Abstract
The present study aimed to assess any associations between resistin gene (RETN) polymorphisms and cancer susceptibility by conducting a meta-analysis. A comprehensive literature search was performed with PubMed, Web of Science, Scopus and Google Scholar for relevant studies published before April 2018. For the rs1862513 polymorphism, data from 9 studies covering 1,951 cancer patients and 2,295 healthy controls were included in this meta-analysis. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. Our meta-analysis revealed that this RETN polymorphism significantly increased the risk of cancer in codominant (OR=1.23, 95% CI= 1.01-1.50, p=0.04, CG vs CC; and OR=1.25, 95% CI= 1.03-1.53, p=0.03, GG vs CC), dominant (OR=1.19, 95% CI= 1.05-1.35, p=0.006, CG+GG vs CC), and allele (OR=1.14, 95% CI= 1.00-1.30, p=0.04, G vs C) inheritance genetic models. Stratification analysis by cancer type revealed that the rs1862513 variant significantly increased the risk of colorectal and breast cancer, and that cancer overall in Caucasians (OR=1.22, 95% CI= 1.04-1.43, p=0.02, CG+GG vs CC; OR=1.18, 95% CI= 1.04-1.34, p=0.01, G vs C). The data revealed no correlation between the rs3745367 polymorphism and cancer risk. Further well-designed studies with larger sample sizes and different ethnicities are warranted to validate the present findings.
Keywords: Resistin, RETN, cancer, polymorphism, meta-analysis
Introduction
Cancer, a major public health issue, is a leading cause of death worldwide. It has been estimated that more than 14.1 million new cases and 8.2 million cancer-related deaths happened annually (Siegel et al., 2016). Cancer is recognized as a multifactorial disease resulting from the integration between genetic and environmental factors (Lichtenstein et al., 2000). Single-nucleotide polymorphisms (SNPs) and small insertions or deletions (indels) are the most common genetic variations in human genome (Hashemi et al., 2018). Several studies showing the association between functional SNPs in various genes and the risk of developing cancer.
Adipokines, such as resistin, leptin, adiponectin and visfatin, are mainly synthesized in white adipose tissue and have been related to the pathogenesis of autoimmune disease, inflammatory diseases and cancer (John et al., 2006; Salageanu et al., 2010; Riondino et al., 2014; Muppala et al., 2017; Li and Han, 2018).
Resistin is a 12.5-kDa cysteine-rich polypeptide that upregulates the expression of proinflammatory cytokines and helps expand the population of regulatory T cells (Steppan et al., 2001; Bokarewa et al., 2005). The RETN gene encode resistin is mapped to chromosome 9 (19p13.2). Resistin is increased in type 2 diabetes and is closely correlated with insulin resistance and obesity (Shuldiner et al., 2001; Steppan et al., 2001; John et al., 2006). Obesity is well recognized as a risk factor for colorectal cancer development (Joshi et al., 2014; Joshi and Lee, 2014). Resistin may also be involved in the pathogenesis of cancer (Gonullu et al., 2010; Danese et al., 2012; Riondino et al., 2014). The serum levels of resistin have been shown to be higher in colorectal cancer (CRC) (Kumor et al., 2009; Gonullu et al., 2010; Nakajima et al., 2010; Danese et al., 2012; Slomian et al., 2017), and breast cancer (Dalamaga et al., 2013; Assiri et al., 2015; Deshmukh et al., 2015; Assiri and Kamel, 2016; Zeidan et al., 2018) than controls subjects.
Previous studies also demonstrated that the RETN gene variants were associated with the regulation of RETN gene expression and serum levels of resistin (Cho et al., 2004; Osawa et al., 2004).
In the last few years, a number of studies on the association between REST gene polymorphisms and risk of cancer have been published, with controversial results (Wagsater et al., 2008; Pechlivanis et al., 2009; Al-Harithy and Al-Ghafari, 2010; Alharithy, 2014; Mahmoudi et al., 2014; Duzkoylu et al., 2015; Mahmoudi et al., 2016; Hu et al., 2017; Kohan, 2017; Munoz-Palomeque et al., 2018). Therefore, we conducted a meta-analysis to exactly establish the association between RETN rs1862513 C>G (-420 C<G) and rs3745367 (+299 G>A gene polymorphisms and the risk of cancer.
Literature search
Literature searching in the databases such as PubMed, Web of Science, Scopus, and Google Scholar was performed for all articles describing an association between resistin polymorphisms and cancer risk published up to April 2018. Comprehensive search strategies involved the Mesh term and (‘resistin’ or ‘RETN’), (‘polymorphism’ or ‘variant’ or ‘genotype’ or ‘SNP’ or ‘mutations’), (‘cancer’ or ‘tumor’). Relevant studies which were eligible for the meta-analysis must meet the following criteria: 1) Original case-control studies of the correlation between the RETN polymorphisms and cancer 2) studies provided sufficient information of the genotype frequencies of RETN polymorphisms in both cases and controls. The criteria for exclusion were: 1) the articles have described case reports, reviews, overlapped data, animal or mechanism studies for RETN polymorphisms and cancer; 2) no genotype frequency or genotype information were provided for RETN polymorphism and cancer.
