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

S2405-8440(24)12380-8
10.1016/j.heliyon.2024.e36349
e36349
Research Article
Characteristics of two different immune infiltrating pyroptosis subtypes in ischemic stroke
Xiao Yilei a
Wang Zhen b
Xin Yexin a
Wang Xingbang c
Dong Zhaogang zhaogang.dong@email.sdu.edu.cn
b⁎
a Department of Neurosurgery, Liaocheng People's Hospital, Liaocheng, 252000, Shangdong, China
b Department of Clinical Laboratory, Qilu Hospital of Shandong University, 107 Wenhuaxi Road, Jinan, 250012, Shandong, China
c Department of Geriatric Medicine, Qilu Hospital of Shandong University, 107 Wenhuaxi Road, Jinan, 250012, Shandong, China
⁎ Corresponding author. zhaogang.dong@email.sdu.edu.cn
22 8 2024
15 9 2024
22 8 2024
10 17 e3634911 6 2024
26 7 2024
14 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/).
Background

Ischemic stroke (IS) is a serious health hazard and identified as the second leading cause of mortality around the world. However, the role of pyroptosis in the immune microenvironment regulation in IS is still unclear. Here, our study aims to elucidate the effect of pyroptosis on immune microenvironment in IS.

Methods

The regulation mode of pyroptosis in IS was systematically evaluated, and its effects on immune microenvironment were explored, including infiltration of immune cells, immune response gene sets, and human leukocyte antigen (HLA) gene. The genes and drugs related to pyroptosis phenotype were also identified. An MCAO rat model was constructed, and the mRNA expression levels in the classifier model were validated by qRT-PCR.

Results

The separator is composed of 11 pyroptosis genes, out of which 10 genes could distinguish between ischemic stroke and control samples. CHMP2A, CHMP4A, and NAIP genes are significantly related to immune infiltrating cells, immune response gene sets, and HLA. However, two different pyroptosis subtypes mediated by 10 pyroptosis genes were identified, which were different in immune cell abundance, HLA genes, and immune response gene sets. Furthermore, 199 genes associated with pyroptosis phenotype was identified along with the analysis of biological functions.

Conclusion

These findings reveal the potential mechanism of pyroptosis in the immune microenvironment of IS, indicating that pyroptosis functions as a vital component in the complexity and diversity of the immune microenvironment in patients with IS.

Keywords

Ischemic stroke
Pyroptosis
Immune microenvironment
GEO
Data mining
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pmc1 Introduction

Ischemic stroke (IS) is one of the most prevalent diseases around the globe, with high mortality and disability rates [1]. The most common treatment for acute IS is the revascularization of occluded vessels by thrombolysis/thrombectomy [2]. However, due to the risk of IS and the narrow treatment time window, many patients do not meet the requirements of these treatments [3]. Although widespread efforts have been made in neuroprotective therapy in the past decades, no substantial progress in clinical rehabilitation has been achieved [4,5]. Moreover, severe aseptic inflammatory reactions of IS occur and last for several days [6], which determine the prognosis of the nervous system. An increasing number of studies reveal that the early diagnosis of IS can improve the therapeutic outcome and prognosis [7]. The data indicates the urgency and importance of elucidating the potential mechanism of IS, as well as identifying new biomarkers and therapeutic targets.

In several diseases, programmed cell death (PCD), including apoptosis, NETosis, necroptosis, and pyroptosis, is considered as an important factor of innate immune defense [8]. In general, moderate cell death can play a protective role, but excessive pyroptosis will destroy host tissues [9]. Pyroptosis is a caspase-activated PCD, and its characteristic is pore development in plasma membrane, followed by swelling, cytolysis, and local release of proinflammatory mediators [10,11]. Recent studies indicated that pyroptosis is a focal point in many disease [12,13], and pyroptosis-related gene can be employed as useful biomarkers for the diagnosis, prognosis, or treatment of various diseases including cancers [[14], [15], [16]].

