MétaCan
Menu
Back to cohort
Record W2077212116 · doi:10.1158/0008-5472.can-13-1706

p53 Is Positively Regulated by miR-542-3p

2014· article· en· W2077212116 on OpenAlexaff
Yemin Wang, Jen‐Wei Huang, María Castellà, David G. Huntsman, Toshiyasu Taniguchi

Bibliographic record

VenueCancer Research · 2014
Typearticle
Languageen
FieldMedicine
TopicCancer-related Molecular Pathways
Canadian institutionsUniversity of British ColumbiaBC Cancer Agency
FundersNational Institute of General Medical SciencesNational Cancer InstituteNational Heart, Lung, and Blood Institute
KeywordsInternal medicineBiologyCancer researchMedicine

Abstract

fetched live from OpenAlex

The tumor suppressor p53 and miRNAs are linked through a complex network. Several miRNAs modulate p53 expression, while p53 regulates the transcription and/or biogenesis of several other miRNAs. Here, we report the development of a cell-based assay used with a library of human miRNA mimics in a high-throughput screen for miRNAs that modulate p53 expression. Overexpression of miRNA (miR)-542-3p in cancer cells elevated p53 expression, stimulated the expression of p53 targets, and inhibited cell proliferation. Mechanistically, miR-542-3p increased p53 protein stability by weakening interactions between p53 and its negative regulator MDM2. Furthermore, miR-542-3p suppressed ribosome biogenesis by downregulating a subset of ribosomal proteins such as RPS23, leading to upregulation of RPL11 and stabilization of p53. The 3'untranslated region in the RPS23 transcript contained a miR-542-3p-binding site, suggesting that RPS23 is a direct target of miR-542-3p. Our results define miR-542-3p as an important new positive regulator of p53 with potential applications in cancer treatment.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.102
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.050
GPT teacher head0.395
Teacher spread0.345 · 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.

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

Citations61
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueCancer ResearchSame topicCancer-related Molecular PathwaysFrench-language works237,207