
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12868-X
10.1016/j.heliyon.2024.e36837
e36837
Research Article
Identification and analysis of key immunity-related genes in experimental ischemic stroke
Li Zekun a
Li Xiaohan a
Guo Hongmin a
Zhang Zibo b
Ge Yihao a
Dong Fang c
Zhang Fan zhangfan86@hebmu.edu.cn
d⁎⁎
Zhang Feng ydsyzf@hebmu.edu.cn
a⁎
a Department of Rehabilitation Medicine, The Third Hospital of Hebei Medical University, Shijiazhuang, 050051, PR China
b Metabolic Diseases and Cancer Research Center, Hebei Medical University, Shijiazhuang, 050017, PR China
c Department of Clinical Laboratory Medicine, The Third Hospital of Hebei Medical University, Shijiazhuang, 050051, PR China
d The Key Laboratory of Neural and Vascular Biology, Ministry of Education and Department of Biochemistry and Molecular Biology, Hebei Medical University, Shijiazhuang, 050017, PR China
⁎ Corresponding author. Department of Rehabilitation Medicine, The Third Hospital of Hebei Medical University, No. 139 Ziqiang Road, 050051, Shijiazhuang, Hebei, PR China. ydsyzf@hebmu.edu.cn
⁎⁎ Corresponding author. The Key Laboratory of Neural and Vascular Biology, Ministry of Education and Department of Biochemistry and Molecular Biology, Hebei Medical University, No. 361 Zhongshan East Road, 050017, Shijiazhuang, Hebei, PR China. zhangfan86@hebmu.edu.cn
23 8 2024
15 9 2024
23 8 2024
10 17 e3683718 4 2024
3 8 2024
22 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
The regulation of the immune system and the occurrence of inflammation are vital factors in the pathophysiology of ischemic stroke. This study aims to screen target molecules which play key roles in alleviating the brain injury following ischemic stroke via regulating neuroinflammation. Several bioinformatics methods were used to identify immune-related genes in ischemic stroke. A total of 218 genes were identified as differentially expressed genes within the GSE97537 dataset. By performing GO, KEGG, and GSEA analyses, DEGs were mainly enriched in pathways related to immunity and inflammation. By utilizing the MCODE plugin in conjunction with Cytoscape software, a total of six crucial genes were identified, including C1qb, C1qc, Fcer1g, Fcgr3a, Tyrobp, and CD14. Based on the above crucial genes, 13 miRNAs were predicted. Furthermore, 71 potential drugs with therapeutic properties that target the crucial genes were screened, including lovastatin, ASPIRIN, and PREDNISOLONE. Moreover, the results of RT-qPCR showed that compared with Sham group, the expressions of C1qb, C1qc, Fcer1g, Fcgr3a, Tyrobp, and CD14 in MCAO group were significantly increased, which was consistent with the expression trend of validation dataset and training dataset. In conclusion, immune-related genes may play a key role in ischemic stroke. In addition, six crucial genes were identified as potential biomarkers and 71 promising drugs were screened to treat ischemic stroke patients.

Graphical abstract

Image 1

Keywords

Ischemic stroke
Immune
Inflammation
Bioinformatics
Crucial genes
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pmc1 Introduction

Ischemic stroke is responsible for about 87 % of all strokes [1,2], causing long-term disability, and ranks the second leading cause of death globally [3]. Although there has been considerable research effort, effective therapeutics for ischemic stroke still remains a challenge.

A series of events involving the central nervous system (CNS) are triggered following an ischemic attack. After ischemic stroke, with the occurrence of intravascular hypoxia and disorders in hemodynamics, platelets, coagulation and complement systems are activated, and inflammatory response occurs [4]. The oxidative stress response and activated complement system caused by hypoxia directly impair the local vascular system, resulting in necrosis and dissociation of vascular endothelial cells, damage of BBB integrity, and exposure of antigens under the vascular endothelium [5]. At the same time, immune cells such as neutrophils and mast cells at the site of ischemic injury release intracellular MMP, destroy the vascular basement membrane and tight junction protein, accelerate the destruction of BBB, and lead to the increase of cerebral infarction size. Neurons are very sensitive to ischemia. Afterwards, ischemia induces the release of damage-associated molecular pattern (DAMP) molecules. DAMPs increase the release of immune cell chemokines through toll-like receptors (TLRs) on the surface of immune cells [[6], [7], [8]]. This further promotes immune cell chemotaxis, activates and amplifies the innate immune response, accelerates blood vessel destruction and cell death, ultimately forming a vicious cycle of blood vessel damage, inflammation, and neuronal death [9].

