Identification of novel IL17-related genes as prognostic and therapeutic biomarkers of psoriasis using comprehensive bioinformatics analysis and machine learning.

Psoriasis is a common chronic skin disorder with a polygenic background. It is widely acknowledged that Th17/IL-17A axis plays a key role in the pathogenesis of psoriasis. However, numerous regulatory genes upstream of the pathway remain undiscovered, creating a knowledge gap in our understanding of the genetic aspects of the Th17/IL-17A axis. In this study, we employed machine learning algorithms to identify three target genes associated with psoriasis: CCR7, IL2RG, and PLEK. The validation of these genes was carried out in specimens from psoriatic patients. In vivo, investigations assessed the relationship between these three genes and IL-17A-related inflammation and their connection to psoriatic phenotypes. To further confirm the significance of the newly discovered gene, PLEK, we performed experiments involving the blockade of its expression. Our bioinformatics analysis revealed three novel genes closely linked to psoriasis: CCR7, IL2RG, and PLEK. These genes exhibited upregulated expression in psoriasis, consistently aligning with the Th17/IL-17A axis. Inhibition of PLEK expression reduced Th17/IL-17A-related inflammation and alleviated psoriatic phenotypes. CCR7, IL2RG, and PLEK show potential as three novel biomarkers for psoriasis, with PLEK being reported for the first time in this context. These genes contribute to pathogenesis by associating with the Th17/IL-17A signaling pathway.