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Research Papers:

NFKB1 -94insertion/deletion ATTG polymorphism and cancer risk: Evidence from 50 case-control studies

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Oncotarget. 2017; 8:9806-9822. https://doi.org/10.18632/oncotarget.14190

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Wen Fu, Zhen-Jian Zhuo, Yung-Chang Chen, Jinhong Zhu, Zhang Zhao, Wei Jia, Jin-Hua Hu, Kai Fu, Shi-Bo Zhu, Jing He and Guo-Chang Liu _

Abstract

Wen Fu1,*, Zhen-Jian Zhuo2,*, Yung-Chang Chen3,*, Jinhong Zhu4, Zhang Zhao1, Wei Jia1, Jin-Hua Hu1, Kai Fu1, Shi-Bo Zhu1, Jing He1, Guo-Chang Liu1

1Department of Pediatric Urology, Department of Pediatric Surgery, Guangzhou Institute of Pediatrics, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, Guangdong, China

2School of Chinese Medicine, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong 999077, China

3Department of Gastroenterology, The First People’s Hospital of Foshan (Affiliated Foshan Hospital of Sun Yat-sen University), Foshan 528000, Guangdong, China

4Molecular Epidemiology Laboratory and Department of Laboratory Medicine, Harbin Medical University Cancer Hospital, Harbin 150040, Heilongjiang, China

*These authors have contributed equally to this work

Correspondence to:

Guo-Chang Liu, email: starbless2003@126.com

Jing He, email: hejing198374@gmail.com

Keywords: NFKB1, -94ins/delATTG, polymorphism, cancer risk, meta-analysis

Received: August 15, 2016    Accepted: November 22, 2016    Published: December 26, 2016

ABSTRACT

Nuclear factor-kappa B1 (NF-𝛋B1) is a pleiotropic transcription factor and key contributor to tumorigenesis in many types of cancer. Numerous studies have addressed the association of a functional insertion (I)/deletion (D) polymorphism (-94ins/delATTG, rs28362491) in the promoter region of NFKB1 gene with the risk of various types of cancer; however, their conclusions have been inconsistent. We therefore conducted a meta-analysis to reevaluate this association. PubMed, EMBASE, China National Knowledge infrastructure (CNKI), and WANFANG databases were searched through July 2016 to retrieve relevant studies. After careful assessment, 50 case-control studies, comprising 18,299 cases and 23,484 controls were selected. Crude odds ratios (ORs) and 95% confidence intervals (CIs) were used to determine the strength of the association. The NFKB1 -94ins/delATTG polymorphism was associated with a decreased risk of overall cancer in the homozygote model (DD vs. II): OR = 0.75, 95% CI = 0.64-0.87); heterozygote model (ID vs. II): OR = 0.91, 95% CI = 0.83-0.99; recessive model (DD vs. ID/II): OR = 0.81, 95% CI = 0.71-0.91; dominant model (ID/DD vs. II): OR = 0.86, 95% CI = 0.78-0.95; and allele contrast model (D vs. I): OR = 0.88, 95% CI = 0.81-0.95). Subgroup and stratified analyses revealed decreased risks for lung cancer, nasopharyngeal carcinoma, prostate cancer, ovarian cancer, and oral squamous cell carcinoma, and this association held true also for Asians (especially Chinese subjects) in hospital-based studies, and in studies with quality scores less than nine. Well-designed, large-scale case-control studies are needed to confirm these results.


INTRODUCTION

Cancer is a substantial public health burden, with an estimated 1.7 million new cancer cases and 0.6 million cancer-related deaths in the United States in 2016 [1]. Although the etiology of carcinogenesis has not yet been fully elucidated, many lines of evidence suggest that cancer is a multifactorial disease caused by intricate interactions between multiple hereditary and environmental factors [2, 3]. Numerous studies have demonstrated that inflammation is critically implicated in the development of some cancers [4, 5]. In view of this, it is plausible that genetic polymorphisms in inflammation-related genes could modify cancer susceptibility [6-8].

Nuclear factor-kappa B (NF-κB) is a pleiotropic transcription factor discovered by Sen and Baltimore in 1986 [9]. In mammals, the NF-κB family consists of five members: c-Rel (Rel), Rel B, p65 (RelA), p50/p105 (NF-κB1), and p52/p100 (NF-κB2) [10]. This group of molecules function as key regulators of a variety of genes implicated in diverse biological events including cell survival, apoptosis, inflammation, differentiation, and autophagy [11, 12]. Recently, high levels of NF-κB have been observed in many cancers, including pancreatic cancer [13], lung cancer [14], colorectal cancer [15], breast cancer [16], melanoma [17], and multiple myeloma [18]. Although various dimeric forms of NF-κB exist, the most common is the p50 and p65/RelA heterodimer, encoded by the NFKB1 and RelA genes, respectively [19].

The human NFKB1 gene, spanning 156-kb, is located on chromosome 4q23-q24 and encodes a 105 kD protein (p105) which is cleaved into an active subunit (p50) [20]. Several variations have been identified in the NFKB1 gene, including rs72696119 (C>G), rs28362491 (-94 ins/del ATTG), rs4648068 (A>G), and rs12509517 (G>C) [21]. Among these, NFKB1 rs28362491, namely the -94insertion/deletion ATTG polymorphism, is potentially functional and the most widely investigated [21]. This modification occurs between two important regulatory elements (activator protein 1 and κB binding site) in the promoter region of the NFKB1 gene. The deletion of four bases (ATTG) reduces or prevents the binding to nuclear proteins and leads to lower transcript levels of the NFKB1 gene, thereby changing mRNA stability and regulating translation efficiency [21, 22].

