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Using a multi-cancer cohort of 413 patients and 239 healthy individuals, the work profiles eccDNA fragment-size distributions with support from a patient-derived xenograft (PDX) model. A fragment-size threshold distinguishes tumor-derived from non-tumor-derived eccDNA, followed by gene-based annotation. Machine learning yields ScanTecc for cancer detection and tissue-of-origin classification.",{"@graph":69,"@context":122},[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/detection-of-primary-cancer-types-via-fragment-size-selection-in-circulating-cell-free-extrachromosomal-circular-dna/346378/",{"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/detection-of-primary-cancer-types-via-fragment-size-selection-in-circulating-cell-free-extrachromosomal-circular-dna/346378.png","ImageObject",300,407,{"name":92,"@type":93},"Patrick","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-24","2026-09-22",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the key biomarker used for detecting cancer in this study?","Question",{"text":112,"@type":113},"The study uses plasma-derived cell-free extrachromosomal circular DNA (eccDNA). 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Genome Medicine (2026) 18:18  \n[https://doi.org/10.1186/s13073-025-01595-6](https://doi.org/10.1186/s13073-025-01595-6)  \nGenome Medicine  \nRESEARCH Open Access  \nDetection of primary cancer types via  fragment size selection in circulating cell-free extrachromosomal circular DNA  \nJingwen Fang1,2†, Songwen Luo1†, Shouzhen Li1†, Yehong Xu3†, Jing Wang4†, Benjie Shan1†, Mingjun Hu5†, Qiaoni Yu1,6, Wen Zhang1,7, Ke Liu1, Yunying Shao2, JiaxuanYang2, YouYang Zhou2, Guangtao Xu1, Xinfeng Yao1, Ruoming Sun1, Mengyuan Zhang4, Kun Li8, Xihai Xu8, Yongliang Zhang5*, Zhihong Zhang3*, Xinghua Han 1*, Yueyin Pan1*, Chuang Guo9,10* and Kun Qu1,6,7*  \nAbstract  \nBackground Extrachromosomal circular DNA has emerged as a pivotal factor in tumor biology, contributing to intratumor heterogeneity, oncogene amplification, and tumor evolution. Despite its relevance, the presence and molecular characteristics of plasma-derived cell-free extrachromosomal circular DNA (eccDNA) in cancer patients remain insufficiently explored.  \nMethods In this study, we profiled plasma-derived cell-free eccDNA from a multi-cancer cohort consisting of 413 cancer patients and 239 healthy individuals. We analyzed eccDNA fragment size distributions using a patient-derived xenograft (PDX) mouse model and identified fragment size features to distinguish tumor-derived eccDNA from non-tumor-derived eccDNA. We further performed in silico fragment size selection and developed a gene-based annotation method to characterize the gene content carried by cell-free eccDNA across different cancer types. By utilizing these cell-free eccDNA signatures, we developed ScanTecc (screening cancer types with cell-free eccDNA), a machine learning-based approach for cancer detection and tissue-of-origin classification. The classification  \n†Jingwen Fang, Songwen Luo, Shouzhen Li, Yehong Xu, Jing Wang, Benjie Shan and Mingjun Hu contributed equally to this work.  \n*Correspondence: Yongliang Zhang [zyl2020@ustc.edu.cn](zyl2020@ustc.edu.cn)[ ](zyl2020@ustc.edu.cn)Zhihong Zhang [zhangzhihope@126.com](zhangzhihope@126.com)[ ](zhangzhihope@126.com)Xinghua Han [hxhmail@ustc.edu.cn](hxhmail@ustc.edu.cn)[ ](hxhmail@ustc.edu.cn)Yueyin Pan [panyueyin@ustc.edu.cn](panyueyin@ustc.edu.cn)[ ](panyueyin@ustc.edu.cn)Chuang Guo [gchuang@ustc.edu.cn](gchuang@ustc.edu.cn)[ ](gchuang@ustc.edu.cn)Kun Qu [qukun@ustc.edu.cn](qukun@ustc.edu.cn)  \nFull list of author information is available at the end of the article  \nBackground  \nCirculating tumor DNA (ctDNA) has attracted widespread attention in clinical oncology as a minimally invasive liquid biopsy biomarker for detecting target mutations and monitoring cancer recurrence or persistence [1–3]. However, most ctDNA clinical research focuses on genomic variations [4], epigenomic characteristics [5] or fragmentation patterns [6], limiting its ability to detect early cancer due to dilution by much larger quantities of DNA of noncancerous origins. To address this issue, researchers have attempted to improve ctDNA detection by increasing sequencing depth or combining other blood biomarkers, such as histone modifications, proteomics, or metabolomics [7, 8]. However, these  \n© The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your","cbCaira9tRtAEZPe","https://ap.wps.com/l/cbCaira9tRtAEZPe","pdf",1862385,14,"English","# Abstract\n## Background\n## Methods\n## Results\n## Conclusions\n## Keywords","[{\"question\":\"What is the key biomarker used for detecting cancer in this study?\",\"answer\":\"The study uses plasma-derived cell-free extrachromosomal circular DNA (eccDNA). It focuses on eccDNA fragment size and gene content carried by eccDNA.\"},{\"question\":\"How does the method distinguish tumor-derived eccDNA from non-tumor-derived eccDNA?\",\"answer\":\"It analyzes eccDNA fragment size distributions and identifies an approximate fragment-size threshold near 1,000 base pairs using a PDX model, separating tumor-derived from non-tumor-derived eccDNA.\"},{\"question\":\"What performance did ScanTecc achieve for cancer detection and cancer type classification?\",\"answer\":\"ScanTecc achieved an overall AUC of 0.92 for distinguishing cancer patients from healthy individuals, and an overall AUC of 0.79 for identifying specific cancer types. Performance remained high across stages, including stage I and stage IV.\"}]","Detection of primary cancer types via fragment size selection in circulating cell-free extrachromosomal circular DNA | PDF",1790061177,35]