Downregulation of MGMT expression by targeted editing of DNA methylation enhances temozolomide sensitivity in glioblastoma
Bibliographic record
Abstract
Glioblastoma is the most common and aggressive primary tumor of the central nervous system with poor outcome. Current gold standard treatment is surgical resection followed by a combination of radio- and chemotherapy. Efficacy of temozolomide (TMZ), the primary chemotherapeutic agent, depends on the DNA methylation status of the O6-methylguanine DNA methyltransferase (MGMT), which has been identified as a prognostic biomarker in glioblastoma patients. Clinical studies revealed that glioblastoma patients with hypermethylated MGMT promoter have a better response to TMZ treatment and a significantly improved overall survival. In this study, we thus used the CRISPRoff genome editing tool to mediate targeted DNA methylation within the MGMT promoter region. The system carrying a CRISPR-deactivated Cas9 (dCas9) fused with a methyltransferase (Dnmt3A/3L) domain downregulated MGMT expression in TMZ-resistant human glioblastoma cell lines through targeted DNA methylation. The reduction of MGMT expression levels reversed TMZ resistance in TMZ-resistant glioblastoma cell lines resulting in TMZ induced dose-dependent cell death rates. In conclusion, we demonstrate targeted RNA-guided methylation of the MGMT promoter as a promising tool to overcome chemoresistance and improve the cytotoxic effect of TMZ in glioblastoma.
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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".