Interferon-α/β enhances temozolomide activity against MGMT-positive glioma stem-like cells
Bibliographic record
Abstract
Glioma is one of the most common primary tumors of the central nervous system in adults. Glioblastoma (GBM) is the most lethal type of glioma, whose 5-year survival is 9.8% at best. Glioma stem-like cells (GSCs) play an important role in recurrence and treatment resistance. MGMT is a DNA repair protein that removes DNA adducts and therefore attenuates treatment efficiency. It has been reported that interferon-α/β (IFN-α/β) downregulates the level of MGMT and sensitizes glioma cells to temozolomide. In the present study, we assessed whether IFN-α/β is able to sensitize GSCs to temozolomide by modulating MGMT expression. Upon the treatment of IFN-α/β, the efficacy of temozolomide against MGMT‑positive GSCs was markedly enhanced by combination treatment with IFN-α/β when compared with the temozolomide single agent group, and MGMT expression was markedly decreased at the same time. Further mechanistic study showed that IFN-α/β suppressed the NF-κB activity, which further mediated the sensitization of MGMT‑positive GSCs to temozolomide. Our data therefore demonstrated that the application of IFN-α/β is a promising agent with which to enhance temozolomide efficiency and reduce drug resistance, and our findings shed light on improving clinical outcomes and prolonging the survival of patients with malignant gliomas.
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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 | 0.001 |
| 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".