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Role of interleukin-12 gene polymorphisms in the onset risk of cancer: a meta-analysis

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Oncotarget. 2017; 8:29795-29807. https://doi.org/10.18632/oncotarget.16080

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Yi Zheng, Meng Wang, Tian Tian, Kang Liu, Xinghan Liu, Yajing Zhai, Shuai Lin, Pengtao Yang, Shanli Li, Zhijun Dai and Jun Lu _

Abstract

Yi Zheng1,2,*, Meng Wang2,*, Tian Tian2,*, Kang Liu2, Xinghan Liu2, Yajing Zhai1, Shuai Lin2, Pengtao Yang2, Shanli Li2, Zhijun Dai2, Jun Lu1

1Clinical Research Center, First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, 710061, China

2Department of Oncology, Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, 710004, China

*These authors contributed equally to this work

Correspondence to:

Jun Lu, email: lujun2006@xjtu.edu.cn

Zhijun Dai, email: dzj0911@126.com

Keywords: interluekin-12, cancer risk, polymorphism, meta-analysis

Received: December 31, 2016     Accepted: February 27, 2017     Published: March 10, 2017

ABSTRACT

Many molecular epidemiologic studies have explored the possible links between interleukin-12 (IL-12) polymorphisms and various cancers. However, results from these studies remain inconsistent. This meta-analysis is aimed to shed light on the associations between three common loci (rs568408, rs2243115, rs3212227) of IL-12 gene and overall cancer risk. Our meta-analysis finally included 33 studies comprising 10,587 cancer cases and 12,040 cancer-free controls. Odds ratios (ORs) and 95% confidence intervals (CIs) were used to assess the cancer risk. We observed a significant association between IL-12B rs3212227 and overall cancer risk, especially in hepatocellular carcinoma, nasopharyngeal cancer, and among Asians. IL-12A polymorphisms (rs2243115 and rs568408) were found no influence on overall cancer risk. Nevertheless, stratification analyses demonstrated that rs568408 polymorphism contributed to increasing cancer risk of Caucasians and cervical cancer. And, rs2243115 may enhance the risk of brain tumor. These findings provided evidence that IL-12 polymorphisms may play a potential role in cancer risk.


Role of interleukin-12 gene polymorphisms in the onset risk of cancer: a meta-analysis | Zheng | Oncotarget

INTRODUCTION

According to the data provided by Cancer statistics, 2017, around 1,688,780 new cancer patients and 600,920 cancer-related deaths will develop in America in 2017 [1]. And, it was estimated that a number of 4,292,000 new cancer patients and 2,814,000 cancer-related deaths would occur in China in 2015 [2]. These data declared that cancer has represented a substantial public health burden. It has been proved that cancer was a multifactorial disease due to dynamic interactions between the environmental exposures and host genetic background. The mechanisms of inherited factors effecting on tumor is still not known clearly, though the carcinogenesis and tumor-immunity function of cytokines have been well established [3]. In fact, one of the pivotal control mechanisms in pathogenesis of tumor was considered to be cytokine-mediated immunity [4]. For instance, interferon, a cytokine with strongly anti-tumor effects, was widely used in treatment of many cancers, especially for hematological malignant tumor such as chronic myeloid leukemia and hairy cell leukemia [5]. Likewise, Interleukins (ILs) were promising in immunotherapy for many cancers because they can enhance the immune response against both infections and tumors. In previous clinical trials several candidates of ILs (such as IL-1a, IL-2, and IL-18) were failed to show corresponding effectiveness on tumors, and other ILs (including IL-7, IL-10, IL-15 and IL-21) showed severe adverse side effects during treatment [6]. Thus, exploration and discovery new, effective immune-modulators are imperative. Interleukin-12 (IL-12), which is characteristic as a link between the innate and acquired immune response [7], is one of the most potential candidates for tumor immunotherapy.

