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Record W2131308193 · doi:10.1038/onc.2013.347

Insulin activates the insulin receptor to downregulate the PTEN tumour suppressor

2013· article· en· W2131308193 on OpenAlexaff
Jun Liu, Stacy Grieve, Jeff R. Boudreau, Benjamin Yeung, S J Lo, Gabriel Chamberlain, Fabian P.S. Yu, Tiansong Sun, Tony Papanicolaou, Annie Lam, Xiaolong Yang, Ian D. Chin-Sang

Bibliographic record

VenueOncogene · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism, Diabetes, and Cancer
Canadian institutionsQueen's University
FundersNational Institutes of HealthOklahoma Medical Research Foundation
KeywordsPTENTensinBiologyHyperinsulinemiaInsulin receptorInsulinCancer researchEndocrinologySignal transductionProtein kinase BInternal medicineDownregulation and upregulationPI3K/AKT/mTOR pathwayCell biologyInsulin resistanceGeneticsMedicineGene

Abstract

fetched live from OpenAlex

Insulin and insulin-like growth factor-1 signaling have fundamental roles in energy metabolism, growth and development. Recent research suggests hyperactive insulin receptor (IR) and hyperinsulinemia are cancer risk factors. However, the mechanisms that account for the link between the hyperactive insulin signaling and cancer risk are not well understood. Here we show that an insulin-like signaling inhibits the DAF-18/(phosphatase and tensin homolog) PTEN tumour suppressor in Caenorhabditis elegans and that this regulation is conserved in human breast cancer cells. We show that inhibiting the IR increases PTEN protein levels, while increasing insulin signaling decreases PTEN protein levels. Our results show that the kinase region of IRβ subunit physically binds to PTEN and phosphorylates on Y27 and Y174. Our genetic results also show that DAF-2/IR negatively regulates DAF-18/PTEN during C. elegans axon guidance. As PTEN is an important tumour suppressor, our results therefore suggest a possible mechanism for increased cancer risk observed in hyperinsulinemia and hyperactive IR individuals.

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 score0.449
Threshold uncertainty score0.730

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.009
GPT teacher head0.232
Teacher spread0.223 · 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

Citations38
Published2013
Admission routes1
Has abstractyes

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