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Tumor Biology

, Volume 36, Issue 5, pp 3179–3189 | Cite as

Note of clarification of data in the paper titled X-ray repair cross-complementing group 1 codon 399 polymorphism and lung cancer risk: an updated meta-analysis

  • Wenlong Zhai
  • Ruo Feng
  • Haiyu Wang
  • Yadong Wang
Editorial

Abstract

We read with great interest the paper titled “X-ray repair cross-complementing group 1 codon 399 polymorphism and lung cancer risk: an updated meta-analysis” published by Wang et al in Tumor Biology, 2014, 35:411–418. Their results suggest that codon 399 polymorphism of XRCC1 gene might contribute to individual’s susceptibility to lung cancer in Asian population and especially in nonsmoking Chinese women. The result is encouraging. Nevertheless, several key issues are worth noticing.

Keywords

XRCC1 Polymorphism Lung cancer Risk 

The X-ray repair cross-complementing group 1 (XRCC1) gene is a major DNA repair gene involved in base excision repair (BER) and single-strand break (SSB) repair. XRCC1 interacts strongly with poly[ADP-ribose] polymerase 1 (PARP1), which recognizes SSBs, and LIGIII that seals SSBs and BER intermediates [1]. Several single nucleotide polymorphisms (SNPs) have been identified in the XRCC1 gene [2], and the potential associations with lung cancer risk have been proposed [3, 4, 5, 6]. Among them, a polymorphism of rs25487 (Arg399Gln, G > A) is one the most extensively studied SNPs, which leads to amino acid substitutions (exon 10). This mutation could alter XRCC1 function, diminish repair kinetics, and influence susceptibility to cancers. To date, a considerable number of studies have investigated the association between XRCC1 Arg399Gln polymorphism and lung cancer risk [7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54]. However, the results remained conflicting rather than conclusive.

Recently, we have read with great interest the paper titled “X-ray repair cross-complementing group 1 codon 399 polymorphism and lung cancer risk: an updated meta-analysis” published online in Tumor Biology, 2014, 35:411–418 [3]. The authors performed a meta-analysis of 46 studies on the association between XRCC1 codon 399 polymorphism and lung cancer risk published before June 2013. In general population, the authors found that the M (Gln) allele and MM (Gln/Gln) genotype were associated with an increased risk of lung cancer compared with C (Arg) allele and CC (Arg/Arg) genotype, and the odds ratios (ORs) were 1.06 [95 % confidence interval (95 %CI) 1.01–1.12] and 1.19 (95 % CI 1.05–1.34), respectively. When it was stratified according to Asian population, the association between XRCC1 codon 399 polymorphism and lung cancer risk was further strengthened. It is an extremely interesting study.

However, after carefully examining the data provided by Wang et al. (Table 1 in the original text) [3], we found that there are several overlapping data that were not properly excluded from Wang et al.’s study [3]. Firstly, the data from Zhang et al.’s study [55] overlapped with the data reported by Hao et al. [17]. Secondly, the data from Liu et al.’s study [56] overlapped with Zhou et al.’s data [47]. Thirdly, the data reported by Yin et al. in 2009 [57] overlapped with the data reported by Yin et al. in 2007 [44]. Fourthly, Hung et al.’s paper published in 2008 [58] was a pooled analysis study, which included the data from Hung et al.’s paper published in 2005 [20], Zhou et al.’s paper published in 2003 [47], Popanda et al.’s paper published in 2004 [35], and Shen et al.’s paper published in 2005 [39]. Fifthly, the data from two papers reported by Li et al. in 2005 [59, 60] overlapped with Li et al.’s paper published in 2008 [26]. Sixthly, the data from Li et al.’s published in 2005 [61] overlapped with the data reported by Su et al. [41]. As a consequence, 5986 cases and 6495 controls were calculated two times in Wang et al.’s paper [3]. In addition, two eligible papers [7, 23] published before 2013 was not included in Wang et al.’s paper [3]. Therefore, it is required to verify the conclusions by Wang et al. [3]. In order to clarify the association between XRCC1 Arg399Gln polymorphism and lung cancer risk, a meta-analysis including the updated data was reconducted, which may provide comprehensive evidence for this association. We also presented the stratified results by mainly confounding factors such as source of control, ethnicity, smoking status, histological subtypes, and Hardy-Weinberg equilibrium (HWE) in control besides giving overall estimates.
Table 1

Characteristics of selected studies in this meta-analysis

Author

Year

Ethnicity

Country

Source of control

Cases

Controls

P value of HWE

Chan [7]

2005

Asians

China

Hospital

75

162

0.879127

Chang [8]

2009

Africans and Latinos

USA

Population

368

578

0.592618

Chen [9]

2002

Asians

China

Population

103

99

0.853812

Cote [10]