Pyroptosis is an important driver in early stages of stroke process. Previous studies indicate that inhibition of pyroptosis can reduce the severity of neuroinflammation and delay stroke progression, providing a larger therapeutic window [16]. Although the critical role of pyroptosis in stroke has been extensively studied, little is known about the impact of pyroptosis genes on the immune microenvironment after stroke. The functions of these genes in the diagnosis, prognosis, or treatment of IS require further elucidation. The purpose of this study is to systematically assess the expression level and the role of pyroptosis gene in IS patients. Moreover, the effect of pyroptosis gene on the immune microenvironment and the potential therapeutic drugs based on hub genes was explored.

2 Materials and methods

2.1 Data preprocessing

GSE16561 [17] data of GPL6883 platform were obtained from Gene Expression Omnibus (GEO) database through R package (https://bioconductor.org/packages/release/bioc/html/GEOquery.html) GEOquery (version 2.62.2) [18] as a training set (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE16561). There were 63 samples in training set, 39 of which were IS patients over the age of 18 and 24 of which were healthy individuals. Furthermore, the GSE58294 [19] data of the GPL570 platform were retrieved from the GEO database as the validation set (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE58294). The data set contained 92 samples, including 69 samples of IS patients and 23 healthy samples. The operation during GEO data processing was to delete the no-load probe, and the probes corresponding to multiple genes. The average expression value of these probes was used as the expression value of the gene.

2.2 Differential expression analysis and classifier construction of pyroptosis genes in ischemic stroke

The pyroptosis gene was derived from the union of three pyroptosis-related pathways with a total of 57 related genes. Three pathways, including GOBP_PYROPTOSIS, GOBP_POSITIVE_REGULATION_OF_PYROPTOSIS, and REACTOME_PYROPTOSIS were obtained from Msigdb database. According to adjust p value < 0.05, the expression of pyroptosis gene in disease group and healthy group was evaluated using the ‘limma’ (https://www.jianshu.com/p/f009bea514af) R package [20]. Correlation analysis was conducted on the pyroptosis genes of all samples. Based on the training set data, univariate logistic regression analysis was performed using the function glm (version 4.1.2) in R. The pyroptosis genes related to IS were selected according to p-value <0.05. Least absolute shrinkage and selection operator (Lasso) analysis was further utilized to identify redundant factors and construct classifier model, and the score box chart of the model gene was drawn as well. Finally, the receiver operating characteristic (ROC) analysis was perform using the R package pROC (version 1.18.4), and the ROC score was used to assess the diagnostic performance of classifier.

2.3 Correlation analysis of pyroptosis genes and immune characteristics of IS

A gene set that marks various types of immune infiltrating cells was obtained from the study of Charoentong [21]. The gene set is rich in various types of human immune cell subtypes, such as natural killer T cells, activated CD8 T cells, regulatory T cells, activated dendritic cells, macrophages, etc. Based on the ‘GSVA’ R package [22], the calculation of the enrichment score was performed by single sample Gene Set Enrichment Analysis (ssGSEA). Enrichment analysis was performed using the ClusterProfiler R package (version 4.2.2), results mapping using the enrichplot R package (version 1.14.2), and enrichment analysis was performed with default parameters. Based on this score, the differential analysis of immune infiltrating cells and immune response gene set infiltration in the diseased and healthy groups was conducted. The difference in HLA expression was analyzed, and the correlation between model genes, immune infiltrating cells, immune response gene set, and HLA was assessed.

2.4 Classification and analysis of classifier gene subtypes

Based on the model gene, the ‘ConsencusClusterPlus’ R package [23] was used for unsupervised clustering. Moreover, km and Euclidean distance was selected as method and distance respectively. According to the CDF decline curve, the best cluster number was the two categories with the most moderate decline. Additionally, the clinical characteristics of different subtypes were analyzed on the basis of subtype classification. The differences in immune cell infiltration, immune response gene set, and HLA expression in different subtypes were also assessed. Furthermore, ssGSEA was conducted for different subtypes using the ‘GSVA’ R package; Moreover, the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were utilized to assess the differentially expressed genes among subtypes.

2.5 Potential drug discovery of subtype HUB gene

PPI networks were analyzed based on the database stringdb, and plotted using the software Cytoscape (version 3.9.1). HUB nodes were mined in the network. DGIdb [24] database was used to identify potential drugs based on HUB nodes.

