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BMC Medical Genetics

, 20:186 | Cite as

Association between lncRNA H19 rs217727 polymorphism and the risk of cancer: an updated meta-analysis

  • Xue Wang
  • Jialing Zhong
  • Fang Chen
  • Kang Hu
  • Suhong Sun
  • Yuanxiu Leng
  • Xumei Chen
  • Fengjiao Gan
  • Yana Pan
  • Qing LuoEmail author
Open Access
Research article
  • 167 Downloads
Part of the following topical collections:
  1. Genetic epidemiology and genetic associations

Abstract

Background

We have performed this study to evaluate the association between H19 rs217727 polymorphism and the risk of cancer.

Methods

An odds ratio (OR) with a 95% confidence interval (CI) was applied to determine a potential association.

Results

A total of 17 case–control publications were selected. This meta-analysis showed that H19 rs217727 has a significant increased association with cancer risk in allelic, homozygous, heterozygote, dominant and recessive models (T vs C: OR = 1.16, 95% CI = 1.06–1.27, I2 = 75.7; TT vs CC: OR = 1.29, 95% CI = 1.06–1.56, I2 = 71.6; CT vs CC: OR = 1.15, 95% CI = 1.01–1.31, I2 = 75.4; CT + TT vs CC: OR = 1.20, 95% CI = 1.05–1.36, I2 = 76.5; TT vs CT + CC: OR = 1.22, 95% CI = 1.02–1.45, I2 = 70.6;). In the subgroup analysis of smoking status, both smokers and nonsmokers showed an increase in cancer risk in allelic, homozygous, dominant and heterozygote models.

Conclusion

This meta-analysis revealed H19 rs217727 may influence cancer susceptibility.

Keywords

Cancer risk H19 rs217727 Polymorphism Meta-analysis 

Abbreviations

CI

Confidence interval

EBV

Epstein-Barr virus

GWAS

Genome-wide association studies

HOTAIR

HOX transcript antisense RNA

HPV

Human papillomavirus

HWE

Hardy-Weinberg Equilibrium

IGF2

Insulin-like growth factor 2

LncRNA

Long non-coding RNA

NOS

Newcastle Ottawa Scale

OR

Odds ratio

PRISMA

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

SNP

Single nucleotide polymorphism

Background

Cancer has become a major public health problem and gives the second leading cause of death after cardiovascular and cerebrovascular disease. Therefore, identification of modifiable risk factors to slow cancer progression is crucial. Environmental factors, smoking [1], alcohol consumption [2], human papillomavirus (HPV) [3], and the Epstein-Barr virus (EBV) [4] was known to play a key role in the pathogenesis and tumorigenesis. In addition, single nucleotide polymorphisms (SNPs) were recognized to be associated with cancer development too. For example, CpG rs1190983, rs155247, and rs62382272 play an important role in oncogenesis in breast cancer [5], and the rs874945 in HOX transcript antisense RNA (HOTAIR) gene increases the risk of bladder cancer in Chinese population [6].

H19 (Gene ID: 283120) is an imprinted gene, located on chromosome 11p15.5, close to the insulin-like growth factor 2 (IGF2) gene, which has 6 exons and can produce long non-coding RNA (lncRNA) with a length of 2326 bp. H19 is mainly involved in the development of the embryo, showing high expression in the fetus, rapidly down-regulated after birth, and only continuously expressed in the heart and skeletal muscle in adults. However, H19 was found to be highly expressed in a variety of cancers. Previous studies have demonstrated that increased levels of H19 contributes to melanoma development and progression [7]. In addition, the introduction of the genome-wide association studies (GWAS) allowed for identification of an increased number of H19 SNPs that were associated with various types of cancer. For instance, H19 rs217727 has been reported to significantly increase the risk of gastric cancer [8], and colorectal cancer [9]. In addition, a large number of studies have found that H19 lncRNA tag SNPs (rs217727, rs2839698, rs3741216, rs3741219, rs2107425, rs3024270, rs2735971, rs2071095) are related to the susceptibility of cervical cancer [10], breast cancer [11, 12, 13, 14, 15], bladder cancer [16, 17, 18], gastric cancer [8], lung cancer [19, 20], osteosarcoma [21], pancreatic cancer [22], and oral squamous cell carcinoma [23, 24]. Among them, rs217727 is located in the exon 5 of the H19 gene. Some original studies and previous meta-analyses reported the relationship between H19 rs217727 and cancer risk, but the results were inconsistent. In addition, several recently published studies provide the basis for updating data sets and more accurately evaluating the relationship between H19 rs 217,727 and cancer risk. Thus, we performed meta-analysis to explore the association between H19 polymorphisms and the risk of cancer.

Methods

For this meta-analysis study, patient consent and ethical approval was not required. We performed this meta-analysis as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [25]. Two independent investigators participated in study selection and data extraction, and any disagreement was solved by discussion and reinterpretation of the data involved.

Selection and exclusion criteria

The eligibility criteria were as follows: (1) case-control studies, in which the relation between H19 rs217727 polymorphism and the risk of cancer was evaluated; (2) 2 or more studies focused on H19 rs217727 polymorphism; (3) the genotype frequency was reported; (4) published as a full-text manuscript in the English language. We excluded meta-analysis, reviews, as well as the articles lack of healthy controls, or polymorphism type not detected.

