Identification of the Molecular Subtypes of Acute Ischemic Stroke Using Bioinformatics and Machine Learning

Identification of the Molecular Subtypes of Acute Ischemic Stroke Using Bioinformatics and Machine Learning | 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 of the Molecular Subtypes of Acute Ischemic Stroke Using Bioinformatics and Machine Learning Zongkai Wu, Hongzhen Fan, Lu Qin, Xiaoli Niu, Bao Chu, Kaihua Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4117025/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jan, 2025 Read the published version in Journal of Molecular Neuroscience → Version 1 posted 8 You are reading this latest preprint version Abstract Acute ischemic stroke (AIS) is a severe disorder characterized by complex pathophysiological processes, which can lead to disability and death. This study aimed to determine necroptosis-associated genes in Acute ischemic stroke (AIS) and to investigate their potential as diagnostic and therapeutic targets for AIS. Expression profiling data were acquired from the Gene Expression Omnibus database, and necroptosis-associated genes were retrieved from GeneCards. The differentially expressed genes (DEGs) and necroptosis-related genes were intersected to obtain the necroptosis-related DEGs (NRDEGs) in AIS. In AIS, a total of 76 genes associated with necroptosis (referred to as NRDEGs) were identified. Enrichment analysis of these genes revealed that they were primarily enriched in pathways known to induce necroptosis. Using Weighted gene co-expression network analysis (WGCNA), five co-expression modules consisting of NRDEGs were identified, along with two modules that exhibited a strong correlation with AIS. Protein-protein interaction (PPI) analysis resulted in the identification of 20 hub genes. The Least absolute shrinkage and selection operator (LASSO) regression model demonstrated promising potential for diagnostic prediction. The receiver operating characteristic (ROC) curve validated the diagnostic model and selected nine characteristic genes that exhibited statistically significant differences (p < 0.05). By employing consensus clustering, distinct patterns of necroptosis were identified using these nine signature genes. The results were verified by quantitative PCR (qPCR) in HT22 cells and an external data set. Furthermore, the analyzed ceRNA network included nine lncRNAs, six miRNAs, and three mRNAs. Overall, this study offers novel insights into the molecular mechanisms underlying NRDEGs in AIS. The findings provide valuable evidence and contribute to our understanding of the disease. Acute ischemic stroke the necroptosis-related differentially expressed genes functional enrichment analyses Weighted gene co-expression network analysis the Least absolute shrinkage and selection operator regression model competitive endogenous RNA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction Stroke is a significant global health issue, leading to disability in adults and causing a substantial number of deaths worldwide (Johnson et al. 2019 ). It poses a considerable burden at both individual and societal levels. An episode of cerebral ischemia arises when a clot or thrombus obstructs the blood supply to the brain, leading to an acute ischemic stroke (AIS). During thrombolytic therapy for AIS, ischemia/reperfusion (I/R) injury is likely to occur. These I/R injuries are particularly susceptible to oxidative stress and inflammation, which exacerbate neuronal cell death (Nagy and Nardai 2017 ). Currently, AIS can be effectively managed with intravenous thrombolysis and mechanical thrombectomy to restore blood flow in occluded vessels. However, the prognosis heavily relies on the timely administration of treatment. Despite the effectiveness of current treatment options in preventing long-term disability in AIS patients, accurate prognostic prediction remains challenging. Existing models for prognostic prediction in AIS patients often lack reliable predictive capabilities (Fahey et al. 2018 ; Quinn et al. 2017 ). The diagnosis of AIS primarily relies on neuroimaging techniques due to the absence of efficient, rapid, and accurate diagnostic biomarkers. Early diagnosis and successful treatment are crucial for reducing mortality rates and improving outcomes in AIS. Therefore, there is an urgent need for systematic studies to understand the biological processes involved in AIS. Cell death plays a crucial role in regulating infarction during ischemia and I/R injury. The condition of ischemic brain tissue can be divided into two clinical regions: the ischemic core and the penumbra (which surrounds the ischemic core) (Kaufmann et al. 1999 ). Neurons in the ischemic core undergo rapid necrotic cell death (Xing et al. 2012 ). On the other hand, neurons in the hypoxic penumbra are functionally suppressed but remain active (Fricker et al. 2018 ). With appropriate treatment, neurons within the penumbra may be potentially rescued. Unfortunately, in humans, we are unable to independently determine the extent of cell death in the ischemic core or penumbra. Therefore, our understanding of the mechanisms underlying neuronal death in ischemic stroke is vital as it can provide valuable insights into potential targets that can be controlled. Necroptosis is a recently identified pathway of programmed cell death that can be induced by the activation of inflammatory receptors such as tumor necrosis factor receptor 1, toll-like receptor (TLR), and Fas/CD95 (Holler et al. 2000 ; Liao et al. 2020 ; Matsumura et al. 2000 ; Vercammen et al. 1998a ; Vercammen et al. 1998b ). This process relies on the activation of receptor-interacting protein kinase (RIPK)1 through its interaction with RIPK3, resulting in the phosphorylation of the pseudokinase mixed lineage kinase domain-like protein (MLKL) by RIPK3 (de Almagro and Vucic 2015 ; Galluzzi et al. 2014 ). In the context of ischemia and I/R injury, neurons experience rapid depletion of adenosine triphosphate (ATP) due to the sudden lack of oxygen and glucose caused by ischemia. This depletion results in plasma membrane depolarization, which triggers the secretion of glutamate and subsequent induction of N-methyl-D-aspartate (NDMA) receptors (Vacher et al. 2008 ). The excitotoxicity caused by glutamate and lactic acidosis further activates RIPK1 (Liao et al. 2020 ; Zhan et al. 2019 ). When caspase-8 is inhibited, RIPK1 is recruited via its receptor-interacting protein (RIP) homotypic interaction motif (RHIM) domain and phosphorylates RIPK3, forming complex IIb (Grootjans et al. 2017 ). Ultimately, MLKL is phosphorylated and polymerized, leading to the induction of necroptosis (Grootjans et al. 2017 ). Previous research has shown that intracerebral injection of Necrostatin-1 (Nec-1), a specific inhibitor of RIPK1, can block ischemic stroke-induced neuronal necroptosis and attenuate delayed ischemic brain injury in mice (Degterev et al. 2005 ). Additionally, various investigations using the Oxygen-Glucose Deprivation/Reoxygenation (OGD/R) model have observed elevated levels of necroptosis markers, such as RIPK1, RIPK3, and MLKL, in neuronal cells (Chen et al. 2018b ; Li et al. 2020b ; Tang et al. 2018 ; Vieira et al. 2014 ; Yang et al. 2017 ). However, effective drugs targeting necroptosis for potential neuroprotection against ischemic brain injury are still lacking. In this study, necroptosis-related differentially expressed genes (NRDEGs) in AIS were identified using a comprehensive analysis. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and Weighted Gene Co-expression Network Analysis (WGCNA) were used to identify the molecular mechanism(s) of the NRDEGs in AIS. Next, a protein-protein interaction (PPI) network as well as the efficient and diagnostic Least absolute shrinkage and selection operator (LASSO) regression models were constructed. The effectiveness of the diagnostic model was confirmed by analyzing receiver operating characteristic (ROC) curves. A total of nine potential NRDEGs were identified as significant indicators for predicting the occurrence of AIS. To further investigate the immune component, we conducted immune infiltration analysis and examined the correlations between the expression of these nine DEGs and 22 immune cell types. Additionally, we selected three DEGs to establish a competing endogenous RNA (ceRNA) network. Altogether, our study successfully unraveled the molecular mechanisms of necroptosis in AIS and established a foundation for its diagnosis and treatment. For a visual representation of our study design, please refer to Fig. 1 in the article. Materials and methods Identification of DEGs and NRDEGs in AIS The gene expression profiling dataset of peripheral whole blood specimens (GSE16561) was retrieved from the Gene Expression Omnibus (GEO) (Barrett et al. 2007 ) database, and divided into 39 ischemic stroke patients (Stroke) and 24 healthy control subjects (Control). Another GEO dataset of peripheral blood specimens (GSE22255) (Krug et al. 2012 ) was obtained and divided into 20 ischemic stroke patients and 20 normal patients. The two datasets were merged and batch effects were mitigated using the combat (package sva) function in R (Leek et al. 2012 ) and verified by principal component analysis (PCA). The third GEO dataset of peripheral blood specimens of Homo sapiens from GPL570 (GSE58294) (Stamova et al. 2014 ) was downloaded and divided into 69 ischemic stroke patients (group name: Stroke) and 23 normal patients (group name: Normal). As the validation dataset, it was used to validate the hub genes and the comparison map between the two groups was drawn. To assess the differential impact of gene expression values on AIS, group comparisons were conducted using the R package limma (Ritchie et al. 2015 ). For differential expression analysis, the following additional thresholds were used: logFC > 0.3 and adjP-value < 0.05 for upregulated genes (up_regulated_genes) and logFC<-0.3 and adjP-value < 0.05 for downregulated genes (down_regulated_genes). We identified necroptosis-associated genes using GeneCards (Stelzer et al. 2016 ), which is an integrative and comprehensive database of human genetic information. A search using the term "necroptosis" yielded 630 necroptosis-related genes (Table S1 ). A Venn diagram was drawn using the DEGs and necroptosis-related genes and the intersecting genes were identified as NRDEGs in AIS. These genes were then subjected to PPI network analysis. DEG Function and Pathway Enrichment analyses GO (Ashburner et al. 2000 ), an international standard for gene functional classification, is widely used as a tool for functional annotation and enrichment analyses. GO terms are classified as cellular component (CC), molecular function (MF), and biological process (BP). KEGG database is comprised of the pathways of experimentally validated metabolic processes and gene sets of human diseases, and it stores an extensive collection of data on genomes, biological pathways, drugs, chemicals, and diseases. The DEGs underwent GO term and KEGG pathway analyses using the clusterProfiler (Yu et al. 2012 ) in R package. P < 0.05 was deemed statistically significant. GSEA evaluation GSEA, a widely recognized computational method, is frequently employed to assess variations in pathway activities and biological processes within expression datasets. It determines whether a predefined set of genes exhibits notable differences between two biological states (Subramanian et al. 2005 ). GSEA was conducted on the gene expression data in the combined dataset of GSE16561 and GSE22255 using the clusterProfiler R package in order to study the biological differences between patients with the AIS and Control groups. The "c2.all.v7.5.2.entrez.gmt" gene set was obtained from the Molecular signatures database (MSigDB) (Liberzon et al. 2015 ) for GSEA of the combined dataset of GSE16561 and GSE22255. Adjusted P < 0.05 was deemed statistically significant. Both p-value and normalized enrichment score (NES) values are presented. Establishment of Co-expression Modules of DEGs through WGCNA WGCNA (Langfelder and Horvath 2008 ) is a systematic biological approach used to characterize the patterns of inter-gene correlations across samples in order to discover modules of highly correlated genes. Therapeutic targets or candidate biomarker genes were identified according to the intensity of the gene set as well as the relationship between the gene set and the phenotype. We analyzed the GSE16561 and GSE22255 datasets using the WGCNA package in R software. For the GSE16561 dataset, we set the minimum gene number, cut height and optimal soft-thresholding power to 50, 135 and 5, respectively, merged modules at a cut height of 0.95, and established a minimum distance of 0.2. For the GSE22255 dataset, the minimum gene number, cut height and optimal soft-thresholding power were set to 50, 80 and 7, respectively, modules were merged at a cut height of 0.4, and the minimum distance was fixed at 0.2. Using this approach, we successfully obtained co-expression modules for the DEGs within the two groups of both datasets. Development of PPI network STRING (Szklarczyk et al. 2019 ) was used to search for known and predicted PPIs and was employed to construct the PPI networks of both DEGs and NRDEGs. Cytoscape v3.6.1 (Smoot et al. 2011 ), a well-known open-source software platform that integrates interaction networks, was utilized for the visualization of PPI networks. Cytoscape CytoHumba plugin (Chin et al. 2014 ) was used to study the network’s hub genes and identified the top 10 genes in the maximum correlation coefficient (MCC). Functional correlations for the key genes were calculated using the R package GOSemSim (Yu 2020 ). Construction of Diagnostic Model via LASSO Regression Model LASSO regression involves the simultaneous screening of variables and complexity adjustment to fit a generalized linear model. Through regularization, a shrinkage penalty is introduced to limit the coefficients. The regularization process uses the sum of the absolute values of all the feature weights, which improves the interpretability of the model to a certain extent. A LASSO regression model was built for the genes in the co-expression modules of the DEGs using the glmnet package in R software (Engebretsen and Bohlin 2019 ; Mazumder and Hastie 2012 ). During the model construction process, we carefully screened the selected features and identified the best model for building a diagnostic model for cerebral infarction. Subsequently, we determined that the genes included in the model were the distinctive genes associated with cerebral infarction. To validate the