Kallikreins as microRNA targets: an<i>in silico</i>and experimental-based analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it