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. 2012 Jan 13;3(4):655–664. doi: 10.3892/etm.2012.454

Association between IL6 -174G/C and cancer: A meta-analysis of 105,482 individuals

RENG-YUN LIU 1,*, XIAOXUE SONG 1,*, PING CHEN 2, ZHE LEI 1, JINGCHENG MIAO 3, NENGJUN YI 4, KUI ZHANG 4, BORIS PASCHE 5,✉, HONG-TAO ZHANG 1,✉
PMCID: PMC3438703  PMID: 22969947

Abstract

Interleukin-6 (IL6) is a pleiotropic inflammatory cytokine, which is implicated in the development and progression of several types of cancer. The -174G/C polymorphism of the IL6 gene controls serum levels of IL6 and may be associated with cancer risk, but the results from the published studies on the association between this polymorphism and cancer risk are conflicting. A comprehensive meta-analysis was conducted to assess the association of IL6 -174G/C with cancer risk. Studies were identified by searches of MEDLINE and HuGE Published Literature databases, with no restrictions. An eligible 83 articles involving 44,735 cancer patients and 60,747 controls were included. Combined odds ratios (ORs) and 95% confidence intervals (CIs) were used to assess the strength of the association between the IL6 -174 G/C polymorphism and cancer risk. Potential sources of heterogeneity were explored by meta-regression and sensitivity analysis. Overall, the IL6 -174G/C polymorphism was not significantly associated with cancer risk. However, cancer risk was increased for individuals with the CC genotype compared to those carrying the GG genotype in African populations (OR=1.83, 95% CI 1.26–2.67, P=0.002), but not in Caucasian populations (OR=1.00, 95% CI 0.92–1.08, P=0.938). The present meta-analysis provides the first evidence of the ethnic-specific association of the IL6 -174G/C polymorphism with cancer risk. Further investigations with a large number of cases and controls are required to confirm the associations between this polymorphism and cancer in Africans.

Keywords: interleukin-6, polymorphism, cancer risk, meta-analysis

Introduction

Clinical and epidemiological studies suggest that chronic inflammation predisposes individuals to different types of cancer, and inflammatory molecules promote the proliferation of malignant cells (1,2). The connection between inflammation and cancer is mediated by several mechanisms, including genetic and epigenetic alterations, that generate an inflammatory microenvironment that further reinforces the development of cancer (3). Moreover, functional polymorphisms of inflammatory cytokine genes are associated with cancer susceptibility (4–6).

Interleukin-6 (IL6) is a pleiotropic inflammatory cytokine that is important for immune responses, cell survival, proliferation and apoptosis (7). Elevated expression of IL6 and its major effector, signal transducer and activator of transcription-3 (STAT3), have been implicated in different stages of tumor development, including initiation, promotion, malignant conversion, invasion and metastasis (8–12). The best characterized genetic variants of IL6 is a G-to-C substitution at position -174, upstream of the transcription start site, which has been reported to influence IL6 levels in vitro and in vivo (13,14). Elucidation of an association, if any, between this polymorphism and cancer risk would support the hypothesis that genetic variants in IL6, resulting in aberrant IL6 expression, play a role in cancer development.

Individual studies and previously published meta-analyses regarding the association of IL6 -174G/C with cancer susceptibility (15,16) enrolled too few subjects to provide conclusive evidence for or against an association of this polymorphism with cancer risk. The aim of this study was to assess the association of IL6 -174G/C polymorphism with cancer risk by conducting a comprehensive meta-analysis of all eligible case-control studies.

Materials and methods

This meta-analysis was performed according to the guidelines of the preferred reporting items for systematic reviews and meta-analyses (PRISMA statement) (17) and the reporting meta-analysis of observational studies in epidemiology (MOOSE) (18).

Data sources and study selection

To identify all studies on the association between the IL6 -174G/C polymorphism and cancer risk, we conducted a systematic search of the literature published before April 2011 using the MEDLINE database and the HuGE Published Literature database (HuGE Pub Lit) (19) with no restrictions. For MEDLINE, keywords ‘IL-6’ OR ‘IL 6’ OR ‘IL6’ OR ‘interleukin-6’ OR ‘interleukin 6’ AND ‘polymorphism’ AND ‘cancer’ were used; For HuGE Pub Lit, keywords ‘IL6’ AND ‘cancer’ were used for searching eligible studies. In addition, a manual review of references from primary or review articles was screened to trace additional relevant studies. Studies were included if they had a case-control design and the available frequency of three genotypes regarding the IL6 -174G/C polymorphism. Of the studies with overlapping data, we selected the ones with the largest number of subjects.