2.6 MCAO model construction

A total of ten healthy male Sprague-Dawley rats, with an average weight of 200g, were procured from Huachuang Sino Pharmatech Co., Ltd (Jiangsu, China). The rats were housed under controlled environmental conditions, including a temperature of 25 ± 3 °C and 50–60 % humidity, with a 12-h light/dark cycle. The rats were provided with ad libitum access to food and water. All rats were administered with Tribromoethanol to induce anesthesia. To confirm occlusion of the middle cerebral artery (MCA), an incision was made in the left common carotid artery (CCA) and a suture was inserted through the internal carotid artery (ICA) to a depth of approximately 17–18 mm. After 90 min, the suture was removed from MCA to allow for reperfusion. The sham rats received surgery without MCAO. 24 h post-operation, all rats were subjected to a 12-h fast prior to sampling. The rats were anesthetized and brain tissue was subsequently collected, stored at −80 °C or in 4 % paraformaldehyde for subsequent experimentation. The animal experiments were approved by the Ethics Committee of Liaocheng People's Hospital (No. 2022015).

2.7 Immunohistochemistry assay

The tissue underwent a meticulous fixation process in a 4 % paraformaldehyde solution, subsequently undergoing a series of intricate steps that encompassed dehydration, clearing, and finally embedding in paraffin wax for further analysis. The endogenous peroxidase activity was blocked with 3 % H2O2. The sections were stained with hematoxylin and eosin (H&E) solution, and observed under the microscope.

2.8 Clinical specimens

Inclusion criteria: 1) aged 18 or older; 2) sudden focal neurological deficit lasting more than 24 h; 3) the diagnosis of ischemic stroke was confirmed by CT or MRI.

Exclusion criteria: 1) history of infarction or hemorrhagic disease in the prior three months; 2) recent use of antiplatelet drugs (aspirin, etc.), thrombolytic, or anticoagulant drugs two weeks before admission; 3) Patients with inflammatory diseases, cancer, severe malnutrition, blood diseases, coronary heart diseases, and organ transplantation.

Peripheral blood from 10 patients were collected and approved by the Ethics Committee of Liaocheng People's Hospital (No. 2022301).

2.9 Flow cytometry of immune cells in human peripheral blood

Antibodies commonly used for immune cells in peripheral blood included anti-CD3 FITC, anti-CD4 APC, anti-CD8 PE, anti-CD16/56 PE (NK cells), and anti-CD19 APC (B cells) from BD Biosciences.

Fifty μL anticoagulated blood was taken, 20 μL antibodies was added. After incubation in dark for 15 min, 450 μL hemolysin was added, followed by another 10 min incubation. Then, samples were detected and the data were collected using a FACScanto II (BD Biosciences) flow cytometer.

2.10 qRT-PCR

Total RNA was extracted using TRIzon Reagent. Evo M-MLV RT Premix for Qpcr kit was used to reverse-transcribe total RNA to cDNA. PCR was conducted using SYBR Green Premix Pro Taq HS qPCR Kit in CFX Connect (BIO-RAD). Amplification cycle conditions were as follows: 95 °C 30s, and then 95 °C 5s, 60 °C 30s for 40 cycles. The qPCR primers were presented in Table 1. Data was normalized with GAPDH, and the relative expression of genes were calculated using 2-△△CT method.Table 1 qPCR primers used in this study.

