BioMed Research International

BioMed Research International / 2019 / Article

Review Article | Open Access

Volume 2019 |Article ID 5897505 | 14 pages | https://doi.org/10.1155/2019/5897505

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

Academic Editor: Giandomenico Roviello
Received25 Feb 2019
Revised16 Jun 2019
Accepted01 Jul 2019
Published28 Jul 2019

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 [1113, 1959]. 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.


AuthorYearEthnicityGenotyping MethodControl of sourceCancer TypeCaseControlHWE
QQQRRRQQQRRRpp(HWE)

Stevens et al.2006CaucasianPCR-RFLPP-BBreast Cancer25918242238198470.380.54Y
Gallicchio et al.2007CaucasianPCR-RFLPP-BBreast Cancer38155469353821.930.19Y
Antognelli et al.2009CaucasianPCR-RFLPP-BBreast Cancer48450133401525227.190.00N
Hussein et al.2011CaucasianPCR-RFLPP-BBreast Cancer514184642120.250.62Y
Naidu et al.2010AsianPCR-RFLPH-BBreast Cancer20015829115115220.810.37Y
Tang et al.2017AsianTaqManP-BEsophagogastric4085011326917762070.230.63Y
Uluocak et al.2017CaucasianPCR-RFLPH-BProstate Cancer241784542110.060.80Y
Wu et al.2017AsianTaqManH-BBreast Cancer15515654167156553.420.06Y
Kaya et al.2016CaucasianTaqManH-BBreast Cancer101111513170.880.35Y
Tomatir et al.2015CaucasianPCR-RFLPH-BHematologic Cancer36204582420.070.79Y
Tomatir et al.2015CaucasianPCR-RFLPH-BHematologic Cancer33216582420.070.08Y
Attar et al.2015CaucasianPCR-RFLPH-BUterine Leiomyoma6088504761.390.24Y
Eom et al.2015AsianPCR-RFLPH-BLung Cancer37170209481881800.010.92Y
Ahmed et al.2015AsianPCR-RFLPP-BColorectal Cancer301642036240.760.38Y
Akkız et al.2013CaucasianPCR-RFLPP-BHepatocellular Carcinoma109951311588140.270.60Y
Vasconcelos et al.2014MixedTaqManH-BEmbryonal Tumors368541104160720.510.48Y
Conesa-Zamora et al.2013CaucasianTaqManH-BLymphomas839933100104107.000.01N
Zhao et al.2012AsianTaqManH-BGlioma16115852159167520.590.44Y
De Aguiar Goncalves et al.2012MixedTaqManH-BHematologic Tumor961024074106541.7900.180Y
Kokouva et al.2012CaucasianPCR-RFLPH-BHematologic Cancer2138815181141290.040.83Y
Aksoy-Sagirli et al.2011CaucasianPCR-RFLPH-BLung Cancer931111912193200.130.72Y
Uyar et al.2011CaucasianPCR-RFLPP-BRenal Cell Cancer38211272760.040.84Y
Lurie et al.2008MixedTaqMANP-BOvarian Cancer66120861222111111.070.30Y
Ergen et al.2010CaucasianPCR-RFLPH-BOsteosarcoma27212153320.060.80Y
Martinez et al.2010CaucasianTaqManH-BBrain Tumor3133922891090.370.54Y
Ozturk et al.2009CaucasianPCR-RFLPH-BBladder Cancer8531537841410.71<0.001N
Gold et al.2009MixedPCR-RFLPP-BMultiple Myeloma101913927190.010.91Y
Arpaci et al.2009CaucasianPCR-RFLPH-BOvarian Cancer3866172961.460.23Y
Rajaraman et al.2008MixedTaqManH-BBrain Tumor26620739244165444.100.04N
Searles Nielsen et al.2005MixedTaqManP-BBrain Tumor32286100105310.170.68Y
Van der Logt et al.2005CaucasianPCR-RFLPP-BColorectal Cancer18015024158120170.870.35Y
Lincz et al.2004CaucasianPCR-RFLPP-BMultiple Myeloma33411610374222.350.13Y
Kerridge et al.2002CaucasianPCR-RFLPP-BLymphoma73503910374222.350.13Y
Antognelli et al.2005CaucasianPCR-RFLPH-BProstate Cancer19716820212856467.85<0.001N
Herrera et al.2015MixedTaqManH-BBrain Tumor1532201232140.640.42Y
Kafadar et al.2006CaucasianPCR-RFLPH-BBrain Tumor432615241881.960.16Y
J. De Roos et al.2006MixedTaqManP-BHematologic Cancer5404531274154031171.530.22Y
Stevens et al.2008MixedTaqManP-BProstate Cancer624537956564871214.740.03N
Antognelli et al.2013CaucasianPCR-RFLPH-BProstate Cancer29125030707258203244.08<0.001N
Wang et al.2012AsianPCR-RFLPP-BLung Cancer361771433884620.930.33Y
Lee et al.2005AsianTaqManP-BLung Cancer2480731189774.9990.025N
Agachan et al.2006CaucasianPCR-RFLPP-BBreast Cancer17412629171.4610.230Y
Hemati et al.2019AsianPCR-RFLPH-BGastric Cancer394110622620.030.87Y

