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Record W2341067531 · doi:10.1007/s12079-016-0321-2

CCN6: a modulator of breast cancer progression

2016· review· en· W2341067531 on OpenAlexaff
Andrew Leask

Bibliographic record

VenueJournal of Cell Communication and Signaling · 2016
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnective Tissue Growth Factor Research
Canadian institutionsWestern University
Fundersnot available
KeywordsCTGFMedicineCancer researchBreast cancerMatricellular proteinCancerFibrosisConnective tissueProtein kinase BMetastasisCYR61OncologyPathologySignal transductionInternal medicineExtracellular matrixBiologyGrowth factorReceptor

Abstract

fetched live from OpenAlex

The expression of the CCN family of matricellular proteins is highly dysregulated in connective tissue pathologies such as fibrosis and highly metastatic cancers. Strategies targeting members of this family, especially CCN2, are under development as novel therapeutic approaches to highly metastatic cancers such as pancreatic cancer. In prior reports, the Kleer laboratory and colleagues have linked reduced expression of CCN6 (WISP3) with aggressive breast cancers. Loss of CCN6 was associated with elevated Akt phosphorylation and TAK1 activation. In a recent report, the same group reports that, by modulating Notch signaling, CCN6 can promote the maintenance of an epithelial phenotype and also reduce cancer cell migration and invasion, tumor initiation, and metastasis (Oncotarget in press DOI: 10.18632/oncotarget.7734 ). These results are consistent with the hypothesis that addition of CCN6 peptides may represent a novel, viable therapeutic approach to blocking aggressive breast cancers.

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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.041
GPT teacher head0.389
Teacher spread0.349 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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