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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Neurol.</journal-id>
<journal-title>Frontiers in Neurology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Neurol.</abbrev-journal-title>
<issn pub-type="epub">1664-2295</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fneur.2024.1354062</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Neurology</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Identification of hub genes significantly linked to tuberous sclerosis related-epilepsy and lipid metabolism via bioinformatics analysis</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" equal-contrib="yes">
<name><surname>Weiliang</surname> <given-names>Wang</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="author-notes" rid="fn001"><sup>&#x02020;</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/2411128/overview"/>
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</contrib>
<contrib contrib-type="author" equal-contrib="yes">
<name><surname>Yinghao</surname> <given-names>Ren</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<xref ref-type="author-notes" rid="fn001"><sup>&#x02020;</sup></xref>
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</contrib>
<contrib contrib-type="author">
<name><surname>Weiliang</surname> <given-names>Hou</given-names></name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
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</contrib>
<contrib contrib-type="author">
<name><surname>Xiaobin</surname> <given-names>Zhang</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
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</contrib>
<contrib contrib-type="author">
<name><surname>Chenglong</surname> <given-names>Yang</given-names></name>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
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</contrib>
<contrib contrib-type="author">
<name><surname>Weimiao</surname> <given-names>An</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
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</contrib>
<contrib contrib-type="author" corresp="yes">
<name><surname>Fei</surname> <given-names>Xu</given-names></name>
<xref ref-type="aff" rid="aff5"><sup>5</sup></xref>
<xref ref-type="corresp" rid="c002"><sup>&#x0002A;</sup></xref>
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</contrib>
<contrib contrib-type="author" corresp="yes">
<name><surname>Fengpeng</surname> <given-names>Wang</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x0002A;</sup></xref>
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<aff id="aff1"><sup>1</sup><institution>Epilepsy Center, Xiamen Humanity Hospital Fujian Medical University, Xiamen</institution>, <addr-line>Fujian</addr-line>, <country>China</country></aff>
<aff id="aff2"><sup>2</sup><institution>Department of Dermatology, Xiamen Humanity Hospital Fujian Medical University, Xiamen</institution>, <addr-line>Fujian</addr-line>, <country>China</country></aff>
<aff id="aff3"><sup>3</sup><institution>Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, State Key Laboratory of Medical Neurobiology, Ministry of Education Frontiers Center for Brain Science and Institutes of Brain Science, Fudan University</institution>, <addr-line>Shanghai</addr-line>, <country>China</country></aff>
<aff id="aff4"><sup>4</sup><institution>Department of Neurosurgery, The Cancer Hospital of Harbin Medical University, Harbin</institution>, <addr-line>Heilongjiang</addr-line>, <country>China</country></aff>
<aff id="aff5"><sup>5</sup><institution>Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University</institution>, <addr-line>Harbin</addr-line>, <country>China</country></aff>
<author-notes>
<fn fn-type="edited-by"><p>Edited by: Francesca Felicia Operto, University of Salerno, Italy</p></fn>
<fn fn-type="edited-by"><p>Reviewed by: Masashi Mizuguchi, The University of Tokyo, Japan</p>
<p>Jianxiang Liao, Shenzhen Children&#x00027;s Hospital, China</p></fn>
<corresp id="c001">&#x0002A;Correspondence: Wang Fengpeng <email>Fengpeng_wang&#x00040;163.com</email></corresp>
<corresp id="c002">Xu Fei <email>bio_fei&#x00040;163.com</email></corresp>
<fn fn-type="equal" id="fn001"><p>&#x02020;These authors have contributed equally to this work and share first authorship</p></fn></author-notes>
<pub-date pub-type="epub">
<day>14</day>
<month>02</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="collection">
<year>2024</year>
</pub-date>
<volume>15</volume>
<elocation-id>1354062</elocation-id>
<history>
<date date-type="received">
<day>11</day>
<month>12</month>
<year>2023</year>
</date>
<date date-type="accepted">
<day>29</day>
<month>01</month>
<year>2024</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x000A9; 2024 Weiliang, Yinghao, Weiliang, Xiaobin, Chenglong, Weimiao, Fei and Fengpeng.</copyright-statement>
<copyright-year>2024</copyright-year>
<copyright-holder>Weiliang, Yinghao, Weiliang, Xiaobin, Chenglong, Weimiao, Fei and Fengpeng</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p></license>
</permissions>
<abstract>
<sec>
<title>Background</title>
<p>Tuberous sclerosis complex (TSC) is one of the most common genetic causes of epilepsy. Identifying differentially expressed lipid metabolism related genes (DELMRGs) is crucial for guiding treatment decisions.</p></sec>
<sec>
<title>Methods</title>
<p>We acquired tuberous sclerosis related epilepsy (TSE) datasets, GSE16969 and GSE62019. Differential expression analysis identified 1,421 differentially expressed genes (DEGs). Intersecting these with lipid metabolism related genes (LMRGs) yielded 103 DELMRGs. DELMRGs underwent enrichment analyses, biomarker selection, disease classification modeling, immune infiltration analysis, weighted gene co-expression network analysis (WGCNA) and AUCell analysis.</p></sec>
<sec>
<title>Results</title>
<p>In TSE datasets, 103 DELMRGs were identified. Four diagnostic biomarkers (ALOX12B, CBS, CPT1C, and DAGLB) showed high accuracy for epilepsy diagnosis, with an AUC value of 0.9592. Significant differences (<italic>p</italic> &#x0003C; 0.05) in Plasma cells, T cells regulatory (Tregs), and Macrophages M2 were observed between diagnostic groups. Microglia cells were highly correlated with lipid metabolism functions.</p></sec>
<sec>
<title>Conclusions</title>
<p>Our research unveiled potential DELMRGs (ALOX12B, CBS, CPT1C and DAGLB) in TSE, which may provide new ideas for studying the psathogenesis of epilepsy.</p></sec></abstract>
<kwd-group>
<kwd>tuberou sclerosis complex</kwd>
<kwd>epilepsy</kwd>
<kwd>lipid metabolism</kwd>
<kwd>bioinformatics analysis</kwd>
<kwd>biomarkers</kwd>
</kwd-group>
<counts>
<fig-count count="9"/>
<table-count count="0"/>
<equation-count count="1"/>
<ref-count count="50"/>
<page-count count="15"/>
<word-count count="7076"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Epilepsy</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="s1">
<title>Introduction</title>
<p>Tuberous sclerosis complex (TSC) is a rare autosomal dominant genetic disorder characterized by the growth of benign tumors in multiple organ systems, including the skin, kidneys, lungs, heart, and brain. A common feature of TSC is epilepsy (<xref ref-type="bibr" rid="B1">1</xref>). Epileptic seizures are a progressively worsening and dynamic process in which several cellular, molecular and pathophysiological mechanisms may be involved, including mammalian target of rapamycin (mTOR) dysregulation and synaptic abnormalities (<xref ref-type="bibr" rid="B2">2</xref>). TSC is a neurodevelopmental disorder caused by mutations in the <italic>TSC1</italic> or <italic>TSC2</italic> genes (<xref ref-type="bibr" rid="B3">3</xref>). The proteins encoded by these genes are responsible for regulating the signal of the mTOR complex (<xref ref-type="bibr" rid="B4">4</xref>). mTOR is part of a complex signal network and plays a crucial role in regulating various cellular processes, including cell growth and metabolism (<xref ref-type="bibr" rid="B5">5</xref>).</p>
<p>Most TSC-related manifestations are the result of over-activation of the mammalian target of rapamycin (mTOR) complex. Rapamycin has been widely used in different animal models of TSC-associated epilepsy and has been shown to have antiepileptic potential as it not only inhibits seizures but also prevents seizure development (<xref ref-type="bibr" rid="B6">6</xref>). The mTOR pathway has been established to be closely associated with lipid metabolism functions (<xref ref-type="bibr" rid="B7">7</xref>). Additionally, the ketogenic diet has shown efficacy in alleviating TSC-associated seizures, and decanoic acid has been found to reduce mTORC1 activity in a model of tuberous sclerosis, including astrocytes derived from TSC patients (<xref ref-type="bibr" rid="B8">8</xref>). The cumulative evidence suggests a close association between lipid metabolism and the occurrence of TSE.</p>
<p>Recent studies have shown that metabolism is critical in regulating homeostasis, dormancy and differentiation of neural stem cells (<xref ref-type="bibr" rid="B9">9</xref>). Neural stem cells can utilize free fatty acid oxidation to generate energy (<xref ref-type="bibr" rid="B10">10</xref>). Under energy-deficient stress conditions, in TSC-deficient cells, high activation of mTORC1 reconfigures metabolism, leading to increased aerobic glycolysis and increased fatty acid synthesis. TSC-deficient cells require autophagy to maintain high mTORC1 activation, possibly through lipid autophagy, to provide lipids as an alternative energy source for oxidative phosphorylation. <italic>In vivo</italic> inhibition of lipid autophagy or its downstream catabolic pathways reversed the defective phenotype induced by TSC1-deficient neural stem cells and reduced tumorigenesis in a mouse model (<xref ref-type="bibr" rid="B7">7</xref>). This evidence suggests an important role for the mTOR pathway in influencing lipid metabolism in TSC patients.</p>
<p>The influence of lipid metabolism on epilepsy is likely due to its function as a &#x0201C;secondary fuel&#x0201D; for the brain. Multiple studies have revealed potential impairments in glucose metabolism within regions of the brain affected by epilepsy. Maintaining normal brain function relies heavily on energy, and deficits in energy may disrupt the ionic gradient, leading to neuronal depolarization and epilepsy (<xref ref-type="bibr" rid="B5">5</xref>). Ketogenic diets offer ketone bodies like acetoacetic acid and beta-hydroxybutyric acid, acting as alternative energy sources for the brain. Around 50% of individuals, including both children and adults with specific types of epilepsy who can tolerate and adhere to these dietary regimens, experience a decrease in the frequency of seizures. Recent data suggests that incorporating medium-chain triglycerides, which provide caprylic and capric acid&#x02014;two medium-chain fatty acids&#x02014;along with ketone bodies as supplementary energy for the brain, proves beneficial in rodent epilepsy models, canines, and human patients with epilepsy (<xref ref-type="bibr" rid="B11">11</xref>).</p>
<p>To identify genes closely associated with lipid metabolism and TSE disease progression, we identified differentially expressed lipid metabolism related genes for possible therapeutic targets.</p></sec>