Data extraction

Three investigators independently extracted data and reached a consensus on all items. The following data were extracted from each study: the first author's last name, publication year, ethnicity of the subjects, cancer type, study design (retrospective case-control or prospective cohort study), and numbers of genotyped cases and controls with GG, GC or CC genotypes. Ethnic group was defined as African, Caucasian or ‘mixed’, including more than one ethnic category. Studies investigating more than one type of cancer with overlapping or same controls were regarded as individual data sets only in subgroup analyses by cancer type.

Statistical analysis

Hardy-Weinberg equilibrium (HWE) analysis for the frequencies of GG, GC and CC genotypes among controls in each study was assessed using Pearson's Chi-square test. The strength of the association between IL6 -174G/C polymorphism and cancer risk was measured by odds ratio (OR) with its 95% confidence interval (CI). The pooled ORs for IL6 -174G/C genotypes CC, GC and C allele carriers (CC or GC) against GG genotype were calculated, respectively. The significance of the pooled OR was determined by the Z-test and P<0.05 was considered statistically significant. Subgroup analysis was performed using stratification by study character, cancer type, ethnicity and study design, respectively. If a cancer type contained less than three independent individual studies, it was categorized into the ‘other types’ group.

Testing for heterogeneity among studies was performed by a Chi-square-based Q-test (20). Since Q-test is poor at detecting true heterogeneity, heterogeneity was considered significant for P<0.10 rather than P<0.05 (21). Additionally, the magnitude of the between-study heterogeneity was also assessed by I2, which can be calculated from the basic results of a typical meta-analysis as I2 = 100% x (Q-df)/Q, ranges form 0 to 100%, and is typically considered low for I2<25%, modest for 25–50% and large for >50% (22). Meta-regression was carried out to investigate whether statistical heterogeneity between the results of the multiple studies was related to one or more characteristics of the studies (23). To identify the studies that led to significant heterogeneity, sensitivity analysis for between-study heterogeneity was implemented by the sequential algorithm proposed by Patsopoulos et al (24). Whenever the P-value of the Q-test was >0.10, the summarized OR estimate of each study was calculated by the fixed-effects model (Mantel-Haenszel method) (25). Otherwise, the random-effects model (DerSimonian and Laird method) was used (26). Funnel plots were used to examine whether the results of a meta-analysis may have been affected by publication bias (27). A modified version of Egger's test proposed by Harbord, Egger and Sterne was implemented to test funnel plot asymmetry (28). All statistical analyses were performed using Stata statistical software (Stata/SE version 10.1 for Windows; Stata Corp, College Station, TX, USA).

Results

Characteristics of the included studies

The detailed steps of our literature search are described in Fig. 1. Eighty-three independent articles that met the inclusion criteria were included in the final analysis. Of these articles, one study provided data on breast and prostate cancer using independent controls (29), therefore each group in this article was treated as an independent study in our meta-analysis. The characteristics of the included studies are summarized in Table I. Overall, the present meta-analysis is based on a total of 105,482 participants, including 44,735 cancer patients and 60,747 controls. The studies were published between April 2000 and March 2011. Seventeen studies were conducted with a prospective cohort design, and 67 were conducted with a retrospective case-control design. Approximately two-thirds of cases and controls (29,019 cases and 42,120 controls) were from 73 studies involving Caucasian populations, a fraction of the data (1,138 cases and 1,299 controls) from seven studies involving African populations, and nearly one-third of the data (14,578 cases and 17,328 controls) from four studies involving ‘mixed’ populations. As shown in Table I, there were two studies from Dossus et al involving Caucasians, African-Americans, Asians, Latinos and native Hawaiians; one study from Ognjanovic et al involving Caucasians, Asians and Hawaiians; and one study from Bushley et al involving Caucasians, Asians and other populations. In the controls, the frequency of the rare C allele among controls varied considerably between Caucasians and Africans (0.417±0.052 and 0.207±0.097, respectively; P<0.001). A significant deviation from HWE was noted in two studies in Africans, nine in Caucasians and four in mixed populations (Table I).