IL-12 is mainly produced by activated antigen-presenting cells, such as macrophages, dendritic cells, and monocytes. As a heterodimeric cytokine, IL-12 protein is formed by two disulfide-linked polypeptide chains comprising p35 and p40, which are encoded by IL-12A gene and IL-12B gene, independently. And it mapped to human chromosomes 3p12-q13.2 and 5q31-33 [8]. IL-12 can potently induce the differentiation of naive CD4+ T cell to Th1 cells and augment cytotoxic T lymphocyte responses, as well as promote activation of natural killer (NK) cells, and secretion of interferon-γ [9]. Moreover, IL-12 can impair the mechanism of neoangiogenesis in tumors by antagonizing the pro-angiogenic signals. Animal experiments extensively showed that both nonspecific and specific antitumor immune responses were enhanced after transfecting IL-12 gene into tumor cells [10, 11]. In addition, clinical trials indicated that serum level of IL-12 was related to the severity of gastric cancer patients [12], and also influenced the progression of colorectal cancer [13].

Because of these pleiotropic activities of IL-12, dozens of molecular epidemiologic studies have explored the influences of IL-12 polymorphisms on susceptibility of various cancers, including hepatocellular carcinoma [1419], colorectal cancer [13, 20, 21] and gastric cancer [2224], etc. The most commonly fascinating loci were rs3212227 in IL-12B gene, rs568408 and rs2243115 in IL-12A genes, perhaps owing to their function to influence IL-12 gene expression, reduce protein synthesis, and subsequently result in cancer. Unfortunately, the functional role on cancer risk that IL-12 gene polymorphisms played was still uncertain. For instance, a study performed by Sun et al. found that IL-12A rs568408 may increase the susceptibility on colorectal cancer [20], being contrary to the conclusion of the study by Huang et al. which suggested IL-12A rs568408 was not associated with colorectal cancer[21]. And, a previous study reported that the loci of rs3212227 in IL-12B gene definitely contributed to increasing cervical cancer risk in Chinese population [25]. However, another study found IL-12B rs3212227 polymorphism exhibited a protective effect [26], while an earlier study showed that there was no relationship between cervical cancer and IL-12B rs3212227 in Korean population [27]. Hence, we conducted this study according to currently published data to investigate the precise relationship between these three common variants and various cancers risk.

RESULTS

Characteristics of eligible studies

As the flow diagram showed Figure 1, we identified a total of 72 publications from the PubMed, web of knowledge, Embase, WanFang, VIP and CNKI databases using the search words, fifteen obviously irrelevant studies and six reviews were excluded firstly. Fourteen studies were excluded from our meta-analysis after screening the titles and abstracts because they were not researching on rs568408, rs2243115 and rs3212227 polymorphisms (2 studies) or cancer (4 studies), and 8 studies were excluded for not case-control studies. The remaining 37 studies evaluated for eligibility by reading the full-text; 3 studies were excluded owing to either lack of complete data or presence of irrelevant data that focused on other IL-12 polymorphisms; one study was written neither in English nor in Chinese. Ultimately, 33 studies with 10,587 cancer cases and 12,040 cancer-free controls were elected in this meta-analysis to explored the relationship of IL-12A rs568408 (10 studies), IL-12A rs2243115 (9 studies), and IL-12B rs3212227 (30 studies) polymorphisms and cancer risk. According to the Newcastle-Ottawa Scale (NOS), all included studies obtained a score of 6 or more, which was defined as a high quality. Among these, 3 studies were written in Chinese and 30 studies in English. The genotype frequencies in 2 studies [17, 18] were not provided completely, we could only evaluated the data based on dominant model. The eligible studies involved several different cancer types including hepatocellular, colorectal, gastric, cervical, breast, prostate, osteosarcoma, esophageal, brain, cervical, ovarian, nasopharyngeal, lung, Non-Hodgkin lymphoma, and cutaneous malignant melanoma. Every study was in according with HWE except for two [20, 28]. The main characteristics of the included studies are presented in Table 1. The distributions of rs568408, rs2243115, and rs3212227 polymorphisms of IL-12 among cases and controls are shown in Table 2.

The flow diagram of the meta-analysis, according to the PRISMA 2009.

Figure 1: The flow diagram of the meta-analysis, according to the PRISMA 2009. CNKI: China National Knowledge Infrastructure.

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

Study

Year

Country

Ethnic

Method

Source of control

Cancer type

Case/control

SNP.

