Introduction

Stroke ranks as the second leading cause of death and the third leading cause of death and disability combined worldwide1,2. In China, stroke remains a severe burden and is still the primary cause of mortality3. Furthermore, without appropriate prevention strategies in place, the incidence of stroke is likely to continue its upward trend. Ischemic stroke (IS) constituted 62.4% of all incident strokes1. Among the subtypes of IS, large artery atherosclerotic stroke (LAA) is the most prevalent and its incidence is increasing at a rate of 5.7% annually4.

In addition to providing effective treatment for patients with IS to minimize mortality and morbidity, prevention of IS occurrence is a practical and efficacious approach towards reducing the burden of stroke5. The reduction in IS incidence through targeted interventions aimed at single or multiple risk factors at both population and individual levels constitutes IS prevention, which necessitates the identification of risk factors for effective implementation6.

Traditional IS risk factors comprise age, gender, hypertension, diabetes mellitus, heart diseases, hyperlipidemia, smoking and drinking habits, high body mass index (BMI), elevated homocysteine levels and carotid stenosis7,8,9. Additionally, significant traditional risk factors include high fasting blood glucose levels and exposure to environmental particulate matter pollution1. Hypertension and hyperlipidemia are prevalent risk factors for IS, both of which are closely associated with atherosclerosis10. In addition to the conventional factors, there exists a hereditary component in IS etiology. Unraveling the genetic contributions to IS could facilitate a more precise definition of causal pathways, identification of novel therapeutic targets, and improved options for diagnosis and prognosis10,11,12. The identification of genetic risk factors for LAA is particularly valuable, given the higher estimated heritability of this type of stroke compared to other subtypes4.

Endothelin type A receptor (EDNRA) is a receptor for the potent vasoconstrictor endothelin-1 (ET-1), which is primarily expressed in vascular smooth muscle cells13. It is now widely used in clinical practice as a therapeutic target for drugs for cardiovascular diseases such as pulmonary hypertension and hypertension14,15. EDNRA promotes the proliferation and migration of vascular smooth muscle cells, inducing the formation of extracellular matrix and fibers that are involved in the development and progression of atherosclerosis16,17. Studies on the EDNRA SNP rs1878406 have demonstrated that polymorphism at this locus is associated with carotid plaque and an increased risk of coronary artery disease18,19. Furthermore, endothelin (EDNs) and EDNRA are implicated in the inflammatory process which also plays a role in LAA pathogenesis20,21,22. Therefore, we propose that EDNRA may be involved in the pathogenesis of LAA. Consistent with our expectations, a recent study has demonstrated that the rs1878406 polymorphism of the EDNRA gene is significantly associated with susceptibility to LAA23. However, to the best of our knowledge, no systematic studies have comprehensively analyzed genetic polymorphisms of EDNRA and their potential association with LAA risk. In the present study, we selected and genotyped 16 tagSNPs in the EDNRA gene using SNPscan technology to examine the correlation between genetic polymorphisms of EDNRA and LAA risk. Additionally, we investigated whether these relevant genetic polymorphisms regulate inflammatory pathways that influence LAA pathogenesis.

Materials and methods

Ethics statement

This study was approved by the Ethics Committee of the Third People’s Hospital of Chengdu in accordance with the Declaration of Helsinki (2019-S-110). Prior to participation, all participants provided written informed consent. This trial was registered at the Chinese Clinical Trial Registry (www.chictr.org.cn) (trial registration number ChiCTR2000032684).

Study populations

We consecutively recruited first-ever LAA patients from the neurology department of the Third People’s Hospital of Chengdu. LAA was diagnosed using Chinese ischemic stroke subclassification24. Control subjects matched by age, gender, and ethnic group were randomly selected from the healthy adults who underwent periodical medical check-ups at the Physical Examination Center of the same Hospital during the same period when patients were recruited. The controls were healthy, without any vascular or neurological diseases by questionnaires, history-taking, and clinical examination. The demographic data and risk factors were recorded in detail, including age, gender, hypertension, diabetes, heart diseases, hyperlipidemia, smoking status, drinking habits, overweight, carotid stenosis, and high homocysteine (HHCY) levels. All data were registered and kept confidential by two staff in the department.

