Identification and Verification of Hub Mitochondrial Dysfunction Genes in Epilepsy Based on Bioinformatics Analysis

Identification and Verification of Hub Mitochondrial Dysfunction Genes in Epilepsy Based on Bioinformatics Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification and Verification of Hub Mitochondrial Dysfunction Genes in Epilepsy Based on Bioinformatics Analysis Yunyun Lu, Yanyan Zhang, Faqiang Li, Feng Chen, Shuaishuai Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7544051/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Feb, 2026 Read the published version in BMC Neurology → Version 1 posted 13 You are reading this latest preprint version Abstract Background Epilepsy is a neurological disorder characterized by recurrent seizures, remaining a significant challenge in terms of understanding its underlying molecular mechanisms. The objective of this study was to investigate the role of mitochondrial energy metabolism-related differentially expressed genes (MRDEGs) in epilepsy, and to construct and validate a diagnostic model based on these genes. Methods Datasets were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis was conducted to identify MRDEGs. Diagnostic models were developed using logistic regression, support vector machine (SVM), and random forest (RF) algorithms. LASSO regression was employed to mitigate overfitting. The diagnostic value of the models was assessed using Receiver Operating Characteristic (ROC) curves. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on hub genes. Protein-protein interaction (PPI) networks were constructed and visualized using Cytoscape software. Additionally, mRNA-miRNA and mRNA-transcription factor (TF) interaction networks were established. Results In dataset GSE143272, logistic regression analysis highlighted 26 statistically significant MRDEGs. The SVM model achieved the highest accuracy with 22 MRDEGs. The RF algorithm identified 11 important MRDEGs based on IncNodePurity > 0.80. LASSO regression yielded a diagnostic model comprising five hub genes: ACAA1 , ALDH3B1 , DLST , GCDH , and NDUFB9 . ROC curves demonstrated high accuracy for DLST (AUC > 0.9). GO and KEGG analyses revealed significant enrichment in processes such as mitochondrial ATP synthesis coupled electron transport. PPI networks illustrated the interactions between hub genes. Conclusions In conclusion, our research elucidates the critical role of MRDEGs in the pathogenesis of epilepsy and develops a robust diagnostic model with potential clinical applications. Epilepsy mitochondrial energy metabolism pathways mitochondrial energy metabolism-related genes diagnostic model bioinformatic analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 1. Introduction Globally, there are about 60 million individuals with epilepsy,and over 4.9 million new cases of epilepsy are reported each year[ 1 ]. Although the impact of the disease has decreased from 1990 to 2016, epilepsy remains a major cause of disability and death [ 2 ]. Despite advancements in medical treatments, approximately 30% of individuals with epilepsy are still resistant to anti-epileptic drugs, which imposes a substantial burden on the healthcare system and significantly affects the quality of life [ 3 ]. Currently, the diagnosis of epilepsy primarily depends on clinical assessments and electroencephalograms. Although these methods are valuable, they often fail to provide a comprehensive understanding of the underlying molecular mechanisms of the disease [ 4 ]. The fact that numerous underlying disease mechanisms contribute to the development of epilepsy, the etiology of the disease remains elusive in the majority of patients [ 5 ]. Thus, the exploration of the pathogenesis, diagnostic biomarkers, and therapeutic targets of epilepsy holds immense significance for assessing and ameliorating the condition of patients. Genetic factors exert a pivotal role in the etiology of epilepsy. Recent studies have identified a multitude of epilepsy-associated genes and genetic variants, which contribute to the intricate nature of the disease[ 6 ] [ 7 ]. For example, genetic generalized epilepsy has been demonstrated to carry a greater burden of copy number variations in comparison with other types of epilepsy [ 8 ]. These genetic insights have laid the foundation for precision medicine strategies that seek to customize treatments according to individual genetic profiles [ 9 ]. Studies have demonstrated that mitochondrial-related genes are associated with multiple neurodegenerative disorders, including Alzheimer’s disease and Parkinson’s disease, indicating their potential involvement in epilepsy as well [ 10 – 12 ]. This association is further corroborated by the finding that specific instances of mitochondrial dysfunction (MD) are frequently accompanied by epileptic seizures, underscoring the significance of mitochondrial in preserving neuronal stability [ 13 ]. Therefore, it is likely that the crucial genes implicated in the mitochondrial energy metabolism will impact the pathogenesis of epilepsy and serve as suitable therapeutic targets. Nonetheless, the specific mitochondrial energy metabolism-related differentially expressed genes (MRDEGs) associated with epilepsy and their diagnostic utility remain uninvestigated. Given our limited comprehension of the underlying mechanisms of epilepsy, there is a necessity to identify novel and dependable screening approaches to augment diagnostic precision and therapeutic results, ultimately contributing to the improvement of patient outcomes. In this study, we utilized datasets obtained from three Gene Expression Omnibus (GEO) repositories. Through a series of bioinformatics analyses, we identified significant MRDEGs that exhibit the potential to serve as biomarkers for the diagnosis of epilepsy. Furthermore, functional enrichment analyses provided insights into the biological processes and pathways associated with these genes. The construction of protein-protein interaction (PPI) networks and the assessment of immune infiltration highlighted the complex interplay between MRDEGs and immune cells, suggesting potential therapeutic targets for further exploration. 2. Materials and Methods 2.1 Data acquisition and processing The methodology flowchart for the present investigation is depicted in Fig. 1 . A total of three epilepsy gene expression microarray datasets (GSE143272 [ 14 ], GSE4290 [ 15 ], GSE32534 [ 16 ]) were retrieved from the National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO, https://www.ncbi.nlm.nih.gov/geo/ ). The samples in GSE143272, GSE4290, and GSE32534 were all obtained from Homo sapiens, with tissue sources being blood, brain, and neocortex, respectively. The R package “limma” was employed to standardize the GSE143272 dataset, annotate and normalize the probes [ 17 ]. A comparison of the expression matrices prior to and following normalization was conducted by categorizing into Control and Epilepsy groups, and Principal Component Analysis (PCA) was performed to evaluate the normalization efficacy [ 18 ]. We searched the PubMed website ( https://pubmed.ncbi.nlm.nih.gov/ ) using “mitochondrial energy metabolism” as keywords to identify published literature related to the mitochondrial energy metabolism-related gene (MRGs) sets [ 19 ]. A total of 188 MRGs were identified, and the detailed information is provided in Table 1 . Table 1 Epilepsy data set information list. GSE143272 GSE4290 GSE32534 Platform GPL10558 GPL570 GPL570 Species Homo sapiens Homo sapiens Homo sapiens Tissue Blood Brain Neocortex Samples in Epilepsy group 34 23 5 Samples in Control group 51 0 5 2.2 MRDEGs related to epilepsy The R package “limma” was used to analyze the differences in gene expression between the Control and Epilepsy cohorts. A threshold of |logFC| >0 and q-value (p-value correction for Benjamini-Hochberg) < 0.05 was established for selecting the differentially expressed genes (DEGs). MRDEGs were obtained by variance analysis in the dataset GSE143272. Intersectional MRGs were identified in the GSE4290 and GSE32534 datasets, leading to the mapping of MRDEGs. To further examine the expression variations of MRDEGs within the GSE14327 dataset, a group-based expression comparison plot was generated based on the expression levels of the MRDEGs. Volcano plots and heatmaps of DEGs were created using the R packages "ggplot2" and "heatmap," respectively. 2.3 Gene set enrichment analysis The genes in the dataset GSE143272 were ranked based on their logFC values. Subsequently, the R package “clusterProfiler” was employed to conduct Gene Set Enrichment Analysis (GSEA) on all genes within GSE143272 dataset. The gene sets “c2.cp.kegg.v6.2.symbols” and “c2.all.v7.2.symbols” were retrieved from the Molecular Signatures Database (MSigDB) for GSEA analysis [ 20 ]. The criteria for GSEA screening were established with a q-value < 0.05. 2.4 Gene set variation analysis All genes in the GSE143272 dataset underwent Gene Set Variation Analysis (GSVA), aimed at quantifying functional enrichment discrepancies across different groups. The significance threshold for GSVA was determined as a p-value < 0.05, with the Benjamini-Hochberg approach applied for p-value correction. 2.5 Construction and validation of diagnostic model for epilepsy To formulate a diagnostic model for epilepsy utilizing MRDEGs from the GSE143272 dataset, logistic regression analysis was conducted. MRDEGs with a p-value < 0.05 were used as the criterion for screening, and a logistic regression model was subsequently constructed. Following this, the SVM algorithm was employed to build the SVM model [ 21 ]. The MRDEGs were further filtered based on the number of genes that achieved the highest accuracy and the lowest error rate. The expression levels of the MRDEGs in the expression matrix of the GSE143272 dataset were utilized to construct the model using the “RF” package with the parameters set.seed (500) and ntree = 1000 specified [ 22 ]. $$\:I(X=xi)=\:-lo{g}_{2}p\left({x}_{i}\right)$$ Finally, DEGs with a p-value < 0.05 identified in the logistic regression analysis were intersected with the DEGs obtained from the SVM model and the RF model, and a Venn diagram was generated. Common Differentially Expressed Genes (CDEGs) were obtained. The parameters set.seed (500) and family = "binomial" were utilized to perform Least Absolute Shrinkage and Selection Operator (LASSO) regression based on the CDEGs using the “glmnet” package in R [ 23 ]. The outcomes of the LASSO regression analysis and linear regression contributed to the formulation of the diagnostic model for MRDEGs. The MRDEGs incorporated in this model were classified as model genes, further identified as hub genes. Ultimately, the LASSO risk score was computed based on the risk coefficients from the LASSO regression analysis, using the following formula: $$\:\text{R}\text{i}\text{s}\text{k}Score\:=\:\sum\:_{i}Coefficient\:\left({ℎub\:gene}_{i}\right)\ast\:mRNA\:Expression\:\left({ℎub\:gene}_{i}\right)$$ The R package was employed to generate a Nomogram based on the outcomes of the LASSO regression analysis, aiming to illustrate the interrelationships among the DEGs incorporated in the diagnostic model [ 24 ]. Additionally, the R package “Decision Curve Analysis (DCA)” was utilized to create DCA plots based on the hub genes in the GSE143272 dataset, with the purpose of assessing the accuracy and discriminative ability of the diagnostic model [ 25 ]. We utilized the “pROC” package in R to generate the Receiver Operating Characteristic (ROC) curves for the hub genes of the MRDEGs diagnostic model in the GSE143272 dataset [ 26 ]. The Area Under the Curve (AUC) was subsequently calculated to assess the diagnostic potential of hub gene expression in epilepsy. Moreover, the risk coefficients obtained from the LASSO regression analysis were employed to compute the corresponding risk scores in the GSE4290 and GSE32534 datasets. The "pROC" package was then utilized to plot ROC curves for the hub genes within these datasets, and the AUC was calculated to validate the diagnostic value of hub gene expression in epilepsy. 2.6 Gene ontology and Kyoto encyclopedia of genes and genomes enrichment analysis The R package “clusterProfiler” was employed to conduct Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the hub genes [ 27 ]. The screening criteria for statistically significant results were set as a q-value < 0.05. 2.7 Protein-protein interaction network The STRING database ( https://cn.string-db.org/ ) was utilized based on the hub genes, with a minimum required interaction score greater than 0.400 (medium confidence) serving as the criterion for constructing the hub gene-related Protein-Protein Interaction (PPI) network [ 28 ]. The Cytoscape software (version 3.9.1) was employed to visualize the network [ 29 ]. 2.8 Construct mRNA-miRNA and mRNA-TF interaction networks To explore the interactions between hub genes and microRNAs (miRNAs), the miRNAs related to the hub genes were retrieved from the miRDB database ( https://mirdb.org/ ) [ 30 ]. The mRNA-miRNA regulatory network was visualized using Cytoscape software [ 29 ].The transcription factors (TFs) were retrieved from the ChIPBase database ( http://rna.sysu.edu.cn/chipbase/ ) to analyze their regulatory effects on the hub genes [ 31 ]. Cytoscape software was employed to visualize the mRNA-TF regulatory network. 2.9 Immune infiltration analysis The first step involved annotating each type of infiltrating immune cell. Next, the enrichment scores generated by Single-Sample Gene-Set Enrichment Analysis (ssGSEA) were employed to depict the relative abundance of immune cell infiltration in each sample, thereby establishing the immune cell infiltration matrix for the GSE143272 dataset [ 32 ]. The R package “ggplot2” was employed to generate group comparison plots to illustrate the differences in the expression of immune cells between the Control and Epilepsy groups in the GSE143272 dataset. Additionally, another R package was used to create a correlation heatmap to display the results of the correlation analysis among the immune cells themselves. The CIBERSORT algorithm, in conjunction with the LM22 signature gene matrix, was employed to filter the data with immune cell enrichment scores greater than zero [ 33 ]. Then, the specific results of the immune cell infiltration matrix for the GSE143272 dataset were obtained, and a proportion bar chart was generated for visualization. R package was utilized to create a correlation heatmap to present the results of the correlation analysis among the immune cells themselves. Additionally, the “ggplot2” package in R was employed to develop a correlation bubble plot, which represented the outcomes of the correlation analysis between hub genes and immune cells. 2.10 Statistical analysis All data processing and analyses in this study were conducted using R software (Version 4.2.2). The statistical significance of normally distributed continuous variables between two groups was evaluated through an independent Student's t-test, unless otherwise indicated. The Mann-Whitney U test (Wilcoxon rank sum test) was employed to analyze the differences between variables that did not follow a normal distribution. Spearman correlation analysis was performed to calculate the correlation coefficient between various molecules. Unless otherwise noted, a p-value threshold of less than 0.05 was established for statistical significance, and all p-values were considered two-tailed. 3. Results 3.1 MRDEGs analysis of data set GSE143272 Distribution boxplots (Fig. 2 A-B) were employed to compare the expression values of the datasets prior to and following normalization. PCA plots (Fig. 2 C-D) were utilized to compare the low-dimensional feature distributions of the datasets before and after standardization. The results from the distribution boxplots and PCA plots indicated that the batch effect among the samples in the dataset GSE143272 was essentially eliminated after batch removal. In the dataset GSE143272, a total of 2166 DEGs met the criteria of |logFC| >0 and p < 0.05. Among these, 1014 genes were upregulated and 1152 genes were downregulated, as indicated by the variance analysis results presented in the volcano plot of the dataset (Fig. 3 A). To obtain the intersection of genes with |logFC| >0 and p < 0.05 from all datasets (GSE143272, GSE4290, and GSE32534), as well as mitochondria-related genes, a mapping was performed (Fig. 3 B). A total of 26 MRDEGs were identified, including ALDH18A1 , MDH2 , ACAA1 , ALDH3B1 , COX6C , NDUFB9 , NDUFV1 , IDH1 , PAAF1 , ECHS1 , PGM2 , NDUFS3 , NDUFA8 , OXCT1 , PDHB , UQCRFS1 , GCDH , COX10 , NDUFA13 , POR , DLST , HADHB , NDUFB7 , HADH , PPA2 , and NDUFS4 . Subsequently, based on the intersection results, the expression differences of these 26 MRDEGs between different sample groups in the dataset GSE143272 were analyzed. An R package was utilized to generate a heatmap to display the analysis results (Fig. 3 C). To explore the expression differences of MRDEGs in the dataset GSE143272, the group comparison figure presents the difference analysis results of the 26 MRDEGs between the two groups (Fig. 3 D). The results demonstrated that the expression of these 26 MRDEGs was statistically significant. 