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Research Article |

Construction of a Clinical Prediction Model for Systemic Sclerosis Cuproptosis-Related Genes Using Machine Learning

Objective: This study aims to identify cuproptosis-related genes in Systemic Sclerosis (SSc) and construct a clinical prediction model. Methods: The GSE33463 dataset was retrieved from the GEO database, and gene set enrichment analysis (GSEA) was used to analyze the expression of pathways related to cuproptosis. Cuproptosis-related genes were extracted, and potential key genes for SSc were selected using the LASSO and Boruta methods to construct a clinical prediction model. The model's predictive ability was evaluated using K-nearest neighbors (KNN) and Lightgbm methods, with assessment based on ROC curves, PR curves, confusion matrices, F-values, and 5-fold cross-validation. The importance of model variables was evaluated using SHAP analysis. Results: Cuproptosis-related pathways were upregulated in SSc. Four key cuproptosis-related genes (PDHB, DLST, PDHA1, DBT) were identified using the LASSO and Boruta methods, leading to the construction of a clinical prediction model through multivariable logistic regression. The model exhibited a C-index of 0.91, an AUC of 0.914 under the ROC curve, and strong performance in 5-fold cross-validation. KNN and Lightgbm models achieved AUC values of 0.9243 and 0.9763, respectively. PR curve AUC values of 0.8492 and 0.9480 demonstrated high precision, while confusion matrix results revealed KNN and Lightgbm model accuracies of 0.8663 and 0.932, respectively. The models provide a basis for the early diagnosis of SSc. Conclusion: The clinical prediction model, based on four cuproptosis-related genes, demonstrates high predictive capability, aiding in the early diagnosis of SSc patients.

Systemic Sclerosis, Cuproptosis, Machine Learning, Prediction Model, Confusion Matrix

APA Style

Huang, X., Cai, X., Chen, X., Hong, Y., Yan, Z., et al. (2023). Construction of a Clinical Prediction Model for Systemic Sclerosis Cuproptosis-Related Genes Using Machine Learning. American Journal of BioScience, 11(6), 142-149. https://doi.org/10.11648/j.ajbio.20231106.12

ACS Style

Huang, X.; Cai, X.; Chen, X.; Hong, Y.; Yan, Z., et al. Construction of a Clinical Prediction Model for Systemic Sclerosis Cuproptosis-Related Genes Using Machine Learning. Am. J. BioScience 2023, 11(6), 142-149. doi: 10.11648/j.ajbio.20231106.12

AMA Style

Huang X, Cai X, Chen X, Hong Y, Yan Z, et al. Construction of a Clinical Prediction Model for Systemic Sclerosis Cuproptosis-Related Genes Using Machine Learning. Am J BioScience. 2023;11(6):142-149. doi: 10.11648/j.ajbio.20231106.12

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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