepsis: integrating WGCNA and machine learning-based classification with immune signatures for the development of an advanced diagnostic model |
| title_full_unstemmed | Deciphering the molecular classification of pediatric sepsis: integrating WGCNA and machine learning-based classification with immune signatures for the development of an advanced diagnostic model |
| title_short | Deciphering the molecular classification of pediatric sepsis: integrating WGCNA and machine learning-based classification with immune signatures for the development of an advanced diagnostic model |
| title_sort | deciphering the molecular classification of pediatric sepsis integrating wgcna and machine learning based classification with immune signatures for the development of an advanced diagnostic model |
| topic | pediatric sepsis cuproptosis-related genes (CRGs) cluster analysis gene regulatory networks immunology |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2024.1294381/full |
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