Identification and immunological characterization of cuproptosis-related molecular clusters in idiopathic pulmonary fibrosis disease
BackgroundIdiopathic pulmonary fibrosis (IPF) has attracted considerable attention worldwide and is challenging to diagnose. Cuproptosis is a new form of cell death that seems to be associated with various diseases. However, whether cuproptosis-related genes (CRGs) play a role in regulating IPF dise...
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| Language: | English |
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Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1171445/full |
| _version_ | 1797825462535192576 |
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| author | Xuefeng Shi Xuefeng Shi Xuefeng Shi Zhilei Pan Weixiu Cai Yuhao Zhang Jie Duo Ruitian Liu Ting Cai |
| author_facet | Xuefeng Shi Xuefeng Shi Xuefeng Shi Zhilei Pan Weixiu Cai Yuhao Zhang Jie Duo Ruitian Liu Ting Cai |
| author_sort | Xuefeng Shi |
| collection | DOAJ |
| description | BackgroundIdiopathic pulmonary fibrosis (IPF) has attracted considerable attention worldwide and is challenging to diagnose. Cuproptosis is a new form of cell death that seems to be associated with various diseases. However, whether cuproptosis-related genes (CRGs) play a role in regulating IPF disease is unknown. This study aims to analyze the effect of CRGs on the progression of IPF and identify possible biomarkers.MethodsBased on the GSE38958 dataset, we systematically evaluated the differentially expressed CRGs and immune characteristics of IPF disease. We then explored the cuproptosis-related molecular clusters, the related immune cell infiltration, and the biological characteristics analysis. Subsequently, a weighted gene co-expression network analysis (WGCNA) was performed to identify cluster-specific differentially expressed genes. Lastly, the eXtreme Gradient Boosting (XGB) machine-learning model was chosen for the analysis of prediction and external datasets validated the predictive efficiency.ResultsNine differentially expressed CRGs were identified between healthy and IPF patients. IPF patients showed higher monocytes and monophages M0 infiltration and lower naive B cells and memory resting T CD4 cells infiltration than healthy individuals. A positive relationship was found between activated dendritic cells and CRGs of LIPT1, LIAS, GLS, and DBT. We also identified cuproptosis subtypes in IPF patients. Go and KEGG pathways analysis demonstrated that cluster-specific differentially expressed genes in Cluster 2 were closely related to monocyte aggregation, ubiquitin ligase complex, and ubiquitin-mediated proteolysis, among others. We also constructed an XGB machine model to diagnose IPF, presenting the best performance with a relatively lower residual and higher area under the curve (AUC= 0.700) and validated by external validation datasets (GSE33566, AUC = 0.700). The analysis of the nomogram model demonstrated that XKR6, MLLT3, CD40LG, and HK3 might be used to diagnose IPF disease. Further analysis revealed that CD40LG was significantly associated with IPF.ConclusionOur study systematically illustrated the complicated relationship between cuproptosis and IPF disease, and constructed an effective model for the diagnosis of IPF disease patients. |
| first_indexed | 2024-03-13T10:54:23Z |
| format | Article |
| id | doaj.art-7639d2a947454f01904b809eed9c3797 |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-03-13T10:54:23Z |
| publishDate | 2023-05-01 |
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| series | Frontiers in Immunology |
| spelling | doaj.art-7639d2a947454f01904b809eed9c37972023-05-17T05:43:24ZengFrontiers Media S.A.Frontiers in Immunology1664-32242023-05-011410.3389/fimmu.2023.11714451171445Identification and immunological characterization of cuproptosis-related molecular clusters in idiopathic pulmonary fibrosis diseaseXuefeng Shi0Xuefeng Shi1Xuefeng Shi2Zhilei Pan3Weixiu Cai4Yuhao Zhang5Jie Duo6Ruitian Liu7Ting Cai8Department of Experimental Medical Science, Ningbo No.2 Hospital, Ningbo, ChinaDepartment of Pulmonary and Critial Care medicine, Qinghai provincial people’s hospital, Xining, ChinaState Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, ChinaDepartment of Pulmonary and Critial Care medicine, Qinghai provincial people’s hospital, Xining, ChinaDepartment of Pulmonary and Critial