Targeting Cholesterol Metabolism in Glioblastoma: A New Therapeutic Approach in Cancer Therapy
Bibliographic record
Abstract
Glioblastoma multiforme (GBM) is the most aggressive malignant brain tumor known with a poor survival rate despite current advances in the field of cancer. Additional research into the pathophysiology of GBM is urgently needed given the devastating nature of this disease. Recent studies have revealed the unique cellular physiology of GBM cells as compared with healthy astrocytes. Intriguingly, GBM cells are incapable of de novo cholesterol synthesis via the mevalonate pathway. Thus, the survival of GBM cells depends on cholesterol uptake via low-density lipoprotein receptors (LDLRs) in the form of apolipoprotein-E-containing lipoproteins and ATP-binding cassette transporter A1 (ABCA1) that efflux surplus cholesterol out of cells. Liver X receptors regulate intracellular cholesterol levels in neurons and healthy astrocytes through changes in the expression of LDLR and ABCA1 in response to cholesterol and its derivatives. In GBM cells, due to the dysregulation of this surveillance pathway, there is an accumulation of intracellular cholesterol. Furthermore, intracellular cholesterol regulates temozolomide-induced cell death in glioblastoma cells via accumulation and activation of death receptor 5 in plasma membrane lipid rafts. The mevalonate pathway and autophagy flux are also fundamentally related with implications for cell health and death. Thus, via cholesterol metabolism, the mevalonate pathway may be a crucial player in the pathogenesis and treatment of GBM where our current understanding is still lacking. Targeting cholesterol metabolism in GBM may hold promise as a novel adjunctive clinical therapy for this devastating cancer.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".