Literature and research strategy

We searched the databases Embase, PubMed, and Web of Science up to January 06, 2019 using the keywords “H19 OR long noncoding RNA H19” AND “cancer OR tumor OR neoplasm” AND “mutation OR variant OR polymorphism”. Studies related to the association of H19 rs217727 polymorphism and cancer risk were obtained. In addition, references and meta-analyses of the studies included were searched manually. The search strategy in PubMed are shown in Additional file 1.

Data extraction and synthesis

Data was extracted and listed on the predesigned data extraction sheet included first author, publication year, country, ethnicity (Asian or Caucasian), source of control, type of cancer, type of polymorphism, number and genotyping distribution of cases and controls, genotyping method, smoking status and P-value of Hardy-Weinberg Equilibrium (HWE) in controls [26]. Authors involved were contacted and asked for data usage, when necessary.

Quality assessment

The quality of the included studies was evaluated by two independent investigators according to the Newcastle Ottawa Scale (NOS) [27]. The points were awarded on selection (case definition adequate, representativeness of the cases, selection of controls, definitions of controls), comparability (comparability of cases and controls on the basis of the design or analysis) and exposure (ascertainment of exposure, uniform method of ascertainment, nonresponse rate) and the total score ranged from 0 to 9. Study with a score of more than 5 was included in the meta-analysis.

Data analysis

We used the OR and 95% CI to present the strength of the association using an allelic model (T vs. C), homozygote model (TT vs. CC), heterozygote model (CT vs. CC), dominant model [(CT + TT) vs. CC] and recessive model [TT vs. (CT + CC)]. Meta-analysis was conducted if 2 or more studies were performed for the same type of polymorphism. Initially, heterogeneity was evaluated by the Chi square-based Q-test, and I2 statistics. A value of P ≥ 0.1 and I2 ≤ 50% indicated that heterogeneity was absent, and the fixed-effect model was used. In other occasions, the random-effect model was used. Moreover, subgroup analyses were conducted based on ethnicity, type of cancer, source of controls, sample size, genotyping approach and smoking status. Evaluation of any publication bias was performed by Begg’s and Egger’s tests, when P < 0.1, publication bias was considered to exist. Sensitive analysis was performed by elimination of each study to observe the effect of a single study on the pooled OR. Statistical analysis was performed using Stata software version 12.0 (Stata Corporation, College Station, TX, USA).

Results

Study identification

In this meta-analysis, a total of 17 case–control publications [8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 21, 22, 23, 24], including 9166 cancer patients and 10,823 healthy controls were selected. A summary of data retrieval and selection is summarized in Fig. 1.
Fig. 1

Study selection flowchart

Characteristics and quality of the study

In these 17 studies, 8 types of cancer were studied, including gastric cancer, breast cancer, lung cancer, bladder cancer, osteosarcoma, cervical cancer, oral squamous cancer, and digestive system tumors. Eight of the studies focused on general population and 9 on hospital data. All studies were performed in Asians, except one in Caucasians. The summary characteristics are described in Table 1. In addition, the relationship between smoking status and genetic polymorphism has been reported in only 4 studies [8, 17, 23, 24], and the summary characteristics are described in Table 2.
Table 1

Characteristics of included studies in the meta-analysis (rs217727 C>T)

Author

Year

Country

Ethnicity

Sample size (case/control)

Source of control

Cancer site and type

Genotype distribution

Genotyping method

P for HWE

Case

Control

CC

CT

TT

CC

CT

TT

Jin [10]

2016

China

Asian

246/284

PB

cervical cancer

117

103

26

169

99

16

MassArray

0.74

Li [9]

2016

China

Asian

1147/1203

PB

colorectal cancer

480

514

153

456

570

177

TaqMan

0.959

Xia [11]

2016

China

Asian

464/467

PB

breast cancer

160

156

148

139

212

116

CRS-RFLP

0.052

Hua [17]

2016

China

Asian

1046/1394

HB

bladder cancer

431

467

148

573

665

156

TaqMan

0.074

Yang [8]

2015

China

Asian

500/500

HB

gastric cancer

160

252

88

193

244

63

TaqMan

0.296

Verhaegh [16]

2008

Netherlands

Caucasian

177/204

PB

bladder cancer

114

59

4

115

80

9

PCR-RFLP

0.288

Hu [22]

2017

China

Asian

416/416

HB

pancreatic cancer

133

200

83

128

196

92

TaqMan

0.302

Guo [23]

2017

China

Asian

362/740

PB

oral squamous cell carcinoma

101

181

80

255

348

137

BeadChip

0.342

Lin [12]

2017

China

Asian

1005/1020

HB

breast cancer

403

471

131

465

450

105

SNPscan

0.801

He [21]

2017

China

Asian

193/383

HB

osteosarcoma

79

102

12

195

165

23

TaqMan

0.121

Hassanzarei [13]

2017

Iranian

Asian

230/240

PB

breast cancer

71

132

27

125

113

2

PCR-RFLP

0

Li [18]

2018

China

Asian

200/200

HB

bladder cancer

51

140

9

84

90

26

TaqMan

0.806

Yuan [24]

2018

China

Asian

431/984

PB

oral squamous cell carcinoma

186

194

51

488

423

73

MassArray

0.151

Cui [14]

2018

China

Asian

1488/1675

PB

breast cancer

611

692

185

685

773

217

TaqMan

0.963

Li [19]

2018

China

Asian

555/618

HB

lung cancer

210

250

95

246

305

67

TaqMan

0.053

Abdollahzadeh [15]

2018

Iranian

Asian

150/100

HB

breast cancer

116

29

5

86

14

0

PCR-RFLP

0.452

Yin [20]

