Abstract
Modern clinical practice lacks rapid, reliable, and non-invasive methods to distinguish between bacterial and viral infections. It negatively impacts the effectiveness of treatment and increases the risk of unnecessary antibiotic administration and thus antibiotic resistance spread. Using microRNA (miRNA) biomarkers in urine may become the solution to the problems listed. miRNAs in urine are relatively stable and can provide an indirect yet specific insight into systemic infections, offering a promising diagnostic tool. In this pilot study, urine small RNA and blood full transcriptome sequencing data were analyzed in children with bacterial infections (n = 7), viral infections (n = 7), and controls (n = 8). The objectives were to identify exploratory urinary miRNA signatures and attempt to indirectly correlate differentially expressed (DE) urinary miRNAs with DE transcripts in blood. Using LASSO regularized logistic regression and ANOVA, miRNA biomarker candidates were prioritized. Cross-compartment miRNA: mRNA interactions were putatively evaluated using correlation analysis and target prediction. LASSO regularized regression identified a 5-miRNA signature yielding an apparent internal AUC score of 0.981, though wide confidence intervals reflect the exploratory nature of this small cohort. The prioritized miRNAs demonstrated a descriptive capacity to cluster the patient groups. Correlation analysis and target prediction highlighted parallel molecular patterns across biological fluids, mapping to key immune processes such as cytokine-cytokine receptor interactions and T-cell activation. Our pilot study suggests that prioritized urine miRNA biomarkers reflect host infectious etiology and merit further investigation. While internal performance metrics require validation in larger, independent cohorts, the non-invasive approach represents a promising step toward targeted, timely treatment and the reduction of antibiotic resistance.