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This study develops and validates a liquid biopsy assay using circulating microbial DNA (cmDNA) to identify EAC and high-grade dysplasia (HGD). Metagenomic sequencing reveals distinct microbial diversity and composition in EAC/HGD versus Barrett’s esophagus (BE) and gastroesophageal reflux disease (GERD). A 6-marker cmDNA panel is selected via qRT-PCR validation and LASSO regression, then modeled with XGBoost, yielding strong AUCs in both training and independent testing cohorts.",{"@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":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/a-machine-learning-informed-circulating-microbial-dna-signature-for-early-diagnosis-of-esophageal-adenocarcinoma/418878/",{"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/a-machine-learning-informed-circulating-microbial-dna-signature-for-early-diagnosis-of-esophageal-adenocarcinoma/418878.png","ImageObject",300,407,{"name":92,"@type":93},"Sarah ","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-28",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main goal of this study?","Question",{"text":112,"@type":113},"To develop and validate a liquid biopsy assay based on circulating microbial DNA (cmDNA) for early detection of esophageal adenocarcinoma (EAC) and high-grade dysplasia (HGD).","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How was the microbial DNA signature identified?",{"text":117,"@type":113},"Metagenomic sequencing identified candidate microbes, qRT-PCR validation confirmed candidates, and LASSO regression selected a 6-marker cmDNA panel for the signature.",{"name":119,"@type":110,"acceptedAnswer":120},"What machine-learning method was used in the diagnostic model?",{"text":121,"@type":113},"The diagnostic model was trained with the XGBoost algorithm, achieving AUCs of 0.93 in the training cohort and 0.91 for EAC and 0.88 for HGD in an independent testing cohort.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},418878,1790790485,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":36,"preview_page_count":4},962085320529,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","GUT MICROBES  \n2025, VOL. 18, NO. 1, 2604334  \n[https://doi.org/10.1080/19490976.2025.2604334](https://doi.org/10.1080/19490976.2025.2604334)  \nRESEARCH ARTICLE     \nA machine-learning informed circulating microbial DNA signature for early diagnosis of esophageal adenocarcinoma  \nYuan Lia, b,c, 1, Caiming Xua,d, 1, Hyun Parke, Ashten N. Omsteade, Muhammad Aneese, Chris Sherrye, Alisha F. Khane, Erin Grayhacke, Benny Wekslerf, Patrick Wagnere, David L. Bartlette, Stephen J. Meltzerg, Ali H. Zaidie and Ajay Goela, h   \naDepartment of Molecular Diagnostics and Experimental Therapeutics, Beckman Research Institute of City of Hope, Biomedical Research Center, Monrovia, California, USA; bDepartment of Clinical Laboratory, Yangpu Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China; cDepartment of Clinical Laboratory, Shanghai Children's Hospital, Shanghai Jiaotong University, Shanghai, People's Republic of China; dDepartment of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, People's Republic of China; eAllegheny Health Network Cancer Institute, Allegheny Health Network, Pittsburgh, PA, USA; fDivision of Thoracic Surgery, Department of Cardiothoracic Surgery, Allegheny Health Network, Pittsburgh, PA, USA; gDivision of Gastroenterology and Hepatology, Department of Medicine and Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; hCity of Hope Comprehensive Cancer Center,  \nDuarte, California, USA  \nABSTRACT  \nEsophageal adenocarcinoma (EAC) has seen a dramatic rise in incidence in developed countries over the past three decades. Early detection of its precursors—gastroesophageal reflux disease (GERD), Barrett's esophagus (BE), and high-grade dysplasia (HGD) is critical for cancer prevention. This study presents the development and validation of a novel liquid biopsy assay based on circulating microbial DNA (cmDNA) for the early detection of EACand HGD. Using metagenomic sequencing, we identified significant differences in microbial diversity and composition between EAC and HGD patients, as well as between BE and GERD patients. A total of 46 microbial candidates in tissue and 419 in serum were upregulated in EAC & HGD, with 11 consistently elevated in both sample types. Following qRT-PCR validation and LASSO regression, a 6-marker cmDNA panel was selected. This signature was incorporated into a diagnostic model trained with the XGBoost algorithm, achieving an AUC of 0.93 in the training cohort (52 HGD & EAC cases vs. 54 BE & GERD controls). Importantly, the model demonstrated robust performance in an independent testing cohort (23 HGD & EAC cases vs. 22 BE & GERD controls), yielding AUCs of 0.91 for EAC and 0.88 for HGD. These findings highlight the diagnostic potential of cmDNA-based profiling and support its utility as a minimally invasive, accurate, and generalizable tool for early detection of esophageal adenocarcinoma.  \nARTICLE HISTORY  \nReceived 23 June 2025 Revised 8 September 2025 Accepted 10 December 2025  \nKEYWORDS  \nEsophageal adenocarcinoma; Barrett's esophagus; aarly diagnostic biomarker; circulating microbiome DNA; XGBoost algorithm  \nIntroduction  \nEsophageal cancer (EC) poses a significant global health challenge, ranking as the seventh most common cancer and the sixth leading cause of cancer-associated mortality worldwide. By 2040, it is projected that EC will account for approximately 957,000 new cases and 880,000 deaths.1 Esophageal adenocarcinoma (EAC) is indeed the most prevalent type of EC in Western countries, and its incidence is significantly increasing, primarily attributed to gastroesophageal reflux disease (GERD) and the development of Barrett's esophagus (BE) .2 BE is a well-known preneoplastic lesion for EAC, characterized by an archetypal metaplastic change in the distal esophagus.3 The relative risk of developing EAC in patients with BE is 11.3 (CI95%, 8.8–14.4) compare","cbCaigPDxN7QnGS3","https://ap.wps.com/l/cbCaigPDxN7QnGS3","pdf",2035636,16,"English","# Abstract\n# Introduction","[{\"question\":\"What is the main goal of this study?\",\"answer\":\"To develop and validate a liquid biopsy assay based on circulating microbial DNA (cmDNA) for early detection of esophageal adenocarcinoma (EAC) and high-grade dysplasia (HGD).\"},{\"question\":\"How was the microbial DNA signature identified?\",\"answer\":\"Metagenomic sequencing identified candidate microbes, qRT-PCR validation confirmed candidates, and LASSO regression selected a 6-marker cmDNA panel for the signature.\"},{\"question\":\"What machine-learning method was used in the diagnostic model?\",\"answer\":\"The diagnostic model was trained with the XGBoost algorithm, achieving AUCs of 0.93 in the training cohort and 0.91 for EAC and 0.88 for HGD in an independent testing cohort.\"}]","A machine-learning informed circulating microbial DNA signature for early diagnosis of esophageal adenocarcinoma | PDF",1790614718]