Regulation of p27<sup>Kip1</sup>by miRNA 221/222 in Glioblastoma
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it