RGMA gene polymorphisms as predictive biomarkers for early relapse in neuromyelitis optica spectrum disorders
Objectives: To explore the potential of RGMA gene polymorphisms as novel biomarkers for predicting disease activity in NMOSD. Methods: We enrolled 117 NMOSD patients and 100 healthy controls. Single nucleotide polymorphism genotyping for the RGMA gene was performed using Sanger sequencing. Associati...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-10-01
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| Series: | Neurobiology of Disease |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0969996125002797 |
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| Summary: | Objectives: To explore the potential of RGMA gene polymorphisms as novel biomarkers for predicting disease activity in NMOSD. Methods: We enrolled 117 NMOSD patients and 100 healthy controls. Single nucleotide polymorphism genotyping for the RGMA gene was performed using Sanger sequencing. Associations between RGMA gene polymorphisms and clinical and imaging characteristics, and immune cell activation were analyzed. Results: Carriers of the rs725458-CC and rs4778099-AA genotypes experienced significantly earlier first relapses, but subsequently showed a lower relapse rate. The rs4778099-GG genotype was associated with a higher rate of AQP4-IgG seronegativity, while the rs4778099-AA genotype correlated with a higher likelihood of presenting circumventricular organ syndrome at onset. Carriers of the rs725458-TT genotype were more prone to longer spinal lesion spans. Elevated levels of CD3+T lymphocytes were observed in carriers of the rs725458-TT and rs4778099-GG genotypes during acute phases. Survival analysis revealed that the CC/AA genotypes were linked to earlier relapse, supported by Cox multivariate analysis which identified these genotypes, along with age of onset and onset symptoms, as key predictors of early relapse. Conclusion: RGMA gene polymorphisms, specifically rs725458-CC and rs4778099-AA, are key predictors of early NMOSD relapse. Integrating these markers with clinical data improves relapse risk prediction, enabling more targeted treatment strategies. |
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| ISSN: | 1095-953X |