BioMed Research International
Volume 2019 (2019), Article ID 5897505, 14 pages
https://doi.org/10.1155/2019/5897505

Review Article

The Association between PON1 (Q192R and L55M) Gene Polymorphisms and Risk of Cancer: A Meta-Analysis Based on 43 Studies

Xiaolan Pan,¹ Lei Huang,¹ Meiqin Li,¹ Dan Mo,² Yihua Liang,¹ Zhiming Liu,¹ Zhaodong Huang,¹ Lingsha Huang,¹ Jinfeng Liu,¹ and Bo Zhu¹

¹Department of Clinical Laboratory, The Affiliated Tumor Hospital of Guangxi Medical University, Nanning, 530021 Guangxi, China
²Department of Surgery, Maternal and Child Health Hospital of the Guangxi Zhuang Autonomous Region, Nanning, China

Correspondence should be addressed to Lingsha Huang; huanglinshagx@126.com, Jinfeng Liu; rainbowgxnn@yeah.net, and Bo Zhu; zhubogxnn@126.com

Received 25 February 2019; Revised 16 June 2019; Accepted 1 July 2019; Published 28 July 2019

Academic Editor: Giandomenico Roviello

Copyright © 2019 Xiaolan Pan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Q192R and L55M polymorphism were considered to be associated with the development of multiple cancers. Nevertheless, the results of these researches were inconclusive and controversial. Therefore, we conducted a meta-analysis of all eligible case-control studies to assess the association between PON1 (Q192R and L55M) gene polymorphisms and risk of cancer. With the STATA 14.0 software, we evaluated the strength of the association by using the odds ratios (ORs) and 95% confidence intervals (CIs). A total of 43 case-control publications 19887 cases and 23842 controls were employed in our study. In all genetic models, a significant association between PON1-L55M polymorphisms and overall cancer risk was observed. Moreover, in the stratified analyses by cancer type, polymorphism of PON1-L55M played a risk factor in the occurrence of breast cancer, hematologic cancer, and prostate cancer. Similarly, an increased risk was observed in the Caucasian and Asian population as well as hospital-based group and population-based group. For PON1-Q192R polymorphisms, in the stratified analyses by cancer type, PON1-Q192R allele was associated with reduced cancer risks in breast cancer. Furthermore, for racial stratification, there was a reduced risk of cancer in recession model in Caucasian population. Similarly, in the stratification analysis of control source, the overall risk of cancer was reduced in the heterozygote comparison and dominant model in the population-based group. In conclusion, PON1-Q192R allele decreased the cancer risk especially breast cancer; there was an association between PON1-L55M allele and increased overall cancer risk. However, we need a larger sample size, well-designed in future and at protein levels to confirm these findings.

1. Introduction

Cancer is one of the diseases caused by a combination of genetic and environmental factors [1]. The PON1 gene, located on the long arm of chromosome 7q21.3, is an antioxidant enzyme that has strong lipophilic antioxidant properties, which can maintain the balance of antioxidant-oxidant [2, 3]. Simultaneously, PON1 is also an esterase involved in scavenging reactive oxygen species by binding to high-density lipoprotein (HDL). Studies have shown that oxidative stress may participate in the process of cell proliferation and malignant transformation and damage DNA as well as other biological molecules, resulting in the occurrence of tumors [4]. The ability of PON1 detoxification of carcinogenic oxidative stress products makes it possible for researchers to predict PON1 gene polymorphism in cancer susceptibility [5].

At present, with the deep development of genetic studies of PON1, studies have found that PON1-Q192R and PON1-L55M, the two most common functional genetic polymorphisms in PON1, were identified at positions 192 and 55 [6]. PON1-Q192R polymorphism (rs662A > G) was caused by the glutamine (Q genotype) substituted for the arginine (R genotype) 192 of the gene 6 exon of the PON 1 gene [7]. PON1-L55M (rs854560) was originated from the replacement of 55 leucines (L genotype) by methionine (M genotype) at third exon 55[8]. In addition, it has been shown that the two functional SNP, Q192R and L55M, were associated with the risk of multiple tumors [9, 10], such as oral cancer [11], lung cancer [12], and embryonal tumors [13].

According to the important role of PON1 in the development of tumor and the correlation between genotype and phenotype, we speculate that the variation of PON1 gene Q192 R and L55M may be related to tumor susceptibility. However, the data of many studies are contradictory and uncertain at present. Therefore, a comprehensive meta-analysis should be conducted to determine the relationship between Q192R and L55M polymorphism and cancer risk.

2. Materials and Methods

2.1. Search Strategy

We conducted a systematic literature search in the PubMed, Embase, and Web of Science for all related studies before June 10, 2019 via utilizing the following terms: “polymorphism OR paraoxonase 1 OR PON1” AND “tumor OR malignancy OR cancer OR carcinoma OR neoplasm”. In addition, we extracted the reference of the original articles on this issue to carry out a hand search for extra studies. The results deduced from these articles were limited to humans. When the publication referred to more than one cancer type or ethnicity, we deled with data respectively. Besides, if different authors published articles based on the same population or one author used similar data in an article, we picked out the report with the latest study and largest sample size.

2.1.1. Inclusion Criteria and Exclusion Criteria

The enrolled studies must contain the following inclusion criteria: (1) publication that evaluated the association between PON1-L55M, or PON1-Q192R polymorphism and the risk of cancer. (2) The genotype frequency may be obtainable from cases and controls, or we could gain it via computing. In addition, studies were excluded when they would meet these exclusion criteria: (1) reviews, case reports, or case-only studies; (2) studies with deficient genotype frequency date; (3) animals reports; and (4) replicate studies.