NOS

Tan[14]

2015

China

Asian

iMLDR

Population

HCC

395/686

1,2,3

7

Sun[20]

2015

China

Asian

PCR-RFLP

Hospital

CRC

257/236

1,2,3

7

Yin[22]

2015

China

Asian

SNPscan

Hospital

GAC

234/476

2,3

7

Jafarzadeh[35]

2015

Iran

Asian

PCR-RFLP

Population

BC

100/100

3

6

Winchester[36]

2015

USA

Caucasian

MassArray

Population

PC

566/530

3

9

Saxena[15]

2014

India

Asian

PCR-RFLP

Population

HCC

59/153

3

6

wang[37]

2013

China

Asian

PCR-RFLP

Population

OSC

106/210

1,2,3

7

Sun[38]

2013

China

Asian

SNPscan

Hospital

EC

380/380

3

8

Jaiswal[39]

2013

Indian

Asian

PCR-RFLP

Hospital

BLC

200/200

3

6

Tao[40]

2012

Mixed

Asian

PCR-RFLP

Population

EC

426/432

1,3

6

Sima[41]

2012

China

Asian

PCR-RFLP

Hospital

BT

170/222

2,3

7

Kaarvatn[42]

2012

Croatia

Caucasian

TaqMan

Mixed

BC

191/194

3

6

Carvalho[26]

2012

Brazil

Mixed

PCR-RFLP

Population

CC

162/76

3

6

Roszak[25]

2012

Poland

Caucasian

PCR-RFLP

Population

CC

405/405

1,3

7

Hu[43]

2012

China

Asian

PCR-RFLP

Hospital

OC

92/38

3

6

Huang[21]

2012

China

Asian

PCR-RFLP

Hospital

CRC

410/450

3

6

Liu[16]

2011

China

Asian

PCR-RFLP

population

HCC

869/891

1,2,3

9

Chaaben[44]

2011

Tunisia

African

Taqman

population

NPC

247/284

3

6

Ter-Minassian[45]

2011

USA

Caucasian

MassArray

Population

BT

261/319

2

8

Yang[19]

2011

China

Asian

Taqman

Hospital

HCC

608/612

3

6

Wu[24]

2009

China

Asian

PCR-RFLP

Population

GC

1035/1073

3

7

Chen[46]

2009

China

Asian

PCR-RFLP

population

CC

404/404

1,2,3

9

Miteva[13]

2009

Bulgaria

Caucasian

PCR–RFLP

Hospital

CRC

85/134

3

6

Wei[47]

2009

China

Asian

PCR–RFLP

Hospital

NPC

302/310

3

6

Zhao[48]

2009

China

Asian

PCR–RFLP

Hospital

BT

210/220

3

6

Ognjanovic[17]

2009

USA

Caucasian

Taqman

population

HCC

117/223

3

9

Han[27]

2008

Korea

Asian

PCR–RFLP

Hospital

CC

150/179

3

7

Hou[23]

2007

USA

Caucasian

TaqMan

Population

GC

257/428

1

6

Lee[49]

2007

USA

Asian

TaqMan

Population

LC

119/113

1

8

Wang[50]

2006

USA

Caucasian

TaqMan

Population

NHL

1130/941

1,3

9

Nieters[18]

2005

China

Asian

PCR–RFLP

Hospital

HCC

250/250

3

8

Howell[51]

2003

UK

Caucasian

ARMS-PCR

Hospital

CMM

145/229

3

7

PCR: polymerase chain reaction; RFLP: restriction fragment length polymorphism; HCC: hepatocellular carcinoma; GC: gastric cancer; CRC: colorectal cancer; BC: breast cancer; PC: prostate cancer; OSC: osteosarcoma; EC: esophageal cancer; BLC: bladder cancer; BT: brain tumor; CC: cervical cancer; OC: ovarian carcinoma; NPC: nasopharyngeal cancer; GC: gastric cancer; LC: lung cancer; NHL: non-Hodgkin lymphoma; CMM: cutaneous malignant melanoma. SNP No.1: rs568408 (3′ UTR G>A) in IL-12A; 2: rs2243115 (5’UTR T>G) in IL-12A; 3: rs3212227 (3′ UTR A>C) in IL-12B; NOS: the Newcastle-Ottawa Scale.

