p53 Is Positively Regulated by miR-542-3p
Bibliographic record
Abstract
The tumor suppressor p53 and miRNAs are linked through a complex network. Several miRNAs modulate p53 expression, while p53 regulates the transcription and/or biogenesis of several other miRNAs. Here, we report the development of a cell-based assay used with a library of human miRNA mimics in a high-throughput screen for miRNAs that modulate p53 expression. Overexpression of miRNA (miR)-542-3p in cancer cells elevated p53 expression, stimulated the expression of p53 targets, and inhibited cell proliferation. Mechanistically, miR-542-3p increased p53 protein stability by weakening interactions between p53 and its negative regulator MDM2. Furthermore, miR-542-3p suppressed ribosome biogenesis by downregulating a subset of ribosomal proteins such as RPS23, leading to upregulation of RPL11 and stabilization of p53. The 3'untranslated region in the RPS23 transcript contained a miR-542-3p-binding site, suggesting that RPS23 is a direct target of miR-542-3p. Our results define miR-542-3p as an important new positive regulator of p53 with potential applications in cancer treatment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".