2009

Africans and Caucasians

USA

Population

502

527

0.893601

David-Beabes [11]

2001

Africans and Caucasians

USA

Population

334

704

0.465443

De-Ruyck [12]

2007

Caucasians

Belgium

Hospital

109

109

0.916778

Divine [13]

2001

Caucasians

USA

Hospital

172

143

0.579995

Du [14]

2012

Asians

China

Hospital

100

100

0.000006

Du [15]

2014

Asians

China

Hospital

120

120

0.000000

Guo [16]

2013

Asians

China

Hospital

684

602

0.005453

Hao [17]

2006

Asians

China

Population

1024

1118

0.101696

Harms [18]

2004

Caucasians

Germany

Population

110

119

0.256632

Hu [19]

2005

Asians

China

Population

710

710

0.679058

Hung [20]

2005

Caucasians

France

Hospital

2049

2015

0.105562

Improta [21]

2008

Caucasians

Italy

Population

94

121

0.049457

Ito [22]

2004

Asians

Japan

Hospital

178

448

0.749648

Janik [23]

2011

Caucasians

Poland

Hospital

88

79

0.055572

Kim [24]

2010

Asians

Korea

Population

139

217

0.318155

Kiyohara [25]

2012

Asians

Japan

Hospital

462

379

0.858615

Letkova [49]

2013

Caucasians

Slovak

Unknown

382

379

0.097863

Li [26]

2008

Asians

China

Hospital

350

350

0.239615

Li [27]

2011

Asians

China

Hospital

455

443

0.052370

Lopez-Cima [28]

2007

Caucasians

Spain

Hospital

516

533

0.153908

Matullo [29]

2006

Caucasians

Italy

Population

116

1094

0.632227

Misra [30]

2003

Caucasians

Finland

Population

315

313

0.835918

Natukula [50]

2013

Asians

India

Unknown

100

101

0.266487

Osawa [31]

2010

Asians

Japan

Hospital

104

120

Not estimable

Ouyang [32]

2013

Asians

China

Population

82

201

0.148702

Pachouri [33]

2007

Asians

India

Population

103

122

0.055915

Park [34]

2002

Asians

Korea

Population

192

135

0.739912

Popanda [35]

2004

Caucasians

Germany

Hospital

463

460

0.845748

Qian [36]

2011

Asians

China

Population

581

603

0.411222

Ratnasinghe [37]

2001

Asians

China

Population

107

208

0.572907

Saikia [51]

2014

Asians

India

Population

272

544

0.354912

Schneider [38]

2005

Caucasians

Germany

Hospital

446

622

0.778779

Shen [39]

2005

Asians

China

Population

116

109

0.053219

Song [40]

2004

Asians

China

Hospital

104

104

0.466350

Su [41]

2008

Asians

China

Hospital

162

244

0.848338

Uppal [52]

2014

Asians

India

Unknown

100

100

0.001582

Vogel [42]

2004

Caucasians

Denmark

Population

256

269

0.522834

Wang [43]

2012

Asians

China

Hospital

209

256

0.302192

Yin [44]

2007

Asians

China

Hospital

205

193

0.198358

Yoo [53]

2015

Asians

Korea

Hospital

599

580

0.217986

Yu [45]

2006

Asians

China

Hospital

104

121

0.288300

Zhang [46]

2005

Asians

China

Hospital

149

157

0.853973

Zhou [47]

2003

Caucasians

USA

Population

1091

1240

0.661362

Zhu [54]

2014

Asians

China

Unknown

320

346

0.941896

Zienolddiny [48]

2006

Caucasians

Norway

Population

331

391

0.784938

HWE Hardy-Weinberg equilibrium

A comprehensive search was performed through the database of Medline/PubMed, Science Direct, Elsevier, China National Knowledge Infrastructure (CNKI), and Wanfang Medical Online with a combination of the following terms: “lung cancer,” “lung neoplasm” or “lung carcinoma” and “XRCC1” or “rs25487” and “polymorphism” or “variant.” Last search was updated on March 20, 2015. The references cited in the publications and review articles were also manually searched.

Data inclusion criteria were as follows: (a) the papers reporting lung cancer risk and XRCC1 codon 399 polymorphism; (b) case-control studies or cohort studies; and (c) sufficient data to estimate the OR and 95 % CI. For overlapping or repeated studies, the results including more information were included. Accordingly, papers lacking essential information were excluded; review papers were also excluded. In total, 69 published papers were identified with the association between XRCC1 Arg399Gln polymorphism and lung cancer risk. We reviewed all papers in the light of the criteria defined above and excluded 12 reviews and 9 overlapping articles. Therefore, 48 studies were determined to enter our study.