<sec sec-type="materials and methods" id="s2">
<title>Materials and methods</title>
<sec>
<title>Data source</title>
<p>Data related to Tuberous Sclerosis and Epilepsy were obtained from the Gene Expression Omnibus (GEO) database (<xref ref-type="bibr" rid="B12">12</xref>). Specifically, we downloaded the datasets GSE16969 (<xref ref-type="bibr" rid="B13">13</xref>) and GSE62019 (<xref ref-type="bibr" rid="B14">14</xref>), as well as single cell sequencing data from <ext-link ext-link-type="DDBJ/EMBL/GenBank" xlink:href="GSE201048">GSE201048</ext-link> (<xref ref-type="bibr" rid="B15">15</xref>) (<xref ref-type="supplementary-material" rid="SM1">Supplementary Table 1</xref>). Lipid metabolism related pathways (LMRPs) were also sourced from the following PubMed articles: <ext-link ext-link-type="DDBJ/EMBL/GenBank" xlink:href="PMID35222371">PMID35222371</ext-link> (<xref ref-type="bibr" rid="B16">16</xref>), <ext-link ext-link-type="DDBJ/EMBL/GenBank" xlink:href="PMID36091041">PMID36091041</ext-link> (<xref ref-type="bibr" rid="B17">17</xref>), <ext-link ext-link-type="DDBJ/EMBL/GenBank" xlink:href="PMID36860853">PMID36860853</ext-link> (<xref ref-type="bibr" rid="B18">18</xref>), and <ext-link ext-link-type="DDBJ/EMBL/GenBank" xlink:href="PMID37469520">PMID37469520</ext-link> (<xref ref-type="bibr" rid="B19">19</xref>) (<xref ref-type="supplementary-material" rid="SM1">Supplementary Table 2</xref>). The design and workflow of this study are shown in <xref ref-type="fig" rid="F1">Figure 1</xref>.</p>
<fig id="F1" position="float">
<label>Figure 1</label>
<caption><p>Workflow. DEG, differential expressed gene; LMRP, lipid metabolism related pathway; ssGSEA, single-sample gene set enrichment analysis; DELMRG, differential expressed lipid metabolism related gene; WGCNA, weighted relation network analysis; SVM-RFE, support vector machine-recursive feature elimination; ROC, receiver operating curve; LMRC, lipid metabolism related cell.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fneur-15-1354062-g0001.tif"/>
</fig>
</sec>
<sec>
<title>Differentially expressed genes in tuberous sclerosis related epilepsy</title>
<p>GSE16969 and GSE62019 were processed using the &#x0201C;sva&#x0201D; package for acquiring an integrated GEO dataset consisting of 14 samples, among which there were seven tuberous sclerosis related epilepsy (TSE) samples and seven control (CTRL) samples (<xref ref-type="bibr" rid="B20">20</xref>). The dataset underwent Principal Component Analysis (PCA). Subsequently, the &#x0201C;limma&#x0201D; package was employed to determine the differentially expressed genes (DEGs) among the various subgroups (TSE vs. CTRL) at |log fold change (FC)| &#x0003E; 0.58 and adjusted <italic>p</italic> &#x0003C; 0.05 (<xref ref-type="bibr" rid="B21">21</xref>).</p>
</sec>
<sec>
<title>Gene set enrichment analysis</title>
<p>Gene sets were obtained from the MSigDB database, including the following: &#x0201C;c5.go.v2023.1.Hs.symbols.gmt,&#x0201D; &#x0201C;c2.cp.kegg.v2023.1.Hs.symbols.gmt,&#x0201D; &#x0201C;c2.cp.reactome.v2023.1.Hs. sym bol.gmt,&#x0201D; &#x0201C;c2.cp.wikipathways.v20 23.1.Hs.symbols.gmt,&#x0201D; and &#x0201C;h.all.v2023.1.Hs.symbols.gmt&#x0201D; (<xref ref-type="bibr" rid="B22">22</xref>). The resulting dataset underwent enrichment analysis utilizing the GSEA method provided by the &#x0201C;clusterProfiler&#x0201D; package, with adjusted <italic>p</italic> &#x0003C; 0.05 (<xref ref-type="bibr" rid="B23">23</xref>). By combining the LMRPs downloaded from the literature, with keywords such as &#x0201C;lipid,&#x0201D; &#x0201C;prostanoid,&#x0201D; &#x0201C;fatty acid,&#x0201D; &#x0201C;cholesterol,&#x0201D; &#x0201C;phosphatidylcholine,&#x0201D; and other metabolism-related keywords, we identified LMRPs that exhibited differential enrichment between the TSE and control groups (<xref ref-type="bibr" rid="B24">24</xref>).</p>
</sec>
<sec>
<title>Single sample gene set enrichment analysis</title>
<p>Gene sets for LMRPs with inter-group differences, based on enrichment, underwent ssGSEA analysis in the integrated dataset related to TSE by comparing TSE and control groups. Enrichment scores for each sample, which indicate the activity levels of these pathways, were calculated using the &#x0201C;ssGSEA&#x0201D; algorithm from the R package (<xref ref-type="bibr" rid="B25">25</xref>). Pathways activity variances were evaluated between the TSE and control groups through the &#x0201C;lmFit&#x0201D; analysis (<xref ref-type="bibr" rid="B21">21</xref>).</p>
</sec>
<sec>
<title>Differentially expressed lipid metabolism related genes</title>
<p>The lipid metabolism related genes (LMRGs) were acquired from the MSigDB database. To identify the DELMRGs, they were intersected with the DEGs. The resulting overlap was illustrated in a Venn diagram. Afterward, we conducted protein-protein interaction (PPI) network analysis on the resulting genes using the STRING database (<xref ref-type="bibr" rid="B26">26</xref>). We employed the Cytoscape (<xref ref-type="bibr" rid="B27">27</xref>) plugin &#x0201C;cytoHubba&#x0201D; (<xref ref-type="bibr" rid="B28">28</xref>) and the Maximal Clique Centrality (MCC) algorithm to pinpoint the ten most pivotal genes within the network based on their MCC scores.</p>
</sec>
<sec>
<title>Weighted gene co-expression network analysis</title>
<p>Hierarchical clustering was conducted using ssGSEA enrichment scores for LMRPs that were associated with inter-group differences. To determine the optimal number of clusters, the &#x0201C;fviz_nbclust&#x0201D; function of the R package &#x0201C;factoextra&#x0201D; was utilized. Clustering results were obtained for samples in the integrated dataset based on their lipid metabolism functions. Additionally, WGCNA was performed on the combined dataset related to TSE (<xref ref-type="bibr" rid="B28">28</xref>). In this investigation, WGCNA utilized the amalgamated dataset for TSE as an input to evaluate the connection between the progression of the disease phenotype and various gene modules. In addition, it documented the genes within each module, considering them as feature genes that are specific to the module.</p>
</sec>
<sec>
<title>Risk model construction</title>
<p>The &#x0201C;ggvenn&#x0201D; package was used to generate a Venn diagram by taking the intersection of DELMRGs and lipid metabolism-related module genes identified via WGCNA. The support vector machine-recursive feature elimination (SVM-RFE) algorithm was utilized for the feature selection of LMRGs linked with TSE progression, using the chosen genes (<xref ref-type="bibr" rid="B29">29</xref>). Following the selection of feature genes, logistic regression was employed to develop a diagnostic model. Subsequently, a risk diagnostic score was determined according to the gene expression levels and coefficients obtained from multiple regression analysis.</p>
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<p>The following formula was used to calculate the diagnosis score: A higher AUC (area under the curve) value indicates better diagnostic performance. The receiver operating characteristic (ROC) curve for the TSE progression status risk model was plotted using the &#x0201C;pROC&#x0201D; package (<xref ref-type="bibr" rid="B30">30</xref>). After SVM-RFE feature selection and model building, a nomogram (<xref ref-type="bibr" rid="B24">24</xref>) was created with the &#x0201C;rms&#x0201D; package.</p>
</sec>
<sec>
<title>Immune infiltration analysis</title>
<p>The expression profile dataset of TSE was uploaded onto the CIBERSORTx website (<xref ref-type="bibr" rid="B27">27</xref>). Samples with immune cell enrichment scores greater than zero were selected via data filtration. Later, the specific outcomes of the immune cell infiltration abundance matrix were retrieved and displayed. The distribution of immune cells in high and low diagnostic score sample groups from the TSE dataset were presented using bar plots and box plots. The correlation between immune cells in the groups with high and low diagnostic scores and TSE risk model genes linked to lipid metabolism were computed via Spearman rank correlation analysis. A correlation heat map was produced utilizing the &#x0201C;ggplot2&#x0201D; package.</p>
</sec>
<sec>
<title>Gene set variation analysis</title>
<p>To acquire the reference gene set &#x0201C;h.all.v7.4.symbols.gmt&#x0201D; from the MSigDB database and execute GSVA on an integrated GEO dataset comprising varied groups (high vs. low diagnostic score group) (<xref ref-type="bibr" rid="B25">25</xref>). GSVA converts the expression matrix into a pathway enrichment score matrix. We employed the &#x0201C;lmFit&#x0201D; analysis to identify the variations in pathways between the high and low diagnostic score groups (<xref ref-type="bibr" rid="B21">21</xref>). After that, we established the Pearson correlation between the feature genes of the diagnostic model and the distinctively regulated pathways of the high and low diagnostic score groups. Visualizations were created in the form of a bubble chart using the &#x0201C;ggcorrplot&#x0201D; package and scatter charts using &#x0201C;ggpmisc&#x0201D; and &#x0201C;ggExtra&#x0201D;.</p>
</sec>
<sec>
<title>AUCell analysis</title>
<p>We conducted an efficient data processing and visualization of the GSE201048 single cell dataset utilizing the &#x0201C;Seurat&#x0201D; package (<xref ref-type="bibr" rid="B31">31</xref>). Following this, we employed t-distributed stochastic neighbor embedding (tSNE) to illustrate the subpopulation annotations of the cells. To investigate the functional disparities of lipid metabolism-related cells (LMRCs) among diverse cellular subpopulations, we utilized the &#x0201C;AUCell&#x0201D; package (<xref ref-type="bibr" rid="B32">32</xref>) to determine the pathway activity of individual cells based on the single cell expression profiles of GSE201048. We then identified cell clusters with active &#x0201C;gene sets&#x0201D; within the single cell data. Lastly, we scored each cell based on the feature genes of the diagnostic model and gene expression ranking information. The AUC score somewhat indicated the ratio of top-performing genes found in a selection of pathway genes in every cell, signifying the action level of specific gene sets in each cell.</p>
</sec>
<sec>
<title>Gene ontology enrichment analysis</title>
<p>Based on the single-cell expression profiles from GSE201048, we employed the &#x0201C;FindMarkers&#x0201D; function from the &#x0201C;Seurat&#x0201D; package to detect DEGs among various cell subpopulations (<xref ref-type="bibr" rid="B31">31</xref>). For identifying the DEGs within the single cell subpopulations, genes satisfying the criteria of |logFC| &#x0003C; = 0.25 and adjusted <italic>p</italic> &#x0003C; 0.05 were selected. Large-scale functional enrichment studies of genes in various dimensions and hierarchical levels were conducted through the widely accepted approach of GO enrichment analysis (<xref ref-type="bibr" rid="B33">33</xref>). The analysis was conducted across three dimensions: Biological Process (BP), Molecular Function (MF), and Cellular Component (CC) (<xref ref-type="bibr" rid="B34">34</xref>). To identify significantly enriched biological processes and pathways, we utilized the &#x0201C;clusterProfiler&#x0201D; package for GO enrichment analysis (<xref ref-type="bibr" rid="B23">23</xref>). The visual representation of the enrichment results was created with the &#x0201C;ggplot2&#x0201D; package (<xref ref-type="bibr" rid="B24">24</xref>).</p>
