Up-regulation of 14-3-3ζ in Lung Cancer and Its Implication as Prognostic and Therapeutic Target
Bibliographic record
Abstract
A functional genomic approach integrating microarray and proteomic analyses done in our laboratory has identified 14-3-3zeta as a putative oncogene whose activation was common and driven by its genomic amplification in lung adenocarcinomas. 14-3-3zeta is believed to function in cell signaling, cycle control, and apoptotic death. Following our initial finding, here, we analyzed its expression in lung tumor tissues obtained from 205 patients with various histologic and stage non-small cell lung cancers (NSCLC) using immunohistochemistry and then explored the effects of specific suppression of the gene in vitro and in a xenograft model using small interfering RNA. The increased 14-3-3zeta expression was positively correlated with a more advanced pathologic stage and grade of NSCLCs (P = 0.001 and P = 0.006, respectively) and was associated with overall and cancer-specific survival rates of the patients (P = 0.022 and P = 0.018, respectively). Down-regulation of 14-3-3zeta in lung cancer cells led to a dose-dependent increased sensitivity to cisplatin-induced cell death, which was associated with the inhibition of cell proliferation and increased G2-M arrest and apoptosis. The result was further confirmed in the animal model, which showed that the A549 lung cancer cells with reduced 14-3-3zeta grew significantly slower than the wild-type A549 cells after cisplatin treatment (P = 0.008). Our results suggest that 14-3-3zeta is a potential target for developing a prognostic biomarker and therapeutics that can enhance the antitumor activity of cisplatin for NSCLC.
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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 | 0.000 | 0.000 |
| 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".