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Meta-Analysis:

Genetic polymorphisms of IL-6 promoter in cancer susceptibility and prognosis: a meta-analysis

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Oncotarget. 2018; 9:12351-12364. https://doi.org/10.18632/oncotarget.24033

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Xingchun Peng _, Jun Shi, Wanqun Sun, Xuzhi Ruan, Yang Guo, Lunhua Zhao, Jue Wang and Bin Li

Abstract

Xingchun Peng2, Jun Shi3, Wanqun Sun1, Xuzhi Ruan2, Yang Guo2, Lunhua Zhao2, Jue Wang2 and Bin Li1

1Department of Pathology, Shanghai Xuhui Central Hospital, Zhongshan-Xuhui Hospital, Fudan University, Shanghai Clincal Center, CAS, Shanghai, 200031, P. R. China

2School of Basic Medical Sciences, Hubei University of Medicine, Shiyan, 442000, Hubei, P. R. China

3Department of Stomatology, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, Hubei, P. R. China

Correspondence to:

Bin Li, email: libin15640@163.com

Keywords: IL-6; cancer; meta-analysis; case-control studies

Received: May 11, 2017     Accepted: November 17, 2017     Published: January 05, 2018

ABSTRACT

IL-6 is critical for tumorigenesis. However, previous studies on the association of IL-6 promoter polymorphisms with predisposition to different cancer types are somewhat contradictory. Therefore, we performed this meta-analysis regarding the relationship between IL-6 promoter single nucleotide polymorphisms and cancer susceptibility and prognosis. Up to April 2017, 97 original publications were identified covering three IL-6 promoter SNPs. Our results showed statistically significant association between IL-6 promoter and cancer risk and prognosis. Subgroup analysis indicated that rs1800795 was significantly associated with increased risk of cervical cancer, colorectal cancer, breast cancer, prostate cancer, lung cancer, glioma, non-Hodgkin’s lymphoma and Hodgkin’s lymphoma but not gastric cancer and multiple myeloma. Furthermore, rs1800796 was significantly associated with increased risk of lung cancer, prostate cancer and colorectal cancer but not gastric cancer. Additionally, rs1800797 was significantly association with breast cancer, non-Hodgkin’s lymphoma, B-cell lymphoma and diffuse large B-cell lymphoma but not gastric cancer. Simultaneously, rs1800795 and rs1800796 were associated with a significantly higher risk of cancer in Asia and Caucasian, rs1800797 was associated with a significantly risk of cancer in Caucasian but not in Asia. Furthermore, IL-6 promoter polymorphisms were significantly associated with the prognosis of cancer. Considering these promising results, IL-6 promoter including rs1800795, rs1800796 and rs1800797 may be a tumor marker for cancer therapy.


Genetic polymorphisms of IL-6 promoter in cancer susceptibility and prognosis: a meta-analysis | Peng | Oncotarget

INTRODUCTION

Interleukin-6 (IL-6) is one of the most widely recognized cytokines. It can regulate immune responses and cell proliferation and differentiation [1]. IL-6 was originally studied as an inflammatory factor, which was later found to be closely related to tumorigenesis, invasion and metastasis [2]. High expression of IL-6 is associated with different cancer types, such as esophageal cancer, non-small cell lung cancer, endometrial cancer, breast cancer, prostate cancer, lung cancer, chronic lymphocytic leukemia and diffuse large B-cell lymphoma [25]. Therefore, IL-6 is closely related to tumor occurrence and development, and understanding the genetic diversity of IL-6 will be helpful for cancer risk prediction and gene therapy.

The human IL-6 gene is located on chromosome 7p21 which is identified as pro-inflammatory cytokine [6], and plays an important role in the pathogenesis of several types of cancers. The single nucleotide polymorphisms (SNPs) at the 50 flanking region of the IL-6 gene promoter (rs1800795, rs1800796 and rs1800797) can effect on IL-6 expression [79]. However, previous studies have conflicting results between IL-6 promoter (rs1800795, rs1800796 and rs1800797) and cancer susceptibility [1099] and prognosis [40, 47, 53, 57, 63, 100-106].