2018

China

Asian

556/395

HB

lung cancer

204

264

88

165

172

58

TaqMan

0.232

Table 2

Smoking status: characteristics of studies included in the meta-analysis

Author

Year

Cancer site and type

Smokers

Nonsmokers

Case

Control

Case

Control

CC

CT

TT

CC

CT

TT

CC

CT

TT

CC

CT

TT

Hua [17]

2016

bladder cancer

187

308

73

250

229

52

219

257

75

368

391

104

Yang [8]

2015

gastric cancer

44

60

20

49

68

24

116

186

74

144

167

48

Guo [23]

2017

oral squamous cell carcinoma

35

75

30

81

131

49

66

106

50

174

217

88

Yuan [24]

2018

oral squamous cell carcinoma

79

76

18

179

138

26

107

118

33

309

285

47

Yin [20]

2018

lung cancer

0

0

0

0

0

0

204

264

88

165

172

58

Quality assessment

According to the NOS, detailed quality assessment for each study included are presented in Table 3, the score of each included study is more than 7 points, higher scores were associated with lower risks of bias. The percentage of quality assessment is presented in Fig. 2.
Table 3

Quality score assessment

Studies

Selection

Comparability

Exposure

Total

Case definition adequate

Representativeness of the cases

Selection of controls

Definition of controls

Comparability of cases and controls on the basis of the design or analysis

Ascertainment of exposure

Uniform method of ascertainment

Nonresponserate

Jin [10]

*

*

*

*

**

*

*

*

9

Li [9]

*

*

*

*

**

*

*

*

9

Xia [11]

*

*

*

*

**

*

*

*

9

Hua [17]

*

*

0

*

**

*

*

0

7

Yang [8]

*

*

0

*

**

*

*

*

8

Verhaegh [16]

*

*

*

*

**

*

*

*

9

Hu [22]

*

*

0

*

**

*

*

*

8

Guo [23]

*

*

*

*

**

*

*

0

8

Lin [12]

*

*

0

*

*

*

*

*

7

He2 [1]

*

*

0

*

**

*

*

*

8

Hassanzarei [13]

*

*

*

*

0

*

*

*

7

Li [18]

*

*

0

*

**

*

*

*

8

Yuan [24]

*

*

*

*

**

*

*

0

8

Cui [14]

*

*

*

*

*

*

*

0

7

Li [19]

*

*

0

*

**

*

*

*

8

Abdollahzadeh [15]

*

*

0

*

**

*

*

*

8

Yin [20]

*

*

0

*

**

*

*

*

8

*indicates a score of 1, **indicates a score of 2. The total score ranged from 0 to 9

Fig. 2

Graph of quality assessments

Statistical analysis

As shown in Table 4, H19 rs217727 was found to increase cancer risk in overall analysis under T vs C (OR = 1.16, 95% CI = 1.06–1.27, I2 = 75.7), TT vs CC (OR = 1.29, 95% CI = 1.06–1.56, I2 = 71.6), CT vs CC (OR = 1.15, 95% CI = 1.01–1.31, I2 = 75.4), CT + TT vs CC (OR = 1.20, 95% CI = 1.05–1.36, I2 = 76.5), TT vs CT + CC (OR = 1.22, 95% CI = 1.02–1.45, I2 = 70.6). When stratifying data by ethnicity, genotyping approach and type of cancer, the allelic, homozygote, heterozygote, dominant and recessive models of rs217727 were observed to increase cancer risk based on Asians (T vs C: OR = 1.18, 95% CI = 1.08–1.29, I2 = 75.3, TT vs CC: OR = 1.32, 95% CI = 1.09–1.59, I2 = 72.1, CT vs CC: OR = 1.18, 95% CI = 1.03–1.34, I2 = 75.9, CT + TT vs CC: OR = 1.23, 95% CI = 1.08–1.39, I2 = 76.4, TT vs CT + CC: OR = 1.24, 95% CI = 1.04–1.47, I2 = 71.4), subgroups for genotyping based on MassArray (T vs C: OR = 1.36, 95% CI = 1.16–1.60, I2 = 13.8, TT vs CC: OR = 1.96, 95% CI = 1.39–2.75, I2 = 0, CT vs CC: OR = 1.29, 95% CI = 1.05–1.57, I2 = 0.4, CT + TT vs CC: OR = 1.39, 95% CI = 1.14–1.71, I2 = 10.9, TT vs CT + CC: OR = 1.75, 95% CI = 1.26–2.42, I2 = 0) and oral squamous cell carcinoma (T vs C: OR = 1.26, 95% CI = 1.11–1.42, I2 = 0, TT vs CC: OR = 1.63, 95% CI = 1.25–2.12, I2 = 0, CT vs CC: OR = 1.25, 95% CI = 1.04–1.50, I2 = 0, CT + TT vs CC: OR = 1.32, 95% CI = 1.11–1.57, I2 = 0, TT vs CT + CC: OR = 1.42, 95% CI = 1.07–1.88, I2 = 28.1). H19 rs217727 significantly increased the risk of lung cancer in the allelic, homozygote models (T vs C: OR = 1.17, 95% CI = 1.03–1.33, I2 = 0, TT vs CC: OR = 1.44, 95% CI = 1.07–1.94, I2 = 19.4), as well as breast cancer in the allelic model (T vs C: OR = 1.29, 95% CI = 1.02–1.62, I2 = 86.8). We also conducted subgroup analysis by source of controls and sample size, the pooled results showed that the allelic, homozygote, heterozygote and dominant model of rs217727 have a positive association with cancer risk in hospital-based controls, as shown in Fig. 3 (T vs C: OR = 1.15, 95% CI = 1.07–1.24, I2 = 29.6, TT vs CC: OR = 1.29, 95% CI = 1.07–1.55, I2 = 41.4, CT vs CC: OR = 1.21, 95% CI = 1.03–1.45, I2 = 68.5, CT + TT vs CC: OR = 1.23, 95% CI = 1.07–1.42, I2 = 57.4); Similarly, a positive relation was observed between the allelic, homozygous, dominant and recessive models and the risk of cancer when the case sample size ≥500 (T vs C: OR = 1.13, 95% CI = 1.04–1.22, I2 = 67.1, TT vs CC: OR = 1.27, 95% CI = 1.08–1.49, I2 = 63.6, CT + TT vs CC: OR = 1.13, 95% CI = 1.01–1.25, I2 = 66.4, TT vs CT + CC: OR = 1.25, 95% CI = 1.08–1.41, I2 = 56.4). As shown in Table 5, when stratifying data by smoking status, all the genetic models of rs217727 have a positive association with cancer risk in smokers, as well as in nonsmokers except in recessive model.
Table 4

