Identification of Hub Genes of Mesio Temporal Lobe Epilepsy and Prognostic Biomarkers of Brain Low-grade Gliomas Based on Bioinformatics Analysis

Abstract

Mesio temporal lobe epilepsy (MTLE) syndrome is the most common form of intractable epilepsies. Meanwhile, seizures are common in patients with cancer as a consequence of brain tumors, including brain low-grade gliomas (LGG). However, the underlying molecular mechanisms of MTLE remain poorly understood. Also, the relationship between MTLE and LGG needs our attention. In this study, we aimed to investigate the hub genes and potential mechanism in MTLE, and the relationship between MTLE and LGG, the gene expression profiles (GSE88992) were downloaded from the Gene Expression Omnibus (GEO) database. Difference analysis for MTLE versus control groups under the three time points was conducted to select the differentially expressed genes (DEGs). Time series clustering analysis was used to select the trend genes. Then a series of bioinformatics analyses including functional enrichment analysis, protein–protein interaction (PPI) network and module analyses, and transcription factor (TF) and miRNA prediction were performed. Also, the overall survival analysis and expression of hub genes in LGG were performed using UALCAN from TCGA database. At 6 h, there were 351 upregulated and 80 downregulated DEGs. At 12 h, there were 499 upregulated and 231 downregulated DEGs. Additionally, 532 upregulated and 402 downregulated DEGs were obtained at 24 h. After time series clustering analysis of the DEGs, we obtained 323 uptrend and 248 downtrend genes. We identified 10 key genes with higher degrees, including C3, TIMP1, PENK, CKAP4, etc. Five PPI modules were identified by MCODE. TF analysis predicted four TFs: JUN, STAT3, NR4A2, and Myc. A total of 26,834 miRNA–mRNA pairs were predicted. Moreover, survival analysis of UALCAN suggested that C3, TIMP1, PENK, GNG2, CKAP4, TNC, JUN, STAT3, NR4A2, and Myc can be potential biomarkers for the prognosis of LGG. In summary, DEGs and hub genes were identified in the present study, which provides novel insight into the development of MTLE.

Genomic evidence for the suitability of Göttingen Minipigs with a rare seizure phenotype as a model for human epilepsy

“…We also identified the KEGG pathway neuroactive ligand-receptor interaction, consisting of DEGs encoding neuroreceptors, such as GRIA4 , which is significantly associated with a molecular mechanism involved in temporal epilepsy [ 63 ]. Indeed, a mutation in the GRIA4 gene, which encodes an AMPA receptor (AMPA-R) subunit expressed in cortical as well as thalamic neurons, leads to the absence of seizures [ 64 ].…”

Inflammation-related genes and immune infiltration landscape identified in kainite-induced temporal lobe epilepsy based on integrated bioinformatics analysis

“…A previous study has indicated that Jun had the potential to serve as a prognostic marker for neuropathic pain ( Yang et al, 2018 ). JUN was identified as a potential biomarker for the prognosis of brain low-grade gliomas ( Xu et al, 2020 ). Besides, the c-JUN-medicated signaling pathway was an important BP involved in the pathogenesis of hippocampal neuronal damage in kainite-induced epilepsy ( Lee et al, 2001 ).…”

MicroRNA-221-3p Suppresses the Microglia Activation and Seizures by Inhibiting of HIF-1α in Valproic Acid-Resistant Epilepsy

“…As we know, it is difficult to collect hippocampal tissue from children with VPA-resistant epilepsy in clinical practice. In order to efficiently find the miRNA that regulates HIF-1α in VPA-resistant epilepsy, this study firstly performed integrated bioinformatics analysis on the hippocampal tissue of patients with drug-resistant epilepsy in public databases referring to the previous method ( Xu et al, 2020 ). Subsequently, the miRNAs that potentially regulate the expression of HIF-1α were selected.…”