CSNK2 in cancer: Pathophysiology and translational applications.
Bibliographic record
Abstract
e15594 Background: Protein kinase CSNK2 (CK2) is a pleiotropic serine/threonine kinase whose expression levels are frequently elevated in solid and hematologic malignancies. CSNK2 has been discovered to hold prognostic and therapeutic significance across multiple cancers and is an excellent target for oncology research. This systematic review summarizes the current knowledge from in vitro and in vivo studies on the biology of this kinase in cancer alongside pre-clinical/clinical investigations from 24 different human cancer types. Methods: PRISMA methodology was used to generate a study protocol and building-block search strategy, from which a total of 796 publications in PubMed were retrieved across 24 human cancers. 245 of these publications met both screening and inclusion criteria. Data was then systematically extracted, including information about CSNK2 subunit mRNA/protein/activity levels, phosphorylation targets, phenotypic changes, in vivo studies, and prognostic/therapeutic data. The data was thereafter summarized and analyzed. Results: Five targets phosphorylated by CSNK2 were identified in at least 4 cancers: AKT, STAT3, RELA, PTEN, and TP53. The most heavily cited was AKT, identified in 15 cancers. Phenotypically, behaviors influenced by CSNK2 that were reported in 11 or more cancers included: evasion of apoptosis, enhancement of proliferation, enhancement of invasion/metastasis, and cell cycle control. Interestingly, these pathways correlated heavily with the most commonly cited CSNK2 targets. From a clinical perspective, CSNK2 held prognostic significance in 17 of the cancers. Additionally, xenograft experiments were found to have been performed in 13 cancers where CSNK2 inhibition resulted in a positive response to treatment. Lastly, early studies have shown promising results through the clinical application of CSNK2-specific inhibitors, with several clinical trials now underway for further assessment. Conclusions: Overall, our analysis supports CSNK2 as an attractive target for cancer therapy and points to specific areas where additional investigation is critical to advance our understanding of CSNK2 biology. The design of targeted therapies by exploiting the pathophysiology of CSNK2 has the potential to generate impactful treatment strategies across a wide range of cancers, promising exciting new discoveries scientifically and clinically.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".