2.2. Data Extraction

The authors were able to excerpt relevant data from these qualified studies independently, and the following information would be seized: first author’s last name, publishing year, the ethnicity of each population, the genotyping methods, the control of source, cancer types, number of cases and controls, and P value of Hardy–Weinberg equilibrium. When encountering divergences, we analyzed the report and reached a consistent agreement lastly.

2.3. Statistical Analysis

95% confidence interval (CI) and odds ratio (OR) were utilized to estimate the relation between PON1-Q192R, or PON1-L55M polymorphism and the risk of cancer with five genetic models: heterozygote comparison (ML versus LL; RQ versus QQ), allele contrast (M versus L; R versus Q), homozygote (MM versus LL; RR versus QQ), recessive (MM versus ML+LL; RR versus RQ+QQ), and dominant (ML+MM versus LL; RR+RQ versus QQ). Besides, stratified analyses were conducted via ethnicity, cancer type, control source, and genotyping method. However, when any cancer type is less than two studies, we would segment it into the “other cancers” group. In addition, -test-based Q-statistic test [14] was taken to assess the research heterogeneity while values and P values [15] were used for quantifying. When I²< 50% and P>0.10, it indicates that there was no significant heterogeneity, and ORs could be pooled by a fixed-effects model. Otherwise, the random effects model would be adopted [16]. Furthermore, sensitivity analysis, from the qualified removing a single research study and revealing the individual data set to merge OR influence, was applied to estimate the stability of these data. (P<0.05 was regarded as statistically significant [17].) Finally, potential publication bias was estimated by symmetry of funnel plot of Begg’s test as well as Egger’s test [15, 18], and being statistically significant was considered when P<0.05. All statistical tests were performed with STATA Software (version 14.0, state Corp), and P<0.05 for any genetic models or tests was identified as statistically significant.

3. Result

3.1. Publication Characteristics

According to the inclusion criteria after detailed examination, a total of 43 case-control publications including 19977 cases and 23932 controls were employed in our study [11–13, 19–59]. The flow chart of the study screening process was summarized in Figure 1. Moreover, there were 43 studies with 11412 cases and 13936 controls for PON1-Q192R polymorphism (Table 1), and, for PON1 L55M polymorphism, 28 studies involved a total of 8565 cases and 9996 controls (Table 2). For PON1 Q192R polymorphism, a total of 8 cancer types were processed, including breast cancer [21, 27, 31, 32, 37, 39, 50], prostate cancer [22, 23, 40, 41], gastrointestinal cancer [19, 20, 48, 59, 60], hematologic tumor [25, 29, 33, 44], lung cancer [11, 12, 54], brain tumors [30, 35, 38, 45, 56, 57], ovarian cancer [34, 43] and other cancers [13, 26, 28, 42, 53, 58] (uterine leiomyoma, childhood embryonal tumors, metastatic gastric cancer, bladder cancer, and renal cell cancer). Besides, we disposed a total of 7 cancer types when dealing with PON1-L55M polymorphism nearly like PON1 Q192R polymorphism. In addition, For PON1 Q192R polymorphism, 9 publications were conducted in Asians, 9 in mixed group, and 25 publications in Caucasians. Besides, there were 15 studies divided by TaqMan assay, while 28 studies conducted by PCR- RFLP. Moreover, the majority of control groups in the case group are gender and age matching, including 23 hospital based and 20 population based. For PON1 L55M polymorphism, we also conducted 6, 6, and 16 studies in Asian, mixed group, Caucasians, respectively. Moreover, 10 studies were divided by TaqMan assay as well as 18 studies conducted by PCR- RFLP.

Table 1: Characteristics of qualified case-control studies included in the meta-analysis of PON1-Q192R.

Author
Year
Ethnicity
Genotyping Method
Control of source
Cancer Type
Case
Control
HWE

QQ
QR
RR
QQ
QR
RR

p
p(HWE)

Stevens et al.
2006
Caucasian
PCR-RFLP
P-B
Breast Cancer
259
182
42
238
198
47
0.38
0.54
Y

Gallicchio et al.
2007
Caucasian
PCR-RFLP
P-B
Breast Cancer
38
15
5
469
353
82
1.93
0.19
Y

Antognelli et al.
2009
Caucasian
PCR-RFLP
P-B
Breast Cancer
484
50
13
340
152
52
27.19
0.00
N

Hussein et al.
2011
Caucasian
PCR-RFLP
P-B
Breast Cancer
51
41
8
46
42
12
0.25
0.62
Y

Naidu et al.
2010
Asian
PCR-RFLP
H-B
Breast Cancer
200
158
29
115
115
22
0.81
0.37
Y

Tang et al.
2017
Asian
TaqMan
P-B
Esophagogastric
408
501
132
691
776
207
0.23
0.63
Y

Uluocak et al.
2017
Caucasian
PCR-RFLP
H-B
Prostate Cancer
24
17
8
45
42
11
0.06
0.80
Y

Wu et al.
2017
Asian
TaqMan
H-B
Breast Cancer
155
156
54
167
156
55
3.42
0.06
Y

Kaya et al.
2016
Caucasian
TaqMan
H-B
Breast Cancer
10
11
11
5
13
17
0.88
0.35
Y

Tomatir et al.
2015
Caucasian
PCR-RFLP
H-B
Hematologic Cancer
36
20
4
58
24
2
0.07
0.79
Y

