Circadian genes polymorphisms, night work and prostate cancer risk: Findings from the <scp>EPICAP</scp> study
Bibliographic record
Abstract
Over the past two decades, several studies have attempted to understand the hypothesis that disrupting the circadian rhythm may promote the development of cancer. Some have suggested that night work and some circadian genes polymorphisms are associated with cancer, including prostate cancer. Our study aims to test the hypothesis that prostate cancer risk among night workers may be modulated by genetic polymorphisms in the circadian pathway genes based on data from the EPICAP study, a population-based case-control study including 1511 men (732 cases/779 controls) with genotyped data. We estimated odds ratio (ORs) and P values of the association between prostate cancer and circadian gene variants using logistic regression models. We tested the interaction between circadian genes variants and night work indicators that were significantly associated with prostate cancer at pathway, gene and SNP levels. Analyses were also stratified by each of these night work indicators and by cancer aggressiveness. The circadian pathway was significantly associated with aggressive prostate cancer among night workers (P = .004), particularly for men who worked at night for <20 years (P = .0002) and those who performed long night shift (>10 hours, P = .001). At the gene level, we observed among night workers significant associations between aggressive prostate cancer and ARNTL, NPAS2 and RORA. At the SNP-level, no significant association was observed. Our findings provide some clues of a potential modulating effect of circadian genes in the relationship between night work and prostate cancer. Further investigation is warranted to confirm these findings and to better elucidate the biological pathways involved.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".