Association of MMP3, MMP14, and MMP25 gene polymorphisms with cerebral stroke risk: a case-control study
BMC Medical Genomics volume 16, Article number: 297 (2023)
Cerebral stroke (CS) is the leading cause of death in China, and a complex disease caused by both alterable risk factors and genetic factors. This study intended to investigate the association of MMP3, MMP14, and MMP25 single nucleotide polymorphisms (SNPs) with CS risk in a Chinese Han population.
A total of 1,348 Han Chinese were recruited in this case-control study. Four candidate loci including rs520540 A/G and rs679620 T/C of MMP3, rs2236302 G/C of MMP14, and rs10431961 T/C of MMP25 were successfully screened. The correlation between the four SNPs and CS risk was assessed by logistic regression analysis. The results were analyzed by false-positive report probability (FPRP) for chance or significance. The interactions between four SNPs associated with CS risk were assessed by multifactor dimensionality reduction (MDR).
rs520540 A/G and rs679620 C/T SNP in MMP3 were associated with risk of CS in allele, codominant, dominant and log-additive models. Ischemic stroke risk were significantly lower in carriers with rs520540-A allele and rs679620-T allele than those with G/G or C/C genotypes. However, rs520540-A allele and rs679620-T allele were associated with higher risk of hemorrhagic stroke. Stratified analysis showed that these two SNPs were associated with reduced risk of CS in aged < 55 years, non-smoking and non-drinking participants, and rs679620 SNP also reduced CS risk in male participants. The levels of uric acid, high-density lipoprotein cholesterol, and eosinophil were different among patients with different genotypes of rs520540 and rs679620. No statistically significant association was found between MMP14 rs2236302 G/C or MMP25 rs10431961 T/C with CS even after stratification by stroke subtypes, age, gender as well as smoking and drinking conditions in all the genetic models.
MMP3 rs520540 A/G and rs679620 C/T polymorphisms were associated with CS risk in the Chinese Han population, which provides useful information for the prevention and diagnosis of CS.
Cerebral stroke (CS), commonly known as “stroke”, also known as cerebrovascular accident, is an acute cerebrovascular disease caused by cerebral vascular obstruction or sudden rupture of blood vessels. CS includes ischemic stroke (IS) and hemorrhagic stroke (HS), of which IS (also known as cerebral infarction) is the most pervasive class of CS in clinic, accounting for about 70% ~ 80% of CS. HS is commonly caused by intracerebral hemorrhage (ICH) or subarachnoid hemorrhage. According to the Global Burden of Disease Study 2019, the overall incidence of CS in China was 39.9%, ranking first in the world . CS has become the second most common cause of death and disability in the world , and the first cause of death in China . CS is a complex disease caused by modifiable clinical risk factors (diet, physical inactivity, hypertension , smoking , diabetes , and dyslipidemia ) and genetic factors. The results of a meta-analysis of twin studies and family history studies have shown that there is a clear genetic predisposition to CS. Moreover, monozygotic twins are more likely to have CS at the same time than fraternal twins, and people with a family history of CS are more likely to suffer from CS . Therefore, achieving a deep insight into the individual’s genetic makeup and clinical exposures will expand our understanding on the occurrence of CS.
Matrix metalloproteinase (MMP) is a kind of calcium ion-dependent endopeptidase that plays critical roles in the degradation of extracellular matrix (ECM) of blood vessels, thereby involving in the occurrence of many vascular diseases including cerebrovascular atherosclerosis, HS and IS . The single nucleotide polymorphisms (SNPs) of MMPs are present since birth and are known to be associated with transcriptional activity, expression and changes in their enzymatic activities of MMPs . Hence, we speculated that MMPs gene polymorphisms might be involved in the occurrence of CS. This case-control study selected four candidate SNPs (MMP3: rs520540 A/G, rs679620 T/C; MMP14: rs2236302 G/C; MMP25: rs10431961 T/C) in 1,348 Han Chinese and assessed the effect of these SNPs on the susceptibility to CS. Meanwhile, we further studied the association between SNPs and CS risk stratified by age, gender, smoking, and drinking. Our study will enrich the data of genetic loci related to CS risk in the Chinese Han population, and the discovered susceptibility loci can also be used as genetic markers for predicting CS risk and screening high-risk CS population, so as to provide theoretical basis for the early prevention and diagnosis of CS.
