Computational discovery of plasma microRNA profiles as biomarkers of temporal lobe epilepsy

<div>Presented at ISMB/ECCB 2015 - July 10 – 14, 2015, Dublin, Ireland</div><div><br></div><div><br></div>Epilepsy is a common neurological disorder affecting approximately 1% of the population and is characterised by recurrent unprovoked seizures. The lack of a clinically accepted biomarker for epilepsy diagnosis as well as the incomplete and vague history often provided by the patient is responsible for up to 30% misdiagnosis. MicroRNAs are a class of small non-coding RNA that regulate gene expression at a post-transcriptional level. MicroRNAs are important contributors to brain function and emerging animal and human data suggest microRNAs control multiple pathways in epilepsy. MicroRNAs are also detectable in various body fluids and their stability as well as link to disease mechanism makes them potentially ideal molecular biomarkers of epilepsy. We determined plasma levels of over 800 microRNAs collected from 20 healthy volunteers and 20 epilepsy patients using highthroughput real-time quantitative reverse transcription PCR. Computational analysis included normalisation, clustering, differential expression analysis, target prediction and pathway analysis. A number of significantly differentially expressed microRNAs were identified between control and epilepsy samples including known brain-expressed microRNAs implicated in epilepsy. Furthermore, we applied feature selection with machine learning algorithms, including support vector machines and bidirectional recurrent neural networks, to build a microRNAs-based predictor of epilepsy, validated on an independent test set. This analyse showed that these classifiers may be useful in supporting the existence of a set of microRNAs implicated in disease pathogenesis that may be biomarkers of human epilepsy