Summary of Study ST002015

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 PR001279. The data can be accessed directly via it's Project DOI: 10.21228/M8CX10 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 IDST002015
Study TitleDysregulated Alanine as a Potential Predictive Marker of Glioma—An Insight from Untargeted HRMAS-NMR and Machine Learning Data
Study TypeUntargeted NMR
Study SummaryMetabolic alterations play a crucial role in glioma development and progression and can be detected even before the appearance of the fatal phenotype. We have compared the circulating metabolic fingerprints of glioma patients versus healthy controls, for the first time, in a quest to identify a panel of small, dysregulated metabolites with potential to serve as a predictive and/or diagnostic marker in the clinical settings. High-resolution magic angle spinning nuclear magnetic resonance spectroscopy (HRMAS-NMR) was used for untargeted metabolomics and data acquisition followed by a machine learning (ML) approach for the analyses of large metabolic datasets. Crossvalidation of ML predicted NMR spectral features was done by statistical methods (Wilcoxon-test) using JMP-pro16 software. Alanine was identified as the most critical metabolite with potential to detect glioma with precision of 1.0, recall of 0.96, and F1 measure of 0.98. The top 10 metabolites identified for glioma detection included alanine, glutamine, valine, methionine, N-acetylaspartate (NAA), γ-aminobutyric acid (GABA), serine, α-glucose, lactate, and arginine. We achieved 100% accuracy for the detection of glioma using ML algorithms, extra tree classifier, and random forest, and 98% accuracy with logistic regression. Classification of glioma in low and high grades was done with 86% accuracy using logistic regression model, and with 83% and 79% accuracy using extra tree classifier and random forest, respectively. The predictive accuracy of our ML model is superior to any of the previously reported algorithms, used in tissue- or liquid biopsy-based metabolic studies. The identified top metabolites can be targeted to develop early diagnostic methods as well as to plan personalized treatment strategies.
Institute
University of the Punjab
DepartmentSchool of Biochemistry and Biotechnology
LaboratoryBiopharmaceuticals and Biomarkers Discovery Lab
Last NameFirdous
First NameSafia
AddressQuaid e Azam Campus, University of the Punjab, Lahore.
Emailsaima.ibb@pu.edu.pk
Phone+924299231098
Submit Date2021-10-26
Num Groups2
Total Subjects42
Num Males25
Num Females17
PublicationsDysregulated Alanine as a Potential Predictive Marker of Glioma—An Insight from Untargeted HRMAS-NMR and Machine Learning Data
Analysis Type DetailNMR
Release Date2022-06-01
Release Version1
Safia Firdous Safia Firdous
https://dx.doi.org/10.21228/M8CX10
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

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Project:

Project ID:PR001279
Project DOI:doi: 10.21228/M8CX10
Project Title:Dysregulated Alanine as a Potential Predictive Marker of Glioma—An Insight from Untargeted HRMAS-NMR and Machine Learning Data
Project Type:Untargeted HRMAS NMR, Glioma
Project Summary:Metabolic alterations play a crucial role in glioma development and progression and can be detected even before the appearance of the fatal phenotype. We have compared the circulating metabolic fingerprints of glioma patients versus healthy controls, for the first time, in a quest to identify a panel of small, dysregulated metabolites with potential to serve as a predictive and/or diagnostic marker in the clinical settings. High-resolution magic angle spinning nuclear magnetic resonance spectroscopy (HRMAS-NMR) was used for untargeted metabolomics and data acquisition followed by a machine learning (ML) approach for the analyses of large metabolic datasets. Cross-validation of ML predicted NMR spectral features was done by statistical methods (Wilcoxon-test) using JMP-pro16 software. Alanine was identified as the most critical metabolite with potential to detect glioma with precision of 1.0, recall of 0.96, and F1 measure of 0.98. The top 10 metabolites identified for glioma detection included alanine, glutamine, valine, methionine, N-acetylaspartate (NAA), γ-aminobutyric acid (GABA), serine, α-glucose, lactate, and arginine. We achieved 100% accuracy for the detection of glioma using ML algorithms, extra tree classifier, and random forest, and 98% accuracy with logistic regression. Classification of glioma in low and high grades was done with 86% accuracy using logistic regression model, and with 83% and 79% accuracy using extra tree classifier and random forest, respectively. The predictive accuracy of our ML model is superior to any of the previously reported algorithms, used in tissue- or liquid biopsy-based metabolic studies. The identified top metabolites can be targeted to develop early diagnostic methods as well as to plan personalized treatment strategies.
Institute:University of the Punjab
Department:School of Biochemistry and Biotechnology
Laboratory:Biopharmaceuticals and Biomarkers Discovery Lab
Last Name:Firdous
First Name:Safia
Address:Quaid e Azam Campus, University of the Punjab, Lahore.
Email:saima.ibb@pu.edu.pk
Phone:+924299231098
Funding Source:HEC-IRSIP, USA NIH grants: S10OD023406 and R21CA243255
Publications:Dysregulated Alanine as a Potential Predictive Marker of Glioma—An Insight from Untargeted HRMAS-NMR and Machine Learning Data
Contributors:Safia Firdous, Rizwan Abid, Zubair Nawaz, Faisal Bukhari, Ammar Anwer, Leo L Cheng, Saima Sadaf

