Renal cell carcinoma (RCC) is diagnosed through expensive cross-sectional imaging, frequently followed by renal mass biopsy, which is not only invasive but also prone to sampling errors. Hence, there is a critical need for a noninvasive diagnostic assay. RCC exhibits altered cellular metabolism combined with the close proximity of the tumor(s) to the urine in the kidney, suggesting that urine metabolomic profiling is an excellent choice for assay development. Here, we acquired liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) data followed by the use of machine learning (ML) to discover candidate metabolomic panels for RCC. The study cohort consisted of 105 RCC patients and 179 controls separated into two subcohorts: the model cohort and the test cohort. Univariate, wrapper, and embedded methods were used to select discriminatory features using the model cohort. Three ML techniques, each with different induction biases, were used for training and hyperparameter tuning. Assessment of RCC status prediction was evaluated using the test cohort with the selected biomarkers and the optimally tuned ML algorithms. A seven-metabolite panel predicted RCC in the test cohort with 88% accuracy, 94% sensitivity, 85% specificity, and 0.98 AUC. Metabolomics Workbench Study IDs are ST001705 and ST001706.
Journal of proteome research. 2021 Jun 23 [Epub ahead of print]
Olatomiwa O Bifarin, David A Gaul, Samyukta Sah, Rebecca S Arnold, Kenneth Ogan, Viraj A Master, David L Roberts, Sharon H Bergquist, John A Petros, Facundo M Fernández, Arthur S Edison
Department of Biochemistry and Molecular Biology, Complex Carbohydrate Research Center, University of Georgia, Athens, Georgia 30602, United States., School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States., Department of Urology, Emory University, Atlanta, Georgia 30308, United States., Department of Medicine, School of Medicine, Emory University, Atlanta, Georgia 30322, United States.