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.

AuthorYearEthnicityGenotyping MethodControl of sourceCancer TypeCaseControlHWE
LLLMMMLLLMMMpp(HWE)

Stevens et al.2006CaucasianPCR-RFLPP-BBreast Cancer17623077202233580.880.77Y
Antognelli et al.2009CaucasianPCR-RFLPP-BBreast Cancer107115325188125231157.20.0001N
Hussein et al.2011CaucasianPCR-RFLPP-BBreast Cancer192160352360.580.44Y
Naidu et al.2010AsianPCR-RFLPP-BBreast Cancer15917850126109171.040.308Y
Tang et al.2017AsianTaqManP-BEsophagogastric Cancer97169115739920.120.73Y
Uluocak et al.2017CaucasianPCR-RFLPH-BProstate Cancer192464345100.130.72Y
Wu et al.2017AsianTaqManH-BBreast Cancer2847293463023.240.064Y
Akkız et al.2013CaucasianPCR-RFLPP-BHepatocellular Carcinoma105813110189271.120.29Y
Geng R et al.2014AsianTaqManH-BMetastatic Gastric Cancer117082700.150.7Y
Vasconcelos et al.2014MixedTaqManH-BEmbryonal Tumors855615177134250.0230.95Y
Metin et al.2013CaucasianPCR-RFLPH-BOvarian Cancer33220331920.130.72Y
Vecka et al.2012CaucasianPCR-RFLPH-BPancreatic Cancer243910283780.670.41Y
De Aguiar Goncalves et al.2012MixedTaqManH-BAcute Leukemia104993413175192.910.09Y
Kokouva et al.2012CaucasianPCR-RFLPH-BHematologic Cancer11713960142159500.260.61Y
Aksoy-Sagirli et al.2011CaucasianPCR-RFLPH-BLung Cancer1199410118102141.750.19Y
Uyar et al.2011CaucasianPCR-RFLPP-BRenal Cell Cancer292562129104.960.998Y
Lurie et al.2008MixedTaqManP-BOvarian Cancer1465192241452760.740.39Y
Ergen et al.2010CaucasianPCR-RFLPH-BOsteosarcoma24233212091.140.29Y
Martínez et al.2010CaucasianTaqManH-BBrain Tumor1132303894882.150.14Y
Arpaci et al.2009CaucasianPCR-RFLPH-BOvarian Cancer27195252722.650.103Y
Van der Logt et al.2005CaucasianPCR-RFLPP-BColorectal Cancer13916659140162500.080.78Y
Antognelli et al.2005CaucasianPCR-RFLPH-BProstate Cancer12019767148169430.650.35Y
Herrera et al.2015MixedTaqManH-BBrain Tumor46174421420.370.56Y
J. De Roos et al.2006MixedTaqManP-BHematologic Cancer299307100282260690.590.44Y
Stevens et al.2008MixedTaqManP-BProstate Cancer4816091654985751891.180.28Y
Wang et al.2012AsianPCR-RFLPP-BLung Cancer3074721661800.490.49Y
Antognelli et al.2013CaucasianPCR-RFLPH-BProstate Cancer1802911004975401310.750.39Y
Hemati et al.2019AsianPCR-RFLPH-BGastric Cancer41409344970.0270.87Y

