The long non-coding RNA PCGEM1 is regulated by androgen receptor activity in vivo
Bibliographic record
Abstract
BACKGROUND: Long non-coding RNAs (lncRNAs) can orchestrate oncogenic or tumor-suppressive functions in cancer biology. Accordingly, PCGEM1 and PRNCR1 were implicated in progression of prostate cancer (PCa) as transcriptional co-regulators of the androgen receptor (AR). However, these findings were recently refuted asserting that neither gene physically binds to the AR. Despite evidence for differing AR transcriptional programs in vivo and in vitro, studies investigating AR-regulation of these genes hitherto have only been conducted in vitro. Here, we further examine the relevance of PCGEM1 and PRNCR1 in PCa, and their relationship with AR signaling, using patient-derived xenograft models. FINDINGS: RNA sequencing of two distinct androgen-dependent models shows PCGEM1 to be considerably expressed, while PRNCR1 showed scant basal expression. PCGEM1 was sharply down-regulated following castration and up-regulated upon AR activation in vivo. However, we found no parallel evidence following AR stimulation in vitro. A PCGEM1-associated gene expression signature (PES) was significantly repressed in response to androgen ablation therapy and in hormone-refractory versus hormone-naïve PCa patients. Furthermore, we found PCGEM1 was uniformly distributed in PCa cell nucleus and cytoplasm which remained unaltered upon AR transcriptional activation. PCGEM1 was up-regulated in primary PCa but not in metastasized PCa. Accordingly, the PES was significantly down-regulated in advanced and higher grade PCa patients from multiple independent studies. CONCLUSION: Our results demonstrate PCGEM1 as an in vivo androgen-regulated transcript with potential nuclear and/or cytoplasmic function(s). Importantly, the clinical expression profile of PCGEM1 implicates it in the early stages of PCa warranting further research in this direction.
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) | 0.002 | 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".