Microbiome landscapes of the bladder, intestine, and vagina in bladder cancer: a systematic review
Bibliographic record
Abstract
BACKGROUND: Microbiomes have been linked to oncogenesis, e.g. the intestinal microbiome and colon cancer or HPV-associated cervical cancer. A connection between microbiomes of different body cavities and tumor oncogenesis was shown. The gut microbiome's influence on bladder cancer was established, raising the question whether nearby microbiomes (rectum, vagina) also influence bladder cancer due to their proximity. OBJECTIVE: Considering the influence of various body cavities and the broader microbial components, this systematic review aims to investigate differences in the bladder, vaginal, and intestinal microbiota-including bacterial, viral, fungal and archaea-between patients with bladder cancer and healthy controls. METHODS: Databases (PubMed, Scopus, Embase) were searched until April 2022. Three types of studies were included: "(1) studies using bladder cancer and control groups (case-controlled studies) (2) studies that provided information on the presence or abundance of microbial taxa (3) studies that provided information on increased or decreased taxa in bladder cancer and/or control groups.". Risk of bias was assessed using the Newcastle Ottawa Scale. RESULTS: Fourteen studies (695 samples: 403 bladder cancer, 292 controls) were analyzed. Bacterial taxa that have been detected in at least two studies, the genera Geobacillus and Rubrobacter were more frequently in bladder cancer patients; while Streptococcus and Roseomonas were more prevalent in controls. No consistent taxa were identified across stool or bladder tissue samples. CONCLUSION: The microbiota in bladder cancer patients show significant variation across studies. Standardized methods and expanded investigations into viral and fungal components are needed to clarify the role of microbiota in bladder 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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".