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.

Show all samples  |  Perform analysis on untargeted data  
Download mwTab file (text)   |  Download mwTab file(JSON)   |  Download data files (Contains raw data)
Study IDST001705
Study TitleMachine learning-enabled renal cell carcinoma status prediction using multi-platform urine-based metabolomics (part-I)
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
DepartmentDepartment of Biochemistry and Molecular Biology
LaboratoryEdison Lab
Last NameBifarin
First NameOlatomiwa
Address315 Riverbend Rd, Athens, GA 30602
Emailolatomiwa.bifarin25@uga.edu
Phone757-405-4379
Submit Date2021-02-11
Num GroupsTwo
Raw Data AvailableYes
Raw Data File Type(s)raw(Thermo)
Analysis Type DetailLC-MS
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:SU001782
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
SA158429N091Control
SA158430N090Control
SA158431N092Control
SA158432N094Control
SA158433N095Control
SA158434N089Control
SA158435N093Control
SA158436N087Control
SA158437N083Control
SA158438N082Control
SA158439N084Control
SA158440N085Control
SA158441N096Control
SA158442N086Control
SA158443N088Control
SA158444N098Control
SA158445N108Control
SA158446N107Control
SA158447N109Control
SA158448N110Control
SA158449N112Control
SA158450N111Control
SA158451N106Control
SA158452N105Control
SA158453N099Control
SA158454N081Control
SA158455N100Control
SA158456N101Control
SA158457N103Control
SA158458N102Control
SA158459N097Control
SA158460N079Control
SA158461N058Control
SA158462N057Control
SA158463N059Control
SA158464N060Control
SA158465N063Control
SA158466N062Control
SA158467N056Control
SA158468N055Control
SA158469N050Control
SA158470N049Control
SA158471N051Control
SA158472N052Control
SA158473N054Control
SA158474N053Control
SA158475N064Control
SA158476N065Control
SA158477N075Control
SA158478N074Control
SA158479N076Control
SA158480N077Control
SA158481N113Control
SA158482N078Control
SA158483N073Control
SA158484N072Control
SA158485N067Control
SA158486N066Control
SA158487N068Control
SA158488N069Control
SA158489N071Control
SA158490N070Control
SA158491N080Control
SA158492N115Control
SA158493N157Control
SA158494N156Control
SA158495N158Control
SA158496N159Control
SA158497N161Control
SA158498N160Control
SA158499N155Control
SA158500N154Control
SA158501N148Control
SA158502N147Control
SA158503N150Control
SA158504N151Control
SA158505N153Control
SA158506N152Control
SA158507N162Control
SA158508N163Control
SA158509N174Control
SA158510N172Control
SA158511N175Control
SA158512N176Control
SA158513N178Control
SA158514N177Control
SA158515N171Control
SA158516N170Control
SA158517N165Control
SA158518N164Control
SA158519N166Control
SA158520N167Control
SA158521N169Control
SA158522N168Control
SA158523N146Control
SA158524N145Control
SA158525N124Control
SA158526N123Control
SA158527N125Control
SA158528N126Control
Showing page 1 of 3     Results:    1  2  3  Next     Showing results 1 to 100 of 256

Collection:

Collection ID:CO001775
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:TR001795
Treatment Summary:There were no treatment in the study, urine samples of healthy subjects and renal cell carcinoma patients were collected.

Sample Preparation:

Sampleprep ID:SP001788
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℃

Combined analysis:

Analysis ID AN002777 AN002778
Analysis type MS MS
Chromatography type HILIC HILIC
Chromatography system Q Exactive HF Q Exactive HF
Column Waters ACQUITY UPLC BEH HILIC (75 x 2.1mm,1.7um) Waters ACQUITY UPLC BEH HILIC (75 x 2.1mm,1.7um)
MS Type ESI ESI
MS instrument type Orbitrap Orbitrap
MS instrument name Thermo Q Exactive HF hybrid Orbitrap Thermo Q Exactive HF hybrid Orbitrap
Ion Mode POSITIVE NEGATIVE
Units A.U. A.U.

Chromatography:

Chromatography ID:CH002056
Instrument Name:Q Exactive HF
Column Name:Waters ACQUITY UPLC BEH HILIC (75 x 2.1mm,1.7um)
Chromatography Type:HILIC

MS:

MS ID:MS002574
Analysis ID:AN002777
Instrument Name:Thermo Q Exactive HF hybrid Orbitrap
Instrument Type:Orbitrap
MS Type:ESI
MS Comments:--
Ion Mode:POSITIVE
Analysis Protocol File:bifarin_20210211_060901_PR_CH_4_Analysis_protocol_RCC_FEB2021.docx
  
MS ID:MS002575
Analysis ID:AN002778
Instrument Name:Thermo Q Exactive HF hybrid Orbitrap
Instrument Type:Orbitrap
MS Type:ESI
MS Comments:--
Ion Mode:NEGATIVE
Analysis Protocol File:bifarin_20210211_060901_PR_CH_4_Analysis_protocol_RCC_FEB2021.docx
  logo