MétaCan
Menu
Back to cohort
Record W3210225758 · doi:10.1038/s41388-021-02039-2

Menin inhibition suppresses castration-resistant prostate cancer and enhances chemosensitivity

2021· article· en· W3210225758 on OpenAlexaff
Chaïma Cherif, Dang Tan Nguyen, Clément Paris, Thi Khanh Le, Thibaud Sefiane, Nadine Carbuccia, Pascal Finetti, Max Chaffanet, Abdessamad El Kaoutari, Julien Vernerey, Ladan Fazli, Martin Gleave, Mohamed Manaï, Philippe Barthélémy, Daniel Birnbaum, François Bertucci, David Taïeb, Palma Rocchi

Bibliographic record

VenueOncogene · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsUniversity of British Columbia
FundersAssociation pour la Recherche sur les Tumeurs de la ProstateAgence Nationale de la Recherche
KeywordsProstate cancerCancer researchBiologyMEN1Hsp27Gene silencingSuppressorCarcinogenesisCancerHeat shock proteinHsp70GeneMultiple endocrine neoplasiaGenetics

Abstract

fetched live from OpenAlex

Disease progression and therapeutic resistance of prostate cancer (PC) are linked to multiple molecular events that promote survival and plasticity. We previously showed that heat shock protein 27 (HSP27) acted as a driver of castration-resistant phenotype (CRPC) and developed an oligonucleotides antisense (ASO) against HSP27 with evidence of anti-cancer activity in men with CRPC. Here, we show that the tumor suppressor Menin (MEN1) is highly regulated by HSP27. Menin is overexpressed in high-grade PC and CRPC. High MEN1 mRNA expression is associated with decreased biochemical relapse-free and overall survival. Silencing Menin with ASO technology inhibits CRPC cell proliferation, tumor growth, and restores chemotherapeutic sensitivity. ChIP-seq analysis revealed differential DNA binding sites of Menin in various prostatic cells, suggesting a switch from tumor suppressor to oncogenic functions in CRPC. These data support the evaluation of ASO against Menin for CRPC.

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 score0.012
Threshold uncertainty score0.389

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 head0.014
GPT teacher head0.301
Teacher spread0.287 · 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

Citations30
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueOncogeneSame topicHeat shock proteins researchFrench-language works237,207