{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T06:50:38Z","timestamp":1764312638897,"version":"3.46.0"},"reference-count":49,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T00:00:00Z","timestamp":1762214400000},"content-version":"vor","delay-in-days":3,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["CNS Neurosci Ther"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aims to screen immune metabolism\u2010associated biomarkers for pediatric opsoclonus myoclonus ataxia syndrome (OMAS) in neuroblastoma.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Immune metabolism\u2013related genes were retrieved from the GeneCards database. The differentially expressed immune metabolism\u2013related 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\u2010molecule analyses was performed. Lastly, validation was performed in eight paired clinical samples.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Total 162 differentially immune metabolism\u2013related 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Four immune metabolism\u2013associated diagnostic genes were identified, including TRAF3IP2, RIPK1, KEAP1, and DPP4 for OMAS.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/cns.70610","type":"journal-article","created":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T05:53:23Z","timestamp":1762322003000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Identification and Verification of Immune Metabolism\u2013Related Biomarkers and Immune Infiltration Landscape for Pediatric Opsoclonus Myoclonus Ataxia Syndrome in Neuroblastoma"],"prefix":"10.1111","volume":"31","author":[{"given":"Minglei","family":"Li","sequence":"first","affiliation":[{"name":"Department of pediatric internal Medicine one Weifang People's Hospital  Weifang Shandong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5697-858X","authenticated-orcid":false,"given":"Jinlei","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery Three Weifang People's Hospital  Weifang Shandong China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,11,4]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00415\u2010021\u201010536\u20103"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.4103\/0028\u20103886.241373"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1097\/00002826\u2010199619010\u201000001"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1136\/adc.2010.204792"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.29271\/jcpsp.2022.08.1086"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/1097-0142(197602)37:2<846::AID-CNCR2820370233>3.0.CO;2-L"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/jnc.15169"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1212\/wnl.62.9.1526"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jns.2017.09.006"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gki022"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btp616"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471\u20102105\u201014\u20107"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471\u20102105\u20109\u2010559"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1093\/database\/baq020"},{"key":"e_1_2_10_16_1","unstructured":"B.Jiang P.Sun J.Tang andB.Luo \u201cGLMNet: Graph Learning\u2010Matching Networks for Feature Matching \u201d(2019)."},{"key":"e_1_2_10_17_1","unstructured":"D.Meyer E.Dimitriadou K.Hornik A.Weingessel andF.Leisch \u201cFunctions TM Wien TU Package \u201d(2012):e1071. 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