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Record W1941095565 · doi:10.1002/path.4099

Oncogene‐dependent control of <scp>miRNA</scp> biogenesis and metastatic progression in a model of undifferentiated pleomorphic sarcoma

2012· article· en· W1941095565 on OpenAlexaff
Jeffrey K. Mito, Hooney Min, Yan Ma, Jessica E. Carter, Brian E. Brigman, Leslie G. Dodd, David Dankort, Martin McMahon, David G. Kirsch

Bibliographic record

VenueThe Journal of Pathology · 2012
Typearticle
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsMcGill University
FundersNational Institute of General Medical SciencesNational Cancer InstituteNational Institute of Allergy and Infectious DiseasesAlexander and Margaret Stewart Trust
KeywordsDicerBiologyCancer researchOncogenemicroRNAMetastasisKRASCancerGeneticsGeneCell cycleRNA interferenceRNA

Abstract

fetched live from OpenAlex

Undifferentiated pleomorphic sarcoma (UPS) is one of the most common soft tissue malignancies. Patients with large, high-grade sarcomas often develop fatal lung metastases. Understanding the mechanisms underlying sarcoma metastasis is needed to improve treatment of these patients. Micro-RNAs (miRNAs) are a class of small RNAs that post-transcriptionally regulate gene expression. Global alterations in miRNAs are frequently observed in a number of disease states including cancer. The signalling pathways that regulate miRNA biogenesis are beginning to emerge. To test the relevance of specific oncogenic mutations in miRNA biogenesis in sarcoma, we used primary soft tissue sarcomas expressing either Braf(V600E) or Kras(G12D). We found that Braf(V600E) mutant tumours, which have increased MAPK signalling, have higher levels of mature miRNAs and enhanced miRNA processing. To investigate the relevance of oncogene-dependent alterations in miRNA biogenesis, we introduced conditional mutations in Dicer and showed that Dicer haploinsufficiency promotes the development of distant metastases in an oncogene-dependent manner. These results demonstrate that a specific oncogenic mutation can cooperate with mutation in Dicer to promote tumour progression in vivo.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.041
GPT teacher head0.307
Teacher spread0.265 · 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 designObservational
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

Citations40
Published2012
Admission routes1
Has abstractyes

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