Numerous case-control studies have assessed the association between the NFKB1 -94ins/delATTG promoter polymorphism and cancer risk, with discrepant results. While some studies indicated an increased risk for some types of cancers [23-25], other studies showed instead a decreased risk, or no association [26, 27]. Several meta-analyses attempted to solve the controversy, but did not yield consistent results [28-33]. To provide a more precise evaluation of such association, we performed a comprehensive, updated meta-analysis. In addition, to minimize random errors and strengthen the robustness of our conclusions, we also performed trial sequential analysis (TSA).

RESULTS

Study characteristics

The study selection process for this meta-analysis is shown in Figure 1, 258 potentially relevant published records were retrieved from PubMed, EMBASE, China National Knowledge infrastructure (CNKI), and WANFANG databases. After screening the titles and reading the abstracts, 69 studies remained and were carefully reviewed. Among these, 23 publications were further excluded: 7 were case-only studies [34-39]; 6 were meta-analyses [28-33]; 5 deviated from Hardy-Weinberg equilibrium (HWE) [40-44]; 3 were duplicated publications [45-47]; 1 was a review [48], and 1 lacked sufficient data to calculate odds ratio (OR) and 95% confidence interval (CI) [49]. Thus, 46 publications were included in the final analysis [23-27, 48, 50-89]. Among these, publications that contained different case groups but used the same controls, or that studied one cancer type in different populations, were considered separate studies.

Flowchart of the study inclusion protocol.

Figure 1: Flowchart of the study inclusion protocol.

After this selection procedure, 50 studies extracted from 46 publications with 18,299 cases and 23,484 controls ultimately entered our final meta-analysis (Table 1). 38 of these studies included Asians subjects, and 12 included Caucasians. Regarding cancer types, 6 studies addressed hepatocellular carcinoma, 5 lung cancer, 4 colorectal cancer, 4 nasopharyngeal carcinoma, 4 prostate cancer, 4 ovarian cancer, 3 bladder cancer, 3 gastric cancer, 3 cervical cancer, 2 oral squamous cell carcinoma, 2 breast cancer, and 10 studies addressed other cancers. Moreover, 40 studies used a population-based design, and 10 were hospital-based. 19 studies had a quality score >9, and the remaining 31 had a quality score ≤9.

Table 1: Characteristics of the studies included in the current meta-analysis

Surname

Year

Cancer type

Country

Ethnicity

Control Source

Genotype method

Case

Control

MAF

HWE

Score

II

ID

DD

All

II

ID

DD

All

Lin [25]

2006

OSCC

China

Asian

HB

PCR-PAGE

59

103

50

212

43

100

58

201

0.54

0.993

7

Riemann [26]

2006

Colorectal cancer

Germany

Caucasian

HB

Pyro sequencing

54

58

27

139

118

141

48

307

0.39

0.586

9

Riemann [26]

2006

CLL

Germany

Caucasian

HB

Pyro sequencing

18

41

13

72

118

141

48

307

0.39

0.586

9

Riemann [26]

2006

RCC

Germany

Caucasian

HB

Pyro sequencing

47

76

17

140

118

141

48

307

0.39

0.586

9

Bu [48]

2007

Melanoma

Sweden

Caucasian

HB

PCR-RFLP

67

84

34

185

116

255

67

438

0.44

0.000

10

Riemann [83]

2007

Bladder cancer

Germany

Caucasian

HB

Pyro sequencing

88

124

30

242

118

141

48

307

0.39

0.586

10

Lehnerdt [27]

2008

HNSCC

Germany

Caucasian

HB

Pyro sequencing

132

179

53

364

118

141

48

307

0.39

0.586

8

He [82]

2009

HCC

China

Asian

HB

PCR-RFLP

83

84

35

202

97

183

124

404

0.53

0.070

9

He [89]

2009

HCC

China

Asian

HB

PCR-RFLP

55

65

30

150

70

136

94

300

0.54

0.130

8

Lo [24]

2009

Gastric cancer

China

Asian

HB

PCR

62

89

31

182

20

62

34

116

0.56

0.361

7

Zhang [81]

2009

Prostate cancer

China

Asian

HB

PCR-PAGE

46

57

14

117

44

68

31

143

0.45

0.624

8

Zhou [80]

2009

NPC

China

Asian

HB

PCR-PAGE

74

67

22

163

71

90

42

203

0.43

0.177

7

Tang [79]

2010

Bladder cancer

China

Asian

HB

PCR–PAGE

89

92

26

207

74

108

46

228

0.44

0.565

10

Zhou [78]

2010

Cervical cancer

China

Asian

HB

PCR–PAGE

108

105

20

233

135

166

64

365

0.40

0.297

9

Fan [77]

2011

Ovarian cancer

China

Asian

HB

PCR-CE

78

84

17

179

76

103

44

223

0.43

0.396

8

Lin [76]

2012

OSCC

China

Asian

HB

TaqMan

116

246

100

462

81

271

168

520

0.58

0.099

9

Song [92]

2012

Colorectal cancer

China

Asian

HB

PCR-RFLP

363

500

138

1001

297

522

186

1005

0.44

0.102

14

Tang [87]

2012

HCC

China

Asian

HB

PCR-RFLP

52

84

14

150

57

82

11

150

0.35

0.011

7

Ungerback [75]

2012

Colorectal cancer

Sweden

Caucasian

HB

TaqMan

114

187

43

344

256

270

96

622

0.37

0.079

8

Vangsted [74]

2012

Multiple myeloma

Denmark

Caucasian

PB

TaqMan

110

163

55

328

655

778

253

1686

0.38

0.303

7

Arisawa [73]

2013

Gastric cancer

Japan

Asian

PB

PCR-SSCP

172

239

68

479

342

435

103

880

0.36

0.046

11

Cheng [23]

2013

HCC

China

Asian

HB

TaqMan

42

64

29

135

81

271

168

520

0.58

0.099

7

Huang [72]

2013

Lung cancer

China

Asian

PB

TaqMan

372

459

225

1056

355

491

210

1056

0.43

0.090

10

Huang [72]

