[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125086-en":3,"doc-seo-125086-105":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},125086,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Predicting Patients with Septic Shock and Sepsis - Whole-Blood Expression of NK Cell-Related Hub Genes","Sepsis is a life-threatening condition with urgent clinical demand for biomarkers that can forecast progression to septic shock. Rapid and reliable prediction methods remain insufficient despite advances in transcriptomics for disease phenotyping and endotyping. This study establishes an advanced machine learning framework to predict sepsis and septic shock from whole-blood transcriptomics, deriving an NK cell-related hub gene panel and training a multistage model. Model performance is validated with RT-qPCR measurements to support timely clinical translation.","TYPE Original Research PUBLISHED 28 November 2024 DOI 10.3389/fimmu.2024.1493895  \nOPEN ACCESS  \nEDITED BY  \nSamithamby Jey Jeyaseelan,  \nLouisiana State University, United States  \nREVIEWED BY  \nRudolf Lucas,  \nAugusta University, United States Monowar Aziz,  \nFeinstein Institute for Medical Research, United States  \n*CORRESPONDENCE  \nXiao-Di Tan  \n [xtan25@uic.edu](xtan25@uic.edu)  \nRECEIVED 09 September 2024  \nACCEPTED 29 October 2024  \nPUBLISHED 28 November 2024  \nCITATION  \nDu C, Tan SC, Bu H-F, Subramanian S, Geng H, Wang X, Xie H, Wu X, Zhou T, Liu R, Xu Z, Liu B and Tan X-D (2024) Predicting patients with septic shock and sepsis through analyzing whole-blood expression of NK cell-related hub genes using an advanced machine learning framework.  \nFront. Immunol. 15:1493895 .  \ndoi: 10.3389/fimmu.2024.1493895  \nCOPYRIGHT  \n© 2024 Du, Tan, Bu, Subramanian, Geng, Wang, Xie, Wu, Zhou, Liu, Xu, Liu and Tan. 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.  \nPredicting patients with septic shock and sepsis through analyzing whole-blood expression of NK cell-related hub genes using an advanced machine learning framework  \nChao Du 1,2,3, Stephanie C. Tan 2,4, Heng-Fu Bu 2,5, Saravanan Subramanian 2,5, Hua Geng 2,5, Xiao Wang 2,5, Hehuang Xie 6, Xiaowei Wu 7, Tingfa Zhou 8, Ruijin Liu 8, Zhen Xu 3, Bing Liu 3 and Xiao-Di Tan 2,5,9*  \n1 Department of Gastroenterology, Weihai Municipal Hospital of Shandong University, Weihai, Shandong, China, 2 Department of Pediatrics, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States, 3 Department of Gastroenterology, Linyi People’s Hospital, Weifang Medical University, Linyi, Shandong, China, 4 Loyola University Chicago Stritch School of Medicine, Maywood, IL, United States, 5Center for Pediatric Translational Research and Education, Department of Pediatrics, College of Medicine, University of Illinois at Chicago, Chicago, IL, United States,  \n6 Department of Biomedical Sciences and Pathobiology, Virginia-Maryland College of Veterinary Medicine, Blacksburg, VA, United States, 7 Department of Statistics, Virginia Tech, Blacksburg,  \nVA, United States, 8 Department of Critical Care Medicine, Linyi People’s Hospital, Weifang Medical University, Linyi, Shandong, China, 9 Department of Research & Development, Jesse Brown Veterans Affairs Medical Center, Chicago, IL, United States  \nBackground: Sepsis is a life-threatening condition that causes millions of deaths globally each year. The need for biomarkers to predict the progression of sepsis to septic shock remains critical, with rapid, reliable methods still lacking. Transcriptomics data has recently emerged as a valuable resource for disease phenotyping and endotyping, making it a promising tool for predicting disease stages. Therefore, we aimed to establish an advanced machine learning framework to predict sepsis and septic shock using transcriptomics datasets with rapid turnaround methods.  \nMethods: We retrieved four NCBI GEO transcriptomics datasets previously generated from peripheral blood samples of healthy individuals and patients with sepsis and septic shock. The datasets were processed for bioinformatic analysis and supplemented with a series of bench experiments, leading to the identification of a hub gene panel relevant to sepsis and septic shock. The hub gene panel was used to establish a novel prediction model to distinguish sepsis from septic shock through a multistage machine learning pipeline, incorporating linear discriminant analysis, risk score analysis, and ensemble method combined with Least Absolute Shr","cbCaibQoDqRsH6zG","https://ap.wps.com/l/cbCaibQoDqRsH6zG","pdf",5562667,1,15,"English","en",105,"# Background\n## Clinical need for biomarkers and prediction\n# Methods\n## Dataset retrieval and transcriptomics processing\n## Hub gene panel discovery and model pipeline\n## RT-qPCR validation\n# Results\n## 6-hub gene panel and NK cell cytotoxicity relevance\n## SepxFindeR predictive performance across databases\n# Conclusions\n## Biomarker panel and rapid-stage prediction framework","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To predict sepsis and septic shock by analyzing whole-blood transcriptomics and using an advanced machine learning framework built on NK cell-related hub genes.\"},{\"question\":\"How were the key gene biomarkers identified and used?\",\"answer\":\"Four NCBI GEO transcriptomics datasets were processed to identify a hub gene panel, which was then used to create a multistage prediction model for distinguishing sepsis from septic shock.\"},{\"question\":\"How was the prediction model validated for practical use?\",\"answer\":\"The framework was validated using a newly generated RT-qPCR hub gene dataset from newly recruited patients with sepsis and septic shock, showing compatibility with the same hub gene panel.\"}]","Predicting Patients with Septic Shock and Sepsis - Whole-Blood Expression of NK Cell-Related Hub Genes | PDF",1785896549,38,{"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},"predicting-patients-with-septic-shock-and-sepsis-whole-blood-expression-of-nk-cell-related-hub-genes","",{"@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/predicting-patients-with-septic-shock-and-sepsis-whole-blood-expression-of-nk-cell-related-hub-genes/125086/",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 is the main goal of the study?","Question",{"text":75,"@type":76},"To predict sepsis and septic shock by analyzing whole-blood transcriptomics and using an advanced machine learning framework built on NK cell-related hub genes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were the key gene biomarkers identified and used?",{"text":80,"@type":76},"Four NCBI GEO transcriptomics datasets were processed to identify a hub gene panel, which was then used to create a multistage prediction model for distinguishing sepsis from septic shock.",{"name":82,"@type":73,"acceptedAnswer":83},"How was the prediction model validated for practical use?",{"text":84,"@type":76},"The framework was validated using a newly generated RT-qPCR hub gene dataset from newly recruited patients with sepsis and septic shock, showing compatibility with the same hub gene panel.","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"]