RAD 51 gene plays an important role in the pathogenesis of squamous cell carcinoma of the head and neck (SCCHN), colorectal cancer, ovarian cancer and acute leukaemia. A number of studies assessed the association between RAD51 135G/C polymorphism and the risk of these cancers in different population. However, the results have been inconclusive. We performed a systematic meta-analysis to evaluate the association between RAD51 135G/C polymorphism and the risk of these four types of cancer.
Methods
Pubmed, Cochrane library and Chinese Biomedical Literature Database (CBM) were searched for case-control studies on RAD 51 135G/C polymorphism and the risk of SCCHN, colorectal cancer, ovarian cancer and acute leukaemia published up to Oct 31, 2013. Odds ratios (ORs) with 95% confidence intervals (CIs) were used to assess the strength of association.
Results
A total of twenty-two published studies, with 6836 cases and 8507 controls were included. Overall, no significant association was found between RAD51 135G/C polymorphism and the risk of the four types of cancers (G/G vs. C/C: OR = 0.83, 95% CI: 0.43-1.59, P = 0.57). However, there was a significant association between this polymorphism and SCCHN risk in the subgroup analysis by cancer type (G/G vs. C/C: OR = 2.46, 95% CI: 1.08-5.61, P = 0.03).
Conclusion
The RAD 51 135G/C polymorphism was associated with the risk of SCCHN.
RAD 51Single nucleotide polymorphismCancer riskMeta-analysis
Introduction
Cancer is one of the most common fatal diseases, which results from complex interactions between environmental and genetic factors [1]. More and more studies have focused on the role of gene polymorphism in the aetiology of cancers. Recently, there is growing evidence that single nucleotide polymorphism (SNP) plays an important role in carcinogenesis [2, 3]. DNA repair systems have been considered to maintain genomic integrity by counting threats posed by DNA lesions. Deficiency in the DNA repair pathways might make these lesions unrepaired or repaired incorrectly, eventually leading to genome instability or mutations which may contribute directly to cancer.
RAD51 gene is located on chromosome 15q15.1 in humans [4]. The RAD51 protein encoding by RAD51 gene is essential for the repair of DNA damage. Growing evidences show that RAD51 has an irreplaceable role in the maintenance of genomic stability and the repair of DNA double-strand breaks [5]. The RAD51 genetic variations may contribute to the development of cancers [6]. A functional single nucleotide polymorphism, 135G/C (rs1801320), has been identified in the 5′ untranslated region of the RAD51 gene [7] and has been reported to affect gene transcription activity [8].
Up to now, a variety of molecular epidemiological studies have been conducted to estimate the association between the RAD51 135G/C polymorphism and risk of various cancers [9–17], including squamous cell carcinoma of the head and neck [18–21], colorectal cancer [22–25], ovarian cancer [26–28] and acute leukaemia [29–37]. However, the results of previous studies on the association between RAD51 135G/C polymorphism and cancer risk have been inconclusive, partially because of the relatively small sample size of most studies. Therefore, we carried out this meta-analysis to evaluate the association between RAD51 135G/C polymorphism and risk of the four common types of cancers.
Methods
Selection of eligible studies
We conducted a comprehensive search in Pubmed, Cochrane library and Chinese Biomedical Literature Database (CBM), covering all articles published up to Oct 31, 2013, using the following terms: “RAD51” AND “polymorphism” AND “(squamous cell carcinoma of the head and neck) OR (colorectal cancer) OR (ovarian cancer) OR (acute leukaemia)”. References of all identified studies and reviews were examined for additional articles.
Study assessment
Included studies in this meta-analysis met the following criteria: (a) a human case-control study on the association between RAD51 135G/C polymorphism and any of the four common cancers; (b) containing available genotype data in cases and controls for estimating an odds ratio (OR) and 95% confidence interval (CI); (c) genotype distributions of control population were consistent with Hardy-Weinberg equilibrium (HWE). The exclusion criteria were: (a) reviews, letters, editorial articles and case reports; (b) studies involving only a case population; (c) research not providing cancer information.
Data extraction
Two investigators (Cheng and Shi) extracted the data from all of the eligible publications according to the inclusion and exclusion criteria mentioned above. Primary extraction data were reviewed by Zhen, and any disagreement was resolved by discussion among the three authors. From each study, the following information was collected: first author’s name, year of publication, study location, cancer type, sample size, source of control, the genotyping method, the number of genotype frequencies in cases and controls.
