Abstract

Leptin receptor (LEPR) signaling may be involved in promoting angiogenesis and proliferation, inhibiting apoptosis and playing a vital role in the progression of carcinogenesis. A number of studies have focused on the association of LEPR rs1137101 variants with susceptibility of cancer, however, the observed results were controversial. We searched literature on the relationship of LEPR rs1137101 G>A polymorphism with cancer risk by using PubMed and Embase databases, covering all publications up to 14 October 2018. In total, 44 case–control studies with 35,936 subjects were included. After combining all eligible studies, we identified null relationship between LEPR gene rs1137101 G>A polymorphism and overall cancer risk [A vs. G: odds ratio (OR ) =  0.97, 95% confidence interval (CI ) =  0.89–1.06, P = 0.547; AA vs. GG: OR  =  0.93, 95% CI  =  0.78–1.13, P = 0.476; AA/GA vs. GG: OR  =  0.99, 95% CI  =  0.91–1.09, P= 0.890 and AA vs. GA/GG: OR  = 0.92, 95% CI  =  0.82–1.04, P= 0.198]. However, in a subgroup analysis, there was an increased susceptibility of oral and oropharyngeal cancer in AA vs. GA/GG genetic model (OR, 1.83; 95% CI, 1.01–3.33; P=0.048). Considering the limited participants were included, the findings might be underpowered. Sensitivity analysis identified that any independent study omitted did not materially influence the pooled ORs and CIs. The results of publication bias detection showed that there was no evidence of bias. In summary, this analysis indicates that no significant association of cancer risk was identified to be correlated with rs1137101 G>A variants, even in stratified analyses.

Introduction

Cancer is one of the common health burden worldwide. Due to the prevalence of smoking and drinking, environmental pollution, as well as population aging, the incidence of malignancy is rising. According to the estimation of Global Cancer Statistics 2018, approximately 18.1 million new cancer cases and 9.6 million cancer-related deaths may have occurred in 2018 [1]. In the developing countries, the prognosis of cancer could be poorer than that in the developed countries. The reason of this phenomenon may be due to the diagnosis at an advanced stage combined with limited treatment. Some effective measures can potentially contribute to relieve the global cancer burden, including the application of precise early detection and treatment, tobacco and alcohol control, vaccine injection, sufficient fruits and vegetables intake and appropriate physical exercise. Overweight/obesity influences the health of more than 200 million people [2]. Overweight/obesity may play a vital role in growing morbidity of malignancy [3,4,5]. Thus, it is believed that overweight/obesity-related genes could affect the development of cancer.

Overweight/obesity is attributable to a chronic energy intake and expenditure imbalance. Leptin (LEP) is a common hormone of regulating energy expenditure by inhibiting hunger. LEP receptor (LEPR) is a type I cytokine receptor which is encoded by the LEPR gene and acts as a receptor for the hormone LEP. LEPR is a single transmembrane-domain receptor and composed of extracellular, transmembrane, and intracellular sections. LEP/LEPR signaling may involve in promoting angiogenesis, facilitating cell proliferation, and inhibiting epithelial cell apoptosis [6]. The long isoform in LEPR cytoplasmic domain may be essential for the signal transduction of Janus kinase/signal transducers and activators of transcription pathway [7].

The LEPR gene lies in chromosome 1 (Position_38: 65420652 – 65637493). There are a number of common single nucleotide polymorphisms (SNPs) of the LEPR genes, which have been established. LEPR rs1137101 G>A polymorphism (Arg223Gln) is the most extensively studied association of this SNP with the development of cancer. LEPR rs1137101 locus is a missense variant, which is a substitution of G→A at nucleotide number 668 in exon 6 of LEPR gene [8]. It leads to an Arg→Gln altering in extracellular region [8]. The potential relationships of LEPR rs1137101 variants with susceptibility of cancer have been elucidated in different malignancies; however, the obtained results were inconsistent. Two previous meta-analyses indicated that LEPR rs1137101 G>A polymorphism might not be a risk factor for cancer [9,10]. Recently, more studies concerning the association of LEPR rs1137101 locus with cancer risk were performed [11–26]. Hence, we carried out an updated meta-analysis on such relationship so as to further explore the role of LEPR rs1137101 variants to susceptibility of cancer.