Figure 1.

Figure 1.

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Flow diagram of the study selection for the meta-analysis.

Table I.

Characteristics of all studies included in the meta-analysis.

Author Year Ethnicity Cancer type Study designa Cases
Controls
P-valueb
GG GC CC GG GC CC
Snoussi 2005 African Breast cancer Retrospective 199 98 8 150 46 4 0.830
Vishnoi 2007 African Gallbladder cancer Retrospective 97 25 2 153 44 3 0.936
Ahirwar 2008 African Bladder cancer Retrospective 86 24 26 130 56 14 0.027
Kesarwani 2008 African Prostate cancer Retrospective 102 84 14 103 87 10 0.120
Upadhyay 2008 African Esophageal cancer Retrospective 135 28 5 131 62 8 0.845
Badr El-Din 2009 African Brain tumor Retrospective 6 27 12 5 87 6 0.000
Gangwar 2009 African Cervical cancer Retrospective 107 36 17 142 51 7 0.372
Foster 2000 Caucasian Kaposi sarcoma Retrospective 61 44 10 44 55 27 0.214
Hulkkonen 2000 Caucasian Leukemia Retrospective 8 13 14 81 201 118 0.785
Zheng 2000 Caucasian Multiple myeloma Retrospective 22 36 15 33 69 26 0.357
Martinez-Escribano 2002 Caucasian Melanoma Retrospective 14 26 2 20 26 2 0.071
El-Omar 2003 Caucasian Esophageal and gastric cancer Retrospective 88 91 34 83 98 28 0.913
Howell 2003 Caucasian Melanoma Retrospective 48 79 34 79 101 44 0.258
Hwang 2003 Caucasian Gastric cancer Retrospective 19 9 2 22 8 0 0.399
Landi 2003 Caucasian Colorectal cancer Retrospective 133 180 48 145 133 33 0.761
Yakupova 2003 Caucasian Multiple myeloma Retrospective 23 33 13 37 53 12 0.286
Campa 2004 Caucasian Lung cancer Retrospective 64 111 68 55 105 47 0.818
Cozen 2004 Caucasian Lymphoma Retrospective 41 37 8 25 39 14 0.858
Gazouli 2004 Caucasian Kaposi sarcoma Retrospective 10 4 1 11 22 7 0.482
Smith 2004 Caucasian Breast cancer Retrospective 57 67 20 79 101 44 0.258
Vasku 2004 Caucasian Lymphoma Retrospective 19 35 9 36 46 23 0.259
Basturk 2005 Caucasian Renal cell carcinoma Retrospective 15 10 0 27 13 9 0.007
Campa 2005 Caucasian Lung cancer Retrospective 629 954 412 615 993 374 0.448
Cordano 2005 Caucasian Lymphoma Retrospective 134 197 77 106 184 59 0.167
Festa 2005 Caucasian Basal cell carcinoma Retrospective 57 126 58 62 130 68 0.993
Hefler 2005 Caucasian Breast cancer Retrospective 78 139 52 91 105 31 0.935
Mazur 2005 Caucasian Multiple myeloma Retrospective 11 31 12 16 28 6 0.239
Seifart 2005 Caucasian Lung cancer Retrospective 47 52 17 90 107 46 0.163
Balasubramanian 2006 Caucasian Breast cancer Retrospective 170 244 83 168 235 87 0.759
Gonzalez-Zuloeta Ladd 2006 Caucasian Breast cancer Prospective 55 86 30 1,286 1,733 632 0.246
Gunter 2006 Caucasian Colorectal cancer Retrospective 79 90 35 83 81 26 0.385
Kamangar 2006 Caucasian Gastric cancer Prospective 21 54 27 51 58 43 0.004
Lan 2006 Caucasian Lymphoma Retrospective 211 231 68 241 264 85 0.358
Michaud 2006 Caucasian Prostate cancer Prospective 170 223 91 230 293 90 0.832
Morgan 2006 Caucasian Aneurysm Retrospective 40 40 6 867 1,358 495 0.360
Nogueira de Souza 2006 Caucasian Cervical cancer Retrospective 24 32 0 148 102 3 0.001
Rothman 2006 Caucasian Lymphoma Retrospective 1,277 1,658 564 1,097 1,470 499 0.860
Theodoropoulos 2006 Caucasian Colorectal cancer Retrospective 111 76 35 64 86 50 0.055
Vogel 2006 Caucasian Breast cancer Prospective 108 167 86 98 177 86 0.728
Wang 2006 Caucasian Lymphoma Retrospective 486 474 174 393 410 138 0.068
Berkovic 2007 Caucasian GEP-NETs Retrospective 25 44 11 69 75 18 0.724