Statistical analysis
For each case-control study, we first examined whether the genotype frequencies in controls were consistent with HWE. ORs and 95% CIs were calculated as a measure of the association between the RAD51 135G/C gene polymorphism and risk of the four cancers. The pooled ORs were performed for the homozygote comparison (G/G vs. C/C), heterozygote comparison (G/C vs. C/C), dominant (G/G + G/C vs. C/C) and recessive (G/G vs. G/C + C/C) genetic model comparison, and the significances of the summary ORs were determined by Z test, P < 0.05 was considered as statistically significant. The chi-square-based Q-test was used to assess the statistical heterogeneity among studies, and it was considered significant if P < 0.10 [38]. If the P value greater than 0.10, indicating the absence of heterogeneity, then a fixed-effects model (the Mantel-Haenszel method) was applied to calculate the summary ORs [39]. Otherwise, the random-effects model (the DerSimonian and Laird method) was used [40]. I2 was also calculated to test heterogeneity among included studies, with I2 < 25%, 25-75%, and >75% considered to represent low, moderate and high degree of heterogeneity, respectively [41]. Sensitivity analysis was performed to estimate the stability of the results, each study involved in this meta-analysis was deleted each time to reflect the influence of the individual data set to pooled ORs. Publication bias within the literature was assessed using Begg’s test [42], an asymmetric funnel plot showed a potential publication bias. Egger’s linear regression test (P < 0.05 was considered significant publication bias) was also used to evaluate the symmetry of the funnel plot [43]. All of the analyses were carried out with RevMan 5.0.23 (Cochrane Library Software, Oxford, UK) and STATA11.0 (STATA Corporation, College Station, TX, USA).
Results
Study characteristics
A total of 133 related publications were identified, of which 19 studies were not accepted since they were not full articles (6 reviews, 7 meta-analysis, 4 comments, 2 case-reports). Fifty-nine articles were not about the above four cancers, 33 publications were excluded because they did not meet the inclusion criteria (11 not case-control studies, 6 not human studies, 7 not present the usable data, 7 not the gene loci, 2 not about polymorphism research). Finally, 22 studies including 6836 cases and 8507 controls were included in this meta-analysis (Figure 1).
Figure 1
Flow diagram of study selection.
The main characteristics of these 22 included studies are summarized in Table 1. There were 14 studies from European countries, 3 studies from Asian countries, 3 studies from American countries, 1 study from Australia and 1 study from Africa. In addition, 9 articles were population-based and 13 articles were hospital-based. The number of publications on SCCHN, colorectal, ovarian cancer and acute leukaemia were 4, 4, 5, and 9, respectively. The diagnosis of most of the cases was based on pathology. Healthy subjects matched for age and sex were used as controls. Polymerase chain reaction (PCR) or restriction fragment length polymorphism(RFLP) were performed as genotyping methods. The genotype distributions and HWE examination results were shown in Table 2.
Table 1
Characteristics of 22 published studies included in this meta-analysis
First author
Year
Study location
Cancer type
Sample size
Source of controls
Genotyping methods
1. Lu JC
2006
USA
SCCHN
716/719
HCC
PCR-RFLP
2. Gil J
2011
Poland
CC
133/100
HCC
PCR-RFLP
3. Webb PM
2005
Australia
OC
548/335
PCC
PCR
4. Gresner P
2012
Poland
SCCHN
81/111
PCC
PCR
5. Seedhouse C
2004
UK
AL
267/186
PCC
PCR
6. Jawad M
2006
UK
AL
267/186
PCC
PCR
7. Krupa R
2011
Poland
CC
100/100
HCC
PCR
8. Sliwinski T
2010
Poland
SCCHN
288/353
HCC
PCR-RFLP
9. Hamdy MS
2011
Egypt
AL
50/30
HCC
PCR-RFLP
10. Liu L
2011
China
AL
625/704
HCC
PCR
11. Romanowicz-MakowskaH
2012
Poland
CC
320/320
HCC
PCR
12. WerbouckJ
2008
Belgium
SCCHN
152/157
HCC
PCR
13. Romanowicz-MakowskaH
2011
Poland
OC
120/120
HCC
PCR
14. Bhatla D
2008
USA
AL
452/646
PCC
PCR
15. Voso MT
2007
Italy
AL
160/161
HCC
PCR-RFLP
16. Mucha B
2012
Poland
CC
200/200
HCC
PCR-RFLP
17. Zhang ZQ
2009
China
AL
166/458
HCC
PCR-RFLP
18. Auranen A (UK)
2005
UK
OC
729/847
PCC
PCR
18. Auranen A (USA)
2005
USA
OC
326/419
PCC
PCR
18. Auranen A (Danish)
2005
Denmark
OC
278/699
PCC
PCR
21. Yang L
2011
China
AL
379/704
HCC
PCR
22. Rollinson S
2006
UK
AL
479/952
PCC
PCR
SCCHN: squamous cell carcinoma of the head and neck; CC: colorectal cancer; OC: ovarian cancer; AL: acute leukaemia; HCC: hospital-based case-control; PCC: population-based case-control; PCR: polymerase chain reaction; RFLP: restriction fragment length polymorphism.
