Urine-Based Metabolomics and Machine Learning Reveals Metabolites Associated with Renal Cell Carcinoma Stage

• Authors: Olatomiwa O Bifarin, University of Georgia; David A Gaul, Georgia Institute of Technology; Samyukta Sah, Georgia Institute of Technology; Rebecca Arnold, Emory University; Kenneth Ogan, Emory University; Viraj Master, Emory University; David Roberts, Emory University; Sharon Bergquist, Emory University; John Petros, Emory University; Arthur S Edison, University of Georgia; Facundo M Fernandez, Georgia Institute of Technology
• Journal Title: CANCERS
• Volume: Volume 13, Number 24
• Publisher: MDPI | 2021-12-01
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.3390/cancers13246253
• Subjects: Chemistry, Biochemistry; Biology, Molecular; Health Sciences, Medicine and Surgery
• Keywords: Science & Technology; Life Sciences & Biomedicine; Oncology; renal cell carcinoma; metabolomics; machine learning; liquid chromatography-mass spectrometry; nuclear magnetic resonance spectroscopy; biomarker; tumor metabolism; MASS-SPECTROMETRY; TRANSFER-RNAS; CANCER; QUEUINE; GRADE; EXPRESSION; BIOMARKERS; DIAGNOSIS; GLYCINE; ACID

Abstract

Urine metabolomics profiling has potential for non-invasive RCC staging, in addition to pro-viding metabolic insights into disease progression. In this study, we utilized liquid chromatography-mass spectrometry (LC-MS), nuclear magnetic resonance (NMR), and machine learning (ML) for the discovery of urine metabolites associated with RCC progression. Two machine learning questions were posed in the study: Binary classification into early RCC (stage I and II) and advanced RCC stages (stage III and IV), and RCC tumor size estimation through regression analysis. A total of 82 RCC patients with known tumor size and metabolomic measurements were used for the regression task, and 70 RCC patients with complete tumor-nodes-metastasis (TNM) staging information were used for the classification tasks under ten-fold cross-validation conditions. A voting ensemble regression model consisting of elastic net, ridge, and support vector regressor predicted RCC tumor size with a R2 value of 0.58. A voting classifier model consisting of random forest, support vector machines, logistic regression, and adaptive boosting yielded an AUC of 0.96 and an accuracy of 87%. Some identified metabolites associated with renal cell carcinoma progression included 4-guanidinobutanoic acid, 7-aminomethyl-7-carbaguanine, 3-hydroxyanthranilic acid, lysyl-glycine, glycine, citrate, and pyruvate. Overall, we identified a urine metabolic phenotype associated with renal cell carcinoma stage, exploring the promise of a urine-based metabolomic assay for staging this disease.