Adiponectin receptor activation inhibits prostate cancer xenograft growth
Bibliographic record
Abstract
Adiponectin is an adipokine originally identified as dysregulated in obesity, with a key role in insulin sensitisation and in maintaining systemic energy balance. However, adiponectin is progressively emerging as having aberrant signalling in multiple disease states, including prostate cancer (PCa). Circulating adiponectin is lower in patients with PCa than in non-malignant disease, and inversely correlates with cancer severity. More severe hypoadiponectinemia is observed in advanced PCa than in organ-confined disease. Given the crossover between adiponectin signalling and several cancer hallmark pathways that influence PCa growth and progression, we hypothesised that targeting dysregulated adiponectin signalling may inhibit tumour growth and progression. We, therefore, aimed to test the efficacy of correcting the hypoadiponectinemia and dysregulated adiponectin signalling observed in PCa, a world-first PCa therapeutic approach, using peptide adiponectin receptor (ADIPOR) agonist ADP355 in mice bearing subcutaneous LNCaP xenografts. We demonstrate significant evidence for PCa growth inhibition by ADP355, which slowed tumour growth and delayed progression of serum PCa biomarker, prostate-specific antigen (PSA), compared to vehicle. ADP355 conferred a significant advantage by increasing time on treatment with a delayed ethical endpoint. mRNA sequencing and protein expression analyses of tumours revealed ADP355 PCa growth inhibition may be through altered cellular energetics, cellular stress and protein synthesis, which may culminate in apoptosis, as evidenced by the increased apoptotic marker in ADP355-treated tumours. Our findings highlight the efficacy of ADP355 in targeting classical adiponectin-associated signalling pathways in vivo and provide insights into the promising future for modulating adiponectin signalling through ADIPOR agonism as a novel anti-tumour treatment modality.
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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) | 0.003 | 0.001 |
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".