To confirm whether IL-6 promoter polymorphisms are related to cancer risk, we performed this meta-analysis, aiming to measure the correlation between IL-6 promoter polymorphisms and cancer susceptibility and prognosis.

RESULTS

Characteristics of published studies

A flow chart was carefully identified of the search process in Figure 1. After duplicates removed, 16843 studies were retrieved (PubMed: 16457, Embase: 18324). Finally, ninety-seven studies were chosen, and the data was extracted. Seventy-eight studies reported the association between rs1800795 and cancer risk, twenty-one studies reported the association between rs1800796 and cancer risk, seventeen studies reported the association between rs1800797 and cancer risk, and twelve studies reported the association between IL-6 promoter polymorphisms and cancer prognosis. The genotype frequencies of IL-6 promoter in controls of each study met the HWE expectation (P > 0.05). The genotype distributions of all studies are summarized in Supplementary Tables 1–6.

Meta-analysis of rs1800795 polymorphism and cancer risk

Seventy-eight studies reported the association between rs1800795 and cancer risk. Our results showed that rs1800795 was significantly associated with increased cancer risk in allelic, dominant, recessive and additive models (OR = 1.05, 95% CI: 1.01, 1.09, P = 0.007, allelic models respectively) (Table 1). Subgroup analysis indicated that rs1800795 was associated with a significantly higher risk of cancer in Asia (OR = 1.05, 95% CI: 1.01, 1.10, P = 0.003, allelic models respectively) (Table 2) and Caucasian (OR= 1.04, 95% CI: 1.02, 1.06, P < 0.001, allelic models respectively) (Table 2) in all four gene model. Meanwhile, rs1800795 was significantly associated with increased risk of cervical cancer (OR = 1.13, 95% CI: 1.05, 1.21, P = 0.004, allelic models respectively) (Table 3), colorectal cancer (OR = 1.10, 95% CI: 1.02, 1.19, P = 0.014, allelic models respectively) (Table 3), breast cancer (OR = 1.08, 95% CI: 1.01, 1.19, P = 0.013, allelic models respectively) (Table 3), prostate cancer (OR = 1.08, 95% CI: 1.03, 1.13, P = 0.005, allelic models respectively) (Table 3), lung cancer(OR = 1.08, 95% CI: 1.02, 1.15, P = 0.003, allelic models respectively) (Table 3), glioma (OR = 1.28, 95% CI: 1.13, 1.46, P < 0.001, allelic models respectively) (Table 3), non-hodgkin’s lymphoma (OR = 1.25, 95% CI: 1.01, 1.51, P = 0.049, allelic models respectively) (Table 3) and hodgkin’s lymphoma (OR = 1.22, 95% CI: 1.02, 1.45, P = 0.030, allelic models respectively) (Table 3) but not gastric cancer (OR = 0.95, 95% CI: 0.83, 1.08, P = 0.435, allelic models respectively)(Table 3) and multiple myeloma (OR = 1.06, 95% CI: 0.88, 1.30, P = 0.559, allelic models respectively) (Table 3) in all four gene model.

Table 1: Meta-analysis of IL-6 promoter polymorphisms and cancer susceptibility

Genetic model

No.of studies

Heterogeneity

OR

95% CI

P value

Model

I2

P value

rs1800795

78 (46096/56969)

G vs. C

55.5%

0.000

1.05

1.01,1.09

0.007

Random-effects model

GG+ GC vs. CC

38.4%

0.000

1.04

1.01,1.08

0.021

Fixed-effects model

GG vs. GC+CC

55.1%

0.000

1.08

1.03,1.13

0.001

Random-effects model

GG vs. GC

53.4%

0.000

1.06

1.00,1.14

0.035

Random-effects model

rs1800796

21 (9930/13080)

C vs. G

47.2%

0.008

1.11

1.04,1.18

< 0.001

Fixed-effects model

CC+ CG vs. GG

34.4%

0.059

1.12

1.02,1.21

0.029

Fixed-effects model

CC vs. CG+GG

50.6%

0.003

1.09

1.03,1.16

0.045

Random-effects model

CC vs. CG

44.5%

0.013

1.04

1.01,1.09

0.010

Fixed-effects model

rs1800797

17 (9162/12724)

