Association of genetic variations in the Wnt signaling pathway genes with myocardial infarction susceptibility in Chinese Han population

Abstract

Numerous studies have implicated the Wnt pathway in the development and progression of myocardial infarction (MI); however, there are very few investigations addressing the effects of polymorphisms in the Wnt pathway genes on MI susceptibility. We investigated the possible correlation between genetic variations in Wnt pathway genes and MI risk. Three polymorphisms (rs7832767 C > T in SFRP1 gene, rs2293303 C > T in CTNNB1 gene, rs16893344 C > T in WISP1 gene) were finally selected and genotyped in 465 MI patients and 485 healthy controls, using the PCR-RFLP method. We found that the SFRP1 rs7832767 variant allele (T) was associated with a significantly increased risk of MI [TT vs. CC: adjusted odds ratio (AOR) = 3.13, 95% CI = 1.78-5.51; CT/TT vs. CC: AOR = 1.53, 95% CI = 1.12-2.08; TT vs. CC/CT: AOR = 2.87, 95% CI = 1.66-4.97)]. The significant association with MI risk was also found for the CTNNB1 rs2293303 (CT vs. CC: AOR = 3.48, 95% CI = 2.28-5.33; TT vs. CC: AOR = 7.37, 95% CI = 2.08-26.16; CT/TT vs. CC: AOR = 3.72, 95% CI = 2.46-5.62; TT vs. CC/CT: AOR = 5.52, 95% CI = 1.58-19.28), and WISP1 rs16893344 polymorphisms (CT vs. CC: AOR = 2.43, 95% CI = 1.70-3.47; TT vs. CC: AOR = 5.17, 95% CI = 1.85-14.41; CT/TT vs. CC: AOR = 2.58, 95% CI = 1.83-3.66; TT vs. CC/CT: AOR = 3.88, 95% CI = 1.41-10.64). The associations remain significant in stratified analysis by demographic and clinical characteristics of participants, with few exceptions. Our study provided the first evidence of the association between polymorphisms in the Wnt pathway genes and MI susceptibility in Chinese Han population. Epidemiological studies with larger samples and functional analyses are warranted to further verify our results.

INTRODUCTION

Myocardial infarction (MI), frequently resulting from coronary thrombosis, has been the major cause of death in humans worldwide, and its incidence is still rapidly increasing in low-, moderate-, and high-income countries [1, 2], Finland and Japan have the highest and lowest MI incidence rate worldwide, respectively [3-5]. The World Bank estimates that by 2030, 23 million Chinese patients will experience accurate MI annually [6]. Therefore, this disease is a serious challenge facing the nation’s healthcare system. There are some conventional predisposing factors for the development of MI, involving tobacco use, diabetic diseases, abnormally elevated blood pressure, hypercholesterolemia, and obesity [7]. Aside from these modifiable risk factors, accumulating studies have demonstrated that MI is a complicated polygenic disease, arising from some different causes and impacts, which is believed to be a result of interactions between an individual’s genetic composition and a variety of environmental risk factors [8-10]. Moremore, some patients with MI might have never exposed to any well-established risk factors. On the contrary, only a fraction of individual exposed to risk factors eventually develop this type of disease in the lifetime. Therefore, genetic factors, such as some uncharacterized genetic component may predispose to MI [11]. Abundant studies have identified some molecular pathways that are associated with MI, including Notch [12], MAPK [13], as well as NF-kB [14] signal transduction pathways. Some genetic alterations (e.g., mutation, gene amplification, and genetic variation) within signaling pathways may contribute to MI susceptibility and also serve as druggable targets for the disease. The identification of pathogenic genetic alterations may lead to new understandings of the development of MI.

In the canonical Wnt signaling pathway, Wnt ligands bind to receptors (e.g., the seven-pass transmembrane Frizzled receptor) and lead to the stabilization of intracellular β-catenin, which permits accumulation of β-catenin and its nuclear translocation, subsequently influencing the association of transcription factors (e.g., Tcf/LEF) with transcriptional co-repressors to activate Wnt-dependent target gene expression, such as Wnt-1 inducible signaling protein 1 (WISP1) [15-17]. On the other hand, the secreted frizzled-related protein (SFRP) family (SFRP1-5) can competitively bind to Wnt proteins and block their interactions with Frizzleds, negatively regulating the activation of canonical Wnt signaling pathway and expression of downstream genes [18, 19]. Compelling evidences have indicated that the Wnt signaling pathway is involved in MI [20, 21], but the underlying mechanisms remain to be further clarified.