Table 2
Distribution of RAD51 genotype and allele among cancer patients and controls
First author
Year
Case
GC
CC
Control
GC
CC
Case
C
Control
C
HWE
GG
GG
G
G
1. Lu JC
2006
624
91
1
622
96
1
1339
93
1340
98
0.17
2. Gil J
2011
100
29
4
73
27
0
229
37
173
27
0.19
3. Webb PM
2005
457
85
4
971
145
10
999
93
2087
165
0.08
4. Gresner P
2012
67
13
1
71
14
2
147
15
156
18
0.22
5. Seedhouse C
2004
210
44
3
166
18
2
464
50
350
22
0.08
6. Jawad M
2006
210
44
3
166
18
2
464
50
350
22
0.08
7. Krupa R
2011
61
36
3
36
35
29
158
42
107
93
0.003
8. Sliwinski T
2010
138
145
5
258
64
32
421
155
580
128
0.28
9. Hamdy MS
2011
39
9
2
26
3
1
87
13
55
5
0.06
10. Liu L
2011
72
25
8
511
175
18
169
25
1197
211
0.52
11. Romanowicz- MakowskaH
2012
51
56
213
91
164
65
158
482
346
294
0.57
12. Werbouck J
2008
136
15
1
134
23
0
287
17
291
23
0.32
13. Romanowicz-MakowskaH
2011
13
15
92
33
69
18
41
199
135
105
0.07
14. Bhatla D
2008
374
73
5
555
85
6
821
83
1195
97
0.18
15. Voso MT
2007
125
33
2
142
18
1
283
37
302
20
0.61
16. Mucha B
2012
161
34
5
157
37
6
356
44
351
49
0.05
17. Zhang ZQ
2009
117
47
2
315
123
20
281
51
753
163
0.08
18. Auranen A (Danish)
2005
241
36
1
616
78
5
518
38
1310
88
0.15
18. Auranen A (UK)
2005
642
84
3
745
100
2
1368
90
1590
104
0.48
18. Auranen A (USA)
2005
270
52
4
357
61
1
592
60
775
63
0.34
21. Yang L
2011
268
101
10
511
175
18
637
121
1197
211
0.52
22. Rollinson S
2006
431
34
1
817
115
4
896
36
1749
123
0.98
HWE: P value for Hardy-Weinberg equilibrium for RAD51 135G/C polymorphism among controls.
Quantitative synthesis
The evaluation of association between RAD 51 135G/C gene polymorphism and the risk of the four types of cancers was summarized in Table 3. Overall, no significant association was found between RAD 51 135G/C gene polymorphism and the risk of the four cancers (G/G vs. C/C: OR = 0.83, 95%CI = 0.43-1.59, P = 0.57; G/C vs. C/C: OR = 0.90, 95%CI = 0.39-2.08, P = 0.81; G/G + G/C vs. C/C: OR = 0.82, 95%CI = 0.39-1.73, P = 0.60; G/G vs. G/C + C/C: OR = 0.84, 95%CI = 0.69-1.02, P = 0.08). However, in the subgroup analysis by cancer type, there was a significant association between this polymorphism and SCCHN under homozygote comparison (G/G vs. C/C: OR = 2.46, 95%CI = 1.08-5.61; P = 0.03) (Figure 2). There was no significant association between this polymorphism and the risk of other three cancers under all comparisons. In the subgroup analyses by ethnicity or source of controls, no significant association was found in different genetic models.
