Ablation of the oncogenic transcription factor ERG by deubiquitinase inhibition in prostate cancer
Bibliographic record
Abstract
The transcription factor E-twenty-six related gene (ERG), which is overexpressed through gene fusion with the androgen-responsive gene transmembrane protease, serine 2 (TMPRSS2) in ∼40% of prostate tumors, is a key driver of prostate carcinogenesis. Ablation of ERG would disrupt a key oncogenic transcriptional circuit and could be a promising therapeutic strategy for prostate cancer treatment. Here, we show that ubiquitin-specific peptidase 9, X-linked (USP9X), a deubiquitinase enzyme, binds ERG in VCaP prostate cancer cells expressing TMPRSS2-ERG and deubiquitinates ERG in vitro. USP9X knockdown resulted in increased levels of ubiquitinated ERG and was coupled with depletion of ERG. Treatment with the USP9X inhibitor WP1130 resulted in ERG degradation both in vivo and in vitro, impaired the expression of genes enriched in ERG and prostate cancer relevant gene signatures in microarray analyses, and inhibited growth of ERG-positive tumors in three mouse xenograft models. Thus, we identified USP9X as a potential therapeutic target in prostate cancer cells and established WP1130 as a lead compound for the development of ERG-depleting drugs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".