Description:Every year, hundreds of thousands of cases of renal carcinoma (RCC) are reported worldwide. Accurate staging of the disease is important for treatment and prognosis purposes; however, contemporary methods such as computerized tomography (CT) and biopsies are expensive and prone to sampling errors, respectively. As such, a non-invasive diagnostic assay for staging would be beneficial. This study aims to investigate urine metabolites as potential biomarkers to stage RCC. In the study, we identified a panel of such urine metabolites with machine learning techniques.