[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127690-en":3,"doc-seo-127690-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127690,962084925782,"Ava Thompson","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Identification of platelet-related subtypes and diagnostic markers in pediatric Crohn’s disease based on WGCNA and machine learning","Pediatric Crohn’s disease incidence is rising worldwide, yet early diagnosis remains difficult due to disease heterogeneity. This original research aims to discover novel molecular subtypes and diagnostic markers to improve prognosis. Candidate platelet-related genes are derived from public datasets using weighted gene co-expression network analysis and differential analysis, followed by five machine-learning algorithms. Predictive models and a nomogram are validated on an independent dataset, and subtype-specific pathway enrichment and immune infiltration differences are assessed.","TYPE Original Research PUBLISHED 14 February 2024 DOI 10.3389/fimmu.2024.1323418  \nOPEN ACCESS  \nEDITED BY Kaya Kuru,  \nUniversity of Central Lancashire, United Kingdom  \nREVIEWED BY Fei Yuan,  \nBaylor College of Medicine, United States Yuan Xiao,  \nShanghai Jiao Tong University, China  \n*CORRESPONDENCE Hongyan Li  \n [lihongyan.de@163.com](lihongyan.de@163.com)  \nRECEIVED 17 October 2023  \nACCEPTED 29 January 2024  \nPUBLISHED 14 February 2024  \nCITATION  \nTang D, Huang Y, Che Y, Yang C, Pu B, Liu Sand Li H (2024) Identification of plateletrelated subtypes and diagnostic markers in pediatric Crohn’s disease based on WGCNA and machine learning.  \nFront. Immunol. 15:1323418 .  \ndoi: 10.3389/fimmu.2024.1323418  \nCOPYRIGHT  \n© 2024 Tang, Huang, Che, Yang, Pu, Liu and Li. 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 platelet-related subtypes and diagnostic markers in pediatric Crohn’s disease based on WGCNA and machine learning  \nDadong Tang 1, Yingtao Huang 2, Yuhui Che 1, Chengjun Yang 3, Baoping Pu 1, Shiru Liu 4 and Hongyan Li 4*  \n1Clinical Medical College, Chengdu University of Traditional Chinese Medicine, Chengdu, China, 2 First Clinical Medical College, Liaoning University of Traditional Chinese Medicine, Shenyang, China,  \n3 Department of Otorhinolaryngology, Zigong Hospital of Traditional Chinese Medicine, Zigong, China, 4Anorectal Disease Department, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China  \nBackground: The incidence of pediatric Crohn’s disease (PCD) is increasing worldwide every year. The challenges in early diagnosis and treatment of PCD persist due to its inherent heterogeneity. This study’s objective was to discover novel diagnostic markers and molecular subtypes aimed at enhancing the prognosis for patients suffering from PCD.  \nMethods: Candidate genes were obtained from the GSE117993 dataset and the GSE93624 dataset by weighted gene co-expression network analysis (WGCNA) and differential analysis, followed by intersection with platelet-related genes. Based on this, diagnostic markers were screened by five machine learning algorithms. We constructed predictive models and molecular subtypes based on key markers. The models were evaluated using the GSE101794 dataset as the validation set, combined with receiver operating characteristic curves, decision curve analysis, clinical impact curves, and calibration curves. In addition, we performed pathway enrichment analysis and immune infiltration analysis for different molecular subtypes to assess their differences.  \nResults: Through WGCNA and differential analysis, we successfully identified 44 candidate genes. Following this, employing five machine learning algorithms, we ultimately narrowed it down to five pivotal markers: GNA15, PIK3R3, PLEK, SERPINE1, and STAT1 . Using these five key markers as a foundation, we developed a nomogram exhibiting exceptional performance. Furthermore, we distinguished two platelet-related subtypes of PCD through consensus clustering analysis. Subsequent analyses involving pathway enrichment and immune infiltration unveiled notable disparities in gene expression patterns, enrichment pathways, and immune infiltration landscapes between these subtypes.  \nFrontiers in Immunology 01 [frontiersin.org](frontiersin.org)  \nConclusion: In this study, we have successfully identified five promising diagnostic markers and developed a robust nomogram with high predictive efficacy. Furthermore, the recognition of distinct PCD subtypes enhances our comprehension of potential pathogenic mechanisms and paves the","cbCaijHrjxqxjaGA","https://ap.wps.com/l/cbCaijHrjxqxjaGA","pdf",14012461,2,1,16,"English","en",105,"# Background\n# Methods\n# Results\n# Conclusion\n# Introduction","[{\"question\":\"What is the study’s primary goal for pediatric Crohn’s disease?\",\"answer\":\"To identify novel diagnostic markers and platelet-related molecular subtypes that can improve diagnosis and prognosis for pediatric Crohn’s disease patients.\"},{\"question\":\"How were the candidate genes and diagnostic markers determined?\",\"answer\":\"Platelet-related genes were obtained from GSE117993 and GSE93624 using WGCNA and differential analysis, then narrowed using five machine-learning algorithms to five key markers.\"},{\"question\":\"What analytical results support the existence of distinct subtypes?\",\"answer\":\"Consensus clustering produced two platelet-related PCD subtypes, and subsequent pathway enrichment plus immune infiltration analyses showed notable differences in gene expression, enriched pathways, and immune infiltration patterns between them.\"}]","Identification of platelet-related subtypes and diagnostic markers in pediatric Crohn’s disease based on WGCNA and machine learning | PDF",1785940901,40,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"identification-of-platelet-related-subtypes-and-diagnostic-markers-in-pediatric-crohns-disease-based-on-wgcna-and-machine-learning","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/identification-of-platelet-related-subtypes-and-diagnostic-markers-in-pediatric-crohns-disease-based-on-wgcna-and-machine-learning/127690/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the study’s primary goal for pediatric Crohn’s disease?","Question",{"text":76,"@type":77},"To identify novel diagnostic markers and platelet-related molecular subtypes that can improve diagnosis and prognosis for pediatric Crohn’s disease patients.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were the candidate genes and diagnostic markers determined?",{"text":81,"@type":77},"Platelet-related genes were obtained from GSE117993 and GSE93624 using WGCNA and differential analysis, then narrowed using five machine-learning algorithms to five key markers.",{"name":83,"@type":74,"acceptedAnswer":84},"What analytical results support the existence of distinct subtypes?",{"text":85,"@type":77},"Consensus clustering produced two platelet-related PCD subtypes, and subsequent pathway enrichment plus immune infiltration analyses showed notable differences in gene expression, enriched pathways, and immune infiltration patterns between them.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":30,"slug":119},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]