[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125286-en":3,"doc-seo-125286-105":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},125286,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Identifying Potential Three Key Targets Gene for Septic Shock in Children Using Bioinformatics and Machine Learning Methods","Septic shock in children is an infection-driven, low-immunity condition with very high mortality, and its lethal death mechanisms remain insufficiently defined. This study applies bioinformatics and machine learning to discover key genes and biological pathways linked to fatal sepsis, aiming to support early risk assessment and a theoretical basis for rational follow-up TCM intervention. Differential expression analysis, GO/KEGG enrichment, and protein-protein interaction network screening are integrated with three machine learning models, followed by ROC validation.","TYPE Original Research PUBLISHED 17 June 2025  \nDOI 10.3389/fimmu.2025.1586584  \nOPEN ACCESS  \nEDITED BY  \nSwapan K. Nath,  \nOklahoma Medical Research Foundation, United States  \nREVIEWED BY  \nIvana Kawikova,  \nNational Institute of Mental Health, Czechia Guojun Qian,  \nGuangzhou Medical University, China  \n*CORRESPONDENCE  \nWei Guo  \n [19904509966@163.com](19904509966@163.com)[ ](19904509966@163.com)Hong Chen  \n [chenrongsheng1977@163.com](chenrongsheng1977@163.com)  \nRECEIVED 03 March 2025  \nACCEPTED 30 May 2025  \nPUBLISHED 17 June 2025  \nCITATION  \nGuo W, Chen H, Wang F, Chi Y, Zhang W, Wang S, Chen K and Chen H (2025) Identifying potential three key targets gene for septic shock in children using bioinformatics and machine learning methods.  \nFront. Immunol. 16:1586584 .  \ndoi: 10.3389/fimmu.2025.1586584  \nCOPYRIGHT  \n© 2025 Guo, Chen, Wang, Chi, Zhang, Wang, Chen and Chen. 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.  \nIdentifying potential three key targets gene for septic shock in children using bioinformatics and machine learning methods  \nWei Guo 1*, Hao Chen 2, Feng Wang 2, Yingjiao Chi 3, Wei Zhang 1, Shan Wang 4, Kezhu Chen 5 and Hong Chen 1*  \n1 Department of Pediatrics, First Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, China, 2 Department of Surgery, Heilongjiang Academy of Traditional Chinese Medicine, Harbin, China, 3 Department of Pediatrics, Harbin First Hospital, Harbin, China, 4 Ning ‘an Hospital of Traditional Chinese Medicine Pediatrics, Ning ‘an, China, 5Graduate School, Heilongjiang University of Chinese Medicine, Harbin, China  \nBackground: Septic shock in children is an infectious disease caused by low immunity, and its mortality is very high. Early prediction of the risk of death in children with septic shock is helpful for clinicians to judge the severity of the disease, take active treatment measures, and improve the adverse outcomes of patients. However, the mechanism of death from sepsis in children remains unclear. This study aims to use bioinformatics and machine learning algorithms to identify key genes and pathways associated with fatal sepsis in children, and provide theoretical basis for rational drug use in follow-up TCM treatment.  \nMethods: Gene expression profiles were obtained from the GEO database (GSE4607) for 15 blank patients and 14 children with sepsis death. Differentially expressed genes (DEGs) were enriched by GO and KEGG pathways. Construct and visualize protein-protein interaction (PPI) networks to identify candidate genes responsible for fatal sepsis in children. Three kinds of machine learning models were established, and the candidate genes were screened by intersection to obtain the core genes with diagnostic value. ROC curve was drawn for core genes to clarify the diagnostic value of genetic markers.  \nResults: Analysis of differences in the preprocessed dataset identified 83 genes, including 78 up-regulated genes and 5 down-regulated genes. 17 candidate genes were screened by protein interaction network analysis. Three machine learning algorithms LASSO, random forest(RF), and support vector machine recursive feature elimination (SVM-RFE) were used to finally screen out three core genes: CD163, MCEMP1 and RETN. CD163, MCEMP1 and RETN may jointly regulate complement and coagulation cascades, toll like receptor signaling pathway, graft versus host disease, type I diabetes mellitus.  \nConclusion: In this study, three core genes(CD163, MCEMP1andRETN)that lead to sepsis death in children were screened out, providing a new understanding of the lethal mec","cbCailX5fj42nQ8f","https://ap.wps.com/l/cbCailX5fj42nQ8f","pdf",10534925,1,12,"English","en",105,"# Abstract\n## Background\n## Methods\n## Results\n## Conclusion\n# Introduction\n## Definition and clinical significance\n## Epidemiology and mortality in children\n## Need for early intervention and biomarker discovery","[{\"question\":\"What problem does the study address in pediatric septic shock?\",\"answer\":\"It targets the high mortality of septic shock in children and the incomplete understanding of mechanisms leading to death, emphasizing the need for early prediction and biomarkers.\"},{\"question\":\"How were candidate genes identified in the workflow?\",\"answer\":\"Gene expression profiles from GEO were analyzed for differentially expressed genes, enriched by GO/KEGG, mapped onto PPI networks, and then screened using intersection across three machine learning models.\"},{\"question\":\"Which three core genes were finally selected and what do the results suggest?\",\"answer\":\"The study screened CD163, MCEMP1, and RETN as core genes, suggesting they may jointly regulate pathways related to complement/coagulation cascades and inflammatory signaling relevant to fatal outcomes.\"}]","Identifying Potential Three Key Targets Gene for Septic Shock in Children Using Bioinformatics and Machine Learning Methods | PDF",1785897976,30,{"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},"identifying-potential-three-key-targets-gene-for-septic-shock-in-children-using-bioinformatics-and-machine-learning-methods","",{"@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/identifying-potential-three-key-targets-gene-for-septic-shock-in-children-using-bioinformatics-and-machine-learning-methods/125286/",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","2026-08-05",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},"What problem does the study address in pediatric septic shock?","Question",{"text":75,"@type":76},"It targets the high mortality of septic shock in children and the incomplete understanding of mechanisms leading to death, emphasizing the need for early prediction and biomarkers.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were candidate genes identified in the workflow?",{"text":80,"@type":76},"Gene expression profiles from GEO were analyzed for differentially expressed genes, enriched by GO/KEGG, mapped onto PPI networks, and then screened using intersection across three machine learning models.",{"name":82,"@type":73,"acceptedAnswer":83},"Which three core genes were finally selected and what do the results suggest?",{"text":84,"@type":76},"The study screened CD163, MCEMP1, and RETN as core genes, suggesting they may jointly regulate pathways related to complement/coagulation cascades and inflammatory signaling relevant to fatal outcomes.","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},[93,97,101,105,110,115,120,122,127,130,134],{"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":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]