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Record W3029265163 · doi:10.3389/fcell.2020.00345

Ran GTPase: A Key Player in Tumor Progression and Metastasis

2020· review· en· W3029265163 on OpenAlexafffund
Zied Boudhraa, Eurı́dice Carmona, Diane Provencher, Anne‐Marie Mes‐Masson

Bibliographic record

VenueFrontiers in Cell and Developmental Biology · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNuclear Structure and Function
Canadian institutionsUniversité de MontréalCentre Hospitalier de l’Université de MontréalMontreal Heart Institute
FundersCanadian Institutes of Health ResearchMitacs
KeywordsRanRas superfamilyGTPaseCell biologyBiologyMetastasisSmall GTPaseMicrotubule polymerizationMitosisCytoplasmCancer cellMicrotubuleSignal transductionCancer researchCancerGTP'GeneticsBiochemistryTubulinEnzyme

Abstract

fetched live from OpenAlex

Ran (Ras-related nuclear protein) GTPase is a member of the Ras superfamily. Like all the GTPases, Ran cycles between an active (GTP-bound) and inactive (GDP-bound) state. However, Ran lacks the CAAX motif at its C-terminus, a feature of other small GTPases that ensures a plasma membrane localization, and largely traffics between the nucleus and the cytoplasm. Ran regulates nucleo-cytoplasmic transport of molecules through the nuclear pore complex and controls cell cycle progression through the regulation of microtubule polymerization and mitotic spindle formation. The disruption of Ran expression has been linked to cancer at different levels - from cancer initiation to metastasis. In the present review, we discuss the contribution of Ran in the acquisition of three hallmarks of cancer, namely, proliferative signaling, resistance to apoptosis, and invasion/metastasis, and highlight its prognostic value in cancer patients. In addition, we discuss the use of this GTPase as a therapeutic target in cancer.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.011
GPT teacher head0.259
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations124
Published2020
Admission routes2
Has abstractyes

Explore more

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