Expression of CD44 3′-untranslated region regulates endogenous microRNA functions in tumorigenesis and angiogenesis
Bibliographic record
Abstract
The non-coding 3'-untranslated region (UTR) plays an important role in the regulation of microRNA (miRNA) functions, since it can bind and inactivate multiple miRNAs. Here, we show the 3'-UTR of CD44 is able to antagonize cytoplasmic miRNAs, and result in the increased translation of CD44 and downstream target mRNA, CDC42. A series of cell function assays in the human breast cancer cell line, MT-1, have shown that the CD44 3'-UTR inhibits proliferation, colony formation and tumor growth. Furthermore, it modulated endothelial cell activities, favored angiogenesis, induced tumor cell apoptosis and increased sensitivity to Docetaxel. These results are due to the interaction of the CD44 3'-UTR with multiple miRNAs. Computational algorithms have predicted three miRNAs, miR-216a, miR-330 and miR-608, can bind to both the CD44 and CDC42 3'-UTRs. This was confirmed with luciferase assays, western blotting and immunohistochemical staining and correlated with a series of siRNA assays. Thus, the non-coding CD44 3'-UTR serves as a competitor for miRNA binding and subsequently inactivates miRNA functions, by freeing the target mRNAs from being repressed.
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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".