Identification of 14‐3‐3zeta associated protein networks in oral cancer
Bibliographic record
Abstract
Advancements in genomics, proteomics, and bioinformatics have improved our understanding of gene/protein networks involved in intra- and intercellular communication and tumor-host interactions. Using proteomics integrated with bioinformatics, previously we reported overexpression of 14-3-3ζ in premalignant oral lesions and oral squamous cell carcinoma tissues in comparison with normal oral epithelium. 14-3-3ζ emerged as a novel molecular target for therapeutics and a potential prognostic marker in oral squamous cell carcinoma patients. However, the role of 14-3-3ζ in development and progression of oral cancer is not known yet. This study aimed to identify the 14-3-3ζ associated protein networks in oral cancer cell lines using IP-MS/MS and bioinformatics. A total of 287 binding partners of 14-3-3ζ were identified in metastatic (MDA1986) and nonmetastatic (SCC4) oral cancer cell lines including other 14-3-3 isoforms (2%), proteins involved in apoptosis (2%), cytoskeleton (9%), metabolism (16%), and maintenance of redox potential (2%). Our bioinformatics analysis revealed involvement of 14-3-3ζ in protein networks regulating cell cycle, proliferation, apoptosis, cellular trafficking, and endocytosis in oral cancer. In conclusion, our data revealed several novel protein interaction networks involving 14-3-3ζ in oral cancer progression and metastasis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".