Unveiling Potential Biomarkers for Urinary Tract Infection: An Integrated Bioinformatics Approach

Abstract

Background: Urinary tract infections (UTIs) are a widespread health concern with high recurrence rates and substantial economic impact, and they can increase the prevalence of antibiotic resistance. This study employed an integrated bioinformatics approach to identify key genes associated with UTI development, offering potential targets for interventions. Materials and Methods: For this study, the microarray dataset GSE124917 from the Gene Expression Omnibus (GEO) database was selected and reanalyzed. The differentially expressed genes (DEGs) between UTIs and healthy samples were identified using the LIMMA package in R software. In this section, Enrichr database was utilized to perform functional enrichment analysis of DEGs. Subsequently, the protein-protein interaction (PPI) network of the DEGs was constructed and visualized through Cytoscape, utilizing the STRING online database. The identification of hub genes was performed using Cytoscape’s cytoHubba plug-in employing various methods. Receiver operating characteristic (ROC) analysis was performed to assess the diagnostic accuracy of hub genes. Results: Among the outcomes of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, the tumor necrosis factor (TNF) signaling pathway was identified as one of the notable pathways. The PPI network of the DEGs was successfully established and visualized in Cytoscape with the aid of the STRING online database. Using cytoHubba with different methods, we identified seven hub genes (STAT1, IL6, IFIT1, IFIT3, IFIH1, MX1, and IRF7). Based on the ROC analysis, all hub genes showed high diagnostic value. Conclusion: These findings provide a valuable baseline for future research aimed at unraveling the intricate molecular mechanisms behind UTI.

Keywords

• bioinformatics
• computational
• molecular biologies
• urinary tract infections