Gene Expression Profile Analysis in Epilepsy by Using the Partial Least Squares Method

Abstract EN

Purpose.

Epilepsy is a common chronic neurological disorder.

We aim to investigate the underlying mechanism of epilepsy with partial least squares- (PLS-) based gene expression analysis, which is more sensitive than routine variance/regression analysis.

Methods.

Two microarray data sets were downloaded from the Gene Expression Omnibus (GEO) database.

PLS analysis was used to identify differentially expressed genes.

Gene ontology and network analysis were also implemented.

Results.

A total of 752 genes were identified to be differentially expressed, including 575 depressed and 177 overexpressed genes in patients.

For GO enrichment analysis, except for processes related to the nervous system, we also identified overrepresentation of dysregulated genes in angiogenesis.

Network analysis revealed two hub genes, CUL3 and EP300, which may serve as potential targets in further therapeutic studies.

Conclusion.

Our results here may provide new understanding for the underlying mechanisms of epilepsy pathogenesis and will offer potential targets for producing new treatments.