Table 1Genes (rat)	Forward primer (5′ to 3′)	Reverse primer (5′ to 3′)	
Casp1	GTACACGTCTTGCCCTCATTATC	ACTGTCAGAAGTCTTGTGCTCTG	
Casp5	TTGGGTCTTTGTGGACGCTT	ATTTCCGCACCGTACCTCAG	
Csap8	TATCCCAGATGAGGCAGACTTTC	CCATGTTCCTCGGGTTGTCTTTA	
Chmp2a	TGGGTGATGAGGAAGATGAAGAG	AAGATCCTAGGCATTGAGTCCAG	
Chmp4a	CTACAGGCTTTGCGGAGGAA	CACCTCCTGTTGTTCCGTGA	
Cycs	ATGGGTGATGTTGAGAAAGGCA	AGACCATGGAGATTTGGCCC	
Naip	AGCAGTGTCAGCAGCTTCAT	CCAACTCCTGCAAGTTTGGC	
Nlrp3	AGTGGATAGGTTTGCTGGGATATC	TTAGTCCTGCCAATGGTCAAGAG	
Plcg1	ACATGTGGATCAAGGGCTTAACT	GTTCTTCAGGTCCTTGGCTGATA	
Tnf	GACCCTCACACTCAGATCATCTT	CCTTGAAGAGAACCTGGGAGTAG	
GAPDH	GCACCGTCAAGGCTGAGAAC	TGGTGAAGACGCCAGTGGA	

2.11 Statistical analysis

R software (R version 4.1.2; https://www.R-project.org) was employed to conduct all statistical analyses, and ‘tidyverse’ R package was utilized for data integration and mapping. Stat_cor in the ‘ggpubr’ R package was used to label the correlation in the graph by using spearman method. The ‘ggalluvial’ R package was utilized to construct a sankey diagram, and the ‘ComplexHeatmap’ R package (version 2.10.0) was utilized for the construction of heat map. The ‘corrplot’ R package (version 0.92) was utilized for the plotting of the correlation graph. P-value less than 0.05 indicates statistical significance for all analyses. Statistical test marks: *: p < 0.05, * *: p < 0.01, * * *: p < 0.001, * * * *: p < 0.0001.

3 Results

3.1 The distribution of pyroptosis gene in ischemic stroke and healthy samples

The distribution of pyroptosis genes on human chromosomes is shown in Fig. 1A. PPI network demonstrated close interaction relationship between pyroptosis genes (Fig. 1B). Additionally, these genes had close expression correlation (Fig. 1C). For example, the Pearson correlation coefficient of IL1A and CHMP4C in all samples was 0.95. A difference analysis revealed overexpression of 6 genes and suppression of 5 genes in disease group compared with the healthy group (Fig. 1D). Box graph (Fig. 1E) and heat map (Fig. 1F) were drawn according to the up and down-regulation of gene expression. The validation of the external independent data set GSE58294 showed that the expression of five genes, CASP1, CASP4, NAIP, CHMP2A and 1L1B, were also up-regulated, and the expression of CHMP7, PLCG1 and CHMP4B (also known as CHMP4A) were down-regulated in disease samples of validation set (Fig. S1).Fig. 1 The expression of the pyroptosis gene in IS. (A) The distribution of pyroptosis genes on human chromosomes. (B) Protein-protein interaction between pyroptosis genes. (C) Correlation of the expression of the pyroptosis genes in all samples and IS samples. These two scatter plots show the two most relevant pyroptosis genes: IL1A and CHMP4C. (D) Volcanic maps show a summary of information on changes in the pyroptosis genes expression between IS and normal samples. (E, F) The box diagram and heat map show the transcriptome expression status of the pyroptosis genes between healthy and IS samples. Ns: p ≥ 0.05, *: p < 0.05, **: p < 0.01, ***: p < 0.001 and ****: p < 0.0001.

Fig. 1

3.2 Construction of classifier based on single factor logistic regression and LASSO analysis

Based on all pyroptosis genes of GSE16561 samples, 14 pyroptosis genes (Fig. 2A) related to IS were identified using univariate logistic regression analysis according to P < 0.05. The redundant factors were selected using Lasso regression to build a classifier model. The classifier consists of 10 genes: CASP1, CASP5, CASP8, CHMP2A, CHMP4A, CYCS, NAIP, NLRP3, PLCG1, and TNF (Fig. 2B–C). GSE16561 was used to draw the box graph of model scores, and the scores were significantly different (Fig. 2D). The construction of ROC curve was performed to assess model reliability, which was extremely high at 0.998 (Fig. 2E). These findings revealed that pyroptosis genes played a crucial role in the development of IS.Fig. 2 Pyroptosis genes distinguishing between IS and healthy samples. (A) The association between the pyroptosis genes and IS was analyzed by employing the univariate logistic regression, and 14 pyroptosis genes were found (p < 0.05). (B) Lasso coefficient curve of 14 IS-associated pyroptosis genes. (C) 10-fold cross-validation used for adjustment parameter selection in Lasso regression. The log(λ) was employed to draw partial likelihood deviation, where λ was the setting parameter. Partial likelihood deviation values were displayed, and the error line represents SE. Dashed vertical lines were drawn at the best value with minimum conditions and 1-SE criteria. (D) The risk distribution between IS and healthy samples, in which the risk score of IS was substantially higher as compared to the healthy samples. (E) AUC value and ROC curve were employed to analyze the differentiation ability of the pyroptosis genes to healthy and IS samples.

