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Record W2015095236 · doi:10.1182/blood-2008-05-158139

Transcriptional repression of microRNA genes by PML-RARA increases expression of key cancer proteins in acute promyelocytic leukemia

2008· article· en· W2015095236 on OpenAlexaff
Anne Saumet, Guillaume Vetter, Manuella Bouttier, Élodie Portales-Casamar, Wyeth W. Wasserman, Thomas Maurin, Bernard Mari, Pascal Barbry, Laurent Vallar, Evelyne Friederich, Khalil Arar, Bruno Cassinat, Christine Chomienne, Charles‐Henri Lecellier

Bibliographic record

VenueBlood · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinoids in leukemia and cellular processes
Canadian institutionsUniversity of British Columbia
FundersCentre National de la Recherche ScientifiqueFonds National de la Recherche LuxembourgInstitut National de la Santé et de la Recherche Médicale
KeywordsAcute promyelocytic leukemiaBiologymicroRNAArsenic trioxideCancer researchPromyelocytic leukemia proteinMyeloid leukemiaRetinoic acidPsychological repressionLeukemiaTretinoinCancerALCAMGeneGene expressionGeneticsApoptosis

Abstract

fetched live from OpenAlex

Micro(mi)RNAs are small noncoding RNAs that orchestrate many key aspects of cell physiology and their deregulation is often linked to distinct diseases including cancer. Here, we studied the contribution of miRNAs in a well-characterized human myeloid leukemia, acute promyelocytic leukemia (APL), targeted by retinoic acid and trioxide arsenic therapy. We identified several miRNAs transcriptionally repressed by the APL-associated PML-RAR oncogene which are released after treatment with all-trans retinoic acid. These coregulated miRNAs were found to control, in a coordinated manner, crucial pathways linked to leukemogenesis, such as HOX proteins and cell adhesion molecules whose expressions are thereby repressed by the chemotherapy. Thus, APL appears linked to transcriptional perturbation of miRNA genes, and clinical protocols able to successfully eradicate cancer cells may do so by restoring miRNA expression. The identification of abnormal miRNA biogenesis in cancer may therefore provide novel biomarkers and therapeutic targets in myeloid leukemias.

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 score0.020
Threshold uncertainty score0.555

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 head0.008
GPT teacher head0.228
Teacher spread0.220 · 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

Citations104
Published2008
Admission routes1
Has abstractyes

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