Tomatir et al.
2015
Caucasian
PCR-RFLP
H-B
Hematologic Cancer
33
21
6
58
24
2
0.07
0.08
Y

Attar et al.
2015
Caucasian
PCR-RFLP
H-B
Uterine Leiomyoma
60
8
8
50
47
6
1.39
0.24
Y

Eom et al.
2015
Asian
PCR-RFLP
H-B
Lung Cancer
37
170
209
48
188
180
0.01
0.92
Y

Ahmed et al.
2015
Asian
PCR-RFLP
P-B
Colorectal Cancer
30
16
4
20
36
24
0.76
0.38
Y

Akkız et al.
2013
Caucasian
PCR-RFLP
P-B
Hepatocellular Carcinoma
109
95
13
115
88
14
0.27
0.60
Y

Vasconcelos et al.
2014
Mixed
TaqMan
H-B
Embryonal Tumors
36
85
41
104
160
72
0.51
0.48
Y

Conesa-Zamora et al.
2013
Caucasian
TaqMan
H-B
Lymphomas
83
99
33
100
104
10
7.00
0.01
N

Zhao et al.
2012
Asian
TaqMan
H-B
Glioma
161
158
52
159
167
52
0.59
0.44
Y

De Aguiar Goncalves et al.
2012
Mixed
TaqMan
H-B
Hematologic Tumor
96
102
40
74
106
54
1.790
0.180
Y

Kokouva et al.
2012
Caucasian
PCR-RFLP
H-B
Hematologic Cancer
213
88
15
181
141
29
0.04
0.83
Y

Aksoy-Sagirli et al.
2011
Caucasian
PCR-RFLP
H-B
Lung Cancer
93
111
19
121
93
20
0.13
0.72
Y

Uyar et al.
2011
Caucasian
PCR-RFLP
P-B
Renal Cell Cancer
38
21
1
27
27
6
0.04
0.84
Y

Lurie et al.
2008
Mixed
TaqMAN
P-B
Ovarian Cancer
66
120
86
122
211
111
1.07
0.30
Y

Ergen et al.
2010
Caucasian
PCR-RFLP
H-B
Osteosarcoma
27
21
2
15
33
2
0.06
0.80
Y

Martinez et al.
2010
Caucasian
TaqMan
H-B
Brain Tumor
31
33
9
22
89
109
0.37
0.54
Y

Ozturk et al.
2009
Caucasian
PCR-RFLP
H-B
Bladder Cancer
8
53
15
37
84
14
10.71
<0.001
N

Gold et al.
2009
Mixed
PCR-RFLP
P-B
Multiple Myeloma
10
19
13
9
27
19
0.01
0.91
Y

Arpaci et al.
2009
Caucasian
PCR-RFLP
H-B
Ovarian Cancer
38
6
6
17
29
6
1.46
0.23
Y

Rajaraman et al.
2008
Mixed
TaqMan
H-B
Brain Tumor
266
207
39
244
165
44
4.10
0.04
N

Searles Nielsen et al.
2005
Mixed
TaqMan
P-B
Brain Tumor
32
28
6
100
105
31
0.17
0.68
Y

Van der Logt et al.
2005
Caucasian
PCR-RFLP
P-B
Colorectal Cancer
180
150
24
158
120
17
0.87
0.35
Y

Lincz et al.
2004
Caucasian
PCR-RFLP
P-B
Multiple Myeloma
33
41
16
103
74
22
2.35
0.13
Y

Kerridge et al.
2002
Caucasian
PCR-RFLP
P-B
Lymphoma
73
50
39
103
74
22
2.35
0.13
Y

Antognelli et al.
2005
Caucasian
PCR-RFLP
H-B
Prostate Cancer
197
168
20
212
85
64
67.85
<0.001
N

Herrera et al.
2015
Mixed
TaqMan
H-B
Brain Tumor
15
32
20
12
32
14
0.64
0.42
Y

Kafadar et al.
2006
Caucasian
PCR-RFLP
H-B
Brain Tumor
43
26
15
24
18
8
1.96
0.16
Y

J. De Roos et al.
2006
Mixed
TaqMan
P-B
Hematologic Cancer
540
453
127
415
403
117
1.53
0.22
Y

Stevens et al.
2008
Mixed
TaqMan
P-B
Prostate Cancer
624
537
95
656
487
121
4.74
0.03
N

Antognelli et al.
2013
Caucasian
PCR-RFLP
H-B
Prostate Cancer
291
250
30
707
258
203
244.08
<0.001
N

Wang et al.
2012
Asian
PCR-RFLP
P-B
Lung Cancer
36
177
143
38
84
62
0.93
0.33
Y

Lee et al.
2005
Asian
TaqMan
P-B
Lung Cancer
24
80
73
11
89
77
4.999
0.025
N

Agachan et al.
2006
Caucasian
PCR-RFLP
P-B
Breast Cancer
17
4
12
6
29
17
1.461
0.230
Y

Hemati et al.
2019
Asian
PCR-RFLP
H-B
Gastric Cancer
39
41
10
62
26
2
0.03
0.87
Y

Abbreviations: PCR-RFlP, polymerase chain reaction-restriction fragment length polymorphism; HWE, Hardy–Weinberg equilibrium; Y, polymorphisms conforming to HWE in the control group; N, polymorphisms not conforming to HWE in the control group; H-B, hospital based; P-B, population based.

Table 2: Characteristics of qualified case-control studies included in the meta-analysis of PON1- L55M.

