An Integrative Transcriptomic Analysis of Systemic Juvenile Idiopathic Arthritis for Identifying Potential Genetic Markers and Drug Candidates

Abstract

Systemic juvenile idiopathic arthritis (sJIA) is a rare subtype of juvenile idiopathic arthritis, whose clinical features are systemic fever and rash accompanied by painful joints and inflammation. Even though sJIA has been reported to be an autoinflammatory disorder, its exact pathogenesis remains unclear. In this study, we integrated a meta-analysis with a weighted gene co-expression network analysis (WGCNA) using 5 microarray datasets and an RNA sequencing dataset to understand the interconnection of susceptibility genes for sJIA. Using the integrative analysis, we identified a robust sJIA signature that consisted of 2 co-expressed gene sets comprising 103 up-regulated genes and 25 down-regulated genes in sJIA patients compared with healthy controls. Among the 128 sJIA signature genes, we identified an up-regulated cluster of 11 genes and a down-regulated cluster of 4 genes, which may play key roles in the pathogenesis of sJIA. We then detected 10 bioactive molecules targeting the significant gene clusters as potential novel drug candidates for sJIA using an in silico drug repositioning analysis. These findings suggest that the gene clusters may be potential genetic markers of sJIA and 10 drug candidates can contribute to the development of new therapeutic options for sJIA.

Identification of hub genes and drug candidates for NF2-related vestibular schwannoma by bioinformatics tools

“…[17] Despite strong evidence that merlin contributes to the stability of the membrane-cytoskeleton interface by suppressing the PI3kinase/Akt, Raf/MEK/ERK, and mTOR networks, [18][19][20] not much is known about the NF2 tumorigenesis process. [21] At present, bioinformatics analyses are used to elucidate the underlying mechanisms and potential therapeutic agents of numerous tumors. Herein, using WGCNA, we generated the NF2-based gene co-expression axes, and identified a key gene co-expression module strongly associated with NF2-VS pathogenesis.…”

An in-silico approach to studying a very rare neurodegenerative disease using a disease with higher prevalence with shared pathways and genes: Cerebral adrenoleukodystrophy and Alzheimer’s disease

“…Originally, a signed WGCNA is designed to cluster gene sets that solely consist of positively correlated genes based on Pearson correlation coefficients ( Langfelder and Horvath, 2008 ). First, we performed a signed WGCNA using the merged datasets of AD ( Jung et al, 2019 ; Kim et al, 2021 ). To describe this in detail, outliers of samples were eliminated by the hierarchical cluster method and the soft thresholding power (β) was calculated via scale-free topology analysis to a value of 10.…”

“…In addition to GSEA, a weighted gene co-expression network analysis (WGCNA) is 10.3389/fnmol.2022.996698 a bioinformatic application for finding co-expression patterns between genes by constructing a network and is used to compare clustered genes with another set of genes. These are powerful analytical tools that can be used to investigate various diseases, even rare diseases, with which several studies have successfully elicited genetic markers (Jung et al, 2019;Bottero et al, 2021;Kim et al, 2021). After identifying these markers, drug-repositioning can be performed to develop novel drug candidates.…”

Large-scale Integrative Analysis of Juvenile Idiopathic Arthritis for New Insight into Its Pathogenesis

“…This study used an Affymetrix microarray dataset (GEO study: GSE13501 40 ) and two RNAseq datasets (GEO study: GSE112057 41 and GSE79970 42 ) containing mRNA expression data on JIA patients and healthy controls. The preprocessing and normalization of datasets were performed in accordance with Kim et al 2 . The top 7,000 most-expressed genes of the GSE13501 40 dataset were selected for simplicity after normalization following Jung et al 43 .…”