After ischemic stroke, when the immune system is activated, immunosuppression will occur simultaneously. Within a few hours after cerebral ischemia, systemic immune function is down-regulated, the cellular immunity is suppressed, the number of monocytes, T lymphocytes, B lymphocytes, and other immune cells is reduced, cell apoptosis is increased, or cell dysfunction occurs. At the same time, various inflammatory factors, including IL-6, IL-1β, TNF-α, and so on are suppressed. This immunosuppressive state is called stroke-induced immunosuppressive syndrome (SIDS). Immune activation and immunosuppression after ischemic stroke are a contradictory unity [10,11]. The former removes dead tissue through inflammatory response, creating adapt circumstance for nerve repair [12], which can also cause secondary nerve damage due to excessive inflammatory response. The latter can decrease the destruction of neurons by the immune system and play a neuroprotective role, but excessive immunosuppression inevitably increases the chance of infection and worsens clinical outcomes [13]. Hence, the inflammation and immunity regulatory mechanisms of ischemic stroke are multifaceted and complex. Scientific research is urgently required to enhance comprehension of ischemic stroke and uncover novel therapeutic mechanisms due to the intricate inflammatory and immune processes involved.

In recent times, the field of bioinformatics has experienced rapid growth and plays a crucial role in identifying potential biomarkers for various diseases [14]. In this study, GSE97537 and GSE97532 datasets were used to perform bioinformatics analysis in order to identify immune-related genes which play key roles in ischemic stroke. The expression levels and diagnostic performance of key genes were evaluated in validation datasets (GSE61616 and GSE16561). Moreover, 71 promising drugs were screened to treat ischemic stroke. Our findings may offer new perspectives to explore candidate targets and provide promising effective agents for ischemic stroke.

2 Methods

2.1 Source of data

Publicly available microarray gene expression datasets for ischemic stroke were retrieved from the Gene Expression Omnibus Database (GEO) (http://www.ncbi.nlm.nih.gov/geo/) using the keywords “MCAO” and “ischemic stroke”. We retrieved four datasets (GSE97537, GSE6161, GSE16561, and GSE97532). Sample outliers of GSM416549 and GSM416567 in the GSE16561 dataset were removed. The specific information of the datasets was shown in Table 1.Table 1 Microarray information.

Table 1GEO ID	Platform	Animals	Organism	Gender	Age	Tissues	Sampling time	
GSE97537	GPL1355	7 MCAO and 5 Sham	Rattus norvegicus	Male	8 weeks old	Brain (Infarcted hemisphere)	24hr after the reperfusion	
GSE61616	GPL1355	5 MCAO and 5 Sham	Rattus norvegicus	Male	8 weeks old	Brain (Right hemisphere)	7d after the reperfusion	
GSE16561	GPL6883	39 ischemic stroke patients and 24 healthy control subjects	Homo sapiens	Male and Female	>18 years	Whole blood		
GSE97532	GPL21572	3 MCAO and 3 Sham	Rattus norvegicus	Male	8 weeks old	Blood	24hr after the reperfusion	

2.2 Identification of differentially expressed genes (DEGs)

DEGs analysis was performed using ‘limma’ R package to identify DEGs between MCAO and Sham groups in the GSE97537 dataset. The stander of DEGs was a P < 0.05 and absolute log fold change (FC) greater than 1. For the visualization of DEGs, we utilized the R packages ‘ggplot2′ and ‘pheatmap’ to create volcano maps and heatmaps.

2.3 Enrichment analysis

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were carried out using the software package “clusterProfiler”. The GSEA is a method based on functional categories that can calculate enrichment scores of gene sets and detect various functional phenotypes. We utilized the “GSVA” R package for GSEA of DEGs. The MCAO samples were classified into 2 groups based on their median gene expression levels: one group with high expression and another group with low expression.

2.4 Identification of MCAO-related genes through weighted gene co-expression network analysis (WGCNA)

WGCNA has been widely used for the identification of disorder candidate targets [15]. Using the R package ‘WGCNA’, the gene expression counts of 12 samples from GSE97537 were used as input data for a co-expression network construction. Initially, tree building was performed using the hclust function with the average method. Furthermore, the soft threshold was identified according to the standard of the scale-free network. The dynamic shear tree algorithm was employed to cut clustering dendrogram branches and generation of the modules. The correlation between each module and MCAO or sham was then explored. The module most correlated with MCAO was regarded as a key module for further enrichment analysis.

2.5 Identification and enrichment analysis of MCAO-immuno-related DEGs

The UniProt database serves as a central repository for gathering comprehensive and precise functional data on proteins, encompassing abundant annotations [16]. The Immuno-related genes were screened in the UniPort database. Venn diagrams were used to take intersections of DEGs, key WGCNA modules, and immune-related genes to obtain MCAO-Immuno DEGs. The Wilcox test was used to analyze the expression of MCAO-Immuno DEGs in both the MCAO group and Sham group. Subsequently, the R software package “clusterProfiler” was employed to perform GO and KEGG enrichment analysis based on MCAO-Immuno DEGs. Meanwhile, we utilized the “GSVA” R package for GSEA [17].

