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Record W2056895315 · doi:10.1016/j.celrep.2014.07.058

The RNA-Binding Protein DDX1 Promotes Primary MicroRNA Maturation and Inhibits Ovarian Tumor Progression

2014· article· en· W2056895315 on OpenAlexfundno aff
Cecil Han, Yunhua Liu, Guohui Wan, Hyun Jin Choi, Luqing Zhao, Cristina Ivan, Xiaoming He, Anil K. Sood, Xinna Zhang, Xiongbin Lu

Bibliographic record

VenueCell Reports · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Cancer InstituteNational Institutes of HealthUniversity of Alberta
KeywordsDroshaBiologymicroRNAOvarian cancerCancer researchRNA-binding proteinRNA interferenceGene knockdownRNAGeneGeneticsCancer

Abstract

fetched live from OpenAlex

Posttranscriptional maturation is a critical step in microRNA (miRNA) biogenesis that determines mature miRNA levels. In addition to core components (Drosha and DGCR8 [DiGeorge syndrome critical region gene 8]) in the microprocessor, regulatory RNA-binding proteins may confer the specificity for recruiting and processing of individual primary miRNAs (pri-miRNAs). Here, we identify DDX1 as a regulatory protein that promotes the expression of a subset of miRNAs, including five members in the microRNA-200 (miR-200) family and four miRNAs in an eight-miRNA signature of a mesenchymal ovarian cancer subtype. A majority of DDX1-dependent miRNAs are induced after DNA damage. This induction is facilitated by the ataxia telangiectasia mutated (ATM)-mediated phosphorylation of DDX1. Inhibiting DDX1 promotes ovarian tumor growth and metastasis in a syngeneic mouse model. Analysis of The Cancer Genome Atlas (TCGA) reveals that low DDX1 levels are associated with poor clinical outcome in patients with serous ovarian cancer. These findings suggest that DDX1 is a key modulator in miRNA maturation and ovarian tumor suppression.

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 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 score0.030
Threshold uncertainty score0.468

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.005
GPT teacher head0.234
Teacher spread0.229 · 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

Citations105
Published2014
Admission routes1
Has abstractyes

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