Androgen receptor–regulated lncRNA PRCAT71 promotes AR signaling through the interaction with KHSRP in prostate cancer
Bibliographic record
Abstract
Mounting evidence indicates that long noncoding RNAs (lncRNAs) play vital roles in tumorigenesis and progression of cancers. However, the functions and regulatory mechanisms of lncRNAs in prostate cancer (PCa) are still largely unknown. In this study, we found an lncRNA, PCa-associated transcript 71 (PRCAT71), highly expressed in metastatic and primary PCa compared to benign prostate tissues. Silencing PRCAT71 inhibited cancerous properties of PCa cells and androgen receptor (AR) signaling. Mechanistically, PRCAT71 acts as a scaffold to recruit K homology (KH)-type splicing regulatory protein (KHSRP) to AR messenger RNA (mRNA) and stabilize AR mRNA, leading to activated AR signaling. KHSRP plays a critical role in PCa progression. PRCAT71 is transcriptionally regulated by AR-driven enhancers, forming a positive regulatory loop between AR and PRCAT71 in PCa. Our study demonstrates a coordinated regulation of AR mRNA by lncRNA PRCAT71 and RNA binding protein KHSRP and provides insight that the PRCAT71-KHSRP-AR axis is a promising therapeutic target for treating PCa.
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 | 0.001 |
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".