2.6 Identification of crucial genes

The STRING (https://string-db.org) database was utilized for building the protein-protein interaction (PPI) network of MCAO-Immuno DEGs, with a confidence level of 0.4(medium confidence = 0.4). Additionally, Cytoscape software was employed for network visualization. Next, the molecular complex detection (MCODE) method was used to identify crucial genes from the PPI network. Furthermore, we conducted GSEA analysis based on crucial genes.

2.7 Assessment of the diagnostic value of the crucial genes

The expression of crucial genes in MCAO group and Sham group was compared in GSE97537 (training dataset) and GSE61616 (validation dataset). Additionally, the diagnostic value of each crucial genes was evaluated, and receiver operating characteristic curve (ROC) was drawn. Then calculate the area under the ROC curve (AUC), with 95 % confidence interval estimate diagnostic value. In addition, to further verify the diagnostic value of crucial genes in ischemic stroke, we used the GSE16561 dataset as the validation dataset, compared the expression levels of 6 crucial genes in ischemic stroke patients and healthy controls, evaluated the diagnostic value of each crucial gene, and plotted ROC.

2.8 Drug prediction analysis

The interaction between drug and gene target was calculated by using DGIdb website (https://dgidb.genome.wustl.edu/).

2.9 Construction of miRNA-mRNA network

The R package ‘limma’ was utilized to examine differentially expressed miRNAs (DEmiRNAs) in Sham and MCAO samples from the GSE97532 dataset, applying screening criteria of P < 0.05 and |logFC| > 0.5. Subsequently, miRWalk (http//mirwalk.umm.uni-heidelberg.de/) was utilized to predict miRNAs of crucial genes. Venn diagrams were used to take intersections of DEmiRNAs and predicted miRNAs. The miRNA-mRNA regulatory network was constructed to explore the relationship between miRNA and mRNA. Cytoscape software (version 3.7.2) was utilized to visualize the regulatory network of miRNA-mRNA.

2.10 Animals

All animal experiments using 8-week-old male rats Sprague–Dawley (SD) were approved by the Animal Care and Use Committee of Hebei Medical University (ethical approval ID: IACUC-Hebmu-2023029). The rats were housed in a chamber with the temperature (22 ± 2 °C) and a 12-h cycle of light and darkness, while free access to food and water.

2.11 Middle cerebral artery occlusion/reperfusion (MCAO/R) surgery

Previous publications provide a comprehensive explanation of the MCAO/R procedure's specifics [18]. Briefly, rats were placed supine, a midline incision made in the neck, and the external carotid artery (ECA) and common carotid artery (CCA) were exposed. The ECA was ligated, and then the internal carotid artery (ICA) was isolated. A vessel clip was placed on the CCA and another on the ICA. Then a cut was made on the ECA, and a silicon coated monofilament suture was inserted into the ECA. The vessel clip on the ICA was removed, and the suture advanced (18–22 mm) until resistance was felt. After a duration of 2 h of obstructing, take out the stitch and seal the ECA. After stitching the wound on the neck, the rats were given time to recuperate. The rats of the Sham group underwent the same surgical procedure without silicon coated monofilament suture insertion. The laser doppler flowmeter was used to confirm the successful occlusions (<20 % baseline) and the reperfusion. TTC staining and neurobehavioral scores [[19], [20]] were provided in the supplementary file.

2.12 The expression levels of crucial genes by RT-qPCR

RNA was extracted on the 72h after surgery (Sham: n = 5; MCAO: n = 5). RT-qPCR was performed to examine the mRNA expression of crucial genes. In short, total RNA was extracted from tissues using TRIzol (Thermo Fisher Technologies). Then, use Hifair® Ⅲ 1 st Strand cDNA Synthesis SuperMix for qPCR (11141 ES) to reverse the total RNA into cDNA. The expression levels of C1qb, C1qc, Fcer1g, Fcgr3a, Tyrobp and CD14 mRNA were quantified by Hieff UNICON® Universal Blue qPCR SYBR Green Master Mix (11184 ES) on Real-Time PCR detection System (Applied Biosystems, USA). The expression of beta-actin (β-actin) mRNA was used as the endogenous reference control. The relative levels of mRNA expression were represented as ΔCT = CT gene-CT reference, and the multiple change of gene expression was calculated by the 2−ΔΔCT method. Primer sequences are shown in Table 2.Table 2 Primer sequences.