3.2 GSEA and GSVA GSEA was employed to investigate the association between the expression profiles of all genes within dataset GSE143272 (Fig. 4 A). The findings are presented in Table 2 . It was observed that all the genes exhibited significant enrichment in potassium channels, endocytosis, selenoamino acid metabolism, mitochondrial translation, as well as other related biological functions and signaling pathways (Fig. 4 B-E). Table 2 GSEA results of GSE143272. Description setSize NES p.adjust qvalues REACTOME_TRANSLATION 243 2.92519 1.91 e-08 1.59 e-08 REACTOME_MITOCHONDRIAL_TRANSLATION 84 2.44629 2.4 e-07 2E-07 REACTOME_SELENOAMINO_ACID_METABOLISM 90 2.49705 7.61 e-08 6.35 e-08 KEGG_ENDOCYTOSIS 116 1.502007 0.047105 0.039282 REACTOME_POTASSIUM_CHANNELS 23 1.760692 0.044348 0.036983 WP_COMPLEMENT_AND_COAGULATION_CASCADES 19 1.990027 0.008987 0.007495 NABA_ECM_AFFILIATED 46 2.010629 0.002981 0.002486 REACTOME_INTERFERON_SIGNALING 148 2.020286 1.77 e-05 1.48 e-05 WP_IMMUNE_RESPONSE_TO_TUBERCULOSIS 23 2.033109 0.004623 0.003855 REACTOME_NEUREXINS_AND_NEUROLIGINS 14 2.082106 0.006433 0.005365 WP_TYPE_II_INTERFERON_SIGNALING 31 2.090758 0.002667 0.002224 WP_AGERAGE_PATHWAY 51 2.107866 0.000951 0.000793 REACTOME_ANTIMICROBIAL_PEPTIDES 28 2.182752 0.000362 0.000302 REACTOME_NEUTROPHIL_DEGRANULATION 379 2.535737 1.91 e-08 1.59 e-08 PID_CERAMIDE_PATHWAY 37 1.96624 0.00776 0.006471 GSVA was conducted on the entire gene set within dataset GSE143272, with the detailed information presented in Table 3 . Pathways exhibiting positive enrichment and negative enrichment in the top 10 were identified. Then, the differential expression of these 20 pathways between the two groups was analyzed. The observed differences were validated using the Mann-Whitney U test. A group comparison diagram was generated to illustrate these results (Fig. 4 F). Table 3 GSVA enrichment analysis results of GSE143272. Description logFC p.value SCHLESINGER_METHYLATED_IN_COLON_CANCER 0.62115 0.006833 REACTOME_RUNX3_REGULATES_YAP1_MEDIATED_TRANSCRIPTION 0.57647 0.011154 OHASHI_AURKA_TARGETS 0.56408 0.005234 VANDESLUIS_COMMD1_TARGETS_GROUP_4_DN 0.55875 0.013484 REACTOME_ERBB2_ACTIVATES_PTK6_SIGNALING 0.55795 0.01417 REACTOME_GRB7_EVENTS_IN_ERBB2_SIGNALING 0.55795 0.01417 REACTOME_THE_RETINOID_CYCLE_IN_CONES_DAYLIGHT_VISION 0.54804 0.015981 REACTOME_FGFR2B_LIGAND_BINDING_AND_ACTIVATION 0.54048 0.017574 BILANGES_SERUM_SENSITIVE_VIA_TSC2 0.4529 2.08 e-06 REACTOME_PROSTANOID_LIGAND_RECEPTORS 0.44478 0.015364 REACTOME_FATTY_ACIDS_BOUND_TO_GPR40_FFAR1_REGULATE_INSULIN_SECRETION 0.547836 0.003795 TESAR_JAK_TARGETS_MOUSE_ES_D3_DN 0.567882 0.013975 MIKKELSEN_IPS_LCP_WITH_H3K4ME3_AND_H3K27ME3 0.572147 0.012695 ZIRN_TRETINOIN_RESPONSE_DN 0.573646 0.002409 KEGG_MATURITY_ONSET_DIABETES_OF_THE_YOUNG 0.602383 0.008444 MCCOLLUM_GELDANAMYCIN_RESISTANCE_DN 0.602383 0.008444 ZHOU_PANCREATIC_EXOCRINE_PROGENITOR 0.602383 0.008444 BERGER_MBD2_TARGETS 0.628249 0.006374 BIOCARTA_TUBBY_PATHWAY 0.638454 0.005914 MEISSNER_ES_ICP_WITH_H3K4ME3_AND_H3K27ME3 0.752315 0.000229 3.3 Construction and validation of diagnostic model for epilepsy We conducted Logistic regression analysis utilizing these 26 most relevant MRDEGs. A Logistic regression model was established, and the outcomes were visually represented through a Forest Plot (Fig. 5 A). The findings indicated that all 26 MRDEGs exhibited statistical significance within the Logistic regression model (p < 0.05). We developed a SVM model based on the 26 MRDEGs using the SVM algorithm. We determined the number of genes associated with the lowest error rate (Fig. 5 B) and the highest accuracy rate (Fig. 5 C). The results demonstrated that the SVM model achieved the highest accuracy when 22 genes were included. These 22 MRDEGs are ALDH18A1 , MDH2 , ACAA1 , ALDH3B1 , COX6C , NDUFB9 , IDH1 , PAAF1 , PGM2 , NDUFS3 , OXCT1 , PDHB , UQCRFS1 , GCDH , COX10 , NDUFA13 , DLST , HADHB , NDUFB7 , HADH , PPA2 , and NDUFS4 . Next, we analyzed the expression levels of the 26 MRDEGs in dataset GSE143272 using RF algorithm (Fig. 5 D). IncNodePurity represents the augmentation in node purity. A higher node purity signifies fewer impurities (i.e., a smaller Gini coefficient). We employed IncNodePurity > 0.80 as the criterion to filter the specific analysis results. The findings revealed that a total of 11 MRDEGs were identified, including DLST , ACAA1 , NDUFV1 , NDUFB9 , NDUFA13 , ALDH3B1 , NDUFB7 , ECHS1 , OXCT1 , GCDH , and IDH1 (Fig. 5 E). We performed an intersection analysis between the genes with p < 0.05 from the Logistic regression analysis results and the MRDEGs identified by the SVM and RF model. This analysis yielded a total of 9 intersecting genes, for which a Venn diagram was constructed (Fig. 6 A). The 9 intersecting genes are ACAA1 , ALDH3B1 , DLST , GCDH , IDH1 , NDUFA13 , NDUFB7 , NDUFB9 and OXCT1 . Next, we generated a LASSO regression model diagram and a LASSO variable trajectory diagram for visualization purposes (Fig. 6 B-C). The results indicated that five MRDEGs incorporated into the LASSO regression model were identified as hub genes, including ACAA1 , ALDH3B1 , DLST , GCDH and NDUFB9 . To further validate the diagnostic value of the MRDEGs model, we developed a Nomogram based on the five hub genes within the diagnostic model, illustrating the interrelationships of these hub genes in the dataset GSE143272 (Fig. 6 D). The findings revealed that the utility of the hub gene NDUFB9 expression for the MRDEGs diagnostic model was significantly higher compared to other variables, whereas the utility of the ACAA1 expression level was significantly lower. The predictive performance of the model on the actual outcomes was assessed by comparing the fitting of the optimal theoretical probability (solid line) and the model-predicted probability (dashed line) under various scenarios depicted in the figure (Fig. 6 E). Figure 6 F demonstrates that, within a certain range, the model’s line consistently remains above both the Epilepsy positive and Epilepsy negative lines, indicating a higher net benefit and superior model performance. Furthermore, the R package pROC was employed to generate ROC curves based on the hub genes in dataset GSE143272 (Figs. 7 A-B). The ROC curve analysis revealed that the expression level of the hub gene DLST in dataset GSE143272 exhibited high accuracy (AUC > 0.9) in distinguishing between the high-risk and low-risk groups within the MRDEGs diagnostic model. The expression level of the hub gene NDUFB9 demonstrated a moderate accuracy (0.9 > AUC > 0.7) in differentiating between these two risk groups. In contrast, the expression levels of the hub genes ACAA1 , ALDH3B1 , and GCDH displayed a relatively low accuracy (0.7 > AUC > 0.5) in this regard. The ROC curves constructed based on the hub genes in datasets GSE4290 (Figs. 7 C-D) and GSE32534 (Figs. 7 E-F) indicated that the expression levels of the hub genes ACAA1 and DLST achieved high accuracy (AUC > 0.9) in distinguishing between the high-risk and low-risk groups within the MRDEGs diagnostic model in dataset GSE4290. The expression level of the hub gene ALDH3B1 exhibited a moderate accuracy (0.9 > AUC > 0.7) in this dataset. In dataset GSE32534, the expression levels of the hub genes ACAA1 and GCDH demonstrated a moderate accuracy (0.9 > AUC > 0.7) in differentiating between the high-risk and low-risk groups. Subsequently, we evaluated the five hub genes using the scores from the functional similarity (Friends) analysis (Fig. 8 A), which revealed that DLST holds a crucial role in epilepsy. The correlation dot plot (Fig. 8 B) illustrating the relationships among the five hub genes in dataset GSE143272 demonstrated significant negative correlations between the hub genes ACAA1 and ALDH3B1 , and the hub genes DLST , GCDH , and NDUFB9 (p < 0.05). Notably, the most pronounced positive correlation was observed between the hub genes ACAA1 and ALDH3B1 (p-value < 0.05). 3.4 GO and KEGG enrichment analysis The five hub genes were subjected to GO and KEGG enrichment analyses, with the specific results presented in Table 4 . The analysis revealed that the genes associated with epilepsy were primarily enriched in biological processes (BP), such as the carboxylic acid catabolic process, organic acid catabolic process, and fatty acid beta-oxidation. In terms of cellular components (CC), they were enriched in the oxidoreductase complex, specific granule, tricarboxylic acid cycle enzyme complex, mitochondrial matrix, and NADH dehydrogenase complex. Regarding molecular functions (MF), the genes exhibited enrichment in acyltransferase activity, myristoyltransferase activity, acyl-CoA dehydrogenase activity, NADH dehydrogenase activity, and electron transfer activity. Additionally, KEGG pathway analysis showed enrichment in tryptophan metabolism, fatty acid degradation, and lysine degradation. The outcomes of the GO and KEGG enrichment analyses were visualized using bar graphs (Fig. 9 A). Concurrently, network diagrams depicting the relationships among BP, CC, and MF were constructed based on the GO and KEGG enrichment results (Figs. 9 B-E). Table 4 GO/KEGG enrichment analysis results. Ontology ID Description GeneRatio BgRatio p.adjust q.value BP GO:0042775 mitochondrial ATP synthesis coupled electron transport 1/5 92/18800 0.037869 0.005486 BP GO:0044282 small molecule catabolic process 4/5 376/18800 8.45 e-05 1.22 e-05 BP GO:0046395 carboxylic acid catabolic process 3/5 238/18800 0.000751 0.000109 BP GO:0016054 organic acid catabolic process 3/5 242/18800 0.000751 0.000109 BP GO:0006635 fatty acid beta-oxidation 2/5 76/18800 0.003629 0.000526 CC GO:1990204 oxidoreductase complex 2/5 120/19594 0.008477 0.002745 CC GO:0042581 specific granule 2/5 160/19594 0.008477 0.002745 CC GO:0045239 tricarboxylic acid cycle enzyme complex 1/5 16/19594 0.035331 0.011443 CC GO:0005759 mitochondrial matrix 2/5 473/19594 0.036015 0.011665 CC GO:0030964 NADH dehydrogenase complex 1/5 49/19594 0.036675 0.011879 MF GO:0016746 acyltransferase activity 2/5 244/18410 0.016269 0.001713 MF GO:0019107 myristoyltransferase activity 1/5 11/18410 0.016269 0.001713 MF GO:0003995 acyl-CoA dehydrogenase activity 1/5 12/18410 0.016269 0.001713 MF GO:0003954 NADH dehydrogenase activity 1/5 45/18410 0.01963 0.002066 MF GO:0009055 electron transfer activity 1/5 125/18410 0.038648 0.004068 KEGG hsa00380 Tryptophan metabolism 2/5 42/8164 0.004024 0.001836 KEGG hsa00071 Fatty acid degradation 2/5 43/8164 0.004024 0.001836 KEGG hsa00310 Lysine degradation 2/5 63/8164 0.005774 0.002634 3.5 The PPI network, mRNA-miRNA and mRNA-TF interaction network The PPI network analysis (Fig. 10 A) revealed interactions among the five hub genes: ACAA1 , ALDH3B1 , DLST , GCDH and NDUFB9 . Transcription factors (TFs) binding to these hub genes were identified using the ChIPBase database, and the mRNA-TF regulatory network was constructed and visualized utilizing Cytoscape software (Fig. 10 B). This network comprises 5 hub genes and 20 TFs, with detailed information provided in Table 5 . Additionally, the miRDB database was employed to identify microRNAs (miRNAs) associated with the hub genes, and the mRNA-miRNA regulatory network was constructed and visualized using Cytoscape software (Fig. 10 C). This network consists of 4 hub genes and 31 miRNAs, with specific details presented in Table 6 . Table 5 mRNA-TF interaction network nodes. TF mRNA TF mRNA CTCF - NDUFB9 GATA2 - ACAA1 SPI1 - ACAA1 MYC - GCDH CTCF - ACAA1 CEBPB - NDUFB9 RAD21 - NDUFB9 ERG - ACAA1 RUNX1 - ACAA1 ERG - NDUFB9 ERG - DLST STAG1 - NDUFB9 GABPA - NDUFB9 EP300 - ACAA1 GATA1 - ACAA1 POLR2A - ACAA1 STAT3 - ACAA1 USF1 - ACAA1 GABPA - DLST POLR2A - NDUFB9 ESR1 - NDUFB9 CREB1 - ACAA1 SPI1 - NDUFB9 SPI1 - ALDH3B1 NRF1 - ACAA1 USF1 - ALDH3B1 ELF1 - NDUFB9 ELF1 - DLST ELF1 - ACAA1 MAX - GCDH GABPA - ACAA1 "TF" and "mRNA" represent node; "-" represent edge Table 6 mRNA-miRNA interaction network nodes. miRNA mRNA miRNA mRNA hsa-miR-3942-3p - DLST hsa-let-7a-5p - DLST hsa-miR-30d-3p - DLST hsa-let-7i-5p - DLST hsa-miR-30a-3p - DLST hsa-let-7e-5p - DLST hsa-miR-30e-3p - DLST hsa-let-7c-5p - DLST hsa-miR-6874-3p - DLST hsa-miR-6870-3p - ACAA1 hsa-miR-148b-5p - DLST hsa-miR-516a-3p - DLST hsa-miR-4773 - DLST hsa-miR-516b-3p - DLST hsa-miR-4500 - DLST hsa-miR-6870-3p - GCDH hsa-miR-7110-3p - DLST hsa-miR-3679-5p - ALDH3B1 hsa-miR-140-5p - DLST hsa-miR-7162-5p - DLST hsa-miR-4458 - DLST hsa-miR-6811-5p - DLST hsa-let-7f-5p - DLST hsa-miR-3074-5p - DLST hsa-let-7d-5p - DLST hsa-miR-124-3p - GCDH hsa-miR-98-5p - DLST hsa-miR-506-3p - GCDH hsa-let-7b-5p - DLST hsa-miR-6511b-5p - DLST hsa-let-7g-5p - DLST hsa-miR-3145-3p - GCDH "miRNA" and "mRNA" represent nodes; "-" represents edge. 3.6 Group-based gene enrichment analysis Utilizing the logFC values from the high and low risk groups of the MRDEGs diagnostic model, GSEA was employed to investigate the associations between the expression profiles of all genes in the dataset GSE143272 (Fig. 11 A) and the corresponding biological processes (BP), cellular components (CC), and molecular functions (MF). The detailed results are presented in Table 7 . The analysis revealed that the genes were significantly enriched in various biological functions and signaling pathways, including hemostasis (Fig. 11 B), aquaporin-mediated transport (Fig. 11 C), mitochondrial translation (Fig. 11 D), and DNA replication (Fig. 11 E). Table 7 GSEA results of GSE143272. Description setSize NES p.adjust qvalue KEGG_DNA_REPLICATION 28 1.863 0.045103 0.035546 REACTOME_MITOCHONDRIAL_TRANSLATION 84 2.45598 7.51 e-07 5.92 e-07 REACTOME_AQUAPORIN_MEDIATED_TRANSPORT 23 1.784593 0.03954 0.031161 REACTOME_HEMOSTASIS 315 1.885517 1.64 e-06 1.29 e-06 REACTOME_ANTIMICROBIAL_PEPTIDES 28 2.054375 0.003039 0.002395 NABA_ECM_REGULATORS 59 2.069537 0.000611 0.000482 REACTOME_INTERLEUKIN_4_AND_INTERLEUKIN_13_SIGNALING 67 2.078161 0.000394 0.00031 NABA_MATRISOME 216 2.10595 1.47 e-08 1.16 e-08 REACTOME_SIGNAL_AMPLIFICATION 19 2.109116 0.000962 0.000758 REACTOME_TRANSCRIPTIONAL_REGULATION_OF_GRANULOPOIESIS 25 2.11226 0.001971 0.001553 NABA_MATRISOME_ASSOCIATED 172 2.115595 3.89 e-07 3.07 e-07 WP_NUCLEOTIDEBINDING_OLIGOMERIZATION_DOMAIN_NOD_PATHWAY 34 2.291655 9.6 e-05 7.57 e-05 WP_COMPLEMENT_SYSTEM 51 2.320945 3.66 e-06 2.88 e-06 REACTOME_NEUTROPHIL_DEGRANULATION 379 2.477168 1.47 e-08 1.16 e-08 REACTOME_TRANSLATION 243 2.93387 1.47 e-08 1.16 e-08 3.7 Immune infiltration analysis The group comparison diagram (Fig. 12 A) illustrated that nine types of immune cells, including activated CD8 + T cells, activated dendritic cells, eosinophils, immature dendritic cells, macrophages, mast cells, monocytes, neutrophils, and T follicular helper cells (Tfh), exhibited statistically significant differences between the Control group and the Epilepsy group (p < 0.05). The correlation results regarding the abundance of these nine immune cell infiltrations in the immune infiltration analysis of dataset GSE143272 are presented in a correlation heatmap (Fig. 12 B). The findings indicate that Activated CD8 + T cells were predominantly negatively correlated with other immune cells, whereas most other immune cells displayed positive correlations. Furthermore, the correlation between the five hub genes and the nine immune cells was analyzed using a correlation bubble plot (Fig. 12 C). The results revealed that the hub genes NDUFB9 , GCDH , and DLST showed significant negative correlations with the immune cells (p < 0.05), while hub genes ALDH3B1 and ACAA1 exhibited significant positive correlations with the immune cells (p < 0.05). Based on the immune infiltration analysis results, a bar chart depicting the proportion of 22 immune cells in dataset GSE143272 was generated (Fig. 13 A). The bar chart illustrated the proportions of 18 types of immune cells, including naive B cells, memory B cells, plasmacytoid dendritic cells, activated CD8 + T cells, activated CD4 + T cells, effector memory CD4 + T cells, resting memory CD4 + T cells, regulatory T cells (Treg), gamma-delta T cells, resting natural killer cells, natural killer cells, monocytes, M1 macrophages, M2 macrophages, dendritic cells, mast cells, eosinophils, and neutrophils. Subsequently, the correlation results of the infiltration abundance of these 18 immune cells were presented in a correlation heatmap (Fig. 13 B). The findings revealed that the majority of these 18 immune cells exhibited negative correlations with each other. Finally, the correlation between hub genes and the infiltration abundance of immune cells in dataset GSE143272 was visualized using a correlation bubble plot (Fig. 13 C). The results from the correlation bubble plot indicated that the most significant negative correlation was observed between immune cells (Activated CD4 + T cells) and the hub gene ACAA1 (p < 0.05). Conversely, the most significant positive correlation was found between immune cells (Neutrophils) and the hub gene ACAA1 (p < 0.05). 