Materials and methods

Searching publications

To obtain the potentially eligible investigations on LEPR rs1137101 G>A polymorphism and cancer susceptibility, we conducted an electronic literature search on PubMed and Embase databases, covering all publications up to 14 October 2018, by using the following searching strategy: (Leptin receptor or LEPR or obese receptor or OBR or CD295) and (carcinoma or cancer or tumor or malignancy or neoplasms) and (polymorphism or SNP or variation). References of the eligible studies and reviews were also screened to identify the additional data. We reported the present study using the Preferred Reporting Items for PRISMA guideline (Supplementary Table S1; PRISMA checklist) [27].

Selection and exclusion criteria

The major selection criteria were: (i) full-text study, (ii) assessing the relationship of LEPR rs1137101 variants with cancer susceptibility, (iii) designed as an unrelated case–control study, (iv) sufficient data could be obtained to calculate the odds ratio (OR) with 95% confidence interval (CI), and (v) genotype distribution conformation to Hardy–Weinberg equilibrium (HWE).

The major exclusion criteria were: (i) genotype data could not be extracted; (ii) not case–control study; (iii) distribution of genotype violated HWE; and (iv) comments, reviews, and letters.

Data extraction

Two authors (G.R. and Y.W.) independently extracted raw data. The following data were collected: the surname of first author, publication year, race, country, number of participants, age, Body Mass Index (BMI), source of control, matching method, genotype frequencies and genotyping method. Ethnicity descents were defined as mixed, Asian, and Caucasian. Cancer types were classified as hepatocellular carcinoma, breast cancer, renal cell carcinoma, esophageal cancer, colorectal cancer, oral and oropharyngeal cancer, non-Hodgkin’s lymphoma and other cancers (lung cancer and bladder cancer). For source of control, the eligible studies were categorized as hospital-based and population-based. When HWE in control group was not available, an online software (http://ihg.gsf.de/cgi-bin/hw/hwa1.pl) was harnessed to calculate the P-value of HWE test. If the information extracted was different, two authors reached consensus on each item.

Statistical analysis

The relationship strength of LEPR rs1137101 SNP with susceptibility of cancer was determined by crude ORs with their 95% CIs. The pooled ORs were calculated for allele (A vs. G), dominant (GA/AA vs. GG), recessive (AA versus GA/GG), and homozygote comparison (AA vs. GG) genetic modes. Additionally, stratified analysis was carried out to assess the influence of confounding risk factors: ethnicity (mixed, Asian, and Caucasian) and cancer type (breast cancer, esophageal cancer, hepatocellular carcinoma, renal cell carcinoma, colorectal cancer, oral and oropharyngeal cancer, non-Hodgkin’s lymphoma, and other cancers). We used Chi-square based Q-test and I2 test to assess the potential heterogeneity among the eligible studies. P<0.10 or I2 ≥ 50% was considered as the level of significant heterogeneity. Then a random-effects model (DerSimonian and Laird method) was used to assess the association of LEPR rs1137101 polymorphism with cancer susceptibility [28,29]; otherwise, a fixed-effects model (Mantel–Haenszel method) was harnessed to pool the data [30]. Stratified analysis was conducted to explore the source of heterogeneity. We performed one-way sensitivity analysis to evaluate the effect of an individual study on pooled ORs and CIs. Begg’s funnel plot and the Egger’s test were used to detect the potential bias in included publications, and a P<0.10 was considered significant [31,32]. Statistical analysis of the present study was calculated with STATA 12.0 software (StataCorp, College Station, Texas, U.S.A.).

