Abstract
Psoriasis and chronic ulcers not only significantly impair quality of life but also pose a challenge in dermatological treatment. This study aimed to identify new therapeutic targets and biomarkers for psoriasis and chronic ulcers by comparing their gene expression profiles. The gene expression profiles of psoriatic, wound and chronic ulcer patients, as well as healthy controls, were determined via RNA extraction and next-generation sequencing of biopsies. In order to identify biomarkers, functional enrichment, differential expression analysis and machine learning algorithms were implemented. It is worth mentioning that the genes IL17A, TNF, KRT16, MMP9, and CD44 exhibited substantial correlations with the pathogenesis of the conditions being studied. As evidenced by their AUC-ROC values approaching 0.90, machine learning models accurately identified these biomarkers. The differential gene expression was consequently validated via qRT-PCR, which highlighted the increased expression of matrix remodelling enzymes and inflammatory cytokines. Additionally, genes essential for maintaining epidermis integrity and facilitating wound healing exhibited downregulation. These insights into the molecular mechanisms of psoriasis and chronic ulcers pave the way for the development of targeted therapies, offering hope for improved treatment strategies.