Identification of a potential non-coding RNA biomarker signature for amyotrophic lateral sclerosis

Joilin, Greig, Gray, Elizabeth, Thompson, Alexander G, Bobeva, Yoana, Talbot, Kevin, Weishaupt, Jochen, Ludolph, Albert, Malaspina, Andrea, Leigh, P Nigel, Newbury, Sarah F, Turner, Martin R and Hafezparast, Majid (2020) Identification of a potential non-coding RNA biomarker signature for amyotrophic lateral sclerosis. Brain Communications, 2 (2). pp. 1-14. ISSN 2632-1297

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Abstract

Objective biomarkers for the clinically heterogeneous adult-onset neurodegenerative disorder amyotrophic lateral sclerosis are crucial to facilitate assessing emerging therapeutics, and improve the diagnostic pathway in what is a clinically heterogeneous syndrome. With non-coding RNA transcripts including microRNA, piwi-RNA, and transfer RNA present in human biofluids, we sought to identify whether non-coding RNA in serum could be biomarkers for amyotrophic lateral sclerosis. Serum samples from our Oxford Study for Biomarkers in MND/ALS discovery cohort of amyotrophic lateral sclerosis patients (n=48), disease mimics (n=16), and age- and sex-matched healthy controls (n=24) were profiled for non-coding RNA expression using RNA-sequencing, which showed a wide range of non-coding RNA to be dysregulated. We confirmed significant alterations with reverse transcription-quantitative PCR in the expression of hsa-miR-16-5p, hsa-miR-21-5p, hsa-miR-92a-3p, hsa-piR-33151, TRV-AAC4-1.1, and TRA-AGC6-1.1. Furthermore, hsa-miR-206, a previously identified amyotrophic lateral sclerosis biomarker, showed a binary-like pattern of expression in our samples. Using the expression of these non-coding RNA, we were able to discriminate amyotrophic lateral sclerosis samples from healthy controls in our discovery cohort using a random forest analysis with 93.7% accuracy with promise in predicting progression rate of patients. Importantly, cross-validation of this novel signature using a new geographically distinct cohort of samples from the United Kingdom and Germany with both amyotrophic lateral sclerosis and control samples (n=156) yielded an accuracy of 73.9%. The high prediction accuracy of this non-coding RNA-based biomarker signature, even across heterogeneous cohorts, demonstrates the strength of our approach as a novel platform to identify and stratify amyotrophic lateral sclerosis patients.

Item Type: Article
Keywords: Amyotrophic lateral sclerosis, ALS, biomarker, non-coding RNA, RNA-seq
Schools and Departments: Brighton and Sussex Medical School > Clinical and Experimental Medicine
School of Life Sciences > Neuroscience
Research Centres and Groups: Sussex Neuroscience
Subjects: Q Science > Q Science (General) > Q0179.9 Research
Q Science > Q Science (General)
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Depositing User: Majid Hafezparast
Date Deposited: 25 Jun 2020 11:16
Last Modified: 25 Jun 2020 11:16
URI: http://sro.sussex.ac.uk/id/eprint/92113

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