Results

Study characteristics

In this meta-analysis, 33 publications involving 44 independent case–control studies on the relationship of LEPR rs1137101 G>A polymorphism with cancer risk were recruited [9,11–26,33–48]. In some publications, they contained several subgroups that we treated as independent studies [12,14,40]. Figure 1 shows the eligible study selecting process. In total, 44 independent case–control studies with 35,936 subjects (13,711 cases and 22,225 controls) were included. Among them, 20 were Caucasians [15,16,20–23,34,36,37,39–43,45–48], 14 were Asians [9,11–14,17,24,26,33,44], 1 was African [38], and 9 were mixed populations [18,19,25,35]. Twenty-two case–control studies were designed as hospital-based investigation [9,11–14,17,18,21,22,24,26,33,34,38,44,46,48], seventeen studies were designed as population-based investigation [16,19,25,35–37,40,41,43,45,47], and the source of control in other five studies were unknown [15,20,23,39,42]. Of all the eligible studies, 24 focused on breast cancer [12,14,15,19,20,22,33,35,36,38,41,42,44,47,48], 4 focused on esophageal cancer [13,40], 4 focused on colorectal cancer [34,37,39,46], 3 focused on hepatocellular carcinoma [9,11,26], 2 focused on non-Hodgkin’s lymphoma [43,45], 3 focused on oral and oropharyngeal cancer [18,21,25], 2 focused on renal cell carcinoma [17,24], and 2 focused on other cancers [16,23]. The detailed information of eligible studies is listed in Table 1. The extracted genotypes and HWE are summarized in Table 2.
Figure 1
Flow diagram of the meta–analysis of the association between LEPR rs1137101 G>A polymorphism and cancer risk
Figure 1
Flow diagram of the meta–analysis of the association between LEPR rs1137101 G>A polymorphism and cancer risk
Table 1
Characteristics of the studies in meta-analysis
Study Publication year Country Ethnicity Cancer type Sample size (case/control) Case age (years) Control age (years) Case BMI (kg/m2Control BMI (kg/m2Source of control Match Genotype method 
Zhang et al. 2018 China Asians Hepatocellular carcinoma 584/923 53.17 ± 11.76 53.72 ± 9.97 NA NA Hospital-based Age, sex, ethnicity SNPscan 
Liu et al. 2018 China Asians Breast cancer 488/463 43.71 ± 6.13 43.35 ± 5.43 BMI < 24: n=300, BMI ≥ 24, n=148 BMI < 24: n=289, BMI ≥ 24, n=174 Hospital-based Age, sex, ethnicity, region MS-TOF 
Liu et al. 2018 China Asians Breast cancer 346/342 58.55 ± 6.87 56.60 ± 6.53 BMI < 24:n=207, BMI ≥ 24, n=139 BMI < 24: n=195, BMI ≥ 24, n=147 Hospital-based Sex, ethnicity, region MS-TOF 
Qiu et al. 2017 China Asians Esophageal cancer 507/1,496 62.77 ± 8.01 62.77 ± 8.84 22.27 ± 2.90 23.91 ± 3.03 Hospital-based Age, ethnicity, sex SNPscan 
Yuna et al. 2018 China Asians Breast cancer 77/805 51.43 ± 11.33 48.98 ± 8.83 Obesity (≥27): n=15, non-obesity, n=62 Obesity (≥27), n=50, non-obesity, n=751 Hospital-based Age, sex, ethnicity, region MS-TOF 