Subject:

Subject ID:SU002096
Subject Type:Human
Subject Species:Homo sapiens
Taxonomy ID:9606
Age Or Age Range:15-60 Years
Gender:Male and female
Human Race:Asian
Human Ethnicity:Asian
Human Lifestyle Factors:N/A
Human Medications:N/A
Human Prescription Otc:N/A
Human Smoking Status:N/A
Human Alcohol Drug Use:N/A
Human Nutrition:N/A
Human Inclusion Criteria:Low and High grade glioma patients confirmed by routine histopathology analysis
Human Exclusion Criteria:Diabetes mellitus, Hypertension, liver (hepatitis/liver cirrhosis), and Cardiovascular disease

Factors:

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

mb_sample_id local_sample_id Class Grade
SA188621N2Control -
SA188622N3Control -
SA188623N1Control -
SA188624N14Control -
SA188625N13Control -
SA188626N15Control -
SA188627N4Control -
SA188628N6Control -
SA188629N11Control -
SA188630N12Control -
SA188631N10Control -
SA188632N9Control -
SA188633N7Control -
SA188634N8Control -
SA188635N5Control -
SA188636N16Control -
SA188637AA1HGG III
SA188638GBM8HGG IV
SA188639GBM9HGG IV
SA188640GBM10HGG IV
SA188641GBM7HGG IV
SA188642GBM6HGG IV
SA188643GBM2HGG IV
SA188644GBM3HGG IV
SA188645GBM4HGG IV
SA188646GBM5HGG IV
SA188647GBM11HGG IV
SA188648GBM12HGG IV
SA188649GBM13HGG IV
SA188650GBM14HGG IV
SA188651GBM15HGG IV
SA188652GBM16HGG IV
SA188653GBM1HGG IV
SA188654PA1LGG I
SA188655PA4LGG I
SA188656PA3LGG I
SA188657PA2LGG I
SA188658DA5LGG II
SA188659DA1LGG II
SA188660DA2LGG II
SA188661DA3LGG II
SA188662DA4LGG II
Showing results 1 to 42 of 42

Collection:

Collection ID:CO002089
Collection Summary:Peripheral blood (3 cc) from each patient (fasting state) was collected in Li-heparin tubes, centrifuged (300× g, 10 min) to prepare plasma within an hour of collection, and preserved in sterile tubes at −80 ◦C, as 200 µL aliquots, until further analyses.
Sample Type:Blood (whole)
Collection Location:Punjab Institute of Neurosciences (PINS), Lahore, Pakistan.
Collection Frequency:Pre-operative
Storage Conditions:-80℃
Collection Vials:Li-Heparin

Treatment:

Treatment ID:TR002108
Treatment Summary:The enrolled patients underwent surgical resection of tumor after sample collection.

Sample Preparation:

Sampleprep ID:SP002102
Sampleprep Summary:Sample was prepared by adding 10 µL plasma sample in a 4 mm zirconia rotor with 12 µL Kel-F inserts; 2 µL D2O (Sigma Aldrich, St. Louis, MO, USA) with reference trimethylsilylpropanoic acid (TSP) was added for field locking.
Processing Storage Conditions:On ice

Analysis:

Analysis ID:AN003283
Laboratory Name:Martinos Center for Biomedical Imaging
Analysis Type:NMR
Acquisition Date:June 2018-January 2019
Software Version:Bruker Biospin NMR System
Operator Name:Leo L Cheng
Detector Type:Topspin
Results File:HRMAS_NMR_data_Glioma.txt
Units:Peak Area

NMR:

NMR ID:NM000225
Analysis ID:AN003283
Instrument Name:Bruker Avence
Instrument Type:Other
NMR Experiment Type:Other
NMR Comments:Triple nucleus (1H,13C,31P) HRMAS probe
Field Frequency Lock:D2O
Spectrometer Frequency:600MHz
NMR Probe:Triple nucleus (1 H, 13 C, 31 P) HRMAS probe
NMR Solvent:D2O
NMR Tube Size:4mm
Shimming Method:Autoshim
Pulse Sequence:90° Pulse Sequence
Water Suppression:PLdB9
Pulse Width:3 μs
Power Level:-14 dB
Chemical Shift Ref Cpd:TSP
Temperature:4℃
Number Of Scans:256
Dummy Scans:4
Relaxation Delay:5 s
Spectral Width:12 ppm
Num Data Points Acquired:4096
Line Broadening:0.5Hz
Chemical Shift Ref Std:TSP at 0ppm, Lactate at 1.318ppm, Alanine at 1.468ppm
Binned Increment:0.01
Binned Data Protocol File:N
A
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