Epigenetic mechanisms in anti‐cancer actions of bioactive food components – the implications in cancer prevention
Bibliographic record
Abstract
The hallmarks of carcinogenesis are aberrations in gene expression and protein function caused by both genetic and epigenetic modifications. Epigenetics refers to the changes in gene expression programming that alter the phenotype in the absence of a change in DNA sequence. Epigenetic modifications, which include amongst others DNA methylation, covalent modifications of histone tails and regulation by non-coding RNAs, play a significant role in normal development and genome stability. The changes are dynamic and serve as an adaptation mechanism to a wide variety of environmental and social factors including diet. A number of studies have provided evidence that some natural bioactive compounds found in food and herbs can modulate gene expression by targeting different elements of the epigenetic machinery. Nutrients that are components of one-carbon metabolism, such as folate, riboflavin, pyridoxine, cobalamin, choline, betaine and methionine, affect DNA methylation by regulating the levels of S-adenosyl-L-methionine, a methyl group donor, and S-adenosyl-L-homocysteine, which is an inhibitor of enzymes catalyzing the DNA methylation reaction. Other natural compounds target histone modifications and levels of non-coding RNAs such as vitamin D, which recruits histone acetylases, or resveratrol, which activates the deacetylase sirtuin and regulates oncogenic and tumour suppressor micro-RNAs. As epigenetic abnormalities have been shown to be both causative and contributing factors in different health conditions including cancer, natural compounds that are direct or indirect regulators of the epigenome constitute an excellent approach in cancer prevention and potentially in anti-cancer therapy.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".