ERG–SOX4 interaction promotes epithelial–mesenchymal transition in prostate cancer cells
Bibliographic record
Abstract
BACKGROUND: Approximately 50% of prostate cancer (PCa) patients in Western countries harbor ERG rearrangement with concurrent ERG overexpression. Overexpression of SOX4 has been shown to play important roles in multiple cancers including PCa. However, the link between these two critical genetic aberrations was unclear. METHODS: Fluorescence in situ hybridization and immunohistochemistry were utilized to detect ERG rearrangement and SOX4 expression. Cellular function was evaluated by transwell, wound healing assays, and cell adhesion assay, respectively. Interaction between ERG and SOX4 was arrayed by co-immunoprecipitation, Real-time PCR, Western blot, and siRNA. Direct binding of ERG to the promoter of SOX4, as well as epigenetic modifications of their promoters after TGF-β1 treatment was monitored by chromatin immunoprecipitation. RESULTS: ERG regulated SOX4 expression via binding to its promoter. Silencing both of them showed duplicate effects on restoring the epithelial characteristics, increasing cellular adhesion and decreasing capacity of cellular migration and invasion. ERG and SOX4 have cooperative roles in TGF-β1-induced epithelial to mesenchymal transition (EMT) process. In addition, TGF-β1 stimulation increased levels of chromatin marks associated with active genes (H3K4me3, H416ac), and decreased levels of repressive marks (H3K27me3) at their promoters. 5-aza and TSA treatment changed expressions of ERG and SOX4. Clinically, overexpression of SOX4 is associated with ERG rearrangement status in PCa and ERG+/SOX4+ defined a subset of PCa patients with poor prognosis. CONCLUSION: Our findings define a key role for ERG/SOX4 in the development of a subset of PCa and highlight the clinical importance of identifying molecularly defined tumor subgroups.
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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) | 0.002 | 0.001 |
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".