Author
Year
Ethnicity
Genotyping Method
Control of source
Cancer Type
Case
Control
HWE

LL
LM
MM
LL
LM
MM

p
p(HWE)

Stevens et al.
2006
Caucasian
PCR-RFLP
P-B
Breast Cancer
176
230
77
202
233
58
0.88
0.77
Y

Antognelli et al.
2009
Caucasian
PCR-RFLP
P-B
Breast Cancer
107
115
325
188
125
231
157.2
0.0001
N

Hussein et al.
2011
Caucasian
PCR-RFLP
P-B
Breast Cancer
19
21
60
35
23
6
0.58
0.44
Y

Naidu et al.
2010
Asian
PCR-RFLP
P-B
Breast Cancer
159
178
50
126
109
17
1.04
0.308
Y

Tang et al.
2017
Asian
TaqMan
P-B
Esophagogastric Cancer
971
69
1
1573
99
2
0.12
0.73
Y

Uluocak et al.
2017
Caucasian
PCR-RFLP
H-B
Prostate Cancer
19
24
6
43
45
10
0.13
0.72
Y

Wu et al.
2017
Asian
TaqMan
H-B
Breast Cancer
284
72
9
346
30
2
3.24
0.064
Y

Akkız et al.
2013
Caucasian
PCR-RFLP
P-B
Hepatocellular Carcinoma
105
81
31
101
89
27
1.12
0.29
Y

Geng R et al.
2014
Asian
TaqMan
H-B
Metastatic Gastric Cancer
11
7
0
82
7
0
0.15
0.7
Y

Vasconcelos et al.
2014
Mixed
TaqMan
H-B
Embryonal Tumors
85
56
15
177
134
25
0.023
0.95
Y

Metin et al.
2013
Caucasian
PCR-RFLP
H-B
Ovarian Cancer
33
22
0
33
19
2
0.13
0.72
Y

Vecka et al.
2012
Caucasian
PCR-RFLP
H-B
Pancreatic Cancer
24
39
10
28
37
8
0.67
0.41
Y

De Aguiar Goncalves et al.
2012
Mixed
TaqMan
H-B
Acute Leukemia
104
99
34
131
75
19
2.91
0.09
Y

Kokouva et al.
2012
Caucasian
PCR-RFLP
H-B
Hematologic Cancer
117
139
60
142
159
50
0.26
0.61
Y

Aksoy-Sagirli et al.
2011
Caucasian
PCR-RFLP
H-B
Lung Cancer
119
94
10
118
102
14
1.75
0.19
Y

Uyar et al.
2011
Caucasian
PCR-RFLP
P-B
Renal Cell Cancer
29
25
6
21
29
10
4.96
0.998
Y

Lurie et al.
2008
Mixed
TaqMan
P-B
Ovarian Cancer
14
65
192
24
145
276
0.74
0.39
Y

Ergen et al.
2010
Caucasian
PCR-RFLP
H-B
Osteosarcoma
24
23
3
21
20
9
1.14
0.29
Y

Martínez et al.
2010
Caucasian
TaqMan
H-B
Brain Tumor
11
32
30
38
94
88
2.15
0.14
Y

Arpaci et al.
2009
Caucasian
PCR-RFLP
H-B
Ovarian Cancer
27
19
5
25
27
2
2.65
0.103
Y

Van der Logt et al.
2005
Caucasian
PCR-RFLP
P-B
Colorectal Cancer
139
166
59
140
162
50
0.08
0.78
Y

Antognelli et al.
2005
Caucasian
PCR-RFLP
H-B
Prostate Cancer
120
197
67
148
169
43
0.65
0.35
Y

Herrera et al.
2015
Mixed
TaqMan
H-B
Brain Tumor
46
17
4
42
14
2
0.37
0.56
Y

J. De Roos et al.
2006
Mixed
TaqMan
P-B
Hematologic Cancer
299
307
100
282
260
69
0.59
0.44
Y

Stevens et al.
2008
Mixed
TaqMan
P-B
Prostate Cancer
481
609
165
498
575
189
1.18
0.28
Y

Wang et al.
2012
Asian
PCR-RFLP
P-B
Lung Cancer
307
47
2
166
18
0
0.49
0.49
Y

Antognelli et al.
2013
Caucasian
PCR-RFLP
H-B
Prostate Cancer
180
291
100
497
540
131
0.75
0.39
Y

Hemati et al.
2019
Asian
PCR-RFLP
H-B
Gastric Cancer
41
40
9
34
49
7
0.027
0.87
Y

Abbreviations: PCR-RFlP, polymerase chain reaction-restriction fragment length polymorphism; HWE, Hardy–Weinberg equilibrium; Y, polymorphisms conforming to HWE in the control group; N, polymorphisms not conforming to HWE in the control group; H-B, hospital based; P-B, population based.

Figure 1: Flow chart of the report selection process.