4. Discussion Despite the available of numerous pharmacological interventions for epilepsy, a significant proportion of individuals still suffer from persistent seizures, underscoring the urgent necessity for improved therapeutic strategies [ 34 ]. In the present study, we found that key MRDEGs in epilepsy, such as DLST , ACAA1 , ALDH3B1 , GCDH , and NDUFB9 , are significantly implicated in pathways associated with mitochondrial ATP synthesis and that their altered expression patterns are closely related to the pathogenesis of epilepsy. These results offer valuable insights into the mitochondrial gene-based therapeutic approaches for epilepsy. Several primary mitochondrial diseases can manifest with epilepsy, including mitochondrial encephalopathy, lactic acidosis, myoclonic epilepsy with ragged red fibers, and Leigh syndrome (LS) [ 35 , 36 ]. Wolfgang et al. have demonstrated that MD represents a pivotal mechanisms underlying epileptic seizures and contributes to their occurrence [ 37 ]. Metabolism-based treatments, such as the high-fat ketogenic diet, have the potential to restore metabolic homeostasis and achieve seizure control[ 38 ]. In this study, we focused on identifying MRDEGs in epilepsy patients and constructing a diagnostic model to improve disease management. This approach holds promise as novel biomarkers and therapeutic targets for early diagnosis, thereby enhancing the clinical management of patients with epilepsy and alleviating the disease burden on patients. Our study has revealed that key MRDEGs in epilepsy, including DLST , ACAA1 , ALDH3B1 , GCDH , and NDUFB9 , are significantly involved in pathways associated with mitochondrial ATP synthesis. DLST serves as a crucial component of the alpha-ketoglutarate dehydrogenase complex, which plays a vital role in the tricarboxylic acid cycle and ATP production [ 39 , 40 ]. The reduced expression of DLST in epilepsy suggests that MD contributes to the progression of the disease. ACAA1 encodes human peroxisomal 3-oxoacyl-CoA thiolase, an enzyme involved in the beta-oxidation pathway of fatty acids, a critical process for energy production in cells [ 41 ]. ALDH3B1 encodes a protein implicated in the detoxification of aldehydes, which can impair mitochondrial function [ 42 , 43 ]. Its role in epilepsy was underscored in a bioinformatic analysis that identified key genes associated with the disease [ 44 ], which is consistent with our findings. GCDH participates in the catabolism of lysine, hydroxylysine, and tryptophan, linking amino acid metabolism with mitochondrial energy production [ 45 ]. Posset et al. reported that deficiencies in GCDH are known to cause glutaric aciduria type I, a metabolic disorder that leads to neurological symptoms, including seizures [ 46 ]. NDUFB9 is a component of Complex I in the electron transport chain and is essential for initiating electron transfer and proton pumping [ 47 ]. Mutations or altered expression of NDUFB9 can impair mitochondrial function, leading to reduced ATP availability and increased oxidative stress, both of which are implicated in epilepsy. Our study shows that NDUFB9 expression is decreased in epilepsy samples, potentially by reducing ATP availability and thereby promoting epilepsy progression. DLST and ACAA1 are the first mitochondrial energy MRDEGs identified in epilepsy patients in our study. These genes may serve as potential biomarkers for epilepsy diagnosis and targets for therapeutic intervention. Following GO and KEGG enrichment analyses, we identified several pathways in which MRDEGs were significantly enriched in epilepsy. These pathways encompass mitochondrial ATP synthesis-coupled electron transport, electron transfer activity, and small-molecule metabolic processes. These processes are essential for maintaining cellular energy homeostasis, and their dysregulation has been associated with a variety of neurological disorders, including epilepsy[ 48 ]. Verweij et al. observed MD in both animal and human brain tissues affected by epilepsy, which was characterized by impaired oxidative phosphorylation and ATP production [ 49 ]. Furthermore, the role of small-molecule metabolic processes, such as the regulation of polyamines, has been extensively explored in the context of epilepsy. Polyamines are small, positively charged alkylamines that modulate ion channels and ionotropic glutamate receptors, and their dysregulation has been closely linked to the pathogenesis of epilepsy [ 50 ]. These pathways are critical for sustaining neuronal function and integrity, especially under the metabolic stress conditions frequently observed in epilepsy [ 51 ]. The high diagnostic accuracy of the model based on these hub genes, including ACAA1 , ALDH3B1 , DLST , GCDH and NDUFB9 , is evidenced by the ROC analysis, highlighting their potential as diagnostic biomarkers for epilepsy. Consistent with the findings of Tsuchiya et al. [ 52 ], we observed alterations in immune cell infiltration, suggesting that there may be a link between MD and the immune response in epilepsy patients, which warrants further investigation. For instance, a recent study utilizing Mendelian randomization confirmed a causal relationship between certain immune cell characteristics and epilepsy risk, reinforcing the notion that immune cells contribute to the onset and progression of the disease [ 53 ]. Luo et al. also reported changes in the expression of immune-related genes and the presence of inflammatory markers in patients with epilepsy [ 54 ]. Furthermore, significant differences were found in immune cell phenotypes, such as activated CD8 + T cells and activated dendritic cells, between the two groups, suggesting a potential role in the disease mechanisms [ 55 ]. After being primed in brain-draining lymph nodes, CD8 + T cells act as specific attackers of neurons, leading to apoptotic degeneration of neurons and compromising hippocampal network function, which results in fundamental hyperexcitability and impaired memory skills, thereby inducing an epileptogenic process [ 56 ]. Additionally, Tröscher et al. discovered that the number of T cells, particularly CD8 + cytotoxic T cells, were significantly elevated in the hippocampus of patients with medial temporal lobe epilepsy [ 57 ]. However, the role of dendritic cells in epilepsy remains poorly understood. One might hypothesize that epilepsy is related to the high excitability of dendrites, but it is still unclear whether the changes in dendritic cells associated with epilepsy are a consequence of repeated seizures. We anticipate that advancements in the technology for assessing dendritic excitability will facilitate a better understanding of epilepsy. The involvement of the immune system in epilepsy is further corroborated by findings that immune-related pathways, including the JAK-STAT pathway, are implicated in the disorder [ 58 , 59 ]. Additionally, research has demonstrated that immune cells, such as mast cells and natural killer cells, exhibit differential regulation in epilepsy, with notable correlations observed between these cell types and specific genes such as epidermal growth factor receptor [ 60 ]. This underscores the complexity of immune involvement in epilepsy, wherein different immune cell subsets play distinct roles at various disease stages. For example, the expression signatures of Th1 and Th2 cells have been found to differ between normal and epileptic tissues, suggesting a shift in immune response profiles in epilepsy [ 61 ]. Overall, our findings regarding immune cell differences in epilepsy reinforce the potential of targeting immune-related pathways and cells for the development of novel therapeutic strategies for epilepsy. 5. Limitations Despite these encouraging findings, this study has several limitations. Firstly, the study primarily relies on bioinformatics analysis without incorporating laboratory experiments, which could offer more direct evidence to support the findings. Secondly, the sample size derived from the datasets (GSE143272, GSE4290, and GSE32534) is relatively limited, which constrains the generalizability of the results. Thirdly, the absence of clinical validation implies that the diagnostic models and identified biomarkers have not yet been evaluated in a real-world clinical context. Furthermore, the utilization of multiple datasets introduces the potential for batch effects, which could influence the results despite normalization efforts. 6. Conclusions In conclusion, this study successfully identified 26 MRGs from the GEO datasets in patients with epilepsy and constructed diagnostic models with high accuracy. The integration of various bioinformatics tools provided comprehensive insights into the underlying genetic mechanisms of epilepsy. The findings highlight potential biomarkers and therapeutic targets for epilepsy, which are beneficial for future research. However, further validation through laboratory experiments and clinical studies is essential to confirm these results and fully realize their potential in clinical applications. Declarations Data availability statement The datasets utilized in this study are accessible through online repositories. The names of the repositories and corresponding accession numbers are provided in the article material. Funding statement This work has no Funding. Conflicts of interest disclosure The authors declare that there are no conflicts of interest associated with this study. Ethics Statement: This study is based entirely on publicly available bioinformatics data. No human participants were directly involved, and no identifiable private information was used. Therefore, institutional review board (IRB) approval and participant consent were not required for this analysis. This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki.Patient consent statement. Author contributions Yunyun Lu and Yanyan Zhang conceptualized the research idea, designed the study workflow, and were responsible for drafting, editing, and reviewing the manuscript. Shuaishuai Wang and Ziguang Zhao performed data analysis and interpretation. Faqiang Li and Feng Chen prepared the figures and tables. All authors made substantial contributions to the study and approved the final submitted version. Consent to publish Not applicable. Clinical trial registration Not applicable. Acknowledgments We would like to express our sincere gratitude to the GEO database for providing the platform and datasets. We also extend our thanks to the physicians and all other participants involved in this study. References McDermott D, Darwin ML, Fetrow K, Coulter I, Biesecker K, Thompson JA. 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Cite Share Download PDF Status: Published Journal Publication published 02 Feb, 2026 Read the published version in BMC Neurology → Version 1 posted Editorial decision: Revision requested 03 Nov, 2025 Reviews received at journal 03 Nov, 2025 Reviewers agreed at journal 30 Oct, 2025 Reviews received at journal 30 Oct, 2025 Reviewers agreed at journal 22 Oct, 2025 Reviewers agreed at journal 22 Oct, 2025 Reviewers agreed at journal 10 Oct, 2025 Reviewers agreed at journal 06 Oct, 2025 Reviewers invited by journal 06 Oct, 2025 Editor assigned by journal 06 Oct, 2025 Editor invited by journal 01 Oct, 2025 Submission checks completed at journal 29 Sep, 2025 First submitted to journal 29 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7544051","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":530785261,"identity":"3750c053-5b30-45ec-b1e3-d2ca6532cb91","order_by":0,"name":"Yunyun 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1","display":"","copyAsset":false,"role":"figure","size":506678,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTechnology Roadmap\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7544051/v1/75bca117a050e676c35aa50d.png"},{"id":93784147,"identity":"92725354-d9e2-49ca-914c-202d75966fe3","added_by":"auto","created_at":"2025-10-17 13:43:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1354077,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBatch effect removal treatment of dataset GSE143272\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Boxplot of distribution of data set GSE143272 before normalization. B. Distribution boxplot of the normalized data set GSE143272. C. PCA plot of the pre-standardized data set GSE143272. D. PCA plot of the normalized data set GSE143272. Red represents the Epilepsy group and blue represents the Control group.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7544051/v1/d816e112dc7613c561da74c0.png"},{"id":93782662,"identity":"b309c0c9-168a-474c-b267-b3cbefa818ef","added_by":"auto","created_at":"2025-10-17 13:27:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1169220,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential expression analysis of GSE143272 in the dataset\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Volcano plot of differentially expressed genes analysis between Control group and Epilepsy group in data set GSE143272. B. Venn diagram of differentially expressed genes and MRGs in data set GSE143272 and genes in datasets GSE4290 and GSE32534. C. Heat map of MRDEGs in data set GSE143272. D. The comparison map of MRDEGs between the Control group and the Epilepsy group in data set GSE143272. Group comparison plot blue represents the Control group and red represents the Epilepsy group. In the heat map, orange represents high expression and purple represents low expression. * represents p\u0026lt; 0.05, which is statistically significant; ** represents p\u0026lt; 0.01, highly statistically significant; *** represents p\u0026lt; 0.001, highly statistically significant.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7544051/v1/bd287a97b7eca944936e69c6.png"},{"id":93783015,"identity":"8ebc1899-4649-48ee-84e3-fcdbfe94751d","added_by":"auto","created_at":"2025-10-17 13:35:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1573088,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGSEA and GSVA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. GSEA of dataset GSE143272 showed four main biological features. B -E.GSE143272 data sets of all genes significantly enriched in potassium channels(B), endocytosis(C),selenoamino acid metabolism(D),mitochondrial translation(E), and other pathways. F. Group comparison of GSVA results between the Control group and the Epilepsy group in dataset GSE143272. The significant enrichment screening criteria for GSEA were p\u0026lt; 0.05 and FDR value (q value) \u0026lt; 0.05, and the correction method for p.value was BH.The symbol ** is equivalent to p\u0026lt; 0.01; The symbol *** is equivalent to p\u0026lt; 0.001. Blue represents the Control group and red represents the Epilepsy group. The screening criteria of GSVA was p.\u0026lt; 0.05, and the correction method of p. Value was BH.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7544051/v1/7be24ca059dea2c15015aafe.png"},{"id":93783014,"identity":"332d4e84-4f3a-4150-a625-02b124d3afe8","added_by":"auto","created_at":"2025-10-17 13:35:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1253553,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLogistic regression, SVM, random forest analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Forest Plot of Logistic regression analysis of 26 MRDEGs. B.the number of genes with the lowest error rate obtained by the SVM algorithm. C. The number of genes with the highest accuracy obtained by SVM algorithm. D. Model training error plot of random forest algorithm. E. Random forest model showing differential genes (in descending order of IncNodePurity).\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7544051/v1/43cdbef23e8c9c2df799ab05.png"},{"id":93782664,"identity":"6e4eda70-1df6-4b51-bc4e-2d045aff9cb6","added_by":"auto","created_at":"2025-10-17 13:27:08","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":907144,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of MRDEGs diagnostic model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Logistic regression analysis results and Venn diagram of MRDEGs in SVM model and random forest model. B. Diagnostic model diagram of LASSO regression model. C. Variable trajectory plot of LASSO regression model. D. Nomogram of 5 hub genes in MRDEGs diagnostic model. E-F. Calibration curve of MRDEGs diagnostic model (E), DCA plot (F). The ordinate of the Calibration Curve is the net benefit, and the abscissa is the Threshold Probability or threshold probability.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7544051/v1/5dd31b597d32f9971106cb32.png"},{"id":93782670,"identity":"db71e8bc-0e46-4006-8ead-539d648628bc","added_by":"auto","created_at":"2025-10-17 13:27:08","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1150770,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of MRDEGs diagnostic model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA-B. ROC curve results of hub genes \u003cem\u003eACAA1, ALDH3B1\u003c/em\u003e and \u003cem\u003eDLST\u003c/em\u003e(A), \u003cem\u003eGCDH\u003c/em\u003e and \u003cem\u003eNDUFB9\u003c/em\u003e (B) for high and low risk scores based on MRDEGs diagnostic model in dataset GSE143272. C-D. ROC curve results of high and low risk scores of hub genes \u003cem\u003eACAA1, ALDH3B1\u003c/em\u003eand \u003cem\u003eDLST\u003c/em\u003e (C), \u003cem\u003eGCDH\u003c/em\u003e and \u003cem\u003eNDUFB9\u003c/em\u003e (D) based on MRDEGs diagnostic model in dataset GSE4290. E-F. ROC curve results of hub genes \u003cem\u003eACAA1, ALDH3B1\u003c/em\u003e and \u003cem\u003eDLST\u003c/em\u003e (E), \u003cem\u003eGCDH\u003c/em\u003eand \u003cem\u003eNDUFB9\u003c/em\u003e (F) in dataset GSE32534 based on MRDEGs diagnostic model for high and low risk scores. The closer the AUC was to 1, the better the diagnostic effect was. When AUC was between 0.5 and 0.7, the accuracy was low, when AUC was between 0.7 and 0.9, the accuracy was certain, and when AUC was above 0.9, the accuracy was high.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7544051/v1/844d06064712ac18a144aa3f.png"},{"id":93783018,"identity":"7e19317a-39ad-4125-a9ab-15966a46732f","added_by":"auto","created_at":"2025-10-17 13:35:08","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":370203,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation analysis of hub genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Results of Friends analysis of the five hub genes. B. Dot plot of hub genes' correlation in dataset GSE143272. The absolute value of correlation coefficient (R value) below 0.3 was weak or no correlation, between 0.3 and 0.5 was weak correlation, between 0.5 and 0.8 was moderate correlation, and above 0.8 was strong correlation. Orange represents positive correlation, purple represents negative correlation,the depth of the color represents the strength of the correlation.