Materials and methods
Study participants including 674 CS patients and 674 healthy individuals were recruited from Xi’an No.3 Hospital of Shaanxi Province. CS patients were newly diagnosed and confirmed by experienced specialists according to the diagnostic criteria based on computed tomography (CT) imaging. The inclusion criteria for cases were patients with a first or definite diagnosis of CS (cerebral infarction, cerebral hemorrhage, subarachnoid hemorrhage, and unclassified stroke) in an outpatient or inpatient setting. Patients with a history of other comorbid diseases (malignant tumors, metabolic disorders, central nervous system infections, etc.) and other genetic disorders were excluded. The control group included healthy people who underwent physical examination in the same hospital during the same period as case group and were not diagnosed with CS, and healthy individuals were matched with case group for age and sex (excluding confounding factors). Patients with complex chronic diseases (excluding patients with cancer or a history of cancer) were excluded. Besides, a professional physician conducted a baseline survey of all participants’ general demographic data (including gender, age, smoking and drinking status).
Blood sample collection and DNA extraction
Admission peripheral blood samples from each participant were collected in EDTA-anticoagulated blood collection tubes and stored in a -20 °C refrigerator. After that, we extracted and purified DNA according to the kit instructions (GoldMag Co. Ltd., Xi’an, China). Eventually, the extracted DNA was stored in an ultra-low temperature freezer at -80 °C for future research.
SNP selection and genotyping
A protein–protein interaction (PPI) network was conducted by STRING database (http://string-db.org) to explore the functional enrichment analysis of MMP3, MMP14, MMP25. Meanwhile, Kyoto Encyclopedia of Genes and Genomes (KEGG) [11, 12] and gene ontology (GO) [13,14,15] analysis was performed by R 4.0.5 software package. They were considered functionally important at the p < 0.05 level. Subsequently, SNPs were selected from the 1000 Genomes Project with minor allele frequency (MAF) > 0.05, Hardy-Weinberg equilibrium (HWE) > 0.05, and Tagger r2 < 0.8. Finally, this study selected two sites rs520540 A/G and rs679620 T/C on the MMP3 gene, one site rs2236302 G/C on the MMP14 gene, and one site rs10431961 T/C on the MMP25 gene. The primers were designed by MassARRAY Assay Design software. All SNPs in this study were genotyped using the MassARRAY system (Agena, San Diego, CA, USA). SNP genotypes were generated using iPLEX chemistry. MALDI-TOF mass spectrometry was used to obtain profiles of different mass peaks of multiple reactions, and finally, genotyping was successfully completed.
Demographic characteristics of the participants, including age (t-test), gender, smoking, and drinking (χ2 test) were tested by SPSS 21.0 (SPSS, Chicago, IL, USA) to determine whether all SNPs met HWE. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated by logistic regression models to evaluate the correlation between all candidate SNPs and CS risk. When OR < 1, it indicates that this SNP is a protective factor against CS; when OR = 1, it shows that this SNP has no effect on CS; when OR > 1, it indicates that this SNP is a risk factor for CS. A variety of genetic models were evaluated using PLINK 1.9 with wild-type alleles as references. All tests were two-sided, and p < 0.05 was considered statistically significant. Afterwards, the detection results were analyzed by false-positive report probability (FPRP) for chance or significance. Haplotype analysis was performed by Haploview to calculate linkage disequilibrium (LD). The interactions between four SNPs associated with CS risk were assessed by multifactor dimensionality reduction (MDR). Differences in clinical characteristics of patients with different genotypes were analyzed by one-way analysis of variance (ANOVA).
Basic demographic and epidemiological information about subjects is presented in Table 1. There were 1,348 unrelated participants in this case-control study, including 674 CS patients (455 males (67.5%) and 219 females (32.5%)) with an average age of 54.85 ± 6.759 years, and 674 healthy individuals (443 males (65.5%) and 231 females (34.3%)) with an average age of 55.61 ± 9.130 years. There were no significant differences in mean age (p = 0.080), gender (p = 0.488), smoking (p = 0.827), and drinking (p = 0.827) between the two groups. However, there were significant discrepancies in the levels of uric acid (UA), high-density lipoprotein cholesterol (HDL-C), eosinophil (EOS), globulin (GLOB), monocyte (MON), low-density lipoprotein cholesterol (LDL-C), and red blood cell (RBC) between these two groups.