2013

Lung cancer

China

Asian

PB

TaqMan

169

230

104

503

189

289

145

623

0.46

0.092

10

Huo [71]

2013

Ovarian cancer

China

Asian

HB

Mass ARRAY

83

82

22

187

71

103

47

221

0.45

0.399

7

Kopp [70]

2013

Prostate cancer

Denmark

Caucasian

PB

RT-PCR

128

152

54

334

109

161

64

334

0.43

0.741

11

Li [69]

2013

Bladder cancer

China

Asian

HB

TaqMan

189

269

151

609

223

324

93

640

0.40

0.156

11

Liu [86]

2013

NPC

China

Asian

PB

TaqMan

116

135

49

300

86

143

71

300

0.48

0.443

12

Song [84]

2013

EC

China

Asian

HB

PCR-RFLP

42

52

6

100

56

39

5

100

0.25

0.588

6

Song [85]

2013

Cervical cancer

China

Asian

HB

PCR-RFLP

34

56

10

100

37

55

8

100

0.36

0.044

5

Umar [68]

2013

ESCC

India

Asian

HB

PCR

131

132

27

290

160

129

22

311

0.28

0.561

10

Gao [67]

2014

HCC

China

Asian

PB

TaqMan

68

102

40

210

171

160

79

410

0.39

0.000

12

Hua [66]

2014

Gastric cancer

China

Asian

HB

Mass ARRAY

92

182

127

401

120

230

83

433

0.46

0.144

9

Oltulu [65]

2014

Lung cancer

Turkey

Caucasian

HB

PCR-RFLP

35

44

16

95

46

47

6

99

0.30

0.194

7

Wang [64]

2014

Breast cancer

China

Asian

HB

PCR-RFLP

93

210

171

474

162

216

123

501

0.46

0.003

9

Zhang [63]

2014

HCC

China

Asian

PB

PCR

205

312

107

624

542

790

274

1606

0.42

0.631

10

Chen [62]

2015

Ovarian cancer

China

Asian

HB

Mass ARRAY

120

195

95

410

85

235

122

442

0.54

0.136

9

Cui [61]

2015

Prostate cancer

China

Asian

HB

PCR-RFLP

198

246

99

543

212

355

186

753

0.48

0.125

10

Han [60]

2015

Prostate cancer

China

Asian

PB

PCR-RFLP

63

339

534

936

38

331

567

936

0.78

0.230

12

Kopp [59]

2015

Colorectal cancer

Denmark

Caucasian

PB

KASP

320

449

146

915

679

787

253

1719

0.38

0.311

11

Li [58]

2015

Osteosarcoma

China

Asian

HB

PCR-RFLP

60

114

46

220

50

106

66

222

0.54

0.551

9

Liu [57]

2015

NPC

China

Asian

HB

TaqMan

236

331

117

684

274

438

195

907

0.46

0.420

9

Liu [57]

2015

NPC

China

Asian

HB

TaqMan

316

438

152

906

336

512

224

1072

0.45

0.262

9

Pallavi [56]

2015

Cervical cancer

Iran

Asian

HB

PCR-RFLP

98

116

26

240

73

104

113

290

0.57

0.000

9

Wang [55]

2015

Lung cancer

China

Asian

HB

PCR-RFLP

113

219

89

421

89

205

131

425

0.55

0.595

10

Wang [54]

2015

Thyroid carcinoma

China

Asian

HB

PCR-PAGE

106

186

60

352

171

209

79

459

0.40

0.273

11

Zhang [53]

2015

Lung cancer

China

Asian

HB

PCR-RFLP

434

252

32

718

352

290

76

718

0.31

0.162

9

Eskandari [52]

2016

Breast cancer

Iran

Asian

HB

AS-PCR

96

122

18

236

62

106

35

203

0.43

0.368

8

Lu [51]

2016

Ovarian cancer

China

Asian

HB

PCR-RFLP

115

351

221

687

95

339

253

687

0.61

0.271

10

Rybka [50]

2016

AML

Poland

Caucasian

HB

PCR

25

30

7

62

43

69

14

126

0.38

0.079

4

MAF, minor allele frequency; HWE, Hardy-Weinberg equilibrium; OSCC, oral squamous cell carcinoma; CLL, chronic lymphocytic leukemia; RCC, renal cell carcinoma; HNSCC, head and neck squamous cell carcinoma; HCC, hepatocellular carcinoma; NPC, nasopharyngeal carcinoma; ESCC, esophageal squamous cell carcinoma; EC, endometrial carcinoma; AML, acute myeloid leukemia; PB, population based; HB, hospital based; PCR-PAGE, polymerase chain reaction-polyacrylamide gel electrophoresis; PCR-RFLP, polymerase chain reaction-restriction fragment length polymorphism; PCR-CE, polymerase chain reaction-capillary electrophoresis; PCR-SSCP, polymerase chain reaction-single strand conformation polymorphism; KASP, kompetitive allele specific PCR; AS-PCR, allele-specific polymerase chain reaction.

Meta-analysis results

The main results of this meta-analysis are shown in Table 2 and Figure 2. Overall, the pooled analysis demonstrated a significant, negative association between the NFKB1 -94ins/delATTG polymorphism and overall cancer risk under all five genetic models (described in the Materials and Methods section): DD vs. II: OR = 0.75, 95% CI = 0.64-0.87; ID vs. II: OR = 0.91, 95% CI = 0.83-0.99; DD vs. ID/II: OR = 0.81, 95% CI = 0.71-0.91; ID/DD vs. II: OR = 0.86, 95% CI = 0.78-0.95; and D vs. I: OR = 0.88, 95% CI = 0.81-0.95.

