Network analysis of potential risk genes for psoriasis

Hereditas. 2021 Jun 16;158(1):21. doi: 10.1186/s41065-021-00186-w.

Abstract

Background: Psoriasis is a complex chronic inflammatory skin disease. The aim of this study was to analyze potential risk genes and molecular mechanisms associated with psoriasis.

Methods: GSE54456, GSE114286, and GSE121212 were collected from gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) between psoriasis and controls were screened respectively in three datasets and common DEGs were obtained. The biological role of common DEGs were identified by enrichment analysis. Hub genes were identified using protein-protein interaction (PPI) networks and their risk for psoriasis was evaluated through logistic regression analysis. Moreover, differentially methylated positions (DMPs) between psoriasis and controls were obtained in the GSE115797 dataset. Methylation markers were identified after comparison with the common genes.

Results: A total of 118 common DEGs were identified, which were mainly involved in keratinocyte differentiation and IL-17 signaling pathway. Through PPI network, we identified top 10 degrees as hub genes. Among them, high expression of CXCL9 and SPRR1B may be risk factors for psoriasis. In addition, we selected 10 methylation-modified genes with the higher area under receiver operating characteristic curve (AUC) value as methylation markers. Nomogram showed that TGM6 and S100A9 may be associated with an increased risk of psoriasis.

Conclusion: This suggests that immune and inflammatory responses are active in keratinocytes of psoriatic skin. CXCL9, SPRR1B, TGM6 and S100A9 may be potential targets for the diagnosis and treatment of psoriasis.

Keywords: Differentially expressed genes; Differentially methylated positions; Pathogenesis; Psoriasis.

MeSH terms

  • Case-Control Studies
  • DNA Methylation
  • Databases, Genetic
  • Gene Regulatory Networks*
  • Humans
  • Keratinocytes
  • Protein Interaction Maps
  • Psoriasis / genetics*
  • Signal Transduction / genetics