乾癬における銅依存性細胞死のメカニズム解明：バイオインフォマティクスと生体外実験に基づく研究

To explore the mechanism of cuprotosis in psoriasis, screen cuprotosis related genes (PDCRGs) in psoriasis, and provide new targets for precise diagnosis and treatment of psoriasis.

Integrate bioinformatics analysis and experimental validation. Firstly, based on the GEO database (GSE161683, GSE166388, GSE277173), differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) in psoriasis were screened; Identification of differential cuprotosis related genes (PDCRGs) in psoriasis using a self built cuprotosis gene database (1098 CRGs); Screen key PDCRGs through PPI network, machine learning, and ROC analysis. Subsequently, a mouse model of psoriasis induced by imiquimod (IMQ) was constructed, and gene expression, copper ion levels, inflammatory factors, and oxidative stress factors were validated using qPCR, Western blot, immunohistochemistry, fluorescence, and ELISA.

Thirty-four PDCRGs were identified, among which STAT1, DLD, GBP1, CXCL10, PDHB, and LIAS are Hub genes. Machine learning and ROC analysis further identified APOL6, CD274, and LIAS as key diagnostic biomarkers. PDCRGs are significantly enriched in the TCA cycle, copper ion transport, and glucose metabolism pathways. The levels of FDX1 and serum copper ions were increased in the skin lesions of psoriasis mice, accompanied by upregulation of TCA cycle key proteins and PDCRGs expression; Copper overload triggers oxidative stress and inflammation cascade.

APOL6, CD274, and LIAS were screened as cuprotosis markers in psoriasis. Overexpression of these PDCRGs in psoriasis model mice can promote copper ion accumulation and interfere with the TCA cycle, increase oxidative stress and inflammation levels, and ultimately lead to the occurrence of psoriasis. Therefore, targeted intervention of cuprotosis is of great significance for the clinical treatment of psoriasis.