Yuna et al. 2019 China Asians Breast cancer 79/805 49.94 ± 10.10 48.98 ± 8.83 Obesity (≥27), n=12, non-obesity, n=67 Obesity (≥27), n=50, non-obesity, n=751 Hospital-based Age, sex, ethnicity, region MS-TOF 
Yuna et al. 2017 China Asians Breast cancer 412/805 49.73 ± 9.38 48.98 ± 8.83 Obesity (≥27), n=45, non-obesity, n=365 Obesity (≥27), n=50, non-obesity, n=751 Hospital-based Age, sex, ethnicity, region MS-TOF 
Yuna et al. 2017 China Asians Breast cancer 135/805 50.50 ± 9.04 48.98 ± 8.83 Obesity (≥27), n=12, non-obesity, n=123 Obesity (≥27), n=50, non-obesity, n=751 Hospital-based Age, sex, ethnicity, region MS-TOF 
El-Hussiny et al. 2017 Egypt Caucasians Breast cancer 48/48 47.7 ± 7.5 43.5 ± 9.2 34.37 ± 6.08 27.28 ± 3.52 NA Sex, ethnicity, region PCR-RFLP 
Ali et al. 2017 Pakistan Caucasians Bladder cancer 200/200 55.5 ± 13.24 54.3 ± 9.9 NA NA Population-based Age, sex, ethnicity PCR 
Zhang et al. 2016 China Asians Renal cell carcinoma 83/161 17–85 (median: 57) NA NA NA Hospital-based Ethnicity, age, and sex PCR-RLFP 
Rodrigues et al. 2015 Brazil Mixed Oral and oropharyngeal cancer 129/186 54.9 ± 10.7 54.2 ± 11.1 NA NA Hospital-based Sex, region PCR-RLFP 
Slattery et al. 2015 America Mixed Breast cancer 239/252 NA NA BMI < 25 BMI < 25 Population-based Sex, region A multiplexed bead array assay 
Slattery et al. 2015 America Mixed Breast cancer 176/150 NA NA BMI = 25–29 BMI = 25–29 Population-based Sex, region A multiplexed bead array assay 
Slattery et al. 2015 America Mixed Breast cancer 111/126 NA NA BMI ≥ 30 BMI ≥ 30 Population-based Sex, region A multiplexed bead array assay 
Slattery et al. 2015 America Mixed Breast cancer 253/239 NA NA BMI < 25 BMI < 25 Population-based Sex, region A multiplexed bead array assay 
Slattery et al. 2015 America Mixed Breast cancer 205/304 NA NA BMI = 25–29 BMI = 25–29 Population-based Sex, region A multiplexed bead array assay 
Slattery et al. 2015 America Mixed Breast cancer 148/224 NA NA BMI ≥ 30 BMI ≥ 30 Population-based Sex, region A multiplexed bead array assay 
Mahmoudi et al. 2015 Iran Caucasians Breast cancer 45/41 47.09 ± 11.45 48.37 ± 8.80 NA NA NA Age, sex PCR-RFLP 
Hussain et al. 2015 India Caucasians Oral carcinoma 306/228 33.5 ± 5.79 32.7 ± 5.73 29.5 ± 5.44 23.8 ± 4.88 Hospital-based Age, sex, ethnicity and low-risk environment PCR-RFLP 
Mohammadzadeh et al. 2014 Iran Caucasians Breast cancer 100/100 48.16 ± 10.47 49.0 ± 7.77 27.16 ± 3.96 28.31 ± 4.71 Hospital-based Age, sex, ethnicity, BMI PCR-RFLP 
Unsal et al. 2014 Turkey Caucasians Lung cancer 162/130 60.96 ± 11.88 57.92 ± 14.96 NA NA NA Age, sex, ethnicity PCR-RFLP 
Mu et al. 2014 China Asians Renal cell carcinoma 77/161 56.22 ± 12.27 NA NA NA Hospital-based Ethnicity, age and sex PCR-RFLP or DNA sequence 
Domingos et al. 2014 Brazil Mixed Oral carcinoma 25/89 58.0 ± 13.6 55.9 ± 13.9 NA NA Population-based Age, sex, and smoking habits RFLP-PCR 
Li et al. 2012 China Asians Hepatocellular carcinoma 417/551 52.45 ± 4.6 51.95 ± 2.8 21.44 ± 3.4 22.56 ± 3.2 Hospital-based Age, sex, ethnicity RFLP 
