Role of Dietary Antioxidants in p53‐Mediated Cancer Chemoprevention and Tumor Suppression
Bibliographic record
Abstract
Cancer arises through a complex interplay between genetic, behavioral, metabolic, and environmental factors that combined trigger cellular changes that over time promote malignancy. In terms of cancer prevention, behavioral interventions such as diet can promote genetic programs that may facilitate tumor suppression; and one of the key tumor suppressors responsible for initiating such programs is p53. The p53 protein is activated by various cellular events such as DNA damage, hypoxia, heat shock, and overexpression of oncogenes. Due to its role in cell fate decisions after DNA damage, regulatory pathways controlled by p53 help to maintain genome stability and thus “guard the genome” against mutations that cause cancer. Dietary intake of flavonoids, a C15 group of polyphenols, is known to inhibit cancer progression and assist DNA repair through p53‐mediated mechanisms in human cells via their antioxidant activities. For example, quercetin arrests human cervical cancer cell growth by blocking the G2/M phase cell cycle and inducing mitochondrial apoptosis through a p53‐dependent mechanism. Other polyphenols such as resveratrol upregulate p53 expression in several cancer cell lines by promoting p53 stability, which in colon cancer cells results in the activation of p53‐mediated apoptosis. Finally, among vitamins, folic acid seems to play an important role in the chemoprevention of gastric carcinogenesis by enhancing gastric epithelial apoptosis in patients with premalignant lesions by significantly increased expression of p53. In this review, we discuss the role of these and other dietary antioxidants in p53‐mediated cell signaling in relation to cancer chemoprevention and tumor suppression in normal and 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".