Effects of specific DNMT gene depletion on cancer cell transformation and breast cancer cell invasion; toward selective DNMT inhibitors
Bibliographic record
Abstract
A hallmark of cancer is aberrant DNA methylation, consisting of global hypomethylation and regional hypermethylation of tumor suppressor genes. DNA methyltransferase inhibitors have been recognized as promising candidate anticancer drugs. Drug development has focused on DNA methylation inhibitors with the goal of activating tumor suppressor genes silenced by DNA methylation. 5-azacytidine (5-AC; Vidaza), a global DNA methyltransferase inhibitor, was Food and Drug Administration approved to treat myelodysplastic syndromes and is clinically tested for solid tumors. In this paper, it was demonstrated that 5'-aza-2'-deoxycytidine (5-azaCdR) activated both silenced tumor suppressor genes and pro-metastatic genes by demethylation, raising the concern that it would promote metastasis. 5-AzaCdR treatment increased the invasiveness of non-invasive breast cancer cell lines MCF-7 cells and ZR-75-1 and dramatically induced pro-metastatic genes; Urokinase plasminogen activator (uPA), matrix metalloproteinase 2 (MMP2), metastasis-associated gene (H-MTS1; S100A4) and C-X-C chemokine receptor 4 (CXCR4). The hypothesis that the blocking of cellular transformation activity of DNA methyltransferase inhibitor could be separated from the pro-metastatic activity was tested using short interfering RNA (siRNA) targeted to the different DNA methyltransferase (DNMT) genes. Although depletion of DNMT1 had the strongest effect on colony growth suppression in cellular transformation assays, it did not result in demethylation and activation of uPA, S100A4, MMP2 and CXCR4 in MCF-7 cells. Depletion of DNMT1 did not induce cellular invasion in MCF-7 and ZR-75-1 non-invasive breast cancer cell lines. These data have implications on the design of new DNA methyltransferase inhibitor and on the proper utilization of current inhibitors.
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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".