Frontiers in Medicine (Nov 2020)

Gene Co-expression Networks Identifies Common Hub Genes Between Cutaneous Sarcoidosis and Discoid Lupus Erythematosus

  • Melissa A. Nickles,
  • Kai Huang,
  • Yi-Shin Chang,
  • Maria M. Tsoukas,
  • Nadera J. Sweiss,
  • David L. Perkins,
  • Patricia W. Finn

DOI
https://doi.org/10.3389/fmed.2020.606461
Journal volume & issue
Vol. 7

Abstract

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In this study we analyzed gene co-expression networks of three immune-related skin diseases: cutaneous sarcoidosis (CS), discoid lupus erythematosus (DLE), and psoriasis. We propose that investigation of gene co-expression networks may provide insights into underlying disease mechanisms. Microarray expression data from two cohorts of patients with CS, DLE, or psoriasis skin lesions were analyzed. We applied weighted gene correlation network analysis (WGCNA) to construct gene-gene similarity networks and cluster genes into modules based on similar expression profiles. A module of interest that was preserved between datasets and corresponded with case/control status was identified. This module was related to immune activation, specifically leukocyte activation, and was significantly increased in both CS lesions and DLE lesions compared to their respective controls. Protein-protein interaction (PPI) networks constructed for this module revealed seven common hub genes between CS lesions and DLE lesions: TLR1, ITGAL, TNFRSF1B, CD86, SPI1, BTK, and IL10RA. Common hub genes were highly upregulated in CS lesions and DLE lesions compared to their respective controls in a differential expression analysis. Our results indicate common gene expression patterns in the immune processes of CS and DLE, which may have indications for future therapeutic targets and serve as Th1-mediated disease biomarkers. Additionally, we identified hub genes unique to CS and DLE, which can help differentiate these diseases from one another and may serve as unique therapeutic targets and biomarkers. Notably, we find common gene expression patterns in the immune processes of CS and DLE through utilization of WGCNA.

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