Disease Markers
Volume 2018 (2018), Article ID 3061974, 16 pages
https://doi.org/10.1155/2018/3061974

Review Article

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 and Yanle Zhang¹

¹Department of Biochemistry and Molecular Biology, Medical College, Henan University of Science and Technology, Luoyang, Henan 471023, China
²School 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

Copyright © 2018 Ping Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

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: , 95% CI = 1.00–1.19; heterozygous: , 95% CI = 1.02–1.12; dominant: , 95% CI = 1.02–1.14; and allele comparison: , 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 () and low-quality studies ().

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 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 (). 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 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: 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

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: , 95% CI = 1.00–1.19, ; heterozygous: , 95% CI = 1.02–1.12, ; dominant: , 95% CI = 1.02–1.14, ; and allele comparison: , 95% CI = 1.02–1.11, ) (Table 2, Figure 2). In the subgroup analysis, a statistically significant association was found for prostate cancer (heterozygous: , 95% CI = 1.05–1.24, ; dominant: , 95% CI = 1.05–1.23, ; and allele comparison: , 95% CI = 1.00–1.15, ), Asians (homozygous: , 95% CI = 1.15–2.88, , and recessive: , 95% CI = 1.16–2.68, ), Caucasians (heterozygous: , 95% CI = 1.03–1.13, ; dominant: , 95% CI = 1.02–1.14, ; and allele comparison: , 95% CI = 1.00–1.09, ), population-based studies (homozygous: , 95% CI = 1.01–1.19, ; heterozygous: , 95% CI = 1.02–1.12, ; dominant: , 95% CI = 1.02–1.13, ; and allele comparison: , 95% CI = 1.00–1.08, ), hospital-based studies (recessive: , 95% CI = 1.01–1.34, , and allele comparison: , 95% CI = 1.03–1.24, ), low-quality studies (allele comparison: , 95% CI = 1.02–1.23, ) and high-quality studies (homozygous: , 95% CI = 1.00–1.17, ; heterozygous: , 95% CI = 1.02–1.13, ; dominant: , 95% CI = 1.02–1.14, ; and allele comparison: , 95% CI = 1.00–1.09, ).

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

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

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.

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: ; heterozygous: ; recessive: ; dominant: ; and allele comparison: ). 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 (), recessive model (), dominant model (), and allele comparison (), but not for the heterozygous model (). 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 (, 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)
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

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 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.

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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