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

Wang, Li; Chen, Duan; Wang, Ruodan; Chen, Lifa; Wang, Yue

doi:10.3389/fnins.2022.996368

Cited by 9 publications

(4 citation statements)

References 46 publications

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“…Studies show that in ammation is a major factor in epilepsy 32,33 . For instance, a study discussed the role of P2X7 receptors in epilepsy and neuroin ammation, particularly their expression on various cells in the brain and their involvement in the in ammatory process during epileptic seizures 13 .…”

Section: Discussionmentioning

confidence: 99%

Mendelian Randomization Analysis of the Causal Relationship Between Immune Cells and Epilepsy: The Mediating Role of Metabolites

Chen,

Yu,

Liu

et al. 2024

Preprint

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Our study investigated the causal relationship between immune cells, metabolites, and epilepsy using two-sample Mendelian Randomization (MR) and mediation MR analysis of 731 immune cell traits and 1,400 metabolites. Our core methodology centered on inverse-variance weighted MR, supplemented by other methods. This approach was crucial in clarifying the potential intermediary functions of metabolites in the genetic links between traits of immune cells and epilepsy. We found a causal relationship between immune cells and epilepsy. Specifically, the genetically predicted levels of CD64 on CD14-CD16 are positively correlated with the risk of epilepsy (p < 0.001, OR = 1.0826, 95% CI 1.0361–1.1312). Similarly, metabolites also exhibit a causal relationship with both immune cells (OR = 1.0438, 95% CI:1.0087–1.0801, p = 0.0140) and epilepsy (p = 0.0334, OR = 1.0897, 95% CI: 1.0068–1.1795), and sensitivity analysis was conducted to further validate these relationships. Importantly, our intermediate MR results suggest that the metabolite Paraxanthine to linoleate (18:2n6) ratio may mediate the causal relationship between immune cell CD64 on CD14-CD16 and epilepsy, with a mediation effect of 5.05%. The results suggest the importance of specific immune cell levels and metabolites in understanding epilepsy's pathogenesis. This is significant for understanding the pathogenesis of epilepsy and its prevention and treatment.

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Section: Discussionmentioning

confidence: 99%

Mendelian Randomization Analysis of the Causal Relationship Between Immune Cells and Epilepsy: The Mediating Role of Metabolites

Chen,

Yu,

Liu

et al. 2024

Preprint

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“…Other investigators have applied a similar method of investigation before. A recent study investigated inflammation‐related genes and the immune cell infiltration patterns involved in the pathological processes of TLE via bioinformatics analysis 66 …”

Section: Limitationsmentioning

confidence: 99%

The genetic link between systemic autoimmune disorders and temporal lobe epilepsy: A bioinformatics study

et al. 2023

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Objective:We aimed to explore the underlying pathomechanisms of the comorbidity between three common systemic autoimmune disorders (SADs) [i.e., insulin-dependent diabetes mellitus (IDDM), systemic lupus erythematosus (SLE), and rheumatoid arthritis (RA)] and temporal lobe epilepsy (TLE), using bioinformatics tools. We hypothesized that there are shared genetic variations among these four conditions. Methods: Different databases (DisGeNET, Harmonizome, and Enrichr) were searched to find TLE-associated genes with variants; their single nucleotide polymorphisms (SNPs) were gathered from the literature. We also did a separate literature search using PubMed with the following keywords for original articles: "TLE" or "Temporal lobe epilepsy" AND "genetic variation," "single nucleotide polymorphism," "SNP," or "genetic polymorphism." In the next step, the SNPs associated with TLE were searched in the LitVar database to find the shared gene variations with RA, SLE, and IDDM.Results: Ninety unique SNPs were identified to be associated with TLE. LitVar search identified two SNPs that were shared between TLE and all three SADs (i.e., IDDM, SLE, and RA). The first SNP was rs16944 on the Interleukin-1β (IL-1β) gene. The second genetic variation was ε4 variation of apolipoprotein E (APOE) gene.Significance: The shared genetic variations (i.e., rs16944 on the IL-1β gene and ε4 variation of the APOE gene) may explain the underlying pathomechanisms of the comorbidity between three common SADs (i.e., IDDM, SLE, and RA) and TLE. Exploring such shared genetic variations may help find targeted therapies for patients with TLE, especially those with drug-resistant seizures who also have comorbid SADs.

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“…Previous study has demonstrated that Jun is immune-related biomarker for TLE [24]. JUN has also been identi ed as a diagnostic of Alzheimer's Disease potential ferroptosis-related biomarkers [25].…”

Section: Correlation Of Ferroptosis-related Hub Gene With Immune Cellsmentioning

confidence: 99%

Bioinformatics analysis and machine learning identification of ferroptosis-related biomarkers in epilepsy

Wang

Zhuo

et al. 2023

Preprint

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Background Epilepsy is one of the common nervous system diseases, and early diagnosis has an important impact on prognosis. Ferroptosis plays an important role in the pathogenesis of epilepsy. Using bioinformatics analysis and machine learning, we aimed to identify hub genes associated with ferroptosis and provide therapeutic targets for epilepsy.Methods Through bioinformatics analysis of datasets GSE49030 and GSE88992, differentially expressed genes(DEGs) related to ferroptosis in epilepsy were identified. Functional enrichment of ferroptosis-related DEGs was performed, and a protein interaction network was used to identify the top 30 node genes. LASSO, SVM-RFE, and random forest machine learning algorithms were employed to screen for hub genes. We analyzed the infiltration of immune cells in epilepsy and the control group and analyzed the correlation between hub genes and immune cells. Finally, the value of hub genes in the diagnosis of epilepsy was evaluated in the training set (GSE49030 and GSE88992) and validation set (GSE16969).Results GSE49030 and GSE88992 obtained a total of 1550 DEGs, including 55 ferroptosis-related DEGs. Enrichment analysis showed that ferroptosis, oxidative stress, and inflammatory signaling pathways were enriched. Jun and Wwtr1 were screened as hub genes. Immunological analysis showed that 9 kinds of cells were differentially expressed between the epilepsy group and control group. Two hub genes were mainly associated with NK cell activation. Jun and Wwtr1 were up-regulated in epilepsy, with AUROC of 0.978 and 0.973 for the diagnosis of epilepsy, respectively, and were verified in dataset DSE16969.Conclusion Our results suggest that Jun and Wwtr1 are potential biomarkers of epilepsy and NK cells ferroptosis can influence both the onset and progression of epilepsy. These results provide more evidence for the role of ferroptosis in the pathogenesis of epilepsy.

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

Cited by 9 publications

References 46 publications

Mendelian Randomization Analysis of the Causal Relationship Between Immune Cells and Epilepsy: The Mediating Role of Metabolites

Mendelian Randomization Analysis of the Causal Relationship Between Immune Cells and Epilepsy: The Mediating Role of Metabolites

The genetic link between systemic autoimmune disorders and temporal lobe epilepsy: A bioinformatics study

Bioinformatics analysis and machine learning identification of ferroptosis-related biomarkers in epilepsy

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