Common genetic aspects between COVID-19 and sarcoidosis: A network-based approach using gene expression data

Abstract

The pandemic situation of novel coronavirus disease 2019 (COVID-19) is a global threat on our current planet, with its rapid spread and high mortality rate. Sarcoidosis patients are at high risk to COVID-19 severity for having lung injuries as well as treating with immunosuppressive agents. So, physicians are in dilemma whether they should use immunosuppressive agents or not for the patients with sarcoidosis history and COVID-19 infection. Therefore, common factors should be identified to provide effective treatment. For determining the common genes between COVID-19 and sarcoidosis, GSE164805 and GSE18781 were retrieved from the Gene Expression Omnibus (GEO) database. Common upregulated genes were identified by using R language to investigate their involved pathways and gene ontologies (GO). With the aid of the STRING Cytoscape plugin tool, protein-protein interactions (PPIs) network was constructed. From the PPIs network, Hub genes and essential modules were detected by using Cytohubba, and MCODE respectively. For hub genes, TFs, TFs-miRNA, and drug, interaction networks were built through the NetworkAnalyst web platform. A total of 34 common upregulated genes were identified and among them, five hub genes, including TET2, MUC5AC, VDR, NFE2L2, and BCL6 were determined. In addition, a cluster having VDR and NFE2L2 was detected from the PPIs network. Moreover, 32 transcription factors and 9 miRNA were recognized for hub genes. Furthermore, vitamin D and some of its analogous compounds were obtained from the drug interaction network. In conclusion, hub genes identified in this study might have potential roles in modulating COVID-19 infection and sarcoidosis. However, further studies are required to corroborate this study.

Integrated bioinformatics analysis reveals upregulated extracellular matrix hub genes in pancreatic cancer: Implications for diagnosis, prognosis, immune infiltration, and therapeutic strategies

“…Enrichment analysis is a well-established method for analyzing the collective behavior of genes in terms of health and disease conditions. 32 The signaling pathways and associated gene ontologies of hub genes (HGs) were acquired through Enrichr (https://maayanlab.cloud/ Enrichr/), an accessible online enrichment analysis tool that provides user-friendly functionality and diverse visualization summaries for gene list analyses. 33,34 KEGG 2021 Human, BioPlanet 2019, Wiki-Pathway 2021 Human, and Reactome 2022 databases were adopted for pathways analysis.…”

“…Network analysis is integral to system biology, providing insightful knowledge about gene sets without studying individual genes. 32,43 The corresponding PPI network for upregulated genes was acquired from the STRING database and displayed by Cytoscape software. The PPI network comprised 99 nodes and 344 edges (Figure 3A).…”