Kim et al. 2012 Korea Asians Breast cancer 400/452 ≤49: 64.8% ≤49: 62.4% ≤25: 68.5% ≤25: 78.5% Hospital-based Age, sex MS-TOF 
Karimi et al. 2011 Iran Caucasians Colorectal cancer 173/173 55.8 ± 12.7 44.8 ± 17.2 25.1 ± 5.3 26.2 ± 7.19 Hospital-based BMI, sex and smoking status RFLP 
Nyante et al. 2011 America Mixed Breast cancer 1972/1776 23–74, mean: 50 21–74, mean: 51 BMI < 25, n=712; BMI ≥ 25, n=1219 BMI < 25, n=545; BMI ≥ 25, n=1194 Population-based Age, sex and region Illumina 
Cleveland et al. 2010 America Caucasians Breast cancer 1065/1108 NA NA BMI < 30, n=291; BMI ≥ 30, n=43 BMI < 30, n=304; BMI ≥ 30, n=62 Population-based Age, sex, region PCR 
Pechlivanis et al. 2009 Czech Republic Caucasians Colorectal cancer 702/752 27–85, mean: 62 29–91, mean: 54 13.1–44.9, mean: 26.5 16.6–44.3, mean: 26.4 Hospital-based Sex, region TaqMan 
Okobia et al. 2008 Nigeria Africans Breast cancer 209/209 46.1 ± 12.63 47.1 ± 13.50 NA NA Hospital-based Sex, region PCR-RFLP 
Vasků et al. 2009 Czech Republic Caucasians Colorectal cancer 102/101 68 ± 10.2 68.1 ± 5.4 Male/Female: 26.7 ± 5.1/26.9 ± 5.2 NA NA Age, ethnicity PCR-RFLP 
Doecke et al. 2008 Australia Caucasians Esophageal cancer 260/1352 NA NA NA NA Population-based Sex, region Sequenom 
Doecke et al. 2008 Australia Caucasians Esophageal cancer 301/1352 NA NA NA NA Population-based Sex, region Sequenom 
Doecke et al. 2008 Australian Caucasians Esophageal cancer 213/1352 NA NA NA NA Population-based Sex, region Sequenom 
Gallicchio et al. 2007 America Caucasians Breast cancer 61/933 mean: 59 mean: 59 NA BMI < 25, n=479; BMI ≥ 25, n=513 Population-based NA TaqMan 
Snoussi et al. 2006 Tunisia Caucasians Breast cancer 308/222 50 ± 24 48 ± 14 NA NA NA Sex, region PCR-RFLP 
Willett et al. 2005 U.K. Caucasians Non-Hodgkin’s lymphoma 1073/754 18–65 18–65 BMI < 25, n=633, BMI ≥ 25, n=603 BMI < 25, n=524; BMI ≥ 25, n=387 Population-based Sex, region TaqMan 
Woo et al. 2006 Korea Asians Breast cancer 45/45 NA NA BMI < 25, n=27, BMI ≥ 25, n=18 BMI < 25, n=37; BMI ≥ 25, n=8 Hospital-based Age, sex DNA sequencing 
Skibola et al. 2004 America Caucasians Non-Hodgkin’s lymphoma 376/805 21–74 NA BMI < 25, n=213; BMI ≥ 25, n=162 BMI < 25: n=480; BMI ≥ 25, n=321 Population-based Age, sex and region TaqMan 
Mahmoudi et al. 2016 Iran Caucasians Colorectal cancer 261/339 56.1 ± 12.6 44.3 ± 16.3 25.6 ± 4.9 25.2 ± 4.2 Hospital-based Sex and BMI PCR-RFLP 
Dai et al. 2010 China Asians Hepatocellular carcinoma 80/102 32–65 28–60 NA NA Hospital-based Age, sex, ethnicity PCR-RFLP 
Teras et al. 2009 America Caucasians Breast cancer 648/659 mean: 69 mean: 69 NA NA Population-based Age, sex SNPstream 
Kuptsova et al. 2008 Russia Caucasians Breast cancer 110/105 56–65 50–70 NA NA Hospital-based Age, sex PCR 
Study Publication year Country Ethnicity Cancer type Sample size (case/control) Case age (years) Control age (years) Case BMI (kg/m2Control BMI (kg/m2Source of control Match Genotype method 