Variants definition

The demographic characteristics, potential stroke risk factors, and medical history were gathered from each participant for analysis. Cigarette smoking was defined as the habitual consumption of at least one cigarette per day for a minimum duration of one year25. The presence of alcohol consumption was defined as drinking alcohol at least 12 times during the past year25. The diagnosis of hypertension was established based on the guidelines provided by the World Health Organization (WHO) and the International Society of Hypertension, which define hypertension as blood pressure equal to or exceeding 140/90 mmHg, or through the administration of antihypertensive medications26,27. Diabetes mellitus was diagnosed based on elevated fasting plasma glucose levels (≥ 7.0 mM), or a documented history of the oral hypoglycemic agent or insulin treatment, in accordance with the criteria set by the WHO28. Dyslipidemia was defined according to the 2016 Chinese guideline for the management of dyslipidemia in adults29. The condition of being overweight is defined as having a body mass index equal to or greater than 24 kg/m2 according to the Chinese criteria30. The definition of HHCY is a plasma homocysteine concentration greater than 10 umol/L31. The definition of carotid stenosis is the presence of more than 50% blockage as determined by angiography32.

SNPs selection and genotyping

Genotypes for SNPs in EDNRA representing the Han Chinese were obtained from the HapMap database (phase II, 8 November, on NCBI B36 assembly, dbSNP b126). We carefully selected 16 tagSNPs (rs1878406, rs6842241, rs6841581, rs1801708, rs10305863, rs702757, rs7657903, rs10305895, rs908581, rs7655892, rs78047355, rs2048894, rs5333, rs5335, rs5342 and rs5343) based on their high linkage disequilibrium with r2 > 0.8 and a minor allele frequency > 0.05 in the Chinese Han population.

A total of 5mL venous blood was collected from the antecubital vein in the morning after an overnight fast using EDTA (Disodium salt, 50 mmol/L) tubes. The collected samples were subsequently frozen at -80 °C until testing was conducted. Genomic DNA was extracted from peripheral blood using the Genomic DNA Extraction Kit (Tiangen)33. The genotypes of selected SNPs were determined using SNPscan technology (Genesky)34. Briefly, the high specificity of the ligase ligation reaction is utilized for SNP loci allele identification, followed by introducing non-specific sequences at the end of the ligation probe and performing a ligase addition reaction to obtain ligated products with varying lengths. The ligated products were amplified by PCR using fluorescence-labeled universal primers, and the resulting amplicons were separated via fluorescence capillary electrophoresis (Table 1). Finally, the genotypes of each SNP locus were determined through analysis of the electrophoretic profiles.

Table 1 Primers for genotyping.
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Quantitative real-time PCR

We randomly selected a subset of LAA patients and control samples to assess the transcript levels of EDNRA and NLRP3. Total RNA was isolated from venous blood by using the TRNzol Universal Total RNA Isolation Kit (Tiangen)35. The quality of total RNA was tested by agarose gel electrophoresis. 1 µg of total RNA was reverse transcribed by the use of TransScript® Uni All-in-One First-Strand cDNA Synthesis SuperMix for qPCR (Transgen). The resultant cDNA was diluted 10-fold before PCR amplification. A reverse transcriptase minus reaction served as a negative control. The mRNA levels were measured by SYBR green real-time PCR (Yeasen)36. Primer sequences of genes used for qRT-PCR analysis are given in Table 2.

