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Association between MnSOD Val16Ala Polymorphism and Cancer Risk: Evidence from 33,098 Cases and 37,831 Controls.

1. Introduction

Cancer is one of the leading causes of death across the world, with an estimate of over 20 million new cancer cases that will occur per year as early as 2025 [1]. Although great efforts have been devoted to cancer treatment, cancer still poses a huge threat to human health. Carcinogenesis is rather complex, and mounting evidence suggests that reactive oxygen species- (ROS-) related oxidative damage is involved in this process [2-4].

Among the endogenous antioxidants, manganese superoxide dismutase (MnSOD) is one of the critical enzymes which defends against ROS in the mitochondria. The MnSOD gene, located on chromosome 6q25.3, is composed of four introns and five extrons. Currently, several single-nucleotide polymorphisms (SNPs) in the MnSOD gene have been reported, of which the most extensively studied one is Val16Ala. Since this residue is 9 amino acids upstream of the cleavage site, it has also been called Val9Ala (rs4880) polymorphism [5]. A previous study has shown that Ala-MnSOD allowed more efficient MnSOD localized to the mitochondria than the Val-variant form [6]. In view of this, it is speculated that the Val form of MnSOD may be associated with higher levels of ROS and increased susceptibility to cancer.

Several studies have found the associations between the Val form of the MnSOD gene and increased cancer risk [7-9], but a majority of studies showed the Ala form to be associated with higher cancer risk, such as breast cancer [10, 11], esophageal cancer [12], colorectal cancer [13], and cervical cancer [14], and some other studies find no significant association between this polymorphism and cancer risk [15-18]. To draw a more comprehensive estimation of this possible association, we conducted the present metaanalysis to evaluate the relevance of this variant with susceptibility of cancer.

2. Materials and Methods

2.1. Search Strategy. We systematically searched the PubMed, Embase, Chinese National Knowledge Infrastructure (CNKI), and Wanfang databases for all related publications using the following keywords: "MnSOD or manganese superoxide dismutase," "polymorphism or variant or variation," and "cancer or carcinoma or tumor or neoplasm" (the last search was updated on February 22, 2018). Additional relevant studies were searched manually from the references or review articles about this topic. If studies had overlapped data, only the one with the most participants was included in this analysis.

2.2. Inclusion and Exclusion Criteria. The inclusion criteria were as follows: (1) case-control studies, (2) studies assessing the association between MnSOD Val16Ala polymorphism and cancer risk, (3) and provision of detailed data about genotype and allele distribution of the studied polymorphism. Studies were excluded if any of the following aspects existed: (1) duplicate publications, (2) review articles or meta-analyses, (3) not a case-control study, and (4) genotype frequencies in the control departure from Hardy-Weinberg equilibrium (HWE).

2.3. Data Extraction. Two authors (Ping Wang and Yanfeng Zhu) independently extracted the data from included studies according to the criteria mentioned above. Disagreement was resolved by discussion until a consensus was reached. The following information was collected from each study: first author's surname, year of publication, country of origin, ethnicity, cancer type, control source (hospital-based or population-based), genotyping methods, and numbers of cases and controls with the Val/Val, Val/Ala, and Ala/Ala genotypes.

2.4. Quality Assessment. The quality of each included study was assessed independently by two authors using the criteria from a previous study [19]. Quality scores were rated from 0 to 15, and the studies were classified as high-quality studies (scores > 9) and low-quality studies (scores [less than or equal to] 9).

2.5. Statistical Analysis. The strength of association between the MnSOD Val16Ala polymorphism and cancer risk was assessed by calculating the odd ratios (ORs) with the corresponding 95% confidence intervals (CIs). The pooled ORs of five comparison models were calculated: homozygous model (Ala/Ala versus Val/Val), heterozygous model (Val/Ala versus Val/Val), recessive model [Ala/Ala versus (Val/Val+ Val/Ala)], dominant model [(Ala/Ala + Val/Ala) versus Val/Val], and an allele comparison (Ala versus Val). We used the chi-square-based Q test to check the between-study heterogeneity, and the fixed-effects model (the Mantel-Haenszel method) [20] was used when no significant heterogeneity was found (P > 01). Otherwise, the random-effects model (the Dersimonian and Laird method) [21] was applied. The stratification analysis was performed by cancer type (cancer types with less than three studies would be merged into the "others" group), ethnicity (Asians, Caucasians, Africans, or mixed which contained more than one ethnic group), control source (hospital-based studies and population-based studies), and quality scores ([less than or equal to] 9 and >9). Publication bias was examined using Begg's funnel plot [22] and Egger's linear regression test [23]. Sensitivity analysis was carried out to assess the results stability by excluding one study each time and revaluating the pooled ORs and 95% CIs.

The false-positive report probability (FPRP) was calculated for all the significant findings in the present study. We set 0.2 as a FPRP threshold and assign a prior probability of 0.1 to detect an OR of 0.67/1.50 (protective/risk effects) for an association with the genotypes under investigation [24, 25]. FPRP values less than 0.2 were considered as noteworthy associations. All the statistical tests were performed with STATA software (version 12.0; Stata Corporation, College Station, TX). Two-sided P values <0.05 were considered statistically significant.

3. Results

3.1. Study Characteristics. As shown in Figure 1, a total of 348 articles were identified from PubMed, Embase, CNKI, and Wanfang databases, and 34 more articles were identified by reading the references of retrieved publications. After reading the titles and abstracts, 266 articles were excluded, leaving 116 articles for further assessment. Among them, six were excluded as case-only studies [26-31], five [32-36] were covered by other included publications [7, 37, 38], three were without detailed data for further analysis [39-41], and 18 deviated from HWE [42-59]. Finally, a total of 84 case-control publications [7-18, 37, 38, 60-129] were included in this meta-analysis. Of the 84 publications, three publications [37, 69, 82] with two ethnic groups were considered as two independent studies and one publication [119] with two cancer types were also considered as two independent studies.

For the two studies in the publication [119] with the same control group, the number of control was only calculated once in the total number. Overall, 88 studies with 33,098 cases and 37,831 controls were included in this meta-analysis. Of the 88 studies, 24 studies focused on breast cancer [9-11, 16, 38, 60, 61, 68, 69, 71, 72, 77, 88, 93, 96, 97, 100, 105, 109, 114, 119, 122, 127]; 17 on prostate cancer [37, 66, 74, 79, 82, 85, 86, 89, 95, 106, 111, 113, 120, 125, 128]; six for each of the following cancer types, such as lung cancer [7, 17, 18, 65, 92, 118], bladder cancer [8, 15, 67, 75, 112, 117], and pancreatic cancer [64, 91, 102, 107, 108, 121]; five on colorectal cancer [13, 63, 73, 94, 101]; three for each of the following cancer types, such as ovarian cancer [70, 81, 87], hepatocellular carcinoma [98, 99, 129], and non-Hodgkin's lymphoma [76, 78,110]; and the other with fewer than three studies for each cancer type. Of all the studies, 56 studies were performed on Caucasians, 18 studies on Asians, and seven studies on Africans and mixed ethnicity, respectively. When classified by source of control, 48 were population-based and 40 were hospital-based. In addition, according to the quality score, 49 studies were considered as high-quality and 39 studies were considered as low-quality. The characteristics of the included studies are shown in Table 1.