G vs. A

0.0%

0.901

1.04

1.01,1.08

0.007

Fixed-effects model

GG+ GA vs. AA

0.0%

0.904

1.07

1.02,1.13

0.007

Fixed-effects model

GG vs. GA+AA

0.0%

0.493

1.06

1.03,1.09

0.004

Fixed-effects model

GG vs. GA

38.0%

0.020

1.03

1.00,1.08

0.035

Fixed-effects model

Table 2: Meta-analysis of IL-6 promoter polymorphisms and cancer risk in ethnicity

rs1800795

No.of studies

Heterogeneity

OR

95% CI

P value

Model

Ethnicity

I2

P value

Asian

3 (1090/1482)

G vs. C

75.5%

0.017

1.05

1.01,1.10

0.003

Random-effects model

GG+ GC vs. CC

47.1%

0.151

1.03

1.01,1.06

< 0.001

Fixed-effects model

GG vs. GC+CC

66.8%

0.049

1.07

1.03,1.12

< 0.001

Random-effects model

GG vs. GC

56.7%

0.100

1.06

1.01,1.12

0.002

Random-effects model

Caucasian

75 (44895/55402)

G vs. C

49.7%

0.000

1.04

1.02,1.06

< 0.001

Fixed-effects model

GG+ GC vs. CC

34.0%

0.001

1.05

1.02,1.09

0.004

Fixed-effects model

GG vs. GC+CC

51.4%

0.000

1.10

1.06,1.15

< 0.001

Random-effects model

GG vs. GC

51.6%

0.000

1.08

1.03,1.14

0.004

Random-effects model

rs1800796

Asian

12 (3574/4423)

C vs. G

52.9%

0.013

1.08

1.03,1.14

< 0.001

Random-effects model

CC+ CG vs. GG

28.3%

0.160

1.12

1.05,1.20

< 0.001

Fixed-effects model

CC vs. CG+GG

54.9%

0.009

1.15

1.06,1.25

0.009

Random-effects model

CC vs. CG

53.2%

0.012

1.06

1.02,1.11

0.018

Random-effects model

Caucasian

9 (5679/8001)

C vs. G

0.0%

0.651

1.05

1.01,1.10

0.003

Fixed-effects model

CC+ CG vs. GG

5.2%

0.392

1.05

1.02,1.09

< 0.001

Fixed-effects model

CC vs. CG+GG

15.2%

0.299

1.06

1.02,1.11

0.002

Fixed-effects model

CC vs. CG

29.9%

0.180

1.04

1.02,1.06

< 0.001

Fixed-effects model

rs1800797

Asian

2 (187/495)

G vs. A

0.0%

0.637

1.23

0.79,2.04

0.326

Fixed-effects model

GG+ GA vs. AA

0.0%

0.954

4.38

1.21,15.9

0.025

Fixed-effects model

GG vs. GA+AA

0.0%

0.589

0.86

0.48,1.34

0.405

Fixed-effects model

GG vs. GA

0.0%

0.377

0.38

0.21,0.68

0.001

Fixed-effects model

Caucasian

14 (8298/11573)

G vs. A

0.0%

0.806

1.04

1.01,1.08

0.041

Fixed-effects model

GG+ GA vs. AA

0.0%

0.900

1.06

1.02,1.11

0.034

Fixed-effects model

GG vs. GA+AA

3.1%

0.418

1.06

1.01,1.11

0.014

Fixed-effects model

GG vs. GA

25.9%

0.122

1.03

1.01,1.06

0.002

Fixed-effects model

Table 3: Subground of analyses of rs1800795 polymorphism and cancer risk

rs1800795

No.of studies

Heterogeneity

OR

95% CI

P value

Model

Cancer type

I2

P value

Cervical cancer

7 (1734/2272)

