Identification of Monotonically Differentially Expressed Genes for IFN-β-Treated Multiple Sclerosis Patients.

Suyan Tian,Lei Zhang

doi:10.1155/2019/5647902

Abstract

Multiple sclerosis (MS) is a common neurological disability of the central nervous system. Immune-modulatory therapy with interferon-β (IFN-β) has been used as a first-line treatment to prevent relapses in MS patients. While the therapeutic mechanism of IFN-β has not been fully elucidated, the data of microarray experiments that collected longitudinal gene expression profiles to evaluate the long-term response of IFN-β treatment have been analyzed using statistical methods that were incapable of dealing with such data. In this study, the GeneRank method was applied to generate weighted gene expression values and the monotonically expressed genes (MEGs) for both IFN-β treatment responders and nonresponders were identified. The proposed procedure identified 13 MEGs for the responders and 2 MEGs for the nonresponders, most of which are biologically relevant to MS. Our work here provides some useful insight into the mechanism of IFN-β treatment for MS patients. A full understanding of the therapeutic mechanism will enable a more personalized treatment strategy possible.

Highlights

Multiple sclerosis (MS) is an immune-mediated, inflammatory demyelinating disease of the central nervous system with varying degrees of axonal loss, characterized by temporal and spatial dissemination of lesions [1]
The differentially expressed genes (DEGs) between MS patients and controls were identified by carrying out moderated t-tests with the help of the R limma package [16]. e issue of multiple testing was adjusted by using the BenjaminiHochberg procedure [17], and the cutoff of adjusted p value was set at 0.1, a less stringent value compared to the default value of 0.05 because the sample size of the longitudinal microarray study was small. en the GeneRanks of DEGs for each patient were calculated, which may be regarded as the weighted expression values of genes balancing between original expression values of the genes and their connectivity levels inside the gene-to-gene interaction network
In these DEGs, the GeneRanks were calculated, and steps 2 and 3 of the proposed procedure were applied to identify monotonically expressed genes (MEGs) for the nonresponder and the responder groups, respectively. e identified MEGs are listed in Table 2, in which the directions of both differential expression change (MS versus control) and monotonic expression change pattern are presented as well. ere were 13 MEGs for the responders and two MEGs for the nonresponders, respectively

Summary

Introduction

Multiple sclerosis (MS) is an immune-mediated, inflammatory demyelinating disease of the central nervous system with varying degrees of axonal loss, characterized by temporal and spatial dissemination of lesions [1]. Immune-modulatory therapy with interferon-β (IFN-β) has been a commonly used first-line treatment to prevent relapses in RRMS patients [3]. The resulting longitudinal gene expression profiles have usually been analyzed using statistical methods incapable of dealing with such data [5]. Erefore, a reanalysis of longitudinal gene expression data using a machine learning method capable of identifying genes that present a consistently changed pattern across time is recommended. To decipher the therapeutic mechanism of IFN-β, a longitudinal feature selection that incorporates pathway information to guide the selection of genes presenting consistently changed patterns over time or, more precisely, the monotonically changing patterns over time are preferred. Ose genes may provide some insightful clues about the therapeutic mechanism of IFN-β treatment and facilitate more personalized treatment strategies to MS patients The GeneRank method [10], an extension of Google’s PageRank method [11], was used to analyze biomedical data and weigh the gene expression value as well as the level of importance of such a gene within the network to generate weighted gene expression values. en, the monotonically expressed genes (MEGs) for both responders and nonresponders (of the IFN-β treatment) were identified, and their biological relevance was investigated. ose genes may provide some insightful clues about the therapeutic mechanism of IFN-β treatment and facilitate more personalized treatment strategies to MS patients

Materials and Methods

Statistical Methods

Results

Conflicts of Interest