Thus far, there are few epidemiologic studies evaluating the association between genetic variations in the Wnt pathway genes and the risk of MI. To precisely determine the association of the Wnt pathway gene polymorphisms with MI risk, we investigated the genotype distributions of several single nucleotide polymorphisms (SNPs) in key genes within this important pathway (i.e., rs7832767 C > T in SFRP1 gene, rs2293303 C > T in CTNNB1 gene, rs16893344 C > T in WISP1 gene) in the case-control study with 465 MI cases and 485 controls.

RESULTS

Characteristics of study participants

Demographic and clinical characteristics of all participants were listed in Table 1. In total, 465 patients with MI and 485 healthy controls were enrolled in this study. Of the MI patients, 255 (54.84%) were women and 210 (45.16%) were men, with mean age 67.32±8.57 years (range, 37-89 years). As to the controls, 273 (56.29%) were women and 212 (43.71%) were men, with mean age 66.98±9.07 years (range, 33-91 years). We did not observe significant differences between patients and controls regarding age (P = 0.552), sex (P = 0.653), drinking status (P = 0.742), body mass index (BMI) (P = 0.112), triglycerides (TG) (P = 0.618), high-density lipoprotein cholesterol (HDL-C) (P = 0.719), uric acid (P = 0.185), blood urea nitrogen (BUN) (P = 0.996), and creatinine (Cr) (P = 0.779). The patients and controls differed significantly with regards to systolic blood pressure (SBP) (P < 0.0001), diastolic blood pressure (DBP) (P < 0.0001), fasting plasma glucose (FPG) (P < 0.0001), total cholesterol (TC) (P < 0.0001), low-density lipoprotein cholesterol (LDL-C) (P < 0.0001), the prevalence of hypertension (P < 0.0001), diabetes (P < 0.0001), dyslipidemia (P = 0.017), smoking (P = 0.0002), and obesity (P = 0.028). In terms of these clinical parameters, cases tended to be unhealthier than controls.

Table 1: Demographic and clinical characteristics of study participants

Variables	Case (n= 465)		Control (n = 485)		Pa
	No	%	No	%
Age range, yr	37-89		33-91		0.552
Mean ± SD	67.32 ± 8.57		66.98 ± 9.07
≤ 60	88	18.92	92	18.97
61-70	191	41.08	192	39.59
71-80	161	34.62	182	37.53
>80	25	5.38	19	3.92
Sex					0.653
Female	255	54.84	273	56.29
Male	210	45.16	212	43.71
Smoking status					0.0002
Never	308	66.24	374	77.11
Ever	157	33.76	111	22.89
Drinking status					0.742
Never	414	89.03	435	89.69
Ever	51	10.97	50	10.31
BMI (kg/m2)	26.07 ± 4.06		25.66 ± 3.83		0.112
<18.5	9	1.94	12	2.47
18.5-23.9	147	31.61	151	31.13
24.0-29.9	230	49.46	264	54.43
≥30.0	79	16.99	58	11.96
Hypertension					<0.0001
No	312	67.10	231	47.63
Yes	153	32.90	254	52.37
Diabetes					<0.0001
No	263	56.56	45	9.28
Yes	202	43.44	440	90.72
Dyslipidemia					0.017
No	198	42.58	244	50.31
Yes	267	57.42	241	49.69
Obesity					0.028
No	79	16.99	58	11.96
Yes	386	83.01	427	88.04
SBP (mmHg)	147.04 ± 23.48		137.36 ± 19.04		<0.0001
DBP (mmHg)	90.40 ± 19.34		84.86 ± 16.11		<0.0001
FPG (mmol/L)	6.74 ± 1.74		5.36 ± 1.76		<0.0001
TG (mmol/L)	1.55 ± 1.14		1.59 ± 1.28		0.618
TC (mmol/L)	4.79 ± 1.13		4.41 ± 1.21		<0.0001
HDL-C (mmol/L)	1.24 ± 0.45		1.23 ± 0.50		0.719
LDL-C (mmol/L)	2.95 ± 0.99		2.67 ± 0.84		<0.0001
Uric acid (umol/L)	300.93 ± 93.12		293.41 ± 80.81		0.185
BUN (mmol/L)	5.35 ± 1.62		5.34 ± 1.75		0.996
Cr (mmol/L)	76.64 ± 23.06		76.06 ± 38.91		0.779

BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; TG, triglyceride; TC, total cholesterol; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol; BUN, blood urea nitrogen; Cr, creatinine

a The P value of the continuous variables was calculated by the independent-sample t-test. The P value of the categorical variables was calculated by χ2 test

The genotypes distribution of selected polymorphisms in MI cases and controls

The genotypes distribution of the three selected polymorphisms in patients with MI and healthy controls were indicated in Table 2. The genotype freqency distributions of the three SNPs in controls were in accordance with the Hardy-Weinberg equilibrium (P = 0.769 for rs7832767, P = 0.754 for rs2293303, and P = 0.743 for rs16893344).

Overall, we found that, compared to non-carriers, carriers of SFRP1 rs7832767 T allele had a significantly elevated MI risk [TT vs. CC: adjusted odds ratio (AOR) = 3.13, 95% confidence interval (CI) = 1.78-5.51; CT/TT vs. CC: AOR = 1.53, 95% CI = 1.12-2.08; TT vs. CC/CT: AOR = 2.87, 95% CI = 1.66-4.97] after adjustment for age, gender, smoking, drinking status, BMI, SBP, DBP, FPG, TG, TC, Uric acid, HDL-C, LDL-C, BUN, and Cr. We also found that the significant association between CTNNB rs2293303 C > T polymorphism and an increased MI risk (CT vs. CC: AOR = 3.48, 95% CI = 2.28-5.33; TT vs. CC: AOR = 7.37, 95% CI = 2.08-26.16; CT/TT vs. CC: AOR = 3.72, 95% CI = 2.46-5.62; TT vs. CC/CT: AOR = 5.52, 95% CI = 1.58-19.28). Similarly, WISP rs16893344 C > T polymorphism was also shown to significantly increase the risk of MI (CT vs. CC: AOR = 2.43, 95% CI = 1.70-3.47; TT vs. CC: AOR = 5.17, 95% CI = 1.85-14.41; CT/TT vs. CC: AOR = 2.58, 95% CI = 1.83-3.66; TT vs. CC/CT: AOR = 3.88, 95% CI = 1.41-10.64).

Table 2: The distribution of genotypes and alleles of Wnt signaling pathway gene between myocardial infarction patients and controls

Genotype	Case (n, %)	Control (n, %)	P a	Crude OR (95% CI)	P	Adjusted OR (95% CI) b	P b
SFRP1 rs7832767 C>T
CC	231 (49.68)	286 (58.97)		1.00		1.00
CT	182 (39.14)	170 (35.05)		1.33 (1.01-1.74)	0.042	1.27 (0.91-1.76)	0.167
TT	52 (11.18)	29 (5.98)		2.22 (1.37-3.61)	0.001	3.13 (1.78-5.51)	<0.0001
Additive			0.002	1.42 (1.16-1.73)	0.0006	1.55 (1.23-1.97)	0.0002
Dominant	234 (50.32)	199 (41.03)	0.004	1.46 (1.13-1.88)	0.004	1.53 (1.12-2.08)	0.007
Recessive	413 (88.82)	456 (94.02)	0.004	1.98 (1.23-3.18)	0.005	2.87 (1.66-4.97)	0.0002
CTNNB1 rs2293303 C>T
CC	358 (76.99)	407 (83.92)		1.00		1.00
CT	95 (20.43)	74 (15.26)		1.46 (1.04-2.04)	0.027	3.48 (2.28-5.33)	<0.0001
TT	12 (2.58)	4 (0.82)		3.41 (1.09-10.67)	0.035	7.37 (2.08-26.16)	0.002
Additive			0.009	1.55 (1.16-2.08)	0.003	3.25 (2.24-4.71)	<0.0001
Dominant	107 (23.01)	78 (16.08)	0.007	1.56 (1.13-2.16)	0.007	3.72 (2.46-5.62)	<0.0001
Recessive	453 (97.42)	481 (99.18)	0.036	3.19 (1.02-9.95)	0.046	5.52 (1.58-19.28)	0.007
WISP1 rs16893344 C>T
CC	314 (67.53)	369 (76.08)		1.00		1.00
CT	136 (29.25)	109 (22.47)		1.47 (1.09-1.97)	0.011	2.43 (1.70-3.47)	<0.0001
TT	15 (3.23)	7 (1.44)		2.52 (1.01-6.25)	0.047	5.17 (1.85-14.41)	0.002
Additive			0.007	1.50 (1.16-1.93)	0.002	2.38 (1.75-3.25)	<0.0001
Dominant	151 (32.47)	116 (23.92)	0.003	1.53 (1.15-2.03)	0.004	2.58 (1.83-3.66)	<0.0001
Recessive	450 (96.77)	478 (98.56)	0.068	2.28 (0.92-5.63)	0.075	3.88 (1.41-10.64)	0.009

