Inhibition of relaxin autocrine signaling confers therapeutic vulnerability in ovarian cancer
Bibliographic record
Abstract
Ovarian cancer (OC) is the most deadly gynecological malignancy, with unmet clinical need for new therapeutic approaches. The relaxin peptide is a pleiotropic hormone with reproductive functions in the ovary. Relaxin induces cell growth in several types of cancer, but the role of relaxin in OC is poorly understood. Here, using cell lines and xenograft models, we demonstrate that relaxin and its associated GPCR RXFP1 form an autocrine signaling loop essential for OC in vivo tumorigenesis, cell proliferation, and viability. We determined that relaxin signaling activates expression of prooncogenic pathways, including RHO, MAPK, Wnt, and Notch. We found that relaxin is detectable in patient-derived OC tumors, ascites, and serum. Further, inflammatory cytokines IL-6 and TNF-α activated transcription of relaxin via recruitment of STAT3 and NF-κB to the proximal promoter, initiating an autocrine feedback loop that potentiated expression. Inhibition of RXFP1 or relaxin increased cisplatin sensitivity of OC cell lines and abrogated in vivo tumor formation. Finally, we demonstrate that a relaxin-neutralizing antibody reduced OC cell viability and sensitized cells to cisplatin. Collectively, these data identify the relaxin/RXFP1 autocrine loop as a therapeutic vulnerability in OC.
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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 | 0.001 |
| 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".