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Abstract Details

Potential Correlation Between the Cuprotosis-Related Genes and Acute-Remitting Course in Neuromyelitis Optica Spectrum Disorder
Autoimmune Neurology
P12 - Poster Session 12 (11:45 AM-12:45 PM)
8-004

To develop a predictive model that enables timely intervention to prevent relapses in neuromyelitis optica spectrum disorder (NMOSD).

 

 The "acute-remitting" course, characterized by the time interval between remission and the next relapse, is a distinctive feature of NMOSD. This pattern poses significant challenges for patients and physicians. Despite this, there is limited evidence on the mechanisms underlying relapses. Emerging evidence suggests that copper-induced cell death (cuprotosis), modulated by the tricarboxylic acid (TCA) cycle, may play a role. The TCA cycle is essential for the differentiation of naïve T cells, including the development of regulatory T cell (Treg) and T helper 1 cells (Th1). The involvement of cuprotosis in NMOSD relapses within the T cell regulatory network remains unclear.

We employed the ssGSEA algorithm to identify a cuprotosis signature and utilized consensus clustering to validate the signature’s robustness. This clustering approach revealed two distinct clusters with clinical prognostic significance. We then combined these findings with WGCNA to pinpoint hub genes associated with prognosis and integrated machine learning algorithms to develop a consensus cuprotosis-related prognosis risk (CRPR) model.
The ssGSEA analysis indicated that the cuprotosis signature correlates with clinical traits, a finding further supported by consensus cluserting. Using this signature, we identified key cuprotosis-related prognostic hub genes and applied machine learning algorithms to refine our model. Our integrative approach yielded a robust CPRP model, which was validated through multidimensional assessments.The mode categorized patients into high and low-risk groups based on clinical outcomes. Furthermore, we developed a nomogram incorporating the CPRP model to facilitate the prediction of NMOSD relapses.
The CRPR model demonstrated significantly enhanced predictive accuracy for relapse probabilities in NMOSD.
Authors/Disclosures
Peidong Liu
PRESENTER 		The institution of Dr. Liu has received research support from China.
Ruoyu Li Miss Li has nothing to disclose.
Xinlin Wang Dr. Wang has nothing to disclose.
Hongbo Liu (the First Affiliated Hospital of Zhengzhou University, Zhengzhou University) The institution of Hongbo Liu has received research support from China.