a c2 test for genotype distributions between myocardial infarction patients and controls

b Adjusted for age, gender, smoking, drinking status, BMI, SBP, DBP, FPG, TG, TC, Uric acid, HDL-C, LDL-C, BUN, Cr

Stratified analysis

We performed stratification analyses in terms of age, sex, smoking and drinking status, hypertension, diabetic diseases, dyslipidemia, and obesity to evaluate how these variables modified the association between the SNPs (SFRP1 rs7832767 C > T, CTNNB rs2293303 C > T and WISP1 rs16893344 C > T) and MI risk (Table 3). The SFRP1 rs7832767 CT/TT genotypes were shown to significantly increase MI risk in subjects of age≤67 and under (AOR = 2.31, 95% CI = 1.43-3.74), men (AOR = 1.98, 95% CI = 1.21-3.24), smokers (AOR = 3.05, 95% CI = 1.59-5.84), drinkers (AOR = 9.75, 95%CI = 2.64-36.10), subgroup with hypertension (AOR = 5.99, 95% = 3.10-11.56), subgroup with diabetes (AOR = 4.77, 95% CI = 3.09-7.35) and without diabetes (AOR = 0.32, 95% = 0.14-0.73), subgroup with dyslipidemia (AOR = 1.59, 95% CI = 1.04-2.41) as well as obesity (AOR = 1.50, 95% CI = 1.07-2.10). As to the CTNNB rs2293303 C > T polymorphism, we found that the increased risk remained statistically significant in all subgroups except for non-diabetes and non-obesity subgroups. Finally, we found that the association between the WISP1 rs16893344 CT/TT genotype and MI risk was prominent in subjects at age of older than 67 (AOR = 3.58, 95% CI = 2.21-5.81), women (AOR = 2.87, 95% CI = 1.83-4.51), men (AOR = 1.97, 95% CI = 1.12-3.47), non-smokers (AOR = 1.96, 95% CI = 1.32-2.91), smokers (AOR = 6.99, 95% CI = 3.04-16.08), non-drinkers (AOR = 2.29, 95%CI = 1.60-3.29), drinkers (AOR = 13.00, 95%CI = 2.44-69.25), non-hypertension (AOR = 1.88, 95% = 1.22-2.90), hypertension (AOR = 4.58, 95% = 2.38-8.82), diabetes (AOR = 9.83, 95% CI = 6.23-15.51), dyslipidemia (AOR = 2.73, 95% CI = 1.71-4.36) as well as obesity subgroups (adjusted OR = 2.80, 95% CI = 1.91-4.12).

Table 3: Logistic regression analysis for association of SNPs with MI risk in Wnt signaling pathway

Variables

rs7832767 (Case/Control)

Adjusted ORa

Pa

rs2293303 (Case/Control)

Adjusted ORa

Pa

rs16893344 (Case/Control)

Adjusted ORa

Pa

CC

CT/TT

95% CI

CC

CT/TT

95% CI

CC

CT/TT

95% CI

Median age, yr

≤67

96/133

112/72

2.31 (1.43-3.74)

0.0007

159/164

49/41

2.39 (1.30-4.37)

0.005

142/148

66/57

1.65 (0.98-2.77)

0.061

>67

135/153

122/127

1.15 (0.75-1.75)

0.529

199/243

58/37

5.36 (2.98-9.62)

<0.0001

172/221

85/59

3.58 (2.21-5.81)

<0.0001

Gender

Females

127/155

128/118

1.20 (0.80-1.82)

0.379

183/232

72/41

4.36 (2.58-7.39)

<0.0001

162/211

93/62

2.87 (1.83-4.51)

<0.0001

Males

104/131

106/81

1.98 (1.21-3.24)

0.007

175/175

35/37

2.88 (1.43-5.80)

0.003

152/158

58/54

1.97 (1.12-3.47)

0.018

Smoking status

Never

159/212

149/162

1.18 (0.82-1.70)

