Summary of Study ST001706

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 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.
Institute
University of Georgia
DepartmentBiochemistry and Molecular Biology
LaboratoryEdison Lab/Fernandez Lab
Last NameBifarin
First NameOlatomiwa
Address315 Riverbend Rd, Athens, GA 30602
Emailolatomiwa.bifarin25@uga.edu
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
https://dx.doi.org/10.21228/M8P97V
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

Select appropriate tab below to view additional metadata details:


Project:

Project ID:PR001091
Project DOI:doi: 10.21228/M8P97V
Project Title:Machine learning-enabled renal cell carcinoma status prediction using multi-platform urine-based metabolomics
Project Type:multi-platform urine-based metabolomics
Project 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: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:(706) 542-4401 Lab: 1045

Subject:

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

Factors:

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

mb_sample_id local_sample_id SampleType
SA1586851000-
SA1586862000-
SA158687207Control
SA158688206Control
SA158689208Control
SA158690212Control
SA158691204Control
SA158692211Control
SA158693200Control
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SA158697215Control
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SA158699220Control
SA158700240Control
SA158701238Control
SA158702243Control
SA158703246Control
SA158704248Control
SA158705231Control
SA158706228Control
SA158707219Control
SA158708190Control
SA158709222Control
SA158710225Control
SA158711216Control
SA158712187Control
SA158713151Control
SA158714149Control
SA158715152Control
SA158716154Control
SA158717155Control
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SA158719143Control
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SA158722137Control
SA158723141Control
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SA158727176Control
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SA158733159Control
SA1587343Control
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SA158738254Control
SA158739320Control
SA158740318Control
SA158741322Control
SA158742323Control
SA158743324Control
SA158744315Control
SA158745314Control
SA158746306Control
SA158747309Control
SA158748310Control
SA158749313Control
SA158750325Control
SA158751329Control
SA158752344Control
SA158753343Control
SA158754346Control
SA158755347Control
SA158756348Control
SA158757340Control
SA158758337Control
SA158759330Control
SA158760333Control
SA158761335Control
SA158762336Control
SA158763304Control
SA158764301Control
SA158765265Control
SA158766264Control
SA158767266Control
SA158768267Control
SA158769268Control
SA158770261Control
SA158771260Control
SA158772133Control
SA158773255Control
SA158774257Control
SA158775259Control
SA158776273Control
SA158777276Control
SA158778294Control
SA158779296Control
SA158780299Control
SA158781300Control
SA158782292Control
SA158783291Control
SA158784279Control
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Collection:

Collection ID:CO001776
Collection Summary:Urine samples were collected at the Emory University Hospital
Collection Protocol Filename:2_Collection_protocol_RCC_FEB2021.docx
Sample Type:Urine

Treatment:

Treatment ID:TR001796
Treatment Summary:There were no treatments in the study, urine samples of healthy subjects and renal cell carcinoma patients were collected.

Sample Preparation:

Sampleprep ID:SP001789
Sampleprep Summary:Urine’s samples were prepared for both NMR and MS experiments
Sampleprep Protocol Filename:3_Sample_preparation_protocol_RCC_FEB2021.docx
Processing Storage Conditions:-80℃

Analysis:

Analysis ID:AN002779
Laboratory Name:Edison Lab
Analysis Type:NMR
Num Factors:3
Num Metabolites:50
Units:Area Under the Curve

NMR:

NMR ID:NM000200
Analysis ID:AN002779
Instrument Name:Bruker Avance lll
Instrument Type:FT-NMR
NMR Experiment Type:1D-1H
NMR Comments:Analysis protocol is in 4_Analysis protocol_RCC_FEB2021 (section on NMR); detailed acquisition and processing parameters are in 5_NMRAcquisition_RCC_FEB2021.
Spectrometer Frequency:600 MHz
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