Role of Antioxidant Vitamins and Other Micronutrients on Regulations of Specific Genes and Signaling Pathways in the Prevention and Treatment of Cancer
Bibliographic record
Abstract
Cancer is an escalating global issue, with 19.3 million new cases and 9.9 million deaths in 2020. Therefore, effective approaches to prevent cancer are urgently required. Diet plays a significant role in determining cancer risk. Nutrients and food bioactives influence specific signaling pathways in the body. Recently, there have been significant advances in cancer prevention research through nutrigenomics or with the effects of dietary components on the genome. Google Scholar, PubMed, and Scopus databases were used to search for peer-reviewed articles between 2017 and 2023. Criteria used were vitamins, minerals, tumors, cancer, genes, inflammation, signaling pathways, and nutrigenomics. Among the total of 1857 articles available, the highest relevant 90 articles that specifically discussed signaling pathways and genes on cancer cell lines and human cancer patients were selected and reviewed. Food sources are rich in antioxidant micronutrients, which are effective in activating or regulating signaling pathways involved in pathogenesis and cancer therapy by activating enzymes such as mitogen-activated protein kinase (MAPK), protein kinase C (PKC), and phosphatidylinositol 3-kinase (PI3K). The micronutrients are involved in the regulation of β-catenin (WNT/β-catenin) including mutations in Kras and epidermal growth factor receptor (EGFR) alongside inhibition of the NF-kB pathway. The most common mechanism of cancer prevention by these micronutrients is their antioxidative, anti-inflammation, and anti-apoptosis effects. This review discusses how nutrigenomics is essential and beneficial for developing cancer prevention and treatment approaches.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".