Gene Expression Profiling and Pathway Analysis for Identification of Molecular Targets in MS

Abstract

Multiple sclerosis (MS) is an inflammatory demyelinating disease affecting exclusively the central nervous system (CNS) white matter, mediated by an autoimmune process triggered by a complex interplay between genetic and environmental factors. MS is a kind of neurological syndrome presenting with a great range of phenotypic variability, caused by different immunological mechanisms, leading to the final common pathway that triggers inflammatory demyelination. The identification of biomarkers responsible for the complex phenotype of MS enables us to establish the molecular mechanism-based personalized therapy of MS. Recently, the global analysis of the genome, transcriptome, proteome, and metabolome promotes us to investigate the genome-wide molecular mechanisms of MS. However, omics studies produce high-throughput experimental data, and it is often difficult to find out the most important biological implications from huge data sets. Recent advances in bioinformatics and systems biology have made major breakthroughs by illustrating the cell-wide map of complex molecular interactions with the aid of the literature-based knowledgebase of molecular pathways. Therefore, the integration of omics data derived from the disease-affected cells and tissues with underlying molecular networks helps us to identify novel MS-relevant pathways and network-based effective drug targets for personalized therapy of MS. Here, this study would introduce our approach to establish the logical hypothesis of molecular mechanisms underlying MS, and to identify molecular targets and biomarkers from publicly accessible omics data of MS by effectively combining gene expression profiling and molecular network analysis.