0.371

230/306

78/68

3.46 (2.18-5.50)

<0.0001

208/274

100/100

1.96 (1.32-2.91)

0.0008

Ever

72/74

85/37

3.05 (1.59-5.84)

0.0008

128/101

29/10

5.13 (1.92-13.75)

0.001

106/95

51/16

6.99 (3.04-16.08)

<0.0001

Drinking status

Never

208/245

206/190

1.25 (0.90-1.74)

0.181

316/360

98/75

3.55 (2.31-5.44)

<0.0001

279/323

135/112

2.29 (1.60-3.29)

<0.0001

Ever

23/41

28/9

9.75 (2.64-36.10)

0.0006

42/47

9/3

11.77 (1.77-78.13)

0.011

35/46

16/4

13.00 (2.44-69.25)

0.003

Hypertension

No

144/96

168/135

0.72 (0.49-1.06)

0.103

250/217

62/14

6.85 (3.49-13.47)

<0.0001

216/179

96/52

1.88 (1.22-2.90)

0.004

Yes

87/190

66/64

5.99 (3.10-11.56)

<0.0001

108/190

45/64

2.64 (1.39-5.00)

0.003

98/190

55/64

4.58 (2.38-8.82)

<0.0001

Diabetes

No

185/21

78/24

0.32 (0.14-0.73)

0.007

244/41

19/4

1.21 (0.33-4.49)

0.777

237/29

26/16

0.21 (0.08-0.52)

0.0007

Yes

46/265

156/175

4.77 (3.09-7.35)

<0.0001

114/366

88/74

9.86 (5.91-16.46)

<0.0001

77/340

125/100

9.83 (6.23-15.51)

<0.0001

Dyslipidemia

No

97/145

101/99

1.49 (0.93-2.40)

0.097

152/199

46/45

3.67 (1.96-6.89)

<0.0001

137/183

61/61

2.45 (1.42-4.23)

0.001

Yes

134/141

133/100

1.59 (1.04-2.41)

0.032

206/208

61/33

3.81 (2.15-6.74)

<0.0001

177/186

90/55

2.73 (1.71-4.36)

<0.0001

Obesity

No

31/30

48/28

1.70 (0.73-3.95)

0.221

61/47

18/11

2.25 (0.78-6.53)

0.135

54/42

25/16

1.47 (0.58-3.72)

0.416

Yes

200/256

186/171

1.50 (1.07-2.10)

0.020

297/360

89/67

4.00 (2.54-6.32)

<0.0001

260/327

126/100

2.80 (1.91-4.12)

<0.0001

a Adjusted for age, gender, smoking, drinking status, BMI, SBP, DBP, FPG, TG, TC, Uric acid, HDL-C, LDL-C, BUN, Cr

Figure 1: A. The restriction fragment length polymorphism analysis to determine rs7832767 C > T polymorphism, the CC genotype shows three bands at 56, 199, 421 bp (2, 3 and 4); the TT genotype shows two bands at 56 bp and 620 bp (6 and 7); and the CT genotype shows four bands at 56, 199, 421 and 620 bp (1 and 5). B. Nucleotide sequences around rs7832767 C > T polymorphism ((1), CC genotype; (2), TT genotype; (3), CT genotype).

Figure 2: A. The restriction fragment length polymorphism analysis to determine rs2293303 C > T polymorphism, the CC genotype shows one 349 bp band (4 and 7); the TT genotype shows two bands at 125 bp and 224 bp (3 and 6); and the CT genotype shows three bands at 125 bp, 224 bp and 349 bp (1, 2 and 5). B. Nucleotide sequences around rs2293303 C > T polymorphism ((1), CC genotype; (2), TT genotype; (3), CT genotype).

Figure 3: A. The restriction fragment length polymorphism analysis to determine rs16893344 C > T polymorphism, the CC genotype shows two bands at 153 bp and 188 bp (2 and 5); the TT genotype shows one 341 bp band (3 and 6); and the CT genotype shows three bands at 153 bp, 188 bp and 341 bp (1, 4 and 7). B. Nucleotide sequences around rs16893344 C > T polymorphism ((1), CC genotype; (2), TT genotype; (3), CT genotype).