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7544051/v1/50daf4a0864ebfacc6a2b017.png"},{"id":93782672,"identity":"43fead07-c682-4458-b486-ebf190f2abdc","added_by":"auto","created_at":"2025-10-17 13:27:08","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":919589,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGO and KEGG enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. The bar chart of GO and KEGG enrichment analysis results of hub genes shows: BP, CC, MF and KEGG.GO terms and KEGG terms are shown on the abscissa. B-E. Network diagram of GO and KEGG enrichment analysis results of hub genes: BP (B), CC (C), MF (D) and KEGG (E). Red nodes represent entries, blue nodes represent molecules, and the line represents the relationship between entries and molecules.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-7544051/v1/e7abccabb4bc1e3764d5effa.png"},{"id":93783020,"identity":"ea99d318-7828-47d3-b754-b24de6048ba3","added_by":"auto","created_at":"2025-10-17 13:35:08","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1843531,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of PPI interaction network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. PPI Network of hub genes calculated by STRING database. B. mRNA-TF Regulatory Network of hub genes. C. mRNA-miRNA Regulatory Network of hub genes.In B-C,mRNAs are shown in red, TFs in purple, and miRNAsin blue.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-7544051/v1/aa62c0dd7910a46136d45715.png"},{"id":93782677,"identity":"3ffedf75-0d68-42d9-b6b9-02404d7afe71","added_by":"auto","created_at":"2025-10-17 13:27:08","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":865932,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGSEA of dataset GSE143272\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. GSEA of dataset GSE143272 showed four main biological features. B-E. All genes in dataset GSE143272 were significantly enriched in hemostasis(B), aquaporin mediated transport(C), mitochondrial translation(D),dna replication(E) and other pathways.The significant enrichment screening criteria for GSEA enrichment analysis were p\u0026lt; 0.05 and FDR value (q value) \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-7544051/v1/07fd127e7c73dc2933d7255c.png"},{"id":93784509,"identity":"3aa8fb1a-f8ba-4057-85ea-56b4282175f7","added_by":"auto","created_at":"2025-10-17 13:51:08","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":871765,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003essGSEA\u003c/strong\u003e \u003cstrong\u003eof dataset GSE143272\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Group comparison of ssGSEA immune infiltration results in dataset GSE143272. B. Results of correlation analysis of immune cell infiltration abundance. C. Bubble plot of correlation between immune cells and hub genes.The symbol ns is equivalent to p≥ 0.05, which has no statistical significance. Symbol * is equivalent to p\u0026lt; 0.05; Symbol ** is equivalent to p\u0026lt; 0.01; The symbol *** is equivalent to p\u0026lt; 0.001. The number in the bottom left corner of the correlation heat map represents the correlation coefficient (R). R below 0.3 is weak or no correlation, between 0.3 and 0.5 is weak correlation, and between 0.5 and 0.8 is moderate correlation. Purple represents negative correlation; Orange represents a positive correlation, the depth of the color represents the strength of the correlation.\u003c/p\u003e","description":"","filename":"image12.png","url":"https://assets-eu.researchsquare.com/files/rs-7544051/v1/6d7411c22dc28991d1d4adba.png"},{"id":93784510,"identity":"111eb0a7-a6ae-46f4-9ea9-5d650fe5673f","added_by":"auto","created_at":"2025-10-17 13:51:08","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":1352069,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCIBERSORT analysis of dataset GSE143272\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. The proportion of CIBERSORT immune infiltration analysis results of dataset GSE143272 bar chart. B. Correlation analysis results of immune cell infiltration abundance.C. Bubble plot of correlation between immune cells and hub genes. The bottom left digit of the correlation heat map represents the correlation coefficient (R). R below 0.3 is weak or no correlation, between 0.3 and 0.5 is weak correlation, and between 0.5 and 0.8 is moderate correlation. Purple represents negative correlation; Orange represents a positive correlation, the depth of the color represents the strength of the correlation.\u003c/p\u003e","description":"","filename":"image13.png","url":"https://assets-eu.researchsquare.com/files/rs-7544051/v1/8a978699e818261a639f98e9.png"},{"id":102234298,"identity":"450c1a8d-81bc-4818-9fec-014fb58aa462","added_by":"auto","created_at":"2026-02-09 16:09:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":16737217,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7544051/v1/b2723b1f-a014-4ecd-8659-dfcf1ebc8173.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification and Verification of Hub Mitochondrial Dysfunction Genes in Epilepsy Based on Bioinformatics Analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlobally, there are about 60\u0026nbsp;million individuals with epilepsy,and over 4.9\u0026nbsp;million new cases of epilepsy are reported each year[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although the impact of the disease has decreased from 1990 to 2016, epilepsy remains a major cause of disability and death [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite advancements in medical treatments, approximately 30% of individuals with epilepsy are still resistant to anti-epileptic drugs, which imposes a substantial burden on the healthcare system and significantly affects the quality of life [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Currently, the diagnosis of epilepsy primarily depends on clinical assessments and electroencephalograms. Although these methods are valuable, they often fail to provide a comprehensive understanding of the underlying molecular mechanisms of the disease [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The fact that numerous underlying disease mechanisms contribute to the development of epilepsy, the etiology of the disease remains elusive in the majority of patients [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Thus, the exploration of the pathogenesis, diagnostic biomarkers, and therapeutic targets of epilepsy holds immense significance for assessing and ameliorating the condition of patients.\u003c/p\u003e\u003cp\u003eGenetic factors exert a pivotal role in the etiology of epilepsy. Recent studies have identified a multitude of epilepsy-associated genes and genetic variants, which contribute to the intricate nature of the disease[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. For example, genetic generalized epilepsy has been demonstrated to carry a greater burden of copy number variations in comparison with other types of epilepsy [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These genetic insights have laid the foundation for precision medicine strategies that seek to customize treatments according to individual genetic profiles [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Studies have demonstrated that mitochondrial-related genes are associated with multiple neurodegenerative disorders, including Alzheimer\u0026rsquo;s disease and Parkinson\u0026rsquo;s disease, indicating their potential involvement in epilepsy as well [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This association is further corroborated by the finding that specific instances of mitochondrial dysfunction (MD) are frequently accompanied by epileptic seizures, underscoring the significance of mitochondrial in preserving neuronal stability [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Therefore, it is likely that the crucial genes implicated in the mitochondrial energy metabolism will impact the pathogenesis of epilepsy and serve as suitable therapeutic targets. Nonetheless, the specific mitochondrial energy metabolism-related differentially expressed genes (MRDEGs) associated with epilepsy and their diagnostic utility remain uninvestigated. Given our limited comprehension of the underlying mechanisms of epilepsy, there is a necessity to identify novel and dependable screening approaches to augment diagnostic precision and therapeutic results, ultimately contributing to the improvement of patient outcomes.\u003c/p\u003e\u003cp\u003eIn this study, we utilized datasets obtained from three Gene Expression Omnibus (GEO) repositories. Through a series of bioinformatics analyses, we identified significant MRDEGs that exhibit the potential to serve as biomarkers for the diagnosis of epilepsy. Furthermore, functional enrichment analyses provided insights into the biological processes and pathways associated with these genes. The construction of protein-protein interaction (PPI) networks and the assessment of immune infiltration highlighted the complex interplay between MRDEGs and immune cells, suggesting potential therapeutic targets for further exploration.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data acquisition and processing\u003c/h2\u003e\u003cp\u003eThe methodology flowchart for the present investigation is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A total of three epilepsy gene expression microarray datasets (GSE143272 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], GSE4290 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], GSE32534 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]) were retrieved from the National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The samples in GSE143272, GSE4290, and GSE32534 were all obtained from Homo sapiens, with tissue sources being blood, brain, and neocortex, respectively. The R package \u0026ldquo;limma\u0026rdquo; was employed to standardize the GSE143272 dataset, annotate and normalize the probes [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A comparison of the expression matrices prior to and following normalization was conducted by categorizing into Control and Epilepsy groups, and Principal Component Analysis (PCA) was performed to evaluate the normalization efficacy [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. We searched the PubMed website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using \u0026ldquo;mitochondrial energy metabolism\u0026rdquo; as keywords to identify published literature related to the mitochondrial energy metabolism-related gene (MRGs) sets [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. A total of 188 MRGs were identified, and the detailed information is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEpilepsy data set information list.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGSE143272\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGSE4290\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGSE32534\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatform\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGPL10558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGPL570\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGPL570\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHomo sapiens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHomo sapiens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHomo sapiens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTissue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBlood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBrain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNeocortex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSamples in Epilepsy group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSamples in Control group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 MRDEGs related to epilepsy\u003c/h2\u003e\u003cp\u003eThe R package \u0026ldquo;limma\u0026rdquo; was used to analyze the differences in gene expression between the Control and Epilepsy cohorts. A threshold of |logFC| \u0026gt;0 and q-value (p-value correction for Benjamini-Hochberg)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was established for selecting the differentially expressed genes (DEGs). MRDEGs were obtained by variance analysis in the dataset GSE143272. Intersectional MRGs were identified in the GSE4290 and GSE32534 datasets, leading to the mapping of MRDEGs. To further examine the expression variations of MRDEGs within the GSE14327 dataset, a group-based expression comparison plot was generated based on the expression levels of the MRDEGs. Volcano plots and heatmaps of DEGs were created using the R packages \"ggplot2\" and \"heatmap,\" respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Gene set enrichment analysis\u003c/h2\u003e\u003cp\u003eThe genes in the dataset GSE143272 were ranked based on their logFC values. Subsequently, the R package \u0026ldquo;clusterProfiler\u0026rdquo; was employed to conduct Gene Set Enrichment Analysis (GSEA) on all genes within GSE143272 dataset. The gene sets \u0026ldquo;c2.cp.kegg.v6.2.symbols\u0026rdquo; and \u0026ldquo;c2.all.v7.2.symbols\u0026rdquo; were retrieved from the Molecular Signatures Database (MSigDB) for GSEA analysis [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The criteria for GSEA screening were established with a q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Gene set variation analysis\u003c/h2\u003e\u003cp\u003eAll genes in the GSE143272 dataset underwent Gene Set Variation Analysis (GSVA), aimed at quantifying functional enrichment discrepancies across different groups. The significance threshold for GSVA was determined as a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, with the Benjamini-Hochberg approach applied for p-value correction.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Construction and validation of diagnostic model for epilepsy\u003c/h2\u003e\u003cp\u003eTo formulate a diagnostic model for epilepsy utilizing MRDEGs from the GSE143272 dataset, logistic regression analysis was conducted. MRDEGs with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were used as the criterion for screening, and a logistic regression model was subsequently constructed.\u003c/p\u003e\u003cp\u003eFollowing this, the SVM algorithm was employed to build the SVM model [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The MRDEGs were further filtered based on the number of genes that achieved the highest accuracy and the lowest error rate. The expression levels of the MRDEGs in the expression matrix of the GSE143272 dataset were utilized to construct the model using the \u0026ldquo;RF\u0026rdquo; package with the parameters set.seed (500) and ntree\u0026thinsp;=\u0026thinsp;1000 specified [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:I(X=xi)=\\:-lo{g}_{2}p\\left({x}_{i}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFinally, DEGs with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 identified in the logistic regression analysis were intersected with the DEGs obtained from the SVM model and the RF model, and a Venn diagram was generated. Common Differentially Expressed Genes (CDEGs) were obtained. The parameters set.seed (500) and family = \"binomial\" were utilized to perform Least Absolute Shrinkage and Selection Operator (LASSO) regression based on the CDEGs using the \u0026ldquo;glmnet\u0026rdquo; package in R [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The outcomes of the LASSO regression analysis and linear regression contributed to the formulation of the diagnostic model for MRDEGs. The MRDEGs incorporated in this model were classified as model genes, further identified as hub genes. Ultimately, the LASSO risk score was computed based on the risk coefficients from the LASSO regression analysis, using the following formula:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\text{R}\\text{i}\\text{s}\\text{k}Score\\:=\\:\\sum\\:_{i}Coefficient\\:\\left({ℎub\\:gene}_{i}\\right)\\ast\\:mRNA\\:Expression\\:\\left({ℎub\\:gene}_{i}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe R package was employed to generate a Nomogram based on the outcomes of the LASSO regression analysis, aiming to illustrate the interrelationships among the DEGs incorporated in the diagnostic model [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Additionally, the R package \u0026ldquo;Decision Curve Analysis (DCA)\u0026rdquo; was utilized to create DCA plots based on the hub genes in the GSE143272 dataset, with the purpose of assessing the accuracy and discriminative ability of the diagnostic model [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe utilized the \u0026ldquo;pROC\u0026rdquo; package in R to generate the Receiver Operating Characteristic (ROC) curves for the hub genes of the MRDEGs diagnostic model in the GSE143272 dataset [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The Area Under the Curve (AUC) was subsequently calculated to assess the diagnostic potential of hub gene expression in epilepsy. Moreover, the risk coefficients obtained from the LASSO regression analysis were employed to compute the corresponding risk scores in the GSE4290 and GSE32534 datasets. The \"pROC\" package was then utilized to plot ROC curves for the hub genes within these datasets, and the AUC was calculated to validate the diagnostic value of hub gene expression in epilepsy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Gene ontology and Kyoto encyclopedia of genes and genomes enrichment analysis\u003c/h2\u003e\u003cp\u003eThe R package \u0026ldquo;clusterProfiler\u0026rdquo; was employed to conduct Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the hub genes [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The screening criteria for statistically significant results were set as a q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Protein-protein interaction network\u003c/h2\u003e\u003cp\u003eThe STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org/\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized based on the hub genes, with a minimum required interaction score greater than 0.400 (medium confidence) serving as the criterion for constructing the hub gene-related Protein-Protein Interaction (PPI) network [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The Cytoscape software (version 3.9.1) was employed to visualize the network [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Construct mRNA-miRNA and mRNA-TF interaction networks\u003c/h2\u003e\u003cp\u003eTo explore the interactions between hub genes and microRNAs (miRNAs), the miRNAs related to the hub genes were retrieved from the miRDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mirdb.org/\u003c/span\u003e\u003cspan address=\"https://mirdb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The mRNA-miRNA regulatory network was visualized using Cytoscape software [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].The transcription factors (TFs) were retrieved from the ChIPBase database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rna.sysu.edu.cn/chipbase/\u003c/span\u003e\u003cspan address=\"http://rna.sysu.edu.cn/chipbase/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to analyze their regulatory effects on the hub genes [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Cytoscape software was employed to visualize the mRNA-TF regulatory network.