Differential Expression of Endocannabinoid Receptors in Lesional and Non-Lesional Skin of Psoriasis Patients: Insights Into Pathogenesis and Potential Therapeutic Targets
Bibliographic record
Abstract
BACKGROUND: Psoriasis is a chronic, immune-mediated inflammatory skin disease with a complex etiology involving genetics, environmental triggers, and immune dysregulation. Research suggests that the endocannabinoid system (ECS) is involved in inflammation and skin homeostasis, prompting interest in its involvement in the pathogenesis of psoriasis. OBJECTIVES: This study was designed to investigate the expression of cannabinoid receptors and signaling channels in psoriatic-affected tissue (lesional), unaffected tissue (non-lesional), and healthy control subjects. METHODS: Data were extracted using bulk RNA sequencing data from the Gene Expression Omnibus public database. Differential gene expression analysis was performed to determine changes in cannabinoid receptor expression between psoriatic lesional skin, non-lesional skin, and healthy controls. RESULTS: We found that in psoriatic lesional skin, GPR12, PPARG, TRPV4, PPARA, and HTR1A were significantly downregulated, while CNR2, TRPA1, TRPV3, PPARD, GPR18, ADORA2A, HTR3B, and HTR3A were notably upregulated compared to healthy controls. In addition, TRPV4, PPARG, PPARA, and GPR12 were markedly downregulated in psoriatic lesional skin compared to non-lesional skin, while PPARD, HTR3A, HTR3B, GPR18, TRPV3, TRPA1, CNR2, and ADORA2A showed significant upregulation. There were no significantly upregulated or downregulated endocannabinoid genes in the non-lesional to healthy control analysis. CONCLUSIONS: These findings provide new insights into the role of the ECS in psoriasis pathogenesis and highlight potential targets for further research or novel therapeutic interventions.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".