3.2. Meta-Analysis Results. The overall results suggested there was a significant association between MnSOD Val16Ala polymorphism and cancer risk (homozygous: OR =1.09, 95% CI = 1.00-1.19, P <0.001; heterozygous: OR =1.07, 95% CI = 1.02-1.12, P = 0.001; dominant: OR = 1.08, 95% CI = 1.02-1.14, P <0.001; and allele comparison: OR = 1.06, 95% CI = 1.02-1.11, P <0.001) (Table 2, Figure 2). In the subgroup analysis, a statistically significant association was found for prostate cancer (heterozygous: OR = 1.14, 95% CI = 1.05-1.24, P = 0.765; dominant: OR =1.14, 95% CI = 1.05-1.23, P = 0.552; and allele comparison: OR = 1.07,95% CI = 1.00-1.15, P = 0.106), Asians (homozygous: OR = 1.82, 95% CI = 1.15-2.88, P = 0.020, and recessive: OR = 1.76, 95% CI = 1.16-2.68, P = 0.065), Caucasians (heterozygous: OR = 1.08, 95% CI = 1.03-1.13, P = 0.208; dominant: OR = 1.08, 95% CI = 1.02-1.14, P = 0.011; and allele comparison: OR = 1.04, 95% CI = 1.00-1.09, P < 0.001), population-based studies (homozygous: OR =1.10, 95% CI = 1.01-1.19, P <0.001; heterozygous: OR =1.07, 95% CI = 1.02-1.12, P = 0.263; dominant: OR = 1.07, 95% CI = 1.02-1.13, P = 0.071; and allele comparison: OR = 1.04, 95% CI = 1.00-1.08, P = 0.006), hospital-based studies (recessive: OR=1.16, 95% CI = 1.01-1.34, P <0.001, and allele comparison: OR = 1.13, 95% CI = 1.03-1.24, P < 0.001), low-quality studies (allele comparison: OR =1.12, 95% CI = 1.02-1.23, P <0.001) and high-quality studies (homozygous: OR = 1.08, 95% CI = 1.00-1.17, P = 0.001; heterozygous: OR = 1.07, 95% CI =1.02-1.13, P = 0.067; dominant: OR = 1.07, 95% CI =1.02-1.14, P = 0.002; and allele comparison: OR = 1.04, 95% CI = 1.00-1.09, P <0.001).

3.3. Heterogeneity and Sensitivity Analysis. As shown in Table 2, substantial heterogeneities were found among all studies for the MnSOD Val16Ala polymorphism and overall cancer risk (homozygous: P < 0.001; heterozygous: P = 0.001; recessive: P < 0.001; dominant: P < 0.001; and allele comparison: P < 0.001). Therefore, the random-effects model was used to generate wider CIs. The leave-one-out sensitivity analysis indicated that no single study could change the pooled ORs obviously (data not shown).

3.4. Publication Bias. Begg's funnel plot and Egger's test were performed to evaluate the publication bias of 88 studies, and we found significant publication bias for the homozygous model (P = 0.049), recessive model (P = 0.007), dominant model (P = 0.042), and allele comparison (P = 0.007), but not for the heterozygous model (P = 0.056). Therefore, the Duval and Tweedie nonparametric "trim and fill" method was used to adjust for publication bias. The "trim and fill" method did not draw different conclusions (data not shown), indicating that our findings were statistically robust.

3.5. False-Positive Report Probability (FPRP) Analysis. The FPRP values were calculated for all the significant findings (Table 3). With the assumption of a prior probability of 0.1, the FPRP results revealed that three genetic models [Val/Ala versus Val/Val, (Ala/Ala +Val/Ala) versus Val/Val, and Ala versus Val] of the MnSOD Val16Ala polymorphism were truly associated with increased cancer risk (FPRP = 0.032, 0.045, and 0.106, resp.). In addition, according to the FPRP results, we confirmed that the MnSOD Val16Ala polymorphism was associated with cancer risk for prostate cancer (heterozygous: FPRP = 0.020 and dominant: FPRP = 0.006), Caucasians (heterozygous: FPRP = 0.008 and dominant: FPRP = 0.045), population-based studies (homozygous: FPRP = 0.136, heterozygous: FPRP = 0.032 and dominant: FPRP = 0.119), hospital-based studies (allele comparison: FPRP = 0.082), low-quality studies (allele comparison: FPRP = 0.138), and high-quality studies (heterozygous: FPRP = 0.119).

4. Discussion

In this meta-analysis, we comprehensively assessed the association between MnSOD Val16Ala polymorphism and cancer risk through 88 studies, and we found that this gene polymorphism was significantly associated with overall cancer risk. Further, stratification analysis revealed that the association was more obvious for risk of prostate cancer, Asians, Caucasians, population-based studies, hospital-based studies, low-quality studies, and high-quality studies. To avoid the false-positive results of the meta-analysis, we performed the FPRP analysis for the significant findings by setting as the prior probability of 0.1, and the results suggested that the association between MnSOD Val16Ala polymorphism and cancer risk for Asians was false positive, which may due to limited sample size.

MnSOD is a mitochondrial enzyme that converts superoxide radical [O.sub.2.sup.-] into [H.sub.2][O.sub.2], and it plays a critical role in human cells. Studies have revealed that the aberrant expression of MnSOD is involved in many types of cancers. Our current study indicated that the MnSOD Val16Ala polymorphism was significantly associated with an increased overall cancer risk. Previous meta-analyses have also assessed the association of MnSOD Val16Ala polymorphism with cancer susceptibility. The study carried out by Kang [130] analyzed MnSOD Val16Ala polymorphism and cancer risk, consisting 52 studies with 26,865 cases and 32,464 controls, in which no significant association was found between this polymorphism and overall cancer risk. In the subgroup analysis, statistically significant associations were found between this polymorphism and non-Hodgkin lymphoma, lung cancer, and colorectal cancer. Another meta-analysis [131] including 7366 cases and 9102 controls found no overall association of MnSOD Val16Ala polymorphism for cancer risk. Some of the significant associations detected in the previous meta-analyses were not found in the present study; for example, MnSOD Val16Ala polymorphism was associated with the risk of hepatocellular carcinoma [132, 133], esophageal cancer [134], and lung cancer [134]. The discrepancy that occurred may be because our current study was based on a much larger sample size, allowing the more precise detection of the association. In the subgroup analysis by cancer type, we found a significant association between MnSOD Val16Ala polymorphism and elevated prostate cancer risk, and no significant association between this polymorphism and breast cancer, which were consistent with previous meta-analyses [131, 134-137].

In spite of genetic importance, environment factors such as dietary pattern and exercise play important roles in the development of cancer. Recently, several studies have investigated the association between dietary intake of antioxidant-rich foods and MnSOD Val16Ala polymorphism in breast cancer [60], prostate cancer [89], and cervical cancer [14]. Despite the lack of consistent data, the results suggested that the MnSOD Val16Ala polymorphism and cancer risk could be modulated by dietary factors. Besides, a previous study had shown that moderate exercise training is beneficial for prostate cancer [138], and evidence showed that exercise training may result in positive MnSOD modulation through redox sensitive pathways [139].

The current meta-analysis has several advantages. First, we included the latest publications in the present study and also the publications written in Chinese. Second, the quality of included studies was assessed by the quality score criteria. Third, the FPRP test was performed to make the results more trustworthy and robust. Although the study is the largest and most comprehensive one regarding the association between MnSOD Val16Ala polymorphism and all cancer types, there were still some limitations that should be addressed. First, the number of cases in each study was small (<1000) in all but seven studies [11, 38, 69, 78, 82, 86, 119], which may have an effect on the investigation of the real association. Second, the results were based on unadjusted estimates, which might make the results imprecise. Third, only publications in English and Chinese were included, which could lead to selection bias. Fourth, in the subgroup analysis by cancer type, less than three studies were included for some types of cancer, which may affect the detection of the real association. Finally, the potential gene-gene, and gene-environment interactions were not investigated due to the lack of original information.

