Insulin-like Growth Factor Pathway Genetic Polymorphisms, Circulating IGF1 and IGFBP3, and Prostate Cancer Survival
Bibliographic record
Abstract
BACKGROUND: The insulin-like growth factor (IGF) signaling pathway has been implicated in prostate cancer (PCa) initiation, but its role in progression remains unknown. METHODS: Among 5887 PCa patients (704 PCa deaths) of European ancestry from seven cohorts in the National Cancer Institute Breast and Prostate Cancer Cohort Consortium, we conducted Cox kernel machine pathway analysis to evaluate whether 530 tagging single nucleotide polymorphisms (SNPs) in 26 IGF pathway-related genes were collectively associated with PCa mortality. We also conducted SNP-specific analysis using stratified Cox models adjusting for multiple testing. In 2424 patients (313 PCa deaths), we evaluated the association of prediagnostic circulating IGF1 and IGFBP3 levels and PCa mortality. All statistical tests were two-sided. RESULTS: The IGF signaling pathway was associated with PCa mortality (P = .03), and IGF2-AS and SSTR2 were the main contributors (both P = .04). In SNP-specific analysis, 36 SNPs were associated with PCa mortality with P (trend) less than .05, but only three SNPs in the IGF2-AS remained statistically significant after gene-based corrections. Two were in linkage disequilibrium (r (2) = 1 for rs1004446 and rs3741211), whereas the third, rs4366464, was independent (r (2) = 0.03). The hazard ratios (HRs) per each additional risk allele were 1.19 (95% confidence interval [CI] = 1.06 to 1.34; P (trend) = .003) for rs3741211 and 1.44 (95% CI = 1.20 to 1.73; P (trend) < .001) for rs4366464. rs4366464 remained statistically significant after correction for all SNPs (P (trend.corr) = .04). Prediagnostic IGF1 (HR(highest vs lowest quartile) = 0.71; 95% CI = 0.48 to 1.04) and IGFBP3 (HR = 0.93; 95% CI = 0.65 to 1.34) levels were not associated with PCa mortality. CONCLUSIONS: The IGF signaling pathway, primarily IGF2-AS and SSTR2 genes, may be important in PCa survival.
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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".