</sec>
<sec>
<title>Statistical analysis</title>
<p>We conducted all data calculations and statistical analyses using R (version 4.2.3). The Benjamini-Hochberg method was applied for multiple testing adjustments. Independent Student&#x00027;s <italic>t</italic>-tests assessed statistical significance for normally distributed variables. For non-normally distributed variables, we used the Wilcoxon test. Spearman&#x00027;s correlation analysis calculated correlation coefficients between different molecules. All <italic>p</italic>-values were two-tailed, and statistical significance was set at <italic>p</italic> &#x0003C; 0.05.</p></sec>
</sec>
<sec sec-type="results" id="s3">
<title>Results</title>
<sec>
<title>Differential expression analysis of TSE data</title>
<p>GSE16969 and GSE62019 underwent batch correction and were merged. Box plots before and after batch correction of the combined epilepsy dataset were presented in <xref ref-type="fig" rid="F2">Figures 2A</xref>, <xref ref-type="fig" rid="F2">B</xref>, while <xref ref-type="fig" rid="F2">Figures 2C</xref>, <xref ref-type="fig" rid="F2">D</xref> illustrated the results of PCA for the combined epilepsy dataset, before and after batch correction, respectively. In <xref ref-type="fig" rid="F2">Figure 2E</xref>, certain differences at the transcriptome level between the TSE group and the control group were shown. The differential expression analysis yielded 1,421 DEGs, comprising 708 upregulated genes and 713 downregulated genes, which were graphically displayed as a volcano plot in <xref ref-type="fig" rid="F2">Figure 2F</xref> and a heatmap in <xref ref-type="fig" rid="F2">Figure 2G</xref>. GSEA was performed on pathways from the MSigDB database, specifically GO, KEGG, HALLMARK, REACTOME, and WIKIPATHWAY (<xref ref-type="supplementary-material" rid="SM1">Supplementary Table 3</xref>). <xref ref-type="fig" rid="F2">Figure 2H</xref> showed that the TSE group was linked to 13 LMRPs, including &#x0201C;GOBP-PROSTANOID-METABOLIC-PROCESS,&#x0201D; &#x0201C;GOBP-ICOSANOID-METABOLIC-PROCESS,&#x0201D; &#x0201C;GOBP-POSITIVE-REGULATION-OF-FATTY-ACID-METABOLIC-PROCESS,&#x0201D; and &#x0201C;GOBP-PHOSPHATIDYLCHOLINE-METABOLIC-PROCESS.&#x0201D;</p>
<fig id="F2" position="float">
<label>Figure 2</label>
<caption><p>Differential expression analysis of an integrated dataset of TSE. Box plots before <bold>(A)</bold> and after <bold>(B)</bold> batch correction of the merged TSE dataset. Inter-batch analysis using PCA before <bold>(C)</bold> and after <bold>(D)</bold> batch correction of the merged TSE dataset. Differential analysis of the TSE group and the control group using PCA after batch correction of the merged TSE dataset <bold>(E)</bold>. Volcano plot <bold>(F)</bold> and heatmap <bold>(G)</bold> illustrating DEGs between the TSE group and the control group. GSEA (TSE vs. CTRL) enrichment analysis bubble chart <bold>(H)</bold>. NES, normalized enrichment score.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fneur-15-1354062-g0002.tif"/>
</fig>
<p>We conducted ssGSEA based on the integrated TSE dataset. <xref ref-type="fig" rid="F3">Figure 3A</xref> displayed LMRPs with differential ssGSEA scores between groups (TSE vs. CTRL). <xref ref-type="fig" rid="F3">Figure 3B</xref> presented ssGSEA scores for 13 LMRPs. <xref ref-type="fig" rid="F3">Figure 3C</xref> shows differential ssGSEA scores for 10 LMRPs related to GO. <xref ref-type="fig" rid="F3">Figure 3D</xref> illustrated differential ssGSEA scores for one pathway related to HALLMARK. <xref ref-type="fig" rid="F3">Figure 3E</xref> displays differential ssGSEA scores for one pathway related to REACTOME, and <xref ref-type="fig" rid="F3">Figure 3F</xref> showed differential ssGSEA scores for one pathway related to KEGG. These results demonstrated that the 13 LMRPs enriched through differential expression analysis also exhibit inter-group differences in ssGSEA scores.</p>
<fig id="F3" position="float">
<label>Figure 3</label>
<caption><p>ssGSEA of LMRPs in the integrated dataset related to TSE. <bold>(A)</bold> Differential analysis of LMRPs in the TSE integrated dataset. <bold>(B)</bold> Heatmap of ssGSEA scores for LMRPs in the TSE integrated dataset. Box plots of ssGSEA scores for lipid metabolism-related GO pathways <bold>(C)</bold>, HALLMARK pathways <bold>(D)</bold>, REACTOME pathways <bold>(E)</bold>, and KEGG pathways <bold>(F)</bold> between the TSE group and the control group.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fneur-15-1354062-g0003.tif"/>
</fig>
<p>We intersected 13 LMRGs (1,175 unique genes after deduplication) with the DEGs between the TSE and control groups, resulting in 103 DELMRGs (<xref ref-type="fig" rid="F4">Figure 4A</xref>). Subsequently, we examined the expression patterns of these genes in the TSE and control groups (<xref ref-type="fig" rid="F4">Figure 4B</xref>). The results showed that 33 genes were downregulated in the TSE group, while 70 genes were upregulated in the TSE group. We constructed a protein-protein interaction (PPI) network (<xref ref-type="fig" rid="F4">Figure 4C</xref>). <xref ref-type="fig" rid="F4">Figure 4D</xref> represented the top 10 hub genes in the PPI network based on MCC scores, with ANXA5 having the highest MCC score and degree.</p>
<fig id="F4" position="float">
<label>Figure 4</label>
<caption><p>DELMRGs analysis. <bold>(A)</bold> Venn diagram showing the intersection of DEG (TSE vs. CTRL) and LMRG. <bold>(B)</bold> Heatmap depicting the expression of DELMRGs in the TSE and control groups. <bold>(C)</bold> PPI network of DELMRGs. <bold>(D)</bold> Top 10 hub genes based on MCC scores.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fneur-15-1354062-g0004.tif"/>
</fig>
</sec>
<sec>
<title>Results of WGCNA</title>
<p><xref ref-type="fig" rid="F5">Figure 5A</xref> indicated the optimal number of clusters obtained through hierarchical clustering, with the result showing that the optimal number of clusters was 2. In <xref ref-type="fig" rid="F5">Figure 5B</xref>, the hierarchical clustering results demonstrated that GSM424827, GSM424826, GSM424825, and GSM1518504 clustered together, while the remaining samples clustered separately. Subsequently, we performed WGCNA on the integrated dataset of TSE to screen for co-expression modules related to lipid metabolism subtypes (<xref ref-type="fig" rid="F5">Figure 5C</xref>) and identified a total of 13 co-expressed gene modules. Finally, based on the expression patterns of the module genes and the grouping information of lipid metabolism subtypes, we assessed the correlation between gene modules and lipid metabolism subtypes (<xref ref-type="fig" rid="F5">Figure 5D</xref>). We selected the gene module with the highest absolute correlation value (turquoise, <italic>r</italic> = &#x02212;0.78, <italic>p</italic> = 0.0004) for subsequent analysis, which included 5,286 genes.</p>
<fig id="F5" position="float">
<label>Figure 5</label>
<caption><p>WGCNA of TSE. <bold>(A)</bold> Optimal number of clusters line graph for hierarchical clustering. <bold>(B)</bold> Hierarchical clustering result dendrogram. <bold>(C)</bold> Gene clustering dendrogram based on topological overlap (above) and module color assignments for different gene clusters (below). <bold>(D)</bold> Heatmap showing the correlation between modules and phenotypic traits. <bold>(E)</bold> Scatterplot of Gene Significant (GS) and Module Membership (MM) in the turquoise module. <bold>(F)</bold> Gene enrichment entries related to lipid metabolism in the turquoise module.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fneur-15-1354062-g0005.tif"/>
</fig>
<p>Further correlation analysis using a correlation scatterplot was conducted to assess the relationship between gene module membership and gene significance (<xref ref-type="fig" rid="F5">Figure 5E</xref>), revealing a correlation of <italic>r</italic> = 0.76 and <italic>p</italic> &#x0003C; 1E-200. Module membership represented the relationship between genes and the module, while gene significance indicated the correlation between genes and phenotypic traits. Notably, genes highly significantly associated with a phenotype were often crucial elements within a module significantly associated with that phenotype.</p>
<p>Subsequently, we performed GO enrichment analysis based on the genes in the turquoise module (see <xref ref-type="supplementary-material" rid="SM1">Supplementary Table 4</xref>). The enriched GO functions primarily focused on lipid metabolism-related functions, such as phospholipid biosynthetic process, glycerophospholipid metabolic process, phospholipid metabolic process, glycerolipid metabolic process and others (<xref ref-type="fig" rid="F5">Figure 5F</xref>).</p>
</sec>
<sec>
<title>Risk model construction</title>
<p>We intersected the WGCNA co-expressed module genes related to DELMRGs and TSE (<xref ref-type="fig" rid="F6">Figure 6A</xref>), resulting in 41 differentially expressed genes (DEGs) that were associated with both lipid metabolism and TSE. Subsequently, we employed the SVM-RFE algorithm to select four feature genes from the 41 candidates, which could serve as diagnostic biomarkers for TSE disease grouping (<xref ref-type="fig" rid="F6">Figure 6B</xref>). These four feature genes were ALOX12B, CBS, CPT1C, and DAGLB. We then used logistic regression to construct a risk diagnostic model for TSE disease grouping related to lipid metabolism, where the diagnosis score was calculated as follows: diagnosis score = (&#x02212;64.748046) <sup>&#x0002A;</sup> expression (ALOX12B) &#x0002B; 99.234770 <sup>&#x0002A;</sup> expression (CBS) &#x0002B; (&#x02212;22.507586) <sup>&#x0002A;</sup> expression (CPT1C) &#x0002B; 22.629042 <sup>&#x0002A;</sup> expression (DAGLB).</p>
<fig id="F6" position="float">
<label>Figure 6</label>
<caption><p>Construction of the diagnostic model in the integrated TSE dataset. <bold>(A)</bold> Venn diagram related to lipid metabolism subgroups in DELMRG and WGCNA. <bold>(B)</bold> Curve graph displaying the highest accuracy achieved through SVM-RFE feature selection. <bold>(C)</bold> ROC of the diagnostic model. <bold>(D)</bold> Nomogram for the diagnostic model. <bold>(E)</bold> Expression differences of the feature genes in the diagnostic model between the TSE group and the control group. <bold>(F)</bold> Ranking of the importance of feature genes in the diagnostic model through Friends analysis.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fneur-15-1354062-g0006.tif"/>
</fig>
<p>ROC curve analysis indicated that the constructed risk model exhibited high diagnostic accuracy for TSE, with an AUC value of 0.9592 (<xref ref-type="fig" rid="F6">Figure 6C</xref>). Nomogram analysis was performed to assess the diagnostic capacity of the risk model, and a column chart (Nomogram) was generated (<xref ref-type="fig" rid="F6">Figure 6D</xref>), which revealed that the genes ALOX12B and CBS made significant contributions to the diagnosis of TSE disease. Subsequently, we examined the expression differences of the four feature genes between the TSE group and the control group (<xref ref-type="fig" rid="F6">Figure 6E</xref>). The results showed that the CBS gene had higher expression in the TSE group, while ALOX12B, CPT1C, and DAGLB had lower expression in the TSE group.</p>