Despite of these limitations, this meta-analysis indicated there was a significant association between MnSOD Val16Ala polymorphism and cancer risk, which should be further validated by single large studies.

https://doi.org/10.1155/2018/3061974

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This work was supported by the Key Research Programs for Institutions of Higher Education in Henan Province (Grant no. 18A180012).

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Ping Wang (iD), (1) Yanfeng Zhu, (2) Shoumin Xi (iD), (1) Sanqiang Li (iD), (1) and Yanle Zhang (1)

(1) Department of Biochemistry and Molecular Biology, Medical College, Henan University of Science and Technology, Luoyang, Henan 471023, China

(2) School of Materials Science and Engineering, Henan University of Science and Technology, Luoyang, Henan 471023, China

Correspondence should be addressed to Ping Wang; glorywangping@163.com and Sanqiang Li; sanqiangli2001@163.com

Received 14 March 2018; Accepted 25 July 2018; Published 2 September 2018

Academic Editor: Roberta Rizzo

Caption: Figure 1: Flowchart of included studies for the association between MnSOD Val16Ala polymorphism and cancer susceptibility.
Table 1: Characteristics of studies included in the meta-analysis.

Surname (ref)              Year    Country    Ethnicity

Ambrosone et al. [60]      1999      USA      Caucasian

Mitrunen et al. [10]       2001    Finland    Caucasian

Wang et al. [7]            2001      USA      Caucasian

Green et al. [61]          2002      UK       Caucasian

Hirvonen et al. [62]       2002    Finland    Caucasian

Levine et al. [63]         2002      USA        Mixed

Li et al. [64]             2002      USA      Caucasian

Stoehlmacher et al. [13]   2002      USA      Caucasian

Egan et al. [16]           2003      USA      Caucasian

Lin et al. [65] (a)        2003     China       Asian

Woodson et al. [66]        2003      USA      Caucasian

Cai et al. [11]            2004     China       Asian

Hung et al. [8]            2004     Italy     Caucasian

Ichimura et al. [67]       2004     Japan       Asian

Knight et al. [68]         2004    Canada     Caucasian

Lan et al. [17]            2004     China       Asian

Millikan et al. [69]       2004      USA       African

Millikan et al. [69]       2004      USA      Caucasian

Olson et al. [70]          2004      USA      Caucasian

Tamimi et al. [71]         2004      USA      Caucasian

Bergman et al. [9]         2005    Sweden     Caucasian

Cheng et al. [72]          2005     China       Asian

Gaudet et al. [38]         2005      USA      Caucasian

Landi et al. [73]          2005     Spain     Caucasian

Li et al. [74]             2005      USA      Caucasian

Terry et al. [75]          2005      USA      Caucasian

Ho et al. [18] (c)         2006     China       Asian

Lightfoot et al. [76]      2006    USA and    Caucasian
                                     UK
Slanger et al. [77]        2006    Germany    Caucasian

Wang et al. [78]           2006      USA        Mixed

Cengiz et al. [15] (b)     2007    Turkey     Caucasian

Choi et al. [37]           2007      USA      Caucasian

Choi et al. [37]           2007      USA       African

Ergen et al. [79] (c)      2007    Turkey     Caucasian

Han et al. [80]            2007      USA      Caucasian

Johnatty et al. [81]       2007   Australia   Caucasian

Kang et al. [82]           2007      USA      Caucasian

Kang et al. [82]           2007      USA       African

Landi et al. [83]          2007     Italy     Caucasian

di Martino et al. [84]     2007      USA      Caucasian

Murphy et al. [12]         2007    Ireland    Caucasian

Arsova-Sarafinovska        2008    Turkey     Caucasian
et al. [85]

Cooper et al. [86]         2008      USA      Caucasian

Dalan et al. [87]          2008    Turkey     Caucasian

Justenhoven et al. [88]    2008    Germany    Caucasian

Mikhak et al. [89]         2008      USA      Caucasian

Rajaraman et al. [90]      2008      USA      Caucasian

Wheatley-Price et          2008      USA      Caucasian
al. [91]

Zienolddiny et al. [92]    2008    Norway     Caucasian

Eras-Erdogan et al. [93]   2009    Turkey     Caucasian

Funke et al. [94]          2009    Germany    Caucasian

Iguchi et al. [95]         2009      USA        Mixed

Kostrykina et al. [96]     2009    Russia     Caucasian

Tong et al. [14] (a)       2009     Korea       Asian

Ermolenko et al. [97]      2010    Russia     Caucasian

Ezzikouri et al. [98]      2010    Morocco    Caucasian

Ibrahim et al. [99]        2010     Egypt      African

Kim et al. [100]           2010     Korea       Asian

Meplan et al. [101]        2010     Czech     Caucasian

Tang et al. [102]          2010      USA        Mixed

Wu et al. [103]            2010     China       Asian

Yi et al. [104]            2010     China       Asian

Cerne et al. [105]         2011   Slovenia    Caucasian

Cheng et al. [106] (b)     2011      USA        Mixed

Mohelnikova-               2011     Czech     Caucasian
Duchonova et al. [107]

Zhang et al. [108] (b)     2011      USA        Mixed

Atoum et al. [109] (c)     2012    Jordan     Caucasian

Farawela et al. [110]      2012     Egypt      African

Hemelrijck et al. [1ll]    2012    Germany    Caucasian

Kucukgergin et al. [112]   2012    Turkey     Caucasian

Kucukgergin et al. [113]   2012    Turkey     Caucasian

Tsai et al. [114] (a)      2012     China       Asian

Ye et al. [115]            2012     China       Asian

Zhao et al. [116]          2012     China       Asian

Amr et al. [117]           2013     Egypt      African

Ashour et al. [118]        2013     Egypt      African

Attatippaholkun and        2013   Thailand      Asian
Wikainapakul [119]

Attatippaholkun et al.     2013   Thailand      Asian
[119]

Eken et al. [120]          2013    Turkey     Caucasian

Han et al. [121]           2013     Korea       Asian

Meplan et al. [122]        2013    Denmark    Caucasian

Atilgan et al. [123]       2014    Turkey     Caucasian

Liu et al [124]            2014     China       Asian

Oskina et al. [125]        2014    Russia     Caucasian

Brown et al. [126]         2015      USA      Mixed IV

Jablonska et al. [127]     2015    Polish     Caucasian

Parlaktas et al. [128]     2015    Turkey     Caucasian

Su et al. [129]            2015     China       Asian

Surname (ref)                Cancer type     Control      Genotype
                                             source        method

Ambrosone et al. [60]          Breast          PB         PCR-RFLP

Mitrunen et al. [10]           Breast          PB         PCR-RFLP

Wang et al. [7]                 Lung           HB      Pyrosequencing

Green et al. [61]              Breast          HB         PCR-RFLP

Hirvonen et al. [62]             MPM           PB         PCR-RFLP

Levine et al. [63]               CRC           PB         PCR-RFLP

Li et al. [64]               Pancreatic        PB         PCR-RFLP

Stoehlmacher et al. [13]         CRC           PB          TaqMan

Egan et al. [16]               Breast          PB         PCR-RFLP

Lin et al. [65] (a)             Lung           HB         PCR-RFLP

Woodson et al. [66]           Prostate         PB        MALDI-TOF
                                                             MS

Cai et al. [11]                Breast          PB         PCR-RFLP

Hung et al. [8]                Bladder         HB         PCR-RFLP

Ichimura et al. [67]           Bladder         HB         PCR-RFLP

Knight et al. [68]             Breast          PB         PCR-SSCP

Lan et al. [17]                 Lung           PB      Real-time PCR

Millikan et al. [69]           Breast          PB          TaqMan

Millikan et al. [69]           Breast          PB          TaqMan

Olson et al. [70]              Ovarian         HB        MALDI-TOF
                                                             MS

