Preventing MEK1 activation influences the responses of human osteosarcoma cells to bone morphogenetic proteins 2 and 9
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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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) | 0.002 | 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".