Simvastatin increases temozolomide‐induced cell death by targeting the fusion of autophagosomes and lysosomes
Bibliographic record
Abstract
Temozolomide (TMZ) is a chemotherapy agent used to treat Grade IV astrocytoma, also known as glioblastoma (GBM). TMZ treatment causes DNA damage that results in tumor cell apoptosis and increases the survival rate of GBM patients. However, chemoresistance as a result of TMZ-induced autophagy significantly reduces this anticancer effects over time. Statins are competitive inhibitors of HMG-CoA reductase, the rate-limiting enzyme of the mevalonate (MEV) cascade. Statins are best known for their cholesterol (CH)-lowering effect. Long-term consumption of statins, prior to and in parallel with other cancer therapeutic approaches, has been reported to increase the survival rate of patients with various forms of cancers. In this study, we investigated the potentiation of TMZ-induced apoptosis by simvastatin (Simva) in human GBM cell lines and patient GBM cells, using cell monolayers and three-dimensional cell culture systems. The incubation of cells with a combination of Simva and TMZ resulted in a significant increase in apoptotic cells compared to cells treated with TMZ alone. Incubation of cells with CH or MEV cascade intermediates failed to compensate the decrease in cell viability induced by the combined Simva and TMZ treatment. Simva treatment inhibited the autophagy flux induced by TMZ by blocking autophago-lysosome formation. Our results suggest that Simva sensitizes GBM cells to TMZ-induced cell death in a MEV cascade-independent manner and identifies the inhibition of autophagosome-lysosome fusion as a promising therapeutic strategy in the treatment of GBM.
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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".