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. 2018 Sep 2;2018:3061974. doi: 10.1155/2018/3061974

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

Ping Wang 1,✉, Yanfeng Zhu 2, Shoumin Xi 1, Sanqiang Li 1,✉, Yanle Zhang 1
PMCID: PMC6139213  PMID: 30245752

Abstract

Manganese superoxide dismutase (MnSOD) plays a critical role in the defense against reactive oxygen species. The association between MnSOD Val16Ala polymorphism and cancer risk has been widely studied, but the results are contradictory. To obtain more precision on the association, we performed the current meta-analysis with 33,098 cases and 37,831 controls from 88 studies retrieved from PubMed, Embase, Chinese National Knowledge Infrastructure (CNKI), and Wanfang databases. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were used to assess the strength of association. We found that the polymorphism was associated with an increased overall cancer risk (homozygous: OR = 1.09, 95% CI = 1.00–1.19; heterozygous: OR = 1.07, 95% CI = 1.02–1.12; dominant: OR = 1.08, 95% CI = 1.02–1.14; and allele comparison: OR = 1.06, 95% CI = 1.02–1.11). Stratification analysis further showed an increased risk for prostate cancer, Asians, Caucasians, population-based studies, hospital-based studies, low quality and high quality studies. However, the increased risk for MnSOD Val16Ala polymorphism among Asians needs further validation based on the false-positive report probability (FPRP) test. To summarize, this meta-analysis suggests that the MnSOD Val16Ala polymorphism is associated with significantly increased cancer risk, which needs further validation in single large studies.

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 meta-analysis 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 ≤ 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 > 0.1). 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 (≤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.

Figure 1.

Figure 1

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Flowchart of included studies for the association between MnSOD Val16Ala polymorphism and cancer susceptibility.

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.

Table 1.

Characteristics of studies included in the meta-analysis.