models, ROC curves were drawn using the pROC package (Robin et al. 2011 ). A box plot was generated to display the characteristic genes of peripheral blood samples from patients with cerebral infarction and normal control patients. The genes with p < 0.05 were selected for visualization. Molecular Subtype Analysis of Cerebral Infarction Consensus clustering is an algorithm based on resampling techniques. Its purpose is to identify individual members and their respective subgroup numbers, while also assessing the reliability and validity of the clustering data. The previously identified NRDEGs based on the combined dataset of GSE16561 and GSE22255 were defined as key necroptosis-associated genes. The ConsensusClusterPlus package (Wilkerson and Hayes 2010 ) in R software was employed to determine the gene expression profiles of the previously screened NRDEGs, and the cluster with the best clustering was selected. Based on these results, different necroptosis patterns were identified. Immune Infiltration Analysis The CIBERSORT package (Chen et al. 2018a ) was employed, utilizing a deconvolution algorithm with linear support vector regression, to predict the expression matrix associated with immune cell subtypes. This approach was applied to evaluate immune cell infiltration in patients with ischemic stroke and the control group using RNA-seq data. The differential enrichment of immune cells between patients with AIS and the control group was identified in the combined dataset of GSE16561 and GSE22255. Furthermore, we determined the Pearson correlation coefficients between immune cells and nine NRDEGs and examined the relationship between NRDEGs and the levels of immune infiltration. Network Construction for the Interactions among Hub-mRNA, Hub-microRNA (miRNA), and Hub-long non-coding RNA (lncRNA) We conducted an analysis of lncRNA and miRNA expression, focusing on their interaction with hub genes at the post-transcriptional level. To identify miRNA-mRNA targeting relationships, we utilized the miRTarBase database, which contains experimentally-validated miRNA-target interactions (MTIs) from over 8,500 articles (Huang et al. 2020 ). The database has been augmented with the newly released CLIP-seq dataset, resulting in over 500,000 MTIs. Leveraging improved natural language processing (NLP) technology, we collected additional target relationship pairs along with their network functions and annotation information. For identification of miRNAs potentially binding to hub genes, we utilized the miRTarBase 2020 database ( https://mirtarbase.cuhk.edu.cn/ ) (Huang et al. 2020 ). Additionally, the TarBase database (version 8), which compiles experimentally-validated miRNA targets across multiple species, was used to predict miRNAs interacting with hub genes (Karagkouni et al. 2018 ). A list of miRNAs predicted by both databases was generated. To search for miRNA targets, we employed the starBase database, which incorporates high-throughput sequencing datasets (CLIP-seq and Degradome-seq) and offers various visualization tools to explore microRNA targets (Cai et al. 2014 ). The database includes extensive data on miRNA-mRNA, miRNA-ncRNA, RNA-RNA, and RBP-RNA interactions. Using the starBase database, we predicted lncRNAs interacting with miRNAs. An interaction network comprising hub lncRNAs, hub miRNAs, and hub mRNAs was constructed, and the Cytoscape software was employed to visualize the network as a Sankey diagram. HT22 Cell Culture and treatment The HT22 cells (Zhejiang Ruyao Biotechnology Co. Ltd., Zhejiang, China), which are an immortalized mouse hippocampal neuronal cell line, were cultured in Dulbecco’s modified Eagle’s medium (DMEM, Corning, NY, United States) containing 10% foetal bovine serum (FBS, BI, Israel) and 1% double antibodies (37℃, 5% CO2 cell culture incubator). When the cells attached to the wall and reached 80–90% confluence, the culture medium were discarded, washed once with PBS, and digested with 3ml of trypsin for 2 minutes. The process of digestion was terminated by adding medium immediately after the cells became round and detached. The cells were transferred to a centrifuge tube, centrifuged at 1000rpm for 5 minutes, re-suspended in fresh culture medium, and distributed at a 1:2 ratio. The original cells were transferred into a new cell culture bottle for continued culture, with medium changed every 2 to 3 days. We divided the cells into both a control group and a model group, seeded them into 96-well plates at a density of 5×10 4 , with 3 replicate wells for each group, and then cultured overnight. After 24 hours, we removed the original culture medium in the model group, washed the cells twice with PBS, and then added 300µl of glucose-free, serum-free DMEM medium to each well before being incubated in an anaerobic culture box for 4 hours. After oxygen-glucose deprivation treatment, the cells in the model group were restored to complete DMEM medium, and returned to e cell incubator at 37℃and 5% CO 2 for 2 hours. The cells in the normal control group were continuously cultured in complete medium and a cell incubator at 37℃, 5% CO 2 . Quantitative polymerase chain reaction (qPCR analysis) The mRNAs were extracted from HT22 cells by adding Trizol reagent. The Fast Plus RT Master mix reverse transcription reagent kit (Supersmart® 6-minute Heat-resistant first-strand cDNA Synthesis Kit) was used to synthesise the extracted mRNAs into cDNAs, called the reverse transcription, RT reaction. PCR was carried out using the SYBR Green qPCR Mix pre-mixed qPCR reagent kit (Superbrilliant® Third generation ZAPA SYBR Green qPCR premix) to amplify the cDNA in a two-step process and perform fluorescence quantification, with the reaction conditions as follows: 95℃ for 300s; 95℃ for 10s and 60℃/65℃ for 20s, for 40 cycles. The final primer concentration was 0.4µM, with GAPDH as the reference gene. The relative expression levels were calculated using the 2^ (−△△Ct) method, and the melting curve of the products were analysed to ensure reaction specificity. See Supplemental Files for primer sequences. Statistical Analysis All statistical tests were conducted with R v4.0.2 ( https://www.r-project.org ). For normally distributed continuous variables, the statistical significance was assessed using an independent Student's t-test. Non-normally distributed continuous variables were compared using the Mann-Whitney U test and the Wilcoxon rank sum test. All p-values were two-sided, and p < 0.05 was deemed statistically significant. Results DEGs in Patients with AIS To compare the patients with AIS with normal patients, we merged the two sets of GSE data as GSE_combine, and the batch effects were then eliminated. PCA analysis showed that batch effects had been removed from the datasets (Fig. 2 (a) and 2(b)). For the identification of DEGs, differential expression analysis was carried out using the limma software package, with |logFC| > 0.3 and adjPvalue < 0.05. A total of 1945 DEGs were acquired, including 1195 upregulated and 750 downregulated genes. The ten up- and downregulated DEGs with the highest logFC are presented as heatmaps (Fig. 2 (c)) and gene correlation coefficient heatmaps (Fig. 2 (d)). We compared the DEGs obtained from the combined dataset of GSE16561 and GSE22255 and necroptosis-related genes to identify 76 NRDEGs (Table S2 and Fig. 2 (e)). A PPI network of these 76 genes is shown in Fig. 2 (f). Functional and Pathway Enrichment Analyses Next, GO functional enrichment analysis was conducted for the NRDEGs (Table 1 ). The DEGs related to AIS were mainly enriched in BP terms such as toll/interferon response factor (TRIF)-dependent TLR signaling pathway, myeloid differentiation factor 88 (MyD88)-independent TLR signaling pathway, and pattern recognition receptor signaling pathway (Fig. 3 (a)); in CC terms such as cytosolic part, myosin complex, and ficolin-1-rich granule (Fig. 3 (b)); and in MF terms such as microfilament motor activity, death receptor activity, and actin binding (Fig. 3 (c)). Next, KEGG analysis demonstrated that the DEGs were enriched in biological pathways, such as hepatitis B, pathogenic Escherichia coli infection, and Tumor necrosis factor (TNF) signaling pathway (Fig. 3 (d), Table 2 ). Table 1 GO functional enrichment analysis of DEGs Ontology ID Description GeneRatio BgRatio pvalue p.adjust qvalue BP GO:0035666 TRIF-dependent toll-like receptor signaling pathway 6/76 29/18670 1.64e-09 4.08e-06 2.96e-06 BP GO:0002756 MyD88-independent toll-like receptor signaling pathway 6/76 33/18670 3.78e-09 4.70e-06 3.41e-06 BP GO:0002221 pattern recognition receptor signaling pathway 10/76 197/18670 7.10e-09 4.84e-06 3.51e-06 CC GO:0044445 cytosolic part 9/76 247/19717 4.51e-07 1.21e-04 8.35e-05 CC GO:0016459 myosin complex 5/76 65/19717 5.13e-06 5.60e-04 3.87e-04 CC GO:0101002 ficolin-1-rich granule 7/76 185/19717 7.24e-06 5.60e-04 3.87e-04 MF GO:0000146 microfilament motor activity 4/76 22/17697 2.17e-06 6.00e-04 4.74e-04 MF GO:0005035 death receptor activity 3/76 11/17697 1.23e-05 0.001 8.91e-04 MF GO:0003779 actin binding 10/76 431/17697 1.50e-05 0.001 8.91e-04 Table 2 KEGG functional enrichment analysis of DEGs Ontology ID Description GeneRatio BgRatio pvalue p.adjust qvalue KEGG hsa04668 TNF signaling pathway 9/53 112/8076 3.65e-08 7.01e-06 4.96e-06 KEGG hsa05130 Pathogenic Escherichia coli infection 10/53 197/8076 4.65e-07 4.46e-05 3.15e-05 KEGG hsa05161 Hepatitis B 8/53 162/8076 9.07e-06 5.81e-04 4.11e-04 GSEA Through GSEA of the combined dataset, we explored the relationships between AIS and the DEGs’ BP, CC, and MF. The results of GSEA with adjPvalue < 0.05 showed an enrichment in neutrophil degranulation, G protein-coupled receptors (GPCR) ligand binding, signaling by interleukins, G-alpha (i) signaling events, neuronal system, rhodopsin-like receptors (class A/1), and NABA secreted factors (Fig. 4 (a)-4(d), Table 3 ). Table 3 GSEA enrichment analysis ID NES pvalue p.adjust qvalues REACTOME_NEUTROPHIL_DEGRANULATION 1.909562 0.001148 0.039119 0.028251 REACTOME_GPCR_LIGAND_BINDING 1.98536 0.001155 0.039119 0.028251 REACTOME_SIGNALING_BY_INTERLEUKINS 1.587387 0.001156 0.039119 0.028251 REACTOME_G_ALPHA_I_SIGNALLING_EVENTS 1.742828 0.001168 0.039119 0.028251 REACTOME_NEURONAL_SYSTEM 2.001512 0.001188 0.039119 0.028251 REACTOME_CLASS_A_1_RHODOPSIN_LIKE_RECEPTORS_ 1.958376 0.001208 0.039119 0.028251 NABA_SECRETED_FACTORS 2.126348 0.001209 0.039119 0.028251 WP_NUCLEAR_RECEPTORS_METAPATHWAY 1.900179 0.00122 0.039119 0.028251 Co-expression Modules of DEGs Identified by WGCNA We performed WGCNA on the Control and AIS groups to screen for co-expression modules. In the process of analyzing the GSE16561 dataset using WGCNA, we successfully identified 23 outlier samples by setting the cut height (Fig. 5 (a)). After employing a scatter plot, it was found that the optimal soft-threshold for our study was 5. Next, we carried out further investigations based on this finding (Fig. 5 (b)). The co-expressed genes in the two groups were subsequently clustered in the dark red and pink modules (Fig. 5 (c)). According to the expression pattern and grouping information of module genes, we found that the dark red and pink modules were positively correlated with ischemic stroke with p < 0.05 and were used for further analyses (Fig. 5 (d)). During WGCNA of the GSE22255 dataset, an outlier sample was detected by setting the cut height (Fig. 5 (e)). Using a scatter plot, we determined that a soft threshold of 7 provided optimal results, prompting us to proceed with subsequent investigations (Fig. 5 (f)). The genes co-expressed in the two groups were subsequently clustered in the brown and dark orange modules (Fig. 5 (g)). According to the expression patterns and grouping information of the module genes, we found that the brown and dark orange modules were positively correlated with cerebral infarction with p < 0.05 and were subjected to further analyses (Fig. 5 (h)). Protein-Protein Interaction Network A total of sixty-nine genes were obtained upon the intersection of AIS-related co-expressed genes in the GSE16561 and GSE22255 datasets and necroptosis-related genes (Fig. 6 (a)). The PPI network of AIS-related genes was constructed, and the data were plotted using Cytoscape software (Fig. 6 (b)). Using the MCC algorithm of cytoHubba, a Cytoscape plugin, the top 20 hub gene scores were calculated. These top 20 genes included RPL4, PTGES3, RBM14, HSP90AA1, RPLP1, HNRNPM, RPL38, CCT3, PABPN1, RPL13A, ELAVL1, RPL12, FUS, AHR, RPL23, STUB1, TLR4, RPL3, BNIP3 , and MYC (Fig. 6 (c)). We generated a correlation coefficient heatmap to visualize the relationships among the hub genes (Fig. 6 (d)). Construction of a Stroke Diagnostic Model and Determination of Eigengenes In order to identify AIS-associated genes and assess their diagnostic potential, we employed the LASSO regression model on the GSE_combine dataset. The dataset was randomly divided into Training and Test groups in a 7:1 ratio. The model was constructed using the Training group and validated using the Test group. Throughout the model-building process, the chosen feature parameters reduced as λ increased, whereas the absolute values of the coefficients increased (Fig. 7 (a) and 7(b)). Through the process of simulation and careful selection, we identified a set of 18 feature genes, namely, RPL4, RBM14, HSP90AA1, RPLP1, HNRNPM, RPL38, CCT3, PABPN1, RPL13A, ELAVL1, RPL12, FUS, AHR, STUB1, TLR4, RPL3, BNIP3 , and MYC . ROC curves were plotted for both datasets and the area under the curve (AUC) was computed to validate the model. The AUC values of the risk model for both Training and Test groups were 0.905 and 0.889, respectively (Fig. 7 (c)). We analyzed the differences between the characteristic genes of the AIS group and Control groups and identified nine genes with statistically significant differences (p < 0.05), including RPL4, RBM14, CCT3, PABPN1, RPL13A, ELAVL1, RPL12, AHR , and TLR4 . Boxplots are shown in Fig. 7 (d). Next, the expression differences between the two groups were verified by dataset GSE58294. We used WilCoxon rank sum test to analyze the expression differences of the six hub genes ( TLR4, RBM14, RPL12, ELAVL1, AHR, PABPN1 ). The results of expression differences were shown by group comparison graph (Fig. 7 (e)). The results showed that the expression levels of TLR4, RBM14, and RPL12 were significantly different and the expression trends remained consistent in both test and validation datasets. Two Distinct Modes of Necroptosis Identified by Signature Genes Consensus clustering of the nine necroptosis-related genes was carried out, and a consensus clustering diagram was drawn when k = 2 (Fig. 8 (a)). Furthermore, we observed variations in the area under the cumulative distribution function (CDF) curve in relation to k = 2–9 (Fig. 8 (b)). Additionally, we plotted the CDF of the consistent clustering (Fig. 8 (c)) and the tracking plot (Fig. 8 (d)). Immune Infiltration Analysis To analyze disparities in the extent of immune infiltration of immune infiltration between patients with AIS and the Control group, variations in the abundance of 22 immune cell infiltrates between the two groups were explored using the CIBERSORT algorithm in the GSE_combine dataset (Fig. 9 (a)). Through the use of the Wilcoxon signed-rank test algorithm, a total of nine types of immune cells in the GSE_combine dataset were found to be significantly different between patients with AIS and the Control group (Fig. 9 (b)): plasma cell, regulatory T cell (Treg), CD8 + T cell, activated NK cell, γδ T cell, M0 macrophage, M2 macrophage, neutrophil, and activated mast cell. Next, the correlations between the nine NRDEGs and 22 types of immune cells were analyzed. In the GSE_combine dataset, significant correlations were found between RPL4 and activated CD4 + memory T cell; RBM14 and activated dendritic cell; CCT3 and naïve B cell and activated mast cell; RPL13A and plasma cell, Treg, M1 macrophage, activated mast cell, and activated dendritic cell; AHR and naïve CD4 + T cell and monocyte; and TLR4 and monocyte and M2 macrophage (Fig. 9 (c)). Network for the Interactions among Hub-mRNA, Hub-miRNA, and Hub-lncRNA We constructed an lncRNA-miRNA-mRNA interaction network, which contained three genes related to necroptosis, namely, TLR4, AHR , and ELAVL1 . We then employed the miRTarBase and TarBase databases to predict miRNAs that could interact with the necroptosis-related genes. A total of 42 sets of interactions were obtained from the intersection set (Fig. 10 (a)), and seven sets of interactions supported by experimental evidence were identified. The lncRNAs that potentially bind to miRNAs were predicted using the starBase database, and the lncRNA-miRNA-mRNA network was established using both Sankey (Fig. 10 (b)) and network (Fig. 10 (c)) diagrams. Validation of the identified mRNAs Oxygen glucose deprivation/re-oxygenation (OGD/R) was used to mimic neural injury. When compared with the normal control group, the mRNA expression levels of PAPBN1 (p < 0.01), CCT3 (p < 0.01) in the model control group were significantly downregulated while the mRNA expression levels of AHR (p < 0.01), RPL12 (p < 0.05), and TLR4 (p < 0.01) were significantly upregulate (Fig. 11 ). Discussion AIS is one of the main causes of human death and is one of the most prevalent leading to disability worldwide. The death of neurons can occur through various pathways, including apoptosis, necroptosis, autophagy, ferroptosis, and pyroptosis. Necroptosis is believed to be a type of necrotic cell death that occurs after cerebral infarction and I/R injury. Several studies have shown that necroptosis occurs within one hour after reperfusion in a brain injury model of I/R, primarily affecting neurons in the hippocampus (Li et al. 2020c ; Naito et al. 2020 ). Moreover, necroptosis is associated with the size of the infarct and the extent of neurological impairment (Han et al. 2019 ; Nikseresht et al. 2019 ). The inhibition of RIPK1, genetically or pharmacologically, can alleviate ischemic brain injury (Naito et al. 2020 ), and a specific inhibitor, known as Nec-1, can target necroptosis. Hence, to identify effective biomarkers for diagnosing and treating AIS through necroptosis, 1945 DEGs were retrieved from the combined dataset of GSE16561 and GSE22255. From GeneCards, 630 genes related to necroptosis were obtained, and 76 genes were found to be both DEGs and NRDEGs. GO, KEGG and GSEA were utilized to enrich the DEGs in TNF pathway, TLR pathway and other pathways associated with necroptosis. Through WGCNA analysis, co-expression modules were identified among the DEGs. PPI analysis resulted in the identification of 20 hub genes. Using LASSO regression, a diagnostic model for STROKE was constructed, determining 18 characteristic genes. Through consensus clustering, two distinct patterns of necroptosis were identified based on the signature genes. However, the precise mechanism underlying the role of necroptosis in ischemic stroke remains unclear, necessitating further research. Therefore, our aim was to systematically study NRDEGs, providing a better framework for exploring diagnostic biomarkers and making predictions regarding targeted therapy associated with necroptosis for the treatment of AIS. NRDEGs were found to be enriched in the GO CC term of the cytoplasmic portion and the myosin complex, which aligns with the characteristics of necroptosis, leading to the rupture of the plasma membrane (Cai et al. 2014 ; Nganga et al. 2019 ). Additionally, the NRDEGs showed enrichment in the GO MF terms microfilament motor activity and death receptor activity. During the progression of necroptosis, the assembly of complex "necrosomes" within cells can be triggered by death receptors. Furthermore, KEGG analysis of NRDEGs demonstrated that TNF signal transduction was the main enriched pathway. TNF plays a pivotal role in the cellular events during necroptosis (Chen and Goeddel 2002 ), and the mouse fibroblast L-M cell line is highly susceptible to TNF-induced necroptosis (Laster et al. 1988 ). GSEA analysis also indicated significant enrichment of interleukin signaling, neutrophil degranulation, and G alpha (i) signaling among the NRDEGs. It has been suggested that inflammation triggered by necroptosis may be driven by inflammasome activation signaling mediated by interleukins (ILs). For instance, in an in vivo experiment, necroptosis-induced IL-1 was found to contribute to necroptosis-driven inflammation in dendritic cells due to the deletion of the key RIPK3-MLKL necroptosis suppressor caspase-8 (Kang et al. 2013 ). Neutrophils, which are involved in neural plasticity, have been observed to increase after a stroke (Amulic et al. 2012 ). TNF-induced neutrophil necroptosis has been reported to depend on RIPK1-RIPK3-MLKL signaling (Wicki et al. 2016 ). Neutrophil degranulation may also activate the fibrinolytic system in patients with ischemic stroke (Wicki et al. 2016 ). Additionally, necroptosis can be triggered by TNF (Liu et al. 2014 ). A study demonstrated that G alpha (i) could inhibit the effect of TNF. The enrichment of G alpha (i) signaling suggests its potential involvement in the necroptosis process (Earl et al. 1990 ). Our research results, revealed for the first time, the involvement of G alpha (i) signaling in the process of necroptosis in AIS. Nevertheless, further studies are needed to fully understand the underlying mechanism. Herein, we applied the WGCNA method to construct five co-expression modules for the two datasets. Each module consisted of a set of genes exhibiting similar expression patterns. To assess the correlation between these modules and AIS, we examined the expression profiles and grouping information of the module genes. Notably, the dark red and pink modules, as well as the brown and dark orange modules, demonstrated the strongest correlations. Subsequently, we employed the ischemic stroke-related genes within these modules and intersected them with the necroptosis-related genes. The overlapping genes obtained from this intersection were subjected to further analysis. LASSO regression models were utilized to screen eigengenes and calculate AUC values under the ROC curve for constructing a diagnostic model of ischemic stroke. The aim was to explore the predictive performance of the risk models. Using the 18 NRDEGs selected through LASSO regression analysis, we successfully constructed a diagnostic model for ischemic stroke, which exhibited promising potential for diagnostic prediction. The model's performance was evaluated by plotting ROC curves and calculating the AUC values, which resulted in an AUC value of 0.905 for the Training group and 0.889 for the Test group. Importantly, this predictive model based on the 18 NRDEGs displayed excellent predictive power, with an AUC value of 0.905 in the Training group, surpassing the significance observed in previous studies. The risk model comprised nine statistically significant NRDEGs with diagnostic capabilities, namely, RPL4, RBM14, CCT3, PABPN1, RPL13A, ELAVL1, RPL12, AHR , and TLR4 . Functional enrichment analyses performed on the NRDEGs revealed an enrichment of GO BP terms related to the TRIF-dependent and MyD88-independent TLR pathways. Previous research suggests that necroptosis can be induced through TLR4 or TLR3 pathways. TLR3 specifically relies on TRIF for its activation, participating in NF-κB activation and induction of type I IFN (Takeuchi and Akira 2010 ). TLR4 is transmitted through TRIF signals and MyD88 adaptor (Takeuchi and Akira 2010 ). Our analysis results highlighted TLR4 as a hub gene, with GO BP terms primarily enriched in both TRIF-dependent and MyD88-independent TLR pathways. This provides further evidence that TLR4 activation may induce these signaling pathways, which play a role in the occurrence and progression of necroptosis. AHR is a ligand-dependent transcription factor. In the absence of ligand binding, the encoded protein remains in the cytoplasm, but upon ligand binding, it translocates to the nucleus and induces the transcription of target genes. A recent study showed that Indole-3-carbinol (I3C) regulates the expression of programmed necroptotic protein in cells in a time-dependent manner through AHR (Peng et al. 2021 ). AHR activated by I3C attenuates RIPK3- and MLKL-dependent necroptosis by promoting the expression of cIAP1 and XIAP while ubiquitinizing RIPK1 (Peng et al. 2021 ). Another study revealed that AHR reduces NF-κB activation and the expression of TNF-α and IL-6 (Kimura et al. 2014 ). AHR exhibits dual functions by reducing necroptosis and inhibiting inflammatory responses. Knockout of ELAVL1 and combined treatment with peptide-conjugated and Epi-loaded liposomes enhance necroptosis and result in cell death. ELAVL1 knockout leads to increased mRNA and protein levels of necroptosis in cells (Wang et al. 2021 ). These findings align with our research results, supporting the significant involvement of NRDEGs in the progression of AIS. The immune infiltration analysis revealed notable changes in the infiltration levels of various immune cell types in the cerebral infarction group. Specifically, activated NK cell, γδ T cell, M0 macrophage, M2 macrophage, neutrophil, and activated mast cell exhibited increased infiltration levels, while plasma cell, CD8 + T cell, and Treg showed decreased infiltration levels. It is known that neutrophils and monocytes/macrophages infiltrate and accumulate in the microvasculature and ischemic brain parenchyma (Garcia et al. 1994 ). γδ T cells, which are a significant population of lymphocytes, reside on the epithelial surface and possess innate immune characteristics. They secrete IL-17 and generate chemotaxis signals that attract peripheral bone marrow cells, such as neutrophils and monocytes, exacerbating ischemic brain injury (Gelderblom et al. 2012 ; Shichita et al. 2009 ). Treg cells have been shown to exert neuroprotective effects by suppressing inflammation following ischemic brain injury (Liesz et al. 2015 ), while CD8 + T cells tend to accumulate in the brain after a stroke (Ahnstedt et al. 2020 ). In addition, Selvaraj et al. demonstrated that CD8 + T cells contribute to functional recovery in the chronic phase following a stroke (Selvaraj et al. 2021 ). Interestingly, our findings indicate that genes such as RPL4, RBM14, CCT3, RPL13A, AHR , and TLR4 are closely associated with the abundance of immune cell infiltration. This suggests their potential involvement in immune responses related to ischemic stroke. Non-coding RNAs (ncRNAs), especially lncRNAs and miRNAs, serve as crucial regulators of gene expression. Previous studies have highlighted the involvement of specific ncRNAs in promoting inflammation and modulating the prognosis of AIS. For instance, TUG1 was found to promote inflammation by competitively binding to miR-145, resulting in the upregulation of AQP4 expression in OGD/R cell models and McAO rat models (Du et al. 2021 ). Another study suggested that the lncRNA NEAT1 may act as a sponge for miR-124 and miR-125a, negatively modulating inflammation and influencing the prognosis of AIS (Li et al. 2020a ). In our study, we established a potential ceRNA regulatory network involving three NRDEGs ( TLR4, ELAVL1, AHR ), providing new insights into AIS diagnosis and more effective targeted therapies against these NRDEGs. Manipulating the expression of these necroptosis-related genes, such as TLR4 , using miRNAs or lncRNA could potentially aid in reducing ischemia-reperfusion injury or the extent of cerebral infarction. Although our necroptosis-related genes exhibited promise as accurate diagnostic and therapeutic targets, there are still certain limitations that need to be addressed. Firstly, the roles of these nine genes in cluster analysis, immune infiltration, and the ceRNA regulatory network should be validated through further in vitro and in vivo experiments. Secondly, given that the relationship between necroptosis and cerebral infarction remains unclear, additional research is needed to elucidate the potential mechanisms involving necroptosis-related genes in the context of cerebral infarction. Conclusions In summary, our study identified a set of novel necroptosis-associated gene signatures that hold promise for accurate diagnosis and treatment of AIS. These gene signatures not only shed light on the underlying biological changes occurring in AIS but also have the potential to serve as valuable indicators for clinical decision-making. Among the identified genes, RPL4, RBM14, CCT3, PABPN1, RPL13A, ELAVL1, RPL12, AHR , and TLR4 emerged as key players in necroptosis-related processes. These genes hold considerable potential as biomarkers for diagnosing and treating AIS. Declarations Author Contributions ZW contributed to conception and design of the study. ZW analyzed the database. ZW performed the statistical analysis. ZW, HF, HW reviewed relevant literature and drafted the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version. Funding This work was supported by the Natural Science Fundation for Hebei Province (Grant number: H2020307041). Acknowledgements The authors would like to express their gratitude to EditSprings (https://www.editsprings.cn) for the expert linguistic services provided. Data Availability The data is available upon request from the corresponding author. Ethics Approval and Consent to Participate Not applicable. Consent for Publication Not applicable. Competing Interests The authors declare no competing interests. References Ahnstedt H, Patrizz A, Chauhan A, Roy-O'Reilly M, Furr JW, Spychala MS et al (2020) Sex differences in T cell immune responses, gut permeability and outcome after ischemic stroke in aged mice. 