Identification of blood-derived candidate gene markers and a new 7-gene diagnostic model for multiple sclerosis

Biological Research

Xin Chen¹  Huiqing Hou¹  Huimin Qiao¹  Haolong Fan¹  Tianyi Zhao¹  Mei Dong¹ 

[1] Department of Neurology, The Second Hospital of Hebei Medical University, 050000, Shijiazhuang, Hebei, China;

关键词: Biomarker;    Support vector machine approach;    Multiple sclerosis;    Bioinformatics;    Protein–protein interaction;   

DOI  :  10.1186/s40659-021-00334-6

来源: Springer

【 摘 要 】

BackgroundMultiple sclerosis (MS) is a central nervous system disease with a high disability rate. Modern molecular biology techniques have identified a number of key genes and diagnostic markers to MS, but the etiology and pathogenesis of MS remain unknown.ResultsIn this study, the integration of three peripheral blood mononuclear cell (PBMC) microarray datasets and one peripheral blood T cells microarray dataset allowed comprehensive network and pathway analyses of the biological functions of MS-related genes. Differential expression analysis identified 78 significantly aberrantly expressed genes in MS, and further functional enrichment analysis showed that these genes were associated with innate immune response-activating signal transduction (p = 0.0017), neutrophil mediated immunity (p = 0.002), positive regulation of innate immune response (p = 0.004), IL-17 signaling pathway (p < 0.035) and other immune-related signaling pathways. In addition, a network of MS-specific protein–protein interactions (PPI) was constructed based on differential genes. Subsequent analysis of network topology properties identified the up-regulated CXCR4, ITGAM, ACTB, RHOA, RPS27A, UBA52, and RPL8 genes as the hub genes of the network, and they were also potential biomarkers of MS through Rap1 signaling pathway or leukocyte transendothelial migration. RT-qPCR results demonstrated that CXCR4 was obviously up-regulated, while ACTB, RHOA, and ITGAM were down-regulated in MS patient PBMC in comparison with normal samples. Finally, support vector machine was employed to establish a diagnostic model of MS with a high prediction performance in internal and external datasets (mean AUC = 0.97) and in different chip platform datasets (AUC = (0.93).ConclusionThis study provides new understanding for the etiology/pathogenesis of MS, facilitating an early identification and prediction of MS.

【 授权许可】

CC BY