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Circulating Neutrophil Extracellular Traps Signature for Identifying Organ Involvement and Response to Glucocorticoid in Adult-Onset Still’s Disease: A Machine Learning Study

Adult-onset Still’s disease (AOSD) is an autoinflammatory disease with multisystem involvement. Early identification of patients with severe complications and those refractory to glucocorticoid is crucial to improve therapeutic strategy in AOSD. Exaggerated neutrophil activation and enhanced formati...

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Autores principales: Jia, Jinchao, Wang, Mengyan, Ma, Yuning, Teng, Jialin, Shi, Hui, Liu, Honglei, Sun, Yue, Su, Yutong, Meng, Jianfen, Chi, Huihui, Chen, Xia, Cheng, Xiaobing, Ye, Junna, Liu, Tingting, Wang, Zhihong, Wan, Liyan, Zhou, Zhuochao, Wang, Fan, Yang, Chengde, Hu, Qiongyi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7680913/
https://www.ncbi.nlm.nih.gov/pubmed/33240258
http://dx.doi.org/10.3389/fimmu.2020.563335
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author Jia, Jinchao
Wang, Mengyan
Ma, Yuning
Teng, Jialin
Shi, Hui
Liu, Honglei
Sun, Yue
Su, Yutong
Meng, Jianfen
Chi, Huihui
Chen, Xia
Cheng, Xiaobing
Ye, Junna
Liu, Tingting
Wang, Zhihong
Wan, Liyan
Zhou, Zhuochao
Wang, Fan
Yang, Chengde
Hu, Qiongyi
author_facet Jia, Jinchao
Wang, Mengyan
Ma, Yuning
Teng, Jialin
Shi, Hui
Liu, Honglei
Sun, Yue
Su, Yutong
Meng, Jianfen
Chi, Huihui
Chen, Xia
Cheng, Xiaobing
Ye, Junna
Liu, Tingting
Wang, Zhihong
Wan, Liyan
Zhou, Zhuochao
Wang, Fan
Yang, Chengde
Hu, Qiongyi
author_sort Jia, Jinchao
collection PubMed
description Adult-onset Still’s disease (AOSD) is an autoinflammatory disease with multisystem involvement. Early identification of patients with severe complications and those refractory to glucocorticoid is crucial to improve therapeutic strategy in AOSD. Exaggerated neutrophil activation and enhanced formation of neutrophil extracellular traps (NETs) in patients with AOSD were found to be closely associated with etiopathogenesis. In this study, we aim to investigate, to our knowledge for the first time, the clinical value of circulating NETs by machine learning to distinguish AOSD patients with organ involvement and refractory to glucocorticoid. Plasma samples were used to measure cell-free DNA, NE-DNA, MPO-DNA, and citH3-DNA complexes from training and validation sets. The training set included 40 AOSD patients and 24 healthy controls (HCs), and the validation set included 26 AOSD patients and 16 HCs. Support vector machines (SVM) were used for modeling and validation of circulating NETs signature for the diagnosis of AOSD and identifying patients refractory to low-dose glucocorticoid treatment. The training set was used to build a model, and the validation set was used to test the predictive capacity of the model. A total of four circulating NETs showed similar trends in different individuals and could distinguish patients with AOSD from HCs by SVM (AUC value: 0.88). Circulating NETs in plasma were closely correlated with systemic score, laboratory tests, and cytokines. Moreover, circulating NETs had the potential to distinguish patients with liver and cardiopulmonary system involvement. Furthermore, the AUC value of combined NETs to identify patients who were refractory to low-dose glucocorticoid was 0.917. In conclusion, circulating NETs signature provide added clinical value in monitoring AOSD patients. It may provide evidence to predict who is prone to be refractory to low-dose glucocorticoid and help to make efficient therapeutic strategy.