Surname (ref) Year Country Ethnicity Cancer type Control source Genotype method Case Control MAF HWE Score
Val/Val Val/Ala Ala/Ala All Val/Val Val/Ala Ala/Ala All
Ambrosone et al. [60] 1999 USA Caucasian Breast PB PCR-RFLP 16 53 45 114 25 62 23 110 0.49 0.181 12
Mitrunen et al. [10] 2001 Finland Caucasian Breast PB PCR-RFLP 124 255 100 479 153 231 98 482 0.44 0.526 13
Wang et al. [7] 2001 USA Caucasian Lung HB Pyrosequencing 305 551 245 1101 288 628 323 1239 0.49 0.609 9
Green et al. [61] 2002 UK Caucasian Breast HB PCR-RFLP 13 17 9 39 8 22 6 36 0.47 0.175 5
Hirvonen et al. [62] 2002 Finland Caucasian MPM PB PCR-RFLP 6 11 3 20 15 36 12 63 0.48 0.248 9
Levine et al. [63] 2002 USA Mixed CRC PB PCR-RFLP 139 209 108 456 140 234 121 495 0.48 0.237 12
Li et al. [64] 2002 USA Caucasian Pancreatic PB PCR-RFLP 10 11 3 24 8 10 5 23 0.43 0.580 6
Stoehlmacher et al. [13] 2002 USA Caucasian CRC PB TaqMan 25 65 35 125 21 64 37 122 0.43 0.456 5
Egan et al. [16] 2003 USA Caucasian Breast PB PCR-RFLP 102 250 118 470 130 240 127 497 0.50 0.446 10
Lin et al. [65]a 2003 China Asian Lung HB PCR-RFLP 139 59 (Val/Ala + Ala/Ala) 198 233 99 (Val/Ala + Ala/Ala) 332 NA NA 10
Woodson et al. [66] 2003 USA Caucasian Prostate PB MALDI-TOF MS 43 98 58 199 49 102 40 191 0.48 0.330 12
Cai et al. [11] 2004 China Asian Breast PB PCR-RFLP 831 266 28 1125 884 290 23 1197 0.14 0.890 15
Hung et al. [8] 2004 Italy Caucasian Bladder HB PCR-RFLP 68 89 44 201 45 115 54 214 0.48 0.262 9
Ichimura et al. [67] 2004 Japan Asian Bladder HB PCR-RFLP 169 41 3 213 157 48 4 209 0.13 0.882 11
Knight et al. [68] 2004 Canada Caucasian Breast PB PCR-SSCP 107 187 105 399 90 195 87 372 0.50 0.350 14
Lan et al. [17] 2004 China Asian Lung PB Real-time PCR 93 23 3 119 81 30 1 112 0.14 0.321 10
Millikan et al. [69] 2004 USA African Breast PB TaqMan 259 372 129 760 196 357 124 677 0.45 0.083 13
Millikan et al. [69] 2004 USA Caucasian Breast PB TaqMan 273 681 311 1265 266 586 283 1135 0.49 0.269 13
Olson et al. [70] 2004 USA Caucasian Ovarian HB MALDI-TOF MS 27 64 27 118 51 87 39 177 0.47 0.869 9
Tamimi et al. [71] 2004 USA Caucasian Breast PB Mixedd 255 468 245 968 297 612 296 1205 0.50 0.584 15
Bergman et al. [9] 2005 Sweden Caucasian Breast PB Sequencing 33 73 12 118 43 88 43 174 0.50 0.879 11
Cheng et al. [72] 2005 China Asian Breast HB MassARRAY 343 115 11 469 545 183 11 739 0.14 0.322 11
Gaudet et al. [38] 2005 USA Caucasian Breast PB MALDI-TOF MS 253 511 270 1034 264 539 281 1084 0.49 0.862 14
Landi et al. [73] 2005 Spain Caucasian CRC HB APEX 94 164 77 335 88 151 64 303 0.46 0.958 5
Li et al. [74] 2005 USA Caucasian Prostate PB PCR-RFLP 132 288 147 567 190 379 195 764 0.50 0.829 14
Terry et al. [75] 2005 USA Caucasian Bladder HB MALDI-TOF MS 54 122 59 235 57 103 54 214 0.49 0.586 8
Ho et al. [18]c 2006 China Asian Lung HB PCR-RFLP 176 58 0 234 180 52 7 239 0.14 0.184 7
Lightfoot et al. [76] 2006 USA and UK Caucasian NHL PB TaqMan 211 463 229 903 358 713 371 1442 0.50 0.676 13
Slanger et al. [77] 2006 Germany Caucasian Breast PB TaqMan 144 318 152 614 263 528 289 1080 0.49 0.477 14
Wang et al. [78] 2006 USA Mixed NHL PB TaqMan 285 545 290 1120 240 486 211 937 0.48 0.240 13