The Cochrane Q statistics test was used to assess the heterogeneity among studies. A fixed-effects model or a random-effects model was applied to estimate the combined effects according to the results of heterogeneity test [62]. A fixed-effects model is used while the effects are assumed to be homogenous; otherwise, a random-effects model is used. The funnel plot was drawn to evaluate publication bias visually. In addition, Begg’s test and Egger’s test were used to assess the publication bias [63, 64]. The χ 2 test was used to check whether the genotype frequencies of the controls were in agreement with HWE.

All of the statistical analyses were conducted by using Review Manager (version 4.2.10, the Cochrane Collaboration) and STATA10.0 software package (Stata Corporation, College Station, TX). Statistical significance was determined as a two-sided P value less than 0.05 for any test or model.

Table 1 lists the characteristics of included studies. Table 2 lists the summary effects of the association between XRCC1 codon 399 polymorphism and lung cancer risk on the basis of 48 published studies including 15,751 cases and 18,688 controls. Overall, we observed a significant association between XRCC1 codon 399 MM genotype variant and lung cancer risk, and the summary OR was 1.19 (95 %CI 1.04–1.37) (Fig. 1); we did not observe any association between XRCC1 codon 399 CM and CM + MM genotype variants and lung cancer risk, and the summary ORs were 0.98 (95 % CI 0.92–1.05) for CM vs. CC (Fig. 2) and 1.02 (95 % CI 0.95–1.10) for CM + MM vs. CC (Fig. 3), respectively. Our results are consistent with Wang et al.’s study [3]. They also found that the MM genotype was associated with increased risk of lung cancer compared with CC genotype in total population. Limiting the analysis to studies of control in agreement with HWE, we did not observe the association between XRCC1 codon 399 polymorphism and lung cancer risk, the summary ORs were 0.99 (95 % CI 0.92–1.07) for CM vs. CC, 1.12 (95 % CI 0.98–1.29) for MM vs. CC, and 1.02 (95 % CI 0.94–1.10) for CM + MM vs. CC, respectively (Table 2). In subgroup analysis by ethnicity, we observed an increased lung cancer risk among subjects carrying XRCC1 codon 399 MM genotype compared with CC genotype carriers (OR = 1.43, 95 % CI 1.16–1.76) among Asians, which is consistent with Wang et al.’s results [3]. We did not observe the association of XRCC1 codon 399 polymorphism with lung cancer risk among Caucasians (Table 2), which is consistent with Wang et al.’s results [3]. When stratified by source of control, we observed an increased lung cancer risk among subjects carrying MM genotype compared with those carrying CC genotype on the basis of hospital-based control (OR = 1.37, 95 % CI 1.11–1.70); we did not observe the association of XRCC1 codon 399 polymorphism with lung cancer risk on the basis of population-based control (Table 2). We did not observe the association between XRCC1 codon 399 polymorphism and lung cancer risk in additional subgroup analyses by smoking status and histological subtypes (Table 2).
Table 2

Summery odds ratios of the relation of XRCC1 codon 399 polymorphism to lung cancer risk

Genotype

Cases/controls

Heterogeneity test

Analysis model

Summery OR (95 % CI)

Hypothesis test

df

Begg’s test

Egger’s test

Q

P

Z

P

Z

P

t

P

Total

 CM vs. CC

14126/16966

86.96

0.0002

Random-effects model

0.98 (0.92–1.05)

0.49

0.62

46

0.26

0.797

0.78

0.440

 MM vs. CC

9502/11120

99.60

<0.00001

Random-effects model

1.19 (1.04–1.37)

2.59

0.01

45

1.06

0.289

1.76

0.085

 CM + MM vs. CC

15751/18688

101.06

<0.00001

Random-effects model

1.02 (0.95–1.10)

0.66

0.51

47

0.76

0.450

0.31

0.756

Stratification by HWE in control

 Yes

  CM vs. CC

13136/15931

79.39

0.0003

Random-effects model

0.99 (0.92–1.07)

0.22

0.82

41

0.24

0.812

0.36

0.723

  MM vs. CC

8757/10473

82.70

<0.0001

Random-effects model

1.12 (0.98–1.29)

1.72

0.09

40

0.89

0.375

1.26

0.217

  CC

14549/17525

94.41

<0.00001

Random-effects model

1.02 (0.94–1.10)

0.40

0.69

41

0.69

0.488

0.14

0.891

Stratification by ethnicity

 Asians

  CM vs. CC

7285/8370

66.07

<0.0001

Random-effects model

1.01 (0.90–1.13)

0.16

0.87

28

0.39

0.694

0.03

0.975

  MM vs. CC

5126/5656

64.88

<0.0001

Random-effects model

1.43 (1.16–1.76)

3.32

0.0009

28

0.66

0.511

1.13

0.269

  CM + MM vs. CC

8009/8992

78.81

<0.00001

Random-effects model

1.09 (0.97–1.22)