Fig. 2

3.3 Analysis of pyroptosis genes and immune microenvironment

The investigation of the differences in immune microenvironment characteristics between IS and healthy samples was carried out by evaluating immune cell infiltration, immune response gene sets, and HLA gene expression. The findings revealed that the infiltration of immune cells in disease group was relatively high as compared to healthy group (Fig. 3A). As for the immune response gene set, disease group had a more active immune response. For example, antimicrobials and TNF family members were highly active in disease group (Fig. 3B). Moreover, the HLA gene expression of HLA_DOA, HLA_DOB, and HLA_DRB1 in healthy group was substantially higher in comparison to disease group (Fig. 3C). Furthermore, the correlation between classifier gene and immune infiltrating cells, immune response gene set, and HLA was analyzed. CHMP2A, CHMP4A, and NAIP genes revealed a significant correlation with immune infiltrating cells, immune response gene set, and HLA (Fig. 3D–F). For example, CHMP2A and NAIP indicated a negative association with activated CD4 T cells, whereas CHMP4A revealed a positive association with effector memory CD8 T cells. Additionally, the immune infiltrating cells were also significantly correlated with CASP1, CASP5, CASP8, and CYCS genes.Fig. 3 Analysis of association between pyroptosis gene and immune characteristics. (A–C) Variations in immune cell infiltration, immune response gene set, and HLA gene expression between IS and healthy samples. (D–F) Correlation between pyroptosis classifier gene and immune infiltrating cells, immune response gene set, and HLA. Ns: p ≥ 0.05, *: p < 0.05, **: p < 0.01, ***: p < 0.001 and ****: p < 0.0001.

Fig. 3

3.4 Identification and characterization of classifier gene subtypes

Based on 10 classifier model genes, unsupervised cluster analysis on IS samples was conducted, and two different subtypes of IS were determined, including 27 samples in cluster 1 and 12 samples in cluster 2 (Fig. 4A–C). PCA analysis and verification were satisfactory (Fig. 4D). Furthermore, the expression of classifier genes in different subtypes was mapped by box graph and heat map, and significant differences were found in CASP5, CHMP2A, NAIP, NLRP3, and PLCG1 (Fig. 4E–F). The clinical correlation analysis found that subtypes were related to the patient's gender (Fig. 4G), not to the patient's age. Subtype 1 was mostly female, while subtype 2 was mostly male (Fig. 4H).Fig. 4 Cluster analysis of pyroptosis-related genes. (A) Consensus clustering cumulative distribution function (CDF) when K = 2–7. (B) Comparative change of area under CDF curve. (C) Heat map of the co-occurrence ratio matrix of IS samples. (D) Principal component analysis. (E–F) box and heat plots, respectively, to plot the classifier gene expression in different subtypes. (G–H) Association analysis of subtypes with age and sex of patients.