Table 2: Genotype distribution and allele frequency of IL-12 polymorphisms

First author

Genotype (N)

Allele frequency (N)

MAF

HWE

Case

Control

Case

Control

Total

AA

AB

BB

Total

AA

AB

BB

A

B

A

B

rs568408

Tan 2015[14]

395

313

76

6

686

511

161

14

702

80

1183

189

0.102

0.750

Sun 2015[20]

257

172

83

2

236

181

55

0

427

89

417

55

0.172

0.040

Wang2013[37]

106

66

37

3

210

156

47

7

169

61

359

61

0.265

0.150

Tao 2012[40]

430

276

142

12

432

304

112

16

694

184

720

144

0.210

0.170

Roszak 2012[25]

405

253

128

24

450

306

125

19

634

140

737

163

0.181

0.180

Liu 2011[16]

802

504

277

21

861

631

220

10

1285

289

1482

240

0.184

0.060

Chen 2009[46]

404

253

145

6

404

285

112

7

651

221

682

126

0.253

0.290

Hou 2007[23]

257

211

8

38

428

306

112

10

430

8

724

132

0.018

0.950

Lee 2007[49]

118

101

17

0

113

81

31

1

219

81

193

33

0.270

0.290

Wang 2006[50]

941

671

238

32

1130

839

270

21

1580

238

1948

312

0.131

0.890

rs2243115

Tan 2015[14]

395

326

63

6

685

549

128

8

715

75

1226

144

0.095

0.861

Sun 2015[20]

257

225

31

1

236

212

24

0

481

33

448

24

0.064

0.411

Yin 2015[22]

235

197

36

2

466

387

75

4

430

40

849

83

0.085

0.862

Wang2013[37]

106

89

16

1

210

175

31

4

194

18

381

39

0.085

0.073

Sun 2013[38]

368

311

56

1

370

308

58

4

678

58

674

66

0.079

0.499

Sima 2012[41]

170

140

25

5

222

198

24

0

305

35

420

24

0.103

0.395

Liu 2011[16]

861

735

125

1

874

732

137

5

1595

127

1601

147

0.074

0.604

Ter-Minassian 2011[45]

248

115

113

20

317

180

114

23

343

153

474

160

0.308

0.403

Chen 2009[46]

404

342

60

2

404

337

64

3

744

64

738

70

0.079

0.984

rs3212227

Tan 2015[14]

395

104

201

90

686

200

347

139

409

381

747

625

0.482

0.605

Sun 2015[20]

257

69

140

48

236

71

124

41

278

236

266

206

0.459

0.295

Yin 2015[22]

234

60

132

42

466

152

221

93

252

216

525

407

0.462

0.436

Jafarzadeh 2015[35]

100

58

32

10

100

59

33

8

148

52

151

49

0.260

0.280

Winchester 2015[36]

866

535

274

57

830

568

227

35

1344

388

1363

297

0.224

0.046

Saxena 2014[15]

59

19

31

9

148

63

71

14

69

49

197

99

0.415

0.345

Wang 2013[37]

106

27

50

29

210

78

101

31

104

108

257

163

0.509

0.585

Sun 2013[38]

368

116

176

76

370

112

179

79

408

328

403

337

0.446

0.635

Jaiswal 2013[39]

200

87

94

19

200

111

74

15

268

132

296

104

0.330

0.590

Tao 2012[40]

426

109

216

101

432

148

213

71

434

418

509

355

0.491

0.701

Sima 2012[41]

170

66

86

18

222

71

116

35

218

122

258

186

0.359

0.275

Kaarvatn 2012[42]

191

126

59

6

194

104

73

17

311

71

281

107

0.186

0.419

Carvalho 2012[26]

162

100

49

13

76

31

37

8

249

75

99

53

0.231

0.531

Roszak 2012[25]

405

212

174

19

450

289

151

10

598

212

729

171

0.262

0.056

Hu 2012[43]

92

16

42

34

38

13

16

9

74

110

42

34

0.598

0.360

Huang 2012[21]

410

93

219

98

450

124

230

96

405

415

478

422

0.506

0.578

Liu 2011[16]

831

249

422

160

844

272

414

158

920

742

958

730

0.446

0.983

Chaaben 2011[44]

247

74

124

49

284

135

118

31

272

222

388

180

0.449

0.497

Yang2011[19]

608

156

309

143

612

195

302

115

621

595

692

532

0.489

0.919

Wu 2009[24]

1035

347

508

180

1073

333

554

186

1202

868

1220

926

0.419

0.086

Chen 2009[46]

404

127

199

78

404

150

185

69

453

355

485

323

0.439

0.357

Miteva 2009[13]

