Summary of Study ST001705

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, 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.

See: https://www.metabolomicsworkbench.org/about/howtocite.php

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.

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Study ID	ST001705
Study Title	Machine learning-enabled renal cell carcinoma status prediction using multi-platform urine-based metabolomics (part-I)
Study Summary	Currently, 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.
Institute	University of Georgia
Department	Department of Biochemistry and Molecular Biology
Laboratory	Edison Lab
Last Name	Bifarin
First Name	Olatomiwa
Address	315 Riverbend Rd, Athens, GA 30602
Email	olatomiwa.bifarin25@uga.edu
Phone	757-405-4379
Submit Date	2021-02-11
Num Groups	Two
Raw Data Available	Yes
Raw Data File Type(s)	raw(Thermo)
Analysis Type Detail	LC-MS
Release Date	2021-04-27
Release Version	1

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Factors:

Subject type: Human; Subject species: Homo sapiens (Factor headings shown in green)

mb_sample_id	local_sample_id	SampleType
SA158429	N091	Control
SA158430	N090	Control
SA158431	N092	Control
SA158432	N094	Control
SA158433	N095	Control
SA158434	N089	Control
SA158435	N093	Control
SA158436	N087	Control
SA158437	N083	Control
SA158438	N082	Control
SA158439	N084	Control
SA158440	N085	Control
SA158441	N096	Control
SA158442	N086	Control
SA158443	N088	Control
SA158444	N098	Control
SA158445	N108	Control
SA158446	N107	Control
SA158447	N109	Control
SA158448	N110	Control
SA158449	N112	Control
SA158450	N111	Control
SA158451	N106	Control
SA158452	N105	Control
SA158453	N099	Control
SA158454	N081	Control
SA158455	N100	Control
SA158456	N101	Control
SA158457	N103	Control
SA158458	N102	Control
SA158459	N097	Control
SA158460	N079	Control
SA158461	N058	Control
SA158462	N057	Control
SA158463	N059	Control
SA158464	N060	Control
SA158465	N063	Control
SA158466	N062	Control
SA158467	N056	Control
SA158468	N055	Control
SA158469	N050	Control
SA158470	N049	Control
SA158471	N051	Control
SA158472	N052	Control
SA158473	N054	Control
SA158474	N053	Control
SA158475	N064	Control
SA158476	N065	Control
SA158477	N075	Control
SA158478	N074	Control
SA158479	N076	Control
SA158480	N077	Control
SA158481	N113	Control
SA158482	N078	Control
SA158483	N073	Control
SA158484	N072	Control
SA158485	N067	Control
SA158486	N066	Control
SA158487	N068	Control
SA158488	N069	Control
SA158489	N071	Control
SA158490	N070	Control
SA158491	N080	Control
SA158492	N115	Control
SA158493	N157	Control
SA158494	N156	Control
SA158495	N158	Control
SA158496	N159	Control
SA158497	N161	Control
SA158498	N160	Control
SA158499	N155	Control
SA158500	N154	Control
SA158501	N148	Control
SA158502	N147	Control
SA158503	N150	Control
SA158504	N151	Control
SA158505	N153	Control
SA158506	N152	Control
SA158507	N162	Control
SA158508	N163	Control
SA158509	N174	Control
SA158510	N172	Control
SA158511	N175	Control
SA158512	N176	Control
SA158513	N178	Control
SA158514	N177	Control
SA158515	N171	Control
SA158516	N170	Control
SA158517	N165	Control
SA158518	N164	Control
SA158519	N166	Control
SA158520	N167	Control
SA158521	N169	Control
SA158522	N168	Control
SA158523	N146	Control
SA158524	N145	Control
SA158525	N124	Control
SA158526	N123	Control
SA158527	N125	Control
SA158528	N126	Control