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. 2024 Apr 25;6(1):16.
doi: 10.1186/s42494-024-00160-9.

Autophagy-related genes in mesial temporal lobe epilepsy: an integrated bioinformatics analysis

Man Yang #  1 , Yinchao Li #  1 , Xianyue Liu #  1 , Shangnan Zou  1 , Lei Lei  1 , Qihang Zou  1 , Yaqian Zhang  1 , Yubao Fang  1 , Shuda Chen  2 , Liemin Zhou  3
Affiliations

Autophagy-related genes in mesial temporal lobe epilepsy: an integrated bioinformatics analysis

Man Yang et al. Acta Epileptol. .

Abstract

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.

Keywords: Autophagy; Bioinformatics analysis; Biomarkers; Mesial temporal lobe epilepsy.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: This study was approved by the Institutional Animal Care and Use Committee, Sun Yat-Sen University (Approval No. SYSU-IACUC-2022-B1339). Consent for publication: Not applicable. Competing interests: Author Liemin Zhou is the member of the Editorial Board for Acta Epileptologica, who was not involved in the journal’s review of, or decisions related to this manuscript.

Figures

Fig. 1
Fig. 1
Workflow analysis Abbreviations: LASSO: Least absolute shrinkage and selection operator; mTLE: Mesial temporal lobe epilepsy; PPI: Protein-protein interaction; ROC: Receiver operating characteristic
Fig. 2
Fig. 2
Identification of autophagy-related genes and functional enrichment analysis. a Screening autophagy-related DEGs in mTLE. b GO enrichment analysis of genes associated with autophagy in mTLE. c Chord diagram illustrating top 10 KEGG enrichment analysis results
Fig. 3
Fig. 3
PPI network of autophagy related candidate genes and central module in mTLE. a Autophagy-related genes associated with mTLE were categorized into three major modules. b The PPI network of mTLE autophagy-related genes depicted red dots representing up-regulated genes, green dots representing down-regulated genes, and black lines indicating interactions between the encoded proteins. c The PPI network of hub module genes
Fig. 4
Fig. 4
DEGs of autophagy and Spearman correlation analysis of the 14 hub genes. a A Volcano plot of the 3399 autophagy-related DEGs in the blood of patients with epilepsy. b Venn diagrams of the screened dysregulated genes in epilepsy and the hub genes. The blue part indicates the epilepsy-related genes in blood and brain tissue, while the yellow part indicates the hub genes. c A heatmap of the 14 autophagy-related DEGs in mTLE and healthy controls. d The violin plots of 14 autophagy-related DEGs in blood of epilepsy patients and controls. e Spearman correlation analysis of the 14 autophagy-related DEGs
Fig. 5
Fig. 5
The model for predicting epilepsy. a LASSO regression model. b ROC curves analysis of dataset. c ROC curves analysis of seven genes
Fig. 6
Fig. 6
mRNA expression of seven autophagy-related genes
See this image and copyright information in PMC

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