Transcriptomic analyses and experimental verification reveal potential biomarkers and biological pathways of urinary tract infection

Urinary tract infection (UTI) is a common infectious disease. Urinary tract pathogenic Escherichia coli (UPEC) is the main cause of UTIs. At present, antibiotics are mainly used for the treatment of UTIs. However, with the increase of drug resistance, the course of the disease is prolonged. Therefore, identifying the receptors and signal pathways of host cells and tissues will further our understanding of the pathogenesis of UTIs and help in the development of new drug treatments. We used two public microarray datasets (GSE43790, GSE124917) in the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) between UTI and normal cell samples. A functional analysis based on Gene Ontology (GO) data, a pathway enrichment analysis based on Kyoto Encyclopedia of Genes and Genomes (KEGG) data and a protein-protein interaction analysis identified the main potential biomarkers and verified them in animal tissues. A total of 147 up-regulated genes and 40 down-regulated genes were identified. GO enrichment analysis showed that these functional changes relate to the terms response to lipopolysaccharide, regulation of cytokine production, and regulation of the inflammatory response. KEGG analysis indicated that urinary tract infections likely involve the TNF-alpha signaling pathways. The 20 hub genes were selected from the protein-protein interaction network, and the highly significant hub genes were verified by animal experiments. Our findings provide potential targets for exploring new treatments for urinary tract infections. After a comprehensive analysis of the GEO database, these results may facilitate development of new diagnosis and treatment strategies for urinary tract infections.

Automated summary

This study utilized public microarray datasets from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes between UTI and normal cell samples, uncovering potential biomarkers and signaling pathways associated with UTIs. The findings were validated through animal experiments, providing new targets for exploring treatments for UTIs.