Genotyping of candidate SNPs
The results of the functional enrichment analysis are shown in Fig. 1. We constructed a PPI network analysis based on MMP3/MMP14/MMP25 genes. The enrichment analysis showed that these genes were enriched in TNF signaling pathway. The possible biological process (BP) of these overlapping genes were related to the extracellular matrix organization and extracellular structure organization. In this study, four candidate loci were successfully screened: MMP3 rs520540 A/G and rs679620 T/C, MMP14 rs2236302 G/C, and MMP25 rs10431961 T/C. These four SNPs were in line with HWE (p > 0.05), and MAF was greater than 5% in the tested population. The specific information about all candidate SNPs is shown in Table 2. In addition, we found significant differences in MMP3 rs520540 A/G genotype frequency (p = 0.049) and allele frequency (p = 0.016) as well as MMP3 rs679620 T/C genotype frequency (p = 0.033) and allele frequency (p = 0.012) between CS patients and healthy controls (Fig. 2).
Association between candidate SNPs and CS risk
The association between the the four candidate SNPs and the risk of CS is shown in Fig. 3. The results showed that MMP3 rs520540 A/G and rs679620 T/C SNPs were significantly associated with CS risk. In particular, rs520540 A/G significantly reduced CS risk under the allele (A vs. G: OR = 0.83, 95% CI 0.70–0.97, p = 0.018), codominant (AG vs. GG: OR = 0.79, 95% CI 0.63-1.00, p = 0.049), dominant (AG-AA vs. GG: OR = 0.77, 95% CI 0.62–0.96, p = 0.022), and log-additive models (OR = 0.82, 95% CI 0.70–0.97, p = 0.018). Meanwhile, rs679620 T/C could notably decrease the risk of CS under the allele (T vs. C: OR = 0.82, 95% CI 0.70–0.96, p = 0.014), codominant (CT vs. CC: OR = 0.77, 95% CI 0.62–0.97, p = 0.028), dominant (CT-TT vs. CC: OR = 0.76, 95% CI 0.61–0.94, p = 0.013), and log-additive models (OR = 0.82, 95% CI 0.69–0.96, p = 0.014). However, no correlation was found between the remaining two SNPs (MMP14 rs2236302 G/C, and MMP25 rs10431961 T/C) and CS risk (p > 0.05).
Furthermore, we stratified the CS patients into ischemic stroke and hemorrhagic stroke groups, and analyzed the association between these four SNPs with stroke subtypes. In this study, 610 patients suffered from IS and 64 patients had HS, and the healthy controls were randomly selected using SPSS 26.0 software at 1:1 of case-control for the two types of stroke, respectively. After adjusting for age, gender, smoking and drinking history, the OR with 95% CI and p values were calculated between SNPs and IS/HS risk using logistic analysis (Table 3). The results showed that MMP3 rs520540 A allele and rs679620 T allele were associated with decreased risk of IS (A vs. G: OR = 0.64, 95% CI 0.07–0.85, p < 0.001; T vs. C: OR = 0.80, 95% CI 0.07–0.84, p = 0.018), their protective effects against IS were also found in codominant, dominant and log-additive models. Meanwhile, MMP3 rs520540 A allele and rs679620 T allele may associate with higher risk of HS, although no significant difference were indicated. The MMP3 rs520540 A/G genotype and rs679620 C/T genotype carried a significant increase in the risk of HS, particularly in dominant and log-additive models. Furthermore, no statistically significant association was found between MMP14 rs2236302 G/C or MMP25 rs10431961 T/C and two stroke subtypes.
Stratified analysis of the association between candidate SNPs and CS risk
Age and gender Stratified by age and gender, the correlation between the four SNPs and CS risk is presented in Table 4. The results indicated that rs520540 could remarkably decrease the risk of CS in subjects aged ≤ 55 years under a variety of genetic models (allele: OR = 0.79, 95% CI 0.63–0.99, p = 0.039; codominant: OR = 0.66, 95% CI 0.47–0.92, p = 0.014; dominant: OR = 0.67, 95% CI 0.49–0.91, p = 0.011; log-additive: OR = 0.77, 95% CI 0.61–0.97, p = 0.030). rs679620 was a protective factor not only in CS patients aged ≤ 55 years under multiple genetic models (allele: OR = 0.80, 95% CI 0.63-1.00, p = 0.048; codominant: OR = 0.63, 95% CI 0.45–0.88, p = 0.007; dominant: OR = 0.65, 95% CI 0.48–0.89, p = 0.008; log-additive: OR = 0.78, 95% CI 0.62–0.99, p = 0.038), but also in CS patients aged > 55 years under two genetic models (codominant: OR = 0.58, 95% CI 0.35–0.98, p = 0.040; recessive: OR = 0.62, 95% CI 0.38-1.00, p = 0.049). Meanwhile, it was also a protective factor in males (dominant: OR = 0.76, 95% CI 0.58–0.99, p = 0.045; log-additive: OR = 0.81, 95% CI 0.67–0.99, p = 0.044). Additionally, rs2236302 could notably reduce CS risk in males (allele: OR = 0.72, 95% CI 0.53–0.99, p = 0.039; codominant: OR = 0.69, 95% CI 0.49–0.97, p = 0.035). Nevertheless, we did not find any association of rs10431961 with CS risk and any association between the four candidate SNPs and CS risk in females (p > 0.05).