Brenner 2007 Caucasian Glioma Retrospective 222 332 100 319 503 211 0.621
Deans 2007 Caucasian Gastro-oesophageal cancer Retrospective 71 83 43 79 101 44 0.258
Duch 2007 Caucasian Multiple myeloma Retrospective 28 22 2 35 23 2 0.442
Gatti 2007 Caucasian Gastric cancer Retrospective 42 13 1 23 27 6 0.642
Gonullu 2007 Caucasian Breast cancer Retrospective 15 17 6 14 3 7 <0.001
Litovkin 2007 Caucasian Breast cancer and uterine leiomyoma Retrospective 44 64 25 30 39 9 0.490
Oliveira 2007 Caucasian Osteosarcoma Retrospective 9 23 32 10 68 82 0.405
Purdue 2007 Caucasian Lymphoma Retrospective 177 245 91 157 210 90 0.194
Slattery 2007 Caucasian Colorectal cancer Retrospective 952 1,043 355 728 897 347 0.015
Talseth 2007 Caucasian Colorectal cancer Retrospective 36 58 24 25 53 22 0.542
Vogel 2007 Caucasian Basal cell carcinoma Prospective 65 176 63 89 157 69 0.988
Vogel 2007 Caucasian Colorectal cancer Prospective 98 168 89 204 364 185 0.371
Zanke 2007 Caucasian Colorectal cancer Retrospective 381 557 195 373 539 213 0.461
Colakogullari 2008 Caucasian Lung cancer Retrospective 10 29 5 27 22 9 0.222
Crusius 2008 Caucasian Gastric cancer Prospective 78 122 43 415 517 206 0.044
Ennas 2008 Caucasian Leukemia Retrospective 17 16 6 64 43 5 0.506
Fontanella 2008 Caucasian Aneurysm Retrospective 144 157 34 66 71 19 0.989
Gu 2008 Caucasian Melanoma Prospective 69 106 32 69 102 33 0.646
Kury 2008 Caucasian Colorectal cancer Retrospective 363 489 171 435 504 182 0.079
Vairaktaris 2008 Caucasian Oral cancr Retrospective 42 102 18 90 60 6 0.298
Vogel 2008 Caucasian Lung cancer Prospective 105 202 96 204 361 179 0.437
Wilkening 2008 Caucasian Colorectal cancer Prospective 79 163 61 162 297 121 0.481
Aladzsity 2009 Caucasian Multiple myeloma Retrospective 37 43 17 36 49 14 0.681
Birmann 2009 Caucasian Multiple myeloma Prospective 21 46 10 52 82 28 0.655
Cherel 2009 Caucasian Breast cancer Retrospective 102 131 60 29 58 25 0.695
Moore 2009 Caucasian Prostate cancer Prospective 191 485 281 196 401 250 0.152
Ozgen 2009 Caucasian Papillary thyroid carcinoma Retrospective 21 14 7 143 171 26 0.009
Pierce 2009 Caucasian Prostate cancer Prospective 82 101 32 864 848 306 0.000
Talar-Wojnarowska 2009 Caucasian Pancreatic cancer Retrospective 13 19 9 22 19 9 0.191
Tsilidis 2009 Caucasian Colorectal cancer Prospective 68 93 39 113 170 71 0.627
Vasku 2009 Caucasian Colorectal cancer Retrospective 22 46 32 22 47 31 0.601
Wang 2009 Caucasian Prostate cancer Prospective 91 116 43 84 128 40 0.448
Cacev 2010 Caucasian Colorectal cancer Retrospective 64 70 26 68 75 17 0.582
Guey 2010 Caucasian Bladder cancer Retrospective 470 438 109 450 495 120 0.356
Jakubowska 2010 Caucasian Breast and ovarian cancer Retrospective 135 227 102 73 144 73 0.907
MARIE-GENICA 2010 Caucasian Breast cancer Retrospective 986 1,571 585 1,774 2,671 1,036 0.586
Consortium
Schonfeld 2010 Caucasian Breast cancer Retrospective 274 408 156 379 487 211 0.017
Giannitrapani 2011 Caucasian Hepatocellular carcinoma Retrospective 63 36 6 51 37 10 0.402
Grimm 2011 Caucasian Cervical cancer Retrospective 55 51 25 85 96 27 0.990
Bushley 2004 Mixed Ovarian cancer Retrospective 5 34 143 9 46 163 0.020
Dossus 2010 Mixed Prostate cancer Prospective 3,594 3,218 1,125 3,832 3,402 1,274 <0.001
Dossus 2010 Mixed Breast cancer Prospective 2,847 2,523 820 3,707 3,324 1,035 <0.001
Ognjanovic 2010 Mixed Colorectal cancer Retrospective 173 74 22 357 136 43 <0.001
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a

