Detection and Prediction of Neurodegenerative Disorders by Long Noncoding RNA Signature Analysis among Bangladeshi Population

Abstract

Long noncoding RNAs (lncRNAs) have appeared lately as a new class of genes that may control and mediate cellular processes. It plays a critical role in many diseases, including neurodegenerative disorders. HOTAIR and MKRN2-42:1 are two lncRNAs that have been seen upregulated in various diseases including in the peripheral blood of the patients suffering from Parkinson’s disease (PD) in different countries. It has also been seen that Meg3v1 and NEAT1 lncRNAs are expressed highly in patients suffering from motor neuron disease (MND) from various countries, though the association between the reason for the onset of this disease and the expression of these specific lncRNAs is not clear. The primary aim of this study was to observe expression variations for specific lncRNAs in the peripheral blood of PD and MND patients compared with healthy individuals and to look for lncRNAs that might be related to the pathogenesis of PD and MND. The aim of this study was also to investigate the association of lncRNA sequence as biomarkers with the risk of having neurodegenerative disorders (PD and MND) in the population of Bangladesh. The purpose of this research also included the possibility of prediction of these neurodegenerative disorders by analyzing and comparing the data which was acquainted from the study where 20 PD patients and 20 healthy subjects and 25 MND patients and 25 healthy subjects of Bangladesh were examined. Their peripheral blood was taken, and plasma was separated. Later, RT-PCR was performed to observe the changes in the expression of specific lncRNAs. Results showed that expression of HOTAIR and MKRN2-42:1 was positively correlated ( $p<0.05$ ) with the symptoms (5) of Parkinson’s suspects in comparison to the healthy subjects. Similarly, Meg3v1 and NEAT1 lncRNAs were expressed significantly ( $p<0.05$ ) in MND patients rather than that in healthy controls. Data were analyzed using GraphPad Prism 7.0, where $p<0.05$ was considered statistically significant. Successful prediction of the possibility of neurodegenerative disorder by analyzing data regarding the expression of lncRNAs as biomarkers may help in advance clinicians to stratify high-risk patients and prepare for appropriate medical intervention.