Table 2: Meta-analysis of the association between the NFKB1 -94ins/delATTG (rs28362491) polymorphism and overall cancer risk

Variables

No. of studies

Sample size

Homozygous

Heterozygous

Recessive

Dominant

Allele

DD vs. II

ID vs. II

DD vs. ID/II

ID/DD vs. II

D vs. I

OR (95% CI)

Phet

OR (95% CI)

Phet

OR (95% CI)

Phet

OR (95% CI)

Phet

OR (95% CI)

Phet

All

50

18299/ 23484

0.75 (0.64-0.87)

<0.001

0.91 (0.83-0.99)

<0.001

0.81 (0.71-0.91)

<0.001

0.86 (0.78-0.95)

<0.001

0.88 (0.81-0.95)

<0.001

Cancer type

HCC

6

1471/ 3390

0.65 (0.38-1.11)

<0.001

0.82 (0.56-1.19)

<0.001

0.74 (0.54-1.02)

0.006

0.75 (0.50-1.15)

<0.001

0.80 (0.61-1.07)

<0.001

Lung

5

2793/ 2921

0.77 (0.48-1.26)

<0.001

0.84 (0.74-0.96)

0.337

0.82 (0.54-1.26)

<0.001

0.83 (0.67-1.03)

0.012

0.88 (0.70-1.09)

<0.001

Colorectal

4

2399/ 3653

0.96 (0.65-1.43)

0.001

1.08 (0.79-1.47)

<0.001

0.92 (0.69-1.22)

0.022

1.05 (0.77-1.44)

<0.001

1.01 (0.82-1.24)

<0.001

NPC

4

2053/ 2482

0.66 (0.56-0.78)

0.467

0.85 (0.75-0.97)

0.537

0.73 (0.63-0.85)

0.755

0.79 (0.69-0.90)

0.371

0.81 (0.74-0.89)

0.316

Prostate

4

1930/ 2166

0.59 (0.48-0.72)

0.684

0.74 (0.62-0.88)

0.803

0.77 (0.65-0.92)

0.267

0.69 (0.59-0.81)

0.760

0.79 (0.72-0.87)

0.540

Ovarian

4

1463/ 1573

0.54 (0.40-0.73)

0.173

0.73 (0.61-0.87)

0.416

0.68 (0.52-0.89)

0.102

0.67 (0.56-0.79)

0.462

0.75 (0.65-0.86)

0.181

Bladder

3

1058/ 1175

0.93 (0.40-2.21)

<0.001

0.95 (0.74-1.22)

0.193

0.97 (0.43-2.17)

<0.001

0.96 (0.67-1.37)

0.026

0.97 (0.66-1.43)

<0.001

Gastric

3

1062/ 1429

0.97 (0.41-2.31)

<0.001

0.88 (0.60-1.30)

0.032

1.11 (0.57-2.15)

<0.001

0.90 (0.54-1.49)

0.002

0.98 (0.65-1.50)

<0.001

Cervical

3

573/ 755

0.41 (0.15-1.11)

0.001

0.85 (0.67-1.08)

0.627

0.44 (0.17-1.12)

0.001

0.69 (0.45-1.04)

0.050

0.66 (0.39-1.10)

<0.001

OSCC

2

674/ 721

0.49 (0.33-0.72)

0.222

0.67 (0.51-0.88)

0.570

0.63 (0.49-0.81)

0.308

0.60 (0.46-0.77)

0.377

0.70 (0.60-0.82)

0.300

Breast

2

710/ 704

0.92 (0.13-6.42)

<0.001

1.13 (0.51-2.54)

0.002

0.85 (0.20-3.61)

<0.001

1.13 (0.38-3.37)

<0.001

1.04 (0.43-2.53)

<0.001

Others

10

2113/ 4263

1.07 (0.88-1.31)

0.281

1.16 (0.94-1.42)

0.006

1.00 (0.86-1.16)

0.483

1.14 (0.94-1.38)

0.008

1.06 (0.95-1.19)

0.062

Ethnicity

 Asians

38

15079/ 18673

0.67 (0.55-0.80)

<0.001

0.86 (0.79-0.94)

<0.001

0.75 (0.65-0.86)

<0.001

0.80 (0.72-0.89)

<0.001

0.83 (0.76-0.91)

<0.001

 Chinese

34

13834/ 16989

0.68 (0.56-0.81)

<0.001

0.84 (0.77-0.93)

<0.001

0.77 (0.67-0.88)

<0.001

0.80 (0.71-0.89)

<0.001

0.84 (0.76-0.91)

<0.001

 Caucasians

12

3220/ 6559

1.08 (0.92-1.27)

0.221

1.11 (0.94-1.30)

0.004

1.02 (0.88-1.17)

0.269

1.10 (0.95-1.28)

0.010

1.06 (0.98-1.15)

0.141

Source of control

 HB

40

12614/ 15682

0.70 (0.58-0.85)

<0.001

0.88 (0.80-0.98)

<0.001

0.76 (0.65-0.89)

<0.001

0.84 (0.74-0.94)

<0.001

0.85 (0.77-0.94)

<0.001

PB

10

5685/ 9550

0.95 (0.79-1.15)

0.001

1.00 (0.86-1.15)

0.002

0.98 (0.88-1.09)

0.161

0.98 (0.84-1.14)

<0.001

0.98 (0.90-1.08)

<0.001

Quality score

>9

19

9894/ 13117

0.87 (0.73-1.04)

<0.001

0.93 (0.84-1.04)

<0.001

0.92 (0.80-1.05)

<0.001

0.91 (0.81-1.03)

<0.001

0.94 (0.86-1.02)

<0.001

≤9

31

8405/ 12115

0.68 (0.53-0.86)

<0.001

0.89 (0.79-1.01)

<0.001

0.73 (0.60-0.88)

<0.001

0.83 (0.71-0.96)

<0.001

0.84 (0.75-0.95)

<0.001

Het, heterogeneity; HCC, hepatocellular carcinoma; NPC, nasopharyngeal carcinoma; OSCC, oral squamous cell carcinoma; HB, hospital-based; PB, population-based.