85

50

31

4

134

80

45

9

131

39

205

63

0.229

0.443

Wei 2009[47]

302

58

165

79

310

98

152

60

281

323

348

272

0.535

0.938

Zhao 2009[48]

210

38

115

57

220

70

106

44

191

229

246

194

0.545

0.736

Ognjanovic 2009[17]

117

57

/

/

223

128

/

/

/

/

/

/

/

/

Tamandani 2009[28]

200

63

134

3

200

88

104

8

260

140

280

120

0.350

0.001

Han 2008[27]

150

32

87

31

179

52

88

39

151

149

192

166

0.497

0.877

Wang 2006[50]

926

565

310

51

1110

641

397

72

1440

412

1679

541

0.222

0.322

Nieters 2005[18]

249

56

/

/

250

72

/

/

/

/

/

/

/

/

Howell2003[51]

145

95

42

8

229

139

77

13

232

58

355

103

0.200

0.591

A: the major allele, B: the minor allele, MAF: minor allele frequencies; HWE: Hardy–Weinberg equilibrium.

Quantitative synthesis of rs568408 and rs2243115 polymorphisms

In the meta-analysis, we derived data from 10 studies including 4115 cancer cases and 4950 cancer-free controls for rs568408, 9 studies containing 3004 cancer cases and 3784 cancer-free controls for rs2243115, respectively.

The pooled analysis proved that IL-12A rs568408 polymorphisms was associated with overall cancer risk on allele comparison (A vs. G: OR = 1.18, 95%CI: 1.01-1.38, P = 0.04). However, no significant association was found after excluding one study [20] not according to the HWE (All P > 0.05, Figure 2). In stratified analysis by ethnicity, we observed an increased risk of overall cancers among Caucasians (recessive model: OR = 2.63, 95% CI = 1.05-6.56; homozygote model: OR = 2.46, 5% CI = 1.20-5.06; allele model: OR = 1.19, 95% CI = 1.05-1.35). The subgroup analysis by cancer type showed rs568408 polymorphism increased cervical cancer risk under three genetic models (allele model: OR = 1.28, 95% CI = 1.07-1.52; heterozygous model: OR = 1.34, 95% CI = 1.09-1.66; dominate model: OR = 1.35, 95% CI = 1.10-1.65). Rs2243115 polymorphism was associated with brain tumor under dominate model (OR = 1.58, 95% CI = 1.18-2.11), heterozygous model (OR = 1.53, 95 % CI = 1.13-2.07) and allele model (OR = 1.52, 95% CI = 1.03-2.24). However, the subgroup analysis based on source of controls proved IL-12A polymorphisms (rs568408 and rs2243115) have no influence on cancer susceptibility in both hospital-based and population-based controls subgroups (all P > 0.05).

Figure 2:

Figure 2: Forest plot of OR with 95%CI for the IL-12 polymorphisms with cancer risk under dominant model (A) rs568408; (B) rs2243115). CI: confidence interval, OR: odds ratio.

Quantitative synthesis of rs3212227 polymorphism

We gained 9950 patients and 11180 control subjects from 30 studies of rs3212227 polymorphism. IL-12B rs3212227 was proved to enhance overall cancer risk (C vs. A: OR = 1.15, 95%CI: 1.05-1.25; CC vs. AA: OR = 1.32, 95%CI: 1.11-1.56; AC vs. AA: OR= 1.21, 95%CI: 1.08-1.35, P = 0.001; AC+CC vs. AA: OR = 1.24, 95%CI: 1.10-1.40, P < 0.001; CC vs. AC+AA: OR = 1.17, 95%CI: 1.04-1.31). The similar results were obtained after excluding studies with HWE disequilibrium (C vs. A: OR = 1.14, 95%CI: 1.05-1.25; CC vs. AA: OR = 1.33, 95%CI: 1.12-1.58; AC vs. AA: OR = 1.19, 95%CI: 1.06-1.34; AC+CC vs. AA: OR = 1.23, 95%CI: 1.09-1.38; CC vs. AC+AA: OR = 1.18, 95%CI: 1.05-1.32).

