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. 2019 Jul 28;2019:5897505. doi: 10.1155/2019/5897505

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,✉, Bo Zhu 1,✉
PMCID: PMC6699405  PMID: 31467900

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, χ2-test-based Q-statistic test [14] was taken to assess the research heterogeneity while I2 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.

Figure 1.

Figure 1

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Flow chart of the report selection process.

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 χ 2 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
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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 χ 2 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
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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.

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) pa I2(%) OR(95% CI) pa I2(%) OR(95% CI) pa I2(%) OR(95% CI) pa I2(%) OR(95% CI) pa 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
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Notes: ∗ statistically significant (P<0.05); P valuea: 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.

Figure 2

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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) pa I2(%) OR(95% CI) pa I2(%) OR(95% CI) pa I2(%) OR(95% CI) pa I2(%) OR(95% CI) pa 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
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Notes: ∗ statistically significant (P<0.05); P valuea: 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.

Figure 3

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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.

Figure 4

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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.

Figure 5

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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.

Acknowledgments

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

Contributor Information

Lingsha Huang, Email: huanglinshagx@126.com.

Jinfeng Liu, Email: rainbowgxnn@yeah.net.

Bo Zhu, Email: zhubogxnn@126.com.

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.

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