Table 2Primer name	Sequence (5′–3′)	
C1qb(Rat)-F	GACGTTTTTGGGAGGGGACA	
C1qb(Rat)-R	GGGCCTCCTGTGTATGGAATC	
C1qc(Rat)-F	ACTTCGTCCACCACACATCC	
C1qc(Rat)-R	ACCATGCCGTTGTAGTCGTT	
Fcer1g(Rat)-F	ATCCCAGCGGTGATCTTGTTCTTG	
Fcer1g(Rat)-R	TCGACAGTAGAGCAGGGTAAGGAC	
Fcgr3a(Rat)-F	TCCGTGGCAGTCTATGAGGA	
Fcgr3a(Rat)-R	CAGATGGTGAGGTCGCAAGT	
Tyrobp(Rat)-F	GTGACAATTACCCAGGATGCGA	
Tyrobp(Rat)-R	CTGTTTCCGGGTCCCGTCTG	
CD14(Rat)-F	CGGATATTCTGGCCTCCGGG	
CD14(Rat)-R	TGTTGAGATCGGGTCCGGTG	

3 Results

3.1 Identification of differentially expressed genes (DEGs) between MCAO and sham samples

A total of 218 DEGs between MCAO and Sham samples. The criteria for identification were a significance level of P < 0.05 and |logFC| > 1. Among the DEGs, 212 genes showed up-regulation and 6 genes showed down-regulation (Additional file: Table S1). The volcanic plot and heatmap of DEGs are shown in Fig. 1A and B. Additionally, the box plot showed the gene expression level among different samples after normalization (Fig. 1C). Principal component analysis (PCA) of the GSE97537 dataset unveiled a significant differentiation in gene signatures among the two groups Fig. 1D.Fig. 1 Identification of DEGs between Sham and MCAO groups. (A) The volcano plot displaying DEGs in Sham vs. MCAO group; (B) Heatmap representing the top 20 upregulated and downregulated DEGs (blue represents MCAO sample and pink represents Sham sample); (C) Box plots of raw data normalized between samples, sample names are on the x-axis and the logarithm transformations of the expression values are shown on the y-axis; (D) PCA analysis results of GSE97537.

Fig. 1

3.2 Enrichment analysis of DEGs

Furthermore, we performed enrichment analysis to identify significantly enriched items of DEGs. The GO and KEGG functional enrichment analysis of DEGs produced 857 biological processes (BP), 32 molecular functions (MF), 29 cellular components (CC), and 42 KEGG signaling pathways. Fig. 2A showed the enrichment results of the top 8 GO, and Fig. 2B showed the enrichment results of the top 20 KEGG signaling pathways, respectively. The results showed that these genes are enriched in biological processes such as the regulation of inflammatory response, as well as KEGG signaling pathways such as IL-17 signaling pathway, TNF signaling pathway. Based on GO and KEGG enrichment analysis, we found that inflammation and immune response play an important role in the pathogenesis of MCAO. The results of the GSEA enrichment analysis showed that these genes were enriched in cell junction and immune effector process (Fig. 2C). Therefore, this study focused on inflammation and immunity.Fig. 2 Enrichment analysis. (A) GO enrichment analysis; (B) KEGG enrichment analysis; (C) GSEA enrichment analysis.

Fig. 2

3.3 Identification of MCAO-related genes by weighted gene co-expression network analysis (WGCNA)

To further accurately identify genes associated with MCAO, we constructed a gene co-expression network using WGCNA algorithm. The results of sample hierarchical cluster analysis showed good clustering among samples, with no significant outliers (Fig. 3A). The function “sft$powerEstimate” determined the soft-power threshold β. As was shown in Fig. 3B, the optimal soft-thresholding power was 12 if the correlational coefficient was >0.85. A gene hierarchy clustering dendrogram was constructed by gene correlation, and a total of nine similar gene modules were identified (Fig. 3C). Then, the gene modules were detected based on TOM. These modules were represented by different colors, with a significant correlation between members of the darkgreen and turquoise modules and MCAO, with the darkgreen module representing the most significant positive relation (6090 genes; correlation coefficient = 0.85; P < 0.0001), and the turquoise module representing the most significant negative relation (3622 genes; correlation coefficient = −0.82; P < 0.0001) (Fig. 3D). The genes of these modules were identified as genes related to MCAO. The interactions of these co-expression modules were analyzed with the Pearson correlation coefficient (Fig. 3E). A heatmap was then generated to visually represent the relationship between the modules and MCAO group (Fig. 3F).Fig. 3 Construction of the co-expression network. (A) The sample dendrogram and feature heat map were drawn using the average clustering method for hierarchical clustering of samples, Height in the vertical coordinate being the clustering distance, and the horizontal coordinate being the grouping information; (B) The soft threshold β is set to 12 and the scale-free topological fit index is use; (C) The clustering tree showed the original and merged modules; (D) Heatmap of module–trait correlations, with red indicating positive correlations and green indicating negative correlations; (E) The heatmap showed the TOM module genes based on co-expression modules; (F) Visualizing the gene network using a heatmap.

