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A total of 911 cancer patients (lung, gastric, colorectal) and 792 non-cancer participants were enrolled and randomly split into discovery and validation cohorts. A urinary panel of 18 metabolites distinguished cancer from healthy controls with AUC 0.96, while additional panels differentiated cancer subtypes and the model predicted origins of lung, gastric, and colorectal cancers with overall accuracy 0.75.",{"@graph":69,"@context":126},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/development-of-a-urine-based-metabolomics-approach-for-multi-cancer-screening-and-tumor-origin-prediction/329706/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/development-of-a-urine-based-metabolomics-approach-for-multi-cancer-screening-and-tumor-origin-prediction/329706.png","ImageObject",300,407,{"name":92,"@type":93},"Genevieve","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-26","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118,122],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main goal of the study?","Question",{"text":112,"@type":113},"To develop a urine-based metabolomics approach for multi-cancer screening and for predicting tumor origin.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were participants organized for analysis?",{"text":117,"@type":113},"Participants were enrolled and randomly allocated into discovery and validation cohorts to build and then assess screening and origin-prediction models.",{"name":119,"@type":110,"acceptedAnswer":120},"Which metabolites and model performance were reported?",{"text":121,"@type":113},"A total of 360 metabolites were identified; 18 urinary metabolites characterized as biomarkers achieved AUC 0.96 for discriminating cancer patients from healthy controls in the validation cohort.",{"name":123,"@type":110,"acceptedAnswer":124},"How well did the model predict tumor origin?",{"text":125,"@type":113},"The model predicted origins of lung, gastric, and colorectal cancers with overall accuracy 0.75, with AUCs of 0.88, 0.88, and 0.80 for each cancer type, respectively.","https://schema.org",{"og:url":83,"og:type":128,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":130,"canonical":83},"index,follow",{"doc_id":132,"site_id":62},329706,1790173611,{"code":4,"msg":5,"data":135},{"doc_id":132,"user_id":136,"nickname":92,"user_avatar":137,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":138,"file_id":139,"file_url":140,"file_type":141,"file_size":142,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":52,"language":143,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":144,"faqs":145,"seo_title":146,"seo_description":67,"update_tm":147,"read_time":148,"preview_page_count":4},1374391974585,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","TYPE Original Research PUBLISHED 13 December 2024 DOI 10.3389/fimmu.2024.1449103  \nOPEN ACCESS  \nEDITED BY  \nZhiwei Zhou,  \nUniversity of Texas Southwestern Medical Center, United States  \nREVIEWED BY  \nDaniele Vergara, University of Salento, Italy Murat Akand,  \nUniversity Hospitals Leuven, Belgium  \n*CORRESPONDENCE  \nYan Li  \n [yan.li@metanotitia.com](yan.li@metanotitia.com)[ ](yan.li@metanotitia.com)Youxiang Chen  \n [chenyx102@126.com](chenyx102@126.com)[ ](chenyx102@126.com)Zhenkun Xia  \n [xwvfye@csu.edu.cn](xwvfye@csu.edu.cn)  \n†These authors have contributed equally to this work  \nRECEIVED 14 June 2024  \nACCEPTED 27 November 2024  \nPUBLISHED 13 December 2024  \nCITATION  \nXu X, Zeng C, Qing B, He Y, Song G, Wang J, Yu S, Zhang T, Wei Q, Liu L, Wen H, Hu J, Zhang W, Li Y, Chen Y and Xia Z (2024) Development of a urine-based metabolomics approach for multi-cancer screening and tumor origin prediction.  \nFront. Immunol. 15:1449103 .  \ndoi: 10.3389/fimmu.2024.1449103  \nCOPYRIGHT  \n© 2024 Xu, Zeng, Qing, He, Song, Wang, Yu, Zhang, Wei, Liu, Wen, Hu, Zhang, Li, Chen and Xia. 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.  \nDevelopment of a urine-based metabolomics approach for multi-cancer screening and tumor origin prediction  \nXinping Xu 1†, Chunyan Zeng 2†, Bei Qing 3†, Yun He 4†, Guodong Song 5, Jiaojiao Wang 4, Shuqi Yu 4, Tao Zhang 4, Qingyan Wei 4, Li Liu 4, He Wen 4, Junyuan Hu 4, Wei Zhang 2, Yan Li 4*, Youxiang Chen 2* and Zhenkun Xia 3*  \n1Jiangxi Institute of Respiratory Disease, The First Affiliated Hospital of Nanchang University, Nanchang, China, 2The First Affiliated Hospital of Nanchang University, Nanchang, China, 3The Second Xiangya Hospital of Central South University, Changsha, China, 4 Metanotitia Inc., Shenzhen, China, 5The Second Hospital of Tianjin Medical University, Tianjin, China  \nBackground: Cancer remains a leading cause of mortality worldwide. A noninvasive screening solution was required for early diagnosis of cancer. Multicancer early detection (MCED) tests have been considered to address the challenge by simultaneously identifying multiple types of cancer within a single test using minimally invasive blood samples. However, a multi-cancer screening strategy utilizing urine-based metabolomics has not yet been developed.  \nMethods: We enrolled 911 cancer patients with 548 lung cancer (LC), 177 with gastric cancer (GC), and 186 with colorectal cancer (CRC), alongside 563 individuals with non-cancerous benign diseases and 229 healthy controls (HC) and investigated the metabolic profiles of urine samples. Participants were randomly allocated to discovery and validation cohorts. The discovery cohort was used for identifying multi-cancer and tissue-specific signatures to build the cancer screening and tumor origin prediction models, while the validation cohort was employed for assessing the performance of these models.  \nResults: We identified and annotated a total of 360 metabolites from the urine samples. Using the LASSO regression algorithm, 18 metabolites were characterized as urinary metabolic biomarkers and exhibited excellent discriminative performance between cancer patients and HC with AUC of 0.96 in the validation cohort. In comparison with the performance of traditional tumor markers CEA, the screening model performed higher sensitivity across the cancer stages, with a particularly increase in sensitivity among early-stage cancer patients. Moreover, the screening model also exhibited in high classification of cancers from non-cancerous group, comprising with HC and benign disease participants. Furthe","cbCaiofHKE8KKfMS","https://ap.wps.com/l/cbCaiofHKE8KKfMS","pdf",4616912,"English","# Background\n# Methods\n# Results\n## Biomarker identification and model performance\n## Differentiation of cancer types and tumor origin prediction\n# Discussion\n# Introduction","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To develop a urine-based metabolomics approach for multi-cancer screening and for predicting tumor origin.\"},{\"question\":\"How were participants organized for analysis?\",\"answer\":\"Participants were enrolled and randomly allocated into discovery and validation cohorts to build and then assess screening and origin-prediction models.\"},{\"question\":\"Which metabolites and model performance were reported?\",\"answer\":\"A total of 360 metabolites were identified; 18 urinary metabolites characterized as biomarkers achieved AUC 0.96 for discriminating cancer patients from healthy controls in the validation cohort.\"},{\"question\":\"How well did the model predict tumor origin?\",\"answer\":\"The model predicted origins of lung, gastric, and colorectal cancers with overall accuracy 0.75, with AUCs of 0.88, 0.88, and 0.80 for each cancer type, respectively.\"}]","Development of a urine-based metabolomics approach for multi-cancer screening and tumor origin prediction | PDF",1789985263,25]