Table 2 Primers for qRT-PCR.
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Inflammation-related factors assay

Plasma was collected using EDTA as an anticoagulant, and samples were centrifuged at 1000×g for 15 min at a temperature of 4 °C. The levels of TNF-α, CCL18, IL-1β, IL-6, IL-8, IL-10, and CIL-18 were quantified by flow cytometry in accordance with the manufacturer’s instructions (Nuohebio).

Enzyme-linked immunosorbent assay (ELISA)

Plasma was collected using EDTA as an anticoagulant, and samples were centrifuged at 1000×g for 15 min at a temperature of 4 °C. Store the sample at -80 °C to prevent repeated freezing and thawing. The contents of NLRP3 were determined strictly following the instructions of the manufacturers (ABclonal).

Statistical analysis

All statistical tests were analyzed using SPSS statistics version 21.0. The chi-square test was utilized to assess the proportions of clinical and environmental variables, as well as Hardy-Weinberg equilibrium (HWE)37. Subsequent genetic association analyses were conducted using three genetic models: dominant, recessive, and allelic comparison. Normally distributed, continuous data were compared with a student’s t-test and expressed as mean ± standard deviation. The BH (Benjamini–Hochberg) method of FDR (False discovery Rate) was used to correct type I errors38. The generalized multifactor dimensionality reduction (GMDR) beta v0.9 software package was used to analyze gene–gene interactions39. GMDR software obtains the best model combination from multiple genes and behavioral indicators through the factor dimensionality reduction principle. The optimal model is obtained from the following results: (1) The model is meaningful only when the p-value is less than 0.05; (2) The larger the testing balance accuracy is, the better the model effect is; (3) The closer the cross validation (CV) consistency is to 10, the better. The influence of high-risk interactive genotypes on functional outcomes was investigated with multivariable logistic regression analysis, after adjusting for the main baseline variables related to each main variable in the univariate analysis (enter approach and probability of entry p < 0.2)40. A p-value of less than 0.05 was considered a statistically significant difference.

Results

Baseline characteristics of the subjects

In this study, a total of 434 patients with LAA and 428 controls were enrolled. The baseline clinical and demographic characteristics are presented in Table 3. Compared to the control group, the patients were significantly older (p < 0.001) and had a higher proportion of males (p = 0.027). Multi-factor logistic regression analysis revealed significant differences in traditional risk factors for IS, such as gender(p = 0.027), hypertension (p < 0.001), diabetes (p < 0.001), heart diseases (p = 0.006), smoking status (p < 0.001), and carotid stenosis (p = 0.021).

Table 3 Baseline characteristics of the study population.
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Genotypic and allelic frequencies

The observed frequency distribution of the sixteen variants conforms to Hardy-Weinberg equilibrium (HWE) (p > 0.05), indicating that the gene frequencies in the selected study population are representative of those in the general population (Table 4).

Table 4 Hardy-Weinberg equilibrium of SNPs genotype in LAA and control group.
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EDNRA rs5343 SNP and the risk of LAA

From univariate analysis, three SNP candidates (rs5343, rs2048894, and rs908581) were selected for comparison with logistic regression analysis between the LAA cases and control groups based on gender, hypertension, diabetes, heart diseases, smoking status, drinking habits, overweightness, carotid stenosis and HHCY. The CC genotype of rs5343 was associated with a lower risk of LAA (OR = 0.734, 95%CI = 0.539–0.999, P = 0.049) while the TT genotype of rs5343 was linked to an increased risk (OR = 3.243, 95%CI = 1.608–6.542, P = 0.001) (Table 5). We also analyzed the association between rs5343 gene polymorphisms and cardiovascular risk factors such as hypertension, diabetes, heart diseases, and carotid stenosis, however, no statistically significant correlation was found (Supplementary Table 1).

Table 5 Association of the EDNRA polymorphism with risk of LAA.
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Linkage disequilibrium (LD) analysis and haplotype block structure

As illustrated in Fig. 1, the LD analysis identified four blocks among the 16 tagSNPs. To minimize potential false positives, haplotypes with frequencies less than 0.01 were excluded from the haplotype analysis. After adjusting for gender, hypertension, diabetes, heart diseases, smoking status, drinking habits, overweightness, carotid stenosis and HHCY, no significant association was observed between any of the haplotypes and LAA risk (Table 6).