3.2. Meta-Analysis

3.2.1. Association between PON1-Q192R and Cancer Susceptibility

In summary, in allele contrast model, we have found that there were not association between PON1-Q192R allele and reduced overall cancer risk (Table 3). In the subgroup analysis of cancer type, we identified a decreased risk in breast cancer (R versus Q: OR=0.643, 95%CI=0.440-0.942; RR versus QQ: OR=0.542, 95%CI=0.331-0.886; RQ versus QQ: OR=0.529, 95%CI=0.325-0.861; and RR+RQ versus QQ: OR=0.534, 95%CI=0.330-0.865). Nevertheless, an increased risk was confirmed in prostate cancer in the dominant model (RR+RQ versus QQ: OR=1.249, 95%CI=1.030-1.514). Furthermore, by racial stratification, there was a reduced risk of cancer in recession model (RR+RQ versus QQ: OR=0.744, 95%CI=0.557-0.993) among Caucasian population. Similarly, in the stratification analysis of control source, the overall risk of cancer is reduced in the heterozygote comparison and dominant model (RQ versus QQ: OR= 0.793, 95%CI=0.638-0.984; RR+RQ versus QQ: OR=0.789, 95%CI=0.630-0.988) in the population-based group. In addition, we did not observe any risk factor by stratified analysis using genotyping method. Figure 2 showed the meta-analysis of the association between PON1-Q192R polymorphism and cancer risk (R versus Q)

Table 3: Results of meta-analysis for PON1-Q192R polymorphism and cancer risk.

Variables
Case/control
R vs. Q
RR vs. QQ
RQ vs. QQ
RR+RQ vs. QQ
RR vs. RQ+QQ

OR(95% CI)

I²(%)
OR(95% CI)

I²(%)
OR(95% CI)

I²(%)
OR(95% CI)

I²(%)
OR(95% CI)

I²(%)

Total
11412/13936
0.897(0.798-1.008)
0
86.8
0.855(0.683-1.073)
0
81.1
0.861(0.724-1.023)
0
86.7
0.857(0.730-1.008)
0
86.7
0.914(0.758-1.102)
0
78.1

    Breast cancer
2005/2748
0.643(0.440-0.942)
0
91.4
0.542(0.331-0.886)
0
74
0.529(0.325-0.861)
0
89.1
0.534(0.330-0.865)
0
90.6
0.720(0.492-1.054)
0.010
62.3

    Gastrointestinal cancer
1752/2356
1.008(0.700-1.450)
0
88.1
0.969(0.463-2.025)
0.000
80.2
1.079(0.761-1.529)
0.002
75.7
1.038(0.682-1.580)
0.000
85.0
0.968(0.547-1.711)
0.013
68.5

    Prostate cancer
2261/2891
0.967(0.886-1.055)
0.748
0
0.563(0.313-1.015)
0.001
83
1.544(0.969-2.458)
0
90.9
1.249(1.030-1.514)
0.083
55
0.498(0.235-1.053)
0
90.3

    Hematologic tumor
2303/2355
1.113(0.852-1.453)
0
85.5
1.358(0.787-2.341)
0
82
0.943(0.740-1.202)
0.007
62.3
1.040(0.774-1.397)
0
78.2
1.364(0.863-2.157)
0
77.8

    Lung cancer
1172/1011
1.179(0.949-1.464)
0.04
63.8
1.244(0.665-2.326)
0.005
76.6
1.214(0.704-2.094)
0.004
77.5
1.262(0.741-2.149)
0.003
78.6
1.201(0.997-1.449)
0.442
0

    Brain tumor
1173/1395
0.759(0.505-1.140)
0
89.2
0.576(0.266-1.248)
0
85.9
0.778(0.539-1.124)
0.006
69
0.696(0.431-1.123)
0
84.4
0.698(0.388-1.256)
0
79.4

    Ovarian cancer
322/496
0.665(0.189-2.337)
0
92.7
0.940(0.314-2.813)
0.087
65.8
0.328(0.030-3.572)
0
94.6
0.443(0.060-3.283)
0
94.5
1.359(0.985-1.875)
0.658
0

    Other cancers
424/684
0.809(0.481-1.362)
0
85.1
1.264(0.515-3.101)
0.022
65
0.670(0.250-1.798)
0
89.4
0.741(0.299-1.838)
0
89.1
1.352(0.797-2.294)
0.209
31.8

Ethnicities

    Caucasian
4424/6292
0.815(0.658-1.011)
0
90.1
0.784(0.516-1.193)
0
84.5
0.731(0.528-1.010)
0
91.1
0.744(0.557-0.993)
0
90.3
0.893(0.608-1.311)
0
83.5

    Asian
3253/3629
0.840(0.840-1.244)
0
89.9
1.019(0.689-1.506)
0
78.5
1.020(0.779-1.337)
0
76.3
1.022(0.758-1.377)
0
83.0
1.052(0.850-1.303)
0.025
54.4

    Mixed
3735/4015
0.981(0.877-1.098)
0.035
51.6
0.913(0.727-1.145)
0.062
46.2
1.012(0.873-1.174)
0.109
38.8
0.990(0.851-1.153)
0.057
47
0.929(0.770-1.119)
0.115
38.1

Control source

    Population based
6871/8354
0.849(0.717-1.004)
0
88.8
0.802(0.604-1.065)
0
79.1
0.793(0.638-0.984)
0
85
0.789(0.630-0.988)
0
87.9
0.920(0.749-1.130)
0
70.3

    Hospital based
4309/5667
0.897(0.798-1.008)
0
85.1
0.948(0.655-1.374)
0
83.2
0.927(0.704-1.221)
0
87.3
0.923(0.726-1.175)
0
85.5
0.967(0.696-1.342)
0
83.3

Genotype method

    PCR-RFLP
5445/6900
0.884(0.735-1.064)
0
88.4
0.888(0.735-1.064)
0
88.4
0.815(0.607-1.094)
0
90.4
0.839(0.646-1.091)
0
89.4
0.938(0.692-1.271)
0
80.5

    TaqMan
5967/7036
0.922(0.801-1.060)
0
83
0.834(0.622-1.117)
0
81.3
0.952(0.824-1.099)
0.001
61.1
0.906(0.762-1.078)
0
76.7
0.915(0.736-1.139)
0
73.5

Notes: statistically significant (P<0.05); P : P value of Q test for heterogeneity test; I²: 0%–25% means no heterogeneity, 25%–50% means modest heterogeneity, and 50% means high heterogeneity. Abbreviations: CI, confidence interval; OR, odds ratio; PCR-RFLP, polymerase chain reaction-restriction fragment length polymorphism.