Smoking and drinking Stratified by smoking and drinking, the correlation between the four SNPs and CS risk is presented in Table 5. The results indicated that rs520540 reduced the risk of CS in both non-smokers (OR = 0.79, 95% CI 0.42–0.98, p = 0.034) and non-drinkers (OR = 0.78, 95% CI 0.62–0.97, p = 0.024) participants in the allele model. And rs679620 reduced the risk of CS in non-smoking subjects in the allele model (OR = 0.77, 95% CI 0.62–0.96, p = 0.018). Meanwhile, it decreased CS risk in non-drinking participants under a variety of genetic models (allele: OR = 0.76, 95% CI 0.61–0.95, p = 0.014; codominant: OR = 0.59, 95% CI 0.36–0.99, p = 0.047; dominant: OR = 0.72, 95% CI 0.52–0.98, p = 0.039; log-additive: OR = 0.76, 95% CI 0.61–0.97, p = 0.025). Furthermore, we found no correlation of rs2236302 and rs10431961 with CS risk and no association between the four candidate SNPs and CS risk in smokers and drinkers (p > 0.05).
Differences in clinical characteristics of patients with different genotypes
The associations between the four candidate SNPs and clinical characteristics of CS patients with different genotypes are presented in Table 6. The results showed that the rs520540 AA genotype was linked with the reduced levels of UA, HDL-C, and EOS in CS patients. And the rs679620 TT genotype was connected with the increased levels of UA, HDL-C, EOS, and GLOB in CS patients. The MMP14 rs2236302 GG genotype was associated with the reduced levels of MON. The MMP25 rs10431961 TT genotype was associated with the increased levels of LDL-C and the reduced levels of RBC.
Detailed results of FPRP analysis are displayed in Table S1. The results showed that the correlation between rs679620 and CS risk in subjects aged > 55 years was not noteworthy at a prior probability level of 0.25 and a FPRP threshold of 0.2. Meanwhile, the association between rs679620 and CS risk in non-drinkers under the homozygous model was not noteworthy. The FPRP values of other results were all less than 0.2 at a prior probability level of 0.25, suggesting that these positive results were noteworthy.
LD and haplotype analysis
LD results of the candidate SNPs are shown in Fig. 4, and haplotype analysis results are presented in Table S2. The results indicated that there was linkage disequilibrium between rs520540 and rs679620. Meanwhile, the results of haplotype analysis showed that the distribution of the haplotype Ars520540Trs679620 was associated with a decreased risk of CS (OR = 0.82, p = 0.018).
MDR analysis of SNP-SNP interactions is shown in Fig. 5. The blue lines represent that SNPs have redundant effects in modulating CS risk. Details of SNP-SNP interactions are presented in Table 7. The results showed that the best prediction model was the rs679620 single-site model (good CVC: 9/10, the largest testing balanced accuracy: 0.520, p = 0.0080).
This study is the first to analyze the association of MMP3 (rs520540 A/G and rs679620 T/C), MMP14 (rs2236302 G/C), and MMP25 (rs10431961 T/C) gene polymorphisms with CS risk in Chinese Han population. We found that MMP3 gene polymorphisms (rs520540 A/G and rs679620 T/C) were associated with CS risk. Notably, they played a protective role against IS development, rather than in HS.
