Non-Invasive and Rapid Diagnosis of Low-Grade Bladder Cancer via SERSomes of Urine

Abstract

The early screening and diagnosis of low-grade bladder cancer (LGBC) can help to guide timely clinical treatments before deterioration, reducing relapse rates and improving patient survival and quality of life. However, the current clinical technologies are mainly invasive, painful, lack of sensitivity and time efficacy, which cannot meet the clinical needs. Surface-enhanced Raman scattering (SERS) is a label-free detection technique with high sensitivity and can provide molecular-specific information. In this work, we adopt SERSome, an advanced SERS characterization approach using SERS spectral set to comprehensively and accurately profile urine metabolites of LGBC patients and healthy control. With the help of machine learning, we achieve high accuracy of LGBC diagnosis (89.47%) and LGBC stratification (90%). The entire diagnostic process is very rapid, convenient, non-invasive, and low-cost, holding the potential for future use in mass population health screenings. Moreover, we explore the metabolite contribution based on the varied SERSome patterns in LGBC patients, aiming at indicating potential urine biomarkers of LGBC.