1.44

0.15

29

0.32

0.748

0.82

0.420

 Caucasians

  CM vs. CC

6229/7690

19.78

0.23

Fixed-effects model

0.99 (0.92–1.06)

0.32

0.75

16

0.70

0.484

2.23

0.041

  MM vs. CC

3924/4812

19.92

0.17

Fixed-effects model

1.00 (0.90–1.11)

0.04

0.97

15

0.14

0.893

0.35

0.728

  CM + MM vs. CC

7105/8754

17.61

0.35

Fixed-effects model

0.99 (0.93–1.06)

0.27

0.79

16

1.03

0.303

2.08

0.055

Stratification by source of control

 Population-based control

  CM vs. CC

6322/8580

35.11

0.02

Random-effects model

0.92 (0.84–1.02)

1.59

0.11

20

1.24

0.216

1.11

0.283

  MM vs. CC

4209/5559

29.53

0.08

Fixed-effects model

0.98 (0.87–1.10)

0.34

0.73

20

0.33

0.740

0.20

0.845

  CM + MM vs. CC

6946/9422

35.23

0.02

Random-effects model

0.93 (0.85–1.02)

1.46

0.14

20

1.12

0.264

0.98

0.340

 Hospital-based control

  CM vs. CC

7032/7535

34.23

0.03

Random-effects model

1.02 (0.93–1.13)

0.43

0.67

21

0.34

0.735

0.01

0.990

  MM vs. CC

4746/4993

52.20

0.0001

Random-effects model

1.37 (1.11–1.70)

2.91

0.004

20

0.45

0.651

1.85

0.080

  CM + MM vs. CC

7903/8340

45.24

0.002

Random-effects model

1.09 (0.98–1.20)

1.56

0.12

22

1.21

0.224

0.89

0.382

Stratification by smoking status

 Smokers

  CM + MM vs. CC

2893/2856

10.97

0.61

Fixed-effects model

1.02 (0.92–1.13)

0.36

0.72

13

0.00

1.000

0.36

0.726

 Nonsmokers

  CM + MM vs. CC

656/1814

18.49

0.02

Random-effects model

1.19 (0.87–1.65)

1.08

0.28

8

0.52

0.602

0.65

0.537

Stratification by histological type

 Squamous cell carcinoma

  CM + MM vs. CC

891/2347

22.13

0.001

Random-effects model

1.06 (0.74–1.54)

0.33

0.74

6

0.00

1.000

0.33

0.751

 Adenocarcinoma

  CM + MM vs. CC

1425/3994

21.69

0.03

Random-effects model

1.05 (0.87–1.26)

0.48

0.63

11

1.03

0.304

0.23

0.822

HWE Hardy-Weinberg equilibrium, OR odds ratio, CI confidence interval

Fig. 1

Forest plots for the association between XRCC1 codon 399 MM genotype variant and lung cancer risk

Fig. 2

Forest plots for the association between XRCC1 codon 399 CM genotype variant and lung cancer risk

Fig. 3

Forest plots for the association between XRCC1 codon 399 CM + MM genotype variant and lung cancer risk

The shape of funnel plots did not reveal any evidence of obvious asymmetry (Figs. 4, 5, and 6) among total studies, which suggested that there was not any potential publication bias. Begg’s test and Egger’s test suggested that there was no obvious publication bias in this study, except for the analysis under the genetic model of CM vs. MM among Caucasians, since the P value was less than 0.05 in Egger’s test (Table 2).
Fig. 4

Funnel plots for the association between XRCC1 codon 399 MM genotype variant and lung cancer risk

Fig. 5

Funnel plots for the association between XRCC1 codon 399 CM genotype variant and lung cancer risk

Fig. 6

Funnel plots for the association between XRCC1 codon 399 CM + MM genotype variant and lung cancer risk

In summary, our results suggest that XRCC1 codon 399 MM genotype variant was associated with an increased lung cancer risk, especially among Asians. To reach a definitive conclusion, further well-designed studies with large sample size are needed to verify the association of XRCC1 codon 399 polymorphism and lung cancer risk. We hope that this remark will contribute to a more accurate elaboration and substantiation of the results presented by Wang et al. [3].

Notes

Conflicts of interest

None

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Copyright information

© International Society of Oncology and BioMarkers (ISOBM) 2015

Authors and Affiliations

  • Wenlong Zhai
    • 1
  • Ruo Feng
    • 2
  • Haiyu Wang
    • 3
  • Yadong Wang
    • 3
  1. 1.Department of General SurgeryFirst Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
  2. 2.Department of Histology and Embryology, School of Basic MedicineZhengzhou UniversityZhengzhouChina
  3. 3.Department of ToxicologyHenan Center for Disease Control and PreventionZhengzhouChina

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