Fig. 3

3.4 Identification and enrichment analysis of MCAO-immuno-related DEGs

In this study, 1482 immune-related genes were retrieved from the UniPort database. Eighteen MCAO-Immuno-related DEGs were obtained by Venn analysis (Fig. 4A, Additional file: Table S2). The expression level of Anxa1, C1qb, C1qc, Cd14, Cd74, Fcer1g, Fcgr3a, Il6, Lbp, Lcn2, Lgals3, Msn, Myd88, RT1-Da, S100a9, Serping1, Spp1 and Tyrobp in the MCAO group exhibited a significant upregulate compared to the Sham group (P < 0.05) (Fig. 4B). The functional enrichment analysis of the 18 MCAO-Immuno-related DEGs included biological processes (BP), cellular components (CC), molecular functions (MF) (Fig. 4C), and KEGG signaling pathways (Fig. 4D). The findings indicated that these genes were enriched in adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains, adaptive immune response and other biological processes, and KEGG signaling pathways including Toll-like receptor signaling pathway.Fig. 4 Identification and enrichment analysis of MCAO-Immuno DEGs. (A) Venn diagram of DEGs, darkgreen and turquoise modules, and Immune-RGs; (B) Boxplot of 18 MCAO-Immuno DEGs in the MCAO vs Sham groups; (C) GO enrichment results (Top 10); (D) KEGG enrichment results (Top 20).

Fig. 4

3.5 Identification of crucial genes

The STRING database was utilized to perform a PPI network analysis on the 18 MCAO-Immuno-related DEGs (Fig. 5A). Next, the information related to 18 MCAO-Immuno-related DEGs was imported into Cytoscape software for visualization (Fig. 5B). MCODE analysis was used to conduct cluster analysis on the gene network to determine crucial PPI network modules (Fig. 5C), including C1qb, C1qc, Fcer1g, Fcgr3a, Tyrobp and CD14, all of these genes were up-regulated in MCAO group. Moreover, we performed GSEA analysis based on crucial genes, the top 30 pathways and top 4 hallmarks for each of the 6 crucial genes are shown in Fig. 6.Fig. 5 Identification of crucial genes. (A) PPI network; (B) The PPI network was constructed and visualized using Cytoscape software (The bigger the green circle, the higher the Betweenness Centrality score). (C) MCODE analysis results.

Fig. 5

Fig. 6 Hallmark and pathway enrichment results of crucial genes. (A, B) C1qb; (C, D) C1qc; (E, F) Fcer1g; (G, H) Fcgr3a; (I, J) Tyrobp; (K, L) CD14.

Fig. 6

3.6 Analysis of the crucial genes for drug prediction

The DGIdb website was used to predict potentially effective therapeutic agents for six crucial genes. Four of the six crucial genes have potential therapeutic agents. There are 11 potential therapeutic agents for CD14, 4 for C1qc, 53 for Fcgr3a, and 3 for Fcer1g (Additional file: Table S3), including the lipid-lowering drug: lovastatin; Anticoagulants: ASPIRIN; Antiinflammatory agent: PREDNISOLONE, TOCILIZUMAB; Immunosuppressant: CYCLOSPORINE, MUROMONAB-CD3, etc.

3.7 Assessment of the diagnostic value of the crucial genes in training dataset and validation datasets

The expression levels of 6 crucial genes in MCAO group and Sham group were compared in GSE97537 training dataset. The expression of these 6 crucial genes showed statistically significant differences between the two groups (Fig. 7A). ROC curves were then generated to evaluate the diagnostic value of these 6 genes, with AUC >0.7 indicating good diagnostic value (Fig. 7B). The expression of these six genes was then evaluated in GSE61616 (validation dataset). The results showed that the expression trends of the 6 crucial genes were consistent with those in the training dataset (Fig. 7C), and all the 6 crucial genes had high diagnostic value for ischemic stroke (Fig. 7D). To further verify the diagnostic value of crucial genes in ischemic stroke, the GSE16561 dataset was included to evaluate the expression of these 6 crucial genes. The expression of these 6 crucial genes increased in patients with ischemic stroke compared to healthy controls (Fig. 7E), but there was no significant difference in TYROBP. Additionally, the AUC values of C1QC, FCER1G, and TYROBP were below (AUC <70 %) (Fig. 7F), which may be due to small patient sample sizes, sample collection method, or disease stage affecting diagnostic performance.Fig. 7 The diagnostic value of crucial genes for ischemic stroke. (A) The expression levels of 6 crucial genes in the GSE97537; (B) ROC curves for 6 crucial genes in the GSE97537; (C) The expression levels of crucial genes in the GSE61616; (D) ROC curves for 6 crucial genes in the GSE61616; (E) The expression levels of crucial genes in the GSE16561; (F) ROC curves for 6 crucial genes in the GSE16561.

