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. 2023 Jun 28;25(6):418-426.
doi: 10.22074/cellj.2023.1982769.1191.

Psoriasis Associated Hub Genes Revealed by Weighted Gene Co-Expression Network Analysis

Zeinab Darvish  1 , Saeed Ghanbari  2 , Saeid Afshar  3 , Leili Tapak  4 , Payam Amini  5
Affiliations

Psoriasis Associated Hub Genes Revealed by Weighted Gene Co-Expression Network Analysis

Zeinab Darvish et al. Cell J. .

Abstract

Objective: Psoriasis, an immune-mediated disorder, is a multifactorial disease with unidentified cause(s). This study aimed to discover possible biomarkers of this papulosquamous skin disease.

Materials and methods: The gene chip GSE55201, resulted from an experimental study, including 44 Psoriasis patients and 30 healthy controls was downloaded from GEO and weighted gene co-expression network analysis was utilized to identify hub genes. Key modules were determined using the module eigenvalues. We used biological functions (BFs), cellular components, and molecular functions in the Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes enrichment analysis in the gene metabolic pathway were used for enrichment analysis.

Results: Adjacency matrix was built by using power adjacency function and the power to turn the correlation to adjacency matrix was four with a topology fit index of 0.92. Using the weighted gene co-expression network analysis, 11 modules were identified. The green-yellow module eigenvalues were significantly associated with Psoriasis (Pearson correlation=0.53, P<0.001). Candidate hub genes were determined by their higher connectivity and relationship with module eigenvalue. The genes including SIGLEC8, IL5RA, CCR3, RNASE2, CPA3, GATA2, c-KIT, and PRSS33 were recorded as the hub genes.

Conclusion: We can conclude that SIGLEC8, IL5RA, CCR3, RNASE2, CPA3, GATA2, c-KIT, and PRSS33 have an important role in the immune response regulation and they could be considered as a potential diagnostic biomarker and therapeutic target for Psoriasis.

Keywords: Gene; Gene Modules; Gene Network; Psoriasis.

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Figures

Fig.1
Fig.1
Module hierarchical clustering tree to detect clusters (horizontal axes) of genes based on the distance between the genes (vertical axes). Dynamic tree cut shows module divided based on clustering output; merged dynamic indicates module divided according to similarity of the module. Based on the figure, merging is not necessary.
Fig.2
Fig.2
Venn diagram to identify the key candidate genes in the green-yellow module based on module eigengenes (ME) and connectivity.
Fig.3
Fig.3
Venn diagram of hub genes in green-yellow module based on maximal clique centrality (MCC), maximum neighborhood component (MNC), and node connect degree (degree).
Fig.4
Fig.4
The resulting network in the green-yellow module for genes with relative connectivity more than 0.60.
See this image and copyright information in PMC

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