Figure 2: Meta-analysis of the association between PON1-Q192R polymorphism and cancer risk (R versus Q). Abbreviations: ID, identification; CI, confidence interval; NA, not available; OR, odds ratio; weights come from random effects analysis.

3.2.2. Association between PON1-L55M and Cancer Susceptibility

Our study had uncovered that the PON1-L55M polymorphism was significantly associated with an increased risk of the overall cancers under all the genetic models (Table 4) (M versus L: OR =1.277, 95% CI =1.127-1.448; MM versus LL: OR =1.507, 95% CI =1.205-1.885; ML versus LL: OR =1.192, 95%CI =1.064-1.337; MM versus ML+LL: OR =1.288, 95%CI =1.120-1.408; ML+MM versus LL: OR =1.417, 95%CI =1.176-1.708). Furthermore, we found an increased risk of breast cancer under all the five models when conducting the cancer type subgroup analysis (M versus L: OR =2.186, 95%CI =1.438-3.323; MM versus LL: OR =3.215, 95%CI=1.756-5.886; ML versus LL: OR =1.579, 95%CI=1.145-2.177; MM versus ML+LL: OR =2.727, 95%CI=1.563-4.756; ML+MM versus LL: OR =2.110, 95%CI =1.397-3.188), prostate cancer in the dominant and heterozygote comparison model (ML versus LL: OR =1.291, 95% CI =1.071-1.557; ML+MM versus LL: OR =1.341, 95%CI=1.024-1.756), and hematologic tumor in the allele contrast model (M versus L: OR =1.271, 95% CI =1.059-1.525), homozygote model (MM versus LL: OR =1.514, 95%CI =1.178-1.946), recessive model (MM versus ML+LL: OR =1.405, 95%CI =1.111-1.778), and dominant model (ML+MM versus LL: OR =1.299, 95%CI =1.017-1.661). Figure 3 showed the meta-analysis of the association between PON1-L55M polymorphism and cancer risk (M versus L).

Table 4: Results of meta-analysis for PON1-L55M polymorphism and cancer risk.

Variables
Case/control
M vs. L
MM vs. LL
ML vs. LL
ML+MM vs. LL
MM vs. ML+LL

OR(95% CI)

I²(%)
OR(95% CI)

I²(%)
OR(95% CI)

I²(%)
OR(95% CI)

I²(%)
OR(95% CI)

I²(%)

Total
8565/9996
1.277(1.127-1.448)
0
81.6
1.507(1.205-1.885)
0
68.5
1.192(1.064-1.337)
0.001
50.6
1.288(1.120-1.480)
0
70.9
1.417(1.176-1.708)
0
64.2

Breast cancer
1882/1731
2.186(1.438-3.323)
0
92.5
3.215(1.756-5.886)
0
81.8
1.579(1.145-2.177)
0.01
69.9
2.110(1.397-3.188)
0
85.3
2.727(1.563-4.756)
0
81.9

Gastrointestinal cancer
1803/2495
1.111(0.898-1.375)
0.071
50.7
1.165(0.848-1.601)
0.988
0
1.097(0.794-1.515)
0.023
61.5
1.120(0.829-1.512)
0.032
59.0
1.185(0.881-1.594)
0.996
0

Prostate cancer
2259/2888
1.233(0.971-1.566)
0
84.3
1.496(0.876-2.556)
0
85.5
1.291(1.071-1.557)
0.144
44.6
1.341(1.024-1.756)
0.008
74.6
1.284(0.838-1.966)
0.002
80.5

Hematologic tumor
1259/1187
1.271(1.059-1.525)
0.121
52.7
1.514(1.178-1.946)
0.376
0
1.212(0.954-1.540)
0.172
43.1
1.299(1.017-1.661)
0.124
52
1.405(1.111-1.778)
0.622
0

Ovarian cancer
377/553
1.219(0.965-1.539)
0.450
0
1.208(0.648-2.253)
0.393
0
0.833(0.536-1.296)
0.579
0
0.952(0.623-1.454)
0.802
0
1.482(0.800-2.743)
0.316
13.2

Lung cancer
579/418
1.095(0.666-1.801)
0.104
62.2
0.781(0.344-1.771)
0.404
0
1.074(0.711-1.622)
0.215
34.8
1.089(0.670-1.767)
0.146
52.7
0.806(0.361-1.799)
0.432
0

Other cancers
406/724
0.932(0.753-1.155)
0.333
12.6
0.884(0.505-1.548)
0.215
31
0.907(0.682-1.206)
0.801
0
0.910(0.696-1.190)
0.625
0
0.930(0.584-1.481)
0.252
25.4

Ethnicities

Caucasian
3616/4392
1.231(1.028-1.474)
0
83.8
1.737(1.519-1.986)
0
72
1.170(1.034-1.324)
0.199
22.4
1.334(1.215-1.465)
0
70.7
1.407(1.092-1.813)
0
67.8