DISCUSSION

Identification of genetic variations that are able to modify individuals’ susceptibility may advance the understanding of the pathophysiological characteristics of MI, and may further facilitate individualized treatment decision making [22]. In this case-control study, we genotyped SNPs in three major Wnt signaling pathway genes (SFRP1, CTNNB1 and WISP1) in Chinese population, and investigated the correlation between these SNPs and MI risk. The results indicated that SFRP1 rs7832767 C > T, CTNNB1 rs2293303 C > T, and WISP1 rs16893344 C > T were all strongly correlated with MI susceptibility.

The Wnt signaling pathway can be competitively inhibited by SFRP1 by sequestrating the Wnt ligands and consequently preventing the activation of downstream signaling cascade. Evidence from transgenic mice overexpressing SFRP1 revealed that overexpression of SFRP1 reduced infarct size and cardiac rupture in a coronary arter lighation-induced MI model and improves cardiac function, when compared to wild-type mice [23]. Previous investigations have revealed the associations of genetic variants in the SFRP1 gene with tuberculosis, inflammation, and cancer susceptibility [24-26]. We explored the rs78332767 C > T polymorphism in the SFRP1gene and found that it was significantly associated with the risk of MI in Chinese Han population. Carriers of SFRP1 rs78332767 TT genotype or CT genotype were at significantly increased risk of MI, suggesting SFRP1 may be a susceptibility gene for the development of MI. To the best of our knowledge, this is the first study to scrutinize of the risk effect of SFRP1 rs7832767 C > T in cardiovascular disease. Larger, well-designed studies, involoving different ethnical populations are warranted to validate our finding, and further functional analysis are also indispensable to provide biological supports for the causal association.

β-catenin encoded by the CTNNB1 gene has multiple functions in the maintenance of cardiac tissue homeostasis. Stabilization of β-catenin has been related to adult cardiac hypertrophy, and down-regulation of this protein initiates heart formation in embryogenesis [27]. Aberrant action and excessively high expression levels of β-catenin have synergistic effects in various diseases. A number of animal experiments confirmed that β-catenin levels were significantly increased in the MI model [28]. There are several lines of evidence showing the contribution of CTNNB1 rs2293303 C > T polymorphism to the risk and prognosis of cancer, elucitating its potential involvment the pathogenesis of human diseases. Wang et al. [29] demonstrated that carriers of polymorphic variant of CTNNB1 rs2293303 C > T polymorphism had significantly increased risk of gastric cancer and survival advantage versus non-carriers in a Chinese population. Jia et al. reported the significant association between CTNNB1 rs2293303 C > T and breast cancer susceptibility [30]. Our results indicated that CTNNB1 rs2293303 C > T was correlated with MI, and carriers of variant T allele had a significantly increased MI risk. Our results suggested that this polymorphism might modify the function of β-catenin, thereby affecting the development and progression of MI. However, the exact underlying mechanism by which this polymorphism conferred MI susceptibility remains to be elucidated.

WISP1 is a gene in the canonical Wnt signaling pathway [31]. Previous studies indicated that mutations in this gene were related to several diseases, involving lung cancer, asthma, hypertension, and spinal osteoarthritis [32-35]. It has been reported recently that genetic variations in the WISP1 gene, including rs16893344 C > T, may confer lung cancer susceptibility [32]. We observed an association of WISP1 rs16893344 C > T polymorphism with MI in Chinese Han population, with TT and CT risk genotype associated with an increase in MI risk. Overall, our findings may help to improve the understanding of the implication of inherited genetic factors in the onset and progression of MI.

Despite the promising findings in this study, several limitations should be mentioned. First of all, the sample size of this study is relatively moderate. The associations of MI risk with SFRP1 rs7832767 C > T, CTNNB1 rs2293303 C > T and WISP1 rs16893344 C > T with MI should be confirmed studies with larger sample size, involving diverse ethnical group. Second, we only drawed conclusions based on a observational association study, and found that in MI patients, the major Wnt signaling pathway genes all had C > T nucleotide polymorphism. These was similar to the previous research about the prevalent C > T nucleotide polymorphism in various human cancers [36, 37], but the researcher further revealed that the C > T nucleotide alteration tends to change an arginine to another amino acid [37]. Thus, mechanistic studies should be performed to explore the functions of the three SNPs in the future. Third, we only investigated three SNPs. Another variants that may also modify MI susceptibility should be also considered, including SNPs positioned in the functional domain. To better understand the impact of genetic factors on the MI susceptibility, genetic analysis on other genes within Wnt pathway should be carried out.

