Vascular Endothelial Growth Factor Genotypes, Haplotypes, Gender, and the Risk of Non–Small Cell Lung Cancer
Bibliographic record
Abstract
PURPOSE: The vascular endothelial growth factor (VEGF) is a major mediator of angiogenesis involving tumor growth and metastasis. Polymorphisms in the VEGF gene may regulate VEGF production. In this large case-control study, we investigated whether functional polymorphisms (-460C/T, +405C/G, +936C/T) in the VEGF gene are associated with the risk of non-small cell lung cancer (NSCLC). EXPERIMENTAL DESIGN: VEGF genotypes and haplotypes were determined in 1,900 Caucasian patients with NSCLC and 1,458 healthy controls. The results were analyzed using logistic regression models, adjusting for age, gender, smoking status, pack-years of smoking, and years since smoking cessation (for ex-smokers). The false-positive report probability was estimated for the observed odds ratios (OR). RESULTS: There were no overall associations between individual VEGF genotypes and the risk of NSCLC. Stratified analysis suggested that the combined +405CC+CG genotype was significantly associated with increased risk of lung adenocarcinoma in males (adjusted OR, 1.40; 95% confidence interval, 1.03-1.87). In haplotype analysis, haplotypes were globally associated with differences between cases and controls in males (P = 0.03). Specifically, the -460T/+405G/+936C haplotype was significantly (P = 0.02) associated with decreased risk of adenocarcinoma in males when compared with the most common CGC haplotype (adjusted OR, 0.76; 95% confidence interval, 0.50-0.98). None of the VEGF genotypes and haplotypes studied significantly influenced the susceptibility to NSCLC in females. CONCLUSIONS: Polymorphisms of -460C/T, +405C/G, and +936C/T in the VEGF gene do not play a major role in NSCLC risk. However, we could not exclude a minor role for the +405CC+CG genotypes and the 460T/+405G/+936C haplotype in lung adenocarcinogenesis in male Caucasians.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".