Forest plot of the association between the NFKB1 -94ins/delATTG polymorphism and overall cancer susceptibility in the allele contrast model.

Figure 2: Forest plot of the association between the NFKB1 -94ins/delATTG polymorphism and overall cancer susceptibility in the allele contrast model. The horizontal lines represent the study-specific ORs and 95% CIs, respectively. The diamond represents the pooled OR and corresponding 95% CI.

Stratified analysis by cancer type revealed that the -94ins/delATTG polymorphism significantly decreased lung cancer risk (ID vs. II: OR = 0.84, 95% CI = 0.74-0.96), nasopharyngeal carcinoma risk (DD vs. II: OR = 0.66, 95% CI = 0.56-0.78; ID vs. II: OR = 0.85, 95% CI = 0.75-0.97; DD vs. ID/II: OR = 0.73, 95% CI = 0.63-0.85; ID/DD vs. II: OR = 0.79, 95% CI = 0.69-0.90; D vs. I: OR = 0.81, 95% CI = 0.74-0.89), prostate cancer risk (DD vs. II: OR = 0.59, 95% CI = 0.48-0.72; ID vs. II: OR = 0.74, 95% CI = 0.62-0.88; DD vs. ID/II: OR = 0.77, 95% CI = 0.65-0.92; ID/DD vs. II: OR = 0.69, 95% CI = 0.59-0.81; D vs. I: OR = 0.79, 95% CI = 0.72-0.87), ovarian cancer risk (DD vs. II: OR = 0.54, 95% CI = 0.40-0.73; ID vs. II: OR = 0.73, 95% CI = 0.61-0.87; DD vs. ID/II: OR = 0.68, 95% CI = 0.52-0.89; ID/DD vs. II: OR = 0.67, 95% CI = 0.56-0.79; D vs. I: OR = 0.75, 95% CI = 0.65-0.86), and oral squamous cell carcinoma risk (DD vs. II: OR = 0.49, 95% CI = 0.33-0.72; ID vs. II: OR = 0.67, 95% CI = 0.51-0.88; DD vs. ID/II: OR = 0.63, 95% CI = 0.49-0.81; ID/DD vs. II: OR = 0.60, 95% CI = 0.46-0.77; D vs. I: OR = 0.70, 95% CI = 0.60-0.82). However, no correlation was observed between NFKB1 -94ins/delATTG polymorphism and other types of cancer.

When stratified by population, a significant association between NFKB1 -94ins/delATTG polymorphism and decreased cancer risk among Asians was detected under all genetic models (DD vs. II: OR = 0.67, 95% CI = 0.55-0.80; ID vs. II: OR = 0.86, 95% CI = 0.79-0.94; DD vs. ID/II: OR = 0.75, 95% CI = 0.65-0.86; ID/DD vs. II: OR = 0.80, 95% CI = 0.72-0.89; D vs. I: OR = 0.83, 95% CI = 0.76-0.91). As most of the studies were performed on the Chinese population, we determined the association of NFKB1 -94ins/delATTG polymorphism with cancer risk on Chinese subjects. In this case, the results also showed a protective role against cancer (DD vs. II: OR = 0.68, 95% CI = 0.56-0.81; ID vs. II: OR = 0.84, 95% CI = 0.77-0.93; DD vs. ID/II: OR = 0.77, 95% CI = 0.67-0.88; ID/DD vs. II: OR = 0.80, 95% CI = 0.71-0.89; D vs. I: OR = 0.84, 95% CI = 0.76-0.91). No association was observed, however, for Caucasians.

Upon stratification based on the sources of controls, the NFKB1 -94ins/delATTG polymorphism had a protective role against cancer in hospital-based groups (DD vs. II: OR = 0.70, 95% CI = 0.58-0.85; ID vs. II: OR = 0.88, 95% CI = 0.80-0.98; DD vs. ID/II: OR = 0.76, 95% CI = 0.65-0.89; ID/DD vs. II: OR = 0.84, 95% CI = 0.74-0.94; D vs. I: OR = 0.85, 95% CI = 0.77-0.94).

After stratification by quality score, a significantly decreased cancer risk was observed for studies with quality scores ≤9 (DD vs. II: OR = 0.68, 95% CI = 0.53-0.86; DD vs. ID/II: OR = 0.73, 95% CI = 0.60-0.88; ID/DD vs. II: OR = 0.83, 95% CI = 0.71-0.96; D vs. I: OR = 0.84, 95% CI = 0.75-0.95).

Heterogeneity and sensitivity analysis

Statistically significant between-study heterogeneity was found in the pooled analysis under the five genetic models (P < 0.001). Thus, the random-effect model was applied to calculate the ORs and 95% CIs. Sensitivity analysis using the leave-one-out cross-validation method was conducted to assess the impact of each single study on the overall risk estimates. The omission of each individual study did not have substantial influence on the risk estimates, supporting the credibility and reliability of this meta-analysis (data not shown).

Publication bias

Publication bias was assessed by Begg’s funnel plot and quantitative Egger’s test. The funnel plot showed a symmetrical shape (Figure 3), suggesting no publication bias, a conclusion further supported by the Egger’s test (DD vs. II: P = 0.158; ID vs. II: P = 0.340, DD vs. ID/II: P = 0.157; ID/DD vs. II: P = 0.221; and D vs. I: P = 0.250).

Funnel plot analysis to detect publication bias for NFKB1 -94ins/delATTG polymorphism under the allele contrast model.

Figure 3: Funnel plot analysis to detect publication bias for NFKB1 -94ins/delATTG polymorphism under the allele contrast model. Each point represents a separate study.

Trial sequential analysis and false-positive report probability (FPRP) analyses

To minimize random errors and strengthen the robustness of our conclusions, we performed TSA (Figure 4). This analysis showed that the cumulative z-curve crossed the trial sequential monitoring boundary before reaching the required information size, suggesting that the cumulative evidence is sufficient and no further evidence is needed to verify the conclusions.

