Pharmacological Inhibition of Endogenous Hydrogen Sulfide Production Slows Bladder Cancer Progression in an Intravesical Murine Model
Bibliographic record
Abstract
Present bladder cancer therapies have relatively limited therapeutic impact and account for one of the highest lifetime treatment costs per patient. Therefore, there is an urgent need to explore novel and optimized treatment strategies. The present study investigated the effects of inhibiting endogenous hydrogen sulfide (H2S) production on bladder cell viability and in vivo tumor progression. We targeted the H2S-producing enzyme, cystathionine γ-lyase, in 5637 cells using propargylglycine (H2S inhibitor) and performed cytofluorimetric analysis to evaluate cell viability. We then tested the efficacy of propargylglycine alone or in combination with gemcitabine (conventional chemotherapy) in an intravesical murine model of bladder cancer. Magnetic resonance imaging and immunohistochemical staining for cell proliferation, apoptosis, immune-cell infiltration, and neovascularization were performed to evaluate tumor response. Compared to control conditions or cohorts, propargylglycine administration significantly attenuated bladder cancer cell viability in vitro (p < 0.0001) and tumor growth (p < 0.002) and invasion in vivo. Furthermore, propargylglycine enhanced the anti-cancer effects of gemcitabine, resulting in tumor regression (p < 0.0001). Moreover, propargylglycine induced cleaved PARP-1-activated apoptosis (p < 0.05), as well as intratumoral CD8+ T cell (p < 0.05) and F4/80+ macrophage (p < 0.002) infiltration. Propargylglycine also reduced intratumoral neovascularization (p < 0.0001) and cell proliferation (p < 0.0002). Importantly, the pro-apoptotic and anti-neovascularization effects of gemcitabine were enhanced by propargylglycine co-administration. Our findings suggest that inhibition of endogenous H2S production can be protective against bladder cancer by enhancing the chemotherapeutic action of gemcitabine and may be a novel pharmacological target and approach for improved bladder cancer diagnosis and treatments in the future.
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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) | 0.001 | 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".