Suppression of relaxin receptor RXFP1 decreases prostate cancer growth and metastasis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Relaxin (RLN) is a small peptide hormone expressed in several cancers of reproductive and endocrine organs. Increased expression of RLN in prostate cancer correlates with aggressive cancer. RLN G-protein-coupled receptor (RLN family peptide receptor 1, RXFP1) is expressed in both androgen receptor (AR)-positive and -negative prostate cancers as well as in prostate cancer cell lines. RLN behaves as a cell growth factor and increases invasiveness and proliferation of cancer cells in vitro and in vivo. The objective of this study is to determine whether downregulation of RXFP1 expression using small interfering RNA (siRNA) reduces cancer growth and metastasis in a xenograft model of prostate cancer. We used two well-characterized prostate adenocarcinoma cell lines, AR-positive LNCaP cells and AR-negative PC3 cells. The tumors were established in nude male mice by s.c. injections. Intratumoral injections of siRNAs loaded on biodegradable chitosan nanoparticles led to a downregulation of RXFP1 receptor expression and a dramatic reduction in tumor growth. In LNCaP tumors, the siRNA treatment led to an extensive necrosis. In PC3 xenografts treated with siRNA against RXFP1, the smaller tumor size was associated with the decreased cell proliferation and increased apoptosis. The downregulation of RXFP1 resulted in significant decrease in metastasis rate in PC3 tumors. Global transcriptional profiling of PC3 cells treated with RXFP1 siRNA revealed genes with significantly altered expression profiles previously shown to promote tumorigenesis, including the downregulation of MCAM, MUC1, ANGPTL4, GPI, and TSPAN8. Thus, the suppression of RLN/RXFP1 may have potential therapeutic benefits in prostate cancer.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it