Trial sequential analysis for NFKB1 -94ins/delATTG polymorphism under the allele contrast model.

Figure 4: Trial sequential analysis for NFKB1 -94ins/delATTG polymorphism under the allele contrast model.

We finally calculated the FPRP values for all observed significant findings. With the assumption of a prior probability of 0.1, the FPRP values were all <0.20, suggesting that these significant associations were noteworthy (Table 3).

Table 3: False-positive report probability values for associations between the NFKB1 -94ins/delATTG polymorphism and overall cancer risk

Variables

OR (95% CI)

P a

Power b

Prior Probability

0.25

0.1

0.01

0.001

0.0001

Homozygous (DD vs. II)

 All

0.75 (0.64-0.87)

2.45*10-4

1.000

0.001

0.002

0.024

0.197

0.710

 NPC

0.66 (0.56-0.78)

1.99*10-6

0.652

0.000

0.000

0.000

0.003

0.030

 Prostate

0.59 (0.48-0.72)

6.40*10-7

0.860

0.000

0.000

0.000

0.001

0.007

 Ovarian

0.54 (0.40-0.73)

6.62*10-6

0.612

0.000

0.000

0.001

0.011

0.098

 OSCC

0.49 (0.33-0.72)

3.26*10-4

0.388

0.003

0.008

0.077

0.457

0.894

 Asians

0.67 (0.55-0.80)

1.81*10-5

1.000

0.000

0.000

0.002

0.018

0.153

 Chinese

0.68 (0.56-0.81)

3.45*10-5

1.000

0.000

0.000

0.003

0.033

0.257

 HB

0.70 (0.58-0.85)

3.30*10-4

1.000

0.001

0.003

0.032

0.248

0.767

 QS ≤9

0.68 (0.53-0.86)

1.63*10-3

1.000

0.005

0.014

0.139

0.620

0.942

Heterozygous (ID vs. II)

 All

0.91 (0.83-0.99)

0.024

1.000

0.066

0.175

0.700

0.959

0.996

 Lung

0.84 (0.74-0.96)

7.74*10-3

1.000

0.023

0.065

0.434

0.885

0.987

 NPC

0.85 (0.75-0.97)

0.017

1.000

0.048

0.131

0.623

0.943

0.994

 Prostate

0.74 (0.62-0.88)

7.44*10-4

0.998

0.002

0.007

0.069

0.427

0.882

 Ovarian

0.73 (0.61-0.87)

5.68*10-4

0.979

0.002

0.005

0.054

0.367

0.853

 OSCC

0.67 (0.51-0.88)

3.99*10-3

0.800

0.015

0.043

0.331

0.833

0.980

 Asians

0.86 (0.79-0.94)

7.59*10-4

1.000

0.002

0.007

0.070

0.431

0.884

 Chinese

0.84 (0.77-0.93)

4.59*10-4

1.000

0.001

0.004

0.043

0.314

0.821

 HB

0.88 (0.80-0.98)

0.015

1.000

0.043

0.118

0.596

0.937

0.993

Recessive (DD vs. ID/II)

 All

0.81 (0.71-0.91)

3.22*10-4

1.000

0.001

0.003

0.031

0.243

0.763

 NPC

0.73 (0.63-0.85)

3.26*10-5

0.841

0.000

0.000

0.004

0.037

0.279

 Prostate

0.77 (0.65-0.92)

3.78*10-3

0.999

0.011

0.033

0.272

0.791

0.974

 Ovarian

0.68 (0.52-0.89)

4.62*10-3

0.976

0.014

0.041

0.319

0.826

0.979

 OSCC

0.63 (0.49-0.81)

2.37*10-4

0.356

0.002

0.006

0.062

0.399

0.869

 Asians

0.75 (0.65-0.86)

6.02*10-5

1.000

0.000

0.001

0.006

0.057

0.376

 Chinese

0.77 (0.67-0.88)

1.44*10-4

1.000

0.000

0.001

0.014

0.126

0.591

 HB

0.76 (0.65-0.89)

5.23*10-4

1.000

0.002

0.005

0.049

0.343

0.840

 QS ≤9

0.73 (0.60-0.88)

1.23*10-3

1.000

0.004

0.011

0.109

0.551

0.925

Dominant (ID/DD vs. II)

 All

0.86 (0.78-0.95)

2.68*10-4

1.000

0.008

0.024

0.210

0.728

0.964

 NPC

0.79 (0.69-0.90)

2.94*10-4

0.997

0.001

0.003

0.028

0.227

0.747

 Prostate

0.69 (0.59-0.81)

8.36*10-6

0.796

0.000

0.000

0.001

0.010

0.095

 Ovarian

0.67 (0.56-0.79)

3.45*10-6

0.531

0.000

0.000

0.001

0.006

0.061

 OSCC

0.60 (0.46-0.77)

9.87*10-5

0.186

0.002

0.005

0.050

0.347

0.842

 Asians

0.80 (0.72-0.89)

9.05*10-5

1.000

0.000

0.001

0.009

0.083

0.475

 Chinese

0.80 (0.71-0.89)

9.39*10-5

1.000

0.000

0.001

0.009

0.086

0.484

 HB

0.84 (0.74-0.94)

2.29*10-3

1.000

0.007

0.020

0.185

0.696

0.958

 QS ≤9

0.83 (0.71-0.96)

0.013

1.000

0.038

0.105

0.563

0.929

0.992

Allele (D vs. I)

 All

0.88 (0.81-0.95)

8.86*10-4

1.000

0.003

0.008

0.081

0.469

0.899

 NPC

0.81 (0.74-0.89)

9.60*10-6

1.000

0.000

0.000

0.001

0.009

0.088

 Prostate

0.79 (0.72-0.87)