Cengiz et al. [15]b 2007 Turkey Caucasian Bladder HB PCR-RFLP 34 (Val/Val + Val/Ala) 17 51 34 (Val/Val + Val/Ala) 19 53 NA NA 7
Choi et al. [37] 2007 USA Caucasian Prostate PB MALDI-TOF MS 112 239 104 455 293 610 311 1214 0.49 0.857 13
Choi et al. [37] 2007 USA African Prostate PB MALDI-TOF MS 7 15 6 28 39 52 31 122 0.47 0.112 10
Ergen et al. [79]c 2007 Turkey Caucasian Prostate HB PCR-RFLP 19 25 6 50 32 18 0 50 0.18 0.121 7
Han et al. [80] 2007 USA Caucasian Skin PB TaqMan 184 402 187 773 196 425 212 833 0.49 0.549 15
Johnatty et al. [81] 2007 Australia Caucasian Ovarian PB Real-time PCR 123 273 147 543 276 546 308 1130 0.49 0.269 11
Kang et al. [82] 2007 USA Caucasian Prostate PB TaqMan 275 578 297 1150 376 686 320 1382 0.48 0.835 13
Kang et al. [82] 2007 USA African Prostate PB TaqMan 31 57 15 103 122 194 79 395 0.45 0.906 11
Landi et al. [83] 2007 Italy Caucasian MPM HB APEX 16 27 37 80 98 170 81 349 0.48 0.661 9
di Martino et al. [84] 2007 USA Caucasian Esophageal HB PCR-RFLP 32 73 35 140 20 39 34 93 0.42 0.171 8
Murphy et al. [12] 2007 Ireland Caucasian Esophageal PB SNaPshot 44 103 60 207 60 113 48 221 0.47 0.703 11
Arsova-Sarafinovska et al. [85] 2008 Turkey Caucasian Prostate HB Real-time PCR 19 46 20 85 41 73 37 151 0.49 0.690 9
Cooper et al. [86] 2008 USA Caucasian Prostate PB TaqMan 602 1352 680 2634 423 789 424 1636 0.50 0.152 15
Dalan et al. [87] 2008 Turkey Caucasian Ovarian PB PCR-RFLP 30 19 6 55 28 17 6 51 0.28 0.196 7
Justenhoven et al. [88] 2008 Germany Caucasian Breast PB MALDI-TOF MS 159 312 133 604 163 313 145 621 0.49 0.824 14
Mikhak et al. [89] 2008 USA Caucasian Prostate PB TaqMan 156 320 166 642 162 331 159 652 0.50 0.695 14
Rajaraman et al. [90] 2008 USA Caucasian Brain HB TaqMan 129 262 123 514 122 220 109 451 0.49 0.617 10
Wheatley-Price et al. [91] 2008 USA Caucasian Pancreatic HB TaqMan 33 58 31 122 61 165 105 331 0.43 0.786 11
Zienolddiny et al. [92] 2008 Norway Caucasian Lung PB APEX 74 175 70 319 119 178 78 375 0.45 0.448 12
Eras-Erdogan et al. [93] 2009 Turkey Caucasian Breast PB PCR-RFLP 107 113 30 250 150 141 39 330 0.33 0.508 8
Funke et al. [94] 2009 Germany Caucasian CRC PB Pyrosequencing 136 321 166 623 146 294 163 603 0.49 0.554 12
Iguchi et al. [95] 2009 USA Mixed Prostate HB PCR-RFLP 41 86 60 187 40 96 39 175 0.50 0.199 6
Kostrykina et al. [96] 2009 Russia Caucasian Breast PB TaqMan 123 233 119 475 103 183 90 376 0.48 0.622 12
Tong et al. [14]a 2009 Korea Asian Cervical HB SNaPshot 72 27 (Val/Ala + Ala/Ala) 99 194 69 (Val/Ala + Ala/Ala) 263 NA NA 7
Ermolenko et al. [97] 2010 Russia Caucasian Breast HB Real-time PCR 228 454 239 921 121 235 104 460 0.48 0.620 9
Ezzikouri et al. [98] 2010 Morocco Caucasian HCC PB PCR-RFLP 21 45 30 96 81 101 40 222 0.41 0.388 11
Ibrahim et al. [99] 2010 Egypt African HCC HB PCR-RFLP 16 32 27 75 19 28 11 58 0.43 0.904 8
Kim et al. [100] 2010 Korea Asian Breast HB TaqMan 234 66 4 304 279 90 7 376 0.14 0.934 11
Méplan et al. [101] 2010 Czech Caucasian CRC HB AS-PCR 172 358 189 719 165 318 174 657 0.49 0.415 9
Tang et al. [102] 2010 USA Mixed Pancreatic HB TaqMan 143 278 137 558 167 309 162 638 0.50 0.429 11
Wu et al. [103] 2010 China Asian Oral HB Real-time PCR 91 28 2 121 88 32 2 122 0.15 0.637 9
