Decitabine-Induced Demethylation of 5′ CpG Island in GADD45A Leads to Apoptosis in Osteosarcoma Cells
Bibliographic record
Abstract
GADD45 genes are epigenetically inactivated in various types of cancer and tumor cell lines. To date, defects of the GADD45 gene family have not been implicated in osteosarcoma (OS) oncogenesis, and the role of this pathway in regulating apoptosis in this tumor is unknown. The therapeutic potential of Gadd45 in OS emerged when our previous studies showed that GADD45A was reexpressed by treatment with the demethylation drug decitabine. In this study, we analyze the OS cell lines MG63 and U2OS and show that on treatment with decitabine, a significant loss of DNA methylation of GADD45A was associated with elevated expression and induction of apoptosis. In vivo affects of decitabine treatment in mice showed that untreated control xenografts exhibited low nuclear staining for Gadd45a protein, whereas the nuclei from xenografts in decitabine-treated mice exhibited increased amounts of protein and elevated apoptosis. To show the specificity of this gene for decitabine-induced apoptosis in OS, GADD45A mRNAs were disrupted using short interference RNA, and the ability of the drug to induce apoptosis was reduced. Understanding the role of demethylation of GADD45A in reexpression of this pathway and restoration of apoptotic control is important for understanding OS oncogenesis and for more targeted therapeutic approaches.
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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".