MC1R gene variants and nonmelanoma skin cancer: a pooled analysis from the M-SKIP project
Bibliographic record
Abstract
BACKGROUND: The melanocortin-1-receptor (MC1R) gene regulates human pigmentation and is highly polymorphic in populations of European origins. The aims of this study were to evaluate the association between MC1R variants and the risk of non-melanoma skin cancer (NMSC), and to investigate whether risk estimates differed by phenotypic characteristics. METHODS: Data on 3527 NMSC cases and 9391 controls were gathered through the M-SKIP Project, an international pooled-analysis on MC1R, skin cancer and phenotypic characteristics. We calculated summary odds ratios (SOR) with random-effect models, and performed stratified analyses. RESULTS: Subjects carrying at least one MC1R variant had an increased risk of NMSC overall, basal cell carcinoma (BCC) and squamous cell carcinoma (SCC): SOR (95%CI) were 1.48 (1.24-1.76), 1.39 (1.15-1.69) and 1.61 (1.35-1.91), respectively. All of the investigated variants showed positive associations with NMSC, with consistent significant results obtained for V60L, D84E, V92M, R151C, R160W, R163Q and D294H: SOR (95%CI) ranged from 1.42 (1.19-1.70) for V60L to 2.66 (1.06-6.65) for D84E variant. In stratified analysis, there was no consistent pattern of association between MC1R and NMSC by skin type, but we consistently observed higher SORs for subjects without red hair. CONCLUSIONS: Our pooled-analysis highlighted a role of MC1R variants in NMSC development and suggested an effect modification by red hair colour phenotype.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".