Abstract
Psoriasis is a chronic inflammatory skin disorder driven by immune dysregulation and excessive cell death. Pyroptosis, a form of inflammatory programmed cell death, has not been extensively studied in the context of psoriasis despite its importance in inflammation. In this study, we systematically analyzed the expression of pyroptosis-related genes (PRGs) in psoriasis to identify critical players involved in disease progression. Using bioinformatics tools and publicly available datasets, we constructed a risk score model based on machine learning algorithms, which identified several key hub genes including CASP1, CASP5, AIM2, GZMB, GZMA, IL1B, and NOD2. The generated risk score model demonstrated robust performance in external validation datasets, showing strong predictive power for psoriasis severity and immune infiltration. High-risk patients exhibited increased inflammatory cell infiltration and worsening clinical symptoms, which was consistent with the model's ability to predict immune response dynamics in psoriatic lesions. To further validate our findings, we analyzed single-cell RNA sequencing data and demonstrated that the risk score was highly correlated with immune cell composition, particularly DCs, T cells, and mast cells, indicating that patients with higher risk scores have more severe disease and stronger immune infiltration. Additionally, we targeted CASP1 and CASP5 using CRISPR-Cas9 delivery via lipid nanoparticles (LNPs) to selectively knock out these genes in keratinocytes, resulting in significant therapeutic effects in the IMQ-induced psoriasis mouse model. Our findings provide comprehensive insights into the role of pyroptosis in psoriasis and propose a novel gene editing strategy for alleviating the disease.