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Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
Published on: December 18, 2016
Autophagy-related genes in mesial temporal lobe epilepsy: an integrated bioinformatics analysis
Man Yang1, Yinchao Li1, Xianyue Liu1
1Department of Neurology, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518107, Guangdong Province, China.
Background:
Autophagy plays essential roles in the development and pathogenesis of mesial temporal lobe epilepsy (mTLE). In this research, we aim to identify and validate the autophagy-related genes associated with mTLE through bioinformatics analysis and experimental validations.
Methods:
We obtained the dataset GSE143272 and high-throughput sequencing results of mTLE from public databases. Potential differentially expressed autophagy-related genes related to mTLE were identified using R software. Subsequently, genomes pathway enrichment analysis, protein-protein interactions (PPIs), and the gene ontology (GO) enrichment were performed for the selected autophagy-related genes. The mRNA expression profiles of hub genes were then used to establish a least absolute shrinkage and selection operator (LASSO) model. Finally, seven hub candidate autophagy-related genes were confirmed in hippocampus using the lithium-pilocarpine chronic epilepsy model.
Results:
A total of 40 differential expression genes (DEGs) among the core autophagy-related genes were identified. The analysis results of PPI revealed that interactions among these DEGs. KEGG pathway and GO analysis of selected candidate autophagy-related genes indicated that those enriched terms mainly focused on macroautophagy, regulation of autophagy, cellular response to extracellular stimulus and mitochondrion disassembly. The results suggested that SQSTM1, VEGFA, BNIP and WIPI2 were consistent with the bioinformatics analysis. The expression levels of SQSTM1 and VEGFA in epilepsy model samples were significantly higher than those in normal control, while BNIP and WIPI2 expression levels were notably decreased. The final hub gene-based LASSO regression model accurately predicted the occurrence of epilepsy (AUC = 0.88).
Conclusions:
Through bioinformatics analysis of public data, we identified 40 candidate autophagy-related genes associated with mTLE. SQSTM1, VEGFA, BNIP and WIPI2 may play significant roles in autophagy, influencing the onset and development of mTLE by regulating autophagy pathway. These findings deepen our understanding of mTLE, and may serve as sensitive and valuable indicators for the prognosis and diagnosis of this condition.
