Document Type : Original Article(s)
Authors
1
MSc Student, Pediatric Inherited Diseases Research Center, Research Institute for Primordial Prevention of Non-Communicable Disease AND Department of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
2
Assistant Professor, Department of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
3
Associate Professor, Department of Neurology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
4
Associate Professor, Pediatric Inherited Diseases Research Center, Research Institute for Primordial Prevention of Non-Communicable Disease AND Department of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
Abstract
Background: microRNAs are non-coding RNAs that modulate different types of cellular processes like differentiation and cell death. Hitherto several studies have been done to determine the expression profile of microRNAs in patients with multiple sclerosis (MS) to obtain appropriate biomarkers. In previous studies, it was found that miR-145 was over-expressed. This up-regulation was in patients who did not start taking medicine. Therefore, in this study we assessed the effect of beta interferon on the expression of this microRNA in patients with multiple sclerosis.Methods: We evaluated the expression pattern of miR-145 in 15 patients who did not start taking medicine and called treatment naive, in 15 patients who were under treatment and also 15 healthy people using real-time polymerase chain reaction method.Findings: The expression level of miR-145 in treatment naive patients was 3.9 fold of healthy people (P = 0.005), whereas expression level of this microRNA between healthy people and under treatment patients was not significantly different.Conclusion: Down-regulation of miR-145 in under-treatment patients to the extent of healthy people suggests that probably, this microRNA could be used as predictive biomarker.
Keywords