Construction of a Cuproptosis-Related Gene Clinical Prediction Model for Juvenile Idiopathic Arthritis Using Machine Learning

Yiwei Hong; Xu Cai; Xinpeng Chen; Xinmin Huang; Zhengbo Yan; Peihu Li; Jianwei Xiao

doi:doi:10.11648/j.ijgg.20231104.14

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Construction of a Cuproptosis-Related Gene Clinical Prediction Model for Juvenile Idiopathic Arthritis Using Machine Learning

Yiwei Hong, Xu Cai, Xinpeng Chen, Xinmin Huang, Zhengbo Yan, Peihu Li, Jianwei Xiao^*

Published in International Journal of Genetics and Genomics (Volume 11, Issue 4)

Received: 23 November 2023 Accepted: 7 December 2023 Published: 22 December 2023

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Abstract

Objective: Juvenile Idiopathic Arthritis (JIA) is a chronic inflammatory joint disease affecting children and adolescents, where early diagnosis and treatment are crucial for improving prognosis. This study aimed to identify cuproptosis-related genes in JIA and develop a clinical predictive model. Methods: The GSE13849 dataset was retrieved from the GEO database to extract cuproptosis-related genes. Key JIA genes were selected using the Boruta and SVM-REF algorithms, followed by the construction of a clinical prediction model. The model's predictive capacity was validated using the concordance index (C-index), Receiver Operating Characteristic (ROC) curves, and calibration curves. Patient net benefit was evaluated through clinical decision curves, with internal validation conducted via Bootstrap. Results: The Boruta and SVM-REF algorithms identified four and five core cuproptosis-related genes, respectively, intersecting to yield three core genes (PDHA1, LIAS, DLAT). A clinical prediction model was established using multivariate logistic regression, exhibiting a C-index of 0.75 and an area under the ROC curve of 0.749. Clinical decision curve analysis demonstrated the highest net clinical benefit at a threshold probability range of 0.15 to 0.9, ensuring no harm to other patients. Internal validation reported a C-index of 0.755 and an area under the ROC curve of 0.736. Conclusion: The JIA clinical prediction model, based on three cuproptosis-related genes, demonstrates substantial predictive diagnostic capability, contributing to the early diagnosis of JIA patients.

Published in	International Journal of Genetics and Genomics (Volume 11, Issue 4)
DOI	10.11648/j.ijgg.20231104.14
Page(s)	133-138
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Juvenile Idiopathic Arthritis, Cuproptosis, Machine Learning, Clinical Prediction Model, Boruta, SVM-REF

References

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APA Style

Hong, Y., Cai, X., Chen, X., Huang, X., Yan, Z., et al. (2023). Construction of a Cuproptosis-Related Gene Clinical Prediction Model for Juvenile Idiopathic Arthritis Using Machine Learning. International Journal of Genetics and Genomics, 11(4), 133-138. https://doi.org/10.11648/j.ijgg.20231104.14

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ACS Style

Hong, Y.; Cai, X.; Chen, X.; Huang, X.; Yan, Z., et al. Construction of a Cuproptosis-Related Gene Clinical Prediction Model for Juvenile Idiopathic Arthritis Using Machine Learning. Int. J. Genet. Genomics 2023, 11(4), 133-138. doi: 10.11648/j.ijgg.20231104.14

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AMA Style

Hong Y, Cai X, Chen X, Huang X, Yan Z, et al. Construction of a Cuproptosis-Related Gene Clinical Prediction Model for Juvenile Idiopathic Arthritis Using Machine Learning. Int J Genet Genomics. 2023;11(4):133-138. doi: 10.11648/j.ijgg.20231104.14

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@article{10.11648/j.ijgg.20231104.14,
  author = {Yiwei Hong and Xu Cai and Xinpeng Chen and Xinmin Huang and Zhengbo Yan and Peihu Li and Jianwei Xiao},
  title = {Construction of a Cuproptosis-Related Gene Clinical Prediction Model for Juvenile Idiopathic Arthritis Using Machine Learning},
  journal = {International Journal of Genetics and Genomics},
  volume = {11},
  number = {4},
  pages = {133-138},
  doi = {10.11648/j.ijgg.20231104.14},
  url = {https://doi.org/10.11648/j.ijgg.20231104.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijgg.20231104.14},
  abstract = {Objective: Juvenile Idiopathic Arthritis (JIA) is a chronic inflammatory joint disease affecting children and adolescents, where early diagnosis and treatment are crucial for improving prognosis. This study aimed to identify cuproptosis-related genes in JIA and develop a clinical predictive model. Methods: The GSE13849 dataset was retrieved from the GEO database to extract cuproptosis-related genes. Key JIA genes were selected using the Boruta and SVM-REF algorithms, followed by the construction of a clinical prediction model. The model's predictive capacity was validated using the concordance index (C-index), Receiver Operating Characteristic (ROC) curves, and calibration curves. Patient net benefit was evaluated through clinical decision curves, with internal validation conducted via Bootstrap. Results: The Boruta and SVM-REF algorithms identified four and five core cuproptosis-related genes, respectively, intersecting to yield three core genes (PDHA1, LIAS, DLAT). A clinical prediction model was established using multivariate logistic regression, exhibiting a C-index of 0.75 and an area under the ROC curve of 0.749. Clinical decision curve analysis demonstrated the highest net clinical benefit at a threshold probability range of 0.15 to 0.9, ensuring no harm to other patients. Internal validation reported a C-index of 0.755 and an area under the ROC curve of 0.736. Conclusion: The JIA clinical prediction model, based on three cuproptosis-related genes, demonstrates substantial predictive diagnostic capability, contributing to the early diagnosis of JIA patients.
},
 year = {2023}
}