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Immune infiltration analysis\u003c/h2\u003e\u003cp\u003eThe first step involved annotating each type of infiltrating immune cell. Next, the enrichment scores generated by Single-Sample Gene-Set Enrichment Analysis (ssGSEA) were employed to depict the relative abundance of immune cell infiltration in each sample, thereby establishing the immune cell infiltration matrix for the GSE143272 dataset [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The R package \u0026ldquo;ggplot2\u0026rdquo; was employed to generate group comparison plots to illustrate the differences in the expression of immune cells between the Control and Epilepsy groups in the GSE143272 dataset. Additionally, another R package was used to create a correlation heatmap to display the results of the correlation analysis among the immune cells themselves.\u003c/p\u003e\u003cp\u003eThe CIBERSORT algorithm, in conjunction with the LM22 signature gene matrix, was employed to filter the data with immune cell enrichment scores greater than zero [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Then, the specific results of the immune cell infiltration matrix for the GSE143272 dataset were obtained, and a proportion bar chart was generated for visualization. R package was utilized to create a correlation heatmap to present the results of the correlation analysis among the immune cells themselves. Additionally, the \u0026ldquo;ggplot2\u0026rdquo; package in R was employed to develop a correlation bubble plot, which represented the outcomes of the correlation analysis between hub genes and immune cells.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.10 Statistical analysis\u003c/h2\u003e\u003cp\u003eAll data processing and analyses in this study were conducted using R software (Version 4.2.2). The statistical significance of normally distributed continuous variables between two groups was evaluated through an independent Student's t-test, unless otherwise indicated. The Mann-Whitney U test (Wilcoxon rank sum test) was employed to analyze the differences between variables that did not follow a normal distribution. Spearman correlation analysis was performed to calculate the correlation coefficient between various molecules. Unless otherwise noted, a p-value threshold of less than 0.05 was established for statistical significance, and all p-values were considered two-tailed.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.1 MRDEGs analysis of data set GSE143272\u003c/h2\u003e\u003cp\u003eDistribution boxplots (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B) were employed to compare the expression values of the datasets prior to and following normalization. PCA plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-D) were utilized to compare the low-dimensional feature distributions of the datasets before and after standardization. The results from the distribution boxplots and PCA plots indicated that the batch effect among the samples in the dataset GSE143272 was essentially eliminated after batch removal.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the dataset GSE143272, a total of 2166 DEGs met the criteria of |logFC| \u0026gt;0 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Among these, 1014 genes were upregulated and 1152 genes were downregulated, as indicated by the variance analysis results presented in the volcano plot of the dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo obtain the intersection of genes with |logFC| \u0026gt;0 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 from all datasets (GSE143272, GSE4290, and GSE32534), as well as mitochondria-related genes, a mapping was performed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). A total of 26 MRDEGs were identified, including \u003cem\u003eALDH18A1\u003c/em\u003e, \u003cem\u003eMDH2\u003c/em\u003e, \u003cem\u003eACAA1\u003c/em\u003e, \u003cem\u003eALDH3B1\u003c/em\u003e, \u003cem\u003eCOX6C\u003c/em\u003e, \u003cem\u003eNDUFB9\u003c/em\u003e, \u003cem\u003eNDUFV1\u003c/em\u003e, \u003cem\u003eIDH1\u003c/em\u003e, \u003cem\u003ePAAF1\u003c/em\u003e, \u003cem\u003eECHS1\u003c/em\u003e, \u003cem\u003ePGM2\u003c/em\u003e, \u003cem\u003eNDUFS3\u003c/em\u003e, \u003cem\u003eNDUFA8\u003c/em\u003e, \u003cem\u003eOXCT1\u003c/em\u003e, \u003cem\u003ePDHB\u003c/em\u003e, \u003cem\u003eUQCRFS1\u003c/em\u003e, \u003cem\u003eGCDH\u003c/em\u003e, \u003cem\u003eCOX10\u003c/em\u003e, \u003cem\u003eNDUFA13\u003c/em\u003e, \u003cem\u003ePOR\u003c/em\u003e, \u003cem\u003eDLST\u003c/em\u003e, \u003cem\u003eHADHB\u003c/em\u003e, \u003cem\u003eNDUFB7\u003c/em\u003e, \u003cem\u003eHADH\u003c/em\u003e, \u003cem\u003ePPA2\u003c/em\u003e, and \u003cem\u003eNDUFS4\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eSubsequently, based on the intersection results, the expression differences of these 26 MRDEGs between different sample groups in the dataset GSE143272 were analyzed. An R package was utilized to generate a heatmap to display the analysis results (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eTo explore the expression differences of MRDEGs in the dataset GSE143272, the group comparison figure presents the difference analysis results of the 26 MRDEGs between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). The results demonstrated that the expression of these 26 MRDEGs was statistically significant.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.2 GSEA and GSVA\u003c/h2\u003e\u003cp\u003eGSEA was employed to investigate the association between the expression profiles of all genes within dataset GSE143272 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The findings are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. It was observed that all the genes exhibited significant enrichment in potassium channels, endocytosis, selenoamino acid metabolism, mitochondrial translation, as well as other related biological functions and signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-E).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGSEA results of GSE143272.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003esetSize\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNES\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep.adjust\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eqvalues\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eREACTOME_TRANSLATION\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.92519\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.91 e-08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.59 e-08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eREACTOME_MITOCHONDRIAL_TRANSLATION\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.44629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.4 e-07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2E-07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eREACTOME_SELENOAMINO_ACID_METABOLISM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.49705\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.61 e-08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.35 e-08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKEGG_ENDOCYTOSIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.502007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.047105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.039282\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eREACTOME_POTASSIUM_CHANNELS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.760692\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.044348\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.036983\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWP_COMPLEMENT_AND_COAGULATION_CASCADES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.990027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.008987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.007495\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNABA_ECM_AFFILIATED\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.010629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002981\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002486\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eREACTOME_INTERFERON_SIGNALING\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.020286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.77 e-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.48 e-05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWP_IMMUNE_RESPONSE_TO_TUBERCULOSIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.033109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.004623\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.003855\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eREACTOME_NEUREXINS_AND_NEUROLIGINS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.082106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.006433\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.005365\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWP_TYPE_II_INTERFERON_SIGNALING\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.090758\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002224\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWP_AGERAGE_PATHWAY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.107866\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000793\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eREACTOME_ANTIMICROBIAL_PEPTIDES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.182752\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000302\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eREACTOME_NEUTROPHIL_DEGRANULATION\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.535737\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.91 e-08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.59 e-08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePID_CERAMIDE_PATHWAY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.96624\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00776\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.006471\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eGSVA was conducted on the entire gene set within dataset GSE143272, with the detailed information presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Pathways exhibiting positive enrichment and negative enrichment in the top 10 were identified. Then, the differential expression of these 20 pathways between the two groups was analyzed. The observed differences were validated using the Mann-Whitney U test. A group comparison diagram was generated to illustrate these results (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGSVA enrichment analysis results of GSE143272.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003elogFC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep.value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSCHLESINGER_METHYLATED_IN_COLON_CANCER\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.62115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.006833\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eREACTOME_RUNX3_REGULATES_YAP1_MEDIATED_TRANSCRIPTION\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.57647\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.011154\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOHASHI_AURKA_TARGETS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.56408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.005234\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVANDESLUIS_COMMD1_TARGETS_GROUP_4_DN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.55875\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.013484\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eREACTOME_ERBB2_ACTIVATES_PTK6_SIGNALING\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.55795\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.01417\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eREACTOME_GRB7_EVENTS_IN_ERBB2_SIGNALING\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.55795\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.01417\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eREACTOME_THE_RETINOID_CYCLE_IN_CONES_DAYLIGHT_VISION\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.54804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.015981\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eREACTOME_FGFR2B_LIGAND_BINDING_AND_ACTIVATION\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.54048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.017574\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBILANGES_SERUM_SENSITIVE_VIA_TSC2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.4529\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.08 e-06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eREACTOME_PROSTANOID_LIGAND_RECEPTORS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.44478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.015364\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eREACTOME_FATTY_ACIDS_BOUND_TO_GPR40_FFAR1_REGULATE_INSULIN_SECRETION\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.547836\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.003795\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTESAR_JAK_TARGETS_MOUSE_ES_D3_DN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.567882\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.013975\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMIKKELSEN_IPS_LCP_WITH_H3K4ME3_AND_H3K27ME3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.572147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.012695\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZIRN_TRETINOIN_RESPONSE_DN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.573646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002409\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKEGG_MATURITY_ONSET_DIABETES_OF_THE_YOUNG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.602383\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.008444\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMCCOLLUM_GELDANAMYCIN_RESISTANCE_DN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.602383\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.008444\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZHOU_PANCREATIC_EXOCRINE_PROGENITOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.602383\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.008444\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBERGER_MBD2_TARGETS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.628249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.006374\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBIOCARTA_TUBBY_PATHWAY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.638454\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.005914\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMEISSNER_ES_ICP_WITH_H3K4ME3_AND_H3K27ME3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.752315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000229\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Construction and validation of diagnostic model for epilepsy\u003c/h2\u003e\u003cp\u003eWe conducted Logistic regression analysis utilizing these 26 most relevant MRDEGs. A Logistic regression model was established, and the outcomes were