ECM remodeling is an essential process in the pathogenesis of atherosclerosis and IS. MMPs act as fundamental mediators for matrix turnover by their protein-digesting enzymatic activities in catalyzing the breakdown of major ECM components, such as collagen, elastin, gelatin, and other glycoproteins and proteoglycans . MMP3 is an important member of MMPs that activates other MMPs, such as MMP1, MMP7, and MMP9, making MMP3 essential in connective tissue and ECM remodeling, resulting in plaque instability [17, 18]. MMP3 is expressed in various vascular tissues and cells, including endothelial cells and vascular smooth muscle cells, thereby modulating various vascular diseases. Alvarez-sabín et al.  have found that the expression of MMP3 is up-regulated in patients with cerebral hemorrhage. A study in a mouse middle cerebral artery embolization model has revealed that tissue-type plasminogen activator (t-PA) treatment induces MMP3 expression levels in peripheral blood and increases hemorrhagic events, which is thought to be related to the disruption of vascular endothelial barrier by MMP3 . MMP3 SNP is associated with reduced in vitro promoter activity and expression . MMP3 (-1171) 5 A/6A + 5 A/5A genotypes and 5 A allele were significantly higher in patients with IS than in controls and contributed to different subtypes of IS susceptibility . Circulating MMP3 level was significantly higher in patients with 5 A/6A or 5 A/5A genotype than in patients with 6 A/6A genotype. MMP3 5 A allele carrier may bear a higher risk of recurrence among patients with the stroke subtype of large-artery atherosclerosis . Kaplan et al. found that MMP3 rs3025058 SNP was associated with increased risk of hemorrhagic stroke . In the overall analysis, this study found that two SNPs of MMP3 (rs520540 A/G and rs679620 T/C) were associated with the reduced risk of IS in the Chinese Han population. In contrary to our findings, the study by Kim et al.  has shown that both rs520540 and rs679620 of MMP3 increase the risk of IS in the Korean population. Some studies have shown that MMP gene polymorphisms can be influenced by racial and ethnic background and ultimately affect the onset of IS . Therefore, we speculate that racial differences may cause the inconsistency between our results and those of previous studies, and we need to further verify our hypothesis in different populations. At the same time, the results demonstrated that rs520540-A allele and rs679620-T allele were associated with higher risk of hemorrhagic stroke. Our finding that the same MMP3 haplotype (rs520540-A/G allele and rs679620-T/C) appeared to protect against thrombotic events (i.e., ischemic stroke) and promote bleeding events (i.e., hemorrhagic stroke) may be explained by cleavage of coagulation and fibrinolytic proteins (plasminogen, plasminogen activator inhibitor-1, fibrinogen) by MMP3.
Former research has revealed that risk factors such as age, gender , and smoking  contribute to the development of CS. Consequently, our study stratified the participants according to the above factors. The results indicated that both rs520540 and rs679620 of MMP3 significantly reduced CS risk in non-smoking and non-drinking participants. Consistent with our results, the study by Larsson et al.  has shown that some gene polymorphisms is positively and statistically associated with IS, large artery stroke, and small vessel stroke in smokers. Another study has shown that there is a correlation between drinking and an increased risk of CS . Thus, the association of rs520540 and rs679620 with the risk of CS may be affected by smoking and drinking. Meanwhile, we noticed rs679620 also reduced the risk of CS in male participants. Some studies have shown that genetic polymorphisms are correlated with CS risk in males [27, 28], which is consistent with our findings, whereas some studies have demonstrated that genetic polymorphisms have a significant correlation with CS risk in females [29, 30]. Therefore, we consider there may be gender differences in susceptibility to CS and further validation is needed to support our conclusions.
Tumor necrosis factor α (TNFα) is mainly secreted by microglia and plays an important role in maintaining homeostasis in the central nervous system . Simultaneously, TNFα is a proinflammatory cytokine related to innate immune response, which is related to a variety of neurodegenerative diseases including Parkinson’s disease, Alzheimer’s disease, multiple sclerosis, ischemic brain injury, etc., and is considered to be one of the important indicators of the severity of IS [32, 33]. TNF modulates tissue damage in experimental CS and is a potential target for future CS therapy . However, excessive TNFα are associated with inflammatory cascade reactions by amplifying and exacerbating the inflammatory response and, in severe cases, triggering systemic inflammatory response syndrome [35, 36]. Therefore, appropriate TNF plays an important proinflammatory role in CS. Our study found that the most important functional terms enriched in MMP3/MMP14 genes was TNF signaling pathway. And the possible BP of MMP3/MMP14/MMP25 genes were related to the extracellular matrix organization and extracellular structure organization. TNF signaling pathway has been shown to induce the expression of MMPs . In addition, TNFα can activate nuclear factor of kappaB (NF-κB), and the activated NF-κB further induces the secretion of TNFα, which leads to the increased release of MMP activity and finally promotes cell invasion and metastasis . Therefore, we hypothesized that MMP3 rs520540/rs679620 and MMP14 rs2236302 may play a role in the occurrence of CS through TNF signaling pathway. However, further experiments are needed to verify our conjecture.