Tamimi et al. [71]             Breast          PB        Mixed (d)

Bergman et al. [9]             Breast          PB        Sequencing

Cheng et al. [72]              Breast          HB        Mass ARRAY

Gaudet et al. [38]             Breast          PB        MALDI-TOF
                                                             MS

Landi et al. [73]                CRC           HB           APEX

Li et al. [74]                Prostate         PB         PCR-RFLP

Terry et al. [75]              Bladder         HB        MALDI-TOF
                                                             MS

Ho et al. [18] (c)              Lung           HB         PCR-RFLP

Lightfoot et al. [76]            NHL           PB          TaqMan

Slanger et al. [77]            Breast          PB          TaqMan

Wang et al. [78]                 NHL           PB          TaqMan

Cengiz et al. [15] (b)         Bladder         HB         PCR-RFLP

Choi et al. [37]              Prostate         PB        MALDI-TOF
                                                             MS

Choi et al. [37]              Prostate         PB        MALDI-TOF
                                                             MS

Ergen et al. [79] (c)         Prostate         HB         PCR-RFLP

Han et al. [80]                 Skin           PB          TaqMan

Johnatty et al. [81]           Ovarian         PB      Real-time PCR

Kang et al. [82]              Prostate         PB          TaqMan

Kang et al. [82]              Prostate         PB          TaqMan

Landi et al. [83]                MPM           HB           APEX

di Martino et al. [84]       Esophageal        HB         PCR-RFLP

Murphy et al. [12]           Esophageal        PB         SNaPshot

Arsova-Sarafinovska           Prostate         HB      Real-time PCR
et al. [85]

Cooper et al. [86]            Prostate         PB          TaqMan

Dalan et al. [87]              Ovarian         PB         PCR-RFLP

Justenhoven et al. [88]        Breast          PB        MALDI-TOF
                                                             MS

Mikhak et al. [89]            Prostate         PB          TaqMan

Rajaraman et al. [90]           Brain          HB          TaqMan

Wheatley-Price et            Pancreatic        HB          TaqMan
al. [91]

Zienolddiny et al. [92]         Lung           PB           APEX

Eras-Erdogan et al. [93]       Breast          PB         PCR-RFLP

Funke et al. [94]                CRC           PB      Pyrosequencing

Iguchi et al. [95]            Prostate         HB         PCR-RFLP

Kostrykina et al. [96]         Breast          PB          TaqMan

Tong et al. [14] (a)          Cervical         HB         SNaPshot

Ermolenko et al. [97]          Breast          HB      Real-time PCR

Ezzikouri et al. [98]            HCC           PB         PCR-RFLP

Ibrahim et al. [99]              HCC           HB         PCR-RFLP

Kim et al. [100]               Breast          HB          TaqMan

Meplan et al. [101]              CRC           HB          AS-PCR

Tang et al. [102]            Pancreatic        HB          TaqMan

Wu et al. [103]                 Oral           HB      Real-time PCR

Yi et al. [104]                Gastric         HB         SNaPshot

Cerne et al. [105]             Breast          HB          TaqMan

Cheng et al. [106] (b)        Prostate         PB        MALDI-TOF
                                                             MS

Mohelnikova-                 Pancreatic        PB      Real-time PCR
Duchonova et al. [107]

Zhang et al. [108] (b)       Pancreatic        PB          TaqMan

Atoum et al. [109] (c)         Breast          HB         PCR-RFLP

Farawela et al. [110]            NHL           PB         PCR-RFLP

Hemelrijck et al. [1ll]       Prostate         PB        Mass ARRAY

Kucukgergin et al. [112]       Bladder         HB         PCR-RFLP

Kucukgergin et al. [113]      Prostate         HB         PCR-RFLP

Tsai et al. [114] (a)          Breast          HB      Real-time PCR

Ye et al. [115]                  NPC           HB           PCR

Zhao et al. [116]               Brain          HB        OpenArray

Amr et al. [117]               Bladder         PB          TaqMan

Ashour et al. [118]             Lung           PB          TaqMan

Attatippaholkun and           Cervical         HB         SNaPshot
Wikainapakul [119]

Attatippaholkun et al.         Breast          HB         SNaPshot
[119]

Eken et al. [120]             Prostate         HB      Real-time PCR

Han et al. [121]             Pancreatic        PB         PCR-SSCP

Meplan et al. [122]            Breast          PB          TaqMan

Atilgan et al. [123]             RCC           HB          Probe

Liu et al [124]                 OSCC           HB         PCR-RFLP

Oskina et al. [125]           Prostate         PB          TaqMan

Brown et al. [126]         Medulloblastoma     PB       Illumina SNP
                                                            chip

Jablonska et al. [127]         Breast          PB      Real-time PCR

Parlaktas et al. [128]        Prostate         HB          Probe

Su et al. [129]                  HCC           HB         PCR-RFLP

Surname (ref)                                      Case

                               Val/           Val/       Ala /   All
                               Val            Ala         Ala

Ambrosone et al. [60]           16             53         45     114

Mitrunen et al. [10]           124            255         100    479

Wang et al. [7]                305            551         245    1101

Green et al. [61]               13             17          9      39

Hirvonen et al. [62]            6              11          3      20

Levine et al. [63]             139            209         108    456

Li et al. [64]                  10             11          3      24

Stoehlmacher et al. [13]        25             65         35     125

Egan et al. [16]               102            250         118    470

Lin et al. [65] (a)            139        59 (Val/Ala            198
                                           + Ala/Ala)

Woodson et al. [66]             43             98         58     199

Cai et al. [11]                831            266         28     1125

Hung et al. [8]                 68             89         44     201

Ichimura et al. [67]           169             41          3     213

Knight et al. [68]             107            187         105    399

Lan et al. [17]                 93             23          3     119

Millikan et al. [69]           259            372         129    760

Millikan et al. [69]           273            681         311    1265

Olson et al. [70]               27             64         27     118

Tamimi et al. [71]             255            468         245    968

Bergman et al. [9]              33             73         12     118

Cheng et al. [72]              343            115         11     469

Gaudet et al. [38]             253            511         270    1034

Landi et al. [73]               94            164         77     335

Li et al. [74]                 132            288         147    567

Terry et al. [75]               54            122         59     235

Ho et al. [18] (c)             176             58          0     234

Lightfoot et al. [76]          211            463         229    903

Slanger et al. [77]            144            318         152    614

Wang et al. [78]               285            545         290    1120

Cengiz et al. [15] (b)     34 (Val/Val                    17      51
                            + Val/Ala)

Choi et al. [37]               112            239         104    455

Choi et al. [37]                7              15          6      28

Ergen et al. [79] (c)           19             25          6      50

Han et al. [80]                184            402         187    773

Johnatty et al. [81]           123            273         147    543

Kang et al. [82]               275            578         297    1150

Kang et al. [82]                31             57         15     103

Landi et al. [83]               16             27         37      80

di Martino et al. [84]          32             73         35     140

Murphy et al. [12]              44            103         60     207

Arsova-Sarafinovska             19             46         20      85
et al. [85]

Cooper et al. [86]             602            1352        680    2634

Dalan et al. [87]               30             19          6      55

Justenhoven et al. [88]        159            312         133    604

Mikhak et al. [89]             156            320         166    642

Rajaraman et al. [90]          129            262         123    514

Wheatley-Price et               33             58         31     122
al. [91]

Zienolddiny et al. [92]         74            175         70     319

Eras-Erdogan et al. [93]       107            113         30     250

Funke et al. [94]              136            321         166    623

Iguchi et al. [95]              41             86         60     187

Kostrykina et al. [96]         123            233         119    475

Tong et al. [14] (a)            72        27 (Val/Ala             99
                                           + Ala/Ala)

