The evolutionarily conserved long non‐coding RNA <i>LINC00261</i> drives neuroendocrine prostate cancer proliferation and metastasis <i>via</i> distinct nuclear and cytoplasmic mechanisms
Bibliographic record
Abstract
Metastatic neuroendocrine prostate cancer (NEPC) is a highly aggressive disease, whose incidence is rising. Long noncoding RNAs (lncRNAs) represent a large family of disease‐ and tissue‐specific transcripts, most of which are still functionally uncharacterized. Thus, we set out to identify the highly conserved lncRNAs that play a central role in NEPC pathogenesis. To this end, we performed transcriptomic analyses of donor‐matched patient‐derived xenograft models (PDXs) with immunohistologic features of prostate adenocarcinoma (AR+/PSA+) or NEPC (AR−/SYN+/CHGA+) and through differential expression analyses identified lncRNAs that were upregulated upon neuroendocrine transdifferentiation. These genes were prioritized for functional assessment based on the level of conservation in vertebrates. Here, LINC00261 emerged as the top gene with over 3229‐fold upregulation in NEPC. Consistently, LINC00261 expression was significantly upregulated in NEPC specimens in multiple patient cohorts. Knockdown of LINC00261 in PC‐3 cells dramatically attenuated its proliferative and metastatic abilities, which are explained by parallel downregulation of CBX2 and FOXA2 through distinct molecular mechanisms. In the cell cytoplasm, LINC00261 binds to and sequesters miR‐8485 from targeting the CBX2 mRNA, while inside the nucleus, LINC00261 functions as a transcriptional scaffold to induce SMAD‐driven expression of the FOXA2 gene. For the first time, these results demonstrate hyperactivation of the LINC00261‐CBX2‐FOXA2 axes in NEPC to drive proliferation and metastasis, and that LINC00261 may be utilized as a therapeutic target and a biomarker for this incurable disease.
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".