ArticleScientific reports2021
Biopsy bacterial signature can predict patient tissue malignancy.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 17 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 24 citations in OpenAlex.
- Intratumoral microbiota composition in women's cancers: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Guideline for designing microbiome studies in neoplastic diseases.GeroScience · 2024Guideline
- IntratumoralAnimals : an open access journal from MDPI · 2026Article
- Microbe-associated molecular patterns differentially mediate carcinogenic alterations of the breast tissue in the context of obesity.Neoplasia (New York, N.Y.) · 2026Article
- The intratumoral microbiota in breast cancer: from basic research to clinical translation.Gut microbes · 2025Review
- Recent advancements in artificial intelligence-powered cancer prediction from oral microbiome.Periodontology 2000 · 2025Review
- The role and significance of the oncobiota in selected cancers: a review.Clinical and experimental medicine · 2025Review
- Bacterial-Mediated In Situ Engineering of Tumour-Associated Macrophages for Cancer Immunotherapy.Cancers · 2025Review
- A Systematic Review and Meta-Analysis of 16S rRNA and Cancer Microbiome Atlas Datasets to Characterize Microbiota Signatures in Normal Breast, Mastitis, and Breast Cancer.Microorganisms · 2025Review
- The oncobiome; what, so what, now what?Microbiome research reports · 2025Review
- A review of machine learning methods for cancer characterization from microbiome data.NPJ precision oncology · 2024Review
- The tremendous clinical potential of the microbiota in the treatment of breast cancer: the next frontier.Journal of cancer research and clinical oncology · 2023Review
- Identification of Bacterial Metabolites Modulating Breast Cancer Cell Proliferation and Epithelial-Mesenchymal Transition.Molecules (Basel, Switzerland) · 2023Article
- Review
- Understanding and harnessing triple-negative breast cancer-related microbiota in oncology.Frontiers in oncology · 2022Review
- The involvement of oncobiosis and bacterial metabolite signaling in metastasis formation in breast cancer.Cancer metastasis reviews · 2021Review
- Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Considerable recent research has indicated the presence of bacteria in a variety of human tumours and matched normal tissue. Rather than focusing on further identification of bacteria within tumour samples, we reversed the hypothesis to query if establishing the bacterial profile of a tissue biopsy could reveal its histology / malignancy status. The aim of the present study was therefore to differentiate between malignant and non-malignant fresh breast biopsy specimens, collected specifically for this purpose, based on bacterial sequence data alone. Fresh tissue biopsies were obtained from breast cancer patients and subjected to 16S rRNA gene sequencing. Progressive microbiological and bioinformatic contamination control practices were imparted at all points of specimen handling and bioinformatic manipulation. Differences in breast tumour and matched normal tissues were probed using a variety of statistical and machine-learning-based strategies. Breast tumour and matched normal tissue microbiome profiles proved sufficiently different to indicate that a classification strategy using bacterial biomarkers could be effective. Leave-one-out cross-validation of the predictive model confirmed the ability to identify malignant breast tissue from its bacterial signature with 84.78% accuracy, with a corresponding area under the receiver operating characteristic curve of 0.888. This study provides proof-of-concept data, from fit-for-purpose study material, on the potential to use the bacterial signature of tissue biopsies to identify their malignancy status.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.