Decoding Psoriasis: Integrated Bioinformatics Approach to Understand Hub Genes and Involved Pathways

Author(s): Saumya Choudhary, Dibyabhaba Pradhan*, Noor S. Khan, Harpreet Singh, George Thomas, Arun K. Jain*

Journal Name: Current Pharmaceutical Design

Volume 26 , Issue 29 , 2020


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Abstract:

Background: Psoriasis is a chronic immune mediated skin disorder with global prevalence of 0.2- 11.4%. Despite rare mortality, the severity of the disease could be understood by the accompanying comorbidities, that has even led to psychological problems among several patients. The cause and the disease mechanism still remain elusive.

Objective: To identify potential therapeutic targets and affecting pathways for better insight of the disease pathogenesis.

Method: The gene expression profile GSE13355 and GSE14905 were retrieved from NCBI, Gene Expression Omnibus database. The GEO profiles were integrated and the DEGs of lesional and non-lesional psoriasis skin were identified using the affy package in R software. The Kyoto Encyclopaedia of Genes and Genomes pathways of the DEGs were analyzed using clusterProfiler. Cytoscape, V3.7.1 was utilized to construct protein interaction network and analyze the interactome map of candidate proteins encoded in DEGs. Functionally relevant clusters were detected through Cytohubba and MCODE.

Results: A total of 1013 genes were differentially expressed in lesional skin of which 557 were upregulated and 456 were downregulated. Seven dysregulated genes were extracted in non-lesional skin. The disease gene network of these DEGs revealed 75 newly identified differentially expressed gene that might have a role in development and progression of the disease. GO analysis revealed keratinocyte differentiation and positive regulation of cytokine production to be the most enriched biological process and molecular function. Cytokines -cytokine receptor was the most enriched pathways. Among 1013 identified DEGs in lesional group, 36 DEGs were found to have altered genetic signature including IL1B and STAT3 which are also reported as hub genes. CCNB1, CCNA2, CDK1, IL1B, CXCL8, MKI 67, ESR1, UBE2C, STAT1 and STAT3 were top 10 hub gene.

Conclusion: The hub genes, genomic altered DEGs and other newly identified differentially dysregulated genes would improve our understanding of psoriasis pathogenesis, moreover, the hub genes could be explored as potential therapeutic targets for psoriasis.

Keywords: Psoriasis, pathogenesis, hub gene, network analysis, bioinformatic approaches, differentially expressed genes.

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Article Details

VOLUME: 26
ISSUE: 29
Year: 2020
Published on: 03 September, 2020
Page: [3619 - 3630]
Pages: 12
DOI: 10.2174/1381612826666200311130133
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