Non-invasive diagnostic tool for Parkinson’s disease by sebum RNA profile with machine learning

Abstract Parkinson's disease (PD) is a progressive neurodegenerative disease presenting with motor and non-motor symptoms, including skin disorders (seborrheic dermatitis, bullous pemphigoid, and rosacea), skin pathological changes (decreased nerve endings and alpha-synuclein deposition), and m...

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Autores principales:	Yuya Uehara, Shin-Ichi Ueno, Haruka Amano-Takeshige, Shuji Suzuki, Yoko Imamichi, Motoki Fujimaki, Noriyasu Ota, Takatoshi Murase, Takayoshi Inoue, Shinji Saiki, Nobutaka Hattori
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8d5af2acda0847b59720e17cde503ddc
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spelling	oai:doaj.org-article:8d5af2acda0847b59720e17cde503ddc2021-12-02T18:48:23ZNon-invasive diagnostic tool for Parkinson’s disease by sebum RNA profile with machine learning10.1038/s41598-021-98423-92045-2322https://doaj.org/article/8d5af2acda0847b59720e17cde503ddc2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98423-9https://doaj.org/toc/2045-2322Abstract Parkinson's disease (PD) is a progressive neurodegenerative disease presenting with motor and non-motor symptoms, including skin disorders (seborrheic dermatitis, bullous pemphigoid, and rosacea), skin pathological changes (decreased nerve endings and alpha-synuclein deposition), and metabolic changes of sebum. Recently, a transcriptome method using RNA in skin surface lipids (SSL-RNAs) which can be obtained non-invasively with an oil-blotting film was reported as a novel analytic method of sebum. Here we report transcriptome analyses using SSL-RNAs and the potential of these expression profiles with machine learning as diagnostic biomarkers for PD in double cohorts (PD [n = 15, 50], controls [n = 15, 50]). Differential expression analysis between the patients with PD and healthy controls identified more than 100 differentially expressed genes in the two cohorts. In each cohort, several genes related to oxidative phosphorylation were upregulated, and gene ontology analysis using differentially expressed genes revealed functional processes associated with PD. Furthermore, machine learning using the expression information obtained from the SSL-RNAs was able to efficiently discriminate patients with PD from healthy controls, with an area under the receiver operating characteristic curve of 0.806. This non-invasive gene expression profile of SSL-RNAs may contribute to early PD diagnosis based on the neurodegeneration background.Yuya UeharaShin-Ichi UenoHaruka Amano-TakeshigeShuji SuzukiYoko ImamichiMotoki FujimakiNoriyasu OtaTakatoshi MuraseTakayoshi InoueShinji SaikiNobutaka HattoriNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yuya Uehara Shin-Ichi Ueno Haruka Amano-Takeshige Shuji Suzuki Yoko Imamichi Motoki Fujimaki Noriyasu Ota Takatoshi Murase Takayoshi Inoue Shinji Saiki Nobutaka Hattori Non-invasive diagnostic tool for Parkinson’s disease by sebum RNA profile with machine learning
description	Abstract Parkinson's disease (PD) is a progressive neurodegenerative disease presenting with motor and non-motor symptoms, including skin disorders (seborrheic dermatitis, bullous pemphigoid, and rosacea), skin pathological changes (decreased nerve endings and alpha-synuclein deposition), and metabolic changes of sebum. Recently, a transcriptome method using RNA in skin surface lipids (SSL-RNAs) which can be obtained non-invasively with an oil-blotting film was reported as a novel analytic method of sebum. Here we report transcriptome analyses using SSL-RNAs and the potential of these expression profiles with machine learning as diagnostic biomarkers for PD in double cohorts (PD [n = 15, 50], controls [n = 15, 50]). Differential expression analysis between the patients with PD and healthy controls identified more than 100 differentially expressed genes in the two cohorts. In each cohort, several genes related to oxidative phosphorylation were upregulated, and gene ontology analysis using differentially expressed genes revealed functional processes associated with PD. Furthermore, machine learning using the expression information obtained from the SSL-RNAs was able to efficiently discriminate patients with PD from healthy controls, with an area under the receiver operating characteristic curve of 0.806. This non-invasive gene expression profile of SSL-RNAs may contribute to early PD diagnosis based on the neurodegeneration background.
format	article
author	Yuya Uehara Shin-Ichi Ueno Haruka Amano-Takeshige Shuji Suzuki Yoko Imamichi Motoki Fujimaki Noriyasu Ota Takatoshi Murase Takayoshi Inoue Shinji Saiki Nobutaka Hattori
author_facet	Yuya Uehara Shin-Ichi Ueno Haruka Amano-Takeshige Shuji Suzuki Yoko Imamichi Motoki Fujimaki Noriyasu Ota Takatoshi Murase Takayoshi Inoue Shinji Saiki Nobutaka Hattori
author_sort	Yuya Uehara
title	Non-invasive diagnostic tool for Parkinson’s disease by sebum RNA profile with machine learning
title_short	Non-invasive diagnostic tool for Parkinson’s disease by sebum RNA profile with machine learning
title_full	Non-invasive diagnostic tool for Parkinson’s disease by sebum RNA profile with machine learning
title_fullStr	Non-invasive diagnostic tool for Parkinson’s disease by sebum RNA profile with machine learning
title_full_unstemmed	Non-invasive diagnostic tool for Parkinson’s disease by sebum RNA profile with machine learning
title_sort	non-invasive diagnostic tool for parkinson’s disease by sebum rna profile with machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/8d5af2acda0847b59720e17cde503ddc
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Non-invasive diagnostic tool for Parkinson’s disease by sebum RNA profile with machine learning

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