Yi et al. [104] 2010 China Asian Gastric HB SNaPshot 85 48 7 140 119 27 1 147 0.10 0.690 9
Cerne et al. [105] 2011 Slovenia Caucasian Breast HB TaqMan 118 269 143 530 65 134 71 270 0.51 0.910 8
Cheng et al. [106]b 2011 USA Mixed Prostate PB MALDI-TOF MS 152 (Val/Val + Val/Ala) 50 202 1054 (Val/Val + Val/Ala) 374 1428 NA NA 13
Mohelnikova-Duchonova et al. [107] 2011 Czech Caucasian Pancreatic PB Real-time PCR 66 121 48 235 73 134 58 265 0.47 0.812 10
Zhang et al. [108]b 2011 USA Mixed Pancreatic PB TaqMan 129 (Val/Val + Val/Ala) 60 189 365 (Val/Val + Val/Ala) 121 486 NA NA 13
Atoum et al. [109]c 2012 Jordan Caucasian Breast HB PCR-RFLP 22 43 0 65 11 6 0 17 0.18 0.377 6
Farawela et al. [110] 2012 Egypt African NHL PB PCR-RFLP 10 50 40 100 12 49 39 100 0.37 0.568 9
Hemelrijck et al. [111] 2012 Germany Caucasian Prostate PB MassARRAY 50 100 53 203 80 190 90 360 0.49 0.285 13
Kucukgergin et al. [112] 2012 Turkey Caucasian Bladder HB PCR-RFLP 52 68 37 157 89 99 36 224 0.38 0.341 8
Kucukgergin et al. [113] 2012 Turkey Caucasian Prostate HB PCR-RFLP 43 65 26 134 66 69 24 159 0.37 0.398 8
Tsai et al. [114]a 2012 China Asian Breast HB Real-time PCR 192 68 (Val/Ala + Ala/Ala) 260 138 86 (Val/Ala + Ala/Ala) 224 NA NA 8
Ye et al. [115] 2012 China Asian NPC HB PCR 88 15 2 105 110 23 3 136 0.11 0.191 8
Zhao et al. [116] 2012 China Asian Brain HB OpenArray 241 107 31 379 293 81 6 380 0.12 0.882 11
Amr et al. [117] 2013 Egypt African Bladder PB TaqMan 127 188 99 414 109 160 87 356 0.47 0.065 13
Ashour et al. [118] 2013 Egypt African Lung PB TaqMan 17 27 6 50 21 25 4 50 0.33 0.355 9
Attatippaholkun and Wikainapakul [119] 2013 Thailand Asian Cervical HB SNaPshot 64 39 4 107 84 48 3 135 0.20 0.184 7
Attatippaholkun et al. [119] 2013 Thailand Asian Breast HB SNaPshot 82 54 5 141 84 48 3 135 0.20 0.184 7
Eken et al. [120] 2013 Turkey Caucasian Prostate HB Real-time PCR 7 17 9 33 31 37 13 81 0.39 0.726 8
Han et al. [121] 2013 Korea Asian Pancreatic PB PCR-SSCP 190 85 19 294 236 59 5 300 0.12 0.558 12
Méplan et al. [122] 2013 Denmark Caucasian Breast PB TaqMan 228 485 226 939 237 494 227 958 0.49 0.331 14
Atilgan et al. [123] 2014 Turkey Caucasian RCC HB Probe 10 17 14 41 23 19 8 50 0.35 0.244 5
Liu et al [124] 2014 China Asian OSCC HB PCR-RFLP 272 83 7 362 296 61 1 358 0.09 0.243 10
Oskina et al. [125] 2014 Russia Caucasian Prostate PB TaqMan 92 194 94 380 86 152 99 337 0.48 0.076 12
Brown et al. [126] 2015 USA Mixed Medulloblastoma PB Illumina SNP chip 3 15 8 26 18 18 9 45 0.40 0.264 5
Jablonska et al. [127] 2015 Polish Caucasian Breast PB Real-time PCR 32 75 29 136 41 92 50 183 0.48 0.915 10
Parlaktas et al. [128] 2015 Turkey Caucasian Prostate HB Probe 23 23 3 49 24 20 5 49 0.31 0.784 7
Su et al. [129] 2015 China Asian HCC HB PCR-RFLP 334 78 10 422 359 107 13 479 0.14 0.150 7
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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. aLin et al. [65], Tong et al. [14], and Tsai et al. [114] were only calculated for the dominant model. bCengiz et al. [15], Cheng et al. [106], and Zhang et al. [108] were only calculated for the recessive model. cHo 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. dMixed: which included more than one genotyping methods.

