{"doi":"10.1128/mbio.01607-23","title":"Major data analysis errors invalidate cancer microbiome findings","abstract":"ABSTRACT 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. IMPORTANCE Recent reports showing that human cancers have a distinctive microbiome 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.","journal":"mBio","year":2023,"id":315185,"datarank":0.8034879412008019,"base_score":5.356586274672012,"endowment":5.356586274672012,"self_citation_contribution":0.8034879412008019,"citation_network_contribution":0.0,"self_endowment_contribution":0.8034879412008019,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":211,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.7256,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":530412,"name":"Yuchen Ge","orcid":"0000-0001-6955-590X","position":1,"is_corresponding":false},{"id":79031,"name":"Jennifer Lu","orcid":"0000-0001-9167-2002","position":2,"is_corresponding":false},{"id":269784,"name":"Daniela Puiu","orcid":"0000-0002-2386-9265","position":3,"is_corresponding":false},{"id":1017129,"name":"Amanda Xu","orcid":null,"position":4,"is_corresponding":false},{"id":13367,"name":"Colin S. Cooper","orcid":"0000-0003-2013-8042","position":5,"is_corresponding":false},{"id":14334,"name":"Daniel Brewer","orcid":"0000-0003-4753-9794","position":6,"is_corresponding":false},{"id":16222,"name":"Mihaela Pertea","orcid":"0000-0003-0762-8637","position":7,"is_corresponding":false},{"id":4334,"name":"Steven L. Salzberg","orcid":"0000-0002-8859-7432","position":8,"is_corresponding":false},{"id":1016021,"name":"Abraham Gihawi","orcid":"0000-0002-3676-5561","position":0,"is_corresponding":true}],"reference_count":33,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T01:06:25.560098Z","pmid":"37811944","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}