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TY  - JOUR
T1  - Construction of a Cuproptosis-Related Gene Clinical Prediction Model for Juvenile Idiopathic Arthritis Using Machine Learning
AU  - Yiwei Hong
AU  - Xu Cai
AU  - Xinpeng Chen
AU  - Xinmin Huang
AU  - Zhengbo Yan
AU  - Peihu Li
AU  - Jianwei Xiao
Y1  - 2023/12/22
PY  - 2023
N1  - https://doi.org/10.11648/j.ijgg.20231104.14
DO  - 10.11648/j.ijgg.20231104.14
T2  - International Journal of Genetics and Genomics
JF  - International Journal of Genetics and Genomics
JO  - International Journal of Genetics and Genomics
SP  - 133
EP  - 138
PB  - Science Publishing Group
SN  - 2376-7359
UR  - https://doi.org/10.11648/j.ijgg.20231104.14
AB  - Objective: Juvenile Idiopathic Arthritis (JIA) is a chronic inflammatory joint disease affecting children and adolescents, where early diagnosis and treatment are crucial for improving prognosis. This study aimed to identify cuproptosis-related genes in JIA and develop a clinical predictive model. Methods: The GSE13849 dataset was retrieved from the GEO database to extract cuproptosis-related genes. Key JIA genes were selected using the Boruta and SVM-REF algorithms, followed by the construction of a clinical prediction model. The model's predictive capacity was validated using the concordance index (C-index), Receiver Operating Characteristic (ROC) curves, and calibration curves. Patient net benefit was evaluated through clinical decision curves, with internal validation conducted via Bootstrap. Results: The Boruta and SVM-REF algorithms identified four and five core cuproptosis-related genes, respectively, intersecting to yield three core genes (PDHA1, LIAS, DLAT). A clinical prediction model was established using multivariate logistic regression, exhibiting a C-index of 0.75 and an area under the ROC curve of 0.749. Clinical decision curve analysis demonstrated the highest net clinical benefit at a threshold probability range of 0.15 to 0.9, ensuring no harm to other patients. Internal validation reported a C-index of 0.755 and an area under the ROC curve of 0.736. Conclusion: The JIA clinical prediction model, based on three cuproptosis-related genes, demonstrates substantial predictive diagnostic capability, contributing to the early diagnosis of JIA patients.

VL  - 11
IS  - 4
ER  -

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Author Information

Yiwei Hong

Rheumatology, Shenzhen Futian Hospital for Rheumatic Diseases, Shenzhen, China
Xu Cai

Rheumatology, Shenzhen Futian Hospital for Rheumatic Diseases, Shenzhen, China
Xinpeng Chen

Rheumatology, Shenzhen Futian Hospital for Rheumatic Diseases, Shenzhen, China
Xinmin Huang

Rheumatology, Shenzhen Futian Hospital for Rheumatic Diseases, Shenzhen, China
Zhengbo Yan

Rheumatology, Shenzhen Futian Hospital for Rheumatic Diseases, Shenzhen, China
Peihu Li

Rheumatology, Shenzhen Futian Hospital for Rheumatic Diseases, Shenzhen, China
Jianwei Xiao

Rheumatology, Shenzhen Futian Hospital for Rheumatic Diseases, Shenzhen, China

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APA Style

Hong, Y., Cai, X., Chen, X., Huang, X., Yan, Z., et al. (2023). Construction of a Cuproptosis-Related Gene Clinical Prediction Model for Juvenile Idiopathic Arthritis Using Machine Learning. International Journal of Genetics and Genomics, 11(4), 133-138. https://doi.org/10.11648/j.ijgg.20231104.14

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ACS Style

Hong, Y.; Cai, X.; Chen, X.; Huang, X.; Yan, Z., et al. Construction of a Cuproptosis-Related Gene Clinical Prediction Model for Juvenile Idiopathic Arthritis Using Machine Learning. Int. J. Genet. Genomics 2023, 11(4), 133-138. doi: 10.11648/j.ijgg.20231104.14

