Mevalonate Promotes the Growth of Tumors Derived from Human Cancer Cells in Vivo and Stimulates Proliferation in Vitro with Enhanced Cyclin-dependent Kinase-2 Activity
Bibliographic record
Abstract
Malignant cells are known to have elevated rates of mevalonate synthesis because of increased levels and catalytic efficiency of 3-hydroxy-3-methylglutaryl-CoA reductase. Whether this increased mevalonate synthesis occurs as a consequence of increased requirements for a mevalonate-derived metabolite in response to rapid growth or whether mevalonate promotes the growth of tumor cells is unknown. To address this question, we administered mevalonate via miniosmotic pumps to nude mice inoculated with MDA-MB-435 human cancer cells. After 13 weeks of growth, tumors in mevalonate-treated mice were significantly larger than tumors in saline-treated, control mice (1.52 +/- 0.26 g versus 0.81 +/- 0.27 g respectively, p < 0.05). The cancer cells treated in culture with mevalonate also demonstrated increased proliferation rates associated with accelerated entry of cells into S phase. These cells had enhanced total and cyclin A-immunoprecipitable cyclin-dependent kinase-2 (CDK-2) activity, increased activating phosphorylation of CDK-2, and decreased inhibitory binding of CDK-2 to p21(Cip1). Our findings demonstrate that mevalonate promotes tumor growth and suggest that an increase in mevalonate synthesis in extrahepatic tissues following cholesterol-lowering therapy may explain the elevated risk of cancer shown in some studies.
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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".