Regulation of p27<sup>Kip1</sup>by miRNA 221/222 in Glioblastoma
Bibliographic record
Abstract
Levels of p27(Kip1), a key negative regulator of the cell cycle, are often decreased in cancer. In most cancers, levels of p27(Kip1) mRNA are unchanged and increased proteolysis of the p27(Kip1) protein is thought to be the primary mechanism for its downregulation. Here we show that p27(Kip1) protein levels are also downregulated by microRNAs in cancer cells. We used RNA interference to reduce Dicer levels in human glioblastoma cell lines and found that this caused an increase in p27(Kip1) levels and a decrease in cell proliferation. When the coding sequence for the 3'UTR of the p27(Kip1) mRNA was inserted downstream of a luciferase reporter gene, Dicer depletion also enhanced expression of the reporter gene product. The microRNA target site software TargetScan predicts that the 3'UTR of p27(Kip1) mRNA contains multiple sites for microRNAs. These include two sites for microRNA 221 and 222, which have been shown to be upregulated in glioblastoma relative to adjacent normal brain tissue. The genes for microRNA 221 and microRNA 222 occupy adjacent sites on the X chromosome; their expression appears to be coregulated and they also appear to have the same target specificity. Antagonism of either microRNA 221 or 222 in glioblastoma cells also caused an increase in p27(Kip1) levels and enhanced expression of the luciferase reporter gene fused to the p27(Kip1) 3'UTR. These data show that p27(Kip1) is a direct target for microRNAs 221 and 222, and suggest a role for these microRNAs in promoting the aggressive growth of human glioblastoma.
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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".