visually represented through a Forest Plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The findings indicated that all 26 MRDEGs exhibited statistical significance within the Logistic regression model (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). We developed a SVM model based on the 26 MRDEGs using the SVM algorithm. We determined the number of genes associated with the lowest error rate (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) and the highest accuracy rate (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe results demonstrated that the SVM model achieved the highest accuracy when 22 genes were included. These 22 MRDEGs are \u003cem\u003eALDH18A1\u003c/em\u003e, \u003cem\u003eMDH2\u003c/em\u003e, \u003cem\u003eACAA1\u003c/em\u003e, \u003cem\u003eALDH3B1\u003c/em\u003e, \u003cem\u003eCOX6C\u003c/em\u003e, \u003cem\u003eNDUFB9\u003c/em\u003e, \u003cem\u003eIDH1\u003c/em\u003e, \u003cem\u003ePAAF1\u003c/em\u003e, \u003cem\u003ePGM2\u003c/em\u003e, \u003cem\u003eNDUFS3\u003c/em\u003e, \u003cem\u003eOXCT1\u003c/em\u003e, \u003cem\u003ePDHB\u003c/em\u003e, \u003cem\u003eUQCRFS1\u003c/em\u003e, \u003cem\u003eGCDH\u003c/em\u003e, \u003cem\u003eCOX10\u003c/em\u003e, \u003cem\u003eNDUFA13\u003c/em\u003e, \u003cem\u003eDLST\u003c/em\u003e, \u003cem\u003eHADHB\u003c/em\u003e, \u003cem\u003eNDUFB7\u003c/em\u003e, \u003cem\u003eHADH\u003c/em\u003e, \u003cem\u003ePPA2\u003c/em\u003e, and \u003cem\u003eNDUFS4\u003c/em\u003e. Next, we analyzed the expression levels of the 26 MRDEGs in dataset GSE143272 using RF algorithm (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). IncNodePurity represents the augmentation in node purity. A higher node purity signifies fewer impurities (i.e., a smaller Gini coefficient). We employed IncNodePurity\u0026thinsp;\u0026gt;\u0026thinsp;0.80 as the criterion to filter the specific analysis results. The findings revealed that a total of 11 MRDEGs were identified, including \u003cem\u003eDLST\u003c/em\u003e, \u003cem\u003eACAA1\u003c/em\u003e, \u003cem\u003eNDUFV1\u003c/em\u003e, \u003cem\u003eNDUFB9\u003c/em\u003e, \u003cem\u003eNDUFA13\u003c/em\u003e, \u003cem\u003eALDH3B1\u003c/em\u003e, \u003cem\u003eNDUFB7\u003c/em\u003e, \u003cem\u003eECHS1\u003c/em\u003e, \u003cem\u003eOXCT1\u003c/em\u003e, \u003cem\u003eGCDH\u003c/em\u003e, and \u003cem\u003eIDH1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003eWe performed an intersection analysis between the genes with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 from the Logistic regression analysis results and the MRDEGs identified by the SVM and RF model. This analysis yielded a total of 9 intersecting genes, for which a Venn diagram was constructed (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The 9 intersecting genes are \u003cem\u003eACAA1\u003c/em\u003e, \u003cem\u003eALDH3B1\u003c/em\u003e, \u003cem\u003eDLST\u003c/em\u003e, \u003cem\u003eGCDH\u003c/em\u003e, \u003cem\u003eIDH1\u003c/em\u003e, \u003cem\u003eNDUFA13\u003c/em\u003e, \u003cem\u003eNDUFB7\u003c/em\u003e, \u003cem\u003eNDUFB9\u003c/em\u003e and \u003cem\u003eOXCT1\u003c/em\u003e. Next, we generated a LASSO regression model diagram and a LASSO variable trajectory diagram for visualization purposes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB-C). The results indicated that five MRDEGs incorporated into the LASSO regression model were identified as hub genes, including \u003cem\u003eACAA1\u003c/em\u003e, \u003cem\u003eALDH3B1\u003c/em\u003e, \u003cem\u003eDLST\u003c/em\u003e, \u003cem\u003eGCDH\u003c/em\u003e and \u003cem\u003eNDUFB9\u003c/em\u003e. To further validate the diagnostic value of the MRDEGs model, we developed a Nomogram based on the five hub genes within the diagnostic model, illustrating the interrelationships of these hub genes in the dataset GSE143272 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). The findings revealed that the utility of the hub gene \u003cem\u003eNDUFB9\u003c/em\u003e expression for the MRDEGs diagnostic model was significantly higher compared to other variables, whereas the utility of the \u003cem\u003eACAA1\u003c/em\u003e expression level was significantly lower. The predictive performance of the model on the actual outcomes was assessed by comparing the fitting of the optimal theoretical probability (solid line) and the model-predicted probability (dashed line) under various scenarios depicted in the figure (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF demonstrates that, within a certain range, the model\u0026rsquo;s line consistently remains above both the Epilepsy positive and Epilepsy negative lines, indicating a higher net benefit and superior model performance.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFurthermore, the R package pROC was employed to generate ROC curves based on the hub genes in dataset GSE143272 (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-B). The ROC curve analysis revealed that the expression level of the hub gene DLST in dataset GSE143272 exhibited high accuracy (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.9) in distinguishing between the high-risk and low-risk groups within the MRDEGs diagnostic model. The expression level of the hub gene \u003cem\u003eNDUFB9\u003c/em\u003e demonstrated a moderate accuracy (0.9\u0026thinsp;\u0026gt;\u0026thinsp;AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7) in differentiating between these two risk groups. In contrast, the expression levels of the hub genes \u003cem\u003eACAA1\u003c/em\u003e, \u003cem\u003eALDH3B1\u003c/em\u003e, and \u003cem\u003eGCDH\u003c/em\u003e displayed a relatively low accuracy (0.7\u0026thinsp;\u0026gt;\u0026thinsp;AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.5) in this regard. The ROC curves constructed based on the hub genes in datasets GSE4290 (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC-D) and GSE32534 (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE-F) indicated that the expression levels of the hub genes \u003cem\u003eACAA1\u003c/em\u003e and \u003cem\u003eDLST\u003c/em\u003e achieved high accuracy (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.9) in distinguishing between the high-risk and low-risk groups within the MRDEGs diagnostic model in dataset GSE4290. The expression level of the hub gene \u003cem\u003eALDH3B1\u003c/em\u003e exhibited a moderate accuracy (0.9\u0026thinsp;\u0026gt;\u0026thinsp;AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7) in this dataset. In dataset GSE32534, the expression levels of the hub genes \u003cem\u003eACAA1\u003c/em\u003e and \u003cem\u003eGCDH\u003c/em\u003e demonstrated a moderate accuracy (0.9\u0026thinsp;\u0026gt;\u0026thinsp;AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7) in differentiating between the high-risk and low-risk groups.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSubsequently, we evaluated the five hub genes using the scores from the functional similarity (Friends) analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA), which revealed that \u003cem\u003eDLST\u003c/em\u003e holds a crucial role in epilepsy. The correlation dot plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB) illustrating the relationships among the five hub genes in dataset GSE143272 demonstrated significant negative correlations between the hub genes \u003cem\u003eACAA1\u003c/em\u003e and \u003cem\u003eALDH3B1\u003c/em\u003e, and the hub genes \u003cem\u003eDLST\u003c/em\u003e, \u003cem\u003eGCDH\u003c/em\u003e, and \u003cem\u003eNDUFB9\u003c/em\u003e (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Notably, the most pronounced positive correlation was observed between the hub genes \u003cem\u003eACAA1\u003c/em\u003e and \u003cem\u003eALDH3B1\u003c/em\u003e (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.4 GO and KEGG enrichment analysis\u003c/h2\u003e\u003cp\u003eThe five hub genes were subjected to GO and KEGG enrichment analyses, with the specific results presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The analysis revealed that the genes associated with epilepsy were primarily enriched in biological processes (BP), such as the carboxylic acid catabolic process, organic acid catabolic process, and fatty acid beta-oxidation. In terms of cellular components (CC), they were enriched in the oxidoreductase complex, specific granule, tricarboxylic acid cycle enzyme complex, mitochondrial matrix, and NADH dehydrogenase complex. Regarding molecular functions (MF), the genes exhibited enrichment in acyltransferase activity, myristoyltransferase activity, acyl-CoA dehydrogenase activity, NADH dehydrogenase activity, and electron transfer activity. Additionally, KEGG pathway analysis showed enrichment in tryptophan metabolism, fatty acid degradation, and lysine degradation. The outcomes of the GO and KEGG enrichment analyses were visualized using bar graphs (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). Concurrently, network diagrams depicting the relationships among BP, CC, and MF were constructed based on the GO and KEGG enrichment results (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB-E).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGO/KEGG enrichment analysis results.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOntology\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGeneRatio\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBgRatio\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep.adjust\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eq.value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGO:0042775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003emitochondrial ATP synthesis coupled electron transport\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1/5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e92/18800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.037869\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.005486\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGO:0044282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003esmall molecule catabolic process\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4/5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e376/18800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.45 e-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.22 e-05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGO:0046395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ecarboxylic acid catabolic process\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3/5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e238/18800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.000751\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000109\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGO:0016054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eorganic acid catabolic process\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3/5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e242/18800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.000751\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000109\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGO:0006635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003efatty acid beta-oxidation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2/5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e76/18800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.003629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000526\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGO:1990204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eoxidoreductase complex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2/5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e120/19594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.008477\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.002745\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGO:0042581\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003especific granule\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2/5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e160/19594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.008477\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.002745\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGO:0045239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003etricarboxylic acid cycle enzyme complex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1/5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16/19594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.035331\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.011443\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGO:0005759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003emitochondrial matrix\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2/5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e473/19594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.036015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.011665\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGO:0030964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNADH dehydrogenase complex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1/5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49/19594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.036675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.011879\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGO:0016746\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eacyltransferase activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2/5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e244/18410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.016269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.001713\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGO:0019107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003emyristoyltransferase activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1/5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11/18410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.016269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.001713\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGO:0003995\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eacyl-CoA dehydrogenase activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1/5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12/18410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.016269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.001713\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGO:0003954\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNADH dehydrogenase activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1/5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45/18410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.01963\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.002066\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGO:0009055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eelectron transfer activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1/5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e125/18410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.038648\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.004068\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKEGG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa00380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTryptophan metabolism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2/5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42/8164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.004024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.001836\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKEGG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa00071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFatty acid degradation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2/5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43/8164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.004024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.001836\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKEGG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa00310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLysine degradation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2/5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e63/8164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.005774\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.002634\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.5 The PPI network, mRNA-miRNA and mRNA-TF interaction network\u003c/h2\u003e\u003cp\u003eThe PPI network analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA) revealed interactions among the five hub genes: \u003cem\u003eACAA1\u003c/em\u003e, \u003cem\u003eALDH3B1\u003c/em\u003e, \u003cem\u003eDLST\u003c/em\u003e, \u003cem\u003eGCDH\u003c/em\u003e and \u003cem\u003eNDUFB9\u003c/em\u003e. Transcription factors (TFs) binding to these hub genes were identified using the ChIPBase database, and the mRNA-TF regulatory network was constructed and visualized utilizing Cytoscape software (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB). This network comprises 5 hub genes and 20 TFs, with detailed information provided in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Additionally, the miRDB database was employed to identify microRNAs (miRNAs) associated with the hub genes, and the mRNA-miRNA regulatory network was constructed and visualized using Cytoscape software (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eC). This network consists of 4 hub genes and 31 miRNAs, with specific details presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003emRNA-TF interaction network nodes.