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Record W2090002637 · doi:10.1158/1535-7163.targ-09-b99

Abstract B99: Dysregulation of HMGCR and the mevalonate pathway in human cancers

2009· article· en· W2090002637 on OpenAlexaff
Linda Z. Penn, James W. Clendening, Aleksandra A. Pandyra, Paul C. Boutros, Grace A. Trentin, Աննա Մարտիրոսյան, Suzanne Trudel, Igor Jurišica

Bibliographic record

VenueMolecular Cancer Therapeutics · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsAllergan (Canada)University Health NetworkOntario Institute for Cancer Research
Fundersnot available
KeywordsCarcinogenesisBiologyMevalonate pathwayCancer researchEctopic expressionCancerLovastatinStatinCell biologyGeneticsReductaseGeneEnzymeEndocrinologyCholesterolBiochemistry

Abstract

fetched live from OpenAlex

Abstract The importance of cancer metabolism has been appreciated for many years, but the intricacies of how metabolic pathways and oncogenic events overlap remains unclear. By understanding how metabolism contributes to tumorigenesis, we will be able to target these fundamental biochemical pathways and impact patient care. The mevalonate (MVA) pathway, paced by its rate-limiting enzyme, hydroxymethylglutaryl coenzyme A reductase (HMGCR), is a metabolic pathway required for the generation of a number of fundamental end-products including cholesterol and isoprenoids. Despite being subject to years of extensive research from the perspective of coronary artery disease, the contribution of the MBA pathway to human cancer remains largely unexplored. Intriguingly, we show that high mRNA levels of 5/6 MVA pathway genes, including HMGCR, correlate with poor prognosis in a meta-analysis of six large expression microarray datasets of primary breast cancer. Indeed, we show that deregulated expression of HMGCR, full-length or splice-variant, increases anchorage-independent growth and tumorigenesis of transformed cells when plated in soft agar or grown as xenografts, respectively. Moreover, we also show that ectopic expression of deregulated HMGCR drives tumorigenesis of non-transformed breast cells a well as normal murine myeloid progenitors. Advancing these studies to an independent tumor-type (multiple-myeloma), shows that loss of the classic feedback response to the blockbuster drugs known as statins, which inhibit HMGCR, further defines dysregulation of the MVA pathway. In addition, we show that this loss of feedback regulation distinguishes the subset of tumor cells that are highly sensitive to statin-induced apoptosis. Taken together, our results suggest that HMGCR is a candidate metabolic oncogene and provides molecular rationale for further exploring HMGCR inhibitors as anticancer agents. Citation Information: Mol Cancer Ther 2009;8(12 Suppl):B99.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.013
GPT teacher head0.275
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

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