Vitamin D receptor polymorphisms in patients with cutaneous melanoma
Bibliographic record
Abstract
The vitamin D receptor (VDR) gene has been associated with cancer risk, but only a few polymorphisms have been studied in relation to melanoma risk and the results have been inconsistent. We examined 38 VDR gene single nucleotide polymorphisms (SNPs) in a large international multicenter population-based case-control study of melanoma. Buccal DNAs were obtained from 1,207 people with incident multiple primary melanoma and 2,469 with incident single primary melanoma. SNPs with known or suspected impact on VDR activity, haplotype tagging SNPs with ≥ 10% minor allele frequency in Caucasians, and SNPs reported as significant in other association studies were examined. Logistic regression was used to calculate the relative risks conferred by the individual SNP. Eight of 38 SNPs in the promoter, coding, and 3' gene regions were individually significantly associated with multiple primary melanoma after adjusting for covariates. The estimated increase in risk for individuals who were homozygous for the minor allele ranged from 25 to 33% for six polymorphisms: rs10875712 (odds ratios [OR] 1.28; 95% confidence interval (CI), 1.01-1.62), rs4760674 (OR 1.33; 95% CI, 1.06-1.67), rs7139166 (OR 1.26; 95%CI, 1.02-1.56), rs4516035 (OR 1.25; 95%CI, 1.01-1.55), rs11168287 (OR 1.27; 95%CI, 1.03-1.57) and rs1544410 (OR 1.30; 95%CI, 1.04-1.63); for two polymorphisms, homozygous carriers had a decreased risk: rs7305032 (OR 0.81; 95%CI 0.65-1.02) and rs7965281 (OR, 0.78; 95%CI, 0.62-0.99). We recognize the potential false positive findings because of multiple comparisons; however, the eight significant SNPs in our study outnumbered the two significant tests expected to occur by chance. The VDR may play a role in melanomagenesis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".