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)


VariablesCase/controlR vs. QRR vs. QQRQ vs. QQRR+RQ vs. QQRR vs. RQ+QQ
OR(95% CI)I2(%)OR(95% CI)I2(%)OR(95% CI)I2(%)OR(95% CI)I2(%)OR(95% CI)I2(%)

Total11412/139360.897(0.798-1.008)086.80.855(0.683-1.073)081.10.861(0.724-1.023)086.70.857(0.730-1.008)086.70.914(0.758-1.102)078.1
 Breast cancer2005/27480.643(0.440-0.942) 091.40.542(0.331-0.886)0740.529(0.325-0.861)089.10.534(0.330-0.865)090.60.720(0.492-1.054)0.01062.3
 Gastrointestinal cancer1752/23561.008(0.700-1.450)088.10.969(0.463-2.025)0.00080.21.079(0.761-1.529)0.00275.71.038(0.682-1.580)0.00085.00.968(0.547-1.711)0.01368.5
 Prostate cancer2261/28910.967(0.886-1.055)0.74800.563(0.313-1.015)0.001831.544(0.969-2.458)090.91.249(1.030-1.514)0.083550.498(0.235-1.053)090.3
 Hematologic tumor2303/23551.113(0.852-1.453)085.51.358(0.787-2.341)0820.943(0.740-1.202)0.00762.31.040(0.774-1.397)078.21.364(0.863-2.157)077.8
 Lung cancer1172/10111.179(0.949-1.464)0.0463.81.244(0.665-2.326)0.00576.61.214(0.704-2.094)0.00477.51.262(0.741-2.149)0.00378.61.201(0.997-1.449)0.4420
 Brain tumor1173/13950.759(0.505-1.140)089.20.576(0.266-1.248)085.90.778(0.539-1.124)0.006690.696(0.431-1.123)084.40.698(0.388-1.256)079.4
 Ovarian cancer322/4960.665(0.189-2.337)092.70.940(0.314-2.813)0.08765.80.328(0.030-3.572)094.60.443(0.060-3.283)094.51.359(0.985-1.875)0.6580
 Other cancers424/6840.809(0.481-1.362)085.11.264(0.515-3.101)0.022650.670(0.250-1.798)089.40.741(0.299-1.838)089.11.352(0.797-2.294)0.20931.8
Ethnicities
 Caucasian4424/62920.815(0.658-1.011)090.10.784(0.516-1.193)084.50.731(0.528-1.010)091.10.744(0.557-0.993)090.30.893(0.608-1.311)083.5
 Asian3253/36290.840(0.840-1.244)089.91.019(0.689-1.506)078.51.020(0.779-1.337)076.31.022(0.758-1.377)083.01.052(0.850-1.303)0.02554.4
 Mixed3735/40150.981(0.877-1.098)0.03551.60.913(0.727-1.145)0.06246.21.012(0.873-1.174)0.10938.80.990(0.851-1.153)0.057470.929(0.770-1.119)0.11538.1
Control source
 Population based6871/83540.849(0.717-1.004)088.80.802(0.604-1.065)079.10.793(0.638-0.984)0850.789(0.630-0.988)087.90.920(0.749-1.130)070.3
 Hospital based4309/56670.897(0.798-1.008)085.10.948(0.655-1.374)083.20.927(0.704-1.221)087.30.923(0.726-1.175)085.50.967(0.696-1.342)083.3
Genotype method
 PCR-RFLP5445/69000.884(0.735-1.064)088.40.888(0.735-1.064)088.40.815(0.607-1.094)090.40.839(0.646-1.091)089.40.938(0.692-1.271)080.5
 TaqMan5967/70360.922(0.801-1.060)0830.834(0.622-1.117)081.30.952(0.824-1.099)0.00161.10.906(0.762-1.078)076.70.915(0.736-1.139)073.5

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.
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).