In addition, studies have shown that the levels of UA, HDL-C and EOS in CS patients are significantly higher than those in normal people [39,40,41]. In our study, we found that the rs520540 A/G genotype was linked with the reduced levels of UA and EOS and higher HDL-C in CS patients. And this pattern was true for MMP3 rs679620 T/C in affecting the levels of UA, EOS and HDL-C. Therefore, rs520540 A/G and rs679620 T/C seemed exhibiting protective roles against CS.
However, our study still has certain limitations. First, it was limited by the relative sample size in elucidating the association between SNPs and the occurance of stroke, particularly for the hemorrhagic stroke. Second, all participants were recruited in one medical center and only Han Chinese population were enrolled, thus the conclusion should be extrapolated with caution since genetic variation strongly related to ethnic background. Meanwhile, the conclusion should be validated in more clinical centers in a large sample size. Third, our study associated the MMP3 SNPs with laboratory parameters, and deep insights into the association between SNPs and IS etiological subtypes, severity and prognosis should be explored in further studies due to the disease heterogeneity. Besides, the interplay between MMP3 SNP and that in other MMPs members should be comprehensively analyzed depending complete sequencing and studies on the underlying mechanism are needed. Despite the above shortcoming, our study sheds new light on the association of MMP3 (rs520540 A/G and rs679620 T/C), MMP14 (rs2236302 G/C), and MMP25 (rs10431961 T/C) gene polymorphisms with CS risk.
In summary, this study first explored the correlation of MMP3 (rs520540 A/G and rs679620 T/C), MMP14 (rs2236302 G/C), and MMP25 (rs10431961 T/C) gene polymorphisms with CS risk in the Chinese Han population. The results showed that there was a certain association between MMP3 gene polymorphisms (rs520540 A/G and rs679620 T/C) and CS risk. MMP3 rs520540 A/G and rs679620 T/C may exhibit different paradigms in affecting the initiation and progression of IS and HS, where rs520540 A and rs679620 T alleles exhibited protective roles against IS while promoted the occurrence of HS. Our study further enriches the data of susceptibility loci for CS in the Chinese Han population, and provides a preliminary molecular basis for the prevention, diagnosis and treatment of CS.
The datasets generated and/or analysed during the current study are available in the [figshare] repository, https://doi.org/10.6084/m9.figshare.21769499.v1.
Cieza A, Causey K, Kamenov K, Hanson SW, Chatterji S, Vos T. Global estimates of the need for rehabilitation based on the Global Burden of Disease study 2019: a systematic analysis for the global burden of Disease Study 2019. Lancet. 2021;396(10267):2006–17.
Katan M, Luft A. Global Burden of Stroke. Semin Neurol. 2018;38(2):208–11.
Zhou M, Wang H, Zeng X, Yin P, Zhu J, Chen W, et al. Mortality, morbidity, and risk factors in China and its provinces, 1990–2017: a systematic analysis for the global burden of Disease Study 2017. Lancet. 2019;394(10204):1145–58.
Boehme AK, Esenwa C, Elkind MS. Stroke risk factors, Genetics, and Prevention. Circ Res. 2017;120(3):472–95.
Larsson SC, Burgess S, Michaëlsson K. Smoking and Stroke: a mendelian randomization study. Ann Neurol. 2019;86(3):468–71.
Khoury JC, Kleindorfer D, Alwell K, Moomaw CJ, Woo D, Adeoye O, et al. Diabetes Mellitus: a risk factor for ischemic Stroke in a large biracial population. Stroke. 2013;44(6):1500–4.
Tirschwell DL, Smith NL, Heckbert SR, Lemaitre RN, Longstreth WT Jr., Psaty BM. Association of cholesterol with Stroke risk varies in Stroke subtypes and patient subgroups. Neurology. 2004;63(10):1868–75.
Flossmann E, Schulz UG, Rothwell PM. Systematic review of methods and results of studies of the genetic epidemiology of ischemic Stroke. Stroke. 2004;35(1):212–27.
