1 Introduction
In 2008, the genome-wide association studies (GWAS) reported that rs17782313 single nucleotide polymorphism (SNP) mapped 188 kb downstream of the melanocortin 4 receptor (MC4R ) gene was strongly associated with body mass index (BMI) and risk of obesity in European populations.[1] MC4R gene and risk of obesity in populations with different races/ethnicities.[2–4]
MC4R is a 332-amino acid protein encoded by a single exon on chromosome 18q22. The rare coding mutations in the MC4R gene have been found to be the main cause of human monogenic obesity,[5] MC4R gene represents a compelling biological candidate. MC4R expression is also associated with risk of early-onset obesity, increased lean mass and bone mineral density, and enhanced linear growth.[6] MC4R gene was associated with risk of obesity[4] [7] MC4R SNPs are associated with cancer risk, which may help illuminate the potential biological mechanism between obesity and cancer development. To date, several studies have investigated the associations of MC4R SNPs with risk of cancer.[8–14]
The present study aimed to perform a systematic meta-analysis to clarify the association between the rs17782313 SNP (or its proxy) near the MC4R gene and risk of cancer.
2 Materials and Methods
2.1 Literature and search strategy
The PubMed and Embase databases were searched for potentially eligible studies. The following key words were used to search for eligible publications: (melanocortin 4 receptor OR MC4R ) and (polymorphism OR variant OR variation OR genotype) and (cancer OR tumor OR carcinoma). The publication language was restricted to the English language. The reference lists of retrieved articles were also hand-searched. The literature search was updated as of September 10, 2019. Since this is a meta-analysis, ethical approval was waived.
2.2 Inclusion criteria and data extraction
The included studies met all the following inclusion criteria:
(1) studies that determined the association of MC4R rs17782313 (or its proxy SNP rs12970134, r 2 > 0.90) with cancer risk;
(2) studies that had case-control design;
(3) studies that provided an odds ratio (OR) with 95% confidence interval (CI) with or without adjustments for BMI.
The following information were extracted from each study:
(1) name of the first author,
(2) year of publication,
(3) country of origin,
(4) race/ethnicity of the study population,
(5) number of cases and controls,
(6) gender ratio,
(7) mean age,
(8) mean BMI,
(9) cancer type,
(10) the studied SNP, and
(11) the determination of whether BMI was adjusted in the statistical model.
Two authors independently reviewed the articles for compliance with the inclusion/exclusion criteria, resolved any disagreement, and reached a consistent decision after discussion with a third author, if necessary.
2.3 Statistical analysis
The association between MC4R rs17782313 and cancer risk was determined by calculating the pooled OR and 95% CI under an additive genetic model. Z -test was used to determine the significance of the OR (P < .05 was considered statistically significant). Cochrane Q -test was conducted to assess the between-study heterogeneity.[15,16] I 2 represented the range for the degree of heterogeneity. A random-effects model (DerSimonian–Laird[15] P ≤ .10 or I 2 ≥ 50%). Otherwise, a fixed-effects model (Mantel–Haenszel[16] [17] P < .05 was considered statistically significant). The data were analyzed using STATA version 11.0 (StataCorp LP, College Station, TX).
3 Results
3.1 Characteristics of the studies
Figure 1 presents a flow chart describing the process of inclusion/exclusion of studies. The literature search identified 35 potentially relevant articles. A total of 6 publications (6517 cancer cases and 16,886 healthy controls) were finally included in the present meta-analysis. The MC4R rs17782313 (or its proxy SNP rs12970134) in each included study was in Hardy-Weinberg Equivalent. The characteristics of the included studies are listed in Table 1 .
Figure 1: Flowchart for the inclusion/exclusion of studies.
Table 1: Characteristics of studies included in the meta-analysis.
3.2 Meta-analysis results
Before adjusting for BMI, the MC4R rs17782313 SNP risk allele was moderately associated with cancer risk (OR = 1.12, 95% CI = 1.01–1.24) in an additive genetic model (Fig. 2 ). In the subgroup analysis by cancer type, there was a significant association with risk of colorectal cancer (OR = 1.12, 95% CI = 1.04–1.21). In contrast, the MC4R rs17782313 SNP was not associated with endometrial cancer (OR = 1.12, 95% CI = 0.87–1.45) or breast cancer (OR = 1.27, 95% CI = 0.77–2.11) (Table 2 ).