1.03*10-6

1.000

0.000

0.000

0.000

0.001

0.010

 Ovarian

0.75 (0.65-0.86)

3.47*10-5

1.000

0.000

0.000

0.003

0.033

0.257

 OSCC

0.70 (0.60-0.82)

1.11*10-5

0.809

0.000

0.000

0.001

0.014

0.121

 Asians

0.83 (0.76-0.91)

4.78*10-5

1.000

0.000

0.000

0.005

0.046

0.323

 Chinese

0.84 (0.76-0.91)

9.24*10-5

1.000

0.000

0.001

0.009

0.085

0.480

 HB

0.85 (0.77-0.94)

9.63*10-4

1.000

0.003

0.009

0.087

0.490

0.906

 QS ≤9

0.84 (0.75-0.95)

4.84*10-3

1.000

0.014

0.042

0.324

0.829

0.980

CI, confidence interval; OR, odds ratio; NPC, Nasopharyngeal carcinoma; OSCC, oral squamous cell carcinoma; HB, Hospital based; QS, quality score.

a Chi-square test was adopted to calculate the genotype frequency distributions.

b Statistical power was calculated using the number of observations in the subgroup and the OR and P values in this table.

DISCUSSION

In this meta-analysis, we found that the NFKB1 -94ins/delATTG promoter polymorphism was significantly associated with decreased overall cancer risk under the five genetic models. To the best of our knowledge, this is the most comprehensive meta-analysis on this topic by now.

Numerous studies have suggested that polymorphisms in genes encoding inflammatory response factors, such as TNF-alpha -308G>A [6], IL6 -174G>C [90], and NFKBIA -826C>T [91] may contribute to cancer susceptibility. Song et al. [92] reported that the NFKB1 -94ins/delATTG polymorphism analyzed here (rs28362491) increased the risk of colorectal cancer in a Southern Chinese population; this association was also observed in several publications [71]. However, contradictory conclusions were also reported, namely a null association, or decreased cancer susceptibility. To address this controversy, at least six meta-analyses were performed. The first one, performed in 2011 by Zou et al. [32], included only 2,743 cases and 2,195 controls from 11 studies. They did not observe any association between the -94ins/delATTG variant and overall cancer. However, an ethno-specific association was detected by subgroup analysis; the D allele was protective against cancer in Asians, but increased the risk in Caucasians. Afterwards, Wang et al. [33] conducted an updated meta-analysis including 5,196 cases and 6,614 controls from 19 publications. They found that variant homozygotes (DD) had a decreased risk of cancer compared with wild-type homozygotes (II). The association was also found under the dominant genetic model (DD+DI vs. II). In subgroup analysis, a significantly decreased risk was observed in Asians but not in Caucasians. In addition, this susceptibility was cancer-specific, as it was observed for all cancer types examined, except for colorectal cancer. In 2014, four updated meta-analyses were published. Upon revision of 6,494 cases and 9,884 controls from 23 studies, Xu et al. [29] found that the -94ins/delATTG polymorphism was significantly associated with increased cancer risk under all the inheritance models. Stratified analysis by cancer type showed significant associations for ovarian cancer, hepatocellular carcinoma, and oral squamous cell carcinoma, but not for bladder cancer or lung cancer. Ethnicity subgroup analysis indicated that the polymorphism contributed to cancer risk in the Asian, but not the Caucasian, population. Another study by Yang et al. [28], which included 21 reports with 6,127 cases and 9,238 controls, also detected an increased overall cancer risk. Stratified analysis revealed a significant association between the polymorphism and ovarian, oral, and prostate cancers. These findings were also specific to the Asian population. Duan et al. [31] reviewed a total of 25 studies that included 8,750 cancer cases and 9,170 controls. They found that the insertion allele of the -94ins/delATTG polymorphism significantly increased cancer risk, both in overall genetic analysis as well as in Asians. Stratified analysis revealed that the polymorphism was associated with increased risk for oral squamous cell carcinoma and ovarian cancer, but not for colorectal cancer, bladder cancer, or renal cell cancer. In another meta-analysis involving 7,281 cases and 10,039 controls from 25 case-control studies, Nian et al. [30] found that the -94ins/delATTG polymorphism was significantly associated with decreased susceptibility to cancer in overall population under homozygous, recessive, dominant, and allele contrast models. Subgroups analysis based on ethnicity revealed that the polymorphism conferred decreased cancer susceptibility in the Asian population.

Since then, approximately 20 new relevant case-control studies in English and Chinese have emerged, some containing large samples and convincing results. Our study re-evaluated the impact of the NFKB1 -94ins/delATTG polymorphism on cancer risk. In line with some previous meta-analyses, our pooled analysis revealed a significant association with decreased cancer risk under all five genetic models. Conversely, we found that the del allele of the NFKB1 -94ins/delATTG polymorphism conferred a significantly decreased risk of cancer in the pooled analysis. Compared with the ins allele, the ins allele significantly enhances the binding ability to nuclear proteins and increases transcriptional activity, which eventually upregulates p50 (the active NF-κB1 subunit) expression [21]. Given the tumor-promoting role of p50 and NF-κB, it is biologically plausible that the -94del allele confers decreased cancer susceptibility.

In line with previous data, our study detected a significant association between the -94ins/delATTG polymorphism and cancer risk in Asians, but not in Caucasians, under all five genetic models. It is thus likely that the allelic distribution of this polymorphism vary geographically and ethnically, thus leading to the discrepancies in cancer risk. This may indicate that these groups have distinct environmental or genetic cancer co-etiologies. Stratification by cancer type showed that the NFKB1 -94ins/delATTG polymorphism was inversely associated with the risk of lung cancer, nasopharyngeal carcinoma, prostate cancer, ovarian cancer, and oral squamous cell carcinoma, but no association was found for hepatocellular carcinoma, colorectal cancer, bladder cancer, gastric cancer, cervical cancer, breast cancer, or other cancers. This phenomenon may be partly attributed to the inherent heterogeneity of oncogenic progression in different cancer types [93], although the insufficient statistical power caused by the relatively small number of studies on each cancer type may also be a factor.

