Kallikreins as microRNA targets: an<i>in silico</i>and experimental-based analysis
Bibliographic record
Abstract
microRNAs (miRNAs) are non-coding RNAs that target specific mRNAs. They have been shown to control many biological processes including cancer pathogenesis. Kallikreins (KLKs) are a family of serine proteases that are attracting interest as cancer biomarkers. The mechanism of regulation of kallikrein expression is largely unknown. We investigated the potential roles of miRNAs in regulating KLK expression. Using a bioinformatics approach, we identified 96 strong KLK/miRNA interactions. KLK10 is the most frequently targeted kallikrein, followed by KLK5 and KLK13. KLK1, KLK3, KLK8 and KLK12 do not have strongly predicted miRNA/KLK interactions. Ten miRNAs are predicted to target more than one KLK. KLK2, KLK4, KLK5 and KLK10 have multiple miRNA-targeting sites on their transcript. Chromosomes 19 and 14 harbor significantly more KLK-targeting miRNAs. Many KLK-targeting miRNAs have been shown to be dysregulated in malignancy. We experimentally verified our bioinformatics data for the let-7f miRNA in a cell line model. let-7f transfection led to a significant decrease in secreted KLK6 and KLK10 protein levels. Co-transfection of let-7f and anti-let-7f inhibitor was able to partially rescue these protein levels. We conclude that miRNAs play a role in the regulation of KLK expression. Further studies are needed to investigate whether this regulation is altered in 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 0.002 |
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".