<p>Furthermore, we conducted functional analysis (<xref ref-type="fig" rid="F6">Figure 6F</xref>) to determine the importance of these four feature genes in GO functions. The analysis suggested that DAGLB and CBS played critical roles, indicating their potential significance as key genes.</p>
</sec>
<sec>
<title>Immune infiltration analysis</title>
<p>In the integrated TSE dataset, we computed the immune cell infiltration abundances of 22 different immune cell types in the high and low diagnostic score groups (<xref ref-type="fig" rid="F7">Figure 7A</xref>). The results indicated a relatively balanced composition of immune cells in samples from the high and low diagnostic score groups. We separately compared the differences in the infiltration abundances of these 22 immune cell types between the high and low diagnostic score groups (<xref ref-type="fig" rid="F7">Figure 7B</xref>). The results revealed that certain cells such as Plasma cells, T cells regulatory (Tregs), and Macrophages M2 showed significant differences (<italic>p</italic> &#x0003C; 0.05) between the high and low diagnostic score groups. Specifically, Macrophages M2 exhibited higher immune infiltration in the high diagnostic score group, while Plasma cells and T cells regulatory (Tregs) showed higher immune infiltration in the low diagnostic score group.</p>
<fig id="F7" position="float">
<label>Figure 7</label>
<caption><p>Analysis of immune cell infiltration in high and low diagnostic score groups. <bold>(A)</bold> Bar chart of immune cell compositions. <bold>(B)</bold> Box plot of immune cell infiltration. Pearson correlations between immune cells and the risk model genes within the high <bold>(C)</bold> and low <bold>(D)</bold> diagnostic score groups.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fneur-15-1354062-g0007.tif"/>
</fig>
<p>Subsequently, we presented the correlations between immune cells and the four diagnostic model genes in both the high and low diagnostic score groups. In the high diagnostic score group, DAGLB exhibited a significant positive correlation (<italic>r</italic> = 0.91, <italic>p</italic> = 0.0039) with Plasma cells (<xref ref-type="fig" rid="F7">Figure 7C</xref>), whereas in the low diagnostic score group, DAGLB had the highest negative correlation (<italic>r</italic> = &#x02212;0.91, <italic>p</italic> = 0.0049) with Macrophages M0 (<xref ref-type="fig" rid="F7">Figure 7D</xref>).</p>
</sec>
<sec>
<title>Differential functional analysis of high and low diagnostic score groups.</title>
<p>Based on the MSigDB HALLMARK gene set, we conducted GSVA. In <xref ref-type="fig" rid="F8">Figure 8A</xref>, GSVA scores for pathways showed differences between the high and low diagnostic score groups (High vs. Low), with UVRESPONSEUP, SPERMATOGENESIS, OXIDATIVEPHOSPHORYLATION, MYCTARGETSV2 and HEDGEHOGSIGNALING exhibiting higher GSVA scores in the low diagnostic score group. <xref ref-type="fig" rid="F8">Figure 8B</xref> presented the GSVA scores for differential HALLMARK pathways in the high and low diagnostic score groups. <xref ref-type="fig" rid="F8">Figure 8C</xref> indicated the correlation between diagnostic genes and pathways, revealing that DAGLB, CPT1C, and ALOX12B were negatively correlated with most HALLMARK pathways, while CBS was positively correlated with most pathways. <xref ref-type="fig" rid="F8">Figure 8D</xref> demonstrated a positive correlation (<italic>r</italic> = 0.8, <italic>p</italic> = 0.0006) between ALOX12B and MYCTARGETSV2, and <xref ref-type="fig" rid="F8">Figure 8E</xref> showed a negative correlation (<italic>r</italic> = &#x02212;0.85, <italic>p</italic> = 0.0001) between DAGLB and INTERFERONGAMMARESPONSE.</p>
<fig id="F8" position="float">
<label>Figure 8</label>
<caption><p>GSVA pathway analysis of high and low diagnostic score groups. <bold>(A)</bold> Differential HALLMARK pathways in high and low diagnostic score groups in the TSE integrated dataset. <bold>(B)</bold> Heatmap of GSVA scores for HALLMARK Pathways in the TSE integrated dataset. <bold>(C)</bold> Pearson correlation between GSVA Scores of HALLMARK pathways and expression of risk model genes. <bold>(D)</bold> Pearson correlation between the gene ALOX12B and the HALLMARK pathway MYCTARGETSV2. <bold>(E)</bold> Pearson correlation between the gene DAGLB and the HALLMARK pathway INTERFERONGAMMARESPONSE.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fneur-15-1354062-g0008.tif"/>
</fig>
</sec>
<sec>
<title>Single cell data analysis</title>
<p>We downloaded single cell data for epilepsy disease samples from PMID 35739273, which included 85,000 cells (without normal controls). The t-SNE plot revealed nine distinct cell subtypes after unsupervised clustering (<xref ref-type="fig" rid="F9">Figure 9A</xref>). <xref ref-type="fig" rid="F9">Figure 9B</xref> displayed the expression of marker genes for each cell type. The markers for various cell subtypes were derived from PMID 35739273. By examining the expression levels of CD45 (PTPRC), non-immune cells (CD45<sup>low</sup>) and immune cells (CD45<sup>high</sup>) could be distinguished. Cells with CD45<sup>low</sup>CD11b<sup>low</sup> (CD11b was ITGAM) expression were identified as Microglia cells. Oligodendrocyte cells (CD45<sup>low</sup>CD56<sup>high</sup>) were marked by genes such as MAG, MOG, and NCAM1 (CD56), while Endothelial cell markers included CLDN5 and VWF. Smooth Muscle cell markers were ABCC9, and Pericyte cell markers were MYH11 and ACTA2. Among immune cells, CD11b<sup>high</sup>CD14<sup>high</sup> cells were identified as Macrophages. T cell markers included CD3D and CD3E, B cell markers included MS4A1, and a subgroup of cells exhibited CD56<sup>low</sup>CD16<sup>high</sup> expression (CD56 was NCAM1 and CD16 was FCGR3A).</p>
<fig id="F9" position="float">
<label>Figure 9</label>
<caption><p>Analysis of GSE201048 single-cell data. <bold>(A)</bold> Cell type annotation for GSE201048 single-cell data, with each point representing a cell. <bold>(B)</bold> Bubble plot showing the expression of marker genes across various cell types, where deeper red indicates higher gene expression and larger points denote a higher proportion of cells expressing the gene within subpopulations. <bold>(C)</bold> Proportions of different cell types in the samples. <bold>(D)</bold> Scatterplot of AUCell scores for diagnostic model feature genes related to lipid metabolism across single-cell subtypes, with each point representing a cell and darker points indicating higher AUCell scores. <bold>(E)</bold> Violin plot depicting AUCell scores of diagnostic model feature genes related to lipid metabolism in single-cell subtypes, with the x-axis showing single-cell subtypes and the y-axis showing AUCell scores. <bold>(F)</bold> GO enrichment results for upregulated differentially expressed genes (DEGs) in Microglia compared to other cells, with the x-axis representing Gene ratio (the proportion of genes in the category out of all genes) and point size indicating the number of differentially expressed genes in the pathway, darker colors signify smaller <italic>p</italic>-adjusted values. GO, gene ontology.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fneur-15-1354062-g0009.tif"/>
</fig>
<p>Subsequently, we examined the proportions of cell subtypes in each sample (<xref ref-type="fig" rid="F9">Figure 9C</xref>). The results showed that the distribution of various cell subtypes in the samples was relatively even, with no significant bias observed.</p>
<p>Using four risk model genes from the GSE201048 single-cell data, we scored each cell subtype with AUCell (<xref ref-type="fig" rid="F9">Figure 9D</xref>) and visualized the results as box plots (<xref ref-type="fig" rid="F9">Figure 9E</xref>). The findings indicated that Microglia subtypes had the highest AUCell scores, suggesting a strong association between Microglia cells and lipid metabolism functions.</p>
<p>Based on upregulated DEGs (Microglia vs. other cells), a GO analysis was conducted (<xref ref-type="fig" rid="F9">Figure 9F</xref>; <xref ref-type="supplementary-material" rid="SM1">Supplementary Table 5</xref>). The results revealed significant enrichment of genes highly expressed in Microglia cells in pathways related to eicosanoid metabolic processes, regulation of lipid metabolic processes, prostanoid metabolic processes, and other LMRPs.</p></sec></sec>
<sec sec-type="discussion" id="s4">
<title>Discussion</title>
<p>In our research, we identified a total of 103 DELMRGs. To further identify genes related to lipid metabolism for the construction of a diagnostic model for TSE, we took the intersection of the DELMRGs and the co-expression module genes related to TSE by WGCNA. This intersection yielded 41 genes that were both related to lipid metabolism and associated with TSE. We then used the SVM-RFE algorithm to select four feature genes that can serve as diagnostic biomarkers for the classification of TSE. These four feature genes are ALOX12B, CBS, CPT1C, and DAGLB.</p>
<p>Defects in ALOX12B, which subsequently lead to reduced epidermal LOX activity, result in the retention of scales in the stratum corneum of the epidermis. Disruption of the permeability barrier triggered by ALOX12B abnormalities due to an early stop mutation has been previously reported in a mouse model, where a complete lack of barrier formation was demonstrated, leading to rapid dehydration and death in the perinatal period (<xref ref-type="bibr" rid="B35">35</xref>).</p>
<p>CBS is a lytic enzyme that is mainly expressed in the liver. It is the rate-limiting enzyme in the transsulfuration pathway and is responsible for the metabolic conversion of homocysteine to the amino acid cysteine (<xref ref-type="bibr" rid="B36">36</xref>). CBS deficiency leads to hyperhomocysteinemia and impaired production of antioxidants such as hydrogen sulfide. Hepatic CBS plays an important role in the pathogenesis of NAFLD and in the defense against oxidative stress (<xref ref-type="bibr" rid="B37">37</xref>).</p>
<p>CPT1C is a member of the carnitine palmitoyltransferase 1 family and is involved in the regulation of physiological functions such as energy metabolism and feeding (<xref ref-type="bibr" rid="B38">38</xref>). CPT1C can have profound effects on brain physiology and total fatty acid profiles, which can be modulated by nutrients in the diet (<xref ref-type="bibr" rid="B39">39</xref>). Mice deficient in CPT1C exhibit behavioral and metabolic deficits. Overexpression of CPT1C in the brains of developmentally transgenic mice results in cerebellar hypoplasia. Thus, it is clear that CPT1C plays an important role in brain function (<xref ref-type="bibr" rid="B40">40</xref>).</p>
<p>Using DAGLB knockout mice, inactivation of DAGLB in mouse peritoneal macrophages reduced 2-AG, arachidonic acid and prostaglandins (<xref ref-type="bibr" rid="B41">41</xref>). A corresponding reduction in lipopolysaccharide-induced TNF-&#x003B1; release was also observed. These findings suggest a role for DAGLB in the lipid network that regulates the inflammatory response in macrophages (<xref ref-type="bibr" rid="B42">42</xref>).</p>
