Improved diagnostic identification of urothelial carcinoma through solid-state nanopore determination of urinary hyaluronan size distribution

Urothelial carcinoma (UC) is among the most common malignancies worldwide and is known to exhibit a high recurrence rate. The relative lack of validated, non-invasive biomarkers for the disease challenges early detection and negatively impacts patient outcomes. The linear polysaccharide hyaluronan (HA) has been recognized as a potential source of diagnostic information for UC, with its urinary concentration shown to be predictive of disease severity. Here, we use solid-state nanopore (SSNP) sensing to investigate the value of urinary HA size distribution as an independent and complementary predictor of UC. We show that, when combined with urinary concentration, HA size distribution provides a significant improvement to the differentiation of healthy individuals from those with urinary tract diseases in general (AUC = 0.91, p < 0.05), as well as differentiation of individuals with UC from those without (AUC = 0.87, p < 0.05). These results establish the potential of SSNP-based HA profiling for non-invasive diagnostics of UC.