Overall and subgroups meta-analysis of H19 rs217727 (C > T) polymorphism and cancer risk

Overall and subgroups

NO.

T versus C

TT versus CC

CT versus CC

CT + TT versus CC

TT versus CT + CC

OR (95% CI)

PQ

I2(%)

OR (95% CI)

PQ

I2(%)

OR (95% CI)

PQ

I2(%)

OR (95% CI)

PQ

I2(%)

OR (95% CI)

PQ

I2(%)

Total

17

1.16 (1.06, 1.27)

0

75.7

1.29 (1.06, 1.56)

0

71.6

1.15 (1.01, 1.31)

0

75.4

1.20 (1.05, 1.36)

0

76.5

1.22 (1.02, 1.45)

0

70.6

Ethnicity

 Asians

16

1.18 (1.08, 1.29)

0

75.3

1.32 (1.09, 1.59)

0

72.1

1.18 (1.03, 1.34)

0

75.9

1.23 (1.08, 1.39)

0

76.4

1.24 (1.04, 1.47)

0

71.4

 Caucasians

1

0.74 (0.52, 1.05)

NA

NA

0.45 (0.13, 1.50)

NA

NA

O.74 (0.49, 1.14)

NA

NA

0.71 (0.47, 1.08)

NA

NA

0.50 (0.15, 1.66)

NA

NA

Method

 TaqMan

9

1.07 (0.98, 1.17)

0.01

60.6

1.12 (0.92, 1.36)

0.01

63.2

1.12 (0.96, 1.31)

0

74.1

1.12 (0.98, 1.29)

0

72.6

1.06 (0.87, 1.31)

0

70.8

 MassArray

2

1.36 (1.16, 1.60)

0.28

13.8

1.96 (1.39, 2.75)

0.53

0

1.29 (1.05, 1.57)

0.32

0.4

1.39 (1.14, 1.71)

0.29

10.9

1.75 (1.26, 2.42)

0.66

0

 PCR-RFLP

3

1.44 (0.68, 3.05)

0

90.9

4.14 (0.21, 80.14)

0

89

1.32 (0.66, 2.64)

0

83.7

1.45 (0.63, 3.35)

0

89.4

3.60 (0.26, 49.72)

0

86.2

 Others

3

1.17 (1.07, 1.28)

0.4

0

1.33 (1.11, 1.61)

0.42

0

1.01 (0.68, 1.51)

0

86

1.12 (0.84, 1.49)

0.01

77

1.33 (1.12, 1.57)

0.83

0

Cancer type

 Breast cancer

5

1.29 (1.02, 1.62)

0

86.8

1.56 (0.95, 2.56)

0

83

1.15 (0.84, 1.55)

0

84.2

1.27 (0.94, 1.71)

0

85.7

1.48 (0.98, 2.26)

0

80.3

 Bladder cancer

3

1.01 (0.82, 1.25)

0.1

56.8

0.80 (0.40, 1.61)

0.06

64

1.20 (0.64, 2.23)

0

90.1

1.13 (0.68, 1.88)

0

85.9

0.63 (0.22, 1.80)

0

85.1

 Digestive system cancera

3

1.02 (0.82, 1.26)

0.04

81.6

1.05 (0.68, 1.62)

0.01

79.8

1.00 (0.79, 1.26)

0.08

61

1.01 (0.77, 1.34)

0.02

76.1

1.03 (0.76, 1.41)

0.04

68.5

 Osteosarcoma

1

1.27 (0.98, 1.66)

NA

NA

1.29 (0.61, 2.71)

NA

NA

1.53 (1.07, 2.19)

NA

NA

1.50 (1.05, 2.12)

NA

NA

1.04 (0.50, 2.13)

NA

NA

 Cervical cancer

1

1.53 (1.17, 2.02)

NA

NA

2.35 (1.21, 4.57)

NA

NA

1.50 (1.05, 2.16)

NA

NA

1.62 (1.15, 2.29)

NA

NA

1.98 (1.04, 3.78)

NA

NA

 Oral squamous cell carcinoma

2

1.26 (1.11, 1.42)

0.72

0

1.63 (1.25, 2.12)

0.42

0

1.25 (1.04, 1.50)