Ermolenko et al. [97]          228            454         239    921

Ezzikouri et al. [98]           21             45         30      96

Ibrahim et al. [99]             16             32         27      75

Kim et al. [100]               234             66          4     304

Meplan et al. [101]            172            358         189    719

Tang et al. [102]              143            278         137    558

Wu et al. [103]                 91             28          2     121

Yi et al. [104]                 85             48          7     140

Cerne et al. [105]             118            269         143    530

Cheng et al. [106] (b)     152 (Val/Val                   50     202
                            + Val/Ala)

Mohelnikova-                    66            121         48     235
Duchonova et al. [107]

Zhang et al. [108] (b)     129 (Val/Val                   60     189
                            + Val/Ala)

Atoum et al. [109] (c)          22             43          0      65

Farawela et al. [110]           10             50         40     100

Hemelrijck et al. [1ll]         50            100         53     203

Kucukgergin et al. [112]        52             68         37     157

Kucukgergin et al. [113]        43             65         26     134

Tsai et al. [114] (a)          192        68 (Val/Ala            260
                                           + Ala/Ala)

Ye et al. [115]                 88             15          2     105

Zhao et al. [116]              241            107         31     379

Amr et al. [117]               127            188         99     414

Ashour et al. [118]             17             27          6      50

Attatippaholkun and             64             39          4     107
Wikainapakul [119]

Attatippaholkun et al.          82             54          5     141
[119]

Eken et al. [120]               7              17          9      33

Han et al. [121]               190             85         19     294

Meplan et al. [122]            228            485         226    939

Atilgan et al. [123]            10             17         14      41

Liu et al [124]                272             83          7     362

Oskina et al. [125]             92            194         94     380

Brown et al. [126]              3              15          8      26

Jablonska et al. [127]          32             75         29     136

Parlaktas et al. [128]          23             23          3      49

Su et al. [129]                334             78         10     422

Surname (ref)                                   Control

                              Val /           Val/       Ala /   All
                               Val            Ala         Ala

Ambrosone et al. [60]           25             62         23     110

Mitrunen et al. [10]           153            231         98     482

Wang et al. [7]                288            628         323    1239

Green et al. [61]               8              22          6      36

Hirvonen et al. [62]            15             36         12      63

Levine et al. [63]             140            234         121    495

Li et al. [64]                  8              10          5      23

Stoehlmacher et al. [13]        21             64         37     122

Egan et al. [16]               130            240         127    497

Lin et al. [65] (a)            233        99 (Val/Ala            332
                                           + Ala/Ala)

Woodson et al. [66]             49            102         40     191

Cai et al. [11]                884            290         23     1197

Hung et al. [8]                 45            115         54     214

Ichimura et al. [67]           157             48          4     209

Knight et al. [68]              90            195         87     372

Lan et al. [17]                 81             30          1     112

Millikan et al. [69]           196            357         124    677

Millikan et al. [69]           266            586         283    1135

Olson et al. [70]               51             87         39     177

Tamimi et al. [71]             297            612         296    1205

Bergman et al. [9]              43             88         43     174

Cheng et al. [72]              545            183         11     739

Gaudet et al. [38]             264            539         281    1084

Landi et al. [73]               88            151         64     303

Li et al. [74]                 190            379         195    764

Terry et al. [75]               57            103         54     214

Ho et al. [18] (c)             180             52          7     239

Lightfoot et al. [76]          358            713         371    1442

Slanger et al. [77]            263            528         289    1080

Wang et al. [78]               240            486         211    937

Cengiz et al. [15] (b)     34 (Val/Val                    19      53
                            + Val/Ala)

Choi et al. [37]               293            610         311    1214

Choi et al. [37]                39             52         31     122

Ergen et al. [79] (c)           32             18          0      50

Han et al. [80]                196            425         212    833

Johnatty et al. [81]           276            546         308    1130

Kang et al. [82]               376            686         320    1382

Kang et al. [82]               122            194         79     395

Landi et al. [83]               98            170         81     349

di Martino et al. [84]          20             39         34      93

Murphy et al. [12]              60            113         48     221

Arsova-Sarafinovska             41             73         37     151
et al. [85]

Cooper et al. [86]             423            789         424    1636

Dalan et al. [87]               28             17          6      51

Justenhoven et al. [88]        163            313         145    621

Mikhak et al. [89]             162            331         159    652

Rajaraman et al. [90]          122            220         109    451

Wheatley-Price et               61            165         105    331
al. [91]

Zienolddiny et al. [92]        119            178         78     375

Eras-Erdogan et al. [93]       150            141         39     330

Funke et al. [94]              146            294         163    603

Iguchi et al. [95]              40             96         39     175

Kostrykina et al. [96]         103            183         90     376

Tong et al. [14] (a)           194        69 (Val/Ala            263
                                           + Ala/Ala)

Ermolenko et al. [97]          121            235         104    460

Ezzikouri et al. [98]           81            101         40     222

Ibrahim et al. [99]             19             28         11      58

Kim et al. [100]               279             90          7     376

Meplan et al. [101]            165            318         174    657

Tang et al. [102]              167            309         162    638

Wu et al. [103]                 88             32          2     122

Yi et al. [104]                119             27          1     147

Cerne et al. [105]              65            134         71     270

Cheng et al. [106] (b)      1054 (Val/                    374    1428
                            Val + Val/
                               Ala)

Mohelnikova-                    73            134         58     265
Duchonova et al. [107]

Zhang et al. [108] (b)     365 (Val/Val                   121    486
                            + Val/Ala)

Atoum et al. [109] (c)          11             6           0      17

Farawela et al. [110]           12             49         39     100

Hemelrijck et al. [1ll]         80            190         90     360

Kucukgergin et al. [112]        89             99         36     224

Kucukgergin et al. [113]        66             69         24     159

Tsai et al. [114] (a)          138        86 (Val/Ala            224
                                           + Ala/Ala)

Ye et al. [115]                110             23          3     136

Zhao et al. [116]              293             81          6     380

Amr et al. [117]               109            160         87     356

Ashour et al. [118]             21             25          4      50

Attatippaholkun and             84             48          3     135
Wikainapakul [119]

Attatippaholkun et al.          84             48          3     135
[119]