When stratification analysis performed by ethnicity (Figure 3), rs3212227 polymorphism exhibited to increase overall cancer risk among Asians (AC+CC vs. AA: OR = 1.29, 95%CI: 1.15-1.46; CC vs. AC+AA: OR = 1.17, 95%CI: 1.06-1.30; CC vs. AA: OR = 1.38, 95%CI: 1.17-1.62; AC vs. AA: OR = 1.25, 95%CI: 1.11-1.40; C vs. A: OR = 1.18, 95%CI: 1.09-1.28). In subgroup analysis by cancer type (Figure 4), rs3212227 was found to increase the risk of hepatocellular carcinoma (AC+CC vs. AA: OR = 1.24, 95%CI: 1.09-1.40; CC vs. AC+AA: OR = 1.17, 95%CI: 1.00-1.37; CC vs. AA: OR = 1.30, 95%CI: 1.07-1.59; AC vs. AA: OR = 1.17, 95%CI: 1.02-1.35; C vs. A: OR = 1.14, 95%CI: 1.04-1.25) and nasopharyngeal carcinoma (AC+CC vs. AA: OR = 2.03, 95%CI: 1.57-2.63; CC vs. AC+AA: OR = 1.66, 95%CI: 1.23-2.25; CC vs. AA: OR = 2.49, 95%CI: 1.75-3.54; AC vs. AA: OR = 1.88, 95%CI: 1.43-2.47; C vs. A: OR = 1.60, 95%CI: 1.34-1.90). When conducted a stratified analysis by the source of controls, rs3212227 polymorphism displayed an increased cancer risk among population-based studies and hospital-based studies in different comparison models. The detail results were listed in Supplementary Table 1.

Stratified analysis by ethnicity for the association between IL-12 rs3212227 polymorphism and cancer risk under dominant model (CC + AC vs. AA) according to the HWE.

Figure 3: Stratified analysis by ethnicity for the association between IL-12 rs3212227 polymorphism and cancer risk under dominant model (CC + AC vs. AA) according to the HWE. CI: confidence interval, OR: odds ratio, HWE: Hardy–Weinberg equilibrium.

Stratified analysis by cancer type for the association between IL-12B rs3212227 polymorphism and cancer risk under dominant model (CC + AC vs. AA) according to the HWE.

Figure 4: Stratified analysis by cancer type for the association between IL-12B rs3212227 polymorphism and cancer risk under dominant model (CC + AC vs. AA) according to the HWE. CI: confidence interval, OR: odds ratio. HCC: hepatocellular carcinoma; GC: gastric cancer; CRC: colorectal cancer; BC: breast cancer; PC: prostate cancer; OSC: osteosarcoma; EC: esophageal cancer; BLC: bladder cancer; BT: brain tumor; CC: cervical cancer; OC: ovarian carcinoma; NPC: nasopharyngeal cancer; GC: gastric cancer; LC: lung cancer; NHL: non-Hodgkin lymphoma; CMM: cutaneous malignant melanoma, HWE: Hardy–Weinberg equilibrium.

Heterogeneity and sensitivity analysis

For the overall study, statistically significant heterogeneity existed in rs568408 polymorphism (AA+GA vs. GG, P < 0.0001, I2 = 82%; AA vs. GA+GG, P = 0.001, I2 = 68%; AA vs. GG, P = 0.009, I2 = 59%; GA vs. GG, P < 0.0001, I2 = 88%; A vs. G, P < 0.0001, I2 = 70%) and rs3212227 polymorphism (AC+CC vs. AA, P < 0.0001, I2 = 72%; CC vs. AA, P < 0.0001, I2 = 65%; AC vs. AA, P < 0.0001, I2 = 66%; C vs. A, P < 0.0001, I2 = 74%). Therefore, we conducted further subgroup analyses by cancer type, ethnicity and source of control (Supplementary Table 1). The sequential leave-one-out sensitivity analysis was adopted to evaluate each individual study’s influence on the overall risk estimates. However, the results proved there was no single study could skew the pooled ORs significantly, indicating the credibility and reliability of this meta-analysis (shown in Figure 5).

Figure 5:

Figure 5: Sensitivity analysis of the associations between IL12 polymorphisms and cancer risk (A) rs568408; (B) rs2243115; (C) rs3212227).

Publication bias

Publication bias of the eligible studies of these IL-12 polymorphisms was accessed by funnel plot and Egger’s test. The symmetrical shapes of the funnel plots (Figure 6) and further statistical evidence provided by Egger’s test (Table 3) showed the absence of publication bias (P > 0.05).

