# Identification and Verification of Immune Metabolism–Related Biomarkers and Immune Infiltration Landscape for Pediatric Opsoclonus Myoclonus Ataxia Syndrome in Neuroblastoma

## TL;DR

This study identifies four immune metabolism-related genes as potential biomarkers for pediatric opsoclonus myoclonus ataxia syndrome in neuroblastoma, validated through bioinformatics and clinical testing.

## Contribution

The study introduces four novel immune metabolism-related biomarkers (TRAF3IP2, RIPK1, KEAP1, DPP4) for diagnosing OMAS in neuroblastoma.

## Key findings

- Four diagnostic genes (TRAF3IP2, RIPK1, KEAP1, DPP4) were identified using machine learning and validated clinically.
- DPP4, RIPK1, and TRAF3IP2 were upregulated, while KEAP1 was downregulated in OMAS patients.
- Predictive accuracy of the biomarkers was confirmed via ROC curves and nomogram models.

## Abstract

This study aims to screen immune metabolism‐associated biomarkers for pediatric opsoclonus myoclonus ataxia syndrome (OMAS) in neuroblastoma.

Immune 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.

Total 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.

Four immune metabolism–associated diagnostic genes were identified, including TRAF3IP2, RIPK1, KEAP1, and DPP4 for OMAS.

Based on the GEO database and GeneCards database, 1785 immune metabolism genes, 1879 DEGs, and 1560 module genes were screened out. After taking the intersection, 162 differential immune metabolic–related genes were obtained. Four diagnostic biomarker genes, TRAF3IP2, RIPK1, KEAP1, and DPP4, were screened out through three machine learning algorithms and verified by ROC curve and nomogram model. Finally, clinical validation demonstrated that compared with the control group, DPP4, RIPK1, and TRAF3IP2 in the OMAS group were significantly upregulated at the mRNA and protein levels, while KEAP1 was significantly downregulated, which was consistent with the bioinformatics analysis. The ROC curve in clinical samples verified the predictive accuracy of the biomarkers.