Cannabinoids Transmogrify Cancer Metabolic Phenotype via Epigenetic Reprogramming and a Novel CBD Biased G Protein-Coupled Receptor Signaling Platform
Bibliographic record
Abstract
The concept of epigenetic reprogramming predicts long-term functional health effects. This reprogramming can be activated by exogenous or endogenous insults, leading to altered healthy and different disease states. The exogenous or endogenous changes that involve developing a roadmap of epigenetic networking, such as drug components on epigenetic imprinting and restoring epigenome patterns laid down during embryonic development, are paramount to establishing youthful cell type and health. This epigenetic landscape is considered one of the hallmarks of cancer. The initiation and progression of cancer are considered to involve epigenetic abnormalities and genetic alterations. Cancer epigenetics have shown extensive reprogramming of every component of the epigenetic machinery in cancer development, including DNA methylation, histone modifications, nucleosome positioning, non-coding RNAs, and microRNA expression. Endocannabinoids are natural lipid molecules whose levels are regulated by specific biosynthetic and degradative enzymes. They bind to and activate two primary cannabinoid receptors, type 1 (CB1) and type 2 (CB2), and together with their metabolizing enzymes, form the endocannabinoid system. This review focuses on the role of cannabinoid receptors CB1 and CB2 signaling in activating numerous receptor tyrosine kinases and Toll-like receptors in the induction of epigenetic landscape alterations in cancer cells, which might transmogrify cancer metabolism and epigenetic reprogramming to a metastatic phenotype. Strategies applied from conception could represent an innovative epigenetic target for preventing and treating human cancer. Here, we describe novel cannabinoid-biased G protein-coupled receptor signaling platforms (GPCR), highlighting putative future perspectives in this field.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".