Fig. 1
figure 1

LD and correlation coefficients (r2) among sixteen selected SNPs of the EDNRA gene.

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Table 6 Association between haplotype and the risk of LAA.
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SNP-SNP interactions

The GMDR model identified the gene-gene interaction between rs2048894 and rs5343 as the most optimal, with a cross-validation consistency of 10/10 and a sign test result of 9 (p = 0.0107) (Table 7). The optimal interaction model between rs2048894 and rs5343 by GMDR is illustrated in Fig. 2. After adjusting for hypertension, diabetes, heart disease, hyperlipidemia, smoking status, drinking habits, overweight, carotid stenosis and HHCY through multivariate logistic regression analysis, it was determined that the combination of rs2048894 and rs5343 is significantly associated with an increased risk of IS (OR = 2.028, 95%CI = 1.189–3.457, p = 0.0101).

Table 7 GMDR model of gene interaction.
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Fig. 2
figure 2

Risk of 9 different combinations of rs2048894 and rs5343 genotypes. High-risk cells are indicated by dark color, low-risk cells are indicated by light color. The high-risk interaction genotype was assigned as one, and low-risk interaction genotype was assigned as zero in multivariable logistic regression analysis.

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Inflammatory pathways and LAA pathogenesis

Numerous reports have confirmed the impact of cardiovascular diseases, including LAA, on inflammatory pathways and factor levels41,42,43,44. Consistent with prior research, this study’s findings also indicate significant distinctions in NLRP3(Ctr = 282.9 ± 58.75, LAA = 541.4 ± 92.75, pg/mL, p < 0.05), CCL18 (Ctr = 4015 ± 20.37, LAA = 4126 ± 19.24, pg/mL, p < 0.0001), IL-10 (Ctr = 5.74 ± 0.21, LAA = 6.56 ± 0.28, pg/mL, p < 0.05), and IL-18 (Ctr = 15.54 ± 1.39, LAA = 20.60 ± 1.62, pg/mL, p < 0.05) between the LAA and control groups (Fig. 3).

Fig. 3
figure 3

Levels of inflammation-related factors in the control group and LAA group. Outlier detection was conducted using the 3-sigma criterion. Values are shown as means ± SEM, *p < 0.05, ****p < 0.0001.

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EDNRA polymorphism is associated with inflammation

We investigated the correlation between genetic variation and inflammation at multiple SNP loci in EDNRA. Some genotypes were excluded from the analysis due to low population frequency. In LAA patients, NLRP3 expression levels were significantly higher in those with the rs5342 GG genotype compared to the AA and AG genotypes. Patients with the rs6841581 GG genotype exhibited higher IL-6 levels than those with the GA genotype The rs6842241 locus, in complete linkage disequilibrium with rs6841581, showed higher IL-6 levels in patients with the CC genotype compared to the CA genotype. Patients carrying the rs7657903 GA genotype had elevated IL-8 levels compared to those with the GG genotype. Carriers of the rs702757 TT genotype had significantly higher IL-10 levels compared to TA genotype carriers, with no significant difference between AA genotype carriers and others at this locus. Conversely, patients with the rs5343 CT genotype had higher IL-18 concentrations than those with the CC genotype (p < 0.05). (Fig. 4).

Fig. 4
figure 4

EDNRA polymorphism is associated with inflammation in LAA patients. No significant differences were observed in the levels of inflammatory factors at the relevant polymorphic loci in the control group. Values are shown as means ± SEM, *p < 0.05, **p < 0.01.

