Oncotarget

Research Papers:

miRNA-based signatures in cerebrospinal fluid as potential diagnostic tools for early stage Parkinson’s disease

Marcia Cristina T. dos Santos _, Miguel Arturo Barreto-Sanz, Bruna Renata S. Correia, Rosie Bell, Catherine Widnall, Luis Tosar Perez, Caroline Berteau, Claudia Schulte, Dieter Scheller, Daniela Berg, Walter Maetzler, Pedro A.F. Galante and Andre Nogueira da Costa

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Oncotarget. 2018; 9:17455-17465. https://doi.org/10.18632/oncotarget.24736

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Abstract

Marcia Cristina T. dos Santos1, Miguel Arturo Barreto-Sanz2, Bruna Renata S. Correia3, Rosie Bell4, Catherine Widnall5, Luis Tosar Perez6, Caroline Berteau5, Claudia Schulte7, Dieter Scheller8, Daniela Berg7,9, Walter Maetzler7,9, Pedro A.F. Galante3 and Andre Nogueira da Costa1

1Experimental Medicine and Diagnostics, Global Exploratory Development, UCB Biopharma SPRL, Braine-l'Alleud, Belgium

2SimplicityBio SA, Monthey, Switzerland

3Hospital Sirio Libanes, São Paulo, Brazil

4Centre for Misfolding Diseases, University of Cambridge, Cambridge, UK

5Leeds Institute of Biomedical and Clinical Sciences, University of Leeds, Leeds, UK

6Bioanalytical Sciences, Non Clinical Development, UCB Biopharma SPRL, Belgium

7Hertie Institute for Clinical Brain Research, Department of Neurodegeneration, University of Tuebingen and German Center for Neurodegenerative Diseases, Tuebingen, Germany

8Consultancy Neuropharm, Neukirchener, Neuss, Germany

9Department of Neurology, Christian-Albrechts-University Kiel, Kiel, Germany

Correspondence to:

Andre Nogueira da Costa, email: [email protected]

Keywords: exosomal miRNA; early stage PD diagnosis; CSF; machine learning

Received: January 06, 2018     Accepted: February 25, 2018     Published: April 03, 2018

ABSTRACT

Parkinson’s Disease is the second most common neurodegenerative disorder, affecting 1–2% of the elderly population. Its diagnosis is still based on the identification of motor symptoms when a considerable number of dopaminergic neurons are already lost. The development of translatable biomarkers for accurate diagnosis at the earliest stages of PD is of extreme interest. Several microRNAs have been associated with PD pathophysiology. Consequently, microRNAs are emerging as potential biomarkers, especially due to their presence in Cerebrospinal Fluid and peripheral circulation. This study employed small RNA sequencing, protein binding ligand assays and machine learning in a cross-sectional cohort comprising 40 early stage PD patients and 40 well-matched controls. We identified a panel comprising 5 microRNAs (Let-7f-5p, miR-27a-3p, miR-125a-5p, miR-151a-3p and miR-423-5p), with 90% sensitivity, 80% specificity and 82% area under the curve (AUC) for the differentiation of the cohorts. Moreover, we combined miRNA profiles with hallmark-proteins of PD and identified a panel (miR-10b-5p, miR-22-3p, miR-151a-3p and α-synuclein) reaching 97% sensitivity, 90% specificity and 96% AUC. We performed a gene ontology analysis for the genes targeted by the microRNAs present in each panel and showed the likely association of the models with pathways involved in PD pathogenesis.


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