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FASEB J 33(1):1313-1329. https://doi.org/10.1096/fj.201800111RR Additional Declarations No competing interests reported. Supplementary Files SupplementalFiles.zip Cite Share Download PDF Status: Published Journal Publication published 02 Jan, 2025 Read the published version in Journal of Molecular Neuroscience → Version 1 posted Editorial decision: Revision requested 24 Mar, 2024 Reviews received at journal 20 Mar, 2024 Reviewers agreed at journal 20 Mar, 2024 Reviewers agreed at journal 19 Mar, 2024 Reviewers invited by journal 19 Mar, 2024 Submission checks completed at journal 18 Mar, 2024 Editor assigned by journal 18 Mar, 2024 First submitted to journal 17 Mar, 2024 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-4117025","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":282021798,"identity":"23cb6626-068c-40b2-b904-668c7775f046","order_by":0,"name":"Zongkai Wu","email":"","orcid":"","institution":"Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zongkai","middleName":"","lastName":"Wu","suffix":""},{"id":282021799,"identity":"87c9d264-4ae9-476b-b09e-72fbbdb60fa2","order_by":1,"name":"Hongzhen Fan","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hongzhen","middleName":"","lastName":"Fan","suffix":""},{"id":282021800,"identity":"fdbcef0b-e647-4bfe-8f99-9cb3674cbad6","order_by":2,"name":"Lu Qin","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Qin","suffix":""},{"id":282021801,"identity":"c61269b1-3a21-4240-a519-71118d6b1eba","order_by":3,"name":"Xiaoli Niu","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaoli","middleName":"","lastName":"Niu","suffix":""},{"id":282021802,"identity":"e1c22fe7-5ae6-40ec-ba7f-7eb847d2f350","order_by":4,"name":"Bao Chu","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bao","middleName":"","lastName":"Chu","suffix":""},{"id":282021803,"identity":"d6c7c9c1-6fdf-4e32-9283-07e73e6657a6","order_by":5,"name":"Kaihua Zhang","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kaihua","middleName":"","lastName":"Zhang","suffix":""},{"id":282021804,"identity":"5c6d0f4b-c64c-4cdf-bf15-332155578932","order_by":6,"name":"Yaran Gao","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yaran","middleName":"","lastName":"Gao","suffix":""},{"id":282021805,"identity":"cab41636-4b35-4504-8d9e-99dd2dc64358","order_by":7,"name":"Hebo Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYJACZsaGAyDqGANjA1jAgFgtbGkka+ExI06LwfGzh18X7rgjZ87e8+3Bxx3bEhvYm7dJMNTcwa3lTF6a9cwzz4wte85uN5x55nZiA8+xMgmGY89wajE7kGNmzNt2OHHDjdxt0rxtQC0SOWYSjA2HcWs5/waq5f6bZ9J/QVrk3xDQciPH+DHEFh42aUawLTz4tdjfeGPGzHvmMNAvaWaSvWduG7fxpBVbJBzDrUWyP8f4M++Ow8AQO/xM4ueO27L97Ic33vhQg1sLELBJgEh4XLCBiAR8GoAx+QFFyygYBaNgFIwCdAAARENfn5CuHLYAAAAASUVORK5CYII=","orcid":"","institution":"Hebei Medical University","correspondingAuthor":true,"prefix":"","firstName":"Hebo","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-03-17 12:44:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4117025/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4117025/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12031-024-02241-3","type":"published","date":"2025-01-02T15:57:34+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":53155417,"identity":"96459ea3-4112-492e-a8f0-1ede4b9e27ca","added_by":"auto","created_at":"2024-03-21 09:40:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":641232,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of the research design.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4117025/v1/843253621bc1cba4158bda64.png"},{"id":53155415,"identity":"21ab4314-8d05-47e5-a4a0-cc7bf65efba1","added_by":"auto","created_at":"2024-03-21 09:40:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":586353,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential expression analysis. (a) PCA analysis of GSE_combine prior to mitigating batch effects. (b) PCA analysis of GSE_combine after removing batch effects. (c) The abscissa and ordinate represent the patient ID and DEGs, respectively. Yellow, upregulated gene expression; blue, downregulated gene expression; green annotation bars, normal patients; dark red annotation bars, cerebral infarction patients. (d) Correlation of the DEGs. Yellow is positive correlation of genes; blue is negative correlation of genes. (e) The blue circle represents the DEGs of GSE_combine, and the yellow circle represents necroptosis-associated genes. The NRDEGs were identified by performing the intersection. (f) PPI network map of NRDEGs.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4117025/v1/1fd5f006a5912d1af73d089f.png"},{"id":53156370,"identity":"8aea2b28-5250-49d3-ae38-15b1eb835fee","added_by":"auto","created_at":"2024-03-21 09:48:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":413429,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional and pathway enrichment analyses. (a-c) GO analysis. The ordinate is -log(p.adjust), the abscissa is the GO terms, and the node’s color indicates the p.adjust value. Less than 0.02, more blue; more than 0.01, more yellow. (d) KEGG analysis. The abscissa and ordinate represent the gene ratio and KEGG pathways, respectively. Thenode’s size corresponds to the number of genes in the enrichment pathway, and the node’s color indicates the p.adjust value. Less than 0.02, more blue; more than 0.01, more yellow.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4117025/v1/c9fce043bd8b410a8a5f742e.png"},{"id":53155419,"identity":"15f6c87e-d07e-49de-8153-1b5ece414825","added_by":"auto","created_at":"2024-03-21 09:40:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":249861,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA results. (a) GSEA analysis of the combined dataset of GSE16561 and GSE22255. The horizontal and vertical axes represent the gene ratio and the number of genes for each enriched GO terms, respectively. (b-d) GSEA showed enrichment in (b) neutrophil degranulation, (c) signaling by interleukins, and (d) G-alpha (i) signaling.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4117025/v1/e982bdd8d296311bcda0536b.png"},{"id":53155423,"identity":"39edd5d9-504e-4777-9893-8ba617405c18","added_by":"auto","created_at":"2024-03-21 09:40:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":561167,"visible":true,"origin":"","legend":"\u003cp\u003eWGCNA identifies co-expression modules related to cerebral infarction. (a) No outlier specimens were found by cut height. (b, c) Selection of the optimal soft threshold. (d) The aggregation process of modular genes. (e) Association between modular genes and cerebral infarction.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4117025/v1/3345f1267632b3654bf0b092.png"},{"id":53156372,"identity":"7dc53aa3-6461-473b-bab4-f927c184eb83","added_by":"auto","created_at":"2024-03-21 09:48:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":664999,"visible":true,"origin":"","legend":"\u003cp\u003ePPI network. (a) Venn diagram of the stroke-related genes of the datasets GSE16561 (blue circle) and GSE22255 (yellow circle) analyzed by WGCNA, and the necroptosis-related genes (gray circle). The genes at the intersection of the three sets of genes were designated as NRDEGs. (b) PPI network of the NRDEGs. (c) Top 20 genes screened by the CytoHubba Screening Maximum Correlation Criterion. (d) Heat map of correlation coefficient for hub genes.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4117025/v1/56403aa4943c26a74fc93539.png"},{"id":53155426,"identity":"f0d8a05f-35e7-4ca3-aa36-7766bfe2902e","added_by":"auto","created_at":"2024-03-21 09:40:59","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":287334,"visible":true,"origin":"","legend":"\u003cp\u003eEstablishment and validation of LASSO regression diagnostic model. (a) Obtaining the optimal and parsimonious models through LASSO regression. (b) Association between the selected features and the absolute value of the coefficient. (c) Model validation using both the Training and Test groups. (d) The characteristic genes of the cerebral infarction and control groups were significantly different. (e) The results of differential expression were displayed by group comparison plots.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-4117025/v1/d2e68eb9b0928ac5d77a13f3.png"},{"id":53155422,"identity":"f492351e-1659-4467-a95b-81c9ac198dc1","added_by":"auto","created_at":"2024-03-21 09:40:59","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":107168,"visible":true,"origin":"","legend":"\u003cp\u003eConsensus cluster analysis of eigengenes in patients with cerebral infarction. (a) Consensus clustering diagram (k=2). (b) Relative variation of area under the CDF curve for k=2–9. (c) Consensus clustering CDF. (d) Tracking plot.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-4117025/v1/e7f47c62bb9fd69c622394d1.png"},{"id":53155427,"identity":"6ebee971-a1b4-418d-a1b0-e4e2e3165752","added_by":"auto","created_at":"2024-03-21 09:40:59","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":705544,"visible":true,"origin":"","legend":"\u003cp\u003eImmune infiltration analysis. (a, b) Variations in the abundance of 22 immune cell infiltrates across samples in the GSE_combine dataset. (b) Variations in the abundance of 22 immune cell infiltrates in the GSE_combine dataset. Blue, Control group; red, patients with cerebral infarction. (c) Heatmap showing the correlations among the 22 immune cell infiltrates as well as the correlations between the 22 cell infiltrates and hub-mRNA.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-4117025/v1/7f2d1cdc7b57c45f13c027cb.png"},{"id":53156371,"identity":"83b8b3e8-c429-44b4-8681-d0c17f0109fe","added_by":"auto","created_at":"2024-03-21 09:48:59","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":383977,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork for the interactions among hub-mRNA, hub-miRNA, and hub-lncRNA. (a) Venn diagram shows the miRNAs predicted to interact with hub-mRNAs according to the miRTarBase and TarBase databases. There are 42 groups of interactions in the intersection. (b, c) lncRNAs interacting with miRNAs were predicted using the starBase database. (b) Sankey and (c) network diagrams of the lncRNA-miRNA-mRNA network were drawn.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-4117025/v1/188ff2ee3c9ca23068b74e55.png"},{"id":53155420,"identity":"e83f6e36-a1f7-4ad9-9492-f6d1e00835dc","added_by":"auto","created_at":"2024-03-21 09:40:59","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":185363,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe relative expression of differentially expressed mRNA in HT22.\u003c/strong\u003e (a) PABPN1; (b) CCT3; (c) AHR; (d) RPL12;(e) TLR4; (f) RBM14; (g) RBL4; (h) ELAVL1; (i) RPL13A. The control group reflects the normal HT22 and the OGE/R group reflects the model group.\u003c/p\u003e\n\u003cp\u003e*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-4117025/v1/49ad8cd3b6a74c9649a225c4.png"},{"id":73093514,"identity":"053548fa-e646-46ab-9f84-2029fdbd68a0","added_by":"auto","created_at":"2025-01-06 16:21:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5288144,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4117025/v1/d950cdf7-1492-46b8-b47f-60d9a15cd10b.pdf"},{"id":53155418,"identity":"0f924e34-9104-4824-9cf8-1b20c079671d","added_by":"auto","created_at":"2024-03-21 09:40:59","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1746739,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFiles.zip","url":"https://assets-eu.researchsquare.com/files/rs-4117025/v1/59c23879a0117c6a30833680.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of the Molecular Subtypes of Acute Ischemic Stroke Using Bioinformatics and Machine Learning","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStroke is a significant global health issue, leading to disability in adults and causing a substantial number of deaths worldwide (Johnson et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It poses a considerable burden at both individual and societal levels. An episode of cerebral ischemia arises when a clot or thrombus obstructs the blood supply to the brain, leading to an acute ischemic stroke (AIS). During thrombolytic therapy for AIS, ischemia/reperfusion (I/R) injury is likely to occur. These I/R injuries are particularly susceptible to oxidative stress and inflammation, which exacerbate neuronal cell death (Nagy and Nardai \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Currently, AIS can be effectively managed with intravenous thrombolysis and mechanical thrombectomy to restore blood flow in occluded vessels. However, the prognosis heavily relies on the timely administration of treatment. Despite the effectiveness of current treatment options in preventing long-term disability in AIS patients, accurate prognostic prediction remains challenging. Existing models for prognostic prediction in AIS patients often lack reliable predictive capabilities (Fahey et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Quinn et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The diagnosis of AIS primarily relies on neuroimaging techniques due to the absence of efficient, rapid, and accurate diagnostic biomarkers. Early diagnosis and successful treatment are crucial for reducing mortality rates and improving outcomes in AIS. Therefore, there is an urgent need for systematic studies to understand the biological processes involved in AIS.\u003c/p\u003e \u003cp\u003eCell death plays a crucial role in regulating infarction during ischemia and I/R injury. The condition of ischemic brain tissue can be divided into two clinical regions: the ischemic core and the penumbra (which surrounds the ischemic core) (Kaufmann et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Neurons in the ischemic core undergo rapid necrotic cell death (Xing et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). On the other hand, neurons in the hypoxic penumbra are functionally suppressed but remain active (Fricker et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). With appropriate treatment, neurons within the penumbra may be potentially rescued. Unfortunately, in humans, we are unable to independently determine the extent of cell death in the ischemic core or penumbra. Therefore, our understanding of the mechanisms underlying neuronal death in ischemic stroke is vital as it can provide valuable insights into potential targets that can be controlled.