Elucidating the roles of the mammary and gut microbiomes in breast cancer development
Bibliographic record
Abstract
The mammary microbiome is a newly characterized bacterial niche that might offer biological insight into the development of breast cancer. Together with in-depth analysis of the gut microbiome in breast cancer, current evidence using next-generation sequencing and metabolic profiling suggests compositional and functional shifts in microbial consortia are associated with breast cancer. In this review, we discuss the fundamental studies that have progressed this important area of research, focusing on the roles of both the mammary tissue microbiome and the gut microbiome. From the literature, we identified the following major conclusions, (I) There are unique breast and gut microbial signatures (both compositional and functional) that are associated with breast cancer, (II) breast and gut microbiome compositional and breast functional dysbiosis represent potential early events of breast tumor development, (III) specific breast and gut microbes confer host immune responses that can combat breast tumor development and progression, and (IV) chemotherapies alter the microbiome and thus maintenance of a eubiotic microbiome may be key in breast cancer treatment. As the field expectantly advances, it is necessary for the role of the microbiome to continue to be elucidated using multi-omic approaches and translational animal models in order to improve predictive, preventive, and therapeutic strategies for breast cancer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".