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spelling pubmed-76809132020-11-24 Circulating Neutrophil Extracellular Traps Signature for Identifying Organ Involvement and Response to Glucocorticoid in Adult-Onset Still’s Disease: A Machine Learning Study Jia, Jinchao Wang, Mengyan Ma, Yuning Teng, Jialin Shi, Hui Liu, Honglei Sun, Yue Su, Yutong Meng, Jianfen Chi, Huihui Chen, Xia Cheng, Xiaobing Ye, Junna Liu, Tingting Wang, Zhihong Wan, Liyan Zhou, Zhuochao Wang, Fan Yang, Chengde Hu, Qiongyi Front Immunol Immunology Adult-onset Still’s disease (AOSD) is an autoinflammatory disease with multisystem involvement. Early identification of patients with severe complications and those refractory to glucocorticoid is crucial to improve therapeutic strategy in AOSD. Exaggerated neutrophil activation and enhanced formation of neutrophil extracellular traps (NETs) in patients with AOSD were found to be closely associated with etiopathogenesis. In this study, we aim to investigate, to our knowledge for the first time, the clinical value of circulating NETs by machine learning to distinguish AOSD patients with organ involvement and refractory to glucocorticoid. Plasma samples were used to measure cell-free DNA, NE-DNA, MPO-DNA, and citH3-DNA complexes from training and validation sets. The training set included 40 AOSD patients and 24 healthy controls (HCs), and the validation set included 26 AOSD patients and 16 HCs. Support vector machines (SVM) were used for modeling and validation of circulating NETs signature for the diagnosis of AOSD and identifying patients refractory to low-dose glucocorticoid treatment. The training set was used to build a model, and the validation set was used to test the predictive capacity of the model. A total of four circulating NETs showed similar trends in different individuals and could distinguish patients with AOSD from HCs by SVM (AUC value: 0.88). Circulating NETs in plasma were closely correlated with systemic score, laboratory tests, and cytokines. Moreover, circulating NETs had the potential to distinguish patients with liver and cardiopulmonary system involvement. Furthermore, the AUC value of combined NETs to identify patients who were refractory to low-dose glucocorticoid was 0.917. In conclusion, circulating NETs signature provide added clinical value in monitoring AOSD patients. It may provide evidence to predict who is prone to be refractory to low-dose glucocorticoid and help to make efficient therapeutic strategy. Frontiers Media S.A. 2020-11-09 /pmc/articles/PMC7680913/ /pubmed/33240258 http://dx.doi.org/10.3389/fimmu.2020.563335 Text en Copyright © 2020 Jia, Wang, Ma, Teng, Shi, Liu, Sun, Su, Meng, Chi, Chen, Cheng, Ye, Liu, Wang, Wan, Zhou, Wang, Yang and Hu http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Jia, Jinchao
Wang, Mengyan
Ma, Yuning
Teng, Jialin
Shi, Hui
Liu, Honglei
Sun, Yue
Su, Yutong
Meng, Jianfen
Chi, Huihui
Chen, Xia
Cheng, Xiaobing
Ye, Junna
Liu, Tingting
Wang, Zhihong
Wan, Liyan
Zhou, Zhuochao
Wang, Fan
Yang, Chengde
Hu, Qiongyi
Circulating Neutrophil Extracellular Traps Signature for Identifying Organ Involvement and Response to Glucocorticoid in Adult-Onset Still’s Disease: A Machine Learning Study
title Circulating Neutrophil Extracellular Traps Signature for Identifying Organ Involvement and Response to Glucocorticoid in Adult-Onset Still’s Disease: A Machine Learning Study
title_full Circulating Neutrophil Extracellular Traps Signature for Identifying Organ Involvement and Response to Glucocorticoid in Adult-Onset Still’s Disease: A Machine Learning Study
title_fullStr Circulating Neutrophil Extracellular Traps Signature for Identifying Organ Involvement and Response to Glucocorticoid in Adult-Onset Still’s Disease: A Machine Learning Study
title_full_unstemmed Circulating Neutrophil Extracellular Traps Signature for Identifying Organ Involvement and Response to Glucocorticoid in Adult-Onset Still’s Disease: A Machine Learning Study
title_short Circulating Neutrophil Extracellular Traps Signature for Identifying Organ Involvement and Response to Glucocorticoid in Adult-Onset Still’s Disease: A Machine Learning Study
title_sort circulating neutrophil extracellular traps signature for identifying organ involvement and response to glucocorticoid in adult-onset still’s disease: a machine learning study
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7680913/
https://www.ncbi.nlm.nih.gov/pubmed/33240258
http://dx.doi.org/10.3389/fimmu.2020.563335
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