Asian
2257/2667
1.604(1.089-2.363)
0
80.7
2.093(1.295-3.381)
0.441
0
1.550(0.995-2.417)
0.000
79.7
1.624(1.041-2.535)
0
80.8
1.967(1.238-3.125)
0.656
0

Mixed
2692/2937
1.177(1.004-1.379)
0.019
63.1
1.137(0.955-1.354)
0.088
47.8
1.112(0.953-1.297)
0.268
22.1
1.126(1.006-1.261)
0.165
36.3
1.262(0.957-1.665)
0.034
58.4

Control source

Population based
5787/6158
1.325(1.085-1.618)
0
88.7
1.568(1.091-2.253)
0
81.9
1.275(1.051-1.548)
0.401
4.5
1.275(1.051-1.548)
0
75.7
1.503(1.110-2.034)
0
80.5

Hospital based
2778/3838
1.240(1.056-1.456)
0
68.9
1.531(1.199-1.955)
0.132
29.8
1.255(1.020-1.543)
0.000
62.3
1.288(1.120-1.480)
0
66.7
1.411(1.173-1.698)
0.324
11.6

Genotype method

PCR-RFLP
4376/4698
1.243(1.053-1.466)
0
82.2
1.571(1.183-2.087)
0
70.1
1.164(1.033-1.311)
0.145
26.5
1.246(1.045-1.487)
0
69.3
1.483(1.167-1.884)
0
62.7

TaqMan
4189/5298
1.330(1.091-1.622)
0
79.5
1.309(0.988-1.735)
0.091
41.4
1.307(1.026-1.665)
0.001
71.5
1.370(1.073-1.748)
0
74.7
1.264(0.986-1.620)
0.05
48.5

Notes: statistically significant (P<0.05); P : P value of Q test for heterogeneity test; I²: 0%–25% means no heterogeneity, 25%–50% means modest heterogeneity, and 50% means high heterogeneity.

Abbreviations: CI, confidence interval; OR, odds ratio; PCR-RFLP, polymerase chain reaction-restriction fragment length polymorphism.

Figure 3: Meta-analysis of the association between PON1-L55M polymorphism and cancer risk (M versus L). Abbreviations: ID, identification; CI, confidence interval; NA, not available; OR, odds ratio; weights come from random effects analysis.

Similarly, an increased risk was observed in the Caucasian population under the five genetic models: M versus L: OR =1.231, 95% CI = 1.028-1.474; MM versus LL: OR =1.737, 95% CI =1.519-1.986; ML versus LL: OR =1.170, 95% CI =1.034-1.324; MM versus ML+LL: OR =1.407, 95%CI =1.092-1.813; ML+MM versus LL: OR =1.334, 95%CI =1.215-1.465, the Asian population (M versus L: OR =1.604, 95% CI =1.089-2.363; MM versus LL: OR =2.093, 95% CI =1.295-3.381; ML versus LL: OR =1.550, 95% CI =0.995-2.417; MM versus ML+LL: OR =1.624, 95%CI =1.041-2.535; ML+MM versus LL: OR =1.967, 95%CI =1.238-3.125), the mixed population (M versus L: OR =1.177, 95%CI =1.004-1.379; ML+MM versus LL: OR =1.126, 95%CI =1.006-1.261) (Table 4), hospital-based group (M versus L: OR =11.240, 95%CI=1.056-1.456; MM versus LL: OR =1.531, 95%CI =1.199-1.955; ML versus LL: OR =1.255, 95%CI =1.020-1.543; MM versus ML+LL: OR =1.288, 95%CI =1.120-1.480; ML+MM versus LL: OR =1.411, 95%CI=1.173-1.698), and population-based group (M versus L: OR =1.325, 95%CI=1.085-1.618; MM versus LL: OR =1.568, 95%CI =1.091-2.253; ML versus LL: OR =1.275, 95%CI =1.051-1.548; MM versus ML+LL: OR =1.503, 95%CI =1.110-2.034; ML+MM versus LL: OR =1.222, 95%CI=1.122-1.331). In addition, we identified an increased risk by stratified analysis using genotyping method.

3.2.3. Publication Bias and Sensitivity Analysis

A sensitivity analysis was carried out to detect the impact of individual papers on whole data by getting rid of one report at a time from the pooled analysis. And no individual report has been significantly affected by the pooled OR. Figure 4 showed the plot of the sensitivity analysis for evaluating the association between PON1-Q192R and cancer risk (RR versus QQ). Besides, we perform Egger’s test and Begg’s funnel plot to evaluate publication bias (Figure 5). And the results of Egger’s test and Begg’s funnel plot did not uncover publication bias in PON1 (Q192R and L55M) gene polymorphisms (PON1 Q192R: R versus Q: Begg’s test: z=1.74 P=0.082; Egger’s test: t= -1.26 P=0.216; PON1-L55M: M versus L: Begg’s test: z=0.06 p=0.953; Egger’s test: t= 0.66; P=0.516). Thus, our results are believable due to the absence of significant publication bias in our meta-analysis.

Figure 4: Sensitivity analysis of PON1-Q192R in overall OR coefficients (RR versus QQ). Abbreviations: OR, odds ratio CI, confidence interval. Sequentially calculated results of each study are omitted. Both ends of the broken line represent 95% of the CI.

Figure 5: Funnel figure of PON1-Q192R in overall OR coefficients (RR versus QQ). Abbreviations: OR, odds ratio.