G vs. C

70.1%

0.003

1.13

1.05,1.21

0.004

Random-effects model

GG+ GC vs. CC

58.5%

0.025

1.16

1.06,1.27

0.039

Random-effects model

GG vs. GC+CC

58.7%

0.024

1.21

1.08,1.34

0.002

Random-effects model

GG vs. GC

44.0%

0.098

1.19

1.08,1.30

0.003

Fixed-effects model

Colorectal cancer

14 (7399/9808)

G vs. C

63.7%

0.000

1.10

1.02,1.19

0.014

Random-effects model

GG+ GC vs. CC

0.0%

0.515

1.13

1.04,1.22

0.003

Fixed-effects model

GG vs. GC+CC

63.6%

0.000

1.11

1.01,1.22

0.047

Random-effects model

GG vs. GC

54.6%

0.006

1.07

1.01,1.19

0.019

Random-effects model

Gastric cancer

4 (672/1614)

G vs. C

0.0%

0.776

0.95

0.83,1.08

0.435

Fixed-effects model

GG+ GC vs. CC

0.0%

0.573

1.03

0.81,1.32

0.799

Fixed-effects model

GG vs. GC+CC

0.0%

0.874

0.87

0.72,1.06

0.181

Fixed-effects model

GG vs. GC

0.0%

0.409

0.85

0.69,1.05

0.122

Fixed-effects model

Breast cancer

13 (9532/15064)

G vs. C

57.8%

0.011

1.08

1.01,1.19

0.013

Random-effects model

GG+ GC vs. CC

61.6%

0.004

1.19

1.04,1.34

0.011

Random-effects model

GG vs. GC+CC

60.5%

0.005

1.20

1.06,1.25

0.028

Random-effects model

GG vs. GC

57.2%

0.012

1.11

1.06,1.17

0.009

Random-effects model

Prostate cancer

5 (12169/13116)

G vs. C

31.0%

0.203

1.08

1.03,1.13

0.005

Fixed-effects model

GG+ GC vs. CC

32.4%

0.193

1.11

1.04,1.18

0.003

Fixed-effects model

GG vs. GC+CC

24.0%

0.254

1.13

1.05,1.22

0.008

Fixed-effects model

GG vs. GC

15.4%

0.315

1.07

1.02,1.12

0.003

Fixed-effects model

Lung Cancer

4 (3203/3332)

G vs. C

0.0%

0.817

1.08

1.02,1.15

0.003

Fixed-effects model

GG+ GC vs. CC

0.0%

0.912

1.06

1.01,1.11

0.002

Fixed-effects model

GG vs. GC+CC

25.4%

0.258

1.07

1.03,1.12

<0.001

Fixed-effects model

GG vs. GC

0.0%

0.745

1.10

1.03,1.17

0.003

Fixed-effects model

Glioma

3 (1082/1701)

G vs. C

0.0%

0.482

1.28

1.13,1.46

< 0.001

Fixed-effects model

GG+ GC vs. CC

67.6%

0.046

1.15

1.05,1.26

0.021

Random-effects model

GG vs. GC+CC

77.9%

0.011

1.50

1.03,2.17

0.035

Random-effects model

GG vs. GC

88.7%

0.000

1.55

1.05,2.72

0.012

Random-effects model

Multiple myeloma

5 (6013/6471)

G vs. C

0.0%

0.901

1.06

0.88,1.30

0.559

Fixed-effects model

GG+ GC vs. CC

0.0%

0.987

1.00

0.66,1.53

0.992

Fixed-effects model

GG vs. GC+CC

0.0%

0.617

0.95

0.70,1.28

0.733

Fixed-effects model

GG vs. GC

0.0%

0.737

1.01

0.73,1.38

0.961

Fixed-effects model

Non-Hodgkin’s lymphoma

4 (5609/5649)

G vs. C

60.9%

0.053

1.25

1.01,1.51

0.049

Random-effects model

GG+ GC vs. CC

11.2%

0.337

1.26

1.03,1.54

0.022

Fixed-effects model

GG vs. GC+CC

50.3%

0.110

1.20

1.04,1.40

0.015

Random-effects model

GG vs. GC

20.1%

0.289

1.15

1.08,1.35

0.008

Fixed-effects model

Hodgkin’s lymphoma

3 (533/484)

