Memory CD4+T cell profile is associated with unfavorable prognosis in IgG4-related disease: Risk stratification by machine-learning
IgG4-related disease (IgG4-RD) is a chronic immune-mediated disease with heterogeneity. In this study, we used machine-learning approaches to characterize the immune cell profiles and to identify the heterogeneity of IgG4-RD. The XGBoost model discriminated IgG4-RD from HCs with an area under the re...
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| Published in | Clinical immunology (Orlando, Fla.) Vol. 252; p. 109301 |
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| Main Authors | , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
01.07.2023
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| Online Access | Get full text |
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| Abstract | IgG4-related disease (IgG4-RD) is a chronic immune-mediated disease with heterogeneity. In this study, we used machine-learning approaches to characterize the immune cell profiles and to identify the heterogeneity of IgG4-RD. The XGBoost model discriminated IgG4-RD from HCs with an area under the receiver operating characteristic curve of 0.963 in the testing set. There were two clusters of IgG4-RD by k-means clustering of immunological profiles. Cluster 1 featured higher proportions of memory CD4+T cell and were at higher risk of unfavorable prognosis in the follow-up, while cluster 2 featured higher proportions of naïve CD4+T cell. In the multivariate logistic regression, cluster 2 was shown to be a protective factor (OR 0.30, 95% CI 0.10–0.91, P = 0.011). Therefore, peripheral immunophenotyping might potentially stratify patients with IgG4-RD and predict those patients with a higher risk of relapse at early time.
•IgG4-RD patients had altered immunological subsets, by which can be distinguished from healthy people.•Machine-learning approach was effective to detect the features of immunophenotype of IgG4-RD and to identify the heterogeneity of IgG4-RD.•IgG4-RD patients with elevated of CD4+ memory T cells were at higher risk for relapse, calling for more attention during follow-up. |
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| AbstractList | IgG4-related disease (IgG4-RD) is a chronic immune-mediated disease with heterogeneity. In this study, we used machine-learning approaches to characterize the immune cell profiles and to identify the heterogeneity of IgG4-RD. The XGBoost model discriminated IgG4-RD from HCs with an area under the receiver operating characteristic curve of 0.963 in the testing set. There were two clusters of IgG4-RD by k-means clustering of immunological profiles. Cluster 1 featured higher proportions of memory CD4+T cell and were at higher risk of unfavorable prognosis in the follow-up, while cluster 2 featured higher proportions of naïve CD4+T cell. In the multivariate logistic regression, cluster 2 was shown to be a protective factor (OR 0.30, 95% CI 0.10-0.91, P = 0.011). Therefore, peripheral immunophenotyping might potentially stratify patients with IgG4-RD and predict those patients with a higher risk of relapse at early time.IgG4-related disease (IgG4-RD) is a chronic immune-mediated disease with heterogeneity. In this study, we used machine-learning approaches to characterize the immune cell profiles and to identify the heterogeneity of IgG4-RD. The XGBoost model discriminated IgG4-RD from HCs with an area under the receiver operating characteristic curve of 0.963 in the testing set. There were two clusters of IgG4-RD by k-means clustering of immunological profiles. Cluster 1 featured higher proportions of memory CD4+T cell and were at higher risk of unfavorable prognosis in the follow-up, while cluster 2 featured higher proportions of naïve CD4+T cell. In the multivariate logistic regression, cluster 2 was shown to be a protective factor (OR 0.30, 95% CI 0.10-0.91, P = 0.011). Therefore, peripheral immunophenotyping might potentially stratify patients with IgG4-RD and predict those patients with a higher risk of relapse at early time. IgG4-related disease (IgG4-RD) is a chronic immune-mediated disease with heterogeneity. In this study, we used machine-learning approaches to characterize the immune cell profiles and to identify the heterogeneity of IgG4-RD. The XGBoost model discriminated IgG4-RD from HCs with an area under the receiver operating characteristic curve of 0.963 in the testing set. There were two clusters of IgG4-RD by k-means clustering of immunological profiles. Cluster 1 featured higher proportions of memory CD4+T cell and were at higher risk of unfavorable prognosis in the follow-up, while cluster 2 featured higher proportions of naïve CD4+T cell. In the multivariate logistic regression, cluster 2 was shown to be a protective factor (OR 0.30, 95% CI 0.10–0.91, P = 0.011). Therefore, peripheral immunophenotyping might potentially stratify patients with IgG4-RD and predict those patients with a higher risk of relapse at early time. •IgG4-RD patients had altered immunological subsets, by which can be distinguished from healthy people.•Machine-learning approach was effective to detect the features of immunophenotype of IgG4-RD and to identify the heterogeneity of IgG4-RD.