Identification of a novel fusion transcript between human relaxin-1 (RLN1) and human relaxin-2 (RLN2) in prostate cancer
Bibliographic record
Abstract
Simultaneous expression of highly homologous RLN1 and RLN2 genes in prostate impairs their accurate delineation. We used PacBio SMRT sequencing and RNA-Seq in LNCaP cells in order to dissect the expression of RLN1 and RLN2 variants. We identified a novel fusion transcript comprising the RLN1 and RLN2 genes and found evidence of its expression in the normal and prostate cancer tissues. The RLN1-RLN2 fusion putatively encodes RLN2 isoform with the deleted secretory signal peptide. The identification of the fusion transcript provided information to determine unique RLN1-RLN2 fusion and RLN1 regions. The RLN1-RLN2 fusion was co-expressed with RLN1 in LNCaP cells, but the two gene products were inversely regulated by androgens. We showed that RLN1 is underrepresented in common PCa cell lines in comparison to normal and PCa tissue. The current study brings a highly relevant update to the relaxin field, and will encourage further studies of RLN1 and RLN2 in PCa and broader.
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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) | 0.001 | 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".