Reduced Zeb1 Expression in Prostate Cancer Cells Leads to an Aggressive Partial-EMT Phenotype Associated with Altered Global Methylation Patterns
Bibliographic record
Abstract
Prostate cancer is the most common cancer in American men and the second leading cause of cancer-related death. Most of these deaths are associated with metastasis, a process involving the epithelial-to-mesenchymal (EMT) transition. Furthermore, growing evidence suggests that partial-EMT (p-EMT) may lead to more aggressive disease than complete EMT. In this study, the EMT-inducing transcription factor Zeb1 was knocked down in mesenchymal PC-3 prostate cancer cells (Zeb1KD) and resulting changes in cellular phenotype were assessed using protein and RNA analysis, invasion and migration assays, cell morphology assays, and DNA methylation chip analysis. Inducible knockdown of Zeb1 resulted in a p-EMT phenotype including co-expression of epithelial and mesenchymal markers, a mixed epithelial/mesenchymal morphology, increased invasion and migration, and enhanced expression of p-EMT markers relative to PC-3 mesenchymal controls (p ≤ 0.05). Treatment of Zeb1KD cells with the global de-methylating drug 5-azacytidine (5-aza) mitigated the observed aggressive p-EMT phenotype (p ≤ 0.05). DNA methylation chip analysis revealed 10 potential targets for identifying and/or targeting aggressive p-EMT prostate cancer in the future. These findings provide a framework to enhance prognostic and/or therapeutic options for aggressive prostate cancer in the future by identifying new p-EMT biomarkers to classify patients with aggressive disease who may benefit from 5-aza treatment.
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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 | 0.001 |
| 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".