Variant NKX3.1 and Serum IGF-1: Investigation of Interaction in Prostate Cancer
Bibliographic record
Abstract
NKX3.1 is a tumor suppressor down-regulated in early prostate cancers. A SNP (rs2228013), which represents a polymorphic NKX3.1(C154T) coding for a variant protein NKX3.1(R52C), is present in 10% of the population and is related to prostatic enlargement and prostate cancer. We investigated rs2228013 in prostate cancer risk for 937 prostate cancer cases and 1,086 age-matched controls from a nested case-control study within the prospective Physicians' Health Study (PHS) and among 798 cases and 527 controls retrospectively collected in the Risk Factors for Prostate Cancer Study of the Victoria Cancer Council (RFPCS). We also investigated the interaction between serum IGF-I levels and NKX3.1 genotype in the populations from PHS and RFPCS. In the PHS, we found no overall association between the variant T allele in rs2228013 in NKX3.1 and prostate cancer risk (odd ratio = 1.25; 95% confidence interval = 0.92-1.71). A subgroup analysis for cases diagnosed before age 70 showed an increased risk (relative risk = 1.55; 95% confidence interval = 1.04-2.31) of overall prostate cancer. In this age-group, the risk of metastatic cancer at diagnosis or of fatal cancer was even higher in carriers of the T allele (relative risk = 2.15; 95% confidence interval = 1.00-4.63). These associations were not replicated in the RFPCS. Serum IGF-I levels were found to be a risk factor for prostate cancer in both study populations. The wild type NKX3.1 protein can induce IGFBP-3 expression in vitro. We report that variant NKX3.1 cannot induce IGFBP-3 expression, but the NKX3.1 genotype does not modify the association between serum IGF-I levels and prostate cancer risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".