Eken et al. [120]               31             37         13      81

Han et al. [121]               236             59          5     300

Meplan et al. [122]            237            494         227    958

Atilgan et al. [123]            23             19          8      50

Liu et al [124]                296             61          1     358

Oskina et al. [125]             86            152         99     337

Brown et al. [126]              18             18          9      45

Jablonska et al. [127]          41           92 50               183

Parlaktas et al. [128]          24             20          5      49

Su et al. [129]                359            107         13     479

Surname (ref)              MAF     HWE    Score

Ambrosone et al. [60]      0.49   0.181    12

Mitrunen et al. [10]       0.44   0.526    13

Wang et al. [7]            0.49   0.609     9

Green et al. [61]          0.47   0.175     5

Hirvonen et al. [62]       0.48   0.248     9

Levine et al. [63]         0.48   0.237    12

Li et al. [64]             0.43   0.580     6

Stoehlmacher et al. [13]   0.43   0.456     5

Egan et al. [16]           0.50   0.446    10

Lin et al. [65] (a)         NA     NA      10

Woodson et al. [66]        0.48   0.330    12

Cai et al. [11]            0.14   0.890    15

Hung et al. [8]            0.48   0.262     9

Ichimura et al. [67]       0.13   0.882    11

Knight et al. [68]         0.50   0.350    14

Lan et al. [17]            0.14   0.321    10

Millikan et al. [69]       0.45   0.083    13

Millikan et al. [69]       0.49   0.269    13

Olson et al. [70]          0.47   0.869     9

Tamimi et al. [71]         0.50   0.584    15

Bergman et al. [9]         0.50   0.879    11

Cheng et al. [72]          0.14   0.322    11

Gaudet et al. [38]         0.49   0.862    14

Landi et al. [73]          0.46   0.958     5

Li et al. [74]             0.50   0.829    14

Terry et al. [75]          0.49   0.586     8

Ho et al. [18] (c)         0.14   0.184     7

Lightfoot et al. [76]      0.50   0.676    13

Slanger et al. [77]        0.49   0.477    14

Wang et al. [78]           0.48   0.240    13

Cengiz et al. [15] (b)      NA     NA       7

Choi et al. [37]           0.49   0.857    13

Choi et al. [37]           0.47   0.112    10

Ergen et al. [79] (c)      0.18   0.121     7

Han et al. [80]            0.49   0.549    15

Johnatty et al. [81]       0.49   0.269    11

Kang et al. [82]           0.48   0.835    13

Kang et al. [82]           0.45   0.906    11

Landi et al. [83]          0.48   0.661     9

di Martino et al. [84]     0.42   0.171     8

Murphy et al. [12]         0.47   0.703    11

Arsova-Sarafinovska        0.49   0.690     9
et al. [85]

Cooper et al. [86]         0.50   0.152    15

Dalan et al. [87]          0.28   0.196     7

Justenhoven et al. [88]    0.49   0.824    14

Mikhak et al. [89]         0.50   0.695    14


Rajaraman et al. [90]      0.49   0.617    10

Wheatley-Price et          0.43   0.786    11
al. [91]

Zienolddiny et al. [92]    0.45   0.448    12

Eras-Erdogan et al. [93]   0.33   0.508     8

Funke et al. [94]          0.49   0.554    12

Iguchi et al. [95]         0.50   0.199     6

Kostrykina et al. [96]     0.48   0.622    12

Tong et al. [14] (a)        NA     NA       7

Ermolenko et al. [97]      0.48   0.620     9

Ezzikouri et al. [98]      0.41   0.388    11

Ibrahim et al. [99]        0.43   0.904     8

Kim et al. [100]           0.14   0.934    11

Meplan et al. [101]        0.49   0.415     9

Tang et al. [102]          0.50   0.429    11

Wu et al. [103]            0.15   0.637     9

Yi et al. [104]            0.10   0.690     9

Cerne et al. [105]         0.51   0.910     8

Cheng et al. [106] (b)      NA     NA      13

Mohelnikova-               0.47   0.812    10
Duchonova et al. [107]

Zhang et al. [108] (b)      NA     NA      13

Atoum et al. [109] (c)     0.18   0.377     6

Farawela et al. [110]      0.37   0.568     9

Hemelrijck et al. [1ll]    0.49   0.285    13

Kucukgergin et al. [112]   0.38   0.341     8

Kucukgergin et al. [113]   0.37   0.398     8

Tsai et al. [114] (a)       NA     NA       8

Ye et al. [115]            0.11   0.191     8

Zhao et al. [116]          0.12   0.882    11

Amr et al. [117]           0.47   0.065    13

Ashour et al. [118]        0.33   0.355     9

Attatippaholkun and        0.20   0.184     7
Wikainapakul [119]

Attatippaholkun et al.     0.20   0.184     7
[119]

Eken et al. [120]          0.39   0.726     8

Han et al. [121]           0.12   0.558    12

Meplan et al. [122]        0.49   0.331    14

Atilgan et al. [123]       0.35   0.244     5

Liu et al [124]            0.09   0.243    10

Oskina et al. [125]        0.48   0.076    12

Brown et al. [126]         0.40   0.264     5

Jablonska et al. [127]     0.48   0.915    10

Parlaktas et al. [128]     0.31   0.784     7

Su et al. [129]            0.14   0.150     7

MAF: minor allele frequency; HWE: Hardy/Weinberg equilibrium; HB:
hospital/based; PB: population based; NA, not applicable; PCR/RFLP:
polymorphism chain reaction/restriction fragment length polymorphism;
MALDI/TOF MS: matrix/assisted laser desorption/
ionization-time-of-flight mass spectrometry; PCR/SSCP: polymorphism
chain reaction/single strand conformation polymorphism; APEX: arrayed
primer extension; AS/PCR: allele specific/polymorphism chain reaction;
MPM: malignant pleural mesothelioma; CRC: colorectal cancer; NHL: non/
Hodgkin's lymphoma; HCC: hepatocellular carcinoma; RCC: renal cell
carcinoma; OSCC: oral squamous cell carcinoma. (a) Lin et al. [65],
Tong et al. [14], and Tsai et al. [114] were only calculated for the
dominant model. (b) Cengiz et al. [15], Cheng et al. [106], and Zhang
et al. [108] were only calculated for the recessive model. (c) Ho et
al. [18], Ergen et al. [79], and Atoum et al. [109] were only
calculated for the heterozygous model, dominant model, and allele
comparison, and the number of Ala/Ala genotype was zero. (d) Mixed:
which included more than one genotyping methods.

Table 2: Meta-analysis of the association between MnSOD Vall6Ala
polymorphism and cancer risk.

Variables             Number       Sample size
                    of studies   (case/controls)

All                     88        33,098/37,831
Cancer type
Breast                  24        12,479/12,603
Prostate                17          7101/9146
Lung                    6           2021/2347
Bladder                 6           1271/1270
Pancreatic              6           1422/2043
CRC                     5           2258/2180
Ovarian                 3           716,1358
HCC                     3            593/759
NHL                     3           2123/2479
Other cancers           15          3114/3646
Ethnicity
Asian                   18          5092/5748
Caucasian               56        23,738/26,121
African                 7           1530/1758
Mixed                   7           2738/4204
Source of control
PB                      48        23,004/27,193
HB                      40        10,094/10,638
Quality score
Low                     39          7625/7608
High                    49        25,473/30,223

Variables                       Homozygous

                         Ala/Ala versus Val/Val

                       OR (95% CI)      [p.sup.het]

All                 1.09 (1.00-1.19)      <0.001
Cancer type
Breast              1.03 (0.95-1.13)       0.276
Prostate            1.04 (0.87-1.24)       0.002
Lung                1.13 (0.63-2.04)       0.019
Bladder             0.66 (0.39-1.13)       0.002
Pancreatic          1.01 (0.59-1.73)       0.007
CRC                 1.02 (0.86-1.20)       0.856
Ovarian             1.10 (0.85-1.42)       0.839
HCC                 1.92 (0.85-4.36)       0.050
NHL                 1.96 (0.96-4.00)      <0.001
Other cancers       1.79 (1.18-2.70)      <0.001
Ethnicity
Asian               1.82 (1.15-2.88)       0.020
Caucasian           1.03 (0.94-1.12)      <0.001
African             1.58 (0.85-2.93)      <0.001
Mixed               1.11 (0.88-1.42)       0.141
Source of control
PB                  1.10 (1.01-1.19)      <0.001
HB                  1.09 (0.88-1.35)      <0.001
Quality score
Low                 1.15 (0.90-1.46)      <0.001
High                1.08 (1.00-1.17)       0.001

Variables                      Heterozygous

                         Val/Ala versus Val/Val

                       OR (95% CI)      [p.sup.het]

