Article

Molecular Biology Reports

, Volume 40, Issue 5, pp 3731-3737

First online:

Microarray gene expression profiling analysis combined with bioinformatics in multiple sclerosis

  • Mingyuan LiuAffiliated withDepartment of Neurology, Changhai Hospital
  • , Xiaojun HouAffiliated withDepartment of Neurology, Changhai Hospital
  • , Ping ZhangAffiliated withDepartment of Neurology, Changhai Hospital
  • , Yong HaoAffiliated withDepartment of Neurology, Changhai Hospital
  • , Yiting YangAffiliated withDepartment of Neurology, Changhai Hospital
  • , Xiongfeng WuAffiliated withDepartment of Neurology, Changhai Hospital
  • , Desheng ZhuAffiliated withDepartment of Neurology, Changhai Hospital
  • , Yangtai GuanAffiliated withDepartment of Neurology, Changhai Hospital Email author 

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Abstract

Multiple sclerosis (MS) is the most prevalent demyelinating disease and the principal cause of neurological disability in young adults. Recent microarray gene expression profiling studies have identified several genetic variants contributing to the complex pathogenesis of MS, however, expressional and functional studies are still required to further understand its molecular mechanism. The present study aimed to analyze the molecular mechanism of MS using microarray analysis combined with bioinformatics techniques. We downloaded the gene expression profile of MS from Gene Expression Omnibus (GEO) and analysed the microarray data using the differentially coexpressed genes (DCGs) and links package in R and Database for Annotation, Visualization and Integrated Discovery. The regulatory impact factor (RIF) algorithm was used to measure the impact factor of transcription factor. A total of 1,297 DCGs between MS patients and healthy controls were identified. Functional annotation indicated that these DCGs were associated with immune and neurological functions. Furthermore, the RIF result suggested that IKZF1, BACH1, CEBPB, EGR1, FOS may play central regulatory roles in controlling gene expression in the pathogenesis of MS. Our findings confirm the presence of multiple molecular alterations in MS and indicate the possibility for identifying prognostic factors associated with MS pathogenesis.

Keywords

Multiple sclerosis Differentially coexpressed genes Regulatory impact factor MS pathogenesis