Simvastatin Induces Apoptosis in Medulloblastoma Brain Tumor Cells via Mevalonate Cascade Prenylation Substrates
Bibliographic record
Abstract
Medulloblastoma is a common pediatric brain tumor and one of the main types of solid cancers in children below the age of 10. Recently, cholesterol-lowering "statin" drugs have been highlighted for their possible anti-cancer effects. Clinically, statins are reported to have promising potential for consideration as an adjuvant therapy in different types of cancers. However, the anti-cancer effects of statins in medulloblastoma brain tumor cells are not currently well-defined. Here, we investigated the cell death mechanisms by which simvastatin mediates its effects on different human medulloblastoma cell lines. Simvastatin is a lipophilic drug that inhibits HMG-CoA reductase and has pleotropic effects. Inhibition of HMG-CoA reductase prevents the formation of essential downstream intermediates in the mevalonate cascade, such as farnesyl pyrophosphate (FPP) and gernaylgerany parophosphate (GGPP). These intermediates are involved in the activation pathway of small Rho GTPase proteins in different cell types. We observed that simvastatin significantly induces dose-dependent apoptosis in three different medulloblastoma brain tumor cell lines (Daoy, D283, and D341 cells). Our investigation shows that simvastatin-induced cell death is regulated via prenylation intermediates of the cholesterol metabolism pathway. Our results indicate that the induction of different caspases (caspase 3, 7, 8, and 9) depends on the nature of the medulloblastoma cell line. Western blot analysis shows that simvastatin leads to changes in the expression of regulator proteins involved in apoptosis, such as Bax, Bcl-2, and Bcl-xl. Taken together, our data suggests the potential application of a novel non-classical adjuvant therapy for medulloblastoma, through the regulation of protein prenylation intermediates that occurs via inhibition of the mevalonate pathway.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".