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 (1). pp. 1-14. ISSN 2632-1297

	PDF (Prepublication version) - Accepted Version Restricted to SRO admin only Download (2MB)
	PDF - Published Version Available under License Creative Commons Attribution. Download (1MB)

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 motor neurone disease/amyotrophic lateral sclerosis 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)
Depositing User:	Majid Hafezparast
Date Deposited:	23 Apr 2020 07:32
Last Modified:	01 Dec 2022 15:39
URI:	http://sro.sussex.ac.uk/id/eprint/90994

View download statistics for this item

📧 Request an update