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: [email protected]
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.
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 [2–5]. 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 [7–9]. However, previous studies have conflicting results between IL-6 promoter (rs1800795, rs1800796 and rs1800797) and cancer susceptibility [10–99] 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.
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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