Cuproptosis Signature Would Reveal the Acute‐Remitting Pattern in Patients with Neuromyelitis Optica Spectrum Disorder

The acute-remitting course (the time interval from remission to the next relapse) is a distinct signature of neuromyelitis optica spectrum disorder (NMOSD), which harms patients and perplexes physicians. However, clinically actionable biomarkers to anticipate NMOSD relapse timing are unavailable. Recently, it is found that Cuproptosis-related genes would have an impact on the acute-remitting course in patients with aquaporin-4 (AQP4)-immunoglobulin G antibody positive NMOSD. A machine learning-based algorithm is developed to construct a consensus Cuproptosis-related prognosis risk (CRPR) model, followed by training a proportional hazards model to divide patients with NMOSD into high/low relapse risk groups. The CRPR is related to the clinical symptoms of patients with NMOSD. Finally, a nomogram is created using CRPR to predict the possibility of relapse in new cases of NMOSD. This prediction model can assist physicians and help develop appropriate interventions to prevent disease relapse.

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Publication Details

Journal
Advanced Science
Published
2025-08-14
DOI
https://doi.org/10.1002/advs.202417124
Primary Topic
Multiple Sclerosis Research Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

Cuproptosis Signature Would Reveal the Acute‐Remitting Pattern in Patients with Neuromyelitis Optica Spectrum Disorder

Peidong Liu, Henan Jiao, Jiali Pan, Huiru Hu et al.
Advanced Science
Multiple Sclerosis Research Studies
article

Cuproptosis Signature Would Reveal the Acute‐Remitting Pattern in Patients with Neuromyelitis Optica Spectrum Disorder

Peidong Liu, Henan Jiao, Jiali Pan, Huiru Hu, Yanmeng Xing, Xianzhi Liu, Weiming Xia, Ao Shen, Hongbo Liu, Di Chen, Bin Yu, Ruoyu Li, Ju Liu, Xinlin Wang, Wenping Zhu, Lixin Wu, Hui Li, Li Liu
article en

Abstract

The acute-remitting course (the time interval from remission to the next relapse) is a distinct signature of neuromyelitis optica spectrum disorder (NMOSD), which harms patients and perplexes physicians. However, clinically actionable biomarkers to anticipate NMOSD relapse timing are unavailable. Recently, it is found that Cuproptosis-related genes would have an impact on the acute-remitting course in patients with aquaporin-4 (AQP4)-immunoglobulin G antibody positive NMOSD. A machine learning-based algorithm is developed to construct a consensus Cuproptosis-related prognosis risk (CRPR) model, followed by training a proportional hazards model to divide patients with NMOSD into high/low relapse risk groups. The CRPR is related to the clinical symptoms of patients with NMOSD. Finally, a nomogram is created using CRPR to predict the possibility of relapse in new cases of NMOSD. This prediction model can assist physicians and help develop appropriate interventions to prevent disease relapse.

Advanced ScienceVol. 12(41)
Capital Medical University (CN), Beijing Sanbo Brain Hospital (CN), Deyang Stomatological Hospital (CN), First Affiliated Hospital of Xinxiang Medical University (CN), Zhengzhou Central Hospital (CN), First Affiliated Hospital of Zhengzhou University (CN), Puyang Vocational and Technical College (CN), Henan Medical University (CN)
National Natural Science Foundation of China, China Postdoctoral Science Foundation, Zhengzhou University, First Affiliated Hospital of Zhengzhou University
Openalex Percentile: Top 70%
Multiple Sclerosis Research Studies
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