0.65

0

1.32 (1.11, 1.57)

0.8

0

1.42 (1.07, 1.88)

0.24

28.1

 Lung cancer

2

1.17 (1.03, 1.33)

0.73

0

1.44 (1.07, 1.94)

0.27

19.4

1.08 (0.84, 1.39)

0.18

44.4

1.15 (0.97, 1.37)

0.47

0

1.37 (0.89, 2.11)

0.08

67.6

Source ofcontrols

 Population-based

8

1.16 (0.98, 1.38)

0

86.5

1.36 (0.96, 1.93)

0

82.4

1.08 (0.87, 1.33)

0

80.9

1.15 (0.92, 1.43)

0

84.7

1.30 (0.98, 1.73)

0

77.4

 Hospital-based

9

1.15 (1.07, 1.24)

0.18

29.6

1.29 (1.07, 1.55)

0.09

41.4

1.21 (1.03, 1.45)

0

68.5

1.23 (1.07, 1.42)

0.02

57.4

1.16 (0.93, 1.46)

0

64.7

Case sample size

 ≥ 500

13

1.13 (1.04, 1.22)

0

67.1

1.27 (1.08, 1.49)

0

63.6

1.08 (0.96, 1.20)

0

65.2

1.13 (1.01, 1.25)

0

66.4

1.25 (1.08, 1.41)

0.01

56.4

 <  500

4

1.36 (0.83, 2.23)

0

87.1

2.29 (0.31, 16.97)

0

88.2

1.57 (0.88, 2.80)

0

83.9

1.60 (0.87, 2.92)

0

85.8

1.77 (0.23, 13.44)

0

89

aIncluding colorectal cancer, gastric cancer and pancreatic cancer

Fig. 3

Forest plots for H19 rs217727 polymorphism associated with risk of cancer in subgroup analysis under hospital-based controls. a Allelic model (T vs. C), b homozygote model (TT vs. CC). c Heterozygote model (CT vs. CC). d Dominant model [(CT + TT) vs. CC]

Table 5

Smoking status: Meta-analysis of the association between the H19 rs217727 polymorphism and cancer risk

Smoking status

NO.

T versus C

TT versus CC

CT versus CC

CT + TT versus CC

TT versus CT + CC

OR (95% CI)

PQ

I2(%)

OR (95% CI)

PQ

I2(%)

OR (95% CI)

PQ

I2(%)

OR (95% CI)

PQ

I2(%)

OR (95% CI)

PQ

I2(%)

smokers

4

1.29 (1.14, 1.46)

0.19

37.9

1.55 (1.17, 2.03)

0.41

0

1.48 (1.23, 1.77)

0.14

44.8

1.49 (1.26, 1.78)

0.11

49.8

1.25 (0.97, 1.61)

0.77

0

nonsmokers

5

1.21 (1.11, 1.32)

0.41

0

1.46 (1.22, 1.76)

0.28

21.1

1.21 (1.07, 1.38)

0.84

0

1.27 (1.12, 1.43)

0.64

0

1.31 (1.10, 1.55)

0.33

13.4

Heterogeneity analysis

In this meta-analysis, heterogeneity was observed, we next performed the stratified analysis to evaluate the source of the heterogeneity. The heterogeneity decreased significantly or disappeared in genotyping approach of MassArray (T vs C:P = 0.28, I2 = 13.8, TT vs CC: P = 0.53, I2 = 0, CT vs CC: P = 0.32, I2 = 0.4, CT + TT vs CC: P = 0.29, I2 = 10.9, TT vs CT + CC: P = 0.66, I2 = 0), oral squamous cell carcinoma (T vs C:P = 0.72, I2 = 0, TT vs CC: P = 0.42, I2 = 0, CT vs CC: P = 0.65, I2 = 0, CT + TT vs CC: P = 0.8, I2 = 0, TT vs CT + CC: P = 0.24, I2 = 28.1) and lung cancer (T vs C:P = 0.73, I2 = 0, TT vs CC: P = 0.27, I2 = 19.4, CT vs CC: P = 0.18, I2 = 44.4). Furthermore, analyses of control subjects demonstrated that heterogeneity was significantly reduced in hospital-based controls in allelic models (T vs C: P = 0.18, I2 = 29.6). Nevertheless, heterogeneity was still present in other subgroups. In Table 4, an overview of all analyses is presented.

Sensitivity analysis and publication bias

Sensitivity analysis was performed by omitting each and every included studies. As shown in Fig. 4, the results indicated that the pooled ORs were not subjective to change, which indicated the stability of our study. To assess the publication bias for the studies, both the Egger’s test and Begg’s funnel plot were performed. Publication bias was found in allelic model (P = 0.04), heterozygote model (P = 0.05), dominant model (P = 0.03). Trim and fill method was used to identify and correct the publication bias. Before and after the trim, ORs does not change, which indicates that despite the publication bias in this study, the publication bias has little impact, and the research results are robust and reliable. The trim and fill method’s funnel plot is shown in Fig. 5.
Fig. 4

Sensitivity analysis through deletion of one study at a time to reflect the influence of the individual dataset to the pooled ORs in H19 rs217727 polymorphism. a Allelic model (T vs. C), b homozygote model (TT vs. CC). c heterozygote model (CT vs. CC). d Dominant model [(CT + TT) vs. CC]

Fig. 5

Trim and fill method’s funnel plot of the association between H19 rs217727 polymorphism and cancer risk. a Allelic model (T vs. C), b dominant model [(CT + TT) vs. CC]

