Cargando…

Dysregulated Alanine as a Potential Predictive Marker of Glioma—An Insight from Untargeted HRMAS-NMR and Machine Learning Data

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 pane...

Descripción completa

Detalles Bibliográficos
Autores principales: Firdous, Safia, Abid, Rizwan, Nawaz, Zubair, Bukhari, Faisal, Anwer, Ammar, Cheng, Leo L., Sadaf, Saima
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8402070/
https://www.ncbi.nlm.nih.gov/pubmed/34436448
http://dx.doi.org/10.3390/metabo11080507
_version_ 1783745700490641408
author Firdous, Safia
Abid, Rizwan
Nawaz, Zubair
Bukhari, Faisal
Anwer, Ammar
Cheng, Leo L.
Sadaf, Saima
author_facet Firdous, Safia
Abid, Rizwan
Nawaz, Zubair
Bukhari, Faisal
Anwer, Ammar
Cheng, Leo L.
Sadaf, Saima
author_sort Firdous, Safia
collection PubMed
description 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.
format Online
Article
Text
id pubmed-8402070
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-84020702021-08-29 Dysregulated Alanine as a Potential Predictive Marker of Glioma—An Insight from Untargeted HRMAS-NMR and Machine Learning Data Firdous, Safia Abid, Rizwan Nawaz, Zubair Bukhari, Faisal Anwer, Ammar Cheng, Leo L. Sadaf, Saima Metabolites Article 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. MDPI 2021-08-01 /pmc/articles/PMC8402070/ /pubmed/34436448 http://dx.doi.org/10.3390/metabo11080507 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Firdous, Safia
Abid, Rizwan
Nawaz, Zubair
Bukhari, Faisal
Anwer, Ammar
Cheng, Leo L.
Sadaf, Saima
Dysregulated Alanine as a Potential Predictive Marker of Glioma—An Insight from Untargeted HRMAS-NMR and Machine Learning Data
title Dysregulated Alanine as a Potential Predictive Marker of Glioma—An Insight from Untargeted HRMAS-NMR and Machine Learning Data
title_full Dysregulated Alanine as a Potential Predictive Marker of Glioma—An Insight from Untargeted HRMAS-NMR and Machine Learning Data
title_fullStr Dysregulated Alanine as a Potential Predictive Marker of Glioma—An Insight from Untargeted HRMAS-NMR and Machine Learning Data
title_full_unstemmed Dysregulated Alanine as a Potential Predictive Marker of Glioma—An Insight from Untargeted HRMAS-NMR and Machine Learning Data
title_short Dysregulated Alanine as a Potential Predictive Marker of Glioma—An Insight from Untargeted HRMAS-NMR and Machine Learning Data
title_sort dysregulated alanine as a potential predictive marker of glioma—an insight from untargeted hrmas-nmr and machine learning data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8402070/
https://www.ncbi.nlm.nih.gov/pubmed/34436448
http://dx.doi.org/10.3390/metabo11080507
work_keys_str_mv AT firdoussafia dysregulatedalanineasapotentialpredictivemarkerofgliomaaninsightfromuntargetedhrmasnmrandmachinelearningdata
AT abidrizwan dysregulatedalanineasapotentialpredictivemarkerofgliomaaninsightfromuntargetedhrmasnmrandmachinelearningdata
AT nawazzubair dysregulatedalanineasapotentialpredictivemarkerofgliomaaninsightfromuntargetedhrmasnmrandmachinelearningdata
AT bukharifaisal dysregulatedalanineasapotentialpredictivemarkerofgliomaaninsightfromuntargetedhrmasnmrandmachinelearningdata
AT anwerammar dysregulatedalanineasapotentialpredictivemarkerofgliomaaninsightfromuntargetedhrmasnmrandmachinelearningdata
AT chengleol dysregulatedalanineasapotentialpredictivemarkerofgliomaaninsightfromuntargetedhrmasnmrandmachinelearningdata
AT sadafsaima dysregulatedalanineasapotentialpredictivemarkerofgliomaaninsightfromuntargetedhrmasnmrandmachinelearningdata