Fig. 7

3.8 Construction of miRNA-mRNA interaction network

We obtained DEmiRNAs of MCAO and sham based on differential expression analysis, with 15 down-regulated miRNAs and 15 up-regulated miRNAs (Table S4). The overall distribution of DEmiRNAs was shown in a volcano (Fig. 8A). We used miRWalk (http://mirwalk.umm.uni-heidelberg.de/) to predict miRNAs of 6 crucial genes, resulting in 557 miRNAs predicted to bind to their targets. By intersecting these miRNAs with 30 DEmiRNAs, a total of 13 intersect miRNAs were acquired (Fig. 8B). Based on the regulatory connection between miRNA and mRNA, the network of miRNA-mRNA was constructed, including 13 miRNAs and 6 crucial mRNAs. Cytoscape software was utilized to visualize the regulatory network of miRNA-mRNA (Fig. 8C).Fig. 8 Prediction of miRNA-mRNA network. (A) Volcano plot showing DEmiRNA in the MCAO vs. the Sham groups; (B) Venn diagram showing predict miRNAs and DEmiRNAs; (C) miRNA-mRNA regulatory network, V-type and circle indicate miRNA and mRNA, respectively.

Fig. 8

3.9 Validation of the key gene levels

qRT-PCR was used to evaluate and compare the rat cortexes in both the MCAO group and the Sham group. The levels of C1qb, C1qc, Fcer1g, Fcgr3a, Tyrobp and CD14 were markedly higher in the MCAO group compared to the Sham group (Fig. 9). The trend of gene expression was consistent with GSE97537 data.Fig. 9 Verify the expression level of crucial genes. (A) The related mRNA levels of C1qb at 72 h after I/R; (B) The related mRNA levels of C1qc at 72 h after I/R; (C) The related mRNA levels of Fcer1g at 72 h after I/R; (D) The related mRNA levels of Fcgr3a at 72 h after I/R; (E) The related mRNA levels of Tyrobp at 72 h after I/R; (F) The related mRNA levels of CD14 at 72 h after I/R. Each value represents the mean ± SEM.*p < 0.05; **p < 0.01; ***p < 0.001.

Fig. 9

4 Discussion

Inflammation and immunity are crucial factors in the pathobiology of stroke [21]. The immune system has a vital function in all phases of the ischemic cascade [22]. The ischemic brain facilitates infection by immunosuppression, which greatly endangers the survival of individuals who have suffered a stroke [23]. As we all know, both the innate and adaptive immune mechanisms are involved in ischemic stroke. Therefore, it is of great significance to reveal the role of immune-related genes in ischemic stroke [24,25].

By analyzing the GSE97537 dataset, we identified 212 genes with increased expression and 6 genes with decreased expression in the MCAO group. GO enrichment analysis indicated that DEGs were mainly related to regulation of inflammatory response, plasma membrane composition and extracellular matrix. KEGG enrichment analysis showed that DEGs were mainly enriched in TNF signaling pathway, cytokine-cytokine receptor interaction, and so on. This suggested that inflammation and immunity played an important role in ischemic stroke.

Furthermore, WGCNA was employed to build a co-expression network, and the genes within the highly correlated module were intersected with the DEGs and immune genes. A grand total of 18 MCAO-Immuno-related DEGs were obtained. Enrichment analysis of 18 MCAO-Immuno-related DEGs were mainly related to biological processes, such as leukocyte activation involved in immune response, immunoglobulin mediated immune response, B cell mediated immunity, and adaptive immune response. These processes were associated with immunity and inflammation. KEGG enrichment analysis showed that MCAO-Immuno-related DEGs were mainly enriched in the related signaling pathways such as IL-17 signaling pathway, Lipid and atherosclerosis, NF-kappa B signaling pathway, and Toll-like receptor signaling pathway. Notably, in addition to inflammation and immune-related signaling pathways, the lipids and atherosclerosis related signaling pathways were also involved in these processes. Atherosclerosis is known to be one of the main culprits in the rise in stroke-related deaths [26]. The results of our enrichment analysis highlight the involvement of immune genes associated with ischemic stroke in the biological pathway of Lipid and atherosclerosis, and further reveal the correlation between ischemic stroke and lipid metabolism and atherosclerosis from the perspective of immune genes.

