Microbiomes in Post–Digital Rectal Exam Urine Samples are Linked to Prostate Cancer Risk
Bibliographic record
Abstract
Abstract Purpose: Bacterial species including Cutibacterium acnes ( C. acnes ) have been associated with different inflammatory and neoplastic conditions in prostate cancer (PCa) tissue samples, but their clinical impact is unknown. Using next-generation sequencing (NGS)–based clinical reports, we investigated the differential abundance and incidence of microbiomes in post–digital rectal exam (DRE) urine samples from patients with PCa and a matched control group at low risk of PCa. Materials and Methods: A total of 200 post-DRE urine samples were analyzed, 100 from patients with histopathologically confirmed PCa and 100 from men at very low risk of PCa with PSA <1.5 ng/mL as controls. Bacterial and fungal communities were characterized by NGS of 16S and internal transcribed spacer (ITS) loci, respectively, with species' relative abundances provided on physicians' clinical reports. The differential abundance and incidence of species between cancer and control groups were evaluated. Results: Microbes were reported in 39% and 56% of PCa and control group samples, respectively. C. acnes had a significantly higher relative abundance in patients with PCa vs controls ( P < .05), and C. acnes incidence rates were also nominally higher in patients with PCa as compared with controls (12.82% and 7.27%, respectively). By contrast, Finegoldia magna ( F. magna ) had a significantly higher relative abundance ( P < .05) and incidence rate ( P < .05) in controls as compared with patients with PCa. Conclusions: C. acnes was among the most prevalent bacterial species in PCa urine samples. F. magna identified in the low-risk group is responsible for production of equol, a soy metabolite associated with lowering risk of PCa, suggesting a role in prostate cancer chemoprevention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".