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).

Table 2.

Meta-analysis of the association between MnSOD Val16Ala polymorphism and cancer risk.

Variables Number of studies Sample size (case/controls) Homozygous Heterozygous Recessive Dominant Allele comparison
Ala/Ala versus Val/Val Val/Ala versus Val/Val Ala/Ala versus (Val/Val + Val/Ala) (Ala/Ala + Val/Ala) versus Val/Val Ala versus Val
OR (95% CI) P het OR (95% CI) P het OR (95% CI) P het OR (95% CI) P het OR (95% CI) P het
All 88 33,098/37,831 1.09 (1.00–1.19) <0.001 1.07 (1.02–1.12) 0.001 1.05 (0.99–1.11) <0.001 1.08 (1.02–1.14) <0.001 1.06 (1.02–1.11) <0.001
Cancer type
Breast 24 12,479/12,603 1.03 (0.95–1.13) 0.276 1.02 (0.96–1.09) 0.302 1.02 (0.94–1.10) 0.157 1.01 (0.94–1.09) 0.066 1.02 (0.97–1.06) 0.226
Prostate 17 7101/9146 1.04 (0.87–1.24) 0.002 1.14 (1.05–1.24) 0.765 1.03 (0.94–1.14) 0.225 1.14 (1.05–1.23) 0.552 1.07 (1.00–1.15) 0.106
Lung 6 2021/2347 1.13 (0.63–2.04) 0.019 1.05 (0.76–1.46) 0.016 0.91 (0.72–1.14) 0.313 1.02 (0.78–1.32) 0.021 0.98 (0.80–1.21) 0.039
Bladder 6 1271/1270 0.66 (0.39–1.13) 0.002 0.91 (0.68–1.23) 0.049 1.01 (0.83–1.24) 0.520 0.93 (0.68–1.26) 0.021 0.97 (0.80–1.19) 0.033
Pancreatic 6 1422/2043 1.01 (0.59–1.73) 0.007 1.07 (0.77–1.49) 0.032 1.08 (0.77–1.50) 0.020 1.04 (0.70–1.55) 0.002 1.04 (0.76–1.43) <0.001
CRC 5 2258/2180 1.02 (0.86–1.20) 0.856 1.04 (0.90–1.20) 0.733 0.99 (0.86–1.13) 0.967 1.03 (0.90–1.18) 0.733 1.01 (0.93–1.09) 0.863
Ovarian 3 716,1358 1.10 (0.85–1.42) 0.839 1.15 (0.92–1.45) 0.773 1.00 (0.81–1.23) 0.973 1.13 (0.92–1.40) 0.748 1.05 (0.92–1.19) 0.836
HCC 3 593/759 1.92 (0.85–4.36) 0.050 1.15 (0.66–2.00) 0.055 1.70 (0.97–2.97) 0.162 1.36 (0.67–2.76) 0.005 1.34 (0.76–2.35) 0.001
NHL 3 2123/2479 1.96 (0.96–4.00) <0.001 1.03 (0.89–1.19) 0.551 1.08 (0.94–1.24) 0.357 1.05 (0.92–1.20) 0.831 1.05 (0.96–1.14) 0.849
Other cancers 15 3114/3646 1.79 (1.18–2.70) <0.001 1.25 (1.05–1.49) 0.058 1.54 (1.07–2.20) <0.001 1.32 (1.08–1.61) 0.001 1.32 (1.08–1.61) <0.001
Ethnicity
Asian 18 5092/5748 1.82 (1.15–2.88) 0.020 1.10 (0.94–1.30) 0.001 1.76 (1.16–2.68) 0.065 1.08 (0.91–1.29) <0.001 1.16 (0.96–1.40) <0.001
Caucasian 56 23,738/26,121 1.03 (0.94–1.12) <0.001 1.08 (1.03–1.13) 0.208 1.02 (0.96–1.08) 0.005 1.08 (1.02–1.14) 0.011 1.04 (1.00–1.09) <0.001
African 7 1530/1758 1.58 (0.85–2.93) <0.001 0.95 (0.80–1.12) 0.442 0.98 (0.79–1.21) 0.314 0.99 (0.81–1.20) 0.289 1.01 (0.87–1.17) 0.168
Mixed 7 2738/4204 1.11 (0.88–1.42) 0.141 0.98 (0.81–1.19) 0.196 1.12 (0.97–1.31) 0.187 1.02 (0.85–1.23) 0.177 1.06 (0.94–1.21) 0.107
Source of control
PB 48 23,004/27,193 1.10 (1.01–1.19) <0.001 1.07 (1.02–1.12) 0.263 1.02 (0.97–1.08) 0.071 1.07 (1.02–1.13) 0.071 1.04 (1.00–1.08) 0.006
HB 40 10,094/10,638 1.09 (0.88–1.35) <0.001 1.08 (0.98–1.20) 0.003 1.16 (1.01–1.34) <0.001 1.10 (0.98–1.23) <0.001 1.13 (1.03–1.24) <0.001
Quality score
Low 39 7625/7608 1.15 (0.90–1.46) <0.001 1.09 (0.98–1.22) 0.025 1.13 (0.99–1.29) 0.015 1.11 (0.98–1.26) <0.001 1.12 (1.02–1.23) <0.001
High 49 25,473/30,223 1.08 (1.00–1.17) 0.001 1.07 (1.02–1.13) 0.067 1.03 (0.97–1.09) 0.002 1.07 (1.02–1.14) 0.002 1.04 (1.00–1.09) <0.001
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Het: heterogeneity; CRC: colorectal cancer; HCC: hepatocellular carcinoma; NHL: non-Hodgkin's lymphoma; PB: population-based; HB: hospital-based.