\u003c/p\u003e \u003cp\u003eNecroptosis is a recently identified pathway of programmed cell death that can be induced by the activation of inflammatory receptors such as tumor necrosis factor receptor 1, toll-like receptor (TLR), and Fas/CD95 (Holler et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Liao et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Matsumura et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Vercammen et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1998a\u003c/span\u003e; Vercammen et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1998b\u003c/span\u003e). This process relies on the activation of receptor-interacting protein kinase (RIPK)1 through its interaction with RIPK3, resulting in the phosphorylation of the pseudokinase mixed lineage kinase domain-like protein (MLKL) by RIPK3 (de Almagro and Vucic \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Galluzzi et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In the context of ischemia and I/R injury, neurons experience rapid depletion of adenosine triphosphate (ATP) due to the sudden lack of oxygen and glucose caused by ischemia. This depletion results in plasma membrane depolarization, which triggers the secretion of glutamate and subsequent induction of N-methyl-D-aspartate (NDMA) receptors (Vacher et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The excitotoxicity caused by glutamate and lactic acidosis further activates RIPK1 (Liao et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhan et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). When caspase-8 is inhibited, RIPK1 is recruited via its receptor-interacting protein (RIP) homotypic interaction motif (RHIM) domain and phosphorylates RIPK3, forming complex IIb (Grootjans et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Ultimately, MLKL is phosphorylated and polymerized, leading to the induction of necroptosis (Grootjans et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Previous research has shown that intracerebral injection of Necrostatin-1 (Nec-1), a specific inhibitor of RIPK1, can block ischemic stroke-induced neuronal necroptosis and attenuate delayed ischemic brain injury in mice (Degterev et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Additionally, various investigations using the Oxygen-Glucose Deprivation/Reoxygenation (OGD/R) model have observed elevated levels of necroptosis markers, such as RIPK1, RIPK3, and MLKL, in neuronal cells (Chen et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e; Tang et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Vieira et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, effective drugs targeting necroptosis for potential neuroprotection against ischemic brain injury are still lacking.\u003c/p\u003e \u003cp\u003eIn this study, necroptosis-related differentially expressed genes (NRDEGs) in AIS were identified using a comprehensive analysis. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and Weighted Gene Co-expression Network Analysis (WGCNA) were used to identify the molecular mechanism(s) of the NRDEGs in AIS. Next, a protein-protein interaction (PPI) network as well as the efficient and diagnostic Least absolute shrinkage and selection operator (LASSO) regression models were constructed. The effectiveness of the diagnostic model was confirmed by analyzing receiver operating characteristic (ROC) curves. A total of nine potential NRDEGs were identified as significant indicators for predicting the occurrence of AIS. To further investigate the immune component, we conducted immune infiltration analysis and examined the correlations between the expression of these nine DEGs and 22 immune cell types. Additionally, we selected three DEGs to establish a competing endogenous RNA (ceRNA) network. Altogether, our study successfully unraveled the molecular mechanisms of necroptosis in AIS and established a foundation for its diagnosis and treatment. For a visual representation of our study design, please refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e in the article.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eIdentification of DEGs and NRDEGs in AIS\u003c/p\u003e\n\u003cp\u003eThe gene expression profiling dataset of peripheral whole blood specimens (GSE16561) was retrieved from the Gene Expression Omnibus (GEO) (Barrett et al. \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e) database, and divided into 39 ischemic stroke patients (Stroke) and 24 healthy control subjects (Control). Another GEO dataset of peripheral blood specimens (GSE22255) (Krug et al. \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e) was obtained and divided into 20 ischemic stroke patients and 20 normal patients. The two datasets were merged and batch effects were mitigated using the combat (package sva) function in R (Leek et al. \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e) and verified by principal component analysis (PCA). The third GEO dataset of peripheral blood specimens of Homo sapiens from GPL570 (GSE58294) (Stamova et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e) was downloaded and divided into 69 ischemic stroke patients (group name: Stroke) and 23 normal patients (group name: Normal). As the validation dataset, it was used to validate the hub genes and the comparison map between the two groups was drawn.\u003c/p\u003e\n\u003cp\u003eTo assess the differential impact of gene expression values on AIS, group comparisons were conducted using the R package limma (Ritchie et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). For differential expression analysis, the following additional thresholds were used: logFC\u0026thinsp;\u0026gt;\u0026thinsp;0.3 and adjP-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for upregulated genes (up_regulated_genes) and logFC\u0026lt;-0.3 and adjP-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for downregulated genes (down_regulated_genes).\u003c/p\u003e\n\u003cp\u003eWe identified necroptosis-associated genes using GeneCards (Stelzer et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e), which is an integrative and comprehensive database of human genetic information. A search using the term \u0026quot;necroptosis\u0026quot; yielded 630 necroptosis-related genes (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eA Venn diagram was drawn using the DEGs and necroptosis-related genes and the intersecting genes were identified as NRDEGs in AIS. These genes were then subjected to PPI network analysis.\u003c/p\u003e\n\u003cp\u003eDEG Function and Pathway Enrichment analyses\u003c/p\u003e\n\u003cp\u003eGO (Ashburner et al. \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e), an international standard for gene functional classification, is widely used as a tool for functional annotation and enrichment analyses. GO terms are classified as cellular component (CC), molecular function (MF), and biological process (BP). KEGG database is comprised of the pathways of experimentally validated metabolic processes and gene sets of human diseases, and it stores an extensive collection of data on genomes, biological pathways, drugs, chemicals, and diseases. The DEGs underwent GO term and KEGG pathway analyses using the clusterProfiler (Yu et al. \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e) in R package. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was deemed statistically significant.\u003c/p\u003e\n\u003cp\u003eGSEA evaluation\u003c/p\u003e\n\u003cp\u003eGSEA, a widely recognized computational method, is frequently employed to assess variations in pathway activities and biological processes within expression datasets. It determines whether a predefined set of genes exhibits notable differences between two biological states (Subramanian et al. \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e). GSEA was conducted on the gene expression data in the combined dataset of GSE16561 and GSE22255 using the clusterProfiler R package in order to study the biological differences between patients with the AIS and Control groups. The \u0026quot;c2.all.v7.5.2.entrez.gmt\u0026quot; gene set was obtained from the Molecular signatures database (MSigDB) (Liberzon et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) for GSEA of the combined dataset of GSE16561 and GSE22255. Adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was deemed statistically significant. Both p-value and normalized enrichment score (NES) values are presented.\u003c/p\u003e\n\u003cp\u003eEstablishment of Co-expression Modules of DEGs through WGCNA\u003c/p\u003e\n\u003cp\u003eWGCNA (Langfelder and Horvath \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e) is a systematic biological approach used to characterize the patterns of inter-gene correlations across samples in order to discover modules of highly correlated genes. Therapeutic targets or candidate biomarker genes were identified according to the intensity of the gene set as well as the relationship between the gene set and the phenotype. We analyzed the GSE16561 and GSE22255 datasets using the WGCNA package in R software. For the GSE16561 dataset, we set the minimum gene number, cut height and optimal soft-thresholding power to 50, 135 and 5, respectively, merged modules at a cut height of 0.95, and established a minimum distance of 0.2. For the GSE22255 dataset, the minimum gene number, cut height and optimal soft-thresholding power were set to 50, 80 and 7, respectively, modules were merged at a cut height of 0.4, and the minimum distance was fixed at 0.2. Using this approach, we successfully obtained co-expression modules for the DEGs within the two groups of both datasets.\u003c/p\u003e\n\u003cp\u003eDevelopment of PPI network\u003c/p\u003e\n\u003cp\u003eSTRING (Szklarczyk et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) was used to search for known and predicted PPIs and was employed to construct the PPI networks of both DEGs and NRDEGs.\u003c/p\u003e\n\u003cp\u003eCytoscape v3.6.1 (Smoot et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e), a well-known open-source software platform that integrates interaction networks, was utilized for the visualization of PPI networks. Cytoscape CytoHumba plugin (Chin et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e) was used to study the network\u0026rsquo;s hub genes and identified the top 10 genes in the maximum correlation coefficient (MCC). Functional correlations for the key genes were calculated using the R package GOSemSim (Yu \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eConstruction of Diagnostic Model via LASSO Regression Model\u003c/p\u003e\n\u003cp\u003eLASSO regression involves the simultaneous screening of variables and complexity adjustment to fit a generalized linear model. Through regularization, a shrinkage penalty is introduced to limit the coefficients. The regularization process uses the sum of the absolute values of all the feature weights, which improves the interpretability of the model to a certain extent. A LASSO regression model was built for the genes in the co-expression modules of the DEGs using the glmnet package in R software (Engebretsen and Bohlin \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mazumder and Hastie \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). During the model construction process, we carefully screened the selected features and identified the best model for building a diagnostic model for cerebral infarction. Subsequently, we determined that the genes included in the model were the distinctive genes associated with cerebral infarction. To validate the models, ROC curves were drawn using the pROC package (Robin et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). A box plot was generated to display the characteristic genes of peripheral blood samples from patients with cerebral infarction and normal control patients. The genes with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were selected for visualization.\u003c/p\u003e\n\u003cp\u003eMolecular Subtype Analysis of Cerebral Infarction\u003c/p\u003e\n\u003cp\u003eConsensus clustering is an algorithm based on resampling techniques. Its purpose is to identify individual members and their respective subgroup numbers, while also assessing the reliability and validity of the clustering data. The previously identified NRDEGs based on the combined dataset of GSE16561 and GSE22255 were defined as key necroptosis-associated genes. The ConsensusClusterPlus package (Wilkerson and Hayes \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e) in R software was employed to determine the gene expression profiles of the previously screened NRDEGs, and the cluster with the best clustering was selected. Based on these results, different necroptosis patterns were identified.\u003c/p\u003e\n\u003cp\u003eImmune Infiltration Analysis\u003c/p\u003e\n\u003cp\u003eThe CIBERSORT package (Chen et al. \u003cspan class=\"CitationRef\"\u003e2018a\u003c/span\u003e) was employed, utilizing a deconvolution algorithm with linear support vector regression, to predict the expression matrix associated with immune cell subtypes. This approach was applied to evaluate immune cell infiltration in patients with ischemic stroke and the control group using RNA-seq data. The differential enrichment of immune cells between patients with AIS and the control group was identified in the combined dataset of GSE16561 and GSE22255. Furthermore, we determined the Pearson correlation coefficients between immune cells and nine NRDEGs and examined the relationship between NRDEGs and the levels of immune infiltration.\u003c/p\u003e\n\u003cp\u003eNetwork Construction for the Interactions among Hub-mRNA, Hub-microRNA (miRNA), and Hub-long non-coding RNA (lncRNA)\u003c/p\u003e\n\u003cp\u003eWe conducted an analysis of lncRNA and miRNA expression, focusing on their interaction with hub genes at the post-transcriptional level. To identify miRNA-mRNA targeting relationships, we utilized the miRTarBase database, which contains experimentally-validated miRNA-target interactions (MTIs) from over 8,500 articles (Huang et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The database has been augmented with the newly released CLIP-seq dataset, resulting in over 500,000 MTIs. Leveraging improved natural language processing (NLP) technology, we collected additional target relationship pairs along with their network functions and annotation information. For identification of miRNAs potentially binding to hub genes, we utilized the miRTarBase 2020 database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mirtarbase.cuhk.edu.cn/\u003c/span\u003e\u003c/span\u003e) (Huang et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, the TarBase database (version 8), which compiles experimentally-validated miRNA targets across multiple species, was used to predict miRNAs interacting with hub genes (Karagkouni et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). A list of miRNAs predicted by both databases was generated.