The Non‐Coding Transcriptome as a Dynamic Regulator of Prostate Cancer Metastasis
Bibliographic record
Abstract
While localized prostate cancer (PCa) is readily treated using surgery or radiotherapy, there are no curative therapeutic options available for metastatic PCa. Long non‐coding RNAs (lncRNAs) play critical roles in cancer, either as oncogenes or tumor suppressors, although the mechanisms by which they regulate tumor development and progression are still poorly understood. We utilized our unique collection of patient‐derived prostate tumor tissue xenograft models to identify lncRNAs differentially expressed in metastatic versus non‐metastatic xenografts. PCAT18 is a previously uncharacterized lncRNA specifically expressed in the prostate compared to 11 other normal tissues and up‐regulated in PCa compared to 15 other neoplasms. PCAT18 silencing significantly inhibited PCa cell proliferation, migration and invasion, and also triggered caspase 3/7 activation, with no effect on non‐neoplastic BPH1 cells. Among the lncRNAs down‐regulated in the metastatic xenograft, we focused on LOC153684, which is classified as an uncharacterized lncRNA. Higher expression of this transcript is associated with longer progression‐free survival after prostatectomy (p=0.03). In keeping with its putative onco‐suppressive role, LOC153684 is significantly down‐regulated in prostate cancer specimens, compared to normal prostatic tissue (p=0.04, average fold change ‐1.99). In addition, expression of this transcript is higher in BPH1 cells (non‐neoplastic) compared to all PCa cell lines tested. Understanding the molecular mechanisms by which these lncRNAs regulate PCa progression may facilitate the development of novel therapeutics.
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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 | 0.001 | 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".