RESUMEN
Background: Psoriasis, a chronic inflammatory disorder with an unknown cause, significantly impacts the physical and psychological well-being of patients. However, current biomarkers related to psoriasis lack clinical specificity, sensitivity, and predictive ability. Methods: In this study, we collected skin lesion tissues from 20 psoriasis patients and 20 normal skin samples. Additionally, we obtained four datasets from the GEO database, which included human psoriasis and healthy specimens. We utilized SVM-RFE analysis and the LASSO regression model to identify potential biomarkers. Furthermore, we examined the composition of immune cell types in psoriasis and their correlation with specific genes. Results: Our investigation revealed 57 differentially expressed genes (DEGs), and we identified significantly enriched pathways through KEGG pathway analysis. The results of machine learning and WGCNA suggested that LCE3D and SPRR1B could potentially be used as marker genes for diagnosing psoriasis. RT-PCR and immunohistochemical detection confirmed the abnormally high expression of the SPRR1B gene in psoriasis. Analysis of immune cell infiltration revealed a strong positive correlation between SPRR1B and Macrophages M0 and T cells follicular helper, while showing the strongest negative correlation with resting Mast cells. In addition, we found that silencing SPRR1B in IFN-γ-treated HaCat cells could significantly reduce the increase in IL-17, IL-22, KRT6, and KRT16 caused by IFN-γ. Conclusion: These findings suggest that SPRR1B may have a significant role in the pathogenesis of psoriasis and could be employed as a novel immunomarker for its development.