\u003c/p\u003e\n\u003cp\u003eTo search for miRNA targets, we employed the starBase database, which incorporates high-throughput sequencing datasets (CLIP-seq and Degradome-seq) and offers various visualization tools to explore microRNA targets (Cai et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). The database includes extensive data on miRNA-mRNA, miRNA-ncRNA, RNA-RNA, and RBP-RNA interactions. Using the starBase database, we predicted lncRNAs interacting with miRNAs. An interaction network comprising hub lncRNAs, hub miRNAs, and hub mRNAs was constructed, and the Cytoscape software was employed to visualize the network as a Sankey diagram.\u003c/p\u003e\n\u003cp\u003eHT22 Cell Culture and treatment\u003c/p\u003e\n\u003cp\u003eThe HT22 cells (Zhejiang Ruyao Biotechnology Co. Ltd., Zhejiang, China), which are an immortalized mouse hippocampal neuronal cell line, were cultured in Dulbecco\u0026rsquo;s modified Eagle\u0026rsquo;s medium (DMEM, Corning, NY, United States) containing 10% foetal bovine serum (FBS, BI, Israel) and 1% double antibodies (37℃, 5% CO2 cell culture incubator). When the cells attached to the wall and reached 80\u0026ndash;90% confluence, the culture medium were discarded, washed once with PBS, and digested with 3ml of trypsin for 2 minutes. The process of digestion was terminated by adding medium immediately after the cells became round and detached. The cells were transferred to a centrifuge tube, centrifuged at 1000rpm for 5 minutes, re-suspended in fresh culture medium, and distributed at a 1:2 ratio. The original cells were transferred into a new cell culture bottle for continued culture, with medium changed every 2 to 3 days.\u003c/p\u003e\n\u003cp\u003eWe divided the cells into both a control group and a model group, seeded them into 96-well plates at a density of 5\u0026times;10\u003csup\u003e4\u003c/sup\u003e, with 3 replicate wells for each group, and then cultured overnight. After 24 hours, we removed the original culture medium in the model group, washed the cells twice with PBS, and then added 300\u0026micro;l of glucose-free, serum-free DMEM medium to each well before being incubated in an anaerobic culture box for 4 hours. After oxygen-glucose deprivation treatment, the cells in the model group were restored to complete DMEM medium, and returned to e cell incubator at 37℃and 5% CO\u003csub\u003e2\u003c/sub\u003e for 2 hours. The cells in the normal control group were continuously cultured in complete medium and a cell incubator at 37℃, 5% CO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003eQuantitative polymerase chain reaction (qPCR analysis)\u003c/p\u003e\n\u003cp\u003eThe mRNAs were extracted from HT22 cells by adding Trizol reagent. The Fast Plus RT Master mix reverse transcription reagent kit (Supersmart\u0026reg; 6-minute Heat-resistant first-strand cDNA Synthesis Kit) was used to synthesise the extracted mRNAs into cDNAs, called the reverse transcription, RT reaction. PCR was carried out using the SYBR Green qPCR Mix pre-mixed qPCR reagent kit (Superbrilliant\u0026reg; Third generation ZAPA SYBR Green qPCR premix) to amplify the cDNA in a two-step process and perform fluorescence quantification, with the reaction conditions as follows: 95℃ for 300s; 95℃ for 10s and 60℃/65℃ for 20s, for 40 cycles. The final primer concentration was 0.4\u0026micro;M, with GAPDH as the reference gene. The relative expression levels were calculated using the 2^\u003csup\u003e(\u0026minus;△△Ct)\u003c/sup\u003e method, and the melting curve of the products were analysed to ensure reaction specificity. See \u003cstrong\u003eSupplemental Files\u003c/strong\u003e for primer sequences.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n \u003cp\u003eAll statistical tests were conducted with R v4.0.2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org\u003c/span\u003e\u003c/span\u003e). For normally distributed continuous variables, the statistical significance was assessed using an independent Student\u0026apos;s t-test. Non-normally distributed continuous variables were compared using the Mann-Whitney U test and the Wilcoxon rank sum test. All p-values were two-sided, and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was deemed statistically significant.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eDEGs in Patients with AIS\u003c/p\u003e \u003cp\u003eTo compare the patients with AIS with normal patients, we merged the two sets of GSE data as GSE_combine, and the batch effects were then eliminated. PCA analysis showed that batch effects had been removed from the datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(a) and 2(b)). For the identification of DEGs, differential expression analysis was carried out using the limma software package, with |logFC| \u0026gt; 0.3 and adjPvalue\u0026thinsp;\u0026lt;\u0026thinsp;0.05. A total of 1945 DEGs were acquired, including 1195 upregulated and 750 downregulated genes. The ten up- and downregulated DEGs with the highest logFC are presented as heatmaps (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(c)) and gene correlation coefficient heatmaps (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(d)). We compared the DEGs obtained from the combined dataset of GSE16561 and GSE22255 and necroptosis-related genes to identify 76 NRDEGs (Table S2 and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(e)). A PPI network of these 76 genes is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(f).\u003c/p\u003e \u003cp\u003eFunctional and Pathway Enrichment Analyses\u003c/p\u003e \u003cp\u003eNext, GO functional enrichment analysis was conducted for the NRDEGs (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The DEGs related to AIS were mainly enriched in BP terms such as toll/interferon response factor (TRIF)-dependent TLR signaling pathway, myeloid differentiation factor 88 (MyD88)-independent TLR signaling pathway, and pattern recognition receptor signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(a)); in CC terms such as cytosolic part, myosin complex, and ficolin-1-rich granule (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(b)); and in MF terms such as microfilament motor activity, death receptor activity, and actin binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(c)). Next, KEGG analysis demonstrated that the DEGs were enriched in biological pathways, such as hepatitis B, pathogenic \u003cem\u003eEscherichia coli\u003c/em\u003e infection, and Tumor necrosis factor (TNF) signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(d), Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\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\u003eGO functional enrichment analysis of DEGs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\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\u003epvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep.adjust\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\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\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0035666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTRIF-dependent toll-like receptor signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6/76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29/18670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.64e-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.08e-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.96e-06\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:0002756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMyD88-independent toll-like receptor signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6/76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33/18670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.78e-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.70e-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.41e-06\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:0002221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003epattern recognition receptor signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10/76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e197/18670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.10e-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.84e-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.51e-06\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:0044445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecytosolic part\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9/76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e247/19717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.51e-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.21e-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.35e-05\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:0016459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emyosin complex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5/76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65/19717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.13e-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.60e-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.87e-04\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:0101002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eficolin-1-rich granule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7/76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e185/19717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.24e-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.60e-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.87e-04\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:0000146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emicrofilament motor activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4/76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22/17697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.17e-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.00e-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.74e-04\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:0005035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003edeath receptor activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3/76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11/17697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.23e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.91e-04\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:0003779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eactin binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10/76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e431/17697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.50e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.91e-04\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 \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\u003eKEGG functional enrichment analysis of DEGs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\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\u003epvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep.adjust\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\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\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa04668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTNF signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9/53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e112/8076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.65e-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.01e-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.96e-06\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\u003ehsa05130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePathogenic Escherichia coli infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10/53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e197/8076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.65e-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.46e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.15e-05\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\u003ehsa05161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHepatitis B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8/53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e162/8076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.07e-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.81e-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.11e-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eGSEA\u003c/h3\u003e\n\u003cp\u003eThrough GSEA of the combined dataset, we explored the relationships between AIS and the DEGs\u0026rsquo; BP, CC, and MF. The results of GSEA with adjPvalue\u0026thinsp;\u0026lt;\u0026thinsp;0.05 showed an enrichment in neutrophil degranulation, G protein-coupled receptors (GPCR) ligand binding, signaling by interleukins, G-alpha (i) signaling events, neuronal system, rhodopsin-like receptors (class A/1), and NABA secreted factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(a)-4(d), Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \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\u003eGSEA enrichment analysis\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epvalue\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_NEUTROPHIL_DEGRANULATION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.909562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.039119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREACTOME_GPCR_LIGAND_BINDING\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.98536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.039119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREACTOME_SIGNALING_BY_INTERLEUKINS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.587387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.039119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREACTOME_G_ALPHA_I_SIGNALLING_EVENTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.742828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.039119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREACTOME_NEURONAL_SYSTEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.001512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.039119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREACTOME_CLASS_A_1_RHODOPSIN_LIKE_RECEPTORS_\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.958376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.039119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNABA_SECRETED_FACTORS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.126348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.039119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWP_NUCLEAR_RECEPTORS_METAPATHWAY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.900179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.039119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028251\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\u003eCo-expression