ArticleScientific reports2023
Prediction of breast cancer using blood microbiome and identification of foods for breast cancer prevention.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
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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.
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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.
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Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 25 citations in OpenAlex.
- Breast cancer and microbiome: a systematic review highlighting challenges for clinical translation.BMC women's health · 2025Pooled it
- Guideline for designing microbiome studies in neoplastic diseases.GeroScience · 2024Guideline
- Microbiota in the Tumor Metastatic Cascade: Emerging Evidence Across Invasion, Circulation, and Colonization.International journal of molecular sciences · 2026Review
- Integrated analysis of human-mouse gut microbiota in RSV infection based on machine learning.BMC microbiology · 2026Article
- A prognostic nomogram for colorectal cancer: integrating blood microbiome and clinical factors.Journal of gastrointestinal oncology · 2025Article
- The role of gut microbiota in breast cancer: biomarker identification and therapeutic applications.Antonie van Leeuwenhoek · 2025Review
- Optimizing breast cancer chemotherapy by harnessing gut microbiota with insights from artificial intelligence.NPJ biofilms and microbiomes · 2025Review
- SMOTE-augmented machine learning model predicts recurrent and metastatic breast cancer from microbiome analysis.Scientific reports · 2025Article
- Integrated insights into gut microbiota and metabolomic landscape in breast cancer patients undergoing adjuvant endocrine therapy.mSystems · 2025Article
- MicroAIbiome: Decoding Cancer Types from Microbial Profiles Using Explainable Machine Learning.Microorganisms · 2025Article
- Microbial Signatures in Breast Cancer: Exploring New Potentials Across Body Niches.International journal of molecular sciences · 2025Review
- Microbiome-Induced Microenvironmental Changes Before and After Breast Cancer Treatment.Microorganisms · 2025Article
- Impact of scented candle use on indoor air quality and airborne microbiome.Scientific reports · 2025Article
- Association between breast cancer risk factors and blood microbiome in patients with breast cancer.Scientific reports · 2025Article
- The gut microbiome as a potential predictive biomarker for breast cancer: emerging association and geographic differences.Frontiers in oncology · 2025Review
- Examining the bacterial diversity including extracellular vesicles in air and soil: implications for human health.PloS one · 2025Article
- Tracking the origin of bacterial DNA in blood: Indication of localized and sporadic transfer from other body sites.Gut microbes reports · 2025Article
- Techniques for learning and transferring knowledge for microbiome-based classification and prediction: review and assessment.Briefings in bioinformatics · 2024Review
- Integrated analysis of microbiota and gut microbial metabolites in blood for breast cancer.mSystems · 2024Article
- A review of machine learning methods for cancer characterization from microbiome data.NPJ precision oncology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The incidence of breast cancer (BC) is increasing in South Korea, and diet is closely related to the high prevalence of BC. The microbiome directly reflects eating habits. In this study, a diagnostic algorithm was developed by analyzing the microbiome patterns of BC. Blood samples were collected from 96 patients with BC and 192 healthy controls. Bacterial extracellular vesicles (EVs) were collected from each blood sample, and next-generation sequencing (NGS) of bacterial EVs was performed. Microbiome analysis of patients with BC and healthy controls identified significantly higher bacterial abundances using EVs in each group and confirmed the receiver operating characteristic (ROC) curves. Using this algorithm, animal experiments were performed to determine which foods affect EV composition. Compared to BC and healthy controls, statistically significant bacterial EVs were selected from both groups, and a receiver operating characteristic (ROC) curve was drawn with a sensitivity of 96.4%, specificity of 100%, and accuracy of 99.6% based on the machine learning method. This algorithm is expected to be applicable to medical practice, such as in health checkup centers. In addition, the results obtained from animal experiments are expected to select and apply foods that have a positive effect on patients with BC.
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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.