[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-330321-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-330321-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","a-novel-machine-learning-algorithm-selects-proteome-signature-to-specifically-identify-cancer-exosomes-330321","A novel machine learning algorithm selects proteome signature to specifically identify cancer exosomes","","Non-invasive early cancer diagnosis remains difficult because current tests often lack sufficient sensitivity and specificity. Exosomes are abundant membrane-bound nanovesicles in biological fluids, carrying parent-cell DNA, RNA, and proteins, making them attractive biomarker candidates. A rapid, flexible approach to distinguish cancers across types and fluids is not yet established. This study presents a machine learning method using exosome protein panels, random-forest models, and universal biomarkers to classify cancer exosomes with strong AUROC performance.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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Martin","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-03","2026-09-21",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",5,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"Why are exosomes considered promising biomarkers for early cancer diagnosis?","Question",{"text":63,"@type":64},"Exosomes are abundant in biological fluids and contain biomolecules from their parent cells, including proteins, making them useful for detecting disease-related patterns.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"What machine learning approach is used to identify cancer exosomes?",{"text":68,"@type":64},"The study uses a novel machine learning-based computational method that builds panels of exosome-associated proteins and applies random forest models to classify cancer exosomes.",{"name":70,"@type":61,"acceptedAnswer":71},"How well do the models perform compared with other classifiers?",{"text":72,"@type":64},"Models trained on plasma, serum, or urine-derived exosomes achieve AUROC scores above 0.91 and outperform Support Vector Machine, K Nearest Neighbor, and Gaussian Naive Bayes.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},330321,1790639257,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,100,105,110,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & 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](rkalluri@mdanderson.org)[Competing interest:](Competing interest: See page)[ See page](Competing interest: See page)[ ](Competing interest: See page)13  \nFunding: See page 13  \nSent for Review  \n02 July 2023  \nPreprint posted  \n19 July 2023  \nReviewed preprint posted  \n03 October 2023  \nReviewed preprint revised  \n04 March 2024  \nVersion of Record published  \n26 March 2024  \nReviewing Editor: Mohammad M Karimi, King's College London, United Kingdom  \n Copyright Li et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use andredistribution provided that the original author and source are credited.  \nA novel machine learning algorithmselects proteome signature to specifically identify cancer exosomes  \nBingrui Li1, Fernanda G Kugeratski1, Raghu Kalluri1,2,3*  \n1 Department of Cancer Biology, University of Texas MD Anderson Cancer Center, Houston, United States; 2 Department of Bioengineering, Rice University, Houston, United States; 3 Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, United States  \n\n| Abstract Non-invasive early cancer diagnosis remains challenging due to the low sensitivity and specificity of current diagnostic approaches. Exosomes are membrane-bound nanovesicles secreted by all cells that contain DNA, RNA, and proteins that are representative of the parent cells. This property, along with the abundance of exosomes in biological fluids makes them compelling candidates as biomarkers. However, a rapid and flexible exosome-based diagnostic method to distinguish human cancers across cancer types in diverse biological fluids is yet to be defined. Here, we describe a novel machine learning-based computational method to distinguish cancers using a panel of proteins associated with exosomes. Employing datasets of exosome proteins from human cell lines, tissue, plasma, serum, and urine samples from a variety of cancers, we identify Clathrin Heavy Chain (CLTC), Ezrin,(EZR), Talin-1 (TLN1), Adenylyl cyclase-associated protein 1 (CAP1), and Moesin (MSN) as highly abundant universal biomarkers for exosomes and define three panels of pan-cancer exosome proteins that distinguish cancer exosomes from other exosomes and aid in classifying cancer subtypes employing random forest models. All the models using proteins from plasma, serum, or urine-derived exosomes yield AUROC scores higher than 0.91 and demonstrate superior performance compared to Support Vector Machine, K Nearest Neighbor Classifier and Gaussian Naive Bayes. This study provides a reliable protein biomarker signature associated with cancer exosomes with scalable machine learning capability for a sensitive and specific non-invasive method of cancer diagnosis. |\n| --- |\n| eLife assessment\u003Cbr>This important study introduces a novel AI method for the analysis of published data, with practical implications for early cancer diagnosis. The results are supported by compelling evidence. |\n\nIntroduction  \nTissue biopsies have traditionally been a definitive way to diagnose and stage cancer; however, a biopsy may not be easily accessible for many tumors such as those in the pancreas, lung, and brain (Distler et al., 2014; Morris et al., 2010; Suram et al., 2012; Mullerat et al., 2003; Hammes et al., 2008) . In addition, the small amount of biopsied tissue does not represent the entire heterogeneous pathological profile of the tumor (Oshi et al., 2021) . In recent years, liquid biopsy has emerged asa plausible diagnostic and monitoring approach with the capability to detect tumor biomarkers in more accessible biological fluids such as plasma, serum and urine (Hu et al., 2022) . Detectable tumor  \nLi et al. eLife 2023;12:RP90390. DOI: [https://doi.org/10.7554/eLife.90390](https://doi.org/10.7554/eLife.90390) 1 of 16  \n Research article Cancer Biology | Computational and Systems Biol","cbCaik4NRHaDtit8","https://ap.wps.com/l/cbCaik4NRHaDtit8","pdf",3463067,16,"English","# Abstract\n# Introduction","[{\"question\":\"Why are exosomes considered promising biomarkers for early cancer diagnosis?\",\"answer\":\"Exosomes are abundant in biological fluids and contain biomolecules from their parent cells, including proteins, making them useful for detecting disease-related patterns.\"},{\"question\":\"What machine learning approach is used to identify cancer exosomes?\",\"answer\":\"The study uses a novel machine learning-based computational method that builds panels of exosome-associated proteins and applies random forest models to classify cancer exosomes.\"},{\"question\":\"How well do the models perform compared with other classifiers?\",\"answer\":\"Models trained on plasma, serum, or urine-derived exosomes achieve AUROC scores above 0.91 and outperform Support Vector Machine, K Nearest Neighbor, and Gaussian Naive Bayes.\"}]","A novel machine learning algorithm selects proteome signature to specifically identify cancer exosomes | PDF",1789988150]