Modules of DEGs Identified by WGCNA\u003c/p\u003e \u003cp\u003eWe performed WGCNA on the Control and AIS groups to screen for co-expression modules. In the process of analyzing the GSE16561 dataset using WGCNA, we successfully identified 23 outlier samples by setting the cut height (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(a)). After employing a scatter plot, it was found that the optimal soft-threshold for our study was 5. Next, we carried out further investigations based on this finding (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(b)). The co-expressed genes in the two groups were subsequently clustered in the dark red and pink modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(c)). According to the expression pattern and grouping information of module genes, we found that the dark red and pink modules were positively correlated with ischemic stroke with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and were used for further analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(d)). During WGCNA of the GSE22255 dataset, an outlier sample was detected by setting the cut height (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(e)). Using a scatter plot, we determined that a soft threshold of 7 provided optimal results, prompting us to proceed with subsequent investigations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(f)). The genes co-expressed in the two groups were subsequently clustered in the brown and dark orange modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(g)). According to the expression patterns and grouping information of the module genes, we found that the brown and dark orange modules were positively correlated with cerebral infarction with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and were subjected to further analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(h)).\u003c/p\u003e \u003cp\u003eProtein-Protein Interaction Network\u003c/p\u003e \u003cp\u003eA total of sixty-nine genes were obtained upon the intersection of AIS-related co-expressed genes in the GSE16561 and GSE22255 datasets and necroptosis-related genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(a)). The PPI network of AIS-related genes was constructed, and the data were plotted using Cytoscape software (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(b)). Using the MCC algorithm of cytoHubba, a Cytoscape plugin, the top 20 hub gene scores were calculated. These top 20 genes included \u003cem\u003eRPL4, PTGES3, RBM14, HSP90AA1, RPLP1, HNRNPM, RPL38, CCT3, PABPN1, RPL13A, ELAVL1, RPL12, FUS, AHR, RPL23, STUB1, TLR4, RPL3, BNIP3\u003c/em\u003e, and \u003cem\u003eMYC\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(c)). We generated a correlation coefficient heatmap to visualize the relationships among the hub genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(d)).\u003c/p\u003e \u003cp\u003eConstruction of a Stroke Diagnostic Model and Determination of Eigengenes\u003c/p\u003e \u003cp\u003eIn order to identify AIS-associated genes and assess their diagnostic potential, we employed the LASSO regression model on the GSE_combine dataset. The dataset was randomly divided into Training and Test groups in a 7:1 ratio. The model was constructed using the Training group and validated using the Test group. Throughout the model-building process, the chosen feature parameters reduced as λ increased, whereas the absolute values of the coefficients increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e(a) and 7(b)). Through the process of simulation and careful selection, we identified a set of 18 feature genes, namely, \u003cem\u003eRPL4, RBM14, HSP90AA1, RPLP1, HNRNPM, RPL38, CCT3, PABPN1, RPL13A, ELAVL1, RPL12, FUS, AHR, STUB1, TLR4, RPL3, BNIP3\u003c/em\u003e, and \u003cem\u003eMYC\u003c/em\u003e. ROC curves were plotted for both datasets and the area under the curve (AUC) was computed to validate the model. The AUC values of the risk model for both Training and Test groups were 0.905 and 0.889, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e(c)). We analyzed the differences between the characteristic genes of the AIS group and Control groups and identified nine genes with statistically significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), including \u003cem\u003eRPL4, RBM14, CCT3, PABPN1, RPL13A, ELAVL1, RPL12, AHR\u003c/em\u003e, and \u003cem\u003eTLR4\u003c/em\u003e. Boxplots are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e(d). Next, the expression differences between the two groups were verified by dataset GSE58294. We used WilCoxon rank sum test to analyze the expression differences of the six hub genes (\u003cem\u003eTLR4, RBM14, RPL12, ELAVL1, AHR, PABPN1\u003c/em\u003e). The results of expression differences were shown by group comparison graph (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e(e)). The results showed that the expression levels of \u003cem\u003eTLR4, RBM14, and RPL12\u003c/em\u003e were significantly different and the expression trends remained consistent in both test and validation datasets.\u003c/p\u003e\u003cp\u003eTwo Distinct Modes of Necroptosis Identified by Signature Genes\u003c/p\u003e \u003cp\u003eConsensus clustering of the nine necroptosis-related genes was carried out, and a consensus clustering diagram was drawn when k\u0026thinsp;=\u0026thinsp;2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e(a)). Furthermore, we observed variations in the area under the cumulative distribution function (CDF) curve in relation to k\u0026thinsp;=\u0026thinsp;2\u0026ndash;9 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e(b)). Additionally, we plotted the CDF of the consistent clustering (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e(c)) and the tracking plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e(d)).\u003c/p\u003e \u003cp\u003eImmune Infiltration Analysis\u003c/p\u003e \u003cp\u003eTo analyze disparities in the extent of immune infiltration of immune infiltration between patients with AIS and the Control group, variations in the abundance of 22 immune cell infiltrates between the two groups were explored using the CIBERSORT algorithm in the GSE_combine dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e(a)). Through the use of the Wilcoxon signed-rank test algorithm, a total of nine types of immune cells in the GSE_combine dataset were found to be significantly different between patients with AIS and the Control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e(b)): plasma cell, regulatory T cell (Treg), CD8\u0026thinsp;+\u0026thinsp;T cell, activated NK cell, γδ T cell, M0 macrophage, M2 macrophage, neutrophil, and activated mast cell. Next, the correlations between the nine NRDEGs and 22 types of immune cells were analyzed. In the GSE_combine dataset, significant correlations were found between \u003cem\u003eRPL4\u003c/em\u003e and activated CD4\u0026thinsp;+\u0026thinsp;memory T cell; \u003cem\u003eRBM14\u003c/em\u003e and activated dendritic cell; \u003cem\u003eCCT3\u003c/em\u003e and na\u0026iuml;ve B cell and activated mast cell; \u003cem\u003eRPL13A\u003c/em\u003e and plasma cell, Treg, M1 macrophage, activated mast cell, and activated dendritic cell; \u003cem\u003eAHR\u003c/em\u003e and na\u0026iuml;ve CD4\u0026thinsp;+\u0026thinsp;T cell and monocyte; and \u003cem\u003eTLR4\u003c/em\u003e and monocyte and M2 macrophage (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e(c)).\u003c/p\u003e \u003cp\u003eNetwork for the Interactions among Hub-mRNA, Hub-miRNA, and Hub-lncRNA\u003c/p\u003e \u003cp\u003eWe constructed an lncRNA-miRNA-mRNA interaction network, which contained three genes related to necroptosis, namely, \u003cem\u003eTLR4, AHR\u003c/em\u003e, and \u003cem\u003eELAVL1\u003c/em\u003e. We then employed the miRTarBase and TarBase databases to predict miRNAs that could interact with the necroptosis-related genes. A total of 42 sets of interactions were obtained from the intersection set (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e(a)), and seven sets of interactions supported by experimental evidence were identified. The lncRNAs that potentially bind to miRNAs were predicted using the starBase database, and the lncRNA-miRNA-mRNA network was established using both Sankey (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e(b)) and network (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e(c)) diagrams.\u003c/p\u003e \u003cp\u003eValidation of the identified mRNAs\u003c/p\u003e \u003cp\u003eOxygen glucose deprivation/re-oxygenation (OGD/R) was used to mimic neural injury. When compared with the normal control group, the mRNA expression levels of PAPBN1 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), CCT3 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in the model control group were significantly downregulated while the mRNA expression levels of AHR (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), RPL12 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and TLR4 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were significantly upregulate (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eAIS is one of the main causes of human death and is one of the most prevalent leading to disability worldwide. The death of neurons can occur through various pathways, including apoptosis, necroptosis, autophagy, ferroptosis, and pyroptosis. Necroptosis is believed to be a type of necrotic cell death that occurs after cerebral infarction and I/R injury. Several studies have shown that necroptosis occurs within one hour after reperfusion in a brain injury model of I/R, primarily affecting neurons in the hippocampus (Li et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020c\u003c/span\u003e; Naito et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, necroptosis is associated with the size of the infarct and the extent of neurological impairment (Han et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nikseresht et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The inhibition of RIPK1, genetically or pharmacologically, can alleviate ischemic brain injury (Naito et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and a specific inhibitor, known as Nec-1, can target necroptosis. Hence, to identify effective biomarkers for diagnosing and treating AIS through necroptosis, 1945 DEGs were retrieved from the combined dataset of GSE16561 and GSE22255. From GeneCards, 630 genes related to necroptosis were obtained, and 76 genes were found to be both DEGs and NRDEGs. GO, KEGG and GSEA were utilized to enrich the DEGs in TNF pathway, TLR pathway and other pathways associated with necroptosis. Through WGCNA analysis, co-expression modules were identified among the DEGs. PPI analysis resulted in the identification of 20 hub genes. Using LASSO regression, a diagnostic model for STROKE was constructed, determining 18 characteristic genes. Through consensus clustering, two distinct patterns of necroptosis were identified based on the signature genes. However, the precise mechanism underlying the role of necroptosis in ischemic stroke remains unclear, necessitating further research. Therefore, our aim was to systematically study NRDEGs, providing a better framework for exploring diagnostic biomarkers and making predictions regarding targeted therapy associated with necroptosis for the treatment of AIS.\u003c/p\u003e \u003cp\u003eNRDEGs were found to be enriched in the GO CC term of the cytoplasmic portion and the myosin complex, which aligns with the characteristics of necroptosis, leading to the rupture of the plasma membrane (Cai et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Nganga et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additionally, the NRDEGs showed enrichment in the GO MF terms microfilament motor activity and death receptor activity. During the progression of necroptosis, the assembly of complex \"necrosomes\" within cells can be triggered by death receptors. Furthermore, KEGG analysis of NRDEGs demonstrated that TNF signal transduction was the main enriched pathway. TNF plays a pivotal role in the cellular events during necroptosis (Chen and Goeddel \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), and the mouse fibroblast L-M cell line is highly susceptible to TNF-induced necroptosis (Laster et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). GSEA analysis also indicated significant enrichment of interleukin signaling, neutrophil degranulation, and G alpha (i) signaling among the NRDEGs. It has been suggested that inflammation triggered by necroptosis may be driven by inflammasome activation signaling mediated by interleukins (ILs). For instance, in an in vivo experiment, necroptosis-induced IL-1 was found to contribute to necroptosis-driven inflammation in dendritic cells due to the deletion of the key RIPK3-MLKL necroptosis suppressor caspase-8 (Kang et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Neutrophils, which are involved in neural plasticity, have been observed to increase after a stroke (Amulic et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). TNF-induced neutrophil necroptosis has been reported to depend on RIPK1-RIPK3-MLKL signaling (Wicki et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Neutrophil degranulation may also activate the fibrinolytic system in patients with ischemic stroke (Wicki et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Additionally, necroptosis can be triggered by TNF (Liu et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). A study demonstrated that G alpha (i) could inhibit the effect of TNF. The enrichment of G alpha (i) signaling suggests its potential involvement in the necroptosis process (Earl et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Our research results, revealed for the first time, the involvement of G alpha (i) signaling in the process of necroptosis in AIS. Nevertheless, further studies are needed to fully understand the underlying mechanism.