Zhang et al. 2018 China Asians Hepatocellular carcinoma 584/923 53.17 ± 11.76 53.72 ± 9.97 NA NA Hospital-based Age, sex, ethnicity SNPscan 
Liu et al. 2018 China Asians Breast cancer 488/463 43.71 ± 6.13 43.35 ± 5.43 BMI < 24: n=300, BMI ≥ 24, n=148 BMI < 24: n=289, BMI ≥ 24, n=174 Hospital-based Age, sex, ethnicity, region MS-TOF 
Liu et al. 2018 China Asians Breast cancer 346/342 58.55 ± 6.87 56.60 ± 6.53 BMI < 24:n=207, BMI ≥ 24, n=139 BMI < 24: n=195, BMI ≥ 24, n=147 Hospital-based Sex, ethnicity, region MS-TOF 
Qiu et al. 2017 China Asians Esophageal cancer 507/1,496 62.77 ± 8.01 62.77 ± 8.84 22.27 ± 2.90 23.91 ± 3.03 Hospital-based Age, ethnicity, sex SNPscan 
Yuna et al. 2018 China Asians Breast cancer 77/805 51.43 ± 11.33 48.98 ± 8.83 Obesity (≥27): n=15, non-obesity, n=62 Obesity (≥27), n=50, non-obesity, n=751 Hospital-based Age, sex, ethnicity, region MS-TOF 
Yuna et al. 2019 China Asians Breast cancer 79/805 49.94 ± 10.10 48.98 ± 8.83 Obesity (≥27), n=12, non-obesity, n=67 Obesity (≥27), n=50, non-obesity, n=751 Hospital-based Age, sex, ethnicity, region MS-TOF 
Yuna et al. 2017 China Asians Breast cancer 412/805 49.73 ± 9.38 48.98 ± 8.83 Obesity (≥27), n=45, non-obesity, n=365 Obesity (≥27), n=50, non-obesity, n=751 Hospital-based Age, sex, ethnicity, region MS-TOF 
Yuna et al. 2017 China Asians Breast cancer 135/805 50.50 ± 9.04 48.98 ± 8.83 Obesity (≥27), n=12, non-obesity, n=123 Obesity (≥27), n=50, non-obesity, n=751 Hospital-based Age, sex, ethnicity, region MS-TOF 
El-Hussiny et al. 2017 Egypt Caucasians Breast cancer 48/48 47.7 ± 7.5 43.5 ± 9.2 34.37 ± 6.08 27.28 ± 3.52 NA Sex, ethnicity, region PCR-RFLP 
Ali et al. 2017 Pakistan Caucasians Bladder cancer 200/200 55.5 ± 13.24 54.3 ± 9.9 NA NA Population-based Age, sex, ethnicity PCR 
Zhang et al. 2016 China Asians Renal cell carcinoma 83/161 17–85 (median: 57) NA NA NA Hospital-based Ethnicity, age, and sex PCR-RLFP 
Rodrigues et al. 2015 Brazil Mixed Oral and oropharyngeal cancer 129/186 54.9 ± 10.7 54.2 ± 11.1 NA NA Hospital-based Sex, region PCR-RLFP 
Slattery et al. 2015 America Mixed Breast cancer 239/252 NA NA BMI < 25 BMI < 25 Population-based Sex, region A multiplexed bead array assay 
Slattery et al. 2015 America Mixed Breast cancer 176/150 NA NA BMI = 25–29 BMI = 25–29 Population-based Sex, region A multiplexed bead array assay 
Slattery et al. 2015 America Mixed Breast cancer 111/126 NA NA BMI ≥ 30 BMI ≥ 30 Population-based Sex, region A multiplexed bead array assay 
Slattery et al. 2015 America Mixed Breast cancer 253/239 NA NA BMI < 25 BMI < 25 Population-based Sex, region A multiplexed bead array assay 
Slattery et al. 2015 America Mixed Breast cancer 205/304 NA NA BMI = 25–29 BMI = 25–29 Population-based Sex, region A multiplexed bead array assay 
