Mature MiR-17-5p and passenger miR-17-3p induce hepatocellular carcinoma by targeting PTEN, GalNT7, and vimentin in different signal pathways
Bibliographic record
Abstract
To study the physiological role of a single microRNA (miRNA), we generated transgenic mice expressing the miRNA precursor miR-17 and found that the mature miR-17-5p and the passenger strand miR-17-3p were abundantly expressed. We showed that mature miR-17-5p and passenger strand miR-17-3p could synergistically induce the development of hepatocellular carcinoma. The mature miR-17-5p exerted this function by repressing the expression of PTEN. In contrast, the passenger strand miR-17-3p repressed expression of vimentin, an intermediate filament with the ability to modulate metabolism, and GalNT7, an enzyme that regulates metabolism of liver toxin galactosamine. Hepatocellular carcinoma cells, HepG2, transfected with miR-17 formed larger tumors with more blood vessels and less tumor cell death than mock-treated cells. Expression of miR-17 precursor modulated HepG2 proliferation, migration, survival, morphogenesis and colony formation and inhibited endothelial tube formation. Silencing of PTEN, vimentin or GalNT7 with their respective siRNAs enhanced proliferation and migration. Re-expressing these molecules reversed their roles in proliferation, migration and tumorigenesis. Further experiments indicated that these three molecules do not interact with each other, but appear to function in different signaling pathways. Our results demonstrated that a mature miRNA can function synergistically with its passenger strand leading to the same phenotype but by regulating different targets located in different signaling pathways. We anticipate that our assay will serve as a helpful model for studying miRNA regulation.
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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 | 0.000 |
| 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".