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Record W4406062129 · doi:10.1016/j.canlet.2025.217441

Metabolic reprogramming, malignant transformation and metastasis: Lessons from chronic lymphocytic leukaemia and prostate cancer

2025· review· en· W4406062129 on OpenAlexaff
Madison T. Hindes, Anthony M. McElligott, Giles Best, Mark P. Ward, Stavros Selemidis, Mark A. Miles, Bukuru D. Nturubika, Philip A. Gregory, Paul Anderson, Jessica M. Logan, Lisa M. Butler, David Waugh, John O’Leary, Shane M. Hickey, Lauren A. Thurgood, Doug A. Brooks

Bibliographic record

VenueCancer Letters · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism, Diabetes, and Cancer
Canadian institutionsTrinity College
FundersAustralian Research CouncilProstate Cancer Foundation of AustraliaProstate Cancer Foundation
KeywordsMetastasisProstateProstate cancerCancerMedicineCancer researchMalignant transformationOncologyReprogrammingChronic lymphocytic leukemiaInternal medicinePathologyBiologyLeukemiaCellGenetics

Abstract

fetched live from OpenAlex

Metabolic reprogramming is a hallmark of cancer, crucial for malignant transformation and metastasis. Chronic lymphocytic leukaemia (CLL) and prostate cancer exhibit similar metabolic adaptations, particularly in glucose and lipid metabolism. Understanding this metabolic plasticity is crucial for identifying mechanisms contributing to metastasis. This review considers glucose and lipid metabolism in CLL and prostate cancer, exploring their roles in healthy and malignant states and during disease progression. In CLL, lipid metabolism supports cell survival and migration, with aggressive disease characterised by increased fatty acid oxidation and altered sphingolipids. Richter's transformation and aggressive lymphoma, however, exhibit a metabolic shift towards increased glycolysis. Similarly, prostate cell metabolism is unique, relying on citrate production in the healthy state and undergoing metabolic reprogramming during malignant transformation. Early-stage prostate cancer cells increase lipid synthesis and uptake, and decrease glycolysis, whereas metastatic cells re-adopt glucose metabolism, likely driven by interactions with the tumour microenvironment. Genetic drivers including TP53 and ATM mutations connect metabolic alterations to disease severity in these two malignancies. The bone microenvironment supports the metabolic demands of these malignancies, serving as an initiation niche for CLL and a homing site for prostate cancer metastases. By comparing these malignancies, this review underscores the importance of metabolic plasticity in cancer progression and highlights how CLL and prostate cancer may be models of circulating and solid tumours more broadly. The metabolic phenotypes throughout cancer cell transformation and metastasis, and the microenvironment in which these processes occur, present opportunities for interventions that could disrupt metastatic processes and improve patient outcomes.

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: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.003
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 head0.021
GPT teacher head0.325
Teacher spread0.304 · 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
GenreReview

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

Citations17
Published2025
Admission routes1
Has abstractyes

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