Exploring the Microbiome’s Impact on Glioma and Brain Metastases: Insights into Development, Progression, and Treatment Response—A Scoping Review
Bibliographic record
Abstract
Background: The human microbiome plays a crucial role in health and disease. Dysbiosis, an imbalance of microorganisms, has been implicated in cancer development and treatment response, including in primary brain tumors and brain metastases, through interactions mediated by the gut–brain axis. This scoping review synthesizes current evidence on the relationship between the human microbiome and brain tumors. Methods: A systematic search of five electronic databases was conducted by an expert librarian, using controlled vocabulary and keywords. A targeted grey literature search in Google Scholar and clinical trial registries was also undertaken. Eligible studies included primary research involving human patients, or in vivo, or in vitro models of glioma or brain metastasis, with a focus on the microbiome’s role in tumor development, treatment response, and outcomes. Results: Out of 584 citations screened, 40 studies met inclusion criteria, comprising 24 articles and 16 conference abstracts. These included 12 human studies, 16 using mouse models, 7 combining both, and 5 employing large datasets or next-generation sequencing of tumor samples. Thirty-one studies focused on primary brain tumors, six on brain metastases, and three on both. Of the 20 studies examining dysbiosis in tumor development, 95% (n = 19) found an association with tumor growth. Additionally, 71.4% (n = 5/7) of studies reported that microbiome alterations influenced treatment efficacy. Conclusions: Although the role of the gut–brain axis in brain tumors is still emerging and is characterized by heterogeneity across studies, existing evidence consistently supports a relationship between the gut microbiome and both brain tumor development and treatment outcomes.
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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 | 0.010 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".