Retrospective case-control or prospective cohort study.

b

P-value for Hardy-Weinberg equilibrium analysis among controls. References provided upon request.

Test of heterogeneity

There was a significant heterogeneity in overall comparison of the CC genotype vs. GG genotype (P<0.001 and I2=43.8%). The meta-regression showed that the strong heterogeneity could not be traditionally explained by cancer types, ethnicities or study designs (P=0.285, 0.129 and 0.306, respectively). Furthermore, the 15 studies that deviate from HWE showed similar heterogeneity with that of studies that were in HWE (Table II), suggesting that the remarkable heterogeneity among the overall analysis was not due to the variability of the control quality. Therefore, we carefully assessed the association of the IL6 -174G/C polymorphism with cancer risk in several subgroups, and carried out sensitivity analyses of between-study heterogeneity to detect studies that have remarkable influence on homogeneity.

Table II.

Summary of ORs and 95% CIs for the IL6 -174G>C polymorphism and cancer risk and heterogeneity test for studies of each group.

Variables No.a Cases/controls CC vs. GG
GC vs. GG
GC/CC vs. GG
OR (95% CI)b PHc I2 (%) OR (95% CI)b PHc I2 (%) OR (95% CI)b PHc I2 (%)
Total 84 44,735/60,747 1.01 (0.95–1.08) <0.001 43.8 1.01 (0.96–1.07) <0.001 52.5 1.01 (0.96–1.07) <0.001 57.0
Ethnicities
  African 7 1,138/1,299 1.83 (1.26–2.67)e 0.294 17.7 0.80 (0.55–1.16) 0.002 71.0 0.94 (0.67–1.31) 0.004 68.9
  Caucasian 73 29019/42120 1.00 (0.92–1.08) <0.001 43.8 1.02 (0.96–1.09) <0.001 52.4 1.02 (0.96–1.08) <0.001 58.2
  Mixed 4 14,578/17,328 0.98 (0.92–1.05) 0.488 0.0 1.00 (0.96–1.05) 0.839 0.0 1.00 (0.95–1.04) 0.789 0.0
Cancer typesd
  Breast cancer 13 12,640/20,281 1.01 (0.94–1.09) 0.374 7.2 1.06 (0.96–1.17) 0.033 46.4 1.05 (0.95–1.15) 0.043 44.3
  Colorectal cancer 13 6,798/7,502 0.97 (0.82–1.14) 0.014 52.4 1.01 (0.89–1.13) 0.030 47.2 1.00 (0.87–1.13) 0.002 61.9
  Prostate cancer 6 10,043/12,438 0.99 (0.91–1.07) 0.254 24.0 1.03 (0.97–1.09) 0.361 8.6 1.01 (0.96–1.07) 0.357 9.3
  Lymphomad 7 6,213/5,586 0.96 (0.86–1.07) 0.574 0.0 0.96 (0.89–1.04) 0.609 0.0 0.96 (0.89–1.03) 0.621 0.0
  Multiple myelomad 6 422/601 1.23 (0.82–1.83) 0.609 0.0 1.07 (0.80–1.41) 0.717 0.0 1.10 (0.84–1.44) 0.731 0.0
  Lung cancerd 5 2,801/3,234 1.06 (0.92–1.23) 0.715 0.0 1.05 (0.83–1.33) 0.071 53.7 1.01 (0.90–1.13) 0.153 40.2
  Gastric cancerd 5 554 /1,585 1.06 (0.78–1.44) 0.114 46.2 0.98 (0.55–1.73) 0.001 78.8 0.95 (0.54–1.76) <0.001 80.4
  Melanomad 3 410/476 1.13 (0.75–1.68) 0.790 0.0 1.18 (0.88–1.58) 0.721 0.0 1.17 (0.88–1.54) 0.672 0.0
  Cervical cancer 3 347/661 1.85 (1.12–3.08)f 0.320 12.2 1.11 (0.68–1.82) 0.068 62.7 1.22 (0.92–1.61) 0.195 38.7
  Other types 28 4,723/9,403 1.10 (0.85–1.43) <0.001 67.6 0.94 (0.79–1.11) <0.001 67.1 0.97 (0.82–1.16) <0.001 71.5
Study design
  Prospective 17 18,759/28,718 1.01 (0.95–1.07) 0.893 0.0 1.03 (0.98–1.07) 0.150 26.6 1.02 (0.98–1.06) 0.316 11.8
  Retrospective 67 25,976/32,029 1.00 (0.91–1.11) <0.001 51.9 0.98 (0.92–1.06) <0.001 56.3 0.99 (0.92–1.06) <0.001 61.9
HWE
  >0.05 69 26,067/36,098 1.01 (0.93–1.09) <0.001 44.7 1.00 (0.94–1.07) <0.001 50.8 1.00 (0.94–1.07) <0.001 59.0
  <0.05 15 18,668/24,649 1.02 (0.90–1.15) 0.043 42.3 1.06 (0.95–1.18) 0.001 61.4 1.04 (0.96–1.13) 0.019 48.3
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a

