Bioinformatic Analysis and Translational Validation of Psoriasis Candidate Genes for Precision Medicine

Abstract

Background: Psoriasis is a recurrent, chronic, inflammation- and immune-mediated skin disease with multiple causative factors. However, the genetic markers associated with recurrence have not yet been fully elucidated. Accordingly, in this study, we aimed to identify markers associated with the recurrence of psoriasis.

Methods: We analyzed differentially expressed genes to determine which targets were associated with the recurrence of psoriasis and used these data to construct a protein-protein interaction network using Cytoscape software. The results were then validated by analysis of core targets using Gene Expression Omnibus (GEO) datasets and clinical samples. Functional enrichment analysis was used to explore the potential mechanisms mediating the recurrence of psoriasis.

Results: We screened out six core targets that played important roles in recurrence of psoriasis, and validation of GEO datasets and clinical samples showed that the expression levels of five core targets were higher in patients with psoriasis than in healthy individuals. Functional enrichment analysis revealed that the cell cycle and oocyte meiosis signaling pathways were involved in the recurrence of psoriasis.

Conclusion: Our findings provided insights into the mechanisms mediating the onset and recurrence of psoriasis.

Keywords: cell cycle; gene expression profiling; pathogenic mechanism; psoriasis; recurrence.

Figure 1

Identification of core targets. (A) Heatmap of differential gene expression between patients with recurrent psoriasis and patients with psoriasis without recurrence (GSE30768). Red rectangles represent high expression, and blue rectangles represent low expression. (B), A PPI network was constructed using the STRING database based on psoriasis recurrence-related targets. Connecting lines represent existing interactions between two proteins. (C) Green points represent targets with major roles in the PPI network, and pink points represent other interacting targets in the PPI network.

Figure 2

GEO database validation. (A) Eleven GEO datasets before merging and normalization. (B) Eleven GEO datasets after merging and normalization. (C) Correlation analysis among CDC20, CCNB1, CCNB2, MAD2L1, ZWINT, and SPC25. (D), Expression of CCNB1, CCNB2, CDC20, MAD2L1, SPC25 and ZWINT in patients with psoriasis compared with that in health individuals. The expression of CDC20, CCNB1, CCNB2, MAD2L1, ZWINT, and SPC25 in patients with psoriasis was significantly higher than that in healthy patients (****P<0.0001, all P values <0.0001).

Figure 3

ELISA results. (A) CCNB1 expression in patients with psoriasis was not significantly higher than that in healthy individuals (P=0.5749). (B) CCNB2 expression in patients with psoriasis was significantly higher than that in healthy individuals (P=0.0105). (C) CDC20 expression in patients with psoriasis was significantly higher than that in healthy individuals (P=0.0004). (D) MAD2L1 expression in patients with psoriasis was significantly higher than that in healthy individuals (P=0.0115). (E) SPC25 expression in patients with psoriasis was significantly higher than that in healthy individuals (P=0.0001). (F) ZWINT expression in patients with psoriasis was significantly higher than that in healthy individuals (P=0.0003) (ns, P>0.05; *, P < 0.05; ***, P < 0.001).

Figure 4

Functional enrichment analysis based on the core targets between patients with recurrent psoriasis and healthy individuals. (A) Bar graph of GO enrichment (longer bars indicate that more genes were enriched, and the increasing depth of red indicates that the differences were more obvious; q-value: the adjusted p value). (B) Bubble graph for KEGG pathways (larger bubbles indicate that more genes were enriched, and the increasing depth of red indicates that the differences were more obvious). (C) Cell cycle signaling pathway. Red points are core targets. (D) Oocyte meiosis signaling pathway. Red points are core targets. (E and F) ANN model construction based on the expression of core targets between psoriasis patients and control persons. The AUC value of the training group is 0.96, and the AUC value of the test group is 0.98, which proves that machine learning can effectively distinguish psoriasis patients from control persons through the protein of the core target.