Circadian genes and risk of prostate cancer: Findings from the EPICAP study
Bibliographic record
Abstract
Circadian rhythms regulate several physiological functions and genes controlling the circadian rhythm were found to regulate cell proliferation, cell cycle and apoptosis. Few studies have investigated the role of those circadian genes in prostate cancer occurrence. We aim to investigate the relationship between circadian genes polymorphisms and prostate cancer risk based on data from the EPICAP study, a population-based case-control study including 1,515 men (732 cases / 783 controls) with genotyped data. Odds Ratios (ORs) for association between prostate cancer and circadian gene variants were estimated for each of the 872 single nucleotide polymorphisms (SNPs) in 31 circadian clock genes. We also used a gene-based and pathway-based approach with a focus on the pathway including 9 core circadian genes. Separate analyses were conducted by prostate cancer aggressiveness. The core-circadian pathway (p = 0.0006) was significantly associated to prostate cancer, for either low (p = 0.002) or high (p = 0.01) grade tumor. At the gene level, we observed significant associations between all prostate cancer and NPAS2 and PER1 after correcting for multiple testing, while only RORA was significant for aggressive tumors. At the SNP-level, no significant association was observed. Our findings provide additional evidence of a potential link between genetic variants in circadian genes and prostate cancer risk. Further investigation is warranted to confirm these findings and to better understand 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".