Table 3
Total and stratified analysis of the RAD51 135G/C polymorphism on risk of the four cancers
Variables
No.a
Case/Control
GG vs. CC
GC vs. CC
GG+GC vs. CC
GG vs. GC+CC
OR(95% CI)
Pb
P
OR(95% CI)
Pb
P
OR(95% CI)
Pb
P
OR(95% CI)
Pb
P
Total cancer types
22
6836/8507
0.83(0.43–1.59)
0.00c
0.57
0.90(0.39–2.08)
0.00c
0.81
0.82(0.39–1.73)
0.00c
0.60
0.84(0.69–1.02)
0.00c
0.08
SCCHN
4
1237/1340
2.46(1.08–5.61)
0.50
0.03
2.20(0.30–16.22)
0.02
0.44
2.50(0.76–8.28)
0.25
0.13
0.84(0.40–1.75)
0.00c
0.64
CC
4
753/720
0.92(0.08–10.55)
0.00c
0.95
0.65(0.05–8.08)
0.00c
0.74
0.79(0.06–10.16)
0.00c
0.85
1.12(0.53–2.35)
0.00c
0.77
OC
5
2001/2420
0.42(0.10–1.78)
0.0007
0.24
0.41(0.06–2.67)
0.00c
0.35
0.40(0.07–2.18)
0.00c
0.29
0.80(0.62–1.03)
0.07
0.09
AL
9
2845/4027
0.82(0.49–1.39)
0.26
0.47
1.00(0.59–2.08)
0.29
0.99
0.85(0.50–1.44)
0.25
0.60
0.82(0.63–1.07)
0.002
0.14
Source of controls
HCC
13
3409/4126
0.77(0.30–1.98)
0.00c
0.58
0.79(0.24–2.60)
0.00c
0.70
0.74(0.26–2.13)
0.00c
0.58
0.82(0.60–1.12)
0.00c
0.20
PCC
9
3427/4381
0.93(0.53–1.62)
0.87
0.79
1.14(0.64–2.02)
0.87
0.66
0.95(0.54–1.67)
0.88
0.87
0.88(0.71–1.09)
0.01
0.25
Ethnicity
Asian
3
1170/1866
0.92(0.27–3.19)
0.01
0.90
0.98(0.28–3.40)
0.01
0.97
0.94(0.27–3.26)
0.009
0.92
0.94(0.77–1.14)
0.64
0.52
Caucasian
18
5616/6611
0.80(0.36–1.79)
0.00c
0.59
0.86(0.31–2.38)
0.00c
0.77
0.79(0.32–1.95)
0.00c
0.61
0.83(0.65–1.05)
0.00c
0.13
African
1
50/30
0.75(0.06–8.70)
0.82
1.50(0.10–23.07)
0.77
0.83(0.07–9.54)
0.88
0.55(0.16–1.90)
0.34
aNumber of studies.
bP value of Q-test for heterogeneity test.
cFixed-effects model was used when P value for heterogeneity test <0.10, otherwise, random-effects model was used.
SCCHN: squamous cell carcinoma of the head and neck; CC: colorectal cancer; OC: ovarian cancer; AL: acute leukaemia.
Figure 2
The association betweenRAD51135G/C polymorphism and the four common cancers risk in the subgroup analysis by cancer type (GG vs. CC).
Test of heterogeneity
For the comprehensive analysis, the I2 showed a stable variation under all comparisons (G/G vs. C/C: P < 0.00001, I2 = 81%; G/C vs. C/C: P < 0.00001, I2 = 89%; G/G + G/C vs. C/C: P < 0.00001, I2 = 88%; G/G vs. G/C + C/C: P < 0.00001, I2 = 78%). In the subgroup analyses of SCHNN and acute leukaemia, the I2 showed a low or moderate variation under all comparisons. In the subgroup analyses of colorectal cancer and ovarian cancer, under most comparisons, the moderate heterogeneity was detected. For source of controls, there was no significant heterogeneity under all comparisons of population-based case-control (PCC), except for heterozygous and dominant model comparisons (G/C vs. C/C: P < 0.00001, I2 = 93%; G/G + G/C vs. C/C: P < 0.00001, I2 = 92%) in hospital-based case-control (HCC). P value for heterogeneity was not significant under all comparisons in the subgroup analyses of Asian population, but in Caucasian group, there were high degree heterogeneity under heterozygous and dominant model comparisons (G/C vs. C/C: P < 0.00001, I2 = 90%; G/G + G/C vs. C/C: P < 0.00001, I2 = 89%).
