Cannabidiol (CBD) and Colorectal Tumorigenesis: Potential Dual Modulatory Roles via the Serotonergic Pathway
Bibliographic record
Abstract
The 2018 Farm Bill legalized hemp-derived cannabidiol (CBD) products containing less than 0.3% tetrahydrocannabinol (THC) in the United States. This legislative shift catalyzed both public and scientific interest in CBD's potential health benefits. However, the rapid expansion of the CBD market has considerably outpaced rigorous scientific research, leaving many health claims largely unsubstantiated. While preclinical studies suggest that CBD may exert antitumorigenic effects in colorectal cancer (CRC) by modulating cell proliferation, apoptosis, and inflammation, clinical evidence supporting these effects remains limited. This review critically examines the current evidence on the role of CBD in colorectal tumorigenesis, with particular attention to its molecular mechanisms and interactions with the serotonergic system-a signaling pathway implicated in the development of CRC and possessing potential dual anti- and pro-tumorigenic properties. By influencing the serotonergic system, CBD may confer both protective and potentially deleterious effects during CRC development. This review underscores the need for further research to elucidate the complex mechanisms of CBD in colorectal tumorigenesis and to evaluate its therapeutic potential in clinical settings. Understanding these interactions could pave the way for novel prevention and treatment strategies, optimizing the anticancer efficacy of CBD while mitigating unintended risks.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".