Subsequently, by MCODE analysis, C1qb, C1qc, Fcer1g, Fcgr3a, Tyrobp and CD14 were identified as crucial genes. In this study, we investigated the expression levels and diagnostic value of 6 crucial genes in ischemic stroke using three independent datasets: GSE97537, GSE61616, and GSE16561. Our initial analysis in the training dataset (GSE97537) revealed statistically significant differences in the expression levels of these genes between the MCAO group and the Sham group. The diagnostic performance, assessed using ROC curves, indicated that crucial genes have good diagnostic value (AUC >0.7). When evaluating the 6 crucial genes in the validation dataset (GSE61616), we found that the expression trends of the 6 crucial genes were consistent with those observed in the training dataset. This consistency supports the reliability of 6 crucial genes as potential biomarkers for ischemic stroke. However, the GSE16561 dataset highlighted some variability in the diagnostic performance of crucial genes. While the expression levels of the 6 crucial genes generally showed an increased trend in ischemic stroke patients compared to healthy controls, TYROBP did not exhibit a significant difference. Additionally, the AUC values for C1QC, FCER1G, and TYROBP were below 70 %, indicating that the diagnostic performance of these three genes was poor in the GSE16561 dataset, which may be due to the small sample size of patients, different disease stages, and large complexity and individual variability of human samples. Our findings underscore the importance of validating potential biomarkers across multiple independent datasets to ensure their robustness and generalizability. The consistent expression trends and high diagnostic value observed in two of the datasets suggest that the six genes studied here hold promise as biomarkers for ischemic stroke. However, the variability observed in the GSE16561 dataset highlights the need for further investigation in clinic.

The complement system, an essential element of the innate immune system, plays a vital part in identifying dangers and fighting pathogen [27]. C1q belongs to the C1q/tumor necrosis factor superfamily and has a vital role in the classical pathway of the complement system [28,29], which can exert various immune and non-immune functions [30]. A previous study showed that neonatal mice who experienced global ischemia-reperfusion injury were safeguarded via genetic deletion of the C1q, resulting in a notable decrease in the severity of ischemia and brain injury in comparison with neonatal mice with the wild-type gene [31]. C1qb as a member of the complement system, attaches to C1q molecules to create the C1q-C1r2s2 enzyme complex, which is in charge of immune cells and initiating inflammation. Initially, C1qb binds to C1q receptors located on the neurons' surface, leading to the activation of immune cells and the secretion of inflammatory mediators. Secondly, C1qb can directly fuse with neuronal membranes, causing neuronal damage and apoptosis. Finally, C1qb can modulate immune cell migration and inflammatory responses, exacerbating neuroinflammatory processes [32]. C1qc, an additional vital element of the complement system, could potentially function as a regulator in neuroinflammation as well. Neuroinflammatory disorders have been associated with changes in C1qc expression. Acquiring a more profound comprehension of the functions and involvement of C1qb and C1qc in neuroinflammation could offer valuable insights into potential therapeutic strategies for neuroinflammatory conditions.

Tyrobp, also known as DAP12, has a crucial function in the regulation of inflammation and is strongly linked to the immune system [33]. The stimulation of Tyrobp has the ability to boost and maintain neuroinflammatory reactions, which may interact with microglia receptors such as TREM2 (Triggering Receptor Expressed on Myeloid Cells 2) and initiate subsequent pathways that trigger the release of inflammatory mediators [34]. Highlighting the significance of Tyrobp in the regulation of neuroinflammation is crucial to fully comprehend the role of Tyrobp in the neuroinflammation process.

FCER1G is an Fc fragment of high affinity immunoglobulin E (IgE) receptor Ig that is responsible for allergic inflammatory signaling [35,36]. FCER1G is involved in diverse immune responses and biological processes of different cell types [37,38]. It is reported that FCER1g has been linked to the progression of atherosclerosis [39]. In addition, CD14, an immune cell surface receptor, plays a significant role in neuroinflammation [40]. After a stroke, the brain tissue experiences ischemic and reperfusion injury, resulting in the stimulation of inflammatory cells and the discharge of mediators [41,42]. CD14, serving as the receptor for LPS, has the capability to trigger and control the inflammatory reaction within the stroke context, which is involved in the activation of inflammatory cells and release of inflammatory mediators in ischemic stroke through interacting with signaling molecules such as TLR4 and TLR2 [43,44]. Insufficient investigation has been carried out on the CD14 mechanism in ischemic stroke, requiring further studies to clarify the pathological mechanism of CD14 in this particular situation. Additionally, Fcgr3a, also known as CD16a, is a receptor found on the surface of immune cells and belongs to the Fcγ receptor family of immunoglobulins [45].