No. of comparisons.

b

Random-effect model was used when P-value for heterogeneity test was <0.10; otherwise, the fixed-effect model was used.

c

P-value of Q-test for heterogeneity.

d

All the studies were from Caucasian populations.

e

P=0.002.

f

P=0.017.

For ethnic-specific subgroup analysis, no heterogeneity was detected within African population studies (P=0.294), but there were significant heterogeneity within Caucasian population studies (P<0.001). Sensitivity analysis of between-study heterogeneity revealed that five studies (30–34) mainly contributed to the heterogeneity within Caucasian population studies. After performing cancer type-specific analyses, we found no heterogeneity in studies of breast, prostate, lung, gastric cancer, lymphoma, multiple myeloma, melanoma and cervical cancer (Table II). However, there was strong heterogeneity for colorectal cancer, which was due to one Caucasian study (32).

Quantitative data synthesis

Overall, the CC genotype was not significantly associated with cancer risk when compared to the GG genotype (OR=1.01, 95% CI 0.95–1.08, P=0.698). Ethnic-specific ORs showed that cancer risk was increased for individuals carrying the CC genotype compared to those with the GG genotype in African populations (OR=1.83, 95% CI 1.26–2.67, P=0.002; Fig. 2), but not in Caucasian populations (OR=1.00, 95% CI 0.92–1.08, P=0.938; Table II). After excluding the studies (30–34) responsible for heterogeneity, we found that Caucasian individuals carrying the CC genotype had no remarkable effect on risk of cancer compared to GG genotype individuals (OR=1.02, 95% CI 0.97–1.07, P=0.561) with no significant between-study heterogeneities (P=0.196, I2=12.6%). Although there were nine data sets in which the genotype distribution did not follow HWE, the corresponding meta-analysis was qualitatively similar with or without excluding them.

Figure 2.

Figure 2.

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Risk of cancer for IL6-174 CC vs. GG genotype in African population. The circles and horizontal lines correspond to the study-specific odds ratio (OR) and 95% confidence interval (CI). The combined ORs and their 95% CIs are indicated by the diamonds.

