Inhibition of Pyruvate Kinase M2 Markedly Reduces Chemoresistance of Advanced Bladder Cancer to Cisplatin
Bibliographic record
Abstract
Chemoresistance to cisplatin is a principal cause of treatment failure and mortality of advanced bladder cancer (BC). The underlying mechanisms remain unclear, which hinders the development of preventive strategies. Recent data indicate that pyruvate kinase M2 (PKM2), a glycolytic enzyme for Warburg effect, is strongly upregulated in BC. This study explores the role of PKM2 in chemoresistance and whether inhibiting PKM2 augments the chemosensitivity to cisplatin and reduces BC growth and progression. We found that Shikonin binds PKM2 and inhibits BC cell survival in a dose-dependent but pyruvate kinase activity-independent manner. Down-regulation of PKM2 by shRNA blunts cellular responses to shikonin but enhances the responses to cisplatin. Shikonin and cisplatin together exhibit significantly greater inhibition of proliferation and apoptosis than when used alone. Induced cisplatin-resistance is strongly associated with PKM2 overexpression, and cisplatin-resistant cells respond sensitively to shikonin. In syngeneic mice, shikonin and cisplatin together, but not as single-agents, markedly reduces BC growth and metastasis. Based on these data, we conclude that PKM2 overexpression is a key mechanism of chemoresistance of advanced BC to cisplatin. Inhibition of PKM2 via RNAi or chemical inhibitors may be a highly effective approach to overcome chemoresistance and improve the outcome of advanced BC.
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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) | 0.001 | 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".