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Record W3129132895 · doi:10.1158/0008-5472.can-20-2511

ELOVL5 Is a Critical and Targetable Fatty Acid Elongase in Prostate Cancer

2021· article· en· W3129132895 on OpenAlexfundno aff
Margaret M. Centenera, Julia S. Scott, Jelle Machiels, Zeyad D. Nassar, Deanna C. Miller, Irene Zinonos, Jonas Dehairs, Ingrid J.G. Burvenich, Giorgia Zadra, Paolo Chetta, Clyde Bango, Emma Evergren, Natalie K. Ryan, Joanna L. Gillis, Chui Y. Mah, Terence Tieu, Adrienne R. Hanson, Ryan Carelli, Katarzyna Bloch, Vasilios Panagopoulos, Etienne Waelkens, Rita Derua, Elizabeth D. Williams, Andreas Evdokiou, Anna Cifuentes‐Rius, Nicolas H. Voelcker, Ian G. Mills, Wayne D. Tilley, Andrew M. Scott, Massimo Loda, Luke A. Selth, Johannes V. Swinnen, Lisa M. Butler

Bibliographic record

VenueCancer Research · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsnot available
FundersInterregNational Cancer InstituteCancer Council South AustraliaMedical Research CouncilNorth Dakota Department of AgricultureFondation contre le CancerVlaamse regeringKU LeuvenFonds Wetenschappelijk OnderzoekProstate Cancer Foundation of AustraliaNational Health and Medical Research CouncilCommonwealth Scientific and Industrial Research OrganisationQueen's University BelfastNational Institutes of HealthQueen's UniversityDepartment of Health and Aged Care, Australian GovernmentU.S. Department of DefenseEuropean CommissionProstate Cancer FoundationMovember FoundationAustralian GovernmentDana-Farber Cancer Institute
KeywordsProstate cancerMetastasisCancer researchCancerAndrogen receptorLipidomeCancer cellProstateFatty acid metabolismLipid metabolismBiologyFatty acidChemistryInternal medicineEndocrinologyBiochemistryMedicine

Abstract

fetched live from OpenAlex

Abstract The androgen receptor (AR) is the key oncogenic driver of prostate cancer, and despite implementation of novel AR targeting therapies, outcomes for metastatic disease remain dismal. There is an urgent need to better understand androgen-regulated cellular processes to more effectively target the AR dependence of prostate cancer cells through new therapeutic vulnerabilities. Transcriptomic studies have consistently identified lipid metabolism as a hallmark of enhanced AR signaling in prostate cancer, yet the relationship between AR and the lipidome remains undefined. Using mass spectrometry–based lipidomics, this study reveals increased fatty acyl chain length in phospholipids from prostate cancer cells and patient-derived explants as one of the most striking androgen-regulated changes to lipid metabolism. Potent and direct AR-mediated induction of ELOVL fatty acid elongase 5 (ELOVL5), an enzyme that catalyzes fatty acid elongation, was demonstrated in prostate cancer cells, xenografts, and clinical tumors. Assessment of mRNA and protein in large-scale data sets revealed ELOVL5 as the predominant ELOVL expressed and upregulated in prostate cancer compared with nonmalignant prostate. ELOVL5 depletion markedly altered mitochondrial morphology and function, leading to excess generation of reactive oxygen species and resulting in suppression of prostate cancer cell proliferation, 3D growth, and in vivo tumor growth and metastasis. Supplementation with the monounsaturated fatty acid cis-vaccenic acid, a direct product of ELOVL5 elongation, reversed the oxidative stress and associated cell proliferation and migration effects of ELOVL5 knockdown. Collectively, these results identify lipid elongation as a protumorigenic metabolic pathway in prostate cancer that is androgen-regulated, critical for metastasis, and targetable via ELOVL5. Significance: This study identifies phospholipid elongation as a new metabolic target of androgen action that is critical for prostate tumor metastasis.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.396
Teacher spread0.359 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations96
Published2021
Admission routes1
Has abstractyes

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