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003emRNA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003emRNA\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCTCF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNDUFB9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGATA2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eACAA1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSPI1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eACAA1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMYC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGCDH\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCTCF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eACAA1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCEBPB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNDUFB9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRAD21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNDUFB9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eERG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eACAA1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRUNX1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eACAA1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eERG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNDUFB9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eERG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDLST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSTAG1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNDUFB9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGABPA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNDUFB9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEP300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eACAA1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGATA1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eACAA1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePOLR2A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eACAA1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSTAT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eACAA1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUSF1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eACAA1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGABPA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDLST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePOLR2A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNDUFB9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eESR1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNDUFB9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCREB1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eACAA1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSPI1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNDUFB9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSPI1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eALDH3B1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNRF1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eACAA1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUSF1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eALDH3B1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eELF1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNDUFB9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eELF1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDLST\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eELF1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eACAA1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMAX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGCDH\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGABPA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eACAA1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\"TF\" and \"mRNA\" represent node; \"-\" represent edge\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003emRNA-miRNA interaction network nodes.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003emiRNA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003emRNA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003emiRNA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003emRNA\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa-miR-3942-3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDLST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehsa-let-7a-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDLST\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa-miR-30d-3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDLST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehsa-let-7i-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDLST\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa-miR-30a-3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDLST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehsa-let-7e-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDLST\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa-miR-30e-3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDLST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehsa-let-7c-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDLST\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa-miR-6874-3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDLST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehsa-miR-6870-3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eACAA1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa-miR-148b-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDLST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehsa-miR-516a-3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDLST\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa-miR-4773\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDLST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehsa-miR-516b-3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDLST\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa-miR-4500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDLST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehsa-miR-6870-3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGCDH\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa-miR-7110-3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDLST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehsa-miR-3679-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eALDH3B1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa-miR-140-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDLST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehsa-miR-7162-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDLST\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa-miR-4458\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDLST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehsa-miR-6811-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDLST\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa-let-7f-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDLST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehsa-miR-3074-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDLST\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa-let-7d-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDLST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehsa-miR-124-3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGCDH\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa-miR-98-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDLST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehsa-miR-506-3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGCDH\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa-let-7b-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDLST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehsa-miR-6511b-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDLST\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehsa-let-7g-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDLST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehsa-miR-3145-3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGCDH\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\"miRNA\" and \"mRNA\" represent nodes; \"-\" represents edge.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Group-based gene enrichment analysis\u003c/h2\u003e\u003cp\u003eUtilizing the logFC values from the high and low risk groups of the MRDEGs diagnostic model, GSEA was employed to investigate the associations between the expression profiles of all genes in the dataset GSE143272 (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA) and the corresponding biological processes (BP), cellular components (CC), and molecular functions (MF). The detailed results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The analysis revealed that the genes were significantly enriched in various biological functions and signaling pathways, including hemostasis (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eB), aquaporin-mediated transport (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eC), mitochondrial translation (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eD), and DNA replication (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGSEA results of GSE143272.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003esetSize\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNES\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep.adjust\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eqvalue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKEGG_DNA_REPLICATION\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.863\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.045103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.035546\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eREACTOME_MITOCHONDRIAL_TRANSLATION\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.45598\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.51 e-07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.92 e-07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eREACTOME_AQUAPORIN_MEDIATED_TRANSPORT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.784593\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03954\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.031161\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eREACTOME_HEMOSTASIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.885517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.64 e-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.29 e-06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eREACTOME_ANTIMICROBIAL_PEPTIDES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.054375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.003039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002395\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNABA_ECM_REGULATORS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.069537\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000611\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000482\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eREACTOME_INTERLEUKIN_4_AND_INTERLEUKIN_13_SIGNALING\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.078161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000394\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNABA_MATRISOME\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.10595\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.47 e-08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.16 e-08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eREACTOME_SIGNAL_AMPLIFICATION\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.109116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000962\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000758\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eREACTOME_TRANSCRIPTIONAL_REGULATION_OF_GRANULOPOIESIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.11226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001971\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001553\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNABA_MATRISOME_ASSOCIATED\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.115595\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.89 e-07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.07 e-07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWP_NUCLEOTIDEBINDING_OLIGOMERIZATION_DOMAIN_NOD_PATHWAY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.291655\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.6 e-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.57 e-05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWP_COMPLEMENT_SYSTEM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.320945\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.66 e-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.88 e-06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eREACTOME_NEUTROPHIL_DEGRANULATION\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.477168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.47 e-08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.16 e-08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eREACTOME_TRANSLATION\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.93387\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.47 e-08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.16 e-08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Immune infiltration analysis\u003c/h2\u003e\u003cp\u003eThe group comparison diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eA) illustrated that nine types of immune cells, including activated CD8\u0026thinsp;+\u0026thinsp;T cells, activated dendritic cells, eosinophils, immature dendritic cells, macrophages, mast cells, monocytes, neutrophils, and T follicular helper cells (Tfh), exhibited statistically significant differences between the Control group and the Epilepsy group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The correlation results regarding the abundance of these nine immune cell infiltrations in the immune infiltration analysis of dataset GSE143272 are presented in a correlation heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eB). The findings indicate that Activated CD8\u0026thinsp;+\u0026thinsp;T cells were predominantly negatively correlated with other immune cells, whereas most other immune cells displayed positive correlations. Furthermore, the correlation between the five hub genes and the nine immune cells was analyzed using a correlation bubble plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eC). The results revealed that the hub genes \u003cem\u003eNDUFB9\u003c/em\u003e, \u003cem\u003eGCDH\u003c/em\u003e, and \u003cem\u003eDLST\u003c/em\u003e showed significant negative correlations with the immune cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while hub genes \u003cem\u003eALDH3B1\u003c/em\u003e and \u003cem\u003eACAA1\u003c/em\u003e exhibited significant positive correlations with the immune cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBased on the immune infiltration analysis results, a bar chart depicting the proportion of 22 immune cells in dataset GSE143272 was generated (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eA). The bar chart illustrated the proportions of 18 types of immune cells, including naive B cells, memory B cells, plasmacytoid dendritic cells, activated CD8\u0026thinsp;+\u0026thinsp;T cells, activated CD4\u0026thinsp;+\u0026thinsp;T cells, effector memory CD4\u0026thinsp;+\u0026thinsp;T cells, resting memory CD4\u0026thinsp;+\u0026thinsp;T cells, regulatory T cells (Treg), gamma-delta T cells, resting natural killer cells, natural killer cells, monocytes, M1 macrophages, M2 macrophages, dendritic cells, mast cells, eosinophils, and neutrophils. Subsequently, the correlation results of the infiltration abundance of these 18 immune cells were presented in a correlation heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eB). The findings revealed that the majority of these 18 immune cells exhibited negative correlations with each other. Finally, the correlation between hub genes and the infiltration abundance of immune cells in dataset GSE143272 was visualized using a correlation bubble plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eC). The results from the correlation bubble plot indicated that the most significant negative correlation was observed between immune cells (Activated CD4\u0026thinsp;+\u0026thinsp;T cells) and the hub gene \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eACAA1\u003c/span\u003e (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, the most significant positive correlation was found between immune cells (Neutrophils) and the hub gene \u003cem\u003eACAA1\u003c/em\u003e (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eDespite the available of numerous pharmacological interventions for epilepsy, a significant proportion of individuals still suffer from persistent seizures, underscoring the urgent necessity for improved therapeutic strategies [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In the present study, we found that key MRDEGs in epilepsy, such as \u003cem\u003eDLST\u003c/em\u003e, \u003cem\u003eACAA1\u003c/em\u003e, \u003cem\u003eALDH3B1\u003c/em\u003e, \u003cem\u003eGCDH\u003c/em\u003e, and \u003cem\u003eNDUFB9\u003c/em\u003e, are significantly implicated in pathways associated with mitochondrial ATP synthesis and that their altered expression patterns are closely related to the pathogenesis of epilepsy. These results offer valuable insights into the mitochondrial gene-based therapeutic approaches for epilepsy. Several primary mitochondrial diseases can manifest with epilepsy, including mitochondrial encephalopathy, lactic acidosis, myoclonic epilepsy with ragged red fibers, and Leigh syndrome (LS) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Wolfgang et al. have demonstrated that MD represents a pivotal mechanisms underlying epileptic seizures and contributes to their occurrence [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Metabolism-based treatments, such as the high-fat ketogenic diet, have the potential to restore metabolic homeostasis and achieve seizure control[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In this study, we focused on identifying MRDEGs in epilepsy patients and constructing a diagnostic model to improve disease management. This approach holds promise as novel biomarkers and therapeutic targets for early diagnosis, thereby enhancing the clinical management of patients with epilepsy and alleviating the disease burden on patients.