Data extraction
The papers were reviewed by two independent researchers. The following data were collected from each study such as the first author’s last name, publication year, ethnicity, the sample size, and the genotype and allele frequencies of cases and controls.
Statistical analysis
Meta-analysis was carried out using Revman 5.3 software (Version 5.3. Copenhagen: The Nordic Cochrane Centre, The Cochrane Collaboration, 2014) and STATA 14.1 software (Stata Corporation, College Station, TX, USA). Hardy-Weinberg Equilibrium (HWE) in the control group was tested by χ2 test. Odds ratios (ORs) and 95% confidence intervals (CIs) were pooled using forest-plots graphs to evaluate the association between RETN polymorphisms and cancer. The significance of the pooled OR was determined with the Z-test, and P-values < 0.05 were considered statistically significant. Heterogeneity among studies was assessed using the I2 statistic and the χ2 test-based Q statistic. A p< 0.10 and an I2 > 50% indicated significant heterogeneity. Once heterogeneity existed among studies, a random-effect model was applied; otherwise a fixed-effect model was used.
Publication bias was assessed by funnel plot. The degree of asymmetry was measured using Egger’s linear regression test; p < 0.05 was considered significant publication bias.
Sensitivity analysis
Sensitivity analysis was achieved using the method of eliminating studies one by one to verify whether our results were influenced by each included study or not.
Results
Study characteristics
10 studies met all the inclusion criteria and were included in this meta-analysis. Characteristics of the eligible studies are summarized in Table 1.
Table 1.
First Author | Year | Country | Ethnicity | Cancer type | Source of control | Genotyping Method | Case/ controls | Genotype and allele distribution of cases and controls | HWE (p) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Cases | controls | |||||||||||||||||
rs1862513 | CC | CG | GG | C | G | CC | CG | GG | C | G | ||||||||
Al-Harithy | 2010 | Saudi Arabia | Asian | Colon cancer | HB | PCR- RFLP | 60/6 | 16 | 33 | 11 | 65 | 55 | 24 | 20 | 16 | 68 | 52 | 0.013 |
Alharithy | 2014 | Saudi Arabia | Asian | Colon cancer | HB | PCR- RFLP | 60/6 | 15 | 33 | 12 | 63 | 57 | 24 | 20 | 16 | 68 | 52 | 0.013 |
Duzkoylu | 2015 | Turkey | Caucasian | Colorectal cancer | HB | PCR- RFLP | 123/79 | 53 | 61 | 9 | 167 | 79 | 31 | 36 | 12 | 98 | 60 | 0.771 |
Hu | 2017 | China | Asian | Lung cancer | HB | Real- time PCR | 371/451 | 164 | 157 | 50 | 485 | 257 | 182 | 203 | 66 | 567 | 335 | 0.444 |
Kohan | 2017 | Iran | Asian | Breast cancer | HB | PCR- RFLP | 150/15 | 50 | 63 | 37 | 163 | 137 | 63 | 63 | 24 | 189 | 111 | 0.225 |
Mahmoudi | 2014 | Iran | Asian | Colorectal cancer | HB | PCR- RFLP | 197/217 | 38 | 83 | 76 | 159 | 235 | 56 | 85 | 76 | 197 | 237 | 0.002 |
Munoz- Palomeque | 2018 | Mexico | Caucasian | Breast cancer | PB | PCR- RFLP | 100/308 | 53 | 42 | 5 | 148 | 52 | 199 | 102 | 7 | 500 | 116 | 0.144 |
Pechlivanis | 2009 | Czech Republic | Caucasian | Colorectal cancer | HB | PCR- RFLP | 642/714 | 317 | 262 | 63 | 896 | 388 | 393 | 265 | 56 | 1051 | 377 | 0.230 |
Wagsater | 2008 | Sweden | Caucasian | Colorectal cancer | HB | Taqman | 248/256 | 127 | 95 | 26 | 349 | 147 | 137 | 103 | 16 | 377 | 135 | 0.563 |
rs3745367 | GG | GA | AA | G | A | GG | AG | AA | G | A | HWE (p) | |||||||
Alharithy | 2014 | Saudi Arabia | Asian | Colon cancer | HB | PCR- RFLP | 60/6 | 3 | 51 | 6 | 57 | 63 | 15 | 39 | 6 | 69 | 51 | 0.011 |
Hu | 2017 | China | Asian | Lung cancer | HB | Real- timePCR | 371/451 | 164 | 164 | 43 | 492 | 250 | 190 | 194 | 67 | 574 | 328 | 0.134 |
Mahmoudi | 2016 | Iran | Asian | Colorectal cancer | HB | PCR- RFLP | 312/438 | 65 | 72 | 35 | 202 | 142 | 78 | 86 | 26 | 242 | 138 | 0.767 |
For rs1862513 polymorphism, data from 9 studies including 1951 cancer patients and 2,295 healthy controls were included in this meta-analysis. Regarding rs3745367 polymorphism, data from 3 studies containing 603 cases and 701 controls were included in this meta-analysis.