Figure 2.

Figure 2

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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.

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).

Table 3.

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

Genotype Crude OR (95% CI) P valuea Statistical powerb Prior probability
0.25 0.1 0.01 0.001 0.0001
All
Homozygous 1.09 (1.00–1.19) 0.054 1.000 0.140 0.328 0.843 0.982 0.998
Heterozygous 1.07 (1.02–1.12) 0.004 1.000 0.011 0.032 0.267 0.787 0.974
Dominant 1.08 (1.02–1.14) 0.005 1.000 0.016 0.045 0.343 0.840 0.981
Allele comparison 1.06 (1.02–1.11) 0.013 1.000 0.038 0.106 0.567 0.930 0.992
Cancer type—prostate cancer
Heterozygous 1.14 (1.05–1.24) 0.002 1.000 0.007 0.020 0.183 0.693 0.958
Dominant 1.14 (1.05–1.23) 0.001 1.000 0.002 0.006 0.067 0.420 0.879
Allele comparison 1.07 (1.00–1.15) 0.066 1.000 0.165 0.372 0.867 0.985 0.998
Ethnicity—Asian
Homozygous 1.82 (1.15–2.88) 0.011 0.204 0.134 0.317 0.836 0.981 0.998
Recessive 1.76 (1.16–2.68) 0.008 0.228 0.100 0.249 0.785 0.974 0.997
Ethnicity–Caucasian
Heterozygous 1.08 (1.03–1.13) 0.001 1.000 0.003 0.008 0.078 0.462 0.896
Dominant 1.08 (1.02–1.14) 0.005 1.000 0.016 0.045 0.343 0.840 0.981
Allele comparison 1.04 (1.00–1.09) 0.102 1.000 0.234 0.478 0.910 0.990 0.999
Control source—PB
Homozygous 1.10 (1.01–1.19) 0.018 1.000 0.050 0.136 0.634 0.946 0.994
Heterozygous 1.07 (1.02–1.12) 0.004 1.000 0.011 0.032 0.267 0.787 0.974
Dominant 1.07 (1.02–1.13) 0.015 1.000 0.043 0.119 0.599 0.938 0.993
Allele comparison 1.04 (1.00–1.08) 0.042 1.000 0.111 0.273 0.805 0.977 0.998
Control source—HB
Recessive 1.16 (1.01–1.34) 0.044 1.000 0.116 0.282 0.812 0.978 0.998
Allele comparison 1.13 (1.03–1.24) 0.010 1.000 0.029 0.082 0.495 0.908 0.990
Quality score—low
Allele comparison 1.12 (1.02–1.23) 0.018 1.000 0.051 0.138 0.637 0.947 0.994
Quality score—high
Homozygous 1.08 (1.00–1.17) 0.059 1.000 0.151 0.349 0.855 0.983 0.998
Heterozygous 1.07 (1.02–1.13) 0.015 1.000 0.043 0.119 0.599 0.938 0.993
Dominant 1.07 (1.02–1.14) 0.036 1.000 0.098 0.247 0.783 0.973 0.997
Allele comparison 1.04 (1.00–1.09) 0.102 1.000 0.234 0.478 0.910 0.990 0.999
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aChi-square test was used to calculate the genotype frequency distributions; bstatistical power was calculated using the number of observations in the subgroup and the OR and P values in this table.

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 O2− into H2O2, 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.

Acknowledgments

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

Contributor Information

Ping Wang, Email: glorywangping@163.com.

Sanqiang Li, Email: sanqiangli2001@163.com.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

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