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The expression of EDNRA is positively associated with the expression of NLRP3 and the levels of IL-18 in patients

To further investigate the association between EDNRA and inflammatory pathways, we conducted an analysis of the correlation between EDNRA expression levels and NLRP3, CCL18, as well as various inflammatory factors. We observed a significant correlation between the expression levels of EDNRA and NLRP3 in both LAA patients (Pearson’s r = 0.437, p < 0.001) and controls (Pearson’s r = 0.234, p = 0.002). Notably, the correlation between EDNRA and NLRP3 was more pronounced in LAA patients compared to controls. Furthermore, only LAA patients showed a correlation between EDNRA and IL-18 levels (Pearson’s r = 0.212, p = 0.009), while no significant correlations were found between EDNRA expression levels and CCL18, IL-6, IL-8 or IL-10 levels in either patient group or controls (Fig. 5) (Table 8).

Fig. 5
figure 5

EDNRA expression is positively correlated with NLRP3 expression and IL18 levels in LAA patients.

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Table 8 Description of the association between EDNRA and diverse levels of inflammatory factors.
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Discussion

In this study, the pathogenesis of LAA was found to be associated with traditional risk factors including age, gender, hypertension, diabetes, heart disease, smoking and carotid artery stenosis. The EDNRA rs5343 gene polymorphism was found to be significantly associated with the risk of LAA development under all genetic models, however, this SNP was not associated with vascular risk factors for LAA (including hypertension, diabetes, heart diseases, and carotid stenosis). These results suggested that the rs5343 polymorphism might directly influence LAA susceptibility and this effect may be independent of traditional risk factors for IS. Individuals carrying the T-allele exhibit an increased susceptibility to LAA. Furthermore, multifactorial logistic regression analysis revealed an interaction between rs2048894 and rs5343. Additionally, we confirmed that inflammation levels differed among patients with different EDNRA gene polymorphisms compared to controls.

In this study, we selected NLRP3, CCL18, IL-6, IL-8, IL-10, and IL-18 to analyze their roles in mediating the effect of EDNRA on susceptibility to LAA. Previous studies have demonstrated an interaction between inflammatory factors, calcium ion signaling pathways, and the EDNRA gene45. In patients with systemic sclerosis, the EDNRA gene has been found to participate in the activation of inflammatory factors such as interleukin-8 (IL-8) and chemokine CC-motif ligand 18 (CCL18)46, highlighting the regulatory role of the EDNRA/inflammatory factor pathway in the inflammatory response47. Additionally, NLRP3 acts as a platform for activating caspase-1, which subsequently induces the release of pro-inflammatory cytokines interleukin-1β (IL-1β) and IL-1848. These findings suggested that the cascade-like interactions among the EDNRA gene, inflammatory factors, and NLRP3 activation might play a role in disease pathogenesis.

By examining the levels of these inflammatory factors, we found that in patients with LAA, genetic polymorphisms at multiple loci of the EDNRA gene were found to be associated with levels of inflammation-related factors, including NLRP3, IL-6, IL-8, IL-10 and IL-18. Furthermore, a positive correlation was observed between EDNRA expression levels and those of both NLRP3 and IL-18 in LAA patients. Previous studies have shown that after stroke, the ischemic cascade response releases large amounts of IL-649, and CD4 + T cells produce more IL-1050. In addition, IL-18 has a pro-atherosclerotic profile, and increased serum IL-18 in patients with coronary artery disease has been shown to play a role in the process of brain cell injury51. The NLRP3 inflammasome has been identified as a key component in the intervention of inflammatory factors in the post-ischemic period. plays an important role52. Our study suggests that inflammatory mechanisms may mediate the association of genetic polymorphisms in EDNRA with the pathogenesis of LAA.