<p>The results of immune infiltration showed Macrophages.M2, higher immune infiltration in the high diagnostic score group and Plasma.cells, Tregs higher immune infiltration in the low diagnostic score group. Cerebral peripheral vascular macrophages are a special population of macrophages, and cerebral peripheral vascular macrophages are involved in the pathogenesis of neurodegenerative diseases, cerebrovascular dysfunction, autoimmune diseases, traumatic brain injury and epilepsy. They can act in a protective or deleterious manner on disease processes and stages (<xref ref-type="bibr" rid="B43">43</xref>). The number of Tregs in the brain was negatively correlated with seizure frequency in patients with epilepsy (<xref ref-type="bibr" rid="B44">44</xref>). Depletion of intracerebral Tregs promoted astrocytosis, microglia, inflammatory cytokine production, oxidative stress and neuronal loss in the hippocampus after status epilepticus seizures (<xref ref-type="bibr" rid="B45">45</xref>). Modulation of Tregs in epileptic brain tissue has therapeutic potential.</p>
<p>Microglia cells are highly correlated with lipid metabolic functions, and GO analysis showed that highly expressed genes in Microglia cells were significantly enriched in lipid metabolic pathways related to icosanoid metabolic process, regulation of lipid metabolic process, prostanoid metabolic process and so on. metabolic process, regulation of lipid metabolic process, prostanoid metabolic process and other lipid metabolic pathways. Myelin is required for the function of nerve axons in the central nervous system, and microglia are essential for maintaining myelin health. Oligodendrocyte status is associated with altered lipid metabolism (<xref ref-type="bibr" rid="B46">46</xref>).</p>
<p>Evidence accumulated over the past two decades has significantly bolstered the hypothesis that neuroinflammation plays a crucial role in epileptogenesis. This includes the activation of microglia and astrocytes, a cascade of inflammatory mediators being released, and the infiltration of peripheral immune cells from the bloodstream into the brain. Concurrently, an expanding corpus of preclinical studies indicates that anti-inflammatory agents, targeting key inflammatory components, demonstrate efficacy and hold promise in the treatment of epilepsy (<xref ref-type="bibr" rid="B47">47</xref>).</p>
<p>The pathophysiological consequences of microglial activation encompass exacerbated inflammation, modulation of neuronal activity, and the provocation of epileptic seizures (<xref ref-type="bibr" rid="B48">48</xref>). These studies collectively reinforce our belief in the significant role of microglia in TSE.</p>
<p>Furthermore, the mTOR signaling pathway is pivotal in neural development and neural circuit formation, primarily through the regulation of protein synthesis and autophagy. In the brain, inhibition of mTOR signaling diminishes the formation of autophagosomes, elevates lipopolysaccharide-induced proinflammatory cytokines in microglia, attenuates microglial activation, and mitigates astrocyte migration and proliferation, ultimately leading to a reduction in seizure severity (<xref ref-type="bibr" rid="B49">49</xref>).</p>
<p>We studied epilepsy due to tuberous sclerosis. But the last single-cell plot was validated with epilepsy due to cortical dysplasia. TSC and focal cortical dysplasia were focal malformations of cortical development highly associated with refractory epilepsy. TSC and FCD were mTOR disorders caused by a series of pathogenic variants in the target of rapamycin mechanism (mTOR) pathway genes leading to differential activation of mTOR signal (<xref ref-type="bibr" rid="B50">50</xref>). Considering that the electrical mechanisms of epilepsy are relatively similar, we extended the diagnostic genes to another type of epilepsy, and in this way did a single-cell analysis.</p></sec>
<sec sec-type="conclusions" id="s5">
<title>Conclusion</title>
<p>Our research identified potential DELMRGs (ALOX12B, CBS, CPT1C, and DAGLB) in epilepsy, which may provide new ideas for studying the pathogenesis of Epilepsy. In the future, more experiments would be needed to further substantiate our conclusions.</p></sec>
<sec sec-type="data-availability" id="s6">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="supplementary-material" rid="SM1">Supplementary material</xref>, further inquiries can be directed to the corresponding authors.</p></sec>
<sec sec-type="ethics-statement" id="s7">
<title>Ethics statement</title>
<p>Ethical approval was not required for the study involving humans in accordance with the local legislation and institutional requirements. Written informed consent to participate in this study was not required from the participants or the participants&#x00027; legal guardians/next of kin in accordance with the national legislation and the institutional requirements.</p></sec>
<sec sec-type="author-contributions" id="s8">
<title>Author contributions</title>
<p>WW: Conceptualization, Data curation, Writing&#x02014;review &#x00026; editing. RY: Software, Writing&#x02014;original draft. HW: Writing&#x02014;original draft. ZX: Methodology, Writing&#x02014;review &#x00026; editing. YC: Writing&#x02014;original draft. AW: Writing&#x02014;original draft. XF: Conceptualization, Data curation, Writing&#x02014;review &#x00026; editing. WF: Conceptualization, Methodology, Writing&#x02014;review &#x00026; editing.</p></sec>
</body>
<back>
<sec sec-type="funding-information" id="s9">
<title>Funding</title>
<p>The author (s) declare that no financial support was received for the research, authorship, and/or publication of this article.</p>
</sec>
<sec sec-type="COI-statement" id="conf1">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="disclaimer" id="s10">
<title>Publisher&#x00027;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec sec-type="supplementary-material" id="s11">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fneur.2024.1354062/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fneur.2024.1354062/full#supplementary-material</ext-link></p>
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<supplementary-material xlink:href="Data_Sheet_2.ZIP" id="SM3" mimetype="application/zip" xmlns:xlink="http://www.w3.org/1999/xlink"/>
<supplementary-material xlink:href="Data_Sheet_3.ZIP" id="SM4" mimetype="application/zip" xmlns:xlink="http://www.w3.org/1999/xlink"/></sec>
<ref-list>
<title>References</title>
<ref id="B1">
<label>1.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>M&#x000FC;ller</surname> <given-names>AR</given-names></name> <name><surname>Luijten</surname> <given-names>MAJ</given-names></name> <name><surname>Haverman</surname> <given-names>L</given-names></name> <name><surname>de Ranitz-Greven</surname> <given-names>WL</given-names></name> <name><surname>Janssens</surname> <given-names>P</given-names></name> <name><surname>Rietman</surname> <given-names>AB</given-names></name> <etal/></person-group>. <article-title>Understanding the impact of tuberous sclerosis complex: development and validation of the TSC-PROM</article-title>. <source>Bmc Med.</source> (<year>2023</year>) <volume>21</volume>:<fpage>298</fpage>. <pub-id pub-id-type="doi">10.1186/s12916-023-03012-4</pub-id><pub-id pub-id-type="pmid">37553648</pub-id></citation></ref>
<ref id="B2">
<label>2.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Aronica</surname> <given-names>E</given-names></name> <name><surname>Specchio</surname> <given-names>N</given-names></name> <name><surname>Luinenburg</surname> <given-names>MJ</given-names></name> <name><surname>Curatolo</surname> <given-names>P</given-names></name></person-group>. <article-title>Epileptogenesis in tuberous sclerosis complex-related developmental and epileptic encephalopathy</article-title>. <source>Brain.</source> (<year>2023</year>) <volume>146</volume>:<fpage>2694</fpage>&#x02013;<lpage>710</lpage>. <pub-id pub-id-type="doi">10.1093/brain/awad048</pub-id><pub-id pub-id-type="pmid">36806388</pub-id></citation></ref>
<ref id="B3">
<label>3.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Curatolo</surname> <given-names>P</given-names></name></person-group>. <article-title>Mechanistic target of rapamycin (mTOR) in tuberous sclerosis complex-associated epilepsy</article-title>. <source>Pediatr Neurol.</source> (<year>2015</year>) <volume>52</volume>:<fpage>281</fpage>&#x02013;<lpage>89</lpage>. <pub-id pub-id-type="doi">10.1016/j.pediatrneurol.2014.10.028</pub-id><pub-id pub-id-type="pmid">25591831</pub-id></citation></ref>
<ref id="B4">
<label>4.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Karalis</surname> <given-names>V</given-names></name> <name><surname>Caval-Holme</surname> <given-names>F</given-names></name> <name><surname>Bateup</surname> <given-names>HS</given-names></name></person-group>. <article-title>Raptor downregulation rescues neuronal phenotypes in mouse models of Tuberous Sclerosis Complex</article-title>. <source>Nat Commun</source>. (<year>2022</year>) <volume>13</volume>:<fpage>4665</fpage>. <pub-id pub-id-type="doi">10.1038/s41467-022-31961-6</pub-id><pub-id pub-id-type="pmid">35945201</pub-id></citation></ref>
<ref id="B5">
<label>5.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Mulder</surname> <given-names>F</given-names></name> <name><surname>Peeters</surname> <given-names>E</given-names></name> <name><surname>Westerink</surname> <given-names>J</given-names></name> <name><surname>Zwartkruis</surname> <given-names>F</given-names></name> <name><surname>de Ranitz-Greven</surname> <given-names>WL</given-names></name></person-group>. <article-title>The long-term effect of mTOR inhibition on lipid and glucose metabolism in tuberous sclerosis complex: data from the Dutch TSC registry</article-title>. <source>Orphanet J Rare Dis.</source> (<year>2022</year>) <volume>17</volume>:<fpage>252</fpage>. <pub-id pub-id-type="doi">10.1186/s13023-022-02385-8</pub-id><pub-id pub-id-type="pmid">35804402</pub-id></citation></ref>
<ref id="B6">
<label>6.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Moavero</surname> <given-names>R</given-names></name> <name><surname>Muhlebner</surname> <given-names>A</given-names></name> <name><surname>Luinenburg</surname> <given-names>MJ</given-names></name> <name><surname>Craiu</surname> <given-names>D</given-names></name> <name><surname>Aronica</surname> <given-names>E</given-names></name> <name><surname>Curatolo</surname> <given-names>P</given-names></name></person-group>. <article-title>Genetic pathogenesis of the epileptogenic lesions in tuberous sclerosis complex: therapeutic targeting of the mTOR pathway</article-title>. <source>Epilepsy Behav.</source> (<year>2022</year>) <volume>131</volume>:<fpage>107713</fpage>. <pub-id pub-id-type="doi">10.1016/j.yebeh.2020.107713</pub-id><pub-id pub-id-type="pmid">33431351</pub-id></citation></ref>
<ref id="B7">