•IgG4-RD patients with elevated of CD4+ memory T cells were at higher risk for relapse, calling for more attention during follow-up. IgG4-related disease (IgG4-RD) is a chronic immune-mediated disease with heterogeneity. In this study, we used machine-learning approaches to characterize the immune cell profiles and to identify the heterogeneity of IgG4-RD. The XGBoost model discriminated IgG4-RD from HCs with an area under the receiver operating characteristic curve of 0.963 in the testing set. There were two clusters of IgG4-RD by k-means clustering of immunological profiles. Cluster 1 featured higher proportions of memory CD4 T cell and were at higher risk of unfavorable prognosis in the follow-up, while cluster 2 featured higher proportions of naïve CD4 T cell. In the multivariate logistic regression, cluster 2 was shown to be a protective factor (OR 0.30, 95% CI 0.10-0.91, P = 0.011). Therefore, peripheral immunophenotyping might potentially stratify patients with IgG4-RD and predict those patients with a higher risk of relapse at early time. |
| ArticleNumber | 109301 |
| Author | Li, Jingna Peng, Linyi Lu, Hui Li, Mengtao Cao, Wei Sun, Ruijie Fei, Yunyun Zhou, Jiaxin Peng, Yu Zeng, Xiaofeng Li, Taisheng Nie, Yuxue Zhang, Wen Liu, Zheng |
| Author_xml | – sequence: 1 givenname: Yuxue surname: Nie fullname: Nie, Yuxue organization: Department of Rheumatology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases, State Key Laboratory of Complex Severe and Rare Diseases, Beijing, China – sequence: 2 givenname: Zheng surname: Liu fullname: Liu, Zheng organization: Department of Rheumatology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases, State Key Laboratory of Complex Severe and Rare Diseases, Beijing, China – sequence: 3 givenname: Wei surname: Cao fullname: Cao, Wei organization: Department of Infectious Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China – sequence: 4 givenname: Yu surname: Peng fullname: Peng, Yu organization: Department of Rheumatology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases, State Key Laboratory of Complex Severe and Rare Diseases, Beijing, China – sequence: 5 givenname: Hui surname: Lu fullname: Lu, Hui organization: Department of Rheumatology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases, State Key Laboratory of Complex Severe and Rare Diseases, Beijing, China – sequence: 6 givenname: Ruijie surname: Sun fullname: Sun, Ruijie organization: Department of Rheumatology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases, State Key Laboratory of Complex Severe and Rare Diseases, Beijing, China – sequence: 7 givenname: Jingna surname: Li fullname: Li, Jingna organization: Department of Rheumatology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases, State Key Laboratory of Complex Severe and Rare Diseases, Beijing, China – sequence: 8 givenname: Linyi surname: Peng fullname: Peng, Linyi organization: Department of Rheumatology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases, State Key Laboratory of Complex Severe and Rare Diseases, Beijing, China – sequence: 9 givenname: Jiaxin surname: Zhou fullname: Zhou, Jiaxin organization: Department of Rheumatology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases, State Key Laboratory of Complex Severe and Rare Diseases, Beijing, China – sequence: 10 givenname: Yunyun surname: Fei fullname: Fei, Yunyun organization: Department of Rheumatology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases, State Key Laboratory of Complex Severe and Rare Diseases, Beijing, China – sequence: 11 givenname: Mengtao surname: Li fullname: Li, Mengtao organization: Department of Rheumatology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases, State Key Laboratory of Complex Severe and Rare Diseases, Beijing, China – sequence: 12 givenname: Xiaofeng surname: Zeng fullname: Zeng, Xiaofeng organization: Department of Rheumatology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases, State Key Laboratory of Complex Severe and Rare Diseases, Beijing, China – sequence: 13 givenname: Wen surname: Zhang fullname: Zhang, Wen email: zhangwen91@sina.com organization: Department of Rheumatology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases, State Key Laboratory of Complex Severe and Rare Diseases, Beijing, China – sequence: 14 givenname: Taisheng surname: Li fullname: Li, Taisheng email: litsh@263.net organization: Department of Infectious Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China |
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| Keywords | SHAP Immunophenotype IM ROC Th2 cells AUC IgG4-related disease RTX Tfh RI PD1 GC TEM IgG4-RD ML PLSDA |
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| Snippet | IgG4-related disease (IgG4-RD) is a chronic immune-mediated disease with heterogeneity. In this study, we used machine-learning approaches to characterize the... |
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| SubjectTerms | CD4-Positive T-Lymphocytes Humans IgG4-related disease Immunoglobulin G4-Related Disease Immunophenotype Machine Learning Prognosis Risk Assessment |
| Title | Memory CD4+T cell profile is associated with unfavorable prognosis in IgG4-related disease: Risk stratification by machine-learning |
| URI | https://www.clinicalkey.com/#!/content/1-s2.0-S1521661623000803 https://dx.doi.org/10.1016/j.clim.2023.109301 https://www.ncbi.nlm.nih.gov/pubmed/36958412 https://www.proquest.com/docview/2791373560 |
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