Previous studies have examined the association between EDNRA single locus and IS in Asian and European populations; however, the findings have yielded inconsistent results11,23,48,53,54. Given that stroke exhibits distinct characteristics across different geographic regions3, investigating genetic polymorphisms and LAA risk in specific populations will facilitate the development of more precise treatment strategies for LAA. Moreover, the aforementioned studies solely investigated a single genetic locus, and currently, there exists an absence of a comprehensive analysis regarding the potential association and underlying mechanisms between the genetic polymorphism of EDNRA and the risk of LAA. In this study, patients and controls were exclusively recruited from the Han population of the Third People’s Hospital of Chengdu. We conducted a comprehensive analysis on the correlation between 16 tagSNPs polymorphisms in the EDNRA and susceptibility to LAA, revealing a potential link to an inflammatory response. The implementation of these initiatives will facilitate the development of precise strategies for preventing and controlling LAA, specifically targeting distinct populations.

The results indicate a strong association between the rs5343 genetic polymorphism and LAA pathogenesis. Of the various inflammatory factors examined, only IL-18 demonstrated a significant correlation with the rs5343 genetic polymorphism. IL-18 belongs to the IL-1 family of cytokines, which comprises 11 cytokines that stimulate innate immune system activity55,56. IL-18 has the ability to activate both innate and adaptive immune responses, exhibiting pleiotropic effects in various cytokine environments. This highlights its crucial pathophysiological role in maintaining health and contributing to disease progression57,58. Specifically, compelling evidence links IL-18 to IS. During the early phase, neuronal cells primarily express IL-18, while microglia express it during later stages. Stroke-induced inflammation is associated with IL-18, and initial serum levels of this cytokine may predict stroke outcomes59. However, further investigation is necessary to determine whether rs5343 affects LAA pathogenesis through immune response and IL-18.

rs5343, also referred to as rs17475065 or rs60414981, is situated within the 3’-untranslated region (3’UTR) of the EDNRA gene. The noncoding regions of mRNAs, known as 3’UTRs, play a crucial role in regulating protein levels by controlling mRNA stability and translation through AU-rich elements and miRNAs60,61,62. Additionally, 3’UTRs facilitate local translation by regulating mRNA localization63,64. Numerous prior studies have demonstrated the association between stroke pathogenesis and 3’UTRs of diverse genes. Lu et al. demonstrated that the SNP rs8679 located in the 3’UTR of PARP1 may serve as a protective factor for patients with IS65. Kim et al. suggested that Thymidylate Synthase 3’UTR variants were linked to the susceptibility of IS and silent brain infarction66. Additionally, they discovered that MTHFR 3’UTR polymorphisms contribute to the pathogenesis of IS. The combined effects of MTHFR 3’UTR polymorphisms and tHcy/folate levels may play a role in the prevalence of stroke67. Furthermore, it has been demonstrated that certain 3’UTRs are associated with the risk of stroke pathogenesis through miRNA regulation68,69. This has inspired us to further investigate the mechanism by which rs5343 affects the pathogenesis of LAA.

There may be some limitations to this study. Firstly, it was a single-center case-control study with patients from the same region and ethnicity, so there may be some bias in gene expression. Secondly, despite the sample size being relatively large, there was still some heterogeneity in the traditional risk factors of the patients enrolled in this study after adjusting for the baseline levels. Although these risk factors were independent of each other, did not interact with the genes we studied and did not affect the primary outcome of this study, future studies should try to minimize these effects. In addition, the EDNRA gene was identified in this study as possibly influencing LAA susceptibility by affecting inflammatory factors such as IL-18, but the mechanism was not elucidated, and further studies are needed to explore the mechanism in the future.

Conclusion

This study provides strong evidence that the rs5343 TT genotype of the EDNRA gene is significantly associated with an increased risk of LAA. LAA cases exhibit higher levels of NLRP3, IL-10, IL-18, and CCL-18, with a significant relationship between EDNRA polymorphisms and these inflammatory markers. The findings suggest that EDNRA may contribute to LAA susceptibility through the NLRP3-mediated inflammatory pathway. These insights provide valuable directions for future research and potential strategies for the prevention and management of LAA.