MicroRNA-34c Inversely Couples the Biological Functions of the Runt-related Transcription Factor RUNX2 and the Tumor Suppressor p53 in Osteosarcoma
Bibliographic record
Abstract
Osteosarcoma (OS) is a primary bone tumor that is most prevalent during adolescence. RUNX2, which stimulates differentiation and suppresses proliferation of osteoblasts, is deregulated in OS. Here, we define pathological roles of RUNX2 in the etiology of OS and mechanisms by which RUNX2 expression is stimulated. RUNX2 is often highly expressed in human OS biopsies and cell lines. Small interference RNA-mediated depletion of RUNX2 inhibits growth of U2OS OS cells. RUNX2 levels are inversely linked to loss of p53 (which predisposes to OS) in distinct OS cell lines and osteoblasts. RUNX2 protein levels decrease upon stabilization of p53 with the MDM2 inhibitor Nutlin-3. Elevated RUNX2 protein expression is post-transcriptionally regulated and directly linked to diminished expression of several validated RUNX2 targeting microRNAs in human OS cells compared with mesenchymal progenitor cells. The p53-dependent miR-34c is the most significantly down-regulated RUNX2 targeting microRNAs in OS. Exogenous supplementation of miR-34c markedly decreases RUNX2 protein levels, whereas 3′-UTR reporter assays establish RUNX2 as a direct target of miR-34c in OS cells. Importantly, Nutlin-3-mediated stabilization of p53 increases expression of miR-34c and decreases RUNX2. Thus, a novel p53-miR-34c-RUNX2 network controls cell growth of osseous cells and is compromised in OS. Background: Osteosarcoma (OS) is associated with loss of tumor suppressor p53 and increased Runx2. Results: Runx2 and p53 levels are inversely correlated in OS. miR-34c, which targets Runx2, is absent in OS and elevated by p53. Conclusion: p53, miR-34c, and Runx2 form a regulatory loop that is compromised in OS. Significance: RUNX2 could be targeted by miR-34c to prevent OS growth.
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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".