An integrated bioinformatics approach for unravelling the molecular insights into psoriasis pathology and therapeutics

Psoriasis is a chronic relapsing inflammatory disease of the skin that affects almost 2–3 % population worldwide. The current treatment strategies include palliative therapeutics targeting the inflammatory pathways that do not completely cure the lesioned skin. The molecular cues in the hyper-proliferative and aberrantly differentiated keratinocytes of the psoriatic lesioned skin remain unknown. Through an integrative in-silico approach, we have analyzed human psoriatic skin samples from 3 RNA-Seq and 3 microarray datasets to identify 340 differentially expressed genes (DEGs). Further, these DEGs were analyzed using gene ontology enrichment, KEGG pathways, and protein-protein interaction networks for their role in disease pathology and the identification of hub genes. The expression of the hub genes was validated in a preclinical murine model of psoriasiform dermatitis. Finally, the ten hub genes were assessed for their drugability, which revealed 74 drugs targeting 7 hub genes (CCNA2, TOP2A, BIRC5, RRM2, CDK1, AURKA, and CCNB1) that can be repurposed for psoriasis treatment. This study provides an understanding of psoriasis pathophysiology and suggests key molecular biomarkers as therapeutic targets for effective mitigation of the disease.