Evaluating the Impact of Febuxostat on 5-Aminolevulinic Acid-Based Photodynamic Therapy in Bladder Cancer Cell Lines
Bibliographic record
Abstract
BACKGROUND: Bladder cancer (BC) is a prevalent malignancy worldwide, with significant challenges in recurrence management following conventional treatments. This study investigates the potential of Febuxostat, an established xanthine oxidase inhibitor, to enhance the efficacy of photodynamic therapy (PDT) utilizing 5-aminolevulinic acid (5-ALA) in bladder cancer cell lines T24 and 5637. METHODS: In this in vitro study, we evaluated the effect of 5-ALA, Febuxostat and 5-ALA- Febuxostat combination therapy in T24 and 5637 cell lines as representatives of human bladder cancer. The assessment includes scratch-wound assay, colony formation assay, ROS measurement, flow cytometric analysis of apoptosis and DNA cell cycle, real-time PCR (BAX/BCL2, E-cadherin, N-cadherin, HIF1α and VEGFC genes). RESULTS: Our findings demonstrate that co-administration of Febuxostat significantly increases ROS output compared to ALA treatment alone (p-value ≤ 0.05), leading to enhanced cytotoxicity and apoptosis. Flow cytometric analyses revealed elevated apoptosis rates in combination treatment groups, and cell cycle assessments indicated a preferential sub-G1 phase arrest associated with the enhanced apoptotic response (P Value = 0.03). Additionally, gene expression profiling showed alterations in key apoptotic markers (BAX/BCL2), with an upregulation of pro-apoptotic genes and a downregulation of anti-apoptotic factors and increase E-Cadherin/N-Cadherin in response to Febuxostat-enhanced PDT (P Value>0.05). CONCLUSION: These results indicate that Febuxostat effectively potentiates the photodynamic effects of ALA in bladder cancer cells, promising a novel therapeutic strategy that warrants further exploration.
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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".