Fig. 4

3.5 Characteristics of the immune microenvironment in various subtypes

In order to reveal the variations of immune microenvironment characteristics between various subtypes, the evaluation of the infiltrating immune cells, immune response gene sets, and HLA gene expression were performed. The findings revealed that as compared to subtype 1, the infiltrating immune cells of subtype 2 were relatively high (Fig. 5A). For example, activated B cells and activated CD4 T cells were significantly higher in subtype 2. Moreover, subtype 1 revealed a highly active immune response (Fig. 5B). For example, antimicrobials and cytokine receptors were highly active in subtype 1. To investigate the relationship between pyroptosis genes and immune cells, we examined the levels of immune cells and the expression of pyroptosis genes in peripheral blood samples from individuals with ischemic stroke. Our analysis revealed a positive correlation between CHMP4A expression and B cells proportion, as well as TNF expression and CD4 T cells (Fig. 5C). These results indicated a potential involvement of CHMP4A and TNF in regulating immune responses. In HLA gene expression (Fig. 5D), HLA_A and HLA_DQB2 of subtype 1 were significantly higher than HLA_DRB1, HLA_DRB6, and HLA_G of subtype 2. These findings indicated the significance of pyroptosis in the development of immune microenvironments in IS.Fig. 5 Differences in immune microenvironment characteristics among different subtypes. Variations in immune cell infiltration (A), immune response gene set (B), Correlation analysis between pyroptosis gene expression and immune cells (C), and HLA gene (D), expression between subtypes 1 and 2.

Fig. 5

3.6 Functional analysis of various subtypes

KEGG and HALLMARKS pathways between subtypes were compared. Their biological reactions were investigated, and GSVA enrichment analysis was employed for the evaluation of the activation status of biological pathways. The number of significant enrichment pathways of subtypes 1 and 2 was almost same (Fig. 6A–B). In order to further understand which genes were involved in the molecular mechanism of pyroptosis regulation, 199 genes between subtype 1 and subtype 2 were identified, and KEGG and GO hypergeometric enrichment analysis for differential genes were carried out. The results indicated that the biological processes enriched in GO database included cytoplasmic translation, response to estradiol, etc. (Fig. 6C). Cell components included ribosomal subunit, rough endoplasmic reticulum, etc. (Fig. 6D). Molecular functions included immunoglobulin binding, hydrolizing N-glycosyl compounds, etc. (Fig. 6E). The channels enriched in KEGG database included ribosome, tuberculosis, etc. (Fig. 6F).Fig. 6 The difference in potential biological function characteristics between two types of pyroptosis gene subtypes. (A–B) The difference between KEGG and HALLMARKS pathways of subtype 1 and subtype 2. (C–E) The enrichment analysis of GO function revealed the biological function of genes related to pyroptosis phenotype. (F) KEGG pathway analysis revealed the signal pathway of genes related to pyroptosis phenotype. BP, biological process; DEGs, differentially expressed gene; CC, cellular component; MF, molecular function; GO, Gene Ontology.

Fig. 6

3.7 Potential drug discovery of subtype HUB genes

PPI network was constructed based on 199 subtype difference genes, and the gene with a degree ≥10 was selected as HUB gene. In the current study, 13 genes were selected including SNCA, RPL23, PTMA, ACTR3, PTGES3, RPS28, RPL9, MCTS1, EIF3M, RPL7, IL10, PABPC1, RPL14 (Fig. 7A). Based on these HUB genes, the DGIdb database gene-drug interaction analysis was used to mine potential drugs. The relationship map between HUB genes and drugs was made, and 4 genes were found (Fig. 7B). The Sangchi diagram of "Up down grouping-HUB gene-potential drug" was drawn (Fig. 7C).Fig. 7 Potential drug discovery of HUB gene. (A) PPI network construction and hub gene selecting. (B) Tapping the potential drugs of hub gene. (C) Sangchi graph of grouping-HUB gene-potential drugs.

Fig. 7

3.8 Gene expression validation for classifier model genes

To validate the expression of classifier model genes, we constructed MCAO model, which revealed pathological damage in brain tissue (Fig. 8A). The sham group did not exhibit any inflammatory cell infiltration. Conversely, MCAO group displayed significant cellular irregularities, including nuclear condensation, nuclear rupture, and inflammatory cell infiltration. Furthermore, we detected the expression of classifier model genes. Results showed that Casp1, Casp5, Cycs, Naip, Nlrp3, Plcg1, and Tnf were upregulated in MCAO group compared with sham group (Fig. 8B, all p < 0.05), while Casp8, Chmp2a, and Chmp4a did not display significant differences.Fig. 8 Gene expression validation for classifier model genes. (A) The HE staining revealed cytoplasmic shrinkage, vacuolization, and widening of intercellular space in nerve cells within the infarcted brain tissue (red arrow). (B) qPCR was utilized to detect the expression levels of genes in the classifier model.

