Physiological Effects of Green-Colored Food-Derived Bioactive Compounds on Cancer
Bibliographic record
Abstract
Green-colored foods, such as broccoli, sprouts, soybean, and green leafy vegetables are considered one of the representative healthy foods for containing various functional ingredients that can combat chronic diseases, including diabetes, obesity, and cancer. Herein, we reviewed the anti-cancer activities and the underlying mechanisms of some important bioactive compounds, such as sulforaphane, catechins, chlorophyll, isoflavone, indole dervatives, and lutein, present in green-colored foods. In vivo and clinical studies suggest that sulforaphane, a sulfur-containing compound found in cruciferous vegetables, can ameliorate prostate and breast cancer symptoms by arresting cell-cycle progression and modulating Ki67 and HDAC expression. A green tea compound, known as epigallocatechin-3-gallate (EGCG), has shown remarkable anti-cancer effects against prostate cancer and lung adenocarcinoma in human trials through its antioxidative defense and immunomodulatory functions. Chlorophyll, a natural pigment found in all green plants, can regulate multiple cancer-related genes, including cyclin D1, CYP1A, CYP1B1, and p53. Epidemiological studies indicate that chlorophyll can substantially reduce aflatoxin level and can mitigate colon cancer in human subjects. Remarkably, the consumption of soy isoflavone has been found to be associated with the lower incidence and mortality of breast and prostate cancers in East Asia and in Canada. In vivo and in vitro data point out that isoflavone has modulatory effects on estrogen and androgen signaling pathways and the expression of MAPK, NfκB, Bcl-2, and PI3K/AKT in different cancer models. Other green food bioactive compounds, such as indole derivatives and lutein, also exhibited suppressing effects in rodent models of lung, liver, stomach, cervical, and prostate cancers. In addition, some micronutrients, such as folate, riboflavin, retinoic acid, and vitamin D3 present in green foods, also showed potential cancer suppressing effects. Taken together, these data suggest potential chemopreventive functions of the bioactive compounds from green-colored foods. This paper could be beneficial for further research on the anti-carcinogenic effects of green-colored food-derived compounds, in order to develop green chemotherapeutics for cancers.
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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 | 0.001 | 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) | 0.002 | 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".