Epigenetic regulation of kallikrein-related peptidases: there is a whole new world out there
Bibliographic record
Abstract
The human kallikreins are a cluster of 15 kallikreins and kallikrein-related peptidases (KLKs). Evidence shows the involvement of KLKs in a wide range of pathophysiological processes, and underscores their potential contribution to cancer, skin and neurodegenerative disorders. The control of KLK expression is not fully elucidated. Understanding the mechanisms controlling KLK expression is an essential step towards exploring the pathogenesis of several diseases and the use of KLKs as disease biomarkers and/or therapeutic targets. Recently, epigenetic changes (including methylation, histone modification and microRNAs [miRNAs]) have drawn attention as a new dimension for controlling KLK expression. Reports showed the effect of methylation on the expression of KLK genes. This was also shown to have potential utility as a prognostic marker in cancer. miRNAs are small RNAs that control the expression of their targets at the post-transcriptional level. Target prediction showed that KLKs are potential targets of miRNAs that are dysregulated in tumors, including prostate, kidney and ovarian cancers, with downstream effect on tumor proliferation. Experimental validation remains an essential step to confirm the KLK-miRNA interaction. Epigenetic regulation of KLKs holds promise for an array of therapeutic applications in many diseases including cancer.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".