Kallikreins are involved in an miRNA network that contributes to prostate cancer progression
Bibliographic record
Abstract
MicroRNAs (miRNAs) are short RNA nucleotides that negatively regulate their target genes. They are differentially expressed in prostate cancer. Kallikreins are genes that encode serine proteases and are dysregulated in cancer. We elucidated a miRNA-kallikrein network that can be involved in prostate cancer progression. Target prediction identified 23 miRNAs that are dysregulated between high and low risk biochemical failure and are predicted to target five kallikreins linked to prostate cancer; KLK2, KLK3, KLK4, KLK14 and KLK15. We also identified 14 miRNAs that are differentially expressed between Gleason grades and are predicted to target these kallikreins. This demonstrates that kallikreins are downstream effectors through which miRNAs influence tumor progression. We show, through in-silico and experimental analysis, that miR-378/422a and its gene targets PIK3CG, GRB2, AKT3, KLK4 and KLK14 form an integrated circuit in prostate cancer. Our analysis shows that a minisatellite sequence in the kallikrein locus consists of a number of microsatellite repeats that represent predicted miRNA response elements. A number of kallikrein and non-kallikrein prostate cancer-related genes share these microsatellite repeats. We validated some of these interactions in prostate cancer cell lines. Finally, we provide preliminary evidence on the presence of a miRNA-mediated cross-talk between kallikreins, including a kallikrein pseudogene.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".