The credibility of our conclusions is supported by the inclusion of Chinese-language studies, exclusion of publications with controls violating the Hardy-Weinberg equilibrium, and inclusion of subgroup, publication bias, and sensitivity analyses. Among the limitations of our meta-analysis are a significant between-study heterogeneity, detected in some comparisons, which may diminish the strength of our conclusions. The source of this heterogeneity may be ascribed to sample size, genotyping methods, ethnicity, source of controls, as well as the studies’ diverse quality scores. Second, we assessed the association between the NFKB1 -94ins/delATTG polymorphism and cancer risk from a genetic perspective only, by using unadjusted ORs. Multiple potentially influential factors such as life style, environmental exposure, and gene-environment interactions should be considered to obtain a more precise risk estimation. Third, the number of studies in certain subgroup analyses was too small to obtain a reliable association. For instance, only six publications were included for hepatocellular carcinoma, and fewer studies were available for breast cancer and oral squamous cell carcinoma, which restrains further analysis for risk factors. Finally, the meta-analysis is a type of retrospective study with several inherent drawbacks: inconsistent qualities of primary studies, incomplete histological details, misclassified genotypes, different definitions of disease status, and improperly matched sources of controls.

In conclusion, despite these limitations, and in agreement with several previous studies, this meta-analysis draws the robust conclusion that NFKB1 -94ins/delATTG polymorphism is associated with decreased cancer risk, especially in the Asian population. These findings indicate a possible involvement of NFKB1 in the etiology of tumorigenesis, and suggest the potentially relevant therapeutic value of NF-κB modulation in cancer prevention. Further multi-center, well-designed investigations with larger sample sizes that include gene-environment interactions assessment are warranted to confirm our findings.

MATERIALS AND METHODS

Publication search

We performed a comprehensive literature search by using the PubMed and EMBASE databases, without language limitations, up to July 1, 2016. The following search terms were used: “polymorphism or SNP or single nucleotide polymorphism or variant” and “NFKB1/NF-κB1 or nuclear factor kappa B1”, and “tumor or cancer or neoplasm or carcinoma”. We also searched the China National Knowledge Infrastructure (CNKI) and WANFANG databases to obtain additional, relevant studies. Retrieved articles were manually screened to determine eligible studies. When two or more publications containing overlapping data were found, the largest study was included in the final meta-analysis.

Inclusion/exclusion criteria

All articles included in the current analysis met the following criteria: 1) evaluation of the association between NFKB1 -94ins/delATTG polymorphism and cancer risk; 2) case-control studies; 3) sufficient information provided to estimate ORs and 95% CIs; 4) NFKB1 -94ins/delATTG genotype frequency in agreement with HWE in controls. Exclusion criteria were as follows: 1) case-only studies; 2) meta-analysis or reviews; 3) studies that lacked detailed genotyping data; 4) duplicates of previous publications.

Data extraction

Two authors (Z.Z. and W.F.) evaluated all eligible studies independently and extracted the following information: first author’s surname, year of publication, cancer type, country, ethnicity, source of controls, genotyping methods, and genetic distribution of cases and controls. Stratification analyses were conducted by cancer type, ethnicity (Asians, Caucasians), source of control (hospital-based and population-based) and quality score (>9 and ≤9). If a study contained two or more ethnic groups or cancer types, we divided the study accordingly.

Trial sequential analysis

TSA was performed as described by us previously [94]. Briefly, after adopting a level of significance of 5% for type I error and of 30% for type II error, the required information size was calculated, and TSA monitoring boundaries were built.

FPRP analysis

The FPRP values at different prior probability levels for all significant findings were calculated as described by us previously [95]. Briefly, 0.2 was set as FPRP threshold and assigned a prior probability of 0.01 to detect an OR of 0.67 (for protective effects) for an association with genotypes under investigation. A FPRP value <0.2 denoted a noteworthy association.

Statistical methods

Goodness-of-fit χ2 test was used to assess HWE in the control subjects. Departure from HWE was assessed using a P < 0.05 as threshold in each study. The strength of the association between NFKB1 -94ins/delATTG polymorphism and cancer risk was assessed by calculating ORs and corresponding 95% CIs. Five genetic models were adopted: homozygote model (DD, homozygous deletion (del/del) vs. II, homozygous insertion (ins/ins) or wild-type); heterozygote model (ID, heterozygous ins/del vs. II); recessive model (DD vs. ID/II); dominant model (ID/DD vs. II); and allele contrast model (D vs. I). Subgroup and stratification analyses were also performed to test the association by ethnicity, cancer type, source of control and quality score. We performed χ2-based Q-test to assess heterogeneity between study results. The fixed-effects model (Mantel-Haenszel method) was used if the studies were found to be homogeneous (with P > 0.10 for the Q-test). Otherwise, the random-effects model (DerSimonian and Laird method) was adopted to estimate the pooled OR [96-99]. Quality assessment for each study was performed using the quality assessment criteria described previously (Supplementary Table 1) [98]. Sensitivity analysis was carried out by individually removing each study and reanalyzing the pooled risk estimates. Potential publication bias was estimated by Begg’s funnel plot and Egger’s linear regression, where an asymmetric plot and a P value < 0.05, respectively, indicate the presence of publication bias. All the data management and statistical analyses were completed using STATA software (Stata Corporation, College Station, TX; version 11.0). All the P values were two-sided. A P value of < 0.05 was considered statistically significant.

ACKNOWLEDGMENTS

This study was supported by grants from the National Natural Science Foundation of China (Grant No. 81502046), and the Special Financial Grant from the China Postdoctoral Science Foundation (Grant No. 2014T70836).

CONFLICTS OF INTEREST

The authors declare no competing financial interests.

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