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AMA Style

Hong Y, Cai X, Chen X, Huang X, Yan Z, et al. Construction of a Cuproptosis-Related Gene Clinical Prediction Model for Juvenile Idiopathic Arthritis Using Machine Learning. Int J Genet Genomics. 2023;11(4):133-138. doi: 10.11648/j.ijgg.20231104.14

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@article{10.11648/j.ijgg.20231104.14,
  author = {Yiwei Hong and Xu Cai and Xinpeng Chen and Xinmin Huang and Zhengbo Yan and Peihu Li and Jianwei Xiao},
  title = {Construction of a Cuproptosis-Related Gene Clinical Prediction Model for Juvenile Idiopathic Arthritis Using Machine Learning},
  journal = {International Journal of Genetics and Genomics},
  volume = {11},
  number = {4},
  pages = {133-138},
  doi = {10.11648/j.ijgg.20231104.14},
  url = {https://doi.org/10.11648/j.ijgg.20231104.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijgg.20231104.14},
  abstract = {Objective: Juvenile Idiopathic Arthritis (JIA) is a chronic inflammatory joint disease affecting children and adolescents, where early diagnosis and treatment are crucial for improving prognosis. This study aimed to identify cuproptosis-related genes in JIA and develop a clinical predictive model. Methods: The GSE13849 dataset was retrieved from the GEO database to extract cuproptosis-related genes. Key JIA genes were selected using the Boruta and SVM-REF algorithms, followed by the construction of a clinical prediction model. The model's predictive capacity was validated using the concordance index (C-index), Receiver Operating Characteristic (ROC) curves, and calibration curves. Patient net benefit was evaluated through clinical decision curves, with internal validation conducted via Bootstrap. Results: The Boruta and SVM-REF algorithms identified four and five core cuproptosis-related genes, respectively, intersecting to yield three core genes (PDHA1, LIAS, DLAT). A clinical prediction model was established using multivariate logistic regression, exhibiting a C-index of 0.75 and an area under the ROC curve of 0.749. Clinical decision curve analysis demonstrated the highest net clinical benefit at a threshold probability range of 0.15 to 0.9, ensuring no harm to other patients. Internal validation reported a C-index of 0.755 and an area under the ROC curve of 0.736. Conclusion: The JIA clinical prediction model, based on three cuproptosis-related genes, demonstrates substantial predictive diagnostic capability, contributing to the early diagnosis of JIA patients.
},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Construction of a Cuproptosis-Related Gene Clinical Prediction Model for Juvenile Idiopathic Arthritis Using Machine Learning
AU  - Yiwei Hong
AU  - Xu Cai
AU  - Xinpeng Chen
AU  - Xinmin Huang
AU  - Zhengbo Yan
AU  - Peihu Li
AU  - Jianwei Xiao
Y1  - 2023/12/22
PY  - 2023
N1  - https://doi.org/10.11648/j.ijgg.20231104.14
DO  - 10.11648/j.ijgg.20231104.14
T2  - International Journal of Genetics and Genomics
JF  - International Journal of Genetics and Genomics
JO  - International Journal of Genetics and Genomics
SP  - 133
EP  - 138
PB  - Science Publishing Group
SN  - 2376-7359
UR  - https://doi.org/10.11648/j.ijgg.20231104.14
AB  - Objective: Juvenile Idiopathic Arthritis (JIA) is a chronic inflammatory joint disease affecting children and adolescents, where early diagnosis and treatment are crucial for improving prognosis. This study aimed to identify cuproptosis-related genes in JIA and develop a clinical predictive model. Methods: The GSE13849 dataset was retrieved from the GEO database to extract cuproptosis-related genes. Key JIA genes were selected using the Boruta and SVM-REF algorithms, followed by the construction of a clinical prediction model. The model's predictive capacity was validated using the concordance index (C-index), Receiver Operating Characteristic (ROC) curves, and calibration curves. Patient net benefit was evaluated through clinical decision curves, with internal validation conducted via Bootstrap. Results: The Boruta and SVM-REF algorithms identified four and five core cuproptosis-related genes, respectively, intersecting to yield three core genes (PDHA1, LIAS, DLAT). A clinical prediction model was established using multivariate logistic regression, exhibiting a C-index of 0.75 and an area under the ROC curve of 0.749. Clinical decision curve analysis demonstrated the highest net clinical benefit at a threshold probability range of 0.15 to 0.9, ensuring no harm to other patients. Internal validation reported a C-index of 0.755 and an area under the ROC curve of 0.736. Conclusion: The JIA clinical prediction model, based on three cuproptosis-related genes, demonstrates substantial predictive diagnostic capability, contributing to the early diagnosis of JIA patients.

VL  - 11
IS  - 4
ER  -

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