Figure 6:

Figure 6: Funnel plots of publication bias (A) rs568408; (B) rs2243115; (C) rs3212227).

Table 3: Egger’s test for publication bias test of IL-12 polymorphisms

Egger’s test

Coefficient

SE

t

P > |t|

95%CI

rs568408

slope

0.552139

0.228511

2.42

0.046

0.0117964–1.092481

bias

–3.03665

1.803099

–1.68

0.136

–7.300299–1.227003

rs2243115

slope

–0.20484

0.284498

–0.72

0.495

–0.877572–0.467887

bias

1.303655

1.599617

0.81

0.442

–2.837692–6.454866

rs3212227

slope

–0.0187827

0.1555829

–0.12

0.905

–3380125–0.300447

bias

1.225427

0.9578139

1.28

0.212

–7398447–3.190699

SE: standard error; 95%CI: 95% confidence interval.

DISCUSSION

Attracted to the effects of IL-12 polymorphisms in the onset risk of cancer, many researchers performed numerous molecular epidemiological studies to explore the connection. Regrettably, they were failed to achieve a coincident conclusion. Existing epidemiological studies showed a higher interest on the SNP of rs3212227 in IL-12B gene, but put less focus on the rs568408 and rs2243115 SNPs of IL-12A genes. Only one meta-analysis (including eighteen studies) focused on IL-12A polymorphisms and suggested that both rs568408 and rs2243115 polymorphisms of IL-12A did not connect with cancer susceptibility [29]. In our meta-analysis, we observed that rs568408 polymorphism increased cervical cancer risk and overall cancer risk among Caucasians, while rs2243115 polymorphism may be a high risk factor of brain tumor. And, the mutant C allele of rs3212227 polymorphism was proved to contribute to overall cancer’s development, especially for hepatocellular carcinoma, nasopharyngeal cancer and among Asians.

What we found in this meta-analysis is contrary to the previous meta-analyses [29, 30], our study proved no association between cervical cancer and rs3212227 polymorphism, while rs568408 and rs2243115 polymorphisms were associated with cervical cancer and brain tumor respectively. Compared to the previous meta-analyses, this study included more eligible studies, conducted more detailed subgroup analyses, and also performed stringent quality control during data statistics. Thus, our results are more comprehensive and persuasive. In the past years, we considered only IL-12B polymorphism played an important role in cancer development and looked down on the function of IL-12A. This meta-analysis indicated that we should perform more comprehensive investigation both on the respective function and interaction of IL-12A and IL-12B.

As is depicted in introduction, IL-12 is a multiple effective cytokine that links the innate (NK cells) and acquirs (cytotoxic T lymphocytes) immune response [7]. Structurally, IL-12 is a heterodimer consisted of a 35 kD α-chain (p35 subunit) and a 40 kD β-chain (p40 subunit), the latter was shared with IL-23, another important member from IL-12 family. P35 and p40 are encoded by IL-12 A and IL-12 B gene, respectively. And, IL-12 p40 is produced in excess over the IL-12 and IL-23 heterodimers in average level [31]. We guess the amount of activated IL-12B gene was much more than activated IL-12A gene, thus we observed more mutation frequency in IL-12B rs3212227. In fact, both IL-12A and IL-12B gene have played a role in the development of cancer. Therefore, we speculated IL-12 polymorphisms may alter the IL-12 gene expression, decrease functional IL-12 protein synthesis, which ultimately brought about immune system dysfunction and lead to the development of malignant tumors. The clinical trials of IL-12 were rather disappointing, because systemic administration in cancer treatment was observed defined anti-tumor efficacy and severe toxic effects. Thus, it was now vigorously promoted to develop the gene therapy vectors which can express activated IL-12 locally in tumors. New avenues for IL-12 in cancer have been opened with technical advances in recent years, which are currently under research [32]. Our meta-analysis provided a direction to the further vivo and in vitro experiments.

