Screening of Skin Lesion-Associated Genes in Patients with Psoriasis by Meta-Integration Analysis

Abstract

Aim: Psoriasis is an inflammatory skin disease, and approximately one quarter of individuals with psoriasis develop painful and debilitating arthritis. As a complex and polygenetic hereditary disease, it is significant to investigate skin lesion-associated genes of psoriasis.

Method: In the present study, a total of 3,047 differentially expressed genes between lesional and nonlesional skin of psoriasis patients were screened based on 4 data sets in GEO DataSets. In the following, network module analyses were performed.

Result: After calculating the correlation coefficients between eigenvalues of each module and disease status (psoriatic lesion and nonlesion), it was observed that the genes in black and green modules showed a significant correlation in each data set. Consequently, 58 significant characteristic genes of black modules and 74 of green modules were chosen for further analysis. The interaction network of the candidate feature genes was constructed based on the BioGRID, HPRD, and BIND databases, which contained 310 nodes. The mutual relationships of 70 genes, including 28 genes in the black module and 42 genes in the green module, were summarized, and 5 drug molecules related to these 70 factors were detected. Mepacrine and camptothecin were indicated as 2 drugs negatively related to psoriasis.

Conclusion: The results suggested a pathogenesis mechanism of psoriasis and indicated novel therapeutic targets for psoriasis.

Keywords: Coexpression network; Lesion; Modular analysis; Psoriasis; Weighted-gene coexpression network analysis.