<label>7.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Wang</surname> <given-names>C</given-names></name> <name><surname>Haas</surname> <given-names>MA</given-names></name> <name><surname>Yang</surname> <given-names>F</given-names></name> <name><surname>Yeo</surname> <given-names>S</given-names></name> <name><surname>Okamoto</surname> <given-names>T</given-names></name> <name><surname>Chen</surname> <given-names>S</given-names></name> <etal/></person-group>. <article-title>Autophagic lipid metabolism sustains mTORC1 activity in TSC-deficient neural stem cells</article-title>. <source>Nat Metab.</source> (<year>2019</year>) <volume>1</volume>:<fpage>1127</fpage>&#x02013;<lpage>40</lpage>. <pub-id pub-id-type="doi">10.1038/s42255-019-0137-5</pub-id><pub-id pub-id-type="pmid">32577608</pub-id></citation></ref>
<ref id="B8">
<label>8.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Schubert-Bast</surname> <given-names>S</given-names></name> <name><surname>Strzelczyk</surname> <given-names>A</given-names></name></person-group>. <article-title>Review of the treatment options for epilepsy in tuberous sclerosis complex: towards precision medicine</article-title>. <source>Ther Adv Neurol Disord.</source> (<year>2021</year>) <volume>14</volume>:<fpage>91655108</fpage>. <pub-id pub-id-type="doi">10.1177/17562864211031100</pub-id><pub-id pub-id-type="pmid">34349839</pub-id></citation></ref>
<ref id="B9">
<label>9.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Rafalski</surname> <given-names>VA</given-names></name> <name><surname>Brunet</surname> <given-names>A</given-names></name></person-group>. <article-title>Energy metabolism in adult neural stem cell fate</article-title>. <source>Prog Neurobiol.</source> (<year>2011</year>) <volume>93</volume>:<fpage>182</fpage>&#x02013;<lpage>203</lpage>. <pub-id pub-id-type="doi">10.1016/j.pneurobio.2010.10.007</pub-id><pub-id pub-id-type="pmid">21056618</pub-id></citation></ref>
<ref id="B10">
<label>10.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Xie</surname> <given-names>Z</given-names></name> <name><surname>Jones</surname> <given-names>A</given-names></name> <name><surname>Deeney</surname> <given-names>JT</given-names></name> <name><surname>Hur</surname> <given-names>SK</given-names></name> <name><surname>Bankaitis</surname> <given-names>VA</given-names></name></person-group>. <article-title>Inborn errors of long-chain fatty acid &#x003B2;-oxidation link neural stem cell self-renewal to autism</article-title>. <source>Cell Rep.</source> (<year>2016</year>) <volume>14</volume>:<fpage>991</fpage>&#x02013;<lpage>99</lpage>. <pub-id pub-id-type="doi">10.1016/j.celrep.2016.01.004</pub-id><pub-id pub-id-type="pmid">26832401</pub-id></citation></ref>
<ref id="B11">
<label>11.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Han</surname> <given-names>FY</given-names></name> <name><surname>Conboy Schmidt</surname> <given-names>L</given-names></name> <name><surname>Rybachuk</surname> <given-names>G</given-names></name> <name><surname>Volk</surname> <given-names>HA</given-names></name> <name><surname>Zanghi</surname> <given-names>B</given-names></name> <name><surname>Pan</surname> <given-names>Y</given-names></name> <etal/></person-group>. <article-title>Dietary medium chain triglycerides for management of epilepsy: new data from human, dog, and rodent studies</article-title>. <source>Epilepsia.</source> (<year>2021</year>) <volume>62</volume>:<fpage>1790</fpage>&#x02013;<lpage>806</lpage>. <pub-id pub-id-type="doi">10.1111/epi.16972</pub-id><pub-id pub-id-type="pmid">34169513</pub-id></citation></ref>
<ref id="B12">
<label>12.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Barrett</surname> <given-names>T</given-names></name> <name><surname>Wilhite</surname> <given-names>SE</given-names></name> <name><surname>Ledoux</surname> <given-names>P</given-names></name> <name><surname>Evangelista</surname> <given-names>C</given-names></name> <name><surname>Kim</surname> <given-names>IF</given-names></name> <name><surname>Tomashevsky</surname> <given-names>M</given-names></name> <etal/></person-group>. <article-title>NCBI GEO: archive for functional genomics data sets&#x02014;update</article-title>. <source>Nucl Acids Res.</source> (<year>2012</year>) <volume>41</volume>:<fpage>D991</fpage>&#x02013;<lpage>95</lpage>. <pub-id pub-id-type="doi">10.1093/nar/gks1193</pub-id></citation>
</ref>
<ref id="B13">
<label>13.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Boer</surname> <given-names>K</given-names></name> <name><surname>Crino</surname> <given-names>PB</given-names></name> <name><surname>Gorter</surname> <given-names>JA</given-names></name> <name><surname>Nellist</surname> <given-names>M</given-names></name> <name><surname>Jansen</surname> <given-names>FE</given-names></name> <name><surname>Spliet</surname> <given-names>WG</given-names></name> <etal/></person-group>. <article-title>Gene expression analysis of tuberous sclerosis complex cortical tubers reveals increased expression of adhesion and inflammatory factors</article-title>. <source>Brain Pathol.</source> (<year>2010</year>) <volume>20</volume>:<fpage>704</fpage>&#x02013;<lpage>19</lpage>. <pub-id pub-id-type="doi">10.1111/j.1750-3639.2009.00341.x</pub-id><pub-id pub-id-type="pmid">19912235</pub-id></citation></ref>
<ref id="B14">
<label>14.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Kan</surname> <given-names>Y</given-names></name> <name><surname>Feng</surname> <given-names>L</given-names></name> <name><surname>Si</surname> <given-names>Y</given-names></name> <name><surname>Zhou</surname> <given-names>Z</given-names></name> <name><surname>Wang</surname> <given-names>W</given-names></name> <name><surname>Yang</surname> <given-names>J</given-names></name></person-group>. <article-title>Pathogenesis and therapeutic targets of focal cortical dysplasia based on bioinformatics analysis</article-title>. <source>Neurochem Res.</source> (<year>2022</year>) <volume>47</volume>:<fpage>3506</fpage>&#x02013;<lpage>21</lpage>. <pub-id pub-id-type="doi">10.1007/s11064-022-03715-9</pub-id><pub-id pub-id-type="pmid">35945307</pub-id></citation></ref>
<ref id="B15">
<label>15.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Kumar</surname> <given-names>P</given-names></name> <name><surname>Lim</surname> <given-names>A</given-names></name> <name><surname>Hazirah</surname> <given-names>SN</given-names></name> <name><surname>Chua</surname> <given-names>CJH</given-names></name> <name><surname>Ngoh</surname> <given-names>A</given-names></name> <name><surname>Poh</surname> <given-names>SL</given-names></name> <etal/></person-group>. <article-title>Single-cell transcriptomics and surface epitope detection in human brain epileptic lesions identifies pro-inflammatory signaling</article-title>. <source>Nat Neurosci.</source> (<year>2022</year>) <volume>25</volume>:<fpage>956</fpage>&#x02013;<lpage>66</lpage>. <pub-id pub-id-type="doi">10.1038/s41593-022-01095-5</pub-id><pub-id pub-id-type="pmid">35739273</pub-id></citation></ref>
<ref id="B16">
<label>16.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Zhu</surname> <given-names>M</given-names></name> <name><surname>Zeng</surname> <given-names>Q</given-names></name> <name><surname>Fan</surname> <given-names>T</given-names></name> <name><surname>Lei</surname> <given-names>Y</given-names></name> <name><surname>Wang</surname> <given-names>F</given-names></name> <name><surname>Zheng</surname> <given-names>S</given-names></name> <etal/></person-group>. <article-title>Clinical significance and immunometabolism landscapes of a novel recurrence-associated lipid metabolism signature in early-stage lung adenoc arcinoma: a comprehensive analysis</article-title>. <source>Front Immunol.</source> (<year>2022</year>) <volume>13</volume>:<fpage>783495</fpage>. <pub-id pub-id-type="doi">10.3389/fimmu.2022.783495</pub-id><pub-id pub-id-type="pmid">35222371</pub-id></citation></ref>
<ref id="B17">
<label>17.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Wang</surname> <given-names>Y</given-names></name> <name><surname>Xu</surname> <given-names>J</given-names></name> <name><surname>Fang</surname> <given-names>Y</given-names></name> <name><surname>Gu</surname> <given-names>J</given-names></name> <name><surname>Zhao</surname> <given-names>F</given-names></name> <name><surname>Tang</surname> <given-names>Y</given-names></name> <etal/></person-group>. <article-title>Comprehensive analysis of a novel signature incorporating lipid metabolism and immune-related genes for assessing prognosis and immune landscape in lung adenocarcinoma</article-title>. <source>Front Immunol.</source> (<year>2022</year>) <volume>13</volume>:<fpage>950001</fpage>. <pub-id pub-id-type="doi">10.3389/fimmu.2022.950001</pub-id><pub-id pub-id-type="pmid">36091041</pub-id></citation></ref>
<ref id="B18">
<label>18.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Li</surname> <given-names>J</given-names></name> <name><surname>Zhang</surname> <given-names>S</given-names></name> <name><surname>Chen</surname> <given-names>S</given-names></name> <name><surname>Yuan</surname> <given-names>Y</given-names></name> <name><surname>Zuo</surname> <given-names>M</given-names></name> <name><surname>Li</surname> <given-names>T</given-names></name> <etal/></person-group>. <article-title>Lipid metabolism-related gene signature predicts prognosis and depicts tumor microenvironment immune landscape in gliomas</article-title>. <source>Front Immunol.</source> (<year>2023</year>) <volume>14</volume>:<fpage>1021678</fpage>. <pub-id pub-id-type="doi">10.3389/fimmu.2023.1021678</pub-id><pub-id pub-id-type="pmid">36860853</pub-id></citation></ref>
<ref id="B19">
<label>19.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Shen</surname> <given-names>L</given-names></name> <name><surname>Huang</surname> <given-names>H</given-names></name> <name><surname>Li</surname> <given-names>J</given-names></name> <name><surname>Chen</surname> <given-names>W</given-names></name> <name><surname>Yao</surname> <given-names>Y</given-names></name> <name><surname>Hu</surname> <given-names>J</given-names></name> <etal/></person-group>. <article-title>Exploration of prognosis and immunometabolism landscapes in ER&#x0002B; breast cancer based on a novel lipid metabolism-related signature</article-title>. <source>Front Immunol.</source> (<year>2023</year>) <volume>14</volume>:<fpage>1199465</fpage>. <pub-id pub-id-type="doi">10.3389/fimmu.2023.1199465</pub-id><pub-id pub-id-type="pmid">37469520</pub-id></citation></ref>
<ref id="B20">
<label>20.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Leek</surname> <given-names>JT</given-names></name> <name><surname>Johnson</surname> <given-names>WE</given-names></name> <name><surname>Parker</surname> <given-names>HS</given-names></name> <name><surname>Jaffe</surname> <given-names>AE</given-names></name> <name><surname>Storey</surname> <given-names>JD</given-names></name></person-group>. <article-title>The sva package for removing batch effects and other unwanted variation in high-throughput experiments</article-title>. <source>Bioinformatics.</source> (<year>2012</year>) <volume>28</volume>:<fpage>882</fpage>&#x02013;<lpage>83</lpage>. <pub-id pub-id-type="doi">10.1093/bioinformatics/bts034</pub-id><pub-id pub-id-type="pmid">22257669</pub-id></citation></ref>
<ref id="B21">
