Variants in autophagy-related genes and clinical characteristics in melanoma: a population-based study
Bibliographic record
Abstract
Autophagy has been linked with melanoma risk and survival, but no polymorphisms in autophagy-related (ATG) genes have been investigated in relation to melanoma progression. We examined five single-nucleotide polymorphisms (SNPs) in three ATG genes (ATG5; ATG10; and ATG16L) with known or suspected impact on autophagic flux in an international population-based case-control study of melanoma. DNA from 911 melanoma patients was genotyped. An association was identified between (GG) (rs2241880) and earlier stage at diagnosis (OR 0.47; 95% Confidence Intervals (CI) = 0.27-0.81, P = 0.02) and a decrease in Breslow thickness (P = 0.03). The ATG16L heterozygous genotype (AG) (rs2241880) was associated with younger age at diagnosis (P = 0.02). Two SNPs in ATG5 were found to be associated with increased stage (rs2245214 CG, OR 1.47; 95% CI = 1.11-1.94, P = 0.03; rs510432 CC, OR 1.84; 95% CI = 1.12-3.02, P = 0.05). Finally, we identified inverse associations between ATG5 (GG rs2245214) and melanomas on the scalp or neck (OR 0.20, 95% CI = 0.05-0.86, P = 0.03); ATG10 (CC) (rs1864182) and brisk tumor infiltrating lymphocytes (TILs) (OR 0.42; 95% CI = 0.21-0.88, P = 0.02), and ATG5 (CC) (rs510432) with nonbrisk TILs (OR 0.55; 95% CI = 0.34-0.87, P = 0.01). Our data suggest that ATG SNPs might be differentially associated with specific host and tumor characteristics including age at diagnosis, TILs, and stage. These associations may be critical to understanding the role of autophagy in cancer, and further investigation will help characterize the contribution of these variants to melanoma progression.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".