Recursive ensemble feature selection provides a robust mRNA expression signature for myalgic encephalomyelitis/chronic fatigue syndrome

Abstract Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic disorder characterized by disabling fatigue. Several studies have sought to identify diagnostic biomarkers, with varying results. Here, we innovate this process by combining both mRNA expression and DNA methylation dat...

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Autores principales:	Paula I. Metselaar, Lucero Mendoza-Maldonado, Andrew Yung Fong Li Yim, Ilias Abarkan, Peter Henneman, Anje A. te Velde, Alexander Schönhuth, Jos A. Bosch, Aletta D. Kraneveld, Alejandro Lopez-Rincon
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d0baf6feb4464600ad1e82326571d72d
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spelling	oai:doaj.org-article:d0baf6feb4464600ad1e82326571d72d2021-12-02T11:37:22ZRecursive ensemble feature selection provides a robust mRNA expression signature for myalgic encephalomyelitis/chronic fatigue syndrome10.1038/s41598-021-83660-92045-2322https://doaj.org/article/d0baf6feb4464600ad1e82326571d72d2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83660-9https://doaj.org/toc/2045-2322Abstract Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic disorder characterized by disabling fatigue. Several studies have sought to identify diagnostic biomarkers, with varying results. Here, we innovate this process by combining both mRNA expression and DNA methylation data. We performed recursive ensemble feature selection (REFS) on publicly available mRNA expression data in peripheral blood mononuclear cells (PBMCs) of 93 ME/CFS patients and 25 healthy controls, and found a signature of 23 genes capable of distinguishing cases and controls. REFS highly outperformed other methods, with an AUC of 0.92. We validated the results on a different platform (AUC of 0.95) and in DNA methylation data obtained from four public studies on ME/CFS (99 patients and 50 controls), identifying 48 gene-associated CpGs that predicted disease status as well (AUC of 0.97). Finally, ten of the 23 genes could be interpreted in the context of the derailed immune system of ME/CFS.Paula I. MetselaarLucero Mendoza-MaldonadoAndrew Yung Fong Li YimIlias AbarkanPeter HennemanAnje A. te VeldeAlexander SchönhuthJos A. BoschAletta D. KraneveldAlejandro Lopez-RinconNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Paula I. Metselaar Lucero Mendoza-Maldonado Andrew Yung Fong Li Yim Ilias Abarkan Peter Henneman Anje A. te Velde Alexander Schönhuth Jos A. Bosch Aletta D. Kraneveld Alejandro Lopez-Rincon Recursive ensemble feature selection provides a robust mRNA expression signature for myalgic encephalomyelitis/chronic fatigue syndrome
description	Abstract Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic disorder characterized by disabling fatigue. Several studies have sought to identify diagnostic biomarkers, with varying results. Here, we innovate this process by combining both mRNA expression and DNA methylation data. We performed recursive ensemble feature selection (REFS) on publicly available mRNA expression data in peripheral blood mononuclear cells (PBMCs) of 93 ME/CFS patients and 25 healthy controls, and found a signature of 23 genes capable of distinguishing cases and controls. REFS highly outperformed other methods, with an AUC of 0.92. We validated the results on a different platform (AUC of 0.95) and in DNA methylation data obtained from four public studies on ME/CFS (99 patients and 50 controls), identifying 48 gene-associated CpGs that predicted disease status as well (AUC of 0.97). Finally, ten of the 23 genes could be interpreted in the context of the derailed immune system of ME/CFS.
format	article
author	Paula I. Metselaar Lucero Mendoza-Maldonado Andrew Yung Fong Li Yim Ilias Abarkan Peter Henneman Anje A. te Velde Alexander Schönhuth Jos A. Bosch Aletta D. Kraneveld Alejandro Lopez-Rincon
author_facet	Paula I. Metselaar Lucero Mendoza-Maldonado Andrew Yung Fong Li Yim Ilias Abarkan Peter Henneman Anje A. te Velde Alexander Schönhuth Jos A. Bosch Aletta D. Kraneveld Alejandro Lopez-Rincon
author_sort	Paula I. Metselaar
title	Recursive ensemble feature selection provides a robust mRNA expression signature for myalgic encephalomyelitis/chronic fatigue syndrome
title_short	Recursive ensemble feature selection provides a robust mRNA expression signature for myalgic encephalomyelitis/chronic fatigue syndrome
title_full	Recursive ensemble feature selection provides a robust mRNA expression signature for myalgic encephalomyelitis/chronic fatigue syndrome
title_fullStr	Recursive ensemble feature selection provides a robust mRNA expression signature for myalgic encephalomyelitis/chronic fatigue syndrome
title_full_unstemmed	Recursive ensemble feature selection provides a robust mRNA expression signature for myalgic encephalomyelitis/chronic fatigue syndrome
title_sort	recursive ensemble feature selection provides a robust mrna expression signature for myalgic encephalomyelitis/chronic fatigue syndrome
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d0baf6feb4464600ad1e82326571d72d
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Recursive ensemble feature selection provides a robust mRNA expression signature for myalgic encephalomyelitis/chronic fatigue syndrome

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