Computational Identification of Guillain-Barre Syndrome-Related Genes by an mRNA Gene Expression Profile and a Protein-Protein Interaction Network.

ABSTRACT:

Background

In the present study, we used a computational method to identify Guillain-Barré syndrome (GBS) related genes based on (i) a gene expression profile, and (ii) the shortest path analysis in a protein-protein interaction (PPI) network.

Materials and methods

mRNA Microarray analyses were performed on the peripheral blood mononuclear cells (PBMCs) of four GBS patients and four age- and gender-matched healthy controls.

Results

Totally 30 GBS-related genes were screened out, in which 20 were retrieved from PPI analysis of upregulated expressed genes and 23 were from downregulated expressed genes (13 overlap genes). Gene ontology (GO) enrichment and KEGG enrichment analysis were performed, respectively. Results showed that there were some overlap GO terms and KEGG

SUBMITTER: Wang C 

PROVIDER: S-EPMC8968047 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature