Genetic Variants and Prostate Cancer Risk: Candidate Replication and Exploration of Viral Restriction Genes
Bibliographic record
Abstract
The genetic variants underlying the strong heritable component of prostate cancer remain largely unknown. Genome-wide association studies of prostate cancer have yielded several variants that have significantly replicated across studies, predominantly in cases unselected for family history of prostate cancer. Additional candidate gene variants have also been proposed, many evaluated within familial prostate cancer study populations. Such variants hold great potential value for risk stratification, particularly for early-onset or aggressive prostate cancer, given the comorbidities associated with current therapies. Here, we investigate a Caucasian study population of 523 independent familial prostate cancer cases and 523 age-matched controls without a personal or family history of prostate cancer. We replicate identified associations at genome-wide association study loci 8q24, 11q13, and 2p15 (P = 2.9 x 10(-4) to P = 4.7 x 10(-5)), showing study population power. We also find evidence to support reported associations at candidate genes RNASEL, EZH2, and NKX3-1 (P = 0.031 to P = 0.0085). We further explore a set of candidate genes related to RNASEL and to its role in retroviral restriction, identifying nominal associations at XPR1 and RBM9. The effects at 8q24 seem more pronounced for those diagnosed at an early age, whereas at 2p15 and RNASEL the effects were more pronounced at a later age. However, these trends did not reach statistical significance. The effects at 2p15 were statistically significantly more pronounced for those diagnosed with aggressive disease.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".