The Role of the DNA Methyltransferase Family and the Therapeutic Potential of DNMT Inhibitors in Tumor Treatment
Bibliographic record
Abstract
Members of the DNA methyltransferase (DNMT) family have been recognized as major epigenetic regulators of altered gene expression during tumor development. They establish and maintain DNA methylation of the CpG island of promoter and non-CpG region of the genome. The abnormal methylation status of tumor suppressor genes (TSGs) has been associated with tumorigenesis, leading to genomic instability, improper gene silence, and immune evasion. DNMT1 helps preserve methylation patterns during DNA replication, whereas the DNMT3 family is responsible for de novo methylation, creating new methylation patterns. Altered DNA methylation significantly supports tumor growth by changing gene expression patterns. FDA-approved DNMT inhibitors reverse hypermethylation-induced gene repression and improve therapeutic outcomes for cancer. Recent studies indicate that combining DNMT inhibitors with chemotherapies and immunotherapies can have synergistic effects, especially in aggressive metastatic tumors. Improving the treatment schedules, increasing isoform specificity, reducing toxicity, and utilizing genome-wide analyses of CRISPR-based editing to create personalized epigenetic therapies tailored to individual patient needs are promising strategies for enhancing therapeutic outcomes. This review discusses the interaction between DNMT regulators and DNMT1, its binding partners, the connection between DNA methylation and tumors, how these processes contribute to tumor development, and DNMT inhibitors' advancements and pharmacological properties.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".