Care medicine, Qinghai provincial people’s hospital, Xining, ChinaCancer Center, Department of Neurosurgery, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, ChinaDepartment of Pulmonary and Critial Care medicine, Qinghai provincial people’s hospital, Xining, ChinaState Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, ChinaDepartment of Experimental Medical Science, Ningbo No.2 Hospital, Ningbo, ChinaBackgroundIdiopathic pulmonary fibrosis (IPF) has attracted considerable attention worldwide and is challenging to diagnose. Cuproptosis is a new form of cell death that seems to be associated with various diseases. However, whether cuproptosis-related genes (CRGs) play a role in regulating IPF disease is unknown. This study aims to analyze the effect of CRGs on the progression of IPF and identify possible biomarkers.MethodsBased on the GSE38958 dataset, we systematically evaluated the differentially expressed CRGs and immune characteristics of IPF disease. We then explored the cuproptosis-related molecular clusters, the related immune cell infiltration, and the biological characteristics analysis. Subsequently, a weighted gene co-expression network analysis (WGCNA) was performed to identify cluster-specific differentially expressed genes. Lastly, the eXtreme Gradient Boosting (XGB) machine-learning model was chosen for the analysis of prediction and external datasets validated the predictive efficiency.ResultsNine differentially expressed CRGs were identified between healthy and IPF patients. IPF patients showed higher monocytes and monophages M0 infiltration and lower naive B cells and memory resting T CD4 cells infiltration than healthy individuals. A positive relationship was found between activated dendritic cells and CRGs of LIPT1, LIAS, GLS, and DBT. We also identified cuproptosis subtypes in IPF patients. Go and KEGG pathways analysis demonstrated that cluster-specific differentially expressed genes in Cluster 2 were closely related to monocyte aggregation, ubiquitin ligase complex, and ubiquitin-mediated proteolysis, among others. We also constructed an XGB machine model to diagnose IPF, presenting the best performance with a relatively lower residual and higher area under the curve (AUC= 0.700) and validated by external validation datasets (GSE33566, AUC = 0.700). The analysis of the nomogram model demonstrated that XKR6, MLLT3, CD40LG, and HK3 might be used to diagnose IPF disease. Further analysis revealed that CD40LG was significantly associated with IPF.ConclusionOur study systematically illustrated the complicated relationship between cuproptosis and IPF disease, and constructed an effective model for the diagnosis of IPF disease patients.https://www.frontiersin.org/articles/10.3389/fimmu.2023.1171445/fullidiopathic pulmonary fibrosis diseasecuproptosismachine learningimmune infiltrationmolecular clusters |
| spellingShingle | Xuefeng Shi Xuefeng Shi Xuefeng Shi Zhilei Pan Weixiu Cai Yuhao Zhang Jie Duo Ruitian Liu Ting Cai Identification and immunological characterization of cuproptosis-related molecular clusters in idiopathic pulmonary fibrosis disease Frontiers in Immunology idiopathic pulmonary fibrosis disease cuproptosis machine learning immune infiltration molecular clusters |
| title | Identification and immunological characterization of cuproptosis-related molecular clusters in idiopathic pulmonary fibrosis disease |
| title_full | Identification and immunological characterization of cuproptosis-related molecular clusters in idiopathic pulmonary fibrosis disease |
| title_fullStr | Identification and immunological characterization of cuproptosis-related molecular clusters in idiopathic pulmonary fibrosis disease |
| title_full_unstemmed | Identification and immunological characterization of cuproptosis-related molecular clusters in idiopathic pulmonary fibrosis disease |
| title_short | Identification and immunological characterization of cuproptosis-related molecular clusters in idiopathic pulmonary fibrosis disease |
| title_sort | identification and immunological characterization of cuproptosis related molecular clusters in idiopathic pulmonary fibrosis disease |
| topic | idiopathic pulmonary fibrosis disease cuproptosis machine learning immune infiltration molecular clusters |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1171445/full |
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