G vs. C

16.7%

0.301

1.22

1.02,1.45

0.030

Fixed-effects model

GG+ GC vs. CC

0.0%

0.460

1.25

1.02,1.73

0.043

Fixed-effects model

GG vs. GC+CC

0.0%

0.434

1.32

1.02,1.73

0.037

Fixed-effects model

GG vs. GC

0.0%

0.601

1.28

1.08,1.68

0.013

Fixed-effects model

Meta-analysis of rs1800796 polymorphism and cancer risk

Twenty-one studies reported the association between rs1800796 and cancer risk. Our results showed that rs1800796 was significantly associated with increased cancer risk in allelic, dominant, recessive, and additive models (OR = 1.11, 95% CI: 1.04, 1.18, P < 0.001, allelic models respectively) (Table 1). Subgroup analysis indicated that rs1800796 was significantly associated with increased risk of lung cancer (OR = 1.23, 95% CI: 1.11, 1.36, P = 0.002, allelic models respectively) (Table 4), prostate cancer (OR = 1.13, 95% CI: 1.04, 1.23, P = 0.002, allelic models respectively) (Table 4) and colorectal cancer (OR = 1.07, 95% CI: 1.04, 1.23, P < 0.001, allelic models respectively) (Table 4) but not gastric cancer (OR = 1.03, 95% CI: 0.82, 1.29, P = 0.786, allelic models respectively) (Table 4) in all four gene model. Furthermore, rs1800796 was associated with a significantly risk of cancer in Asia (OR = 1.08, 95% CI: 1.03, 1.14, P < 0.001, allelic models respectively) (Table 2) and Caucasian (OR = 1.05, 95% CI: 1.01, 1.10, P-0.003, allelic models respectively) (Table 2) in all four gene model.

Table 4: Subground of analyses of rs1800796 polymorphism and cancer risk

rs1800796

No.of studies

Heterogeneity

OR

95% CI

P value

Model

Cancer type

I2

P value

Lung cancer

6 (1974/2879)

C vs. G

55.6%

0.046

1.23

1.11,1.36

0.002

Random-effects model

CC+ CG vs. GG

57.4%

0.039

1.17

1.09,1.26

0.012

Random-effects model

CC vs. CG+GG

62.0%

0.022

1.15

1.05,1.26

0.012

Random-effects model

CC vs. CG

66.2%

0.011

1.18

1.11,1.27

0.008

Random-effects model

Prostate cancer

5 (2360/3872)

C vs. G

0.0%

0.803

1.13

1.04,1.23

0.002

Fixed-effects model

CC+ CG vs. GG

0.0%

0.623

1.18

1.09,1.25

0.018

Fixed-effects model

CC vs. CG+GG

0.0%

0.493

1.19

1.07,1.32

0.015

Fixed-effects model

CC vs. CG

13.5%

0.328

1.16

1.06,1.28

0.014

Fixed-effects model

Colorectal cancer

2 (2581/3363)

C vs. G

0.0%

0.826

1.07

1.03,1.12

< 0.001

Fixed-effects model

CC+ CG vs. GG

0.0%

0.859

1.08

1.02,1.15

< 0.001

Fixed-effects model

CC vs. CG+GG

0.0%

0.865

1.10

1.02,1.19

0.006

Fixed-effects model

CC vs. CG

0.0%

0.905

1.15

1.04,1.27

0.009

Fixed-effects model

Gastric cancer

2 (365/395)

