Vitamin D in Melanoma: Potential Role of Cytochrome P450 Enzymes
Bibliographic record
Abstract
Vitamin D is a promising anticancer agent for the prevention and treatment of several cancers, including melanoma. Low 25-hydroxyvitamin D levels, a routinely used marker for vitamin D, have been suggested as one of the factors in the development and progression of melanoma. The parent vitamin D needs activation by cytochrome P450 (CYP) enzymes to exert its actions via the vitamin D receptor (VDR). This review discusses the role of vitamin D in melanoma and how CYP-mediated metabolism can potentially affect the actions of vitamin D. Through interacting with the retinoid X receptor, VDR signaling leads to anti-inflammatory, antioxidative, and anticancer actions. Calcitriol, the dihydroxylated form of vitamin D3, is the most active and potent ligand of VDR. CYP27A1, CYP27B1, and CYP2R1 are involved in the activation of vitamin D, whereas CYP24A1 and CYP3A4 are responsible for the degradation of the active vitamin D. CYP24A1, the primary catabolic enzyme of calcitriol, is overexpressed in melanoma tissues and cells. Several drug classes and natural health products can modulate vitamin D-related CYP enzymes and eventually cause lower levels of vitamin D and its active metabolites in tissues. Although the role of vitamin D in the development of melanoma is yet to be fully elucidated, it has been proposed that melanoma prevention may be significantly aided by increased vitamin D signaling. Furthermore, selective targeting of the catabolic enzymes responsible for vitamin D degradation could be a plausible strategy in melanoma therapy. Vitamin D signaling can be improved by utilizing dietary supplements or by modulating CYP metabolism. A positive association exists between the intake of vitamin D supplements and improved prognosis for melanoma patients. Further investigation is required to determine the function of vitamin D supplementation and specific enzyme targeting in the prevention of melanoma.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".