All                 1.07 (1.02-1.12)       0.001
Cancer type
Breast              1.02 (0.96-1.09)       0.302
Prostate            1.14 (1.05-1.24)       0.765
Lung                1.05 (0.76-1.46)       0.016
Bladder             0.91 (0.68-1.23)       0.049
Pancreatic          1.07 (0.77-1.49)       0.032
CRC                 1.04 (0.90-1.20)       0.733
Ovarian             1.15 (0.92-1.45)       0.773
HCC                 1.15 (0.66-2.00)       0.055
NHL                 1.03 (0.89-1.19)       0.551
Other cancers       1.25 (1.05-1.49)       0.058
Ethnicity
Asian               1.10 (0.94-1.30)       0.001
Caucasian           1.08 (1.03-1.13)       0.208
African             0.95 (0.80-1.12)       0.442
Mixed               0.98 (0.81-1.19)       0.196
Source of control
PB                  1.07 (1.02-1.12)       0.263
HB                  1.08 (0.98-1.20)       0.003
Quality score
Low                 1.09 (0.98-1.22)       0.025
High                1.07 (1.02-1.13)       0.067

Variables                          Recessive

                               Ala/Ala versus
                             (Val/Val + Val/Ala)

                       OR (95% CI)      [p.sup.het]

All                 1.05 (0.99-1.11)      <0.001
Cancer type
Breast              1.02 (0.94-1.10)       0.157
Prostate            1.03 (0.94-1.14)       0.225
Lung                0.91 (0.72-1.14)       0.313
Bladder             1.01 (0.83-1.24)       0.520
Pancreatic          1.08 (0.77-1.50)       0.020
CRC                 0.99 (0.86-1.13)       0.967
Ovarian             1.00 (0.81-1.23)       0.973
HCC                 1.70 (0.97-2.97)       0.162
NHL                 1.08 (0.94-1.24)       0.357
Other cancers       1.54 (1.07-2.20)      <0.001
Ethnicity
Asian               1.76 (1.16-2.68)       0.065
Caucasian           1.02 (0.96-1.08)       0.005
African             0.98 (0.79-1.21)       0.314
Mixed               1.12 (0.97-1.31)       0.187
Source of control
PB                  1.02 (0.97-1.08)       0.071
HB                  1.16 (1.01-1.34)      <0.001
Quality score
Low                 1.13 (0.99-1.29)       0.015
High                1.03 (0.97-1.09)       0.002

Variables                        Dominant

                           (Ala/Ala + Val/Ala)
                              versus Val/Val

                       OR (95% CI)      [p.sup.het]

All                 1.08 (1.02-1.14)      <0.001
Cancer type
Breast              1.01 (0.94-1.09)       0.066
Prostate            1.14 (1.05-1.23)       0.552
Lung                1.02 (0.78-1.32)       0.021
Bladder             0.93 (0.68-1.26)       0.021
Pancreatic          1.04 (0.70-1.55)       0.002
CRC                 1.03 (0.90-1.18)       0.733
Ovarian             1.13 (0.92-1.40)       0.748
HCC                 1.36 (0.67-2.76)       0.005
NHL                 1.05 (0.92-1.20)       0.831
Other cancers       1.32 (1.08-1.61)       0.001
Ethnicity
Asian               1.08 (0.91-1.29)      <0.001
Caucasian           1.08 (1.02-1.14)       0.011
African             0.99 (0.81-1.20)       0.289
Mixed               1.02 (0.85-1.23)       0.177
Source of control
PB                  1.07 (1.02-1.13)       0.071
HB                  1.10 (0.98-1.23)      <0.001
Quality score
Low                 1.11 (0.98-1.26)      <0.001
High                1.07 (1.02-1.14)       0.002

Variables                   Allele comparison

                              Ala versus Val

                       OR (95% CI)      [p.sup.het]

All                 1.06 (1.02-1.11)      <0.001
Cancer type
Breast              1.02 (0.97-1.06)       0.226
Prostate            1.07 (1.00-1.15)       0.106
Lung                0.98 (0.80-1.21)       0.039
Bladder             0.97 (0.80-1.19)       0.033
Pancreatic          1.04 (0.76-1.43)      <0.001
CRC                 1.01 (0.93-1.09)       0.863
Ovarian             1.05 (0.92-1.19)       0.836
HCC                 1.34 (0.76-2.35)       0.001
NHL                 1.05 (0.96-1.14)       0.849
Other cancers       1.32 (1.08-1.61)      <0.001
Ethnicity
Asian               1.16 (0.96-1.40)      <0.001
Caucasian           1.04 (1.00-1.09)      <0.001
African             1.01 (0.87-1.17)       0.168
Mixed               1.06 (0.94-1.21)       0.107
Source of control
PB                  1.04 (1.00-1.08)       0.006
HB                  1.13 (1.03-1.24)      <0.001
Quality score
Low                 1.12 (1.02-1.23)      <0.001
High                1.04 (1.00-1.09)      <0.001

Het: heterogeneity; CRC: colorectal cancer; HCC: hepatocellular
carcinoma; NHL: non-Hodgkin's lymphoma; PB: population-based; HB:
hospital-based.

Table 3: False-positive report probability values for associations
between cancer risk and MnSOD Val16Ala polymorphism.

Genotype                Crude OR (95% CI)   P value   Statistical
                                              (a)      power (b)

All
Homozygous              1.09 (1.00-1.19)     0.054       1.000
Heterozygous            1.07 (1.02-1.12)     0.004       1.000
Dominant                1.08 (1.02-1.14)     0.005       1.000
Allele comparison       1.06 (1.02-1.11)     0.013       1.000
Cancer type--prostate
cancer
Heterozygous            1.14 (1.05-1.24)     0.002       1.000
Dominant                1.14 (1.05-1.23)     0.001       1.000
Allele comparison       1.07 (1.00-1.15)     0.066       1.000
Ethnicity--Asian
Homozygous              1.82 (1.15-2.88)     0.011       0.204
Recessive               1.76 (1.16-2.68)     0.008       0.228
Ethnicity-Caucasian
Heterozygous            1.08 (1.03-1.13)     0.001       1.000
Dominant                1.08 (1.02-1.14)     0.005       1.000
Allele comparison       1.04 (1.00-1.09)     0.102       1.000
Control source--PB
Homozygous              1.10 (1.01-1.19)     0.018       1.000
Heterozygous            1.07 (1.02-1.12)     0.004       1.000
Dominant                1.07 (1.02-1.13)     0.015       1.000
Allele comparison       1.04 (1.00-1.08)     0.042       1.000
Control source--HB
Recessive               1.16 (1.01-1.34)     0.044       1.000
Allele comparison       1.13 (1.03-1.24)     0.010       1.000
Quality score--low
Allele comparison       1.12 (1.02-1.23)     0.018       1.000
Quality score--high
Homozygous              1.08 (1.00-1.17)     0.059       1.000
Heterozygous            1.07 (1.02-1.13)     0.015       1.000
Dominant                1.07 (1.02-1.14)     0.036       1.000
Allele comparison       1.04 (1.00-1.09)     0.102       1.000

Genotype                           Prior probability

                        0.25    0.1     0.01    0.001   0.0001
All
Homozygous              0.140   0.328   0.843   0.982   0.998
Heterozygous            0.011   0.032   0.267   0.787   0.974
Dominant                0.016   0.045   0.343   0.840   0.981
Allele comparison       0.038   0.106   0.567   0.930   0.992
Cancer type--prostate
cancer
Heterozygous            0.007   0.020   0.183   0.693   0.958
Dominant                0.002   0.006   0.067   0.420   0.879
Allele comparison       0.165   0.372   0.867   0.985   0.998
Ethnicity--Asian
Homozygous              0.134   0.317   0.836   0.981   0.998
Recessive               0.100   0.249   0.785   0.974   0.997
Ethnicity-Caucasian
Heterozygous            0.003   0.008   0.078   0.462   0.896
Dominant                0.016   0.045   0.343   0.840   0.981
Allele comparison       0.234   0.478   0.910   0.990   0.999
Control source--PB
Homozygous              0.050   0.136   0.634   0.946   0.994
Heterozygous            0.011   0.032   0.267   0.787   0.974
Dominant                0.043   0.119   0.599   0.938   0.993
Allele comparison       0.111   0.273   0.805   0.977   0.998
Control source--HB
Recessive               0.116   0.282   0.812   0.978   0.998
Allele comparison       0.029   0.082   0.495   0.908   0.990
Quality score--low
Allele comparison       0.051   0.138   0.637   0.947   0.994
Quality score--high
Homozygous              0.151   0.349   0.855   0.983   0.998
Heterozygous            0.043   0.119   0.599   0.938   0.993
Dominant                0.098   0.247   0.783   0.973   0.997
Allele comparison       0.234   0.478   0.910   0.990   0.999

(a) Chi-square test was used to calculate the genotype frequency
distributions; (b) statistical power was calculated using the number
of observations in the subgroup and the OR and P values in this table.