Discussion

In recent years, many studies have focused on the relationship between genotype and phenotype, and the personalized prevention and treatment of cancer based on genetic information is the current research trend and hotspot [28]. SNP is the most common type of gene polymorphism, which may affect gene expression and function through indirect influence of related transcription factors or microRNAs, and further participate in the occurrence and development of tumors. LncRNA H19 has been widely recognized for its aberrant expression profile and role in carcinogenesis, and it is suggested to be a novel biomarker for the diagnosis of cancer [29, 30]. In addition, numerous studies have focused on the relation between H19 SNPs and cancer susceptibility. A study conducted by Yang et al. revealed that the TT + CT genotype of rs2839698 could increase the risk of hepatocellular cancer [31]. In terms of H19 rs217727, it was found to increase the risk of breast cancer [12, 13, 15]. Further functional experiments found that the expression level of H19 in breast cancer tissues was higher than that in normal tissues, and rs217727 CT or TT genotype was helpful to improve the expression level of H19 (P<0.001, 12]. However, no significant correlation was found in the study conducted by Xia et al. [11]. Furthermore, a study [17] included 1049 cancer cases and 1399 controls, showed that the AA genotype increased the risk of bladder cancer up to 1.31 times compared with the GG/GA genotype. Similarly, a positive relation was also found in gastric cancer [8] and cervical cancer [10]. However, in another study it was demonstrated that rs217727 did not associate with risk of colorectal cancer in additive model [9]. The results were inconsistent and inconclusive, and might be due to the limited sample size, the difference in genetic background, or the type of cancer. Therefore, in this study, we performed meta-analysis to comprehensively evaluate the association between H19 SNPs and susceptibility to cancer.

In the current meta-analysis, which included 17 case-control studies, people with the T, TT, CT and CT + TT genotypes of SNP rs217727 got a higher risk of cancer. Similarly, subgroup analysis based on ethnicity, type of cancer and genotyping method showed an increased risk for all genetic models in Asian, oral squamous cell carcinoma and genotyping approach according to MassArray. In addition, the risk of lung cancer increased in the allelic, homozygote models, and for breast cancer, the risk increased in the allelic model. The significant association was also found in allelic, homozygote, heterozygote and dominant models in the subgroup of hospital-based controls, as well as in allelic, homozygote, dominant and recessive models in the subgroup with a sample size of more than 500. Overall, the study revealed that H19 rs217727 might increase the risk of cancer. Interestingly, we also found that smoking was not significantly associated with the development of cancer in H19 rs217727.

Our results differ from those previously published [32, 33, 34, 35]. Lv et al. [32] and Li et al. [35] included 5 studies and concluded that the rs217727 C > T might not be associated with the risk of cancer. Chu et al [33] used differently 3 genetic models, and the pooled results showed that the heterozygote and dominant model of rs217727 appeared to be a protective factor to cancer in hospital-based controls, as well as in the subgroup of population-based controls. Lu’s study, which included 4 literatures, subgroup analyses only stratified by genotyping approach and failed to reveal the relationship between rs217727 C > T and cancer risk [34]. The increased sample size and newly incorporated studies in our study may explain this difference. For the relation observed in subgroup meta-analysis, but not in overall meta-analysis, there are several possibilities to explain this difference, such as differences in genetic background, and the complex process of cancer formation. Interestingly, we also found that H19 rs217727 was associated with a neoplastic predisposition, and had little to do with smoking.

Our meta-analysis has several limitations, which should be addressed. First, despite the comprehensive analysis that has been performed to determine a possible relation, potential covariates (age, sex, drinking status, and smoking status) cannot be extracted from all included cases. Thus, the pooled results were based on unadjusted data. Second, the sample size of this study is still limited, which may reduce the power of analysis. Therefore, the data should be validated in a larger study. Third, only English databases were used in our search, which may affect our results. If literatures of other languages were included in this study, it would be possible that additional estimations could have been conducted. Finally, after subgroup analyses, heterogeneity could still be observed in a variety of SNPs, therefore, our conclusions should be treated with caution.

Conclusions

LncRNA H19 rs217727 could increase cancer risk in overall population, as well as in Asians, subgroups for genotyping based on MassArray, oral squamous cell carcinoma, lung cancer, breast cancer, hospital-based controls and subgroups with a case sample size ≥500. Because of the limitations in our study, well-designed studies with a larger sample size, and adjusted risk factors are required to further confirm the conclusions.

Notes

Acknowledgements

Not applicable.

Authors’ contributions

QL obtained funding and designed the study. XW and JLZ performed the analysis and interpretation of the data, and wrote the manuscript. FC, KH and SHS performed the analysis and interpretation of data. YXL, XMC, FJG, YNP provided technical support for the analysis and critical revision of the manuscript. All authors have read and approved the final version of the manuscript.

Funding

1. The National Natural Science Foundation of China (No.81860469) provided ideas for the direction of the topic.

2. The University Excellent Science and Technology Innovation Talent Support Plan Fund of Guizhou Province (Qian Jiao He KY zi [2015]495) provided help for literature retrieval and literature screening.

3. The Key Technologies R&D Program of Guizhou (Qian Ke He LH zi [2016]7479, Qian Ke He LH Zi [2015]7485) and Science Foundation Project of Guizhou provincial health and family planning commission (gzwjkj2107–1-024) provided help for data extraction and synthesis.

