ArticleCNS neuroscience & therapeutics2025
Identification and Verification of Immune Metabolism-Related Biomarkers and Immune Infiltration Landscape for Pediatric Opsoclonus Myoclonus Ataxia Syndrome in Neuroblastoma.
Article in CNS neuroscience & therapeutics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Identification and Verification of Immune Metabolism-Related Biomarkers and Immune Infiltration Landscape for Pediatric Opsoclonus Myoclonus Ataxia Syndrome in Neuroblastoma.CNS neuroscience & therapeutics · 2025Article
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Authors and funding
2 authors.
Funding
Abstract
purposeThis study aims to screen immune metabolism-associated biomarkers for pediatric opsoclonus myoclonus ataxia syndrome (OMAS) in neuroblastoma.
methodsImmune metabolism-related genes were retrieved from the GeneCards database. The differentially expressed immune metabolism-related genes in OMAS were identified by bioinformatics, immune infiltration, and WGCNA analyses. The diagnostic genes were screened by three machine learning algorithms and validated by ROC curve and nomogram model. Correlation between diagnostic genes and differential immune infiltrated cells, GSEA, and drug chemistry small-molecule analyses was performed. Lastly, validation was performed in eight paired clinical samples.
resultsTotal 162 differentially immune metabolism-related genes were obtained. Four diagnostic genes were selected by machine learning methods. The predictive accuracy of biomarker genes for OMAS was determined by nomograms and calibration curves. The targeted drugs for the four diagnostic genes contained bardoxolone methyl, alogliptin, and teneligliptin. Finally, clinical validation showed TRAF3IP2, DPP4, and RIPK1 upregulation and KEAP1 downregulation, consistent with bioinformatics analysis. The predictive accuracy of biomarkers was validated by ROC curve in clinical samples.
conclusionFour immune metabolism-associated diagnostic genes were identified, including TRAF3IP2, RIPK1, KEAP1, and DPP4 for OMAS.
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