FGF12 Enhances Prostate Cancer Cell Survival via the YB1-lncRNA Axis
Bibliographic record
Abstract
Treatment-induced neuroendocrine prostate cancer (t-NEPC) is a highly aggressive and therapy-resistant subtype of prostate cancer characterized by lineage plasticity and poor response to standard chemotherapy and androgen deprivation therapy. Although transcriptional mechanisms driving t-NEPC have been extensively studied, the contribution of post-transcriptional regulation remains less defined. Here, we report fibroblast growth factor 12 (FGF12) as a critical post-transcriptional regulator of t-NEPC progression. Transcriptomic analyses of patient biopsies, patient-derived xenografts, and prostate cancer cell models consistently demonstrated elevated FGF12 expression in t-NEPC, which was further validated by immunohistochemistry in archival specimens. Functional assays revealed that FGF12 expression conferred survival of cancer cells to chemotherapeutic agents, including etoposide and camptothecin. Integrative RNA sequencing and affinity purification-mass spectrometry showed that FGF12 mediates these functions mainly through interaction with the RNA-binding protein YB1, leading to stabilization of oncogenic long noncoding RNAs, including NEAT1 and MALAT1, whereas RNA silencing of YB1 abrogated the ability of FGF12 to upregulate these transcripts. Collectively, these findings uncover a previously unrecognized FGF12-YB1-lncRNA signaling axis that drives t-NEPC progression. Targeting this pathway may provide new therapeutic opportunities for patients with this aggressive disease.
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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 | 0.000 | 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".