Rempe RG, Hartz AMS, Bauer B. Matrix metalloproteinases in the brain and blood-brain barrier: versatile breakers and makers. J Cereb Blood Flow Metab. 2016;36(9):1481–507.
Chang JJ, Stanfill A, Pourmotabbed T. The role of Matrix metalloproteinase polymorphisms in ischemic Stroke. Int J Mol Sci. 2016;17(8):1323.
Kanehisa M, Furumichi M, Sato Y, Kawashima M, Ishiguro-Watanabe M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 2023;51(D1):D587–92.
Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30.
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25(1):25–9.
Gene Ontology C, Aleksander SA, Balhoff J, Carbon S, Cherry JM, Drabkin HJ, et al. The Gene Ontology knowledgebase in 2023. Genetics. 2023;224(1):iyad031.
Thomas PD, Ebert D, Muruganujan A, Mushayahama T, Albou LP, Mi H. PANTHER: making genome-scale phylogenetics accessible to all. Protein Sci. 2022;31(1):8–22.
Huang XY, Han LY, Huang XD, Guan CH, Mao XL, Ye ZS. Association of Matrix Metalloproteinase-1 and Matrix Metalloproteinase-3 Gene variants with ischemic Stroke and its subtype. J Stroke Cerebrovasc Dis. 2017;26(2):368–75.
Burrage PS, Mix KS, Brinckerhoff CE. Matrix metalloproteinases: role in arthritis. Front Biosci. 2006;11:529–43.
Li A, Han T, Li Y, Yang G, Zhang Y, Huang Y, et al. Polymorphisms of the Matrix metalloproteinase genes are Associated with Acute ischemic Stroke in Chinese Han Population. Int J Gen Med. 2023;16:619–29.
Alvarez-Sabín J, Delgado P, Abilleira S, Molina CA, Arenillas J, Ribó M, et al. Temporal profile of matrix metalloproteinases and their inhibitors after spontaneous intracerebral Hemorrhage: relationship to clinical and radiological outcome. Stroke. 2004;35(6):1316–22.
Suzuki Y, Nagai N, Yamakawa K, Kawakami J, Lijnen HR, Umemura K. Tissue-type plasminogen activator (t-PA) induces stromelysin-1 (MMP-3) in endothelial cells through activation of lipoprotein receptor-related protein. Blood. 2009;114(15):3352–8.
Tsukahara S, Shinozaki M, Ikari K, Mochizuki T, Inoue E, Tomatsu T, et al. Effect of matrix metalloproteinase-3 functional SNP on serum matrix metalloproteinase-3 level and outcome measures in Japanese RA patients. Rheumatology (Oxford). 2008;47(1):41–4.
Huang X, Ye Q, Zhang Z, Huang X, Zhu Z, Chen Y, et al. Association of matrix metalloproteinase-3 gene 5A/6A polymorphism with the recurrence of ischemic Stroke: a prospective observational study. Brain Res. 2017;1674:55–61.
Kaplan RC, Smith NL, Zucker S, Heckbert SR, Rice K, Psaty BM. Matrix metalloproteinase-3 (MMP3) and MMP9 genes and risk of Myocardial Infarction, ischemic Stroke, and hemorrhagic Stroke. Atherosclerosis. 2008;201(1):130–7.
Kim SK, Kang SW, Kim DH, Yun DH, Chung JH, Ban JY. Matrix metalloproteinase-3 gene polymorphisms are associated with ischemic Stroke. J Interferon Cytokine Res. 2012;32(2):81–6.
Gu L, Huang J, Li J, Huang S, Li M, Gong L, et al. Association of CALM1 rs3179089 polymorphism with ischemic Stroke in Chinese Han Population. Neuromolecular Med. 2018;20(2):271–9.
Larsson SC, Burgess S, Mason AM, Michaëlsson K. Alcohol Consumption and Cardiovascular Disease: a mendelian randomization study. Circ Genom Precis Med. 2020;13(3):e002814.
Gu L, Huang J, Liang B, Chen Q, Xie J, Yang J, et al. TLR4 polymorphisms affect Stroke risk and inflammatory response in Chinese ischemic Stroke patients. Neurol Sci. 2018;39(1):127–33.
Titov BV, Matveeva NA, Martynov MY, Favorova OO. [Multilocus analysis of the association of polymorphic variants of inflammation genes with ischemic Stroke in russians]. Mol Biol (Mosk). 2016;50(4):674–84.