\u003c/p\u003e\u003cp\u003eOur study has revealed that key MRDEGs in epilepsy, including \u003cem\u003eDLST\u003c/em\u003e, \u003cem\u003eACAA1\u003c/em\u003e, \u003cem\u003eALDH3B1\u003c/em\u003e, \u003cem\u003eGCDH\u003c/em\u003e, and \u003cem\u003eNDUFB9\u003c/em\u003e, are significantly involved in pathways associated with mitochondrial ATP synthesis. \u003cem\u003eDLST\u003c/em\u003e serves as a crucial component of the alpha-ketoglutarate dehydrogenase complex, which plays a vital role in the tricarboxylic acid cycle and ATP production [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The reduced expression of \u003cem\u003eDLST\u003c/em\u003e in epilepsy suggests that MD contributes to the progression of the disease. \u003cem\u003eACAA1\u003c/em\u003e encodes human peroxisomal 3-oxoacyl-CoA thiolase, an enzyme involved in the beta-oxidation pathway of fatty acids, a critical process for energy production in cells [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. \u003cem\u003eALDH3B1\u003c/em\u003e encodes a protein implicated in the detoxification of aldehydes, which can impair mitochondrial function [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Its role in epilepsy was underscored in a bioinformatic analysis that identified key genes associated with the disease [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], which is consistent with our findings. \u003cem\u003eGCDH\u003c/em\u003e participates in the catabolism of lysine, hydroxylysine, and tryptophan, linking amino acid metabolism with mitochondrial energy production [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Posset et al. reported that deficiencies in \u003cem\u003eGCDH\u003c/em\u003e are known to cause glutaric aciduria type I, a metabolic disorder that leads to neurological symptoms, including seizures [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. \u003cem\u003eNDUFB9\u003c/em\u003e is a component of Complex I in the electron transport chain and is essential for initiating electron transfer and proton pumping [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Mutations or altered expression of \u003cem\u003eNDUFB9\u003c/em\u003e can impair mitochondrial function, leading to reduced ATP availability and increased oxidative stress, both of which are implicated in epilepsy. Our study shows that \u003cem\u003eNDUFB9\u003c/em\u003e expression is decreased in epilepsy samples, potentially by reducing ATP availability and thereby promoting epilepsy progression. \u003cem\u003eDLST\u003c/em\u003e and \u003cem\u003eACAA1\u003c/em\u003e are the first mitochondrial energy MRDEGs identified in epilepsy patients in our study. These genes may serve as potential biomarkers for epilepsy diagnosis and targets for therapeutic intervention.\u003c/p\u003e\u003cp\u003eFollowing GO and KEGG enrichment analyses, we identified several pathways in which MRDEGs were significantly enriched in epilepsy. These pathways encompass mitochondrial ATP synthesis-coupled electron transport, electron transfer activity, and small-molecule metabolic processes. These processes are essential for maintaining cellular energy homeostasis, and their dysregulation has been associated with a variety of neurological disorders, including epilepsy[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Verweij et al. observed MD in both animal and human brain tissues affected by epilepsy, which was characterized by impaired oxidative phosphorylation and ATP production [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Furthermore, the role of small-molecule metabolic processes, such as the regulation of polyamines, has been extensively explored in the context of epilepsy. Polyamines are small, positively charged alkylamines that modulate ion channels and ionotropic glutamate receptors, and their dysregulation has been closely linked to the pathogenesis of epilepsy [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. These pathways are critical for sustaining neuronal function and integrity, especially under the metabolic stress conditions frequently observed in epilepsy [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The high diagnostic accuracy of the model based on these hub genes, including \u003cem\u003eACAA1\u003c/em\u003e, \u003cem\u003eALDH3B1\u003c/em\u003e, \u003cem\u003eDLST\u003c/em\u003e, \u003cem\u003eGCDH\u003c/em\u003e and \u003cem\u003eNDUFB9\u003c/em\u003e, is evidenced by the ROC analysis, highlighting their potential as diagnostic biomarkers for epilepsy.\u003c/p\u003e\u003cp\u003eConsistent with the findings of Tsuchiya et al. [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], we observed alterations in immune cell infiltration, suggesting that there may be a link between MD and the immune response in epilepsy patients, which warrants further investigation. For instance, a recent study utilizing Mendelian randomization confirmed a causal relationship between certain immune cell characteristics and epilepsy risk, reinforcing the notion that immune cells contribute to the onset and progression of the disease [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Luo et al. also reported changes in the expression of immune-related genes and the presence of inflammatory markers in patients with epilepsy [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Furthermore, significant differences were found in immune cell phenotypes, such as activated CD8\u0026thinsp;+\u0026thinsp;T cells and activated dendritic cells, between the two groups, suggesting a potential role in the disease mechanisms [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. After being primed in brain-draining lymph nodes, CD8\u0026thinsp;+\u0026thinsp;T cells act as specific attackers of neurons, leading to apoptotic degeneration of neurons and compromising hippocampal network function, which results in fundamental hyperexcitability and impaired memory skills, thereby inducing an epileptogenic process [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Additionally, Tr\u0026ouml;scher et al. discovered that the number of T cells, particularly CD8\u0026thinsp;+\u0026thinsp;cytotoxic T cells, were significantly elevated in the hippocampus of patients with medial temporal lobe epilepsy [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. However, the role of dendritic cells in epilepsy remains poorly understood. One might hypothesize that epilepsy is related to the high excitability of dendrites, but it is still unclear whether the changes in dendritic cells associated with epilepsy are a consequence of repeated seizures. We anticipate that advancements in the technology for assessing dendritic excitability will facilitate a better understanding of epilepsy. The involvement of the immune system in epilepsy is further corroborated by findings that immune-related pathways, including the JAK-STAT pathway, are implicated in the disorder [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAdditionally, research has demonstrated that immune cells, such as mast cells and natural killer cells, exhibit differential regulation in epilepsy, with notable correlations observed between these cell types and specific genes such as epidermal growth factor receptor [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. This underscores the complexity of immune involvement in epilepsy, wherein different immune cell subsets play distinct roles at various disease stages. For example, the expression signatures of Th1 and Th2 cells have been found to differ between normal and epileptic tissues, suggesting a shift in immune response profiles in epilepsy [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Overall, our findings regarding immune cell differences in epilepsy reinforce the potential of targeting immune-related pathways and cells for the development of novel therapeutic strategies for epilepsy.\u003c/p\u003e"},{"header":"5. Limitations","content":"\u003cp\u003eDespite these encouraging findings, this study has several limitations. Firstly, the study primarily relies on bioinformatics analysis without incorporating laboratory experiments, which could offer more direct evidence to support the findings. Secondly, the sample size derived from the datasets (GSE143272, GSE4290, and GSE32534) is relatively limited, which constrains the generalizability of the results. Thirdly, the absence of clinical validation implies that the diagnostic models and identified biomarkers have not yet been evaluated in a real-world clinical context. Furthermore, the utilization of multiple datasets introduces the potential for batch effects, which could influence the results despite normalization efforts.\u003c/p\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eIn conclusion, this study successfully identified 26 MRGs from the GEO datasets in patients with epilepsy and constructed diagnostic models with high accuracy. The integration of various bioinformatics tools provided comprehensive insights into the underlying genetic mechanisms of epilepsy. The findings highlight potential biomarkers and therapeutic targets for epilepsy, which are beneficial for future research. However, further validation through laboratory experiments and clinical studies is essential to confirm these results and fully realize their potential in clinical applications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets utilized in this study are accessible through online repositories. The names of the repositories and corresponding accession numbers are provided in the article material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work has no Funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest disclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest associated with this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is based entirely on publicly available bioinformatics data. No human participants were directly involved, and no identifiable private information was used. Therefore, institutional review board (IRB) approval and participant consent were not required for this analysis. This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki.Patient consent statement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYunyun Lu and\u0026nbsp;Yanyan Zhang\u0026nbsp;conceptualized the research idea, designed the study workflow, and were responsible for drafting, editing, and reviewing the manuscript.\u0026nbsp;Shuaishuai Wang and Ziguang Zhao\u0026nbsp;performed data analysis and interpretation.\u0026nbsp;Faqiang Li and Feng Chen\u0026nbsp;prepared the figures and tables. All authors made substantial contributions to the study and approved the final submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to the GEO database for providing the platform and datasets. We also extend our thanks to the physicians and all other participants involved in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMcDermott D, Darwin ML, Fetrow K, Coulter I, Biesecker K, Thompson JA. Cannabis use patterns in drug-resistant and pharmacoresponsive epilepsy: Single tertiary referral center survey investigation. 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Curr Neurol Neurosci Rep. 2018;18(3):10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNowak M, Bauer S, Haag A, Cepok S, Todorova-Rudolph A, Tackenberg B, Norwood B, Oertel WH, Rosenow F, Hemmer B, et al. Interictal alterations of cytokines and leukocytes in patients with active epilepsy. Brain Behav Immun. 2011;25(3):423\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGranata T, Cross H, Theodore W, Avanzini G. Immune-mediated epilepsies. Epilepsia. 2011;52(Suppl 3):5\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Epilepsy, mitochondrial energy metabolism pathways, mitochondrial energy metabolism-related genes, diagnostic model, bioinformatic analysis","lastPublishedDoi":"10.21203/rs.3.rs-7544051/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7544051/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEpilepsy is a neurological disorder characterized by recurrent seizures, remaining a significant challenge in terms of understanding its underlying molecular mechanisms. The objective of this study was to investigate the role of mitochondrial energy metabolism-related differentially expressed genes (MRDEGs) in epilepsy, and to construct and validate a diagnostic model based on these genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDatasets were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis was conducted to identify MRDEGs. Diagnostic models were developed using logistic regression, support vector machine (SVM), and random forest (RF) algorithms. LASSO regression was employed to mitigate overfitting. The diagnostic value of the models was assessed using Receiver Operating Characteristic (ROC) curves. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on hub genes. Protein-protein interaction (PPI) networks were constructed and visualized using Cytoscape software. Additionally, mRNA-miRNA and mRNA-transcription factor (TF) interaction networks were established.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn dataset GSE143272, logistic regression analysis highlighted 26 statistically significant MRDEGs. The SVM model achieved the highest accuracy with 22 MRDEGs. The RF algorithm identified 11 important MRDEGs based on IncNodePurity \u0026gt; 0.80. LASSO regression yielded a diagnostic model comprising five hub genes: \u003cem\u003eACAA1\u003c/em\u003e, \u003cem\u003eALDH3B1\u003c/em\u003e, \u003cem\u003eDLST\u003c/em\u003e, \u003cem\u003eGCDH\u003c/em\u003e, and \u003cem\u003eNDUFB9\u003c/em\u003e. ROC curves demonstrated high accuracy for \u003cem\u003eDLST\u003c/em\u003e (AUC \u0026gt; 0.9). GO and KEGG analyses revealed significant enrichment in processes such as mitochondrial ATP synthesis coupled electron transport. PPI networks illustrated the interactions between hub genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn conclusion, our research elucidates the critical role of MRDEGs in the pathogenesis of epilepsy and develops a robust diagnostic model with potential clinical applications.\u003c/p\u003e","manuscriptTitle":"Identification and Verification of Hub Mitochondrial Dysfunction Genes in Epilepsy Based on Bioinformatics Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 13:27:03","doi":"10.21203/rs.3.rs-7544051/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-04T04:56:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-03T10:01:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179077653073933437379201233985980139825","date":"2025-10-30T23:25:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-30T06:42:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"210465454607146906885237060745054030105","date":"2025-10-22T13:08:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"209559567390044642764547811753038033720","date":"2025-10-22T06:19:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"92051728540625307453634019449332789658","date":"2025-10-10T13:23:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"90539783365264603589344367903065204759","date":"2025-10-06T16:51:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-06T16:44:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-06T16:41:23+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-01T17:03:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-29T14:25:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Neurology","date":"2025-09-29T14:19:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"187197bf-4681-4870-8aa9-6eff1a0b5608","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T16:05:13+00:00","versionOfRecord":{"articleIdentity":"rs-7544051","link":"https://doi.org/10.1186/s12883-026-04638-6","journal":{"identity":"bmc-neurology","isVorOnly":false,"title":"BMC Neurology"},"publishedOn":"2026-02-02 15:59:45","publishedOnDateReadable":"February 2nd, 2026"},"versionCreatedAt":"2025-10-17 13:27:03","video":"","vorDoi":"10.1186/s12883-026-04638-6","vorDoiUrl":"https://doi.org/10.1186/s12883-026-04638-6","workflowStages":[]},"version":"v1","identity":"rs-7544051","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7544051","identity":"rs-7544051","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}