C vs. G

0.0%

0.910

1.03

0.82,1.29

0.786

Fixed-effects model

CC+ CG vs. GG

0.0%

0.380

1.05

0.62,1.80

0.848

Fixed-effects model

CC vs. CG+GG

0.0%

0.602

1.04

0.78,1.38

0.807

Fixed-effects model

CC vs. CG

26.6%

0.256

1.05

0.78,1.41

0.757

Fixed-effects model

Meta-analysis of rs1800797 polymorphism and cancer risk

Seventeen studies reported the association between rs1800797 and cancer risk. Our results showed that rs1800797 was significantly associated with increased cancer risk in allelic, dominant, recessive, and additive models (OR = 1.04, 95% CI: 1.01, 1.08, P = 0.002, allelic models respectively) (Table 1). Subgroup analysis indicated that rs1800797 has significant association in breast cancer (OR = 1.14, 95% CI: 1.06, 1.23, P = 0.002, allelic models respectively) (Table 5), non-Hodgkin’s lymphoma (OR = 1.09, 95% CI: 1.03, 1.05, P = 0.006, allelic models respectively) (Table 5), B-NHL (OR= 1.10, 95% CI: 1.03, 1.18, P = 0.006, allelic models respectively) (Table 5) and DLCBL (OR = 1.10, 95% CI: 1.01, 1.20, P = 0.006, allelic models respectively) (Table 5) but not gastric cancer (OR = 1.04, 95% CI: 0.93, 1.15, P = 0.530, allelic models respectively) (Table 5) in all four gene model. Besides, rs1800797 was associated with a significantly higher risk of cancer in Caucasian (OR= 1.04, 95% CI: 1.01, 1.08, P = 0.041, allelic models respectively) (Table 2) but not in Asia (OR = 1.23, 95% CI: 0.79, 2.04, P = 0.326, allelic models respectively) (Table 2) in all four gene model.

Table 5: Subground of analyses of rs1800797 polymorphism and cancer risk

rs1800797

No.of studies

Heterogeneity

OR

95% CI

P value

Model

Cancer type

I2

P value

Breast Cancer

2 (1164/1388)

G vs. A

0.0%

0.705

1.14

1.06,1.23

0.002

Fixed-effects model

GG+ GA vs. AA

0.0%

0.923

1.09

1.02,1.16

< 0.001

Fixed-effects model

GG vs. GA+AA

0.0%

0.454

1.17

1.09,1.15

0.003

Fixed-effects model

GG vs. GA

0.0%

0.365

1.06

1.02,1.11

0.003

Fixed-effects model

Gastric cancer

2 (286/316)

G vs. A

0.0%

0.879

1.04

0.93,1.15

0.530

Fixed-effects model

GG+ GA vs. AA

0.0%

0.692

1.01

0.82,1.24

0.936

Fixed-effects model

GG vs. GA+AA

0.0%

0.662

1.06

0.92,1.23

0.429

Fixed-effects model

GG vs. GA

4.0%

0.353

0.99

0.72,1.35

0.934

Fixed-effects model

Non-Hodgkin’s lymphoma

4 (5729/6036)

G vs. A

0.0%

0.554

1.09

1.03,1.15

0.006

Fixed-effects model

GG+ GA vs. AA

0.0%

0.497

1.07

1.02,1.13

0.002

Fixed-effects model

GG vs. GA+AA

32.2%

0.219

1.12

1.04,1.21

0.008

Fixed-effects model

GG vs. GA

67.6%

0.026

1.19

1.06,1.32

0.015

Random-effects model

B-cell lymphoma

3 (2161/2018)

G vs. A

0.0%

0.736

1.10

1.03,1.18

0.006

Fixed-effects model

GG+ GA vs. AA

0.0%

0.389

0.83

0.50,1.37

0.462

Fixed-effects model

GG vs. GA+AA

0.0%

0.603

1.39

1.12,1.67

0.007

Fixed-effects model

GG vs. GA

58.3%

0.091

1.52

1.21,1.84

0.018

Random-effects model

DLCBL

4 (5388/7026)

G vs. A

6.3%

0.344

1.10

1.01,1.20

0.006

Fixed-effects model

GG+ GA vs. AA

0.0%

0.759

1.06

1.01,1.12

< 0.001

Fixed-effects model

GG vs. GA+AA

0.0%

0.683

1.13

1.03,1.24

0.003

Fixed-effects model

GG vs. GA

0.0%

0.830

1.16

1.05,1.28

0.006

Fixed-effects model

DLCBL: diffuse large B-cell lymphoma.