4. Discussion

Several studies have indicated that PON1, which is one of xenobiotic metabolising enzymes, plays a crucial role in the detoxification of carcinogenic compounds and decreases oxidative stress. Genetic polymorphisms can influence the enzyme and modify its activity, resulting in an impact on individual sensitivity to certain pathologies [61]. Indeed, a great deal of researches have showed that polymorphisms encoding the gene of these enzymes have been linked to the progression of cancer [49, 62]. Furthermore, several variants of PON1, including Q192R and L55M, have been found to be a biologically reasonable candidate which has an obvious influence on cancer. PON1 (Q192R and L55M) gene polymorphisms were related to many types of cancer, such as breast, prostate, and hepatocellular carcinoma [20, 50, 63]. For instance, PON1-L55M polymorphism may increase the risk in multiple cancer types, such as prostate and breast cancers but decrease renal cell carcinoma and ovarian cancer risk. As for PON1-Q192R, it has been revealed to suppress expression in lung [64] and pancreatic cancer [65] and reduce the risk of breast and prostate cancers. And the results of these researches were inconclusive and controversial.

In our work, in all genetic models we have identified the significant association between PON1-L55M polymorphism and overall cancer risk, while PON1-Q192R allele was not associated with reduced overall cancer risks. In the stratified analysis, we observed an increased risk in the Caucasian population and the Asian population, as well as the hospital-based group and population-based group under all the five genetic models in the PON1-L55M polymorphism. Similarly, a significantly increased risk of the overall cancers under the homozygote, allele contrast, recessive, and dominant models was uncovered in hematological tumor in the PON1-L55M polymorphism. Nevertheless, in the PON1-Q192R polymorphism, we also observe a reduced risk of the overall cancers in the allele contrast and dominant models. Meanwhile, we could obtain an interesting phenomenon that PON1-L55M polymorphism acts as a risk factor in all the five genetic models and there was an association between Q192R polymorphism and a reduced risk for cancer progression (except recessive model) after stratified analyses by cancer type, especially breast cancer. Thus, we can obtain that PON1 (Q192R and L55M) gene polymorphisms play a vital role in the development of breast cancer, whose mechanism maybe as follows: there was a critical association between L allele and higher PON1 serum concentrations while M variant decreased the stability of this enzyme. Therefore, the blood concentration of PON1 was reduced in this way; then, the activity of the enzyme was influenced, which may increase the vulnerability to genomic damage by reducing the inflammatory oxidant and the detoxifying ability of dietary carcinogens, thereby increasing the risk of breast cancer [5]. Furthermore, breast cancer becomes more susceptible to genomic damage as a result of lower levels of PON1 which could decrease the ability to detoxify inflammatory oxidants and dietary carcinogens [5]. Similarly, the exchange of Q and R could produce an enzyme which has a higher detoxification activity when there were potential carcinogenic products of oxidative stress and lipid peroxidation [66, 67]. In addition, not only genetic factors but also other contributors including nutrition and lifestyle can significantly affect PON1 enzyme activity, thereby reducing the risk of breast cancer [68]. To sum up, PON1, as a member of lipid peroxidation scavenging systems, may have an impact on malignant transformation and cell proliferation in the progression of breast cancer [69]. In the ethnographic analysis, we found ethnic groups having different results, which may be due to ethnic living habits, living environment, and genetic factors.

Previous meta-analysis also reported the association of PON1 polymorphism with cancer risk [10, 70]. As far as we know, we are the first of the typical functional polymorphism of the PON1 gene including all the published and defined case-control studies that have been conducted in a comprehensive meta-analysis. Compared with previous researches, our report was more persuasive and we have carried out a more detailed analysis to demonstrate our results. First and most obviously, the data we collected in our study was up-to-date, and we could keep up with the research front. Secondly, we included more qualified studies and larger sample size, which indicates that we are relatively more accurate in assessing that association between the PON1 gene SNPs and the risk of cancer.

Despite the association between PON1 (Q192R and L55M) gene polymorphism and cancer risk which has been studied in detail, we should note some limitations at the same time. First of all, the quantity of publications collected in our study was limited and there was a relatively small sample size of the report. What is more, Caucasian accounted for the most of the registered publications and there were no Africans. Furthermore, some of publications would only publish positive results, which could make the meta-analysis less credible. Lastly, our results were based on the estimates of single-factor, which could lead to serious confusion and bias due to the lack of raw data, and there is a need to adjust the effect size with possible confounders related to lifestyle risk factors, such as age, obesity, alcohol consumption, and smoking.

In conclusion, our study has demonstrated that PON1-Q192R can significantly reduce the risk of cancer and the polymorphism of PON1-L55M is a risk factor leading to cancer, especially breast cancer. Next, we need a larger sample size at protein levels to confirm whether PON1 polymorphisms may be potential genetic markers of tumor prognosis and identify its role in the risk of women developing breast cancer.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Authors’ Contributions

Bo Zhu, Lingsha Huang, and Jinfeng Liu conceived and designed the experiments; Xiaolan Pan, Lei Huang, and Meiqin Li conduced literature review and data abstraction; Xiaolan Pan and Bo Zhu analyzed data; and Dan Mo, Yihua Liang, and Zhaodong Huang conducted a hand search for extra studies. Xiaolan Pan, Bo Zhu, Lingsha Huang, and Meiqin Li wrote the manuscript. All authors reviewed and approved the manuscript. Xiaolan Pan, Lei Huang, and Meiqin Li have contributed equally to this work.

Acknowledgments

This work was supported by grants from National Science Foundation of China (81760530) and National Science Foundation of Guangxi (2017GXNSFBA198047).

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