<label>21.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Ritchie</surname> <given-names>ME</given-names></name> <name><surname>Phipson</surname> <given-names>B</given-names></name> <name><surname>Wu</surname> <given-names>D</given-names></name> <name><surname>Hu</surname> <given-names>Y</given-names></name> <name><surname>Law</surname> <given-names>CW</given-names></name> <name><surname>Shi</surname> <given-names>W</given-names></name> <etal/></person-group>. <article-title>limma powers differential expression analyses for RNA-sequencing and microarray studies</article-title>. <source>Nucleic Acids Res.</source> (<year>2015</year>) <volume>43</volume>:<fpage>e47</fpage>. <pub-id pub-id-type="doi">10.1093/nar/gkv007</pub-id><pub-id pub-id-type="pmid">25605792</pub-id></citation></ref>
<ref id="B22">
<label>22.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Liberzon</surname> <given-names>A</given-names></name> <name><surname>Birger</surname> <given-names>C</given-names></name> <name><surname>Thorvaldsd&#x000F3;ttir</surname> <given-names>H</given-names></name> <name><surname>Ghandi</surname> <given-names>M</given-names></name> <name><surname>Mesirov</surname> <given-names>JP</given-names></name> <name><surname>Tamayo</surname> <given-names>P</given-names></name></person-group>. <article-title>The molecular signatures database (MSigDB) hallmark gene set collection</article-title>. <source>Cell Syst.</source> (<year>2015</year>) <volume>1</volume>:<fpage>417</fpage>&#x02013;<lpage>25</lpage>. <pub-id pub-id-type="doi">10.1016/j.cels.2015.12.004</pub-id><pub-id pub-id-type="pmid">26771021</pub-id></citation></ref>
<ref id="B23">
<label>23.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Wu</surname> <given-names>T</given-names></name> <name><surname>Hu</surname> <given-names>E</given-names></name> <name><surname>Xu</surname> <given-names>S</given-names></name> <name><surname>Chen</surname> <given-names>M</given-names></name> <name><surname>Guo</surname> <given-names>P</given-names></name> <name><surname>Dai</surname> <given-names>Z</given-names></name> <etal/></person-group>. <article-title>clusterProfiler 40: a universal enrichment tool for interpreting omics data</article-title>. <source>Innovation.</source> (<year>2021</year>) <volume>2</volume>:<fpage>100141</fpage>. <pub-id pub-id-type="doi">10.1016/j.xinn.2021.100141</pub-id><pub-id pub-id-type="pmid">34557778</pub-id></citation></ref>
<ref id="B24">
<label>24.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Gustavsson</surname> <given-names>EK</given-names></name> <name><surname>Zhang</surname> <given-names>D</given-names></name> <name><surname>Reynolds</surname> <given-names>RH</given-names></name> <name><surname>Garcia-Ruiz</surname> <given-names>S</given-names></name> <name><surname>Ryten</surname> <given-names>M</given-names></name></person-group>. <article-title>ggtranscript: an R package for the visualization and interpretation of transcript isoforms usingggplot2</article-title>. <source>Bioinformatics.</source> (<year>2022</year>) <volume>38</volume>:<fpage>3844</fpage>&#x02013;<lpage>46</lpage>. <pub-id pub-id-type="doi">10.1093/bioinformatics/btac409</pub-id><pub-id pub-id-type="pmid">35751589</pub-id></citation></ref>
<ref id="B25">
<label>25.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>H&#x000E4;nzelmann</surname> <given-names>S</given-names></name> <name><surname>Castelo</surname> <given-names>R</given-names></name> <name><surname>Guinney</surname> <given-names>J</given-names></name></person-group>. <article-title>GSVA: gene set variation analysis for microarray and RNA-seq data</article-title>. <source>BMC Bioinformat.</source> (<year>2013</year>) <volume>14</volume>:<fpage>7</fpage>. <pub-id pub-id-type="doi">10.1186/1471-2105-14-7</pub-id><pub-id pub-id-type="pmid">23323831</pub-id></citation></ref>
<ref id="B26">
<label>26.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Szklarczyk</surname> <given-names>D</given-names></name> <name><surname>Gable</surname> <given-names>AL</given-names></name> <name><surname>Nastou</surname> <given-names>KC</given-names></name> <name><surname>Lyon</surname> <given-names>D</given-names></name> <name><surname>Kirsch</surname> <given-names>R</given-names></name> <name><surname>Pyysalo</surname> <given-names>S</given-names></name> <etal/></person-group>. <article-title>The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets</article-title>. <source>Nucl Acids Res.</source> (<year>2021</year>) <volume>49</volume>:<fpage>D605</fpage>&#x02013;<lpage>12</lpage>. <pub-id pub-id-type="doi">10.1093/nar/gkaa1074</pub-id><pub-id pub-id-type="pmid">33237311</pub-id></citation></ref>
<ref id="B27">
<label>27.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Shannon</surname> <given-names>P</given-names></name> <name><surname>Markiel</surname> <given-names>A</given-names></name> <name><surname>Ozier</surname> <given-names>O</given-names></name> <name><surname>Baliga</surname> <given-names>NS</given-names></name> <name><surname>Wang</surname> <given-names>JT</given-names></name> <name><surname>Ramage</surname> <given-names>D</given-names></name> <etal/></person-group>. <article-title>Cytoscape: a software environment for integrated models of biomolecular interaction networks</article-title>. <source>Genome Res.</source> (<year>2003</year>) <volume>13</volume>:<fpage>2498</fpage>&#x02013;<lpage>504</lpage>. <pub-id pub-id-type="doi">10.1101/gr.1239303</pub-id><pub-id pub-id-type="pmid">14597658</pub-id></citation></ref>
<ref id="B28">
<label>28.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Chin</surname> <given-names>C</given-names></name> <name><surname>Chen</surname> <given-names>S</given-names></name> <name><surname>Wu</surname> <given-names>H</given-names></name> <name><surname>Ho</surname> <given-names>C</given-names></name> <name><surname>Ko</surname> <given-names>M</given-names></name> <name><surname>Lin</surname> <given-names>C</given-names></name></person-group>. <article-title>cytoHubba: identifying hub objects and sub-networks from complex interactome</article-title>. <source>BMC Syst Biol.</source> (<year>2014</year>) 8(<supplement>Suppl. 4</supplement>):S11. <pub-id pub-id-type="doi">10.1186/1752-0509-8-S4-S11</pub-id><pub-id pub-id-type="pmid">25521941</pub-id></citation></ref>
<ref id="B29">
<label>29.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Sanz</surname> <given-names>H</given-names></name> <name><surname>Valim</surname> <given-names>C</given-names></name> <name><surname>Vegas</surname> <given-names>E</given-names></name> <name><surname>Oller</surname> <given-names>JM</given-names></name> <name><surname>Reverter</surname> <given-names>F</given-names></name></person-group>. <article-title>SVM-RFE: selection and visualization of the most relevant features through non-linear kernels</article-title>. <source>BMC Bioinformat.</source> (<year>2018</year>) <volume>19</volume>:<fpage>432</fpage>. <pub-id pub-id-type="doi">10.1186/s12859-018-2451-4</pub-id><pub-id pub-id-type="pmid">30453885</pub-id></citation></ref>
<ref id="B30">
<label>30.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Gu</surname> <given-names>Z</given-names></name> <name><surname>Eils</surname> <given-names>R</given-names></name> <name><surname>Schlesner</surname> <given-names>M</given-names></name></person-group>. <article-title>Complex heatmaps reveal patterns and correlations in multidimensional genomic data</article-title>. <source>Bioinformatics.</source> (<year>2016</year>) <volume>32</volume>:<fpage>2847</fpage>&#x02013;<lpage>49</lpage>. <pub-id pub-id-type="doi">10.1093/bioinformatics/btw313</pub-id><pub-id pub-id-type="pmid">27207943</pub-id></citation></ref>
<ref id="B31">
<label>31.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Hao</surname> <given-names>Y</given-names></name> <name><surname>Hao</surname> <given-names>S</given-names></name> <name><surname>Andersen-Nissen</surname> <given-names>E</given-names></name> <name><surname>Mauck</surname> <given-names>WMR</given-names></name> <name><surname>Zheng</surname> <given-names>S</given-names></name> <name><surname>Butler</surname> <given-names>A</given-names></name> <etal/></person-group>. <article-title>Integrated analysis of multimodal single-cell data</article-title>. <source>Cell.</source> (<year>2021</year>) <volume>184</volume>:<fpage>3573</fpage>&#x02013;<lpage>87</lpage>. <pub-id pub-id-type="doi">10.1016/j.cell.2021.04.048</pub-id></citation>
</ref>
<ref id="B32">
<label>32.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Van de Sande</surname> <given-names>B</given-names></name> <name><surname>Flerin</surname> <given-names>C</given-names></name> <name><surname>Davie</surname> <given-names>K</given-names></name> <name><surname>De Waegeneer</surname> <given-names>M</given-names></name> <name><surname>Hulselmans</surname> <given-names>G</given-names></name> <name><surname>Aibar</surname> <given-names>S</given-names></name> <etal/></person-group>. <article-title>A scalable SCENIC workflow for single-cell gene regulatory network analysis</article-title>. <source>Nat Protoc.</source> (<year>2020</year>) <volume>15</volume>:<fpage>2247</fpage>&#x02013;<lpage>76</lpage>. <pub-id pub-id-type="doi">10.1038/s41596-020-0336-2</pub-id><pub-id pub-id-type="pmid">32561888</pub-id></citation></ref>
<ref id="B33">
<label>33.</label>
<citation citation-type="journal"><person-group person-group-type="author"><collab>The Gene Ontology Consortium</collab></person-group>. <article-title>The gene ontology resource: 20 years and still GOing strong</article-title>. <source>Nucleic Acids Res.</source> (<year>2019</year>) <volume>47</volume>:<fpage>D330</fpage>&#x02013;<lpage>38</lpage>. <pub-id pub-id-type="doi">10.1093/nar/gky1055</pub-id></citation>
</ref>
<ref id="B34">
<label>34.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Ashburner</surname> <given-names>M</given-names></name> <name><surname>Ball</surname> <given-names>CA</given-names></name> <name><surname>Blake</surname> <given-names>JA</given-names></name> <name><surname>Botstein</surname> <given-names>D</given-names></name> <name><surname>Butler</surname> <given-names>H</given-names></name> <name><surname>Cherry</surname> <given-names>JM</given-names></name> <etal/></person-group>. <article-title>Gene ontology: tool for the unification of biology. The Gene Ontology Consortium</article-title>. <source>Nat Genet.</source> (<year>2000</year>) <volume>25</volume>:<fpage>25</fpage>&#x02013;<lpage>9</lpage>. <pub-id pub-id-type="doi">10.1038/75556</pub-id><pub-id pub-id-type="pmid">10802651</pub-id></citation></ref>
<ref id="B35">
<label>35.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Bland</surname> <given-names>PJ</given-names></name> <name><surname>Chronnell</surname> <given-names>C</given-names></name> <name><surname>Plagnol</surname> <given-names>V</given-names></name> <name><surname>Kayserili</surname> <given-names>H</given-names></name> <name><surname>Kelsell</surname> <given-names>DP</given-names></name></person-group>. <article-title>A severe collodion phenotype in the newborn period associated with a homozygous missense mutation in ALOX12B</article-title>. <source>Br J Dermatol.</source> (<year>2015</year>) <volume>173</volume>:<fpage>285</fpage>&#x02013;<lpage>87</lpage>. <pub-id pub-id-type="doi">10.1111/bjd.13627</pub-id><pub-id pub-id-type="pmid">25524567</pub-id></citation></ref>