Some possible limitations in this study need to be attended. Firstly, some potential source of heterogeneity, such as sex and environmental exposures, may have influence on the final results of the present study. Secondly, environmental factors were failed to evaluate, because lack of relevant data, such as family history, age, lifestyle, which might alter the real relation of IL-12 polymorphisms and the cancer risk. Thirdly, studies written in a third language were excluded (not in English or Chinese), thus, selection bias is inevitable. Fourthly, although we pooled all eligible publications together in our study, the sample size for some types of cancer were still small. For instance, there was only one or two literature available for cervical cancer. Thus, we should be cautious to explain our results. Finally, publication bias may exist in our meta-analysis because we could not gain the unpublished data.

In conclusion, this meta-analysis draws a reliable conclusion that these IL-12 polymorphisms (rs568408, rs2243115 and rs3212227) might be high risk factors for cancer, which indicated IL-12 may serve as a promising marker for cancer screening. Further well-designed multi-center studies with larger sample sizes (especially for rs568408 and rs2243115 polymorphisms of IL-12A) and detailed gene-environmental interactions are warranted to confirm our findings.

MATERIALS AND METHODS

We followed the PRISMA statement to guide the process of this meta-analysis [33]

Search strategy

We obtained eligible studies prior to October 2016 from the PubMed, web of knowledge, Embase, WanFang, VIP and CNKI databases, using the terms: ‘‘cancer or carcinoma or tumor or neoplasm’’ and “polymorphism or variant or variation” and ‘‘interleukin-12 or IL-12’’. In addition, we manually screened the references in the relevant reviews to ensure all relevant studies were not missed. Studies elected in this meta-analysis met the criteria as follows: (1) case-control studies evaluated the association of IL-12A rs568408, IL-12A rs2243115, or IL-12B rs3212227 polymorphisms with various cancer risks; (2) genotype distributions were sufficient for both cases and controls for data extraction; (3) published in English or Chinese. And we adopted following exclusion criteria: (1) repeat of previous studies, reviews, or abstracts; (2) not case-control design; (3) absence of detailed genotyping data; (4) duplication of previous data. If the overlap of data appeared in different publications, the paper included more samples were chosen.

Data extraction and quality assessment

Two investigators independently elected for following information from the all eligible studies: first author, publication year, cancer type, country, ethnicity, source of controls, genotyping platform, total number of cases and controls, genetic distribution among cases and controls, and Hardy–Weinberg equilibrium (HWE) P value of controls. The subgroup analyses were performed according to cancer type, ethnicity (Asians, Caucasians), and source of controls. All patients in case group were confirmed by histology or pathology. The Newcastle-Ottawa Scale (NOS) was used to assess the quality of the eligible studies [34], The total scores ranged from 0 to 10, with higher scores indicating better quality. Score of 0–5, 6-9 was considered as indicative of a low-, high-quality study, respectively. To resolve all disagreements of two investigators, a discussion with a senior investigator was conducted until a consensus was reached.

Statistical analysis

To evaluate the cancer risk associated IL-12 polymorphisms (rs568408, rs2243115, rs3212227), we calculated ORs and 95% CI based on the genotypes in cases and controls. Five different genetic models were evaluated: (i) BB+ AB vs. AA (dominant model), (ii) BB vs. AA (homozygote model), (iii) AB vs. AA (heterozygote model), (iv) BB vs. AA+ AB (recessive model), (v) B vs. A (allele comparison). “A” represents the wild allele, while “B” represents the mutation allele. Heterogeneity among the studies was checked by Chi-square-based Q statistic and I2 test, and it was regarded as homogeneous if P > 0.10 in Chi-square-based Q statistic. When the effects were assumed to be homogeneous, we used the fixed-effects model to assess the pooled OR. Otherwise, the random-effects model was chosen. Furthermore, source of heterogeneity among studies was explored by subgroup analyses base on cancer type, ethnicity and source of control. We adopted Begg’s and Egger’s test to evaluate the possible sources of bias. Sensitivity analysis was chiefly performed by orderly removing individual study and rechecked the pooled ORs to assess the stability of the final results. All statistical analyses in this study were performed by software STATA (Version 14.0; Stata Corp, College Station, TX). A statistical significance was considered if P < 0.05 and all the statistics were two-sided.

ACKNOWLEDGMENTS AND FUNDING

This work was supported by the National Social Science Foundation of China (16BGL183), the Natural Science Foundation of Shaanxi Province (2015JM8415) and the Fundamental Research Funds for the Central Universities of China (2011jdhz55).

CONFLICTS OF INTEREST

We declare that we have no conflicts of interest.

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