VariablesCase/controlM vs. LMM vs. LLML vs. LLML+MM vs. LLMM vs. ML+LL
OR(95% CI)I2(%)OR(95% CI)I2(%)OR(95% CI)I2(%)OR(95% CI)I2(%)OR(95% CI)I2(%)

Total8565/99961.277(1.127-1.448)081.61.507(1.205-1.885)068.51.192(1.064-1.337)0.00150.61.288(1.120-1.480)070.91.417(1.176-1.708)064.2
Breast cancer1882/17312.186(1.438-3.323) 092.53.215(1.756-5.886)081.81.579(1.145-2.177) 0.0169.92.110(1.397-3.188)085.32.727(1.563-4.756)081.9
Gastrointestinal cancer1803/24951.111(0.898-1.375)0.07150.71.165(0.848-1.601)0.98801.097(0.794-1.515)0.02361.51.120(0.829-1.512)0.03259.01.185(0.881-1.594)0.9960
Prostate cancer2259/28881.233(0.971-1.566)084.31.496(0.876-2.556)085.51.291(1.071-1.557)0.14444.61.341(1.024-1.756)0.00874.61.284(0.838-1.966)0.00280.5
Hematologic tumor1259/11871.271(1.059-1.525)0.12152.71.514(1.178-1.946)0.37601.212(0.954-1.540)0.17243.11.299(1.017-1.661)0.124521.405(1.111-1.778)0.6220
Ovarian cancer377/5531.219(0.965-1.539)0.45001.208(0.648-2.253)0.39300.833(0.536-1.296)0.57900.952(0.623-1.454)0.80201.482(0.800-2.743)0.31613.2
Lung cancer579/4181.095(0.666-1.801)0.10462.20.781(0.344-1.771)0.40401.074(0.711-1.622)0.21534.81.089(0.670-1.767)0.14652.70.806(0.361-1.799)0.4320
Other cancers406/7240.932(0.753-1.155)0.33312.60.884(0.505-1.548)0.215310.907(0.682-1.206)0.80100.910(0.696-1.190)0.62500.930(0.584-1.481)0.25225.4
Ethnicities
Caucasian3616/43921.231(1.028-1.474)083.81.737(1.519-1.986)0721.170(1.034-1.324)0.19922.41.334(1.215-1.465)070.71.407(1.092-1.813)067.8
Asian2257/26671.604(1.089-2.363)080.72.093(1.295-3.381)0.44101.550(0.995-2.417)0.00079.71.624(1.041-2.535)080.81.967(1.238-3.125)0.6560
Mixed2692/29371.177(1.004-1.379)0.01963.11.137(0.955-1.354)0.08847.81.112(0.953-1.297)0.26822.11.126(1.006-1.261)0.16536.31.262(0.957-1.665)0.03458.4
Control source
Population based5787/61581.325(1.085-1.618)088.71.568(1.091-2.253)081.91.275(1.051-1.548)0.4014.51.275(1.051-1.548)075.71.503(1.110-2.034)080.5
Hospital based2778/38381.240(1.056-1.456)068.91.531(1.199-1.955)0.13229.81.255(1.020-1.543)0.00062.31.288(1.120-1.480)066.71.411(1.173-1.698)0.32411.6
Genotype method
PCR-RFLP4376/46981.243(1.053-1.466)082.21.571(1.183-2.087)070.11.164(1.033-1.311)0.14526.51.246(1.045-1.487)069.31.483(1.167-1.884)062.7
TaqMan4189/52981.330(1.091-1.622)079.51.309(0.988-1.735)0.09141.41.307(1.026-1.665)0.00171.51.370(1.073-1.748)074.71.264(0.986-1.620)0.0548.5

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.

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.

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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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.


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