Genitourinary microbiomes and prostate cancer: a systematic review and meta-analysis of tumorigeneses and cancer characteristics
Bibliographic record
Abstract
Introduction: We assessed the association of genitourinary microbiomes with prostate cancer (PCa) tumorigeneses and cancer characteristics. Material and methods: A systematic search and meta-analysis was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. The primary endpoints were the association between relative abundance of genitourinary microbiomes and PCa compared to non-cancerous men/prostate specimen, high grade disease, and disease progression. The odds ratio (OR) was used as the summary statistic, and results were reported with 95% confidence intervals (CI). Results: Seventeen studies, comprising 2,195 patients were eligible for review and meta-analysis. The specific microbiomes in urine, prostate tissue, and prostate (or seminal) secretions were significantly more abundant in patients with PCa compared to men in the control groups in individual studies. Certain bacterial phyla, genuses, and species were significantly associated with PCa aggressiveness and disease progression in individual studies. The relative abundance meta-analysis of five urine microbiomes revealed no statistically significant differences between PCa patients and control groups (pooled OR, 1.35; 95% CI: 0.70-2.59). Conclusions: Our systematic review indicates that specific genitourinary microbiomes are more abundant in PCa and have a potential to predict/prognosticate disease aggressiveness and clinical outcomes. Nevertheless, these findings should be interpreted with caution owing to the significant heterogeneity among studies in terms of microbiome analysis method, assessed sample's characteristics, and individual biological behavior of microbiomes for analysis. Further studies are needed to validate these observations and shed more light on the role of the microbiome across the development and natural history of PCa.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.030 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".