4. The Scientific research project of Sichuan provincial health and Family Planning Commission (18PJ115) provided help for quality assessment and funds for language modification of the manuscript.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Supplementary material

12881_2019_904_MOESM1_ESM.docx (14 kb)
Additional file 1. PubMed search strategy.

References

  1. 1.
    Yan H, Ying Y, Xie H, Li J, Wang X, He L, Jin K, Tang J, Xu X, Zheng X. Secondhand smoking increases bladder cancer risk in nonsmoking population: a meta-analysis. Cancer Manag Res. 2018;10:3781–91.CrossRefGoogle Scholar
  2. 2.
    Rossi M, Jahanzaib Anwar M, Usman A, Keshavarzian A, Bishehsari F. Colorectal cancer and alcohol consumption-populations to molecules. Cancers (Basel). 2018;10(2).Google Scholar
  3. 3.
    Wei-Min X, Qiu-Ping X, Xu L, Ren-Dong X, Lin C, Fei H. The association between human papillomavirus infection and lung cancer a system review and meta-analysis. Oncotarget. 2017;8:96419.Google Scholar
  4. 4.
    Fei Q, Tian XK, Wu J, Zhu HM, Wang Y, Peng FY, Zhang WJ, Yin L, He X. Prognostic significance of Epstein-Barr virus DNA in NK/T-cell lymphoma: a meta-analysis. Onco Targets Ther. 2018;11:997–1004.CrossRefGoogle Scholar
  5. 5.
    Chen J, Jiang Y, Zhou J, Liu S, Qin N, Du J, Jin G, Hu Z, Ma H, Shen H, et al. Evaluation of CpG-SNPs in miRNA promoters and risk of breast cancer. Gene. 2018;651:1–8.CrossRefGoogle Scholar
  6. 6.
    Wang X, Wang W, Zhang Q, Gu D, Zhang K, Ge Y, Chu H, Du M, Xu B, Wang M, et al. Tagging SNPs in the HOTAIR gene are associated with bladder cancer risk in a Chinese population. Gene. 2018;664:22–6.CrossRefGoogle Scholar
  7. 7.
    Shi G, Li H, Gao F, Tan Q. lncRNA H19 predicts poor prognosis in patients with melanoma and regulates cell growth, invasion, migration and epithelial-mesenchymal transition in melanoma cells. Onco Targets Ther. 2018;11:3583–95.CrossRefGoogle Scholar
  8. 8.
    Yang C, Tang R, Ma X, Wang Y, Luo D, Xu Z, Zhu Y, Yang L. Tag SNPs in long non-coding RNA H19 contribute to susceptibility to gastric cancer in the Chinese Han population. Oncotarget. 2015;6(17):15311–20.PubMedPubMedCentralGoogle Scholar
  9. 9.
    Li S, Hua Y, Jin J, Wang H, Du M, Zhu L, Chu H, Zhang Z, Wang M. Association of genetic variants in lncRNA H19 with risk of colorectal cancer in a Chinese population. Oncotarget. 2016;7(18):25470–7.PubMedPubMedCentralGoogle Scholar
  10. 10.
    Jin T, Wu X, Yang H, Liu M, He Y, He X, Shi X, Wang F, Du S, Ma Y, et al. Association of the miR-17-5p variants with susceptibility to cervical cancer in a Chinese population. Oncotarget. 2016;7(47):76647–55.PubMedPubMedCentralGoogle Scholar
  11. 11.
    Xia Z, Yan R, Duan F, Song C, Wang P, Wang K. Genetic polymorphisms in long noncoding RNA H19 are associated with susceptibility to breast Cancer in Chinese population. Medicine. 2016;95(7):e2771.CrossRefGoogle Scholar
  12. 12.
    Lin Y, Fu F, Chen Y, Qiu W, Lin S, Yang P, Huang M, Wang C. Genetic variants in long noncoding RNA H19 contribute to the risk of breast cancer in a Southeast China Han population. Onco Targets Ther. 2017;10:4369–78.CrossRefGoogle Scholar
  13. 13.
    Hassanzarei S, Hashemi M, Sattarifard H, Hashemi SM, Bahari G. Genetic polymorphisms in long noncoding RNA H19 are associated with breast cancer susceptibility in Iranian population. Meta Gene. 2017;14:1–5.CrossRefGoogle Scholar
  14. 14.
    Cui P, Zhao YR, Chu XL, He N, Zheng H, Han JL, Song FJ, Chen KX. SNP rs2071095 in LincRNA H19 is associated with breast cancer risk. Breast Cancer Res Treat. 2018;171(1):161–71.CrossRefGoogle Scholar
  15. 15.
    Abdollahzadeh S, Ghorbian S. Association of the study between LncRNA-H19 gene polymorphisms with the risk of breast cancer. J Clin Lab Anal. 2019;33(3):e22826.Google Scholar
  16. 16.
    Verhaegh GW, Verkleij L, Vermeulen S, den Heijer M, Witjes JA, Kiemeney LA. Polymorphisms in the H19 gene and the risk of bladder Cancer. Eur Urol. 2008;54(5):1118–26.CrossRefGoogle Scholar
  17. 17.
    Hua Q, Lv X, Gu X, Chen Y, Chu H, Du M, Gong W, Wang M, Zhang Z. Genetic variants in lncRNA H19 are associated with the risk of bladder cancer in a Chinese population. Mutagenesis. 2016;31(5):531–8.CrossRefGoogle Scholar
  18. 18.
