Summary of Study ST001706

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench,, where it has been assigned Project ID PR001091. The data can be accessed directly via it's Project DOI: 10.21228/M8P97V This work is supported by NIH grant, U2C- DK119886.


This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.

Perform statistical analysis  |  Show all samples  |  Show named metabolites  |  Download named metabolite data  
Download mwTab file (text)   |  Download mwTab file(JSON)   |  Download data files (Contains raw data)
Study IDST001706
Study TitleMachine learning-enabled renal cell carcinoma status prediction using multi-platform urine-based metabolomics NMR (part-II)
Study SummaryCurrently, Renal Cell Carcinoma (RCC) is identified through expensive cross-sectional imaging, frequently followed by renal mass biopsy, which is invasive and subject to sampling errors. Hence, there is a critical need for a non-invasive diagnostic assay. RCC is a disease of altered cellular metabolism with the tumor(s) in close proximity to the urine in the kidney suggesting metabolomic profiling would be an excellent choice for assay development. Here, we applied liquid chromatography-mass spectrometry (LC-MS), nuclear magnetic resonance (NMR), and machine learning (ML) for the discovery of candidate metabolic panels for RCC. The study cohort consists of 82 RCC patients and 174 healthy controls, these were separated into two sub-cohorts: model cohort and the test cohort. Discriminatory metabolic features were selected in the model cohort, using univariate, wrapper, and embedded methods of feature selection. Three ML techniques with different induction biases were used for training and hyperparameter tuning. Final assessment of RCC status prediction was made using the test cohort with the selected biomarkers and the tuned ML algorithms. A seven-metabolite panel consisting of endogenous and exogenous metabolites enabled the prediction of RCC with 88% accuracy, 94% sensitivity, and 85% specificity in the test cohort, with an AUC of 0.98.
University of Georgia
DepartmentBiochemistry and Molecular Biology
LaboratoryEdison Lab/Fernandez Lab
Last NameBifarin
First NameOlatomiwa
Address315 Riverbend Rd, Athens, GA 30602
Phone(706) 542-4401 Lab: 1045
Submit Date2021-02-11
Raw Data AvailableYes
Raw Data File Type(s)fid
Analysis Type DetailNMR
Release Date2021-04-27
Release Version1
Olatomiwa Bifarin Olatomiwa Bifarin application/zip

Select appropriate tab below to view additional metadata details:


Subject ID:SU001783
Subject Type:Human
Subject Species:Homo sapiens
Taxonomy ID:9606