Table 6: Meta-analysis of IL-6 promoter polymorphisms and cancer prognosis

Genetic model

No.of studies

Heterogeneity

HR

95% CI

P value

Model

I2

P value

rs1800795

10 (7640/8361)

GG vs. GC+CC

0.088

43.6%

1.17

1.07,1.36

< 0.001

Fixed-effects model

CC VS. GC+GG

0.610

0.0%

1.51

1.09,2.13

< 0.001

Fixed-effects model

rs1800796

2 (452/538)

GG vs. GC+CC

0.326

0.0%

1.16

1.07,2.42

< 0.001

Fixed-effects model

rs1800797

3 (892/951)

GG vs. GA+AA

0.416

0.0%

1.23

1.11,1.37

< 0.001

Fixed-effects model

Meta-analysis of IL-6 promoter polymorphisms and cancer prognosis

Twelve studies reported the association between IL-6 promoter polymorphisms and cancer prognosis. Prognostic meta-analyses were performed in a double gene model: CC vs. GC+GG and GG vs. GC+CC in rs1800795, GG vs. GC+CC in rs1800796 and GG vs. GA+AA in rs1800797. Our results showed that rs1800795, rs1800796 and rs1800797 were significantly associated with cancer prognosis (Table 6).

Sensitivity analysis

Sensitivity analysis was conducted to assess the stability of the results. The results show four genetic model were stable in Supplementary Figures 1–3.

Publication bias

Each study in this meta-analysis was performed to evaluate the publication bias by both Begg’s funnel plot and Egger’s test. The results show no obvious evidence of publication bias was found in allelic, dominant, recessive or additive genetic model in Table 7.

Table 7: Publication bias analysis of the meta-analysis

Genetic model

Test

t

95% CI

P

rs1800795

G vs. C

Begg’s test

0.853

Egger’s test

-1.49

-3.43,0.48

0.139

GG+ GC vs. CC

Begg’s test

0.272

Egger’s test

-4.09

-0.84,-027

0.125

GG vs. GC+CC

Begg’s test

0.472

Egger’s test

-3.27

-5.21,1.11

0.086

GG vs. GC

Begg’s test

0.791

Egger’s test

-1.74

-0.48,6.99

0.403

rs1800796

C vs. G

Begg’s test

0.602

Egger’s test

-4.82

-2.60,1.17

0.130

CC+ CG vs. GG

Begg’s test

0.117

Egger’s test

-9.04

-0.09,0.02

0.070

CC vs. CG+GG

Begg’s test

0.602

Egger’s test

-5.03

-3.15,1.36

0.125

CC vs. CG

Begg’s test

0.602

Egger’s test

-5.22

-2.82,1.17

0.121

rs1800797

G vs. A

Begg’s test

0.713

Egger’s test

-1.23

-8.24,2.07

0.230

GG+ GA vs. AA

Begg’s test

/p>

0.890

Egger’s test

-1.29

-0.87,0.20

0.211

GG vs. GA+AA

Begg’s test

0.931

Egger’s test

-0.86

-17.0,6.89

0.395

GG vs. GA

Begg’s test

0.973

Egger’s test

2.28

0.73,14.4

0.531

DISCUSSION

Cancer is now a public health crisis, affecting millions of people in both developed and developing countries. By 2020, the disease is forecasted to be the major cause of morbidity and mortality in most developing nations [107]. To improve this embarrassing situation, risk factors concerning cancer should be identified timely and controlled effectively. The etiology of cancer involves both genetic and environmental factors. Therefore, understanding the impact of genetic factors on cancer will help to prevent cancer. IL-6 is a confirmed pleiotropic pro-inflammatory cytokine associated with cardiovascular diseases. Elevated expression of IL-6 and its major effector have been implicated in the different stages of cancer development, including initiation, promotion, malignant conversion, invasion, and metastasis [2].

Several recent meta-analysis have focused on the association between IL-6 promoter polymorphisms and cancer risk. Two meta-analysis showed that rs1800795 polymorphism increased the risk of prostate cancer and cervical cancer [108, 109]. Though, the result same with ours, it still exist some problems. On the one hand, single case-control studies with small sample sizes may have weak statistical power, thereby interfering with the precision of their results. On the another hand, the quantity of SNPs involving in their meta-analysis was smaller, which weak the persuasive power of their research. Additionally, no meta-analyses concerning the relationship between IL-6 promoter polymorphisms and cancer prognosis.

