Antiproliferative Mechanisms of the Flavonoids 2,2′-Dihydroxychalcone and Fisetin in Human Prostate Cancer Cells
Bibliographic record
Abstract
We have previously demonstrated the antiproliferative effect of two flavonoids-2,2'-dihydroxychalcone (DHC), a novel synthetic flavonoid, and fisetin, a naturally occurring flavonol-in prostate cancer cells. In this study, we further examine the mechanisms of these compounds on survival and proliferation pathways. DHC and fisetin (1-50 microM) caused a dose-dependent reduction in viability, a concomitant increase in apoptosis in PC3 cells at 72 h, and a decrease in clonogenic survival at 24 h treatment. DHC was considerably more potent than fisetin in these cytotoxicity assays. The mechanism of accelerated cellular senescence was not activated by either compound in PC3 or lymph node carcinoma of the prostate (LNCaP) cells. Gene expression alterations in PC3 and LNCaP cells treated with 15 muM DHC and 25 microM fisetin for 6 to 24 h were determined by oligonucleotide array. Amongst the most highly represented functional categories of genes altered by both compounds was the cell cycle category. In total, 100 cell cycle genes were altered by DHC and fisetin including 27 genes with key functions in G2/M phase that were downregulated by both compounds. Other functional categories altered included chromosome organization, apoptosis, and stress response. These results demonstrate the multiple mechanisms of antitumor activity of DHC and fisetin in prostate cancer cells in vitro.
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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".