Wang Y, Yin X, Li L, Deng S, He Z. Association of Apolipoprotein C3 Genetic Polymorphisms with the risk of ischemic Stroke in the Northern Chinese Han Population. PLoS ONE. 2016;11(9):e0163910.
Xu Z, Li Y, Huang X, Shen W, Bai J, Shen C, et al. ESR2 genetic variants and combined oral contraceptive Use Associated with the risk of Stroke. Arch Med Res. 2017;48(2):203–11.
Low PC, Manzanero S, Mohannak N, Narayana VK, Nguyen TH, Kvaskoff D, et al. PI3Kδ inhibition reduces TNF secretion and neuroinflammation in a mouse cerebral Stroke model. Nat Commun. 2014;5:3450.
Olmos G, Lladó J. Tumor necrosis factor alpha: a link between neuroinflammation and excitotoxicity. Mediators Inflamm. 2014; 2014:861231.
Vanamee ÉS, Faustman DL. Structural principles of Tumor necrosis factor superfamily signaling. Sci Signal. 2018;11(511):eaao4910.
Lambertsen KL, Biber K, Finsen B. Inflammatory cytokines in experimental and human Stroke. J Cereb Blood Flow Metab. 2012;32(9):1677–98.
Jafari M, Nasiri MR, Sanaei R, Anoosheh S, Farnia P, Sepanjnia A, et al. The NRAMP1, VDR, TNF-α, ICAM1, TLR2 and TLR4 gene polymorphisms in Iranian patients with pulmonary Tuberculosis: a case-control study. Infect Genet Evol. 2016;39:92–8.
Chen S, Yin W, Bi K, Lu B. MicroRNA–497 attenuates cerebral infarction in patients via the TLR4 and CREB signaling pathways. Int J Mol Med. 2018;42(1):547–56.
Du H, Zhang X, Zeng Y, Huang X, Chen H, Wang S, et al. A Novel Phytochemical, DIM, inhibits Proliferation, Migration, Invasion and TNF-α Induced Inflammatory Cytokine production of synovial fibroblasts from rheumatoid arthritis patients by targeting MAPK and AKT/mTOR Signal Pathway. Front Immunol. 2019;10:1620.
Tanaka T, Imamura T, Yoneda M, Irie A, Ogi H, Nagata M, et al. Enhancement of active MMP release and invasive activity of lymph node metastatic tongue cancer cells by elevated signaling via the TNF-α-TNFR1-NF-κB pathway and a possible involvement of angiopoietin-like 4 in lung Metastasis. Int J Oncol. 2016;49(4):1377–84.
Ndrepepa G. Uric acid and Cardiovascular Disease. Clin Chim Acta. 2018;484:150–63.
Li Z, Zhang J, Luo Y. Impact of triglyceride playing on Stroke severity correlated to bilirubin. Med (Baltim). 2020;99(36):e21792.
Soremekun O, Soremekun C, Machipisa T, Soliman M, Nashiru O, Chikowore T, et al. Genome-Wide Association and Mendelian Randomization Analysis Reveal the causal relationship between White Blood Cell subtypes and Asthma in africans. Front Genet. 2021;12:749415.
We are very grateful to all volunteers, clinicians and hospital staff who participated in this study.
This study were supported by Natural Science Foundation of China (NO.82104155), Key Research and Development Program of Shaanxi (Program No.2020ZDLSF04-03, 2021SF-096), Xi ’an Science and Technology Planning Project (21YXYY0038, 21YXYJ0004), Natural Science Basic Research Program of Shaanxi (Program No. 2022JM-452).
Ethical approval and consent to participation
This study fully followed the principles of the Declaration of Helsinki and was approved by the Ethics Committee of Xi’an No.3 Hospital. And the informed consent was obtained from all participants.
Consent for publication
Conflict of interest
The authors have declared that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary Material 1: Table S1 The FPRP and statistical power values of the positive results in this study. Table S2 Haplotype frequencies and the association with the risk of cerebral stroke.
About this article
Cite this article
Yin, Y., Zhang, Y., Zhang, X. et al. Association of MMP3, MMP14, and MMP25 gene polymorphisms with cerebral stroke risk: a case-control study. BMC Med Genomics 16, 297 (2023). https://doi.org/10.1186/s12920-023-01734-1