Both mature miR-17-5p and passenger strand miR-17-3p target TIMP3 and induce prostate tumor growth and invasion
Bibliographic record
Abstract
MicroRNAs (miRNA) precursor (pre-miRNA) molecules can be processed to release a miRNA/miRNA* duplex. In the canonical model of miRNA biogenesis, one strand of the duplex is thought to be the biologically active miRNA, whereas the other strand is thought to be inactive and degraded as a carrier or passenger strand called miRNA* (miRNA star). However, recent studies have revealed that miRNA* strands frequently play roles in the regulatory networks of miRNA target molecules. Our recent study indicated that miR-17 transgenic mice could abundantly express both the mature miR-17-5p and the passenger strand miR-17-3p. Here, we showed that miR-17 enhanced prostate tumor growth and invasion by increasing tumor cell proliferation, colony formation, cell survival and invasion. miRNA target analysis showed that both miR-17-5p and miR-17-3p repressed TIMP metallopeptidase inhibitor 3 (TIMP3) expression. Silencing with small interfering RNA against TIMP3 promoted cell survival and invasion. Ectopic expression of TIMP3 decreased cell invasion and cell survival. Our results demonstrated that mature miRNA can function coordinately with its passenger strand, enhancing the repressive ability of a miRNA by binding the same target. Within an intricate regulatory network, this may be among the mechanisms by which miRNA can augment their regulatory capacity.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".