Thus far, this study is the first attempt to explore the relation of MI susceptibility with genetic variants in the Wnt signaling pathway in Chinese Han population. Overall, the studied SNPs (SFRP1 rs7832767 C > T, CTNNB1 rs2293303 C > T, and WISP1 rs16893344 C > T polymorphisms) were shown to significantly increase MI risk. These susceptibility loci in the major Wnt signaling pathway genes can be applied to prediction of individual genetic risk to MI, identify the high-risk subpopulation, and encourage prevention. In the future, large-scale case-control studies and functional analyses are essential to verify these findings.

MATERIALS AND METHODS

Ethical approval of the study protocol

This study was approved by the Ethics Committee of the First Affiliated Hospital in the Xinjiang Medical University, Xinjiang, China. We conducted the study according to the standards of the Declaration of Helsinki. All of the patients provided written informed consents and explicitly provided permission for DNA analyses, as well as the collection of relevant clinical data.

Subjects

All patients with MI and control subjects were recruited from the First Affiliated Hospital of Xinjiang Medical University from 2007 to 2014. We enrolled a total of 465 patients who suffered from first MI. Moreover, 485 sex-and age-matched healthy individual served as control subjects. MI diagnosis was established by the presence of chest pain lasting 20 min combined with ST-segment elevation or pathological Q waves on a surface electrocardiogram [38]. Patients with MI had to exhibit either an angiographically occluded infarct-related artery or regional wall motion abnormalities corresponding to the electrocardiographic infarct localization or both. Patients were excluded if they presented with ascertained congenital hypercoagulable status, a proven disease limiting life expectancy, or declared cocaine abuse. The control subjects were selected from volunteers who had visited our hospital between 2007 and 2014 for regular medical check-ups and were found to be healthy. Individuals would be considered eligible disease-free controls if they had angiographically normal coronary arteries and if they had no history of MI, no symptoms suggestive of MI, no electrocardiographic signs of MI, no regional wall motion abnormalities, and no relevant valvular abnormalities in echocardiograms [39]. Coronary angiography in the control individuals was performed for the evaluation of chest pain. Control subjects with coronary heart disease and any neoplasm, cardiomyopathy or severe illness limiting life expectancy or refusing consent were excluded. The response rates of participants were 100%.

Laboratory examination and definition of cardiovascular risk factors

Serum concentrations of TC, TG, FPG, HDL-C, LDL-C, BUN, Cr and uric acid were measured using standard methods in the Department of Clinical Laboratory of First Affiliated Hospital, Xinjiang Medical University as described previously [40, 41]. Major coronary artery disease (CAD) risk factors were defined based on current national guidelines. Dyslipidemia was defined as TG ≥ 2.26mmol/L, TC ≥ 6.22 mmol/L, LDL -C ≥ 4.14 mmol/L, HDL-C < 1.04 mmol/L, or a prior Dyslipidemia diagnosis and/or receiving a lipid-lowering drug [42]. Hypertension was defined as a SBP ≥ 140 mmHg, DBP ≥ 90 mmHg, or a prior hypertension diagnosis and/or receiving an antihypertensive drug [43]. Obesity was defined as a BMI ≥ 30.0 [44]. Diabetes was defined as FPG ≥ 6.99mmol/L, or a prior diabetes diagnosis and/or using a diabetes drug [45]. Smoking was defined as currently smoking cigarettes.

DNA extraction

Blood samples were collected from all participants using a standard venipuncture technique and EDTA-containing tubes. DNA was extracted from the peripheral blood leukocytes using a whole blood genome extraction kit (Beijing Bioteke Corporation, Beijing, China) as described previously [40, 41].

SNP selection

Candidate genes must be well characterized and key components of the Wnt signaling pathway in terms of functional significance. After carefully literature reviewing [26, 29, 32], we finally included three key genes (SFRP1, CTNNB1 and WISP1) within Wnt signaling pathway. Using Haploview 4.2 software and International HapMap Project website phase I &II database (http: //www.hapmap.org), we obtained one tagging SNP for each candidate gene, rs7832767 C > T in SFRP1, rs2293303 C > T in CTNNB1, and rs16893344 C > T in WISP1. All these SNPs have a minor allele frequency (MAF) of ≥ 0.05 and not in linkage disequilibrium with each other (r2 < 0.8).

