Variants of the hK2 Protein Gene (<i>KLK2</i>) Are Associated with Serum hK2 Levels and Predict the Presence of Prostate Cancer at Biopsy
Bibliographic record
Abstract
PURPOSE: Increased levels of serum human kallikrein-2 (hK2) and an hK2 gene (KLK2) variant are positively associated for prostate cancer, but the relationships between them remain unclear. We examined five variants of the KLK2 gene to further define its relevance to prostate cancer susceptibility and hK2 levels. EXPERIMENTAL DESIGN: We genotyped 645 men with biopsy-proven prostate cancer (cases) and 606 males with biopsies negative for prostate cancer (controls) for five additional single nucleotide polymorphisms (SNP) across the KLK2 gene and also tested for serum hK2 levels. These SNPs were identified from sequencing the KLK2 gene among 20 patients with aggressive prostate cancer. Odds ratios (OR) for prostate cancer detection and haplotype analysis were done. RESULTS: Among the SNPs studied, the A allele of the KLK2-SNP1 (G>A, rs2664155) and the T allele of the KLK2-SNP5 (C>T, rs198977) polymorphisms showed positive associations with prostate cancer, adjusted ORs for KLK2-SNP1 AG and AA genotypes being 1.4 [95% confidence interval (95% CI), 1.2-1.8; P=0.002] and for KLK2-SNP5 TT or CT genotypes being 1.3 (95% CI, 1.1-1.6; P=0.05). Haplotype analyses also revealed a significant association between prostate cancer and the haplotype containing both risk alleles (ACCTT), OR being 5.1 (95% CI, 1.6-6.5; P=0.005). Analysis of serum hK2 revealed hK2 levels to be significantly increased in association with KLK2-SNP1 AA and AG risk genotypes compared with the GG genotype (P=0.001) and also in association with the ACCTT risk haplotype compared with the most common non-risk haplotype (P=0.05). CONCLUSIONS: These findings suggest a role for the KLK2 gene in prostate cancer susceptibility and imply that this role may be realized at least in part by the induction of increases in hK2 production.
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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 | 0.000 | 0.001 |
| 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".