The Role of Natural Chalcones and Their Derivatives in Targeting Prostate Cancer: Recent Updates
Bibliographic record
Abstract
Prostate cancer (PCa) is the second most prevalent cancer among men and a major cause of cancer-related mortality worldwide. Despite an initial favorable response to hormone-based therapies, many patients ultimately develop an advanced and lethal form of the disease, referred to as castration-resistant PCa (CRPC). CRPC is associated with poor prognosis and a lack of effective curative treatments. As a result, new alternatives or improved therapeutic strategies to combat this life-threatening condition are urgently needed. Chalcones, also referred to as 1,3-diphenyl-2-propen-1-ones, have attracted significant attention because of their potent antitumor properties. Owing to their distinctive chemical structure and diverse biological activities, these compounds are promising candidates for treating various cancers, including PCa. Both naturally occurring and synthetically derived chalcones have demonstrated anticancer potential by modulating key cellular processes, including apoptosis, cell cycle regulation, cell migration, invasion, metastasis and angiogenesis, as well as major signaling pathways, such as PI3K/Akt/mTOR, androgen signaling, and NF-κB. This review aims to outline the recent advances in the therapeutic potential of chalcone derivatives in prostate cancer, with a focus on their molecular targets, mechanisms of action, and translational relevance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".