Slattery et al. 2015 America Mixed Breast cancer 148/224 NA NA BMI ≥ 30 BMI ≥ 30 Population-based Sex, region A multiplexed bead array assay 
Mahmoudi et al. 2015 Iran Caucasians Breast cancer 45/41 47.09 ± 11.45 48.37 ± 8.80 NA NA NA Age, sex PCR-RFLP 
Hussain et al. 2015 India Caucasians Oral carcinoma 306/228 33.5 ± 5.79 32.7 ± 5.73 29.5 ± 5.44 23.8 ± 4.88 Hospital-based Age, sex, ethnicity and low-risk environment PCR-RFLP 
Mohammadzadeh et al. 2014 Iran Caucasians Breast cancer 100/100 48.16 ± 10.47 49.0 ± 7.77 27.16 ± 3.96 28.31 ± 4.71 Hospital-based Age, sex, ethnicity, BMI PCR-RFLP 
Unsal et al. 2014 Turkey Caucasians Lung cancer 162/130 60.96 ± 11.88 57.92 ± 14.96 NA NA NA Age, sex, ethnicity PCR-RFLP 
Mu et al. 2014 China Asians Renal cell carcinoma 77/161 56.22 ± 12.27 NA NA NA Hospital-based Ethnicity, age and sex PCR-RFLP or DNA sequence 
Domingos et al. 2014 Brazil Mixed Oral carcinoma 25/89 58.0 ± 13.6 55.9 ± 13.9 NA NA Population-based Age, sex, and smoking habits RFLP-PCR 
Li et al. 2012 China Asians Hepatocellular carcinoma 417/551 52.45 ± 4.6 51.95 ± 2.8 21.44 ± 3.4 22.56 ± 3.2 Hospital-based Age, sex, ethnicity RFLP 
Kim et al. 2012 Korea Asians Breast cancer 400/452 ≤49: 64.8% ≤49: 62.4% ≤25: 68.5% ≤25: 78.5% Hospital-based Age, sex MS-TOF 
Karimi et al. 2011 Iran Caucasians Colorectal cancer 173/173 55.8 ± 12.7 44.8 ± 17.2 25.1 ± 5.3 26.2 ± 7.19 Hospital-based BMI, sex and smoking status RFLP 
Nyante et al. 2011 America Mixed Breast cancer 1972/1776 23–74, mean: 50 21–74, mean: 51 BMI < 25, n=712; BMI ≥ 25, n=1219 BMI < 25, n=545; BMI ≥ 25, n=1194 Population-based Age, sex and region Illumina 
Cleveland et al. 2010 America Caucasians Breast cancer 1065/1108 NA NA BMI < 30, n=291; BMI ≥ 30, n=43 BMI < 30, n=304; BMI ≥ 30, n=62 Population-based Age, sex, region PCR 
Pechlivanis et al. 2009 Czech Republic Caucasians Colorectal cancer 702/752 27–85, mean: 62 29–91, mean: 54 13.1–44.9, mean: 26.5 16.6–44.3, mean: 26.4 Hospital-based Sex, region TaqMan 
Okobia et al. 2008 Nigeria Africans Breast cancer 209/209 46.1 ± 12.63 47.1 ± 13.50 NA NA Hospital-based Sex, region PCR-RFLP 
Vasků et al. 2009 Czech Republic Caucasians Colorectal cancer 102/101 68 ± 10.2 68.1 ± 5.4 Male/Female: 26.7 ± 5.1/26.9 ± 5.2 NA NA Age, ethnicity PCR-RFLP 
Doecke et al. 2008 Australia Caucasians Esophageal cancer 260/1352 NA NA NA NA Population-based Sex, region Sequenom 
Doecke et al. 2008 Australia Caucasians Esophageal cancer 301/1352 NA NA NA NA Population-based Sex, region Sequenom 
Doecke et al. 2008 Australian Caucasians Esophageal cancer 213/1352 NA NA NA NA Population-based Sex, region Sequenom 
Gallicchio et al. 2007 America Caucasians Breast cancer 61/933 mean: 59 mean: 59 NA BMI < 25, n=479; BMI ≥ 25, n=513 Population-based NA TaqMan 
Snoussi et al. 2006 Tunisia Caucasians Breast cancer 308/222 50 ± 24 48 ± 14 NA NA NA Sex, region PCR-RFLP 
Willett et al. 2005 U.K. Caucasians Non-Hodgkin’s lymphoma 1073/754 18–65 18–65 BMI < 25, n=633, BMI ≥