Quantitative DNA methylation analysis of genes coding for kallikrein-related peptidases 6 and 10 as biomarkers for prostate cancer
Bibliographic record
Abstract
DNA methylation plays an important role in carcinogenesis and is being recognized as a promising diagnostic and prognostic biomarker for a variety of malignancies including Prostate cancer (PCa). The human kallikrein-related peptidases (KLKs) have emerged as an important family of cancer biomarkers, with KLK3, encoding for Prostate Specific Antigen, being most recognized. However, few studies have examined the epigenetic regulation of KLKs and its implications to PCa. To assess the biological effect of DNA methylation on KLK6 and KLK10 expression, we treated PC3 and 22RV1 PCa cells with a demethylating drug, 5-aza-2'deoxycytidine, and observed increased expression of both KLKs, establishing that DNA methylation plays a role in regulating gene expression. Subsequently, we have quantified KLK6 and KLK10 DNA methylation levels in two independent cohorts of PCa patients operated by radical prostatectomy between 2007-2011 (Cohort I, n = 150) and 1998-2001 (Cohort II, n = 124). In Cohort I, DNA methylation levels of both KLKs were significantly higher in cancerous tissue vs. normal. Further, we evaluated the relationship between DNA methylation and clinicopathological parameters. KLK6 DNA methylation was significantly associated with pathological stage only in Cohort I while KLK10 DNA methylation was significantly associated with pathological stage in both cohorts. In Cohort II, low KLK10 DNA methylation was associated with biochemical recurrence in univariate and multivariate analyses. A similar trend for KLK6 DNA methylation was observed. The results suggest that KLK6 and KLK10 DNA methylation distinguishes organ confined from locally invasive PCa and may have prognostic value.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".