\u003c/p\u003e \u003cp\u003eHerein, we applied the WGCNA method to construct five co-expression modules for the two datasets. Each module consisted of a set of genes exhibiting similar expression patterns. To assess the correlation between these modules and AIS, we examined the expression profiles and grouping information of the module genes. Notably, the dark red and pink modules, as well as the brown and dark orange modules, demonstrated the strongest correlations. Subsequently, we employed the ischemic stroke-related genes within these modules and intersected them with the necroptosis-related genes. The overlapping genes obtained from this intersection were subjected to further analysis.\u003c/p\u003e \u003cp\u003eLASSO regression models were utilized to screen eigengenes and calculate AUC values under the ROC curve for constructing a diagnostic model of ischemic stroke. The aim was to explore the predictive performance of the risk models. Using the 18 NRDEGs selected through LASSO regression analysis, we successfully constructed a diagnostic model for ischemic stroke, which exhibited promising potential for diagnostic prediction. The model's performance was evaluated by plotting ROC curves and calculating the AUC values, which resulted in an AUC value of 0.905 for the Training group and 0.889 for the Test group. Importantly, this predictive model based on the 18 NRDEGs displayed excellent predictive power, with an AUC value of 0.905 in the Training group, surpassing the significance observed in previous studies. The risk model comprised nine statistically significant NRDEGs with diagnostic capabilities, namely, \u003cem\u003eRPL4, RBM14, CCT3, PABPN1, RPL13A, ELAVL1, RPL12, AHR\u003c/em\u003e, and \u003cem\u003eTLR4\u003c/em\u003e. Functional enrichment analyses performed on the NRDEGs revealed an enrichment of GO BP terms related to the TRIF-dependent and MyD88-independent TLR pathways. Previous research suggests that necroptosis can be induced through TLR4 or TLR3 pathways. TLR3 specifically relies on TRIF for its activation, participating in NF-κB activation and induction of type I IFN (Takeuchi and Akira \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). TLR4 is transmitted through TRIF signals and MyD88 adaptor (Takeuchi and Akira \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Our analysis results highlighted TLR4 as a hub gene, with GO BP terms primarily enriched in both TRIF-dependent and MyD88-independent TLR pathways. This provides further evidence that TLR4 activation may induce these signaling pathways, which play a role in the occurrence and progression of necroptosis. AHR is a ligand-dependent transcription factor. In the absence of ligand binding, the encoded protein remains in the cytoplasm, but upon ligand binding, it translocates to the nucleus and induces the transcription of target genes. A recent study showed that Indole-3-carbinol (I3C) regulates the expression of programmed necroptotic protein in cells in a time-dependent manner through AHR (Peng et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). AHR activated by I3C attenuates RIPK3- and MLKL-dependent necroptosis by promoting the expression of cIAP1 and XIAP while ubiquitinizing RIPK1 (Peng et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Another study revealed that AHR reduces NF-κB activation and the expression of TNF-α and IL-6 (Kimura et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). AHR exhibits dual functions by reducing necroptosis and inhibiting inflammatory responses. Knockout of ELAVL1 and combined treatment with peptide-conjugated and Epi-loaded liposomes enhance necroptosis and result in cell death. ELAVL1 knockout leads to increased mRNA and protein levels of necroptosis in cells (Wang et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These findings align with our research results, supporting the significant involvement of NRDEGs in the progression of AIS.\u003c/p\u003e \u003cp\u003eThe immune infiltration analysis revealed notable changes in the infiltration levels of various immune cell types in the cerebral infarction group. Specifically, activated NK cell, γδ T cell, M0 macrophage, M2 macrophage, neutrophil, and activated mast cell exhibited increased infiltration levels, while plasma cell, CD8\u0026thinsp;+\u0026thinsp;T cell, and Treg showed decreased infiltration levels. It is known that neutrophils and monocytes/macrophages infiltrate and accumulate in the microvasculature and ischemic brain parenchyma (Garcia et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). γδ T cells, which are a significant population of lymphocytes, reside on the epithelial surface and possess innate immune characteristics. They secrete IL-17 and generate chemotaxis signals that attract peripheral bone marrow cells, such as neutrophils and monocytes, exacerbating ischemic brain injury (Gelderblom et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Shichita et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Treg cells have been shown to exert neuroprotective effects by suppressing inflammation following ischemic brain injury (Liesz et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), while CD8\u0026thinsp;+\u0026thinsp;T cells tend to accumulate in the brain after a stroke (Ahnstedt et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition, Selvaraj et al. demonstrated that CD8\u0026thinsp;+\u0026thinsp;T cells contribute to functional recovery in the chronic phase following a stroke (Selvaraj et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Interestingly, our findings indicate that genes such as \u003cem\u003eRPL4, RBM14, CCT3, RPL13A, AHR\u003c/em\u003e, and \u003cem\u003eTLR4\u003c/em\u003e are closely associated with the abundance of immune cell infiltration. This suggests their potential involvement in immune responses related to ischemic stroke.\u003c/p\u003e \u003cp\u003eNon-coding RNAs (ncRNAs), especially lncRNAs and miRNAs, serve as crucial regulators of gene expression. Previous studies have highlighted the involvement of specific ncRNAs in promoting inflammation and modulating the prognosis of AIS. For instance, \u003cem\u003eTUG1\u003c/em\u003e was found to promote inflammation by competitively binding to miR-145, resulting in the upregulation of AQP4 expression in OGD/R cell models and McAO rat models (Du et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Another study suggested that the lncRNA \u003cem\u003eNEAT1\u003c/em\u003e may act as a sponge for miR-124 and miR-125a, negatively modulating inflammation and influencing the prognosis of AIS (Li et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e). In our study, we established a potential ceRNA regulatory network involving three NRDEGs (\u003cem\u003eTLR4, ELAVL1, AHR\u003c/em\u003e), providing new insights into AIS diagnosis and more effective targeted therapies against these NRDEGs. Manipulating the expression of these necroptosis-related genes, such as \u003cem\u003eTLR4\u003c/em\u003e, using miRNAs or lncRNA could potentially aid in reducing ischemia-reperfusion injury or the extent of cerebral infarction.\u003c/p\u003e \u003cp\u003eAlthough our necroptosis-related genes exhibited promise as accurate diagnostic and therapeutic targets, there are still certain limitations that need to be addressed. Firstly, the roles of these nine genes in cluster analysis, immune infiltration, and the ceRNA regulatory network should be validated through further in vitro and in vivo experiments. Secondly, given that the relationship between necroptosis and cerebral infarction remains unclear, additional research is needed to elucidate the potential mechanisms involving necroptosis-related genes in the context of cerebral infarction.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, our study identified a set of novel necroptosis-associated gene signatures that hold promise for accurate diagnosis and treatment of AIS. These gene signatures not only shed light on the underlying biological changes occurring in AIS but also have the potential to serve as valuable indicators for clinical decision-making. Among the identified genes, \u003cem\u003eRPL4, RBM14, CCT3, PABPN1, RPL13A, ELAVL1, RPL12, AHR\u003c/em\u003e, and \u003cem\u003eTLR4\u003c/em\u003e emerged as key players in necroptosis-related processes. These genes hold considerable potential as biomarkers for diagnosing and treating AIS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e ZW contributed to conception and design of the study. ZW analyzed the database. ZW performed the statistical analysis. ZW, HF, HW reviewed relevant literature and drafted the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding \u003c/strong\u003eThis work was supported by\u0026nbsp;the\u0026nbsp;Natural Science Fundation\u0026nbsp;for\u0026nbsp;Hebei Province (Grant number: H2020307041).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements \u003c/strong\u003eThe authors would like to express their gratitude to EditSprings (https://www.editsprings.cn) for the expert linguistic services provided.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003eThe data is available upon request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhnstedt H, Patrizz A, Chauhan A, Roy-O\u0026apos;Reilly M, Furr JW, Spychala MS et al (2020) Sex differences in T cell immune responses, gut permeability and outcome after ischemic stroke in aged mice. 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FASEB J 33(1):1313-1329. https://doi.org/10.1096/fj.201800111RR\u003c/li\u003e\n\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":"journal-of-molecular-neuroscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jomn","sideBox":"Learn more about [Journal of Molecular Neuroscience](https://www.springer.com/journal/12031)","snPcode":"12031","submissionUrl":"https://submission.nature.com/new-submission/12031/3","title":"Journal of Molecular Neuroscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Acute ischemic stroke, the necroptosis-related differentially expressed genes, functional enrichment analyses, Weighted gene co-expression network analysis, the Least absolute shrinkage and selection operator regression model, competitive endogenous RNA","lastPublishedDoi":"10.21203/rs.3.rs-4117025/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4117025/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAcute ischemic stroke (AIS) is a severe disorder characterized by complex pathophysiological processes, which can lead to disability and death. This study aimed to determine necroptosis-associated genes in Acute ischemic stroke (AIS) and to investigate their potential as diagnostic and therapeutic targets for AIS. Expression profiling data were acquired from the Gene Expression Omnibus database, and necroptosis-associated genes were retrieved from GeneCards. The differentially expressed genes (DEGs) and necroptosis-related genes were intersected to obtain the necroptosis-related DEGs (NRDEGs) in AIS. In AIS, a total of 76 genes associated with necroptosis (referred to as NRDEGs) were identified. Enrichment analysis of these genes revealed that they were primarily enriched in pathways known to induce necroptosis. Using Weighted gene co-expression network analysis (WGCNA), five co-expression modules consisting of NRDEGs were identified, along with two modules that exhibited a strong correlation with AIS. Protein-protein interaction (PPI) analysis resulted in the identification of 20 hub genes. The Least absolute shrinkage and selection operator (LASSO) regression model demonstrated promising potential for diagnostic prediction. The receiver operating characteristic (ROC) curve validated the diagnostic model and selected nine characteristic genes that exhibited statistically significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). By employing consensus clustering, distinct patterns of necroptosis were identified using these nine signature genes. The results were verified by quantitative PCR (qPCR) in HT22 cells and an external data set. Furthermore, the analyzed ceRNA network included nine lncRNAs, six miRNAs, and three mRNAs. Overall, this study offers novel insights into the molecular mechanisms underlying NRDEGs in AIS. The findings provide valuable evidence and contribute to our understanding of the disease.\u003c/p\u003e","manuscriptTitle":"Identification of the Molecular Subtypes of Acute Ischemic Stroke Using Bioinformatics and Machine Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-21 09:40:54","doi":"10.21203/rs.3.rs-4117025/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-24T12:04:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-20T12:50:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"341654b9-034c-463a-8218-5a1274600682","date":"2024-03-20T12:09:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"d2702cff-9a0e-4eb3-badb-76e37e934469_SNPRID","date":"2024-03-20T01:41:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-19T14:44:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-18T15:38:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-18T15:38:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Molecular Neuroscience","date":"2024-03-17T12:31:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-molecular-neuroscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jomn","sideBox":"Learn more about [Journal of Molecular Neuroscience](https://www.springer.com/journal/12031)","snPcode":"12031","submissionUrl":"https://submission.nature.com/new-submission/12031/3","title":"Journal of Molecular Neuroscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"724912a6-7d25-4b24-836c-7fd9ad83998d","owner":[],"postedDate":"March 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-06T16:05:29+00:00","versionOfRecord":{"articleIdentity":"rs-4117025","link":"https://doi.org/10.1007/s12031-024-02241-3","journal":{"identity":"journal-of-molecular-neuroscience","isVorOnly":false,"title":"Journal of Molecular Neuroscience"},"publishedOn":"2025-01-02 15:57:34","publishedOnDateReadable":"January 2nd, 2025"},"versionCreatedAt":"2024-03-21 09:40:54","video":"","vorDoi":"10.1007/s12031-024-02241-3","vorDoiUrl":"https://doi.org/10.1007/s12031-024-02241-3","workflowStages":[]},"version":"v1","identity":"rs-4117025","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4117025","identity":"rs-4117025","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}