Knockdown of Ki-67 by Dicer-Substrate Small Interfering RNA Sensitizes Bladder Cancer Cells to Curcumin-Induced Tumor Inhibition
Bibliographic record
Abstract
Transitional cell carcinoma (TCC) of the urinary bladder is the most common cancer of the urinary tract. Most of the TCC cases are of the superficial type and are treated with transurethral resection (TUR). However, the recurrence rate is high and the current treatments have the drawback of inducing strong systemic toxicity or cause painful cystitis. Therefore, it would be of therapeutic value to develop novel concepts and identify novel drugs for the treatment of bladder cancer. Ki-67 is a large nucleolar phosphoprotein whose expression is tightly linked to cell proliferation, and curcumin, a phytochemical derived from the rhizome Curcuma longa, has been shown to possess powerful anticancer properties. In this study, we evaluated the combined efficacy of curcumin and a siRNA against Ki-67 mRNA (Ki-67-7) in rat (AY-27) and human (T-24) bladder cancer cells. The anticancer effects were assessed by the determination of cell viability, apoptosis and cell cycle analysis. Ki-67-7 (10 nM) and curcumin (10 µM), when treated independently, were moderately effective. However, in their combined presence, proliferation of bladder cancer cells was profoundly (>85%) inhibited; the rate of apoptosis in the combined presence of curcumin and Ki-67-7 (36%) was greater than that due to Ki-67-7 (14%) or curcumin (13%) alone. A similar synergy between curcumin and Ki-67-7 in inducing cell cycle arrest was also observed. Western blot analysis suggested that pretreatment with Ki-67-7 sensitized bladder cancer cells to curcumin-mediated apoptosis and cell cycle arrest by p53- and p21-independent mechanisms. These data suggest that a combination of anti-Ki-67 siRNA and curcumin could be a viable treatment against the proliferation of bladder cancer cells.
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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".