Ancestry-Dependent Immunologic and Prognostic Effects Characterize the Prostate Cancer Urinary Proteome
Bibliographic record
Abstract
Urine is an attractive biomarker analyte for non-invasive longitudinal monitoring of health and disease, particularly for diseases of the genitourinary tract, like prostate and bladder cancer. The composition of an individual's urine reflects both genetic and lifestyle characteristics that differ across geographies and populations, like diet, hydration and other socio-economic factors. While men of African ancestry have elevated prostate cancer risk, it is unclear to what extent this influences urinary biomarkers. We therefore quantified the urinary proteomes of 329 localized prostate cancer patients: 135 self-identifying as White and 194 self-identifying as Black. We identified 110 proteins that significantly differed between these groups after controlling for age, PSA, and cISUP. Immune pathways were particularly dysregulated. The urinary proteome of Black patients harboured more features of aggressive cancers than those of grade- and PSA-matched White patients. These observations highlight the importance of controlling for race- and ancestry-associated differences in the development of urinary biomarkers.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".