14-3-3 zeta as novel molecular target for cancer therapy
Bibliographic record
Abstract
INTRODUCTION: 14-3-3ζ acts as a central hub in signaling networks, which promotes cell proliferation, adhesion and survival and inhibits apoptosis in multiple cancers. Development of inhibitors or agents that interfere with 14-3-3ζ-dependent signaling networks are likely to serve as novel molecular agents for targeted cancer therapy. AREAS COVERED: The role of 14-3-3ζ in cancer and its potential as a novel molecular target for therapy. The involvement of 14-3-3ζ in chemoresistance in multiple cancers provides a rationale for developing novel molecular therapies targeting this protein for more effective cancer management. The keywords used to conduct the literature search for this paper were '14-3-3/14-3-3zeta and cancer', '14-3-3 structure', '14-3-3 inhibitors', '14-3-3 cancer prognosis', '14-3-3 and cancer therapy', 'role/ functions of 14-3-3'. EXPERT OPINION: 14-3-3ζ is a central cellular hub protein regulating multiple signaling pathways involved in cancer development, progression and therapeutic resistance. Thus, 14-3-3ζ may serve as a novel molecular target for cancer therapy. New approaches including synthetic and/or natural inhibitors that interfere with 14-3-3ζ-client interactions need to be developed for effective cancer therapy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".