In this current meta-analysis was based on 97 case-control study, with 80361 cases and 78712 control from sixteen countries, thus, this meta-analysis provides the most up-to-date epidemiological evidence supporting IL-6 promoter polymorphisms were significantly associated with the susceptibility and prognosis of cancer. To our knowledge, this is the first complete study to identify the potential association between IL-6 promoter and cancer risk and prognosis. However, we also found rs1800795 was not associated with gastric cancer and multiple myeloma, this may be due to tumor heterogeneity or insufficient statistical power to check an association. therefore, a greater number of original case-control studies must be performed to further evaluate the association between the IL-6 promoter polymorphisms and different cancer types.

Although, we performed this meta-analysis very carefully, however, some limitations must be considered in the current meta-analysis. Firstly, we performed stratification only by ethnicity and cancer type, without referring other factors. Further research should be conducted in different sex of population. Secondly, we only select literature that written by English, other language should be chosen in the further. Thirdly, a lack of original data limited further evaluations of the potential gene-gene and gene-environment interactions.

In conclusion, our findings underscore the notion that IL-6 promoter polymorphisms were significantly associated with the susceptibility and prognosis of cancer. In the future, large-scale case-control and population based association studies must be performed in the future to validate the risk identified in the current meta-analysis, and investigate the effect of potential gene-gene and gene-environment interactions on cancer risk.

MATERIALS AND METHODS

Search strategy and selection criteria

The selection process is shown in the flow chart (Figure 1). We searched PubMed and Embase databases up to April, 2017, with keywords including “IL-6” or “interleukin-6” and “single nucleotide polymorphism” or “SNP” and “cancer” or “tumor”. Eligible studies were choosing and other relevant publications were also examined.

Flow diagram of the study selection process.

Figure 1: Flow diagram of the study selection process.

Data extraction

The following information in studies were investigated by two independent researchers: (1) first author; (2) publication year; (3) country; (4) cancer type; (5) cases and controls sample size; (6) genotype.

Statistical analysis

STATA software 12.0 (STATA Corp, College Station, TX, USA) was used to evaluate the relationships between IL-6 promoter polymorphisms and cancer risk and prognosis. Studies were assessed by chi-square in control group under Hardy-Weinberg equilibrium (HWE) to calculate frequencies of IL-6 promoter, and if P < 0.05, study was considered to be disequilibrium. The strength of the relationship between IL-6 promoter polymorphisms and the risk of cancer were evaluated by odd ratios with corresponding 95% confidence intervals. The correlation between IL-6 promoter polymorphisms and prognosis of cancer were measured by hazard ratios (HRs). By using Q test and I2 statistic to assess heterogeneity among studies in rs1800795 in the allelic (G vs. C), dominant (GG+ GC vs. CC), recessive (GG vs. GC+CC) and additive (GG vs. GC), in rs1800796 in the allelic (C vs. G), dominant (CC+CG vs. GG), recessive (CC vs. CC+GG), and additive (CC vs. CG) genetic models and in rs1800797 in the allelic (G vs. A), dominant (GG+GA vs. AA), recessive (GG vs.GA+AA, and additive (GG vs. GA) genetic models. Random-effect model was chosen if PQ<0.10 or I2 >50%, otherwise, fixed-effect mode was applied. Sensitivity analysis was conducted to assess the stability of the results. Begg’s and Egger’s tests were used to assess the publication bias of each study.

Author contributions

Xingchun Peng, Jun Shi and Ming Sang performed search, Wanqun Sun, Xiaodong Sun and Jue Wang prepared tables and figures, Bin Li wrote the manuscript and performed power calculation.

ACKNOWLEDGMENTS AND FUNDING

No financial support and sponsorship support this work.

CONFLICTS OF INTERESTS

No conflicts of financail interests is stated by authors.

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