[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120133-en":3,"doc-seo-120133-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120133,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Identification of Novel Markers for Neuroblastoma Immunoclustering Using Machine Learning","Neuroblastoma shows marked biological heterogeneity, making tumor behavior and patient outcomes closely tied to the characteristics of the tumor immune microenvironment. Public transcriptomic data were used to estimate sample immunity scores, perform hierarchical clustering to separate high- and low-immunity groups, and compare clinicopathological features and treatment response. Machine-learning feature selection (LASSO, SVM-RFE, and Random Forest) identified six genes linked to immune pathways and immune-cell infiltration. The resulting biomarkers may support immunoclustering-guided neuroblastoma treatment strategies.","TYPE Original Research PUBLISHED 04 November 2024 DOI 10.3389/fimmu.2024.1446273  \nOPEN ACCESS  \nEDITED BY  \nRobin Parihar,  \nBaylor College of Medicine, United States  \nREVIEWED BY  \nZbigniew Starosolski,  \nTexas Children’s Hospital, United States Susana Galli,  \nGeorgetown University Medical Center, United States  \n*CORRESPONDENCE  \nJiarui Chen  \n [chenjr228@mail.sysu.edu.cn](chenjr228@mail.sysu.edu.cn)[ ](chenjr228@mail.sysu.edu.cn)Ranyao Yang  \nyangry@hku. hk  \n†These authors have contributed equally to this work and share first authorship  \nRECEIVED 09 June 2024  \nACCEPTED 15 October 2024  \nPUBLISHED 04 November 2024  \nCITATION  \nZhang L, Li H, Sun F, Wu Q, Jin L, Xu A, Chen J and Yang R (2024) Identification of novel markers for neuroblastoma immunoclustering using machine learning. Front. Immunol. 15:1446273 .  \ndoi: 10.3389/fimmu.2024.1446273  \nCOPYRIGHT  \n© 2024 Zhang, Li, Sun, Wu, Jin, Xu, Chen and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nIdentification of novel markers for neuroblastoma immunoclustering using machine learning  \nLongguo Zhang 1,2†, Huixin Li 1,2†, Fangyan Sun 1,2, Qiuping Wu 1,2, Leigang Jin 1,2, Aimin Xu 1,2, Jiarui Chen 3* and Ranyao Yang 1,2,4*  \n1State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong,  \nHong Kong, Hong Kong SAR, China, 2 Department of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China, 3Guangdong Provincial Key Laboratory of Food, Nutrition and Health, and Department of Nutrition, School of Public Health, Sun Yat-sen University, Guangzhou, China, 4 Department of Clinical Pharmacy, Jining First People’s Hospital, Shandong First Medical University, Jining, China  \nBackground: Due to the unique heterogeneity of neuroblastoma, its treatment and prognosis are closely related to the biological behavior of the tumor. However, the effect of the tumor immune micro environment on neuroblastoma needs to be investigated, and there is a lack of biomarkers toreflect the condition of the tumor immune microenvironment.  \nMethods: The GEO Database was used to download transcriptome data (both training dataset and test dataset) on neuroblastoma. Immunity scores were calculated for each sample using ssGSEA, and hierarchical clustering was used to categorize the samples into high and low immunity groups. Subsequently, the differences in clinicopathological characteristics and treatment between the different groups were examined. Three machine learning algorithms (LASSO, SVM-RFE, and Random Forest) were used to screen biomarkers and synthesize their function in neuroblastoma.  \nResults: In the training set, there were 362 samples in the immunity_L group and 136 samples in the immunity_H group, with differences in age, MYCN status, etc. Additionally, the tumor microenvironment can also affect the therapeutic response of neuroblastoma. Six characteristic genes (BATF, CXCR3, GIMAP5, GPR18, ISG20, and IGHM) were identified by machine learning, and these genes are associated with multiple immune-related pathways and immune cells in neuroblastoma.  \nConclusions: BATF, CXCR3, GIMAP5, GPR18, ISG20, and IGHM may serve as biomarkers that reflect the conditions of the immune microenvironment of neuroblastoma and hold promise in guiding neuroblastoma treatment.  \nKEYWORDS  \nbiomarker, tumor microenvironment, immunoclustering, machine learning, neuroblastoma  \nFrontiers in Immunology 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  \nNeuroblastoma (NBL) is a malignant pediatric tumor originating from neural crest cells, representing the most common extracran","cbCaihMdsHT17QQ1","https://ap.wps.com/l/cbCaihMdsHT17QQ1","pdf",11579462,1,15,"English","en",105,"# Introduction\n## Background and clinical relevance\n# Methods\n## Data source and grouping strategy\n## Machine-learning biomarker selection\n# Results\n## High- vs low-immunity group differences\n## Identified characteristic genes and immune associations\n# Conclusions","[{\"question\":\"Why is identifying biomarkers for neuroblastoma’s immune microenvironment important?\",\"answer\":\"Neuroblastoma’s treatment and prognosis depend on tumor biology, and the immune microenvironment affects clinical behavior. Biomarkers reflecting immune microenvironment conditions are limited, motivating this study.\"},{\"question\":\"How were neuroblastoma samples classified into immunity groups?\",\"answer\":\"Transcriptome data from the GEO database were used, ssGSEA calculated immunity scores for each sample, and hierarchical clustering separated samples into high- and low-immunity groups.\"},{\"question\":\"Which genes were identified as potential biomarkers and what supports their relevance?\",\"answer\":\"Six genes—BATF, CXCR3, GIMAP5, GPR18, ISG20, and IGHM—were selected by multiple machine-learning algorithms. They are associated with immune-related pathways and immune cells in neuroblastoma.\"}]","Identification of Novel Markers for Neuroblastoma Immunoclustering Using Machine Learning | PDF",1785728371,38,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"identification-of-novel-markers-for-neuroblastoma-immunoclustering-using-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/identification-of-novel-markers-for-neuroblastoma-immunoclustering-using-machine-learning/120133/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is identifying biomarkers for neuroblastoma’s immune microenvironment important?","Question",{"text":75,"@type":76},"Neuroblastoma’s treatment and prognosis depend on tumor biology, and the immune microenvironment affects clinical behavior. Biomarkers reflecting immune microenvironment conditions are limited, motivating this study.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were neuroblastoma samples classified into immunity groups?",{"text":80,"@type":76},"Transcriptome data from the GEO database were used, ssGSEA calculated immunity scores for each sample, and hierarchical clustering separated samples into high- and low-immunity groups.",{"name":82,"@type":73,"acceptedAnswer":83},"Which genes were identified as potential biomarkers and what supports their relevance?",{"text":84,"@type":76},"Six genes—BATF, CXCR3, GIMAP5, GPR18, ISG20, and IGHM—were selected by multiple machine-learning algorithms. They are associated with immune-related pathways and immune cells in neuroblastoma.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]