Tracking miR-17-5p Levels following Expression of Seven Reported Target mRNAs
Bibliographic record
Abstract
As the most prominent member of the miR-17-92 cluster, miR-17-5p is well associated with tumorigenesis and cancer progression. It can exert both oncogenic and tumor-suppressive functions by inducing translational repression and/or mRNA decay. The complexity of the tissue-specific expression of the targeted transcripts seems to contribute to the differential functions of miR-17-5p in different types of cancers. In this study, we selected 12 reported miR-17-5p targeting genes with mRNA levels unaffected by miR-17-5p expression and analyzed their expression in 31 organ tissues in transgenic mice by real-time PCR. Surprisingly, miR-17-5p expressing transgenic mice showed a positive correlation in these tissues between miR-17-5p expression levels and the selected miR-17-5p targeted transcripts; with high expression of the miRNA in organs with high selected miRNA-targeted mRNA levels. In cancer cell lines, overexpression of 7 reported miR-17-5p targeted genes' 3'-UTRs promoted miR-17-5p expression; meanwhile, transfection of 3'-UTRs with mutations had no significant effect. Moreover, an increase in AGO2 mRNA was associated with 3'-UTR expression as confirmed by real-time PCR. Hence, miR-17-5p regulation by these target genes might be an alternative mechanism to maintain miR-17-5p expression at tissue-specific levels.
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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) | 0.001 | 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".