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pmid:33327771

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

Jian Xu · Mingqiang Sun · Yuanyuan Wang · Anmu Xie · Jian Gao

Cell transplantation · 2020 doi:10.1177/0963689720978722

Paper

complete

Experiment

complete

Data

partial

Analysis

partial

Execution

absent

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title
Identification of Hub Genes of Mesio Temporal Lobe Epilepsy and Prognostic Biomarkers of Brain Low-grade Gliomas Based on Bioinformatics Analysis.
doi
10.1177/0963689720978722
pmid
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Jian Xu
[1]
Mingqiang Sun
[2]
Yuanyuan Wang
[3]
Anmu Xie
[4]
Jian Gao
journal
Cell transplantation
year
2020
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.
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Kaplan–Meier curve was used to evaluate the effects of DEGs on the overall survival of LGG patients.
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Global expression profiling of hippocampus in a mouse model for Mesio-Temporal Lobe Epilepsy
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Complete file
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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. 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