Genotyping

The genotyping in the present study was performed via polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) analysis. We designed the sequencing primers using Primer Premier 5.0. The primers were synthesize by Shanghai Generay Biological Technology Company Limited (Shanghai, China). The PCR amplification was performed in a 50 μL final reaction volume, containing 25μL of 2*powder Taq PCR master mix (Beijing Biotech, Beijing, China), 50 ng of genomic DNA, 21 μL of distilled water, and 1 μL of each forward and reverse primer. The thermal cycling conditions were as follows: (1) SFRP1: an initial denaturation step at 95°C for 5 min, 35 cycles of 95°C for 30 s, 51°C for 30 s and 72°C for 45 s, followed by a final extension step of 72°C for 10 min; (2) CTNNB1: an initial denaturation step at 95°C for 5 min, 35 cycles of 95°C for 30 s, 49°C for 30 s and 72°C for 45 s, and a final extension step of 72°C for 10 min; (3)WISP1: an initial denaturation step at 95°C for 5 min; 35 cycles of 95°C for 30 s, 53°C for 30 s and 72°C for 45 s, and a final extension step of 72°C for 10 min. The thermal cycling was performed using the GeneAmp 9700 system (Applied Biosystems, Foster City, USA). PCR products were further digested by restriction enzymes according to the manufacturer’s instructions. Sequences of the primer pairs, annealing temperatures, lengths of PCR products, and restriction enzymes for the three SNPs were shown in Table 4. After digestion, resulting fragments were separated on 3% agarose gels and stained with ethidium bromide for further analysis. A total of 10% of the genotyped samples were randomly duplicated and results were 100% concordant, and there were at least one positive and one negative control per 96-well DNA plate in our test. The PCR products were also sequenced by ABI 3730XL sequencer (Applied Biosystems, Foster City, USA) to detect the genotype (Figures 1, 2, 3).

Table 4: The primer sequences for each SNPs in Wnt signaling pathway

Polymorphism	PCR Primers (5’→3’)	Denaturation temperature	Products length	Restriction endonuclease
rs7832767	Forward: GAGTTCCACCCTCAATCTGT	51°C	676bp	Tfil
	Reverse: TTCCAGGGATGGTCTGTTAT
rs2293303	Forward: TTGTTGACACCCTGACTCTT	49°C	349bp	BsaBI
	Reverse: TACAAATAGCCTAAACCACTC
rs16893344	Forward: AGTCCCTGCCCGACAGAGTT	53°C	341bp	Acel
	Reverse: CTGATACAGGAGGGAGGATG

SNP, single nucleotide polymorphism; PCR, polymerase chain reaction

Statistical analysis

We analyzed whether genotype frequencies of all studied SNPs in control subjects were in Hardy-Weinberg equilibrium using the goodness-of-fit χ2 analysis. The differences in the continuous variables [shown as mean ± standard deviation (SD)] between the MI patients and the control subjects were assessed using an independent-sample t-test. Categorical variables and distribution of genotypes and alleles were shown as numbers and percentages (%), and the differences between the two groups were tested by χ2 test or Fisher’s exact test. Moreover, logistic regression analysis was performed to assess the contribution of a certain genotype to MI. ORs and 95% CIs were calculated to determine the strength of the association between SNPs and MI. Multivariate analysis was conducted after adjustment for age, gender, smoking, drinking status, BMI, SBP, DBP, FPG, TG, TC, Uric acid, HDL-C, LDL-C, BUN, Cr. All statistical analyses were performed with using SPSS version 22.0 for Windows (SPSS Inc., USA), and statistical significance was established at an alpha level of 0.05.

ACKNOWLEDGMENTS AND FUNDING

This work was supported by grants from Natural science foundation of China grant (81470468), Graduate Research Innovation Project in Xinjiang (XJGRI2015064), Natural science foundation of Xinjiang (2015211C0092), and State Key Laboratory Incubation Base of Xinjiang Major Diseases Research Fund (SKLIB-XJMDR-2012-3).

CONFLICTS OF INTEREST

The authors declare no competing financial interests.

Authors’ contribution

All authors contributed significantly to this work. J.T., Y.-T. W., M. A., Y.-N. W., X.-M. L., B.-D. C., F.L., C.-H. H., and H.-Y. L. performed the research study and collected the data; J.T., and X.X. analyzed the data; J.T. and Y.-T. M. designed the research study; J.T., and Y.-T. M. wrote the paper, and J.T. and Y.-T. W. prepared all the Tables. All authors reviewed the manuscript. In addition, all authors approved the final draft.