Gut microbiota in patients with prostate cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Increasing evidence indicates that gut microbiota are closely related to prostate cancer. This study aims to assess the gut microbiota composition in patients with prostate cancer compared to healthy participants, thereby advancing understanding of gut microbiota's role in prostate cancer. Methods A systematic search was conducted across PubMed, Web of Science, and Embase databases, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The methodological quality of included studies was evaluated using the Newcastle–Ottawa Scale (NOS), and pertinent data were analyzed. The kappa score assessed interrater agreement. Results This study encompassed seven research papers, involving 250 prostate cancer patients and 192 controls. The kappa was 0.93. Meta-analysis results showed that alpha-diversity of gut microbiota in prostate cancer patients was significantly lower than in the control group. In terms of gut microbiota abundance, the ratio of Proteobacteria, Bacteroidia, Clostridia, Bacteroidales, Clostridiales, Prevotellaceae, Lachnospiraceae, Prevotella, Escherichia-Shigella, Faecalibacterium, and Bacteroides was higher in prostate cancer patients. Conversely, the abundance ratio of Actinobacteria, Bacteroidetes, Firmicutes, Selenomonadales, Veillonella, and Megasphaera was higher in the control group. Conclusion Our study reveals differences in alpha-diversity and abundance of gut microbiota between patients with prostate cancer and controls, indicating gut microbiota dysbiosis in those with prostate cancer. However, given the limited quality and quantity of selected studies, further research is necessary to validate these findings.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.031 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".