The miRNA-kallikrein axis of interaction: a new dimension in the pathogenesis of prostate cancer
Bibliographic record
Abstract
Kallikrein-related peptidases (KLKs) are a family of serine proteases that were shown to be useful cancer biomarkers. KLKs have been shown to be dysregulated in prostate cancer (PCa). microRNAs (miRNAs) are short RNA nucleotides that negatively regulate gene expression and have been reportedly dysregulated in PCa. We compiled a comprehensive list of 55 miRNAs that are differentially expressed in PCa from previous microarray analysis and published literature. Target prediction analyses showed that 29 of these miRNAs are predicted to target 10 KLKs. Eight of these miRNAs were predicted to target more than one KLK. Quantitative real-time (qRT)-PCR demonstrated that there was an inverse correlation pattern in the expression (normal vs. cancer) between dysregulated miRNAs and their target KLKs. In addition, we experientially validated the miRNA-KLK interaction by transfecting miR-331-3p and miR-143 into a PCa cell line. Decreased expression of targets KLK4 and KLK10, respectively, and decreased cellular growth were observed. In addition to KLKs, dysregulated miRNAs were predicted to target other genes involved in the pathogenesis of PCa. These data show that miRNAs can contribute to KLK regulation in PCa. The miRNA-KLK axis of interaction projects a new element in the pathogenesis of PCa that may have therapeutic implications.
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".