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Record W2284329206 · doi:10.1016/j.celrep.2016.01.068

miR-137 Modulates a Tumor Suppressor Network-Inducing Senescence in Pancreatic Cancer Cells

2016· article· en· W2284329206 on OpenAlexafffund
Mathieu Neault, Frédérick A. Mallette, Stéphane Richard

Bibliographic record

VenueCell Reports · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsUniversité de MontréalHôpital Maisonneuve-RosemontMcGill UniversityJewish General Hospital
FundersCanadian Institutes of Health Research
KeywordsSenescencePancreatic cancerSuppressorRetinoblastomaCancer researchBiologymicroRNADemethylaseCancerRetinoblastoma proteinTumor suppressor geneCell biologyCarcinogenesisCell cycleEpigeneticsGeneticsGene

Abstract

fetched live from OpenAlex

Activating K-Ras mutations occurs frequently in pancreatic cancers and is implicated in their development. Cancer-initiating events, such as oncogenic Ras activation, lead to the induction of cellular senescence, a tumor suppressor response. During senescence, the decreased levels of KDM4A lysine demethylase contribute to p53 activation, however, the mechanism by which KDM4A is downregulated is unknown. We show that miR-137 targets KDM4A mRNA during Ras-induced senescence and activates both p53 and retinoblastoma (pRb) tumor suppressor pathways. Restoring the KDM4A expression contributed to bypass of miR-137-induced senescence and inhibition of endogenous miR-137 with an miRNA sponge-compromised Ras-induced senescence. miR-137 levels are significantly reduced in human pancreatic tumors, consistent with previous studies revealing a defective senescence response in this cancer type. Restoration of miR-137 expression inhibited proliferation and promoted senescence of pancreatic cancer cells. These results suggest that modulating levels of miR-137 may be important for triggering tumor suppressor networks in pancreatic cancer.

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 score0.008
Threshold uncertainty score0.518

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 head0.007
GPT teacher head0.226
Teacher spread0.219 · 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

Citations76
Published2016
Admission routes2
Has abstractyes

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