Figure 2: Forest plot of overall cancer risk associated with MnSOD
Val16Ala polymorphism by dominant model. For each study, the estimated
of OR and its 95% CI are plotted with a box and a horizontal line.
[??], pooled ORs and its 95% CIs.

Study                                                           %
ID                                          OR (95% CI)       weight

Ambrosone (1999)                         1.80 (0.90, 3.60)     0.50
Mitrunen (2001)                          1.33 (1.01, 1.76)     1.64
Wang (2001)                              0.79 (0.66, 0.95)     2.20
Green (2002)                             0.57 (0.20, 1.60)     0.25
Hirvonen (2002)                          0.73 (0.24, 2.23)     0.21
Levine (2002)                            0.90 (0.68, 1.19)     1.65
Li (2002)                                0.75 (0.23, 2.43)     0.19
Stoehlmacher (2002)                      0.83 (0.44, 1.58)     0.56
Egan (2003)                              1.28 (0.95, 1.72)     1.56
Lin (2003)                               1.00 (0.68, 1.47)     1.17
Woodson (2003)                           1.25 (0.78, 2.00)     0.91
Cai (2004)                               1.00 (0.83, 1.20)     2.21
Hung (2004)                              0.52 (0.34, 0.81)     0.99
Ichimura (2004)                          0.79 (0.50, 1.24)     0.94
Knight (2004)                            0.87 (0.63, 1.20)     1.42
Lan (2004)                               0.73 (0.40, 1.33)     0.63
Millikan (2004)                          0.79 (0.63, 0.99)     1.97
Millikan (2004)                          1.11 (0.92, 1.35)     2.17
Olson (2004)                             1.36 (0.80, 2.34)     0.74
Tamimi (2004)                            0.91 (0.75, 1.11)     2.15
Bergman (2005)                           0.85 (0.50, 1.44)     0.76
Cheng (2005)                             1.03 (0.79, 1.34)     1.74
Gaudet (2005)                            0.99 (0.82, 1.21)     2.12
Landi (2005)                             1.05 (0.74, 1.48)     1.34
Li (2005)                                1.09 (0.85, 1.41)     1.79
Terry (2005)                             1.22 (0.79, 1.87)     1.02
Ho (2006)                                1.01 (0.66, 1.53)     1.06
Lightfoot (2006)                         1.08 (0.89, 1.32)     2.15
Slanger (2006)                           1.05 (0.83, 1.33)     1.91
Wang (2006)                              1.01 (0.83, 1.23)     2.12
Choi (2007)                              0.97 (0.76, 1.25)     1.81
Choi (2007)                              1.41 (0.55, 3.59)     0.29
Ergen (2007)                             2.90 (1.29, 6.53)     0.38
Han (2007)                               0.98 (0.78, 1.24)     1.93
Johnatty (2007)                          1.10 (0.87, 1.41)     1.86
Landi (2007)                             1.56 (0.86, 2.83)     0.63
Martino (2007)                           0.92 (0.49, 1.74)     0.58
Murphy (2007)                            1.38 (0.88, 2.16)     0.97
Kang (2007)                              1.19 (0.99, 1.42)     2.24
Kang (2007)                              1.04 (0.65, 1.66)     0.90
Arsova-Sarafinovska (2008)               1.29 (0.69, 2.42)     0.59
Cooper (2008)                            1.18 (1.02, 1.36)     2.47
Dalan (2008)                             1.01 (0.47, 2.18)     0.42
Justenhoven (2008)                       1.00 (0.77, 1.28)     1.79
Mikhak (2008)                            1.03 (0.80, 1.33)     1.79
Rajaraman (2008)                         1.11 (0.83, 1.48)     1.60
Wheatley-Price (2008)                    0.61 (0.37, 0.99)     0.86
Zienolddiny (2008)                       1.54 (1.10, 2.16)     1.36
Eras-Erdogan (2009)                      1.11 (0.80, 1.55)     1.39
Funke (2009)                             1.14 (0.88, 1.49)     1.72
Iguchi (2009)                            1.06 (0.64, 1.73)     0.84
Kostrykina (2009)                        1.08 (0.80, 1.47)     1.51
Tong (2009)                              1.05 (0.63, 1.77)     0.78
Tang (2010)                              1.03 (0.79, 1.33)     1.76
Ermolenko (2010)                         1.08 (0.84, 1.40)     1.78
Ezzikouri (2010)                         2.05 (1.18, 3.58)     0.71
Ibrahim (2010)                           1.80 (0.82, 3.91)     0.41
Kim (2010)                               0.86 (0.60, 1.22)     1.30
Meplan (2010)                            1.07 (0.83, 1.36)     1.84
Wu (2010)                                0.85 (0.48, 1.51)     0.68
Yi (2010)                                2.75 (1.61, 4.69)     0.75
Cerne (2011)                             1.11 (0.78, 1.56)     1.33
Mohelnikova-Duchonova (2011)             0.97 (0.66, 1.44)     1.15
Atoum (2012)                             3.58 (1.17, 10.98)    0.21
Farawela (2012)                          1.23 (0.50, 2.99)     0.32
Hemelrijck (2012)                        0.87 (0.58, 1.31)     1.10
Kucukgergin (2012)                       1.50 (0.93, 2.43)     0.87
Kucukgergin (2012)                       1.33 (0.87, 2.04)     1.03
Tsai (2012)                              0.57 (0.39, 0.84)     1.17
Ye (2012)                                0.82 (0.42, 1.60)     0.52
Zhao (2012)                              1.93 (1.40, 2.65)     1.46
Amr (2013)                               1.00 (0.73, 1.36)     1.51
Ashour (2013)                            1.41 (0.62, 3.16)     0.38
Attatippaholkun (2013)                   1.11 (0.66, 1.86)     0.78
Attatippaholkun (2013)                   1.19 (0.73, 1.92)     0.87
Eken (2013)                              2.30 (0.89, 5.94)     0.29
Han (2013)                               2.02 (1.40, 2.91)     1.25
Meplan (2013)                            1.03 (0.83, 1.26)     2.06
Atilgan (2014)                           2.64 (1.07, 6.52)     0.31
Liu (2014)                               1.58 (1.10, 2.27)     1.26
Oskina (2014)                            1.07 (0.76, 1.51)     1.36
Brown (2015)                             5.11 (1.33, 19.57)    0.15
Jablonska (2015)                         0.94 (0.55, 1.59)     0.77
Parlaktas (2015)                         1.09 (0.49, 2.40)     0.39
Su (2015)                                0.79 (0.58, 1.08)     1.48
Overall (I-squared = 47.4%, P = 0.000)   1.08 (1.02, 1.14)    100.00

Note. Weights are from random-effects analysis
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Author:Wang, Ping; Zhu, Yanfeng; Xi, Shoumin; Li, Sanqiang; Zhang, Yanle
Publication:Disease Markers
Date:Jan 1, 2018
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