    Li Z, Niu Y. Association between lncRNA H19 (rs217727, rs2735971 and rs3024270) polymorphisms and the risk of bladder cancer in Chinese population. Minerva Urol Nefrol. 2018;71:161.PubMedGoogle Scholar
  19. 19.
    Li LL, Guo GY, Zhang HB, Zhou BS, Bai L, Chen H, Zhao YX, Yan Y. Association between H19 SNP rs217727 and lung cancer risk in a Chinese population: a case control study. BMC Med Genet. 2018;19:136.Google Scholar
  20. 20.
    Yin Z, Cui Z, Li H, Li J, Zhou B. Polymorphisms in the H19 gene and the risk of lung Cancer among female never smokers in Shenyang, China. J Clin Lab Anal. 2018;18(1):893.Google Scholar
  21. 21.
    He TD, Xu D, Sui T, Zhu JK, Wei ZX, Wang YM. Association between H19 polymorphisms and osteosarcoma risk. Eur Rev Med Pharmacol Sci. 2017;21(17):3775–80.PubMedGoogle Scholar
  22. 22.
    Hu P, Qiao O, Wang J, Li J, Jin H, Li Z, Jin Y. rs1859168 A > C polymorphism regulates HOTTIP expression and reduces risk of pancreatic cancer in a Chinese population. Cancer Med. 2017;15(1):155.Google Scholar
  23. 23.
    Guo QY, Wang H, Wang Y. LncRNA H19 polymorphisms associated with the risk of OSCC in Chinese population. Eur Rev Med Pharmacol Sci. 2017;21(17):3770–4.PubMedGoogle Scholar
  24. 24.
    Yuan Z, Yu Y, Zhang B, Miao L, Wang L, Zhao K, Ji Y, Wang R, Ma H, Chen N, et al. Genetic variants in lncRNA H19 are associated with the risk of oral squamous cell carcinoma in a Chinese population. Oncotarget. 2018;9(35):23915–22.CrossRefGoogle Scholar
  25. 25.
    Moher D, Liberati A, Tetzlaff J, Altman DG, Group P. Reprint--preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Phys Ther. 2009;89(9):873–80.PubMedGoogle Scholar
  26. 26.
    Salanti G, Amountza G, Ntzani EE, Ioannidis JP. Hardy-Weinberg equilibrium in genetic association studies: an empirical evaluation of reporting, deviations, and power. Eur J Hum Genet. 2005;13(7):840–8.CrossRefGoogle Scholar
  27. 27.
    Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol. 2010;25(9):603–5.CrossRefGoogle Scholar
  28. 28.
    Tan H. On the protective effects of gene SNPs against human Cancer. EBioMedicine. 2018;33:4–5.CrossRefGoogle Scholar
  29. 29.
    Chen S, Bu D, Ma Y, Zhu J, Chen G, Sun L, Zuo S, Li T, Pan Y, Wang X, et al. H19 Overexpression Induces Resistance to 1,25(OH)2D3 by Targeting VDR Through miR-675-5p in Colon Cancer Cells. Neoplasia (New York, NY). 2017;19(3):226–36.CrossRefGoogle Scholar
  30. 30.
    Chen JS, Wang YF, Zhang XQ, Lv JM, Li Y, Liu XX, Xu TP. H19 serves as a diagnostic biomarker and up-regulation of H19 expression contributes to poor prognosis in patients with gastric cancer. Neoplasma. 2016;63(2):223–30.PubMedGoogle Scholar
  31. 31.
    Yang ML, Huang Z, Wang Q, Chen HH, Ma SN, Wu R, Cai WS. The association of polymorphisms in lncRNA-H19 with hepatocellular cancer risk and prognosis. Biosci Rep. 2018;38(5).Google Scholar
  32. 32.
    Lv Z, Xu Q, Yuan Y. A systematic review and meta-analysis of the association between long non-coding RNA polymorphisms and cancer risk. Mutat Res. 2017;771:1–14.CrossRefGoogle Scholar
  33. 33.
    Chu MJ, Yuan WY, Wu SS, Wang ZQ, Mao LP, Tian T, Lu YH, Zhu BW, Yang Y, Wang B, et al. Quantitative assessment of polymorphisms in H19 lncRNA and cancer risk: a meta-analysis of 13,392 cases and 18,893 controls. Oncotarget. 2016;7(48):78631–9.PubMedPubMedCentralGoogle Scholar
  34. 34.
    Lu Y, Tan L, Shen N, Peng J, Wang C, Zhu Y, Wang X. Association of lncRNA H19 rs217727 polymorphism and cancer risk in the Chinese population: a meta-analysis. Oncotarget. 2016;7(37):59580–8.CrossRefGoogle Scholar
  35. 35.
    Li XF, Yin XH, Cai JW, Wang MJ, Zeng YQ, Li M, Niu YM, Shen M. Significant association between lncRNA H19 polymorphisms and cancer susceptibility: a meta-analysis. Oncotarget. 2017;8(28):45143–53.PubMedPubMedCentralGoogle Scholar

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  1. 1.Department of OncologyAffiliated Hospital of Zunyi Medical UniversityZunyiChina
  2. 2.Clinical Laboratory, Sichuan Academy of Medical Science and Sichuan Provincial People’s Hospital, School of MedicineUniversity of Electronic Science and Technology of ChinaChengduChina
  3. 3.Breast & Thyroid Disease Medical CenterAffiliated Hospital of Zunyi Medical UniversityZunyiChina
  4. 4.The people’s Hospital of Tongnan DistrictChongqingChina

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