Figure 2: The meta-analysis of the association between MC4R rs17782313 and cancer risk without adjusting for body mass index.
Table 2: Meta-analysis of the association between MC4R rs17782313 and cancer risk by cancer type.
After adjusting for BMI, the MC4R rs17782313 SNP risk allele was not associated with cancer risk (OR = 1.08, 95% CI = 0.94–1.23; Fig. 3 ). In the subgroup analysis by cancer type, the MC4R rs17782313 SNP was moderately associated with the risk of colorectal cancer (OR = 1.11, 95% CI = 1.03–1.20; Table 2 ). While the risk factor of the other 2 cancer type (endometrial cancer and breast cancer) were both not associated with the rs17782313 with and without adjustment for BMI.
Figure 3: The meta-analysis of the association between MC4R rs17782313 and cancer risk with the adjustment for body mass index.
3.3 Publication bias
There was no publication bias for the MC4R rs17782313 SNP using Begg test (P = .452) or Egger test (P = .275) before adjusting for BMI, as well as after adjusting for BMI (P = .308 and.310, respectively).
4 Discussion
To our knowledge, this is the first meta-analysis that investigated the association between a SNP near the MC4R gene and risk of cancer. The present meta-analysis revealed that the MC4R rs17782313 SNP is moderately associated with risk of cancer, without adjusting for BMI. However, this association disappeared after adjusting for BMI. It appears that the association between the MC4R gene SNP and cancer risk may be mediated through adiposity.
Several previous GWAS have identified a large number of SNPs associated with obesity. The FTO gene is one of the first loci identified for obesity risk by GWAS. A most recent meta-analysis conducted by Kang et al revealed that the FTO gene rs9939609 SNP was not significantly associated with risk of cancer, regardless of the adjustment for BMI. However, in the subgroup analysis, this variant moderately increased the risk of endometrial cancer and pancreatic cancer, which was mediated by adiposity.[18] MC4R gene rs17782313 SNP and risk of cancer, which mediated through BMI. Notably, a recent large-scale study suggested that MC4R gene SNPs are not associated with risk of colorectal cancer, regardless of adjusting for BMI.[14]
The mechanism underlying the association between the MC4R SNP and cancer risk remains unclear. Similar to the FTO gene, the MC4R gene is also highly expressed in the central nervous system, which regulates the energy metabolism.[19] [20,21] MC4R SNPs increase the risk of obesity and cancer.
It is important to focus on an organ system, which might encompass two or more different cancer types (eg, genitourinary cancer). However, merely 6 studies met the inclusion criteria. Thus, an analysis that focused on an organ system could not be performed. In addition, it is also important to assess the association between SNPs and different endocrine-driven cancers. However, due to the unavailability of data, subgroup analysis was performed by cancer type (colorectal cancer, endometrial cancer and breast cancer).
The present study had 2 strengths. First, the OR was extracted with 95% CI, with the adjustment of covariates from individual studies, to calculate the summary estimate, which represents an accurate estimate. Second, a total 6517 cancer cases and 16,886 healthy controls were included in the present meta-analysis, which greatly improved the statistical power. However, 2 limitations should be considered. First, although the total sample size was sufficiently large, merely 6 studies were included. In addition, the subgroup analysis by cancer type should be interpreted with caution due to the limited studies available for each cancer type. Second, there was a significant between-study heterogeneity in the meta-analysis, even although a random effects model was used to overcome this limitation. In addition, the further meta-regression analysis did not reveal any potential confounders that may explain the between-study heterogeneity.
In summary, there might be an association between the MC4R rs17782313 SNP and risk of cancer, which might be mediated by adiposity. Further studies are necessary to identify the causal variant near the MC4R gene, as well as the underlying mechanism between the MC4R gene SNP and risk of cancer.
Author contributions
Conceptualization: Zeng Tian, Hongjun Xie.
Data curation: Xiaojiao Wang.
Formal analysis: Xiaojiao Wang.
Investigation: Zeng Tian.
Methodology: Jing Zhao, Yu Kang.
Supervision: Yu Kang.
Validation: Jing Zhao.
Visualization: Jing Zhao.
Writing – original draft: Zeng Tian, Jing Zhao, Hongjun Xie.
Writing – review & editing: Hongjun Xie.
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