Fig. 8

4 Discussion

Ischemic stroke (IS) is an inflammatory disease involving interactions of complicated nature between immune responses and pathogens [14]. Increasing evidence has confirmed that pyroptosis plays an indispensable role in adaptive and innate immune responses [25]. So far, numerous studies have been conducted to elucidate the role of pyroptosis in immunity, particularly in tumor microenvironment infiltrating cells. The findings confirmed the significant role of pyroptosis in tumor immunity [[26], [27], [28]]. In the current study, we systematically studied the pyroptosis pattern in the immune microenvironment. In order to clarify how pyroptosis affects the immune response of IS, activates the immune pathway, and enriches and infiltrates immune cells, a series of analyses were carried out, which revealed the following findings.

First of all, drastic changes in the expression of most pyroptosis genes were observed between healthy and IS, indicating that pyroptosis genes were involved in the development of IS. These pyroptosis genes are from the known msigdb database and have been reported to be associated with inflammatory diseases, neurodegenerative diseases, acute kidney injury, melanoma, lung adenocarcinoma and other diseases. Moreover, the classifier could distinguish IS and healthy samples, which again confirmed the significance of pyroptosis gene in IS.

Secondly, inflammasomes in the immune system respond to host threats by recognizing different molecules, termed pathogen or damage-associated molecular patterns (PAMPs/DAMPs), or disrupting cellular homeostasis, termed homeostasis altering molecular processes (HAMP) or effector triggered immunity (ETI). For example, NLRP3 is reported to respond to the gamut of innate immune stimuli, recognizing HAMPs from cellular dysfunction, binding PAMPs/DAMPs in the form of nucleic acids, and mediating ETI by responding to pore-forming toxins that disrupt cellular ion gradients. Therefore, this study focused on the association between pyroptosis genes and immune characteristics in IS, such as infiltrating immune cells, immune response gene set, and HLA gene. A close association between pyroptosis genes and the immune characteristics suggested its essential role in regulating the immune microenvironment. Furthermore, macrophages are an integral constituent of innate immunity and play a vital role in the homeostasis of IS [29]. The abundance of macrophages revealed a positive correlation with the NLRP3 gene and a negative correlation with PLCG1 and CYCS genes. Moreover, CHMP2A showed a positive association with several immune responses, especially cytokines, which have been reported to participate in the progression of IS and play an important role in the recruitment of specific immune cells [30].

Furthermore, IS samples were unsupervised and clustered by utilizing the expression profile of pyroptosis genes. These samples were divided into two subtypes according to its unique immune characteristics. Subtype 2 had relatively higher infiltrating immune cells as compared to subtype 1, exhibiting a highly active immune response. A recently conducted study employed this method to identify two different types of pyroptosis in colon cancer, which could facilitate the development of more effective immunotherapy strategies [14]. Another therapeutic strategy extensively utilized in the field of cancer is the molecular typing strategy, which helps in the identification of new molecular subtypes useful in developing a better treatment plan [31]. Finally, the genes associated with the pyroptosis phenotype and the potential drugs were identified. The expression and regulation of these genes might be affected by pyroptosis, and revealing their biological functions would help clarify the pathogenesis of IS.

Although pyroptosis is not the only form of cell death in brain tissue, its effect on stroke tends to be stronger in the early stage. Targeted inhibition of pyroptosis may improve the symptoms of stroke patients by extending the therapeutic window period and reducing nerve cells damage [16]. Specific inhibition of pyroptosis may be a feasible strategy for clinical treatment of stroke, and many specific inhibitors targeting the pyroptosis pathway have shown promising effects. For example, the use of inhibitors targeting NLRP3 and caspase-1 can effectively improve stroke symptoms, which is associated with the reduction of pro-inflammatory factor IL-1 and pore-forming protein N-GSDMD [32,33]. Reducing injury and recovering function of nerve cells after stroke is the key to treatment challenges, and the neuroinflammation related pyroptosis may be the potential target.