With the deepening of the research, the correlation between miRNA and the occurrence and development of ischemic stroke will be gradually clarified, which may provide new insights for the diagnosis, treatment and prognosis assessment of ischemia stroke [46]. miRNAs are involved in inflammation and oxidative stress after ischemic stroke [47,48]. After focal cerebral ischemia and cerebral ischemia-reperfusion, the expression of miRNA in brain tissue and blood is widely and persistently altered [47,49,50]. It has been reported that miR-19a/b-3p promotes inflammation during I/R by targeting the SIRT1/FoxO3/SPHK1 axis [48]. Additionally, the overexpression of miR-124 in astrocytes can improve the neurological deficits in rat with ischemic stroke through DLL4 regulation [51]. In this study, 13 intersect miRNAs were obtained, including miR-346, miR-23b-5p, miR-290, miR-196b-5p, miR-3552, miR-410-5p, miR-328b-3p, miR-128-2-5p, miR-182, miR-24-1-5p, miR-146a-3p, miR-429, and miR-466b-5p. Previous studies also confirmed that the expression of miR-328b-3p [52], miR-182 [53,54], miR-429 [55], and others were changed in ischemic stroke comparing to control. Therefore, the screened miRNAs and their targeted genes may also play a key role in ischemic stroke.

The limitation of this study is that the two datasets come from different types of tissue, with GSE97537 using brain samples and GSE97532 using blood samples. Nevertheless, extracellular miRNAs have been observed to be remarkably stable in a variety of body fluids. It was reported that blood miRNAs were ideal molecular biomarkers for the diagnosis and treatment of various disorders [56,57]. In addition, tissue cells also actively release miRNA into the blood circulation system. For example, many tumor-specific miRNAs are detected in the circulatory system at different stages of disease development [58,59]. Therefore, the miRNAs from both tissue and blood can act as molecule biomarkers for treating disease.

The study predicts potentially effective therapeutic drugs for crucial genes. These include the lipid-lowering drug: lovastatin; Anticoagulants: ASPIRIN; Antiinflammatory agent: PREDNISOLONE, TOCILIZUMAB; Immunosuppressant: CYCLOSPORINE, MUROMONAB-CD3, etc. These potential drugs were screened according to the 6 identified crucial genes, which might provide effective therapeutics to treat ischemic stroke.

5 Conclusion

Here, we used bioinformatics technology to screen the intersecting DEGs between ischemic stroke and immune, including C1qb, C1qc, Fcer1g, Fcgr3a, Tyrobp and CD14, focusing on the relationship between ischemic stroke and immunity. The results of this study provide further useful evidence for the exploration of therapeutic targets of ischemic stroke. However, further in vitro and in vivo experiments are needed to confirm the functional pathways and pivotal genes associated with ischemic stroke.

Ethics approval

All animal experiments using 8-week-old male rats Sprague–Dawley (SD) were approved by the Animal Care and Use Committee of Hebei Medical University (ethical approval ID: IACUC-Hebmu-2023029).

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Data availability statement

This study analyzed publicly accessible datasets, including GSE97532, GSE61616, GSE16561, and GSE97537 datasets from Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/query).

Funding

The present study was supported by the 10.13039/501100001809 National Natural Science Foundation of China (Nos.82072531 ), 10.13039/501100003787 Natural Science Foundation of Hebei Province (Nos.H2021206160 ).

CRediT authorship contribution statement

Zekun Li: Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Conceptualization. Xiaohan Li: Writing – review & editing, Writing – original draft, Formal analysis, Conceptualization. Hongmin Guo: Writing – review & editing, Writing – original draft, Formal analysis, Data curation, Conceptualization. Zibo Zhang: Writing – original draft, Methodology, Formal analysis. Yihao Ge: Writing – original draft, Formal analysis, Data curation. Fang Dong: Writing – original draft, Formal analysis, Data curation. Fan Zhang: Writing – review & editing, Writing – original draft, Methodology, Investigation, Data curation, Conceptualization. Feng Zhang: Writing – review & editing, Writing – original draft, Supervision, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Abbreviations

DAMP Damage-associated molecular pattern

TNF-α Tumor necrosis factor-alpha

IL-1β Interleukin-1 beta

TLR4 Toll-like receptors 4

IL-6 Interleukin-6

GO Gene Ontology

KEGG Kyoto Encyclopedia of Genes and Genomes

WGCNA Weighted gene co-expression network analysis

MCODE Molecular complex detection

DEG Differentially expressed gene

BP Biological processes

MF Molecular functions

CC 29 cellular components

TOM Topological overlap matrix

Appendix A Supplementary data

The following is/are the supplementary data to this article:Multimedia component 1

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Acknowledgements

Not applicable.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e36837.
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