Preventing MEK1 activation influences the responses of human osteosarcoma cells to bone morphogenetic proteins 2 and 9
Why this work is in the frame
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Bibliographic record
Abstract
It was recently suggested that bone morphogenetic protein (BMP)-2 may be useful for treating osteosarcoma cells. BMP-9, which has been patented to treat breast and prostate cancers, has a higher osteoinductive potential than BMP-2. Peptides derived from the knuckle epitope of BMPs (pBMPs) also induced osteogenic differentiation. However, the effect of BMP-9 and pBMPs on osteosarcoma cells is unclear. We analyzed the effects of BMP-2, BMP-9, pBMP-2, and pBMP-9 on the behavior of human MG-63 and SaOS-2 osteosarcoma cells. An inhibitor of MEK1 activation (PD98059) that prevents downstream extracellular signal-regulated kinase 1/2 (ERK1/2) phosphorylation and a specific inhibitor of p38 were also used as mitogen activated protein kinase-targeting therapy is being investigated as a treatment modality for osteosarcoma. BMP-2 and BMP-9 (1.92 nmol/l) induced the phosphorylation of Smad1/5/8 in both osteosarcoma cells within 1 h but had different effects on mitogen activated protein kinase pathways. Whereas BMP-2 mainly activated ERK1/2, BMP-9 phosphorylated p38 within 1 h. pBMP-2 did not activate either the Smad or ERK/p38, whereas pBMP-9, like BMP-9, induced both Smad1/5/8 and p38 phosphorylation. p38 activation by BMP-9 or pBMP-9 was also enhanced by PD98059. However, BMP-2 or BMP-9 increased the amounts of distal-less homeobox 5 and Osterix mRNAs in SaOS-2 cells within 6 h, whereas pBMP-9 had no effect. PD98059 promoted the highest level of Osterix mRNA in SaOS-2 cells incubated with BMP-2 or BMP-9, whereas p38 inhibitor had no effect. Furthermore, PD98059 induced the lowest proliferation of MG-63 cells incubated with BMP-2, whereas p38 inhibitor did not affect the proliferation of either osteosarcoma cell line. Therefore a combination of BMP-2 or BMP-9 and an inhibitor of MEK1 may be a promising tool for regulating osteosarcoma cell behavior.
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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 | 0.000 | 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