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Record W2483933081 · doi:10.1016/j.celrep.2016.07.036

The miR-17∼92 microRNA Cluster Is a Global Regulator of Tumor Metabolism

2016· article· en· W2483933081 on OpenAlexafffund
Said Izreig, Bożena Samborska, Radia Marie Johnson, Alexey Sergushichev, H. Eric, Carine Lussier, Ekaterina Loginicheva, Ariel O. Donayo, Maya C. Poffenberger, Selena M. Sagan, Emma E. Vincent, Maxim N. Artyomov, Thomas F. Duchaîne, Russell G. Jones

Bibliographic record

VenueCell Reports · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersCanadian Institutes of Health Research
KeywordsmicroRNABiologymTORC1ReprogrammingCell biologyCancer researchRegulatorGlycolysisTumor progressionTumor microenvironmentMetabolismCell growthSuppressorCellCancerTumor cellsSignal transductionBiochemistryGeneticsPI3K/AKT/mTOR pathwayGene

Abstract

fetched live from OpenAlex

A central hallmark of cancer cells is the reprogramming of cellular metabolism to meet the bioenergetic and biosynthetic demands of malignant growth. Here, we report that the miR-17∼92 microRNA (miRNA) cluster is an oncogenic driver of tumor metabolic reprogramming. Loss of miR-17∼92 in Myc(+) tumor cells leads to a global decrease in tumor cell metabolism, affecting both glycolytic and mitochondrial metabolism, whereas increased miR-17∼92 expression is sufficient to drive increased nutrient usage by tumor cells. We mapped the metabolic control element of miR-17∼92 to the miR-17 seed family, which influences cellular metabolism and mammalian target of rapamycin complex 1 (mTORC1) signaling through negative regulation of the LKB1 tumor suppressor. miR-17-dependent tuning of LKB1 levels regulates both the metabolic potential of Myc(+) lymphomas and tumor growth in vivo. Our results establish metabolic reprogramming as a central function of the oncogenic miR-17∼92 miRNA cluster that drives the progression of MYC-dependent tumors.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.252
Threshold uncertainty score0.349

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.005
GPT teacher head0.221
Teacher spread0.217 · 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 teacher head, 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

Citations73
Published2016
Admission routes2
Has abstractyes

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