Characterization of Genetic and Chemical Drivers in the Pathogenesis of DRESS Syndrome: In Silico Study

The pathophysiology of drug rash with eosinophilia and systemic symptoms (DRESS) syndrome is complex and poorly understood. Genetic predispositions play a significant role. We aimed to explore the genetic factors and molecular mechanisms driving DRESS, focusing on gene expression, transcription factors (TFs), microRNAs (miRNAs), and chemical interactions.

We utilized RNA-seq data from the GSE160369 data set in the gene expression omnibus (GEO) database to identify differentially expressed genes (DEGs) related to DRESS. The analysis was conducted using GEO2R for identifying upregulated and downregulated genes. Protein-protein interaction (PPI) networks were constructed using STRING and further analyzed with Cytoscape and CytoHubba. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to identify biological pathways. miRNAs and TFs were predicted using bioinformatics tools like TargetScan, miRDB, and ChEA3, while chemical interactions with key genes were explored using CTDbase.

A total of 336 DEGs were identified, including 239 upregulated and 97 downregulated genes. The PPI network highlighted TNF, IL2, and CD40 as central genes involved in immune-related pathways. Functional enrichment analyses revealed significant pathways related to immune activation, such as leukocyte-mediated immunity. We predicted 15 miRNAs, including hsa-miR-1296-5p, and identified 10 TFs, such as MTF1 and NFKB2, which regulate the expression of key genes. Chemical interaction analysis revealed decitabine and tetradecanoylphorbol acetate as prominent agents modulating gene expression.

miRNAs, TFs, and chemical modulators, which play a key role in the development of DRESS syndrome. Knowledge of the molecular underpinnings of DRESS, imperative for therapeutic targets.