Subsequently, we stratified the association between the IL6 -174G/C polymorphism and cancer risk by cancer types. When compared to individuals with the GG genotype, those with the CC genotype were associated with increased risk of cervical cancer (OR=1.85, 95% CI 1.12–3.08, P=0.017), but not with that of other types of cancer (Table II). Furthermore, no significant association of the IL6 -174G/C polymorphism with risk of breast, colorectal and prostate cancer was observed in individuals with Caucasian ancestry (Fig. 3). A few studies involving African populations did not allow us to perform subgroup analysis in Africans (Table I).

Figure 3.

Figure 3.

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Risk of breast, colorectal and prostate cancer for IL6 -174 CC vs. GG genotype in Caucasians. The circles and horizontal lines correspond to the study-specific odds ratio (OR) and 95% confidence interval (CI). The combined ORs and their 95% CIs are indicated by the diamonds for each type of cancer.

Lastly, we also assessed the ORs of cancer for individuals with the GC genotype or C allele carriers (GC or CC genotype) compared to those with the GG genotype, and found no significant association in overall and subgroup analyses (Table II).

Publication bias

The shape of the funnel plot did not reveal any evidence of obvious asymmetry in comparison of the CC genotype vs. GG genotype (Fig. 4A). Then, we used Harbord's test to provide statistical evidence of funnel plot symmetry, and the result did not show evidence of publication bias (t=0.91, P=0.366; Fig. 4B). Subgroup analyses by ethnicity and cancer type did not provide any evidence of publication bias (t=−1.59, P=0.172 for African populations; t=0.27, P=0.786 for Caucasian populations; t=−0.58, P=0.575 for breast cancer and t=1.02, P=0.329 for colorectal cancer). Similarly, no publication bias was detected when comparing the GC genotype to the GG genotype (t=0.18, P=0.861), the GC/CC genotype to the GG genotype (t=0.42, P=0.677) and in any comparison of the corresponding subgroup analyses.

Figure 4.

Figure 4.

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Publication bias test for the comparison of the CC genotype vs. GG genotype. (A) Funnel plot (with pseudo 95% percent confidence limits) for publication bias test. The natural logarithm of odds ratio (OR) and its standard error were used in the funnel plot. The points correspond to the log OR from individual trials, and the diagonal lines show the expected 95% confidence intervals (CIs) around the summary estimate. (B) Harbord's modified test for funnel plot asymmetry: regress Z/sqrt(V) on sqrt(V) -174G/C, where Z is efficient score and V is score variance.

Discussion

To the best of our knowledge, the present meta-analysis of 83 studies, involving 44,735 cases and 60,747 controls (counting every study's cases and controls only once), provides the most comprehensive assessment of an association of the IL6 -174G/C polymorphism with cancer risk. It provides evidence that African individuals with the CC genotype have higher odds of cancer than individuals with the GG genotype; the findings of our meta-analysis do not show any association of the IL6 -174G/C with cancer risk in Caucasian populations. These findings suggest an ethnic-specific effect of IL6 -174G/C polymorphism on risk of cancer. The discrepancies among different populations suggest a possible role of ethnic differences in genetic backgrounds and the environment they lived in (35).

Recent studies have shown that IL6 and its major effector STAT3 play a role in the epigenetic switch from non-transformed epithelia to cancer cell (36,37). Elevated expression of IL6 via autocrine and paracrine mechanisms leading to subsequent chronic inflammation also exhibits a strong association with cancer (38–41). In the present study, we found that the IL6 -174 CC genotype was significantly associated with increased risk of cervical cancer compared to the GG genotype. However, the smaller number of individuals genotyped in these studies precludes any formal conclusion. As compared to previous analyses based on substantially less data (15,16), the present analysis essentially shows null associations between IL6 -174G/C and several common types of cancer, including breast, colorectal, prostate, lung, gastric cancer, lymphoma, multiple myeloma and melanoma.

Assessment of the between-study heterogeneity and identification of its sources are essential requirements in meta-analyses (23,42). In this study, we systematically examined the effect of IL6 -174G/C on cancer risk across all reliable studies, and the results of the overall analysis showed a strong heterogeneous effect among the 83 studies. Given the fact that clinical characteristics of studies, including study population, design approach and type of cancer, are likely to be potential sources of heterogeneity, we first used meta-regression to detect whether any of the characteristics could explain the between-study variation. However, none of the potential sources considered were able to systematically explain the observed variation across studies. It seems likely that there exist more than one answer to the nature of overall heterogeneity. We, therefore, induced a new approach to perform sensitivity analysis of between-study heterogeneity (24), and detected that several studies with different clinical characteristics were responsible for the heterogeneity (30–34).

