Data from Association of Folate-Pathway Gene Polymorphisms with the Risk of Prostate Cancer: a Population-Based Nested Case-Control Study, Systematic Review, and Meta-analysis
Bibliographic record
Abstract
Abstract Folate-pathway gene polymorphisms have been implicated in several cancers and investigated inconclusively in relation to prostate cancer. We conducted a systematic review, which identified nine case-control studies (eight included, one excluded). We also included data from four genome-wide association studies and from a case-control study nested within the UK population–based Prostate Testing for Cancer and Treatment study. We investigated by meta-analysis the effects of eight polymorphisms: MTHFR C677T (rs1801133; 12 studies; 10,745 cases; 40,158 controls), MTHFR A1298C (rs1801131; 5 studies; 3,176 cases; 4,829 controls), MTR A2756G (rs1805087; 8 studies; 7,810 cases; 37,543 controls), MTRR A66G (rs1801394; 4 studies; 3,032 cases; 4,515 controls), MTHFD1 G1958A (rs2236225; 6 studies; 7,493 cases; 36,941 controls), SLC19A1/RFC1 G80A (rs1051266; 4 studies; 6,222 cases; 35,821 controls), SHMT1 C1420T (rs1979277; 2 studies; 2,689 cases; 4,110 controls), and FOLH1 T1561C (rs202676; 5 studies; 6,314 cases; 35,190 controls). The majority (10 of 13) of eligible studies had 100% Caucasian subjects; only one study had <90% Caucasian subjects. We found weak evidence of dominant effects of two alleles: MTR 2756A>G [random effects pooled odds ratio, 1.06 (1.00-1.12); P = 0.06 (P = 0.59 for heterogeneity across studies)] and SHMT1 1420C>T [random effects pooled odds ratio, 1.11 (1.00-1.22); P = 0.05 (P = 0.38 for heterogeneity across studies)]. We found no effect of MTHFR 677C>T or any of the other alleles in dominant, recessive or additive models, or in comparing a/a versus A/A homozygous. Neither did we find any difference in effects on advanced or localized cancers. Our meta-analysis suggests that known common folate-pathway single nucleotide polymorphisms do not have significant effects on susceptibility to prostate cancer.(Cancer Epidemiol Biomarkers Prev 2009;18(9):2528–39)
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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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.022 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".