Blood metabolites as predictors of skin cancer risk: a comprehensive analysis
Bibliographic record
Abstract
Abstract Introduction This study aimed to investigate the potential causal effects of plasma metabolites on skin cancer (SC) risk through a two-sample Mendelian randomization (MR) analysis. Skin cancer, including melanoma and non-melanoma types, is a prevalent malignancy worldwide, necessitating the identification of novel biomarkers for early detection and prevention. Materials and Methods We utilized genome-wide association study (GWAS) data from 8,299 individuals of European ancestry in the Canadian Longitudinal Study of Aging (CLSA) cohort, encompassing 1,400 metabolites. The analysis also incorporated GWAS data from FinnGen, including 20,951 SC patients and 287,137 controls of European ancestry. The association between metabolites and SC risk was assessed using the inverse-variance weighted (IVW) method, complemented by sensitivity analyses such as MR-Egger and MR-PRESSO tests. Results The results revealed significant associations between 78 unique metabolites and SC risk. Among these, 42 metabolites were associated with a significant increase in SC risk, while 36 metabolites were linked to a significant reduction in SC risk. Conclusions This study highlights novel blood metabolites that are closely related to SC risk, emphasizing their potential importance in prioritizing metabolic features for SC mechanistic research. Further evaluation of these metabolites in SC risk assessment could lead to new insights into SC prevention and treatment strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 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".