Dysregulation of the mevalonate pathway promotes transformation
Bibliographic record
Abstract
The importance of cancer metabolism has been appreciated for many years, but the intricacies of how metabolic pathways interconnect with oncogenic signaling are not fully understood. With a clear understanding of how metabolism contributes to tumorigenesis, we will be better able to integrate the targeting of these fundamental biochemical pathways into patient care. The mevalonate (MVA) pathway, paced by its rate-limiting enzyme, hydroxymethylglutaryl coenzyme A reductase (HMGCR), is required for the generation of several fundamental end-products including cholesterol and isoprenoids. Despite years of extensive research from the perspective of cardiovascular disease, the contribution of a dysregulated MVA pathway to human cancer remains largely unexplored. We address this issue directly by showing that dysregulation of the MVA pathway, achieved by ectopic expression of either full-length HMGCR or its novel splice variant, promotes transformation. Ectopic HMGCR accentuates growth of transformed and nontransformed cells under anchorage-independent conditions or as xenografts in immunocompromised mice and, importantly, cooperates with RAS to drive the transformation of primary mouse embryonic fibroblasts cells. We further explore whether the MVA pathway may play a role in the etiology of human cancers and show that high mRNA levels of HMGCR and additional MVA pathway genes correlate with poor prognosis in a meta-analysis of six microarray datasets of primary breast cancer. Taken together, our results suggest that HMGCR is a candidate metabolic oncogene and provide a molecular rationale for further exploring the statin family of HMGCR inhibitors as anticancer agents.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".