<i>S</i>‐adenosylmethionine blocks osteosarcoma cells proliferation and invasion <i>in vitro</i> and tumor metastasis <i>in vivo</i>: therapeutic and diagnostic clinical applications
Bibliographic record
Abstract
Osteosarcoma (OS) is an aggressive and highly metastatic form of primary bone cancer affecting young children and adults. Previous studies have shown that hypomethylation of critical genes is driving metastasis. Here, we examine whether hypermethylation treatment can block OS growth and pulmonary metastasis. Human OS cells LM-7 and MG-63 were treated with the ubiquitous methyl donor S-adenosylmethionine (SAM) or its inactive analog S-adenosylhomocystine (SAH) as control. Treatment with SAM resulted in a dose-dependent inhibition of tumor cell proliferation, invasion, cell migration, and cell cycle characteristics. Inoculation of cells treated with 150 μmol/L SAM for 6 days into tibia or via intravenous route into Fox Chase severe combined immune deficient (SCID) mice resulted in the development of significantly smaller skeletal lesions and a marked reduction in pulmonary metastasis as compared to control groups. Epigenome wide association studies (EWAS) showed differential methylation of several genes involved in OS progression and prominent signaling pathways implicated in bone formation, wound healing, and tumor progression in SAM-treated LM-7 cells. Real-time polymerase chain reaction (qPCR) analysis confirmed that SAM treatment blocked the expression of several prometastatic genes and additional genes identified by EWAS analysis. Immunohistochemical analysis of normal human bone and tissue array from OS patients showed significantly high levels of expression of one of the identified gene platelet-derived growth factor alpha (PDGFA). These studies provide a possible mechanism for the role of DNA demethylation in the development and metastasis of OS to provide a rationale for the use of hypermethylation therapy for OS patients and identify new targets for monitoring OS development and progression.
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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) | 0.001 | 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".