Apart from between-study heterogeneity, publication bias has also been recognized as a major concern in robust meta-analyses. Thereby, in this study, we used funnel plots to assess whether the studies included could be affected by publication bias. According to the recommendations by Sterne and Egger (43), the log OR and its standard error are used for the horizontal and vertical axis, respectively. No evidence of publication bias was found when testing by a visual inspection of funnel plots. In support of this, Harbord's linear regression test confirmed the evidence of funnel plot symmetry across all constituent data sets.

Notably, social factors are believed to interact with genetic variants to govern complex human phenotypes (44,45). Cole et al recently demonstrated a strong interaction between the IL6 -174G/C polymorphism and social environment factors, which may further affect the risk of inflammation-related disease (46). However, in the absence of the original data of the reviewed studies, our evaluation of potential interactions of gene-environment with cancer risk was limited. This may explain why previous genetic association studies and some subgroup analyses in our meta-analysis, especially the Caucasian studies, failed to show an association between the IL6 -174G/C polymorphism and risk of cancer. Furthermore, two other polymorphisms (-6331T/C and -572G/C) and several haplotypes in the IL6 promoter affect the transcriptional activity of IL6 and may influence susceptibility to inflammation-related diseases (47–49). However, most studies included in our meta-analysis restricted their analysis to the IL6 -174G/C polymorphism and few carried out the IL6 haplotypic analysis on cancer susceptibility. It is difficult to estimate the role of a particular haplotype on cancer risk in the present meta-analysis.

Despite these limitations, our meta-analysis provides a leap in knowledge when compared to a previous study (15) that reviewed the association between the IL6 -174G/C polymorphism and risk of cancer. First, our updated review is more comprehensive than the previous, as we identified 83 independent articles with a total of 105,482 individuals on the association of IL6 -174G/C with cancer risk compared to 47 articles with 67,116 individuals in the previous report. Thus, our meta-analysis had significantly higher statistical power. Second, we noticed the potentially different roles of the IL6 -174G/C polymorphism in the development of cancer among various populations, and found different associations of this polymorphism with cancer risk between Africans and Caucasians. Third, sensitivity analysis of heterogeneity was used to detect studies that were responsible for between-study heterogeneity (24). Fourth, we assessed the pooled effect of the IL6 -174G/C polymorphism on cancer risk within or without the studies that did not follow HWE, and qualitatively similar results were found, suggesting that the estimations of our analyses are stable and convincing. Finally, for publication bias analysis, a modified method for testing funnel plot asymmetry was used (28), which maintains better control of the false-positive rate than the commonly used Egger's test. No publication bias was detected, suggesting that the pooled results should be unbiased.

In summary, the present meta-analysis provides evidence of the ethnic-specific association of the IL6 -174G/C polymorphism with cancer risk. More sophisticated gene-environment interactions should be considered in future analyses, which may result in better understanding of the relevance between the IL6 -174G/C polymorphism and risk of cancer. Moreover, this study reinforces the need to undertake investigations with very large number of cases and controls (including updated meta-analyses) to provide conclusive evidence for the associations between high-frequency genetic variants in low-penetrance genes and complex diseases, such as cancer.

Acknowledgments

This study was, in part, supported by grants from the National Natural Science Foundation of China (81171894, 30973425 and 30672400 to H.T. Zhang), the Program for New Century Excellent Talents in University (NCET-09-0165 to H.T. Zhang), the Science and Technology Committee of Jiangsu Province (BK2008162 to H.T. Zhang), the SRF for ROCS, State Education Ministry (2008890 to H.T. Zhang), the Qing-Lan Project of Education Bureau of Jiangsu Province (to H.T. Zhang), the ‘333’ Project of Jiangsu Province Government (to H.T. Zhang), and the Soochow Scholar Project of Soochow University (to H.T. Zhang). This study was also supported by grants #2R01GM069430-06, CA 137000, CA 112520 and CA 108741 from the NIH (to B. Pasche).

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