In this study, we also validated pyroptosis genes, TNF and CHMP4A, in clinical samples, and their expression levels were significantly correlated with the number of inflammatory cells. TNF is an important cytokine in immune response, increasing significantly in the process of cerebral ischemia. TNF and its receptor TNFR1 can bind and activate caspase-8 to induce apoptosis by downstream effector caspases, or turn to cleave gasdermin D (GSDMD) to trigger pyroptosis, thereby worsening inflammation [34]. CHMP4A is highly expressed in tumor cells and promote hypoxia-related tumor progression by upregulating HIF-1 [35]. As was known, HIF-1 is also an important regulator after cerebral ischemic injury [36]. The role of CHMP4A/HIF-1 signaling pathway in cerebral ischemic injury needs to be further verified.

Despite the novel findings above, this study has also some limitations to be taken into consideration. This study represents a preliminary investigation into the role of pyroptosis in immune microenvironment following ischemic stroke, and does not reveal the underlying molecular mechanisms. Thus, the direct clinical application of this study is limited. In addition, the sample size was small and the assessment of immune microenvironment in IS peripheral blood might be biased. Multi-center, large-sample prospective clinical studies can provide more reliable information for clinical decision-making and improve the prognosis of patients [37]. The stroke animal model is an indispensable tool to simulate the stroke process, and can be used to explore mechanism and discover new therapeutic targets. MCAO model is the most commonly used in the study of focal cerebral ischemia, because the cerebral vascular anatomy of rats is close to that of humans. The vascular injury is constant, and the repeatability is good [38]. Nevertheless, there are species differences between rats and humans that cannot be ignored, such as differences in immune system, circulatory system, metabolic system, etc., including changes in the immune microenvironment after stroke. These differences pose a challenge to the clinical translation of our results. A humanized mouse model of the immune system with a human immune system may overcome this challenge and provide more reliable data on the changes of the immune microenvironment after stroke [39].

5 Conclusion

These findings showed the effect of pyroptosis on immune microenvironment, providing basis for studying the underlying regulatory mechanism of pyroptosis in the immune microenvironment of IS. A comprehensive analysis of the mechanism of pyroptosis of IS cells will help understand the underlying mechanism of the immune regulatory network of IS, and explore more effective treatment methods. We will conduct further large sample size cohort validation to provide more precise clinical evidence. In the future, in vivo and in vitro experiments will be further conducted by means of gene editing and drug intervention. The stroke models of mice with human immune system will be used to verify the regulatory mechanism of pyroptosis genes on immune microenvironment.

Data availability

GSE16561 data of GPL6883 platform were obtained from Gene Expression Omnibus (GEO) database through R package (https://bioconductor.org/packages/release/bioc/html/GEOquery.html) GEOquery (version 2.62.2) as training set (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE16561). There were 63 samples in this training set, 39 of which are IS patients over the age of 18 and 24 healthy individuals. Furthermore, the GSE58294 data of the GPL570 platform were retrieved from the GEO database as the validation set (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE58294). The data set contained 92 samples, including 69 samples of IS patients and 23 normal samples.

Funding

This work was supported by the 10.13039/501100007129 Natural Science Foundation of Shandong Province of China (grant number: ZR2021MH303 , ZR2021MH241 ); the 10.13039/501100010040 Taishan Scholar Project of Shandong Province of China (grant number: tsqn202103200 ).

CRediT authorship contribution statement

Yilei Xiao: Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Funding acquisition. Zhen Wang: Writing – review & editing, Validation, Data curation. Yexin Xin: Writing – review & editing, Validation, Formal analysis. Xingbang Wang: Writing – review & editing, Visualization, Validation, Funding acquisition. Zhaogang Dong: Writing – review & editing, Validation, Methodology, 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.

Appendix A Supplementary data

The following is the Supplementary data to this article:Fig. S1

The independent data set GSE58294 was used to verify the difference in disease samples gene expression. Ns: p ≥ 0.05, *: p < 0.05, **: p < 0.01, ***: p < 0.001 and ****: p < 0.0001.

Fig. S1

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e36349.
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