Bioinformatics Evolution of Gene Biomarkers in Multiple Sclerosis

Multiple sclerosis (MS) is an autoimmune disease in whsich a person’s immune system destroys the myelin around nerve cells in the central nervous system (CNS), yet the peripheral nervous system remains intact. The aim of this study is to investigate the bioinformatics of gene biomarkers in multiple sclerosis. In this study, after reviewing the texts and searching for the bioinformatics databases of NCBI, Gencards, Swiss-prot, Diseasome, etc. the genes involved in the disease based on at, least one of the methods in-vivo, in-vitro, and in-silico has been suggested to be extracted will be considered as candidate genes. In order to compare the results in case and control groups, the expression data obtained from each group was standardized compared to the control group. Then, the connection network of expression data of candidate genes in patients and healthy people was drawn separately with the help of MATLAB software (Version 9.1), and the correctness of these networks and determined biomarkers was checked using the rectome and diseasome database. All statistical calculations were done using R and Matlab software. In the present study, using 5 central criteria including: maximum neighborhood component, degree, closeness, radiality and betweeness, the set of essential genes of MS disease was identified. Based on the results of the central criteria method, TNF, CD40, IL2, IL2RA, IL 7 genes had the most repetitions. According to the identification of the most effective genes related to MS disease in the present study, it is suggested that further studies be designed at the in vitro and clinical levels on the identified effective genes as diagnostic biomarkers of MS disease.