[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-386463-105":59,"doc-detail-386463-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","major-data-analysis-errors-invalidate-cancer-microbiome-findings","Major data analysis errors invalidate cancer microbiome findings","","Re-analysis of a large-scale study that linked microbial DNA signatures to 33 cancer types and built near-perfect machine-learning classifiers identifies two core methodological failures. Genome database and computational errors produced millions of bacterial false positives because most sequences labeled as bacteria were actually human. Transformation errors further generated artificial signatures, assigning distinct signals to tumor types even when no microbial reads were detected, yielding misleading classification accuracy. These flaws likely invalidate the original cancer microbiome conclusions and subsequent derivative studies using the same data.",{"@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/major-data-analysis-errors-invalidate-cancer-microbiome-findings/386463/",{"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/major-data-analysis-errors-invalidate-cancer-microbiome-findings/386463.png","ImageObject",300,407,{"name":92,"@type":93},"Franzy","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-01","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What were the two fundamental flaws found in the original cancer microbiome study?","Question",{"text":112,"@type":113},"The re-analysis found errors in the genome database and computational pipeline that created millions of bacterial false positives, largely because labeled bacterial sequences matched human sequences. It also found errors in transforming raw data that produced artificial microbial signatures even when no microbial reads were detected.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How did the database contamination affect the classification of cancer samples?",{"text":117,"@type":113},"Contaminated or mislabeled bacterial genomes can cause misclassification of human reads that match those contaminated genomes. This is especially problematic for metagenomics analyses where microbial reads are rare compared with human reads.",{"name":119,"@type":110,"acceptedAnswer":120},"Why are the microbiome-based cancer classifiers concluded to be wrong?",{"text":121,"@type":113},"Because both sources of error undermine the reported microbial signatures, the re-analysis concludes that the machine-learning classifiers were trained on artifacts rather than genuine microbial signals, making the reported cancer-specific microbiome distinctions invalid.","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},386463,1791140972,{"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":39,"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":145},2336478945519,"https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","| Human Microbiome | Research Article  \nMajor data analysis errors invalidate cancer microbiome findings  \nAbraham Gihawi,1 Yuchen Ge,2,3 Jennifer Lu,2,3 Daniela Puiu,2,3 Amanda Xu,2 Colin S. Cooper,1 Daniel S. Brewer,1,4 Mihaela Pertea,2,3,5 Steven L. Salzberg2,3,5,6  \nAUTHOR AFFILIATIONS See affiliation list on p. 13.  \nABSTRACT We re-analyzed the data from a recent large-scale study that reported strong correlations between DNA signatures of microbial organisms and 33 different cancer types and that created machine-learning predictors with near-perfect accuracy at distinguishing among cancers. We found at least two fundamental flaws in the reported data and in the methods: (i) errors in the genome database and the associated computational methods led to millions of false-positive findings of bacterial reads across all samples, largely because most of the sequences identified as bacteria were instead human; and (ii) errors in the transformation of the raw data created an artificial signature, even for microbes with no reads detected, tagging each tumor type with a distinct signal that the machine-learning programs then used to create an apparently accurate classifier. Each of these problems invalidates the results, leading to the conclusion that the microbiome-based classifiers for identifying cancer presented in the study are entirely wrong. These flaws have subsequently affected more than a dozen additional published studies that used the same data and whose results are likely invalid as well.  \nIMPORTANCE Recent reports showing that human cancers have a distinctive micro biome have led to a flurry of papers describing microbial signatures of different cancer types. Many of these reports are based on flawed data that, upon re-analysis, completely overturns the original findings. The re-analysis conducted here shows that most of the microbes originally reported as associated with cancer were not present at all in the samples. The original report of a cancer microbiome and more than a dozen follow-up studies are, therefore, likely to be invalid.  \nKEYWORDS microbiome, cancer, bioinformatics, computational biology, metagenomics  \nB acteria and viruses have been implicated as the cause of multiple types of can  \ncer, including human papillomavirus for cervical cancer (1), Helicobacter pylori for stomach cancer (2), and Fusobacterium nucleatum for colon cancer (3), among others. However, until a few years ago, little evidence indicated that a complex microbiome—a mixture of various bacteria and viruses—might affect the etiology of other cancer types. This changed after a large-scale analysis of 17,625 samples from the Cancer Genome Atlas (TCGA) reported that, in the sequence data from 33 types of cancer, a distinctive microbial signature was present in 32 of the cancer types (4) . These signatures were remarkably accurate at discriminating between each tumor type and all other cancers. For 15 cancer types, signatures were created that could distinguish between tumor and normal tissue, and for 20 cancer types, signatures were developed to identify tumors based on microbial DNA found in the blood of those patients. The machine-learning models created in this study had surprisingly high accuracy, with most models ranging from 95 to 100% accurate.  \nEditor Igor B. Zhulin, The Ohio State University, Columbus, Ohio, USA  \nAddress correspondence to Steven L. Salzberg, [salzberg@jhu.edu](salzberg@jhu.edu).  \nAbraham Gihawi, Yuchen Ge, and Jennifer Lu contributed equally to this article. Author order was determined by mutual agreement among the cofirst authors.  \nauthors declare conflict of interest  \nHowever, despite efforts taken by Poore et al. to remove contaminating species and to avoid common biases like batch effects, we were concerned because many of the machine-learning models reported in the study were based on genera that did not make sense in the context of human disease. The models included species that had never been repor","cbCaimdsJRu3pogL","https://ap.wps.com/l/cbCaimdsJRu3pogL","pdf",978589,15,"English","# Abstract\n# Importance\n# Background and rationale\n# Results","[{\"question\":\"What were the two fundamental flaws found in the original cancer microbiome study?\",\"answer\":\"The re-analysis found errors in the genome database and computational pipeline that created millions of bacterial false positives, largely because labeled bacterial sequences matched human sequences. It also found errors in transforming raw data that produced artificial microbial signatures even when no microbial reads were detected.\"},{\"question\":\"How did the database contamination affect the classification of cancer samples?\",\"answer\":\"Contaminated or mislabeled bacterial genomes can cause misclassification of human reads that match those contaminated genomes. This is especially problematic for metagenomics analyses where microbial reads are rare compared with human reads.\"},{\"question\":\"Why are the microbiome-based cancer classifiers concluded to be wrong?\",\"answer\":\"Because both sources of error undermine the reported microbial signatures, the re-analysis concludes that the machine-learning classifiers were trained on artifacts rather than genuine microbial signals, making the reported cancer-specific microbiome distinctions invalid.\"}]","Major data analysis errors invalidate cancer microbiome findings | PDF",1790273895,38]