Macrophage Scavenger Receptor 1 <i>999C&gt;T</i> (R293X) Mutation and Risk of Prostate Cancer
Bibliographic record
Abstract
BACKGROUND: Variants in the gene encoding the macrophage scavenger receptor 1 (MSR1(4)) protein have been identified in men with prostate cancer, and several small studies have suggested that the 999C>T (R293X) protein-truncating mutation may be associated with an increased risk for this disease. METHODS: Using large case-control, cohort, and prostate cancer family studies conducted in several Western countries, we tested for the 999C>T mutation in 2,943 men with invasive prostate carcinoma, including 401 males from multiple-case families, 1,982 cases unselected for age, and 575 men diagnosed before the age of 56 years, and in 2,870 male controls. Risk ratios were estimated by unconditional logistic regression adjusting for country and by a modified segregation analysis. A meta-analysis was conducted pooling our data with published data. RESULTS: The prevalence of MSR1*999C>T mutation carriers was 0.027 (SE, 0.003) in cases and 0.022 (SE, 0.002) in controls, and did not differ by country, ethnicity, or source. The adjusted risk ratio for prostate cancer associated with being a 999C>T carrier was 1.31 [95% confidence interval (CI), 0.93-1.84; P = 0.16]. The modified segregation analysis estimated the risk ratio to be 1.20 (95% CI, 0.87-1.66; P = 0.16). The risk ratio estimated from the meta-analysis was 1.34 (95% CI, 0.94-1.89; P = 0.10). CONCLUSION: Our large-scale analysis of case and controls from several countries found no evidence that the 999C>T mutation is associated with increased risk of prostate cancer. The meta-analysis suggests it is unlikely that this mutation confers more than a 2-fold increased risk.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.006 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".