Sensitivity analysis
Sensitivity analyses were performed to assess the stability of the results in this meta-analysis. Statistically similar data were obtained after sequentially excluding each study, indicating that our results were statistically reliable.
Publication bias
Begg’s funnel plot and Egger’s test were used to assess the publication bias of included studies. Publication bias was not observed in Begg’s funnel plot. The shape of the funnel plots showed to be symmetrical (G/G vs. C/C) and the Egger’s test did not show any evidence of publication bias (P = 0.248 for G/G vs. C/C) (Figure 3). These data indicate that there is no significant publication bias in this meta-analysis.
Figure 3
Begg’s funnel plot for publication bias in selection of studies onRAD51135G/C polymorphism (GG vs. CC;Pfor bias = 0.248).
Discussion
The RAD51 protein encoding by RAD51 gene is essential for the repair of DNA damage. A number of original studies have reported the association between RAD51 135G/C polymorphism and the risk of cancer with inconclusive results, These inconsistent results are possibly because of a small effect of the polymorphism on cancers risk or the relatively low statistical power of the published studies. To better understanding of this association,a meta-analysis, which potentially investigates a large number of individuals and could estimate the effect of a genetic factor on the risk of cancers, was needed to provid a quantitative approach for combining the results of various studies with the same topic, and for estimating and explaining their diversity [44, 45]. We performed a meta-analysis including 6836 cases and 8507 controls from 22 case-control studies to evaluate the association between RAD51 135G/C polymorphism and risk of SCCHN, colorectal cancer, ovarian cancer and acute leukaemia.
The overall population analysis showed no significant association between RAD51 135G/C polymorphism and risk of SCCHN, colorectal cancer, ovarian cancer and acute leukaemia in any genetic model. However, in the subgroup analysis by cancer type, we found that the 135G/C polymorphism of the RAD51 gene was associated with a significantly increased SCCHN risk. There was an aggregated OR of 2.46 (95% CI = 1.08-5.61) for increased SCCHN susceptibility under homozygote comparison. This indicates that the RAD51 135G/C polymorphism may contribute to pathogenesis of SCCHN. GG genotype has been reported to enhance RAD51 gene transcription activity [8], individuals with GG genotype may be more likely to develop SCCHN than those with CC or GC genotype. No associations were found between this polymorphism and the risk of colorectal cancer, ovarian cancer and acute leukaemia, which was consistent with previous reports [22, 23, 27–29, 32, 36].
Heterogeneity is one of the important issues in performing a meta-analysis. In the present meta-analysis, heterogeneity was found in almost all comparisons. Using random-effect models and the stratified analyses by cancer type, sources of control and ethnicity, the heterogeneity was significantly decreased in most of the comparisons. The sensitivity analysis did not alter the results of our meta-analysis, indicating the results are stable. Meanwhile, the publication bias for the association between RAD51 135G/C polymorphism and the risk of the four types of cancers were not detected.
The present meta-analysis has some limitations. First, the control subjects were not uniformly defined. Selection bias and classification bias were possible because the included controls may have other different risks of developing cancers. Second, in the subgroup analyses, the sample sizes of Asian and African population were relatively small, not having enough statistical power to explore the real association. Third, cancer is a multi-factorial disease, our meta-analysis was based on unadjusted estimates.
In conclusion, the GG genotype of RAD51 135G/C was associated with a significantly increased risk of SCCHN. However, there was no significant association between this polymorphism and colorectal cancer, ovarian cancer or acute leukaemia susceptibility.
Declarations
Funding
This study was supported by National Natural Science Foundation of China Grant 81170022, and National Key Technology R&D Program of the 12th Five-year Development Plan 2012BAI05B01.
Authors’ Affiliations
(1)
Department of Respiratory and Critical Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
(2)
Key Laboratory of Respiratory Diseases, Ministry of Health
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