<ref id="B36">
<label>36.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Latorre</surname> <given-names>J</given-names></name> <name><surname>Aroca</surname> <given-names>A</given-names></name> <name><surname>Fern&#x000E1;ndez-Real</surname> <given-names>JM</given-names></name> <name><surname>Romero</surname> <given-names>LC</given-names></name> <name><surname>Moreno-Navarrete</surname> <given-names>JM</given-names></name></person-group>. <article-title>The combined partial knockdown of CBS and MPST genes induces inflammation, impairs adipocyte function-related gene expression and disrupts protein persulfidation in human adipocytes</article-title>. <source>Antioxidants.</source> (<year>2022</year>) <volume>11</volume>:<fpage>1095</fpage>. <pub-id pub-id-type="doi">10.3390/antiox11061095</pub-id><pub-id pub-id-type="pmid">35739994</pub-id></citation></ref>
<ref id="B37">
<label>37.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Lambooy</surname> <given-names>S</given-names></name> <name><surname>Heida</surname> <given-names>A</given-names></name> <name><surname>Joschko</surname> <given-names>C</given-names></name> <name><surname>Nakladal</surname> <given-names>D</given-names></name> <name><surname>van Buiten</surname> <given-names>A</given-names></name> <name><surname>Kloosterhuis</surname> <given-names>N</given-names></name> <etal/></person-group>. <article-title>Selective hepatic Cbs knockout aggravates liver damage, endothelial dysfunction and ROS stress in mice fed a western diet</article-title>. <source>Int J Mol Sci.</source> (<year>2023</year>) <volume>24</volume>:<fpage>7019</fpage>. <pub-id pub-id-type="doi">10.3390/ijms24087019</pub-id><pub-id pub-id-type="pmid">37108182</pub-id></citation></ref>
<ref id="B38">
<label>38.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Zhang</surname> <given-names>H</given-names></name> <name><surname>Li</surname> <given-names>X</given-names></name> <name><surname>Liao</surname> <given-names>D</given-names></name> <name><surname>Luo</surname> <given-names>P</given-names></name> <name><surname>Jiang</surname> <given-names>X</given-names></name></person-group>. <article-title>Alpha/beta-hydrolase domain-containing 6: signaling and function in the central nervous system</article-title>. <source>Front Pharmacol.</source> (<year>2021</year>) <volume>12</volume>:<fpage>784202</fpage>. <pub-id pub-id-type="doi">10.3389/fphar.2021.784202</pub-id><pub-id pub-id-type="pmid">34925039</pub-id></citation></ref>
<ref id="B39">
<label>39.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Reamy</surname> <given-names>AA</given-names></name> <name><surname>Wolfgang</surname> <given-names>MJ</given-names></name></person-group>. <article-title>Carnitine palmitoyltransferase-1c gain-of-function in the brain results in postnatal microencephaly</article-title>. <source>J Neurochem.</source> (<year>2011</year>) <volume>118</volume>:<fpage>388</fpage>&#x02013;<lpage>98</lpage>. <pub-id pub-id-type="doi">10.1111/j.1471-4159.2011.07312.x</pub-id><pub-id pub-id-type="pmid">21592121</pub-id></citation></ref>
<ref id="B40">
<label>40.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Lee</surname> <given-names>J</given-names></name> <name><surname>Wolfgang</surname> <given-names>MJ</given-names></name></person-group>. <article-title>Metabolomic profiling reveals a role for CPT1c in neuronal oxidative metabolism</article-title>. <source>BMC Biochem.</source> (<year>2012</year>) <volume>13</volume>:<fpage>23</fpage>. <pub-id pub-id-type="doi">10.1186/1471-2091-13-23</pub-id><pub-id pub-id-type="pmid">23098614</pub-id></citation></ref>
<ref id="B41">
<label>41.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Liu</surname> <given-names>Z</given-names></name> <name><surname>Yang</surname> <given-names>N</given-names></name> <name><surname>Dong</surname> <given-names>J</given-names></name> <name><surname>Tian</surname> <given-names>W</given-names></name> <name><surname>Chang</surname> <given-names>L</given-names></name> <name><surname>Ma</surname> <given-names>J</given-names></name> <etal/></person-group>. <article-title>Deficiency in endocannabinoid synthase DAGLB contributes to early onset Parkinsonism and murine nigral dopaminergic neuron dysfunction</article-title>. <source>Nat Commun.</source> (<year>2022</year>) <volume>13</volume>:<fpage>3416</fpage>&#x02013;<lpage>90</lpage>. <pub-id pub-id-type="doi">10.1038/s41467-022-31168-9</pub-id><pub-id pub-id-type="pmid">35715418</pub-id></citation></ref>
<ref id="B42">
<label>42.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Zhou</surname> <given-names>D</given-names></name> <name><surname>Zhang</surname> <given-names>D</given-names></name> <name><surname>Sun</surname> <given-names>X</given-names></name> <name><surname>Li</surname> <given-names>Z</given-names></name> <name><surname>Ni</surname> <given-names>Y</given-names></name> <name><surname>Shan</surname> <given-names>Z</given-names></name> <etal/></person-group>. <article-title>A novel variant associated with HDL-C levels by modifying DAGLB expression levels: an annotation-based genome-wide association study</article-title>. <source>Eur J Hum Genet.</source> (<year>2018</year>) <volume>26</volume>:<fpage>838</fpage>&#x02013;<lpage>47</lpage>. <pub-id pub-id-type="doi">10.1038/s41431-018-0108-4</pub-id><pub-id pub-id-type="pmid">29476167</pub-id></citation></ref>
<ref id="B43">
<label>43.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Wen</surname> <given-names>W</given-names></name> <name><surname>Cheng</surname> <given-names>J</given-names></name> <name><surname>Tang</surname> <given-names>Y</given-names></name></person-group>. <article-title>Brain perivascular macrophages: current understanding and future prospects</article-title>. <source>Brain J Neurol</source>. (<year>2024</year>) <volume>147</volume>:<fpage>39</fpage>&#x02013;<lpage>55</lpage>. <pub-id pub-id-type="doi">10.1093/brain/awad304</pub-id><pub-id pub-id-type="pmid">37691438</pub-id></citation></ref>
<ref id="B44">
<label>44.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Xu</surname> <given-names>D</given-names></name> <name><surname>Robinson</surname> <given-names>AP</given-names></name> <name><surname>Ishii</surname> <given-names>T</given-names></name> <name><surname>Duncan</surname> <given-names>DAS</given-names></name> <name><surname>Alden</surname> <given-names>TD</given-names></name> <name><surname>Goings</surname> <given-names>GE</given-names></name> <etal/></person-group>. <article-title>Peripherally derived T regulatory and &#x003B3;&#x003B4; T cells have opposing roles in the pathogenesis of intractable pediatric epilepsy</article-title>. <source>J Exp Med.</source> (<year>2018</year>) <volume>215</volume>:<fpage>1169</fpage>&#x02013;<lpage>86</lpage>. <pub-id pub-id-type="doi">10.1084/jem.20171285</pub-id><pub-id pub-id-type="pmid">29487082</pub-id></citation></ref>
<ref id="B45">
<label>45.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Yue</surname> <given-names>J</given-names></name> <name><surname>Xu</surname> <given-names>R</given-names></name> <name><surname>Yin</surname> <given-names>C</given-names></name> <name><surname>Yang</surname> <given-names>H</given-names></name> <name><surname>Zhang</surname> <given-names>C</given-names></name> <name><surname>Zhao</surname> <given-names>D</given-names></name></person-group>. <article-title>Negative effects of brain regulatory T cells depletion on epilepsy</article-title>. <source>Prog Neurobiol.</source> (<year>2022</year>) <volume>217</volume>:<fpage>102335</fpage>. <pub-id pub-id-type="doi">10.1016/j.pneurobio.2022.102335</pub-id><pub-id pub-id-type="pmid">35931355</pub-id></citation></ref>
<ref id="B46">
<label>46.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>McNamara</surname> <given-names>NB</given-names></name> <name><surname>Munro</surname> <given-names>DAD</given-names></name> <name><surname>Bestard-Cuche</surname> <given-names>N</given-names></name> <name><surname>Uyeda</surname> <given-names>A</given-names></name> <name><surname>Bogie</surname> <given-names>JFJ</given-names></name> <name><surname>Hoffmann</surname> <given-names>A</given-names></name> <etal/></person-group>. <article-title>Microglia regulate central nervous system myelin growth and integrity</article-title>. <source>Nature.</source> (<year>2023</year>) <volume>613</volume>:<fpage>120</fpage>&#x02013;<lpage>29</lpage>. <pub-id pub-id-type="doi">10.1038/s41586-022-05534-y</pub-id><pub-id pub-id-type="pmid">36517604</pub-id></citation></ref>
<ref id="B47">
<label>47.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Li</surname> <given-names>W</given-names></name> <name><surname>Wu</surname> <given-names>J</given-names></name> <name><surname>Zeng</surname> <given-names>Y</given-names></name> <name><surname>Zheng</surname> <given-names>W</given-names></name></person-group>. <article-title>Neuroinflammation in epileptogenesis: from pathophysiology to therapeutic strategies</article-title>. <source>Front Immunol.</source> (<year>2023</year>) <volume>14</volume>:<fpage>1269241</fpage>. <pub-id pub-id-type="doi">10.3389/fimmu.2023.1269241</pub-id><pub-id pub-id-type="pmid">38187384</pub-id></citation></ref>
<ref id="B48">
<label>48.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Hiragi</surname> <given-names>T</given-names></name> <name><surname>Ikegaya</surname> <given-names>Y</given-names></name> <name><surname>Koyama</surname> <given-names>R</given-names></name></person-group>. <article-title>Microglia after seizures and in epilepsy</article-title>. <source>Cells.</source> (<year>2018</year>) <volume>7</volume>:<fpage>26</fpage>. <pub-id pub-id-type="doi">10.3390/cells7040026</pub-id></citation>
</ref>
<ref id="B49">
<label>49.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Zeng</surname> <given-names>C</given-names></name> <name><surname>Hu</surname> <given-names>J</given-names></name> <name><surname>Chen</surname> <given-names>F</given-names></name> <name><surname>Huang</surname> <given-names>T</given-names></name> <name><surname>Zhang</surname> <given-names>L</given-names></name></person-group>. <article-title>The coordination of mTOR signaling and non-coding RNA in regulating epileptic neuroinflammation</article-title>. <source>Front Immunol.</source> (<year>2022</year>) <volume>13</volume>:<fpage>924642</fpage>. <pub-id pub-id-type="doi">10.3389/fimmu.2022.924642</pub-id><pub-id pub-id-type="pmid">35898503</pub-id></citation></ref>
<ref id="B50">
<label>50.</label>
<citation citation-type="journal"><person-group person-group-type="author"><name><surname>Nguyen</surname> <given-names>LH</given-names></name> <name><surname>Mahadeo</surname> <given-names>T</given-names></name> <name><surname>Bordey</surname> <given-names>A</given-names></name></person-group>. <article-title>mTOR hyperactivity levels influence the severity of epilepsy and associated neuropathology in an experimental model of tuberous sclerosis complex and focal cortical dysplasia</article-title>. <source>J Neurosci.</source> (<year>2019</year>) <volume>39</volume>:<fpage>2762</fpage>&#x02013;<lpage>73</lpage>. <pub-id pub-id-type="doi">10.1523/JNEUROSCI.2260-18.2019</pub-id><pub-id pub-id-type="pmid">30700531</pub-id></citation></ref>
</ref-list>
</back>
</article>