Quantitative synthesis
All the calculated results were summarized in Table 2. Our meta-analysis revealed that rs1862513 polymorphism of RETN significantly increased the risk of cancer in codominant (OR=1.23, 95%CI= 1.01-1.50, p=0.04, CG vs CC; and OR=1.25, 95%CI= 1.03-1.53, p=0.03, GG vs CC), dominant (OR=1.19, 95%CI= 1.05-1.35, p=0.006, CG+GG vs CC), and allele (OR=1.14, 95%CI= 1.00-1.30, p=0.04, G vs C) inheritance genetic models (Figure 1 and Table 2).
Table 2.
Polymorphism | Association test | Heterogeneity test | Egger’s test P | Begg’s test P | ||||
---|---|---|---|---|---|---|---|---|
OR (95%CI) | Z | p | χ2 | I2 (%) | p | |||
rs1862513 C>G | ||||||||
CG vs CC | 1.23 (1.01-1.50) | 2.05 | 0.04 | 14.00 | 43 | 0.08 | 0.891 | 0.532 |
GG vs CC | 1.25 (1.03-1.53) | 2.21 | 0.03 | 13.10 | 39 | 0.11 | 0.607 | 0.621 |
CG+GG vs CC | 1.19 (1.05-1.35) | 2.74 | 0.006 | 13.24 | 40 | 0.10 | 0.451 | 0.211 |
GG vs CG+CC | 1.11 (0.85-1.35) | 0.75 | 0.45 | 14.01 | 43 | 0.08 | 0.926 | 0.118 |
CG vs GG+CC | 1.17 (0.97-1.40 | 1.68 | 0.09 | 13.86 | 42 | 0.09 | 0.153 | 0.466 |
G vs C | 1.14 (1.00-1.30) | 2.01 | 0.04 | 13.58 | 41 | 0.09 | 0.520 | 0.532 |
rs3745367 G>A | ||||||||
AG vs GG | 1.32 (0.72-2.45) | 0.90 | 0.37 | 7.80 | 74 | 0.002 | 0.407 | 0.602 |
AA vs GG | 1.38 (0.60-3.17) | 0.76 | 0.44 | 7.67 | 74 | 0.02 | 0.883 | 0.117 |
AG+GG vs AA | 1.34 (0.73-2.46) | 0.96 | 0.34 | 8.43 | 76 | 0.01 | 0.368 | 0.602 |
AA vs AG+GG | 1.05 (0.60-1.84) | 0.18 | 0.92 | 4.69 | 57 | 0.10 | 0.193 | 0.117 |
AG vs AA+GG | 1.19 (0.74-1.93) | 0.73 | 0.47 | 6.34 | 68 | 0.04 | 0.679 | 0.602 |
A vs G | 1.11 (0.83-1.50) | 0.71 | 0.48 | 5.43 | 63 | 0.07 | 0.187 | 0.117 |
Table 3.
Type of cancer | NO. | CG vs CC | GG vs CC | CG+GG vs CC | GG vs CG+CC | CG vs GG+CC | G vs C | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
OR (95%CI) | P | OR (95%CI) | P | OR (95%CI) | P | OR (95%CI) | P | OR (95%CI) | P | OR (95%CI) | P | ||
Cancer type | |||||||||||||
Colorectal cancer | 6 | 1.25 (1.06-1.47) | 0.009 | 1.31 (1.02-1.68) | 0.04 | 1.25 (1.07-1.46) | 0.005 | 1.01 (0.72-1.43) | 0.93 | 1.19 (1.02-1.39) | 0.03 | 1.16 (1.03-1.30) | 0.01 |
Breast cancer | 2 | 1.41 (0.99-1.99) | 0.05 | 2.07 (1.18-3.63) | 0.01 | 1.53 (1.10-2.13) | 0.01 | 1.80 (1.07-3.02) | 0.03 | 1.20 (0.87-1.67) | 0.26 | 1.47 (1.15-1.87) | 0.002 |
Lung cancer | 1 | 0.86 (0.64-1.15) | 0.31 | 0.84 (0.55-1.28) | 0.42 | 0.85 (0.65-1.13) | 0.27 | 0.91 (0.61-1.35) | 0.64 | 0.90 (0.68-1.7) | 0.44 | 0.90 (0.73-1.10) | 0.29 |
Ethnicities | |||||||||||||
Asian | 5 | 1.42 (0.94-2.14) | 0.10 | 1.19 (0.91-1.55) | 0.21 | 1.33 (0.94-1.89) | 0.11 | 1.05 (0.83-1.32) | 0.70 | 1.27 (0.89-1.81) | 0.81 | 1.09 (0.95-1.24) | 0.24 |
Caucasian | 4 | 1.19 (1.00-1.41) | 0.05 | 1.31 (0.75-2.30) | 0.35 | 1.22 (1.04-1.43) | 0.02 | 1.23 (0.71-2.11) | 0.46 | 1.14 (0.97-1.35) | 0.11 | 1.18 (1.04-1.34) | 0.01 |
Stratification analysis by cancer type showed that rs1862513 variant significantly increased the risk of colorectal cancer as well as breast cancer (Table 2).
As shown in Table 2, the rs1862513 variant significantly increased the risk of cancer in Caucasian in dominant (OR=1.22, 95% CI= 1.04-1.43, p=0.02, CG+GG vs CC) and allele (OR=1.18, 95% CI= 1.04-1.34, p=0.01, G vs C) genetics model.
Regarding rs3745367 variant, the finding showed no significant association between the variant and cancer risk (Table 2).
Publication bias
The potential publication bias was assessed using a Begg’s funnel plot (Figure 2) and Egger’s test (Table 2). Begg’s and Egger’s tests proposed no evident publication bias in codominant, dominant recessive, overdominant, and allele inheritance models.
Sensitivity analysis
To verify the outcome of our analyses, we conducted a sensitivity analysis by excluding studies one by one, and then calculating the pooled estimate for the remaining studies (Figure 3). The sensitivity analysis proposed that certain studies significantly affect the association between RETN polymorphism and risk of cancer. We believe that the small number of studies included in our meta-analysis may contribute to the influence of the abovementioned studies; if more studies had been included, the influence of any one study would be decreased.
Discussion
Cancer is a complex disease and it has been proposed that individual genetic variants may only have a modest independent effect on the disease. Adipokines, secreted by the adipose tissue, are convincing candidates for the relationship between obesity and cancer risk (Guadagni et al., 2009; Li et al., 2017; Zhang et al., 2017; Malvi et al., 2018). Obesity leads to insulin resistance and hyperinsulinemia, and insulin levels are positively correlated with colorectal cancer risk (Schoen et al., 1999; Giovannucci, 2007).
Up to now, a number of studies have been carefully designed and investigated the effect of genetic polymorphisms of RETN gene on the risk of cancer. Most of these studies were based on a small sample size and the findings were inconsistent (Wagsater et al., 2008; Pechlivanis et al., 2009; Al-Harithy and Al-Ghafari, 2010; Alharithy, 2014; Mahmoudi et al., 2014; Duzkoylu et al., 2015; Mahmoudi et al., 2016; Hu et al., 2017; Kohan, 2017; Munoz-Palomeque et al., 2018). This is the first meta-analysis conducted to specify the effect of RETN rs1862513 and rs3745367 polymorphisms on susceptibility to cancer. Data from 9 studies indicated that RETN rs1862513 variant significantly increased the risk of cancer in codominant, dominant, and allele inheritance genetic models. We did not find any publication bias, which shows the reliability of the pooled results. Heterogeneity across studies suggests that there is a variation among the outcomes of studies than expected by chance. Sensitivity analysis also revealed an evidence of heterogeneity.
Stratified analyses based on cancer type showed that the rs1862513 variant significantly increased the risk of colorectal cancer as well as breast cancer.
The rs1862513 (-420 C>G) polymorphism is located in the promoter region of RETN and has been shown to be associated with RETN protein expression (Cho et al., 2004; Osawa et al., 2004).
The RETN is a polymorphic and a functional polymorphism at -420 (rs186513) affects promoter activity and increases the expression of resistin. The molecular mechanism by which resistin affect cancer risk is not fully understood.
Regarding rs3745367 variant, data from 3 studies did not support an association between variant and risk of cancer.
A significant deviation from HWE was found in 3 studies included the meta-analysis (Al-Harithy and Al-Ghafari, 2010; Alharithy, 2014). There is no clear clarification for deviation from HWE. The possible cause may be due to genetic drift.
In summary, our metanalysis investigation showed that rs1862513 polymorphism of RETN is a risk factor for cancer development. More studies with larger sample sizes are necessary to clarify the possible roles of RETN polymorphisms in cancer.
Conflict of interest
The Authors declare that there is no conflict of interest to disclose.
Acknowledgements
Saeid Ghavami has been supported by Research Manitoba New Investigator Operating grant and CHRIM operating grant.
References
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