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Record W3211988828 · doi:10.1038/s41416-021-01616-2

CSNK2 in cancer: pathophysiology and translational applications

2021· review· en· W3211988828 on OpenAlexafffund
Scott Strum, Laszlo Gyenis, David W. Litchfield

Bibliographic record

VenueBritish Journal of Cancer · 2021
Typereview
Languageen
FieldMedicine
TopicCancer-related Molecular Pathways
Canadian institutionsWestern University
FundersCanadian Institutes of Health ResearchAmerican Society of Clinical Oncology
KeywordsCancerCancer researchMetastasisProtein kinase BKinasePTENBiologyMedicineCancer cellTranslational researchCell cyclePI3K/AKT/mTOR pathwayBioinformaticsSignal transductionPathologyInternal medicineCell biology

Abstract

fetched live from OpenAlex

Protein kinase CSNK2 (CK2) is a pleiotropic serine/threonine kinase frequently dysregulated in solid and hematologic malignancies. To consolidate a wide range of biological and clinically oriented data from this unique kinase in cancer, this systematic review summarises existing knowledge from in vitro, in vivo and pre-clinical studies on CSNK2 across 24 different human cancer types. CSNK2 mRNA transcripts, protein levels and activity were found to be routinely upregulated in cancer, and commonly identified phosphotargets included AKT, STAT3, RELA, PTEN and TP53. Phenotypically, it frequently influenced evasion of apoptosis, enhancement of proliferation, cell invasion/metastasis and cell cycle control. Clinically, it held prognostic significance across 14 different cancers, and its inhibition in xenograft experiments resulted in a positive treatment response in 12. In conjunction with commentary on preliminary studies of CSNK2 inhibitors in humans, this review harmonises an extensive body of CSNK2 data in cancer and reinforces its emergence as an attractive target for cancer therapy. Continuing to investigate CSNK2 will be crucial to advancing our understanding of CSNK2 biology, and offers the promise of important new discoveries scientifically and clinically.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score0.921

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.361
Teacher spread0.333 · 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 designOther design
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

Citations50
Published2021
Admission routes2
Has abstractyes

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