[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119152-en":3,"doc-seo-119152-105":30,"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":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},119152,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Linking gene expression to clinical outcomes in pediatric Crohn’s disease using machine learning","Pediatric Crohn’s disease often follows a severe, relapsing course with frequent complications such as strictures, fistulas, and progression to surgery. This study applies machine learning models to predict future complication risk using ileal and colonic gene expression from formalin-fixed, paraffin-embedded (FFPE) biopsies collected from treatment-naïve pediatric Crohn’s patients and controls. Differential expression highlights reduced inflammation- and extracellular-matrix-related pathways in patients with strictures, while predictive models achieve strong performance (AUROC 0.84 for strictures, 0.83 for remission, 0.75 for surgery), identifying prognostic genes through multigene contributions.","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nLinking gene expression to clinical outcomes in pediatric Crohn’s disease using machine learning  \nKevin A. Chen1,2, Nina C. Nishiyama1,3, Meaghan M. Kennedy Ng1,3, Alexandria Shumway5, Chinmaya U. Joisa6, Matthew R. Schaner1, Grace Lian1, Caroline Beasley1, Lee‑Ching Zhu4, Surekha Bantumilli4, Muneera R. Kapadia2, Shawn M. Gomez6, Terrence S. Furey1,3* & ShehzadZ. Sheikh1*  \nPediatric Crohn’s disease (CD) is characterized by a severe disease course with frequent complications. We sought to apply machine learning‑based models to predict risk of developing future complications in pediatric CD using ileal and colonic gene expression. Gene expression data was generated from  \n101 formalin‑fixed, paraffin‑embedded (FFPE) ileal and colonic biopsies obtained from treatment‑ naïve CD patients and controls. Clinical outcomes including development of strictures or fistulasand progression to surgery were analyzed using differential expression and modeled using  \nmachine learning. Differential expression analysis revealed downregulation of pathways related to inflammation and extra‑cellular matrix production in patients with strictures. Machine learning‑ based models were able to incorporate colonic gene expression and clinical characteristics to predict outcomes with high accuracy. Models showed an area under the receiver operating characteristic curve (AUROC) of 0.84 for strictures, 0.83 for remission, and 0.75 for surgery. Genes with potential prognostic importance for strictures (REG1A, MMP3, and DUOX2) were not identified in single gene differential analysis but were found to have strong contributions to predictive models. Our findings in FFPEtissue support the importance of colonic gene expression and the potential for machine learning‑ based models in predicting outcomes for pediatric CD.  \nPediatric Crohn’s disease (CD) is the fastest growing age group for incidence of the disease with about 80,000 children in the US affected1–3. CD is characterized by a relapsing, remitting disease course with complications, such as strictures or perforation, affecting around 50% of patients within 5 years of diagnosis4,5. Pediatric CD follows a more severe disease course, more often involving strictures and fistulas6–8. These complications drive further morbidity and healthcare utilization associated with CD including growth failure, delayed puberty, hospitalizations, and surgery4,8.  \nAnalysis of gene expression and identification of biological pathways which drive development of CD and CD complications may give insight into more precise treatment decision-making to prevent a complicated CD course. Genes associated with immune and cytokine pathways have been associated with CD development9–13. Further, specific genes including oncostatin M, IL1B, S100A8, and CXCL1 have been associated with response to anti-tumor necrosis factor therapy14–16. Genes controlling extracellular matrix production and inflammatory processes have been associated with strictures17–19. Predictive modeling which incorporates this genetic information to prognosticate disease course could assist with clinical decision-making.  \nPrevious studies have developed predictive models for CD outcomes based on gene expression and other risk factors, most notably using the RISK cohort17. However, these studies relied on logistic regression models, which may fail to capture the multi-factorial, non-linear interactions between genes and clinical characteristics  \n1Center for Gastrointestinal Biology and Disease, University of North Carolina at Chapel Hill, 7314 Medical Biomolecular Research Building, 111 Mason Farm Road, Chapel Hill, NC 27599, USA. 2Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, USA. 3Departments of Genetics and Biology, Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, 5022 Genetic Medicine Building, 120 ","cbCaipPn91zRhoGQ","https://ap.wps.com/l/cbCaipPn91zRhoGQ","pdf",3010513,1,10,"English","en",105,"# Materials and methods\n## Study design and outcomes\n## Differential expression analysis\n## Machine learning-based predictive modeling","[{\"question\":\"What clinical outcomes are predicted for pediatric Crohn’s disease in this study?\",\"answer\":\"The models predict the development of future complications, including strictures, progression to surgery, and remission status.\"},{\"question\":\"How is gene expression data generated and used for prediction?\",\"answer\":\"Gene expression is measured from ileal and colonic FFPE biopsies from treatment-naïve patients and controls, and then used in differential expression analysis and machine learning model training.\"},{\"question\":\"Why might multigene machine learning models find prognostic genes missed by single-gene differential analysis?\",\"answer\":\"Genes such as REG1A, MMP3, and DUOX2 may not show significance in single-gene testing, but they can contribute strongly when combined within multivariable predictive models.\"}]","Linking gene expression to clinical outcomes in pediatric Crohn’s disease using machine learning | PDF",1785722756,25,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"linking-gene-expression-to-clinical-outcomes-in-pediatric-crohns-disease-using-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@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/linking-gene-expression-to-clinical-outcomes-in-pediatric-crohns-disease-using-machine-learning/119152/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",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 clinical outcomes are predicted for pediatric Crohn’s disease in this study?","Question",{"text":76,"@type":77},"The models predict the development of future complications, including strictures, progression to surgery, and remission status.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is gene expression data generated and used for prediction?",{"text":81,"@type":77},"Gene expression is measured from ileal and colonic FFPE biopsies from treatment-naïve patients and controls, and then used in differential expression analysis and machine learning model training.",{"name":83,"@type":74,"acceptedAnswer":84},"Why might multigene machine learning models find prognostic genes missed by single-gene differential analysis?",{"text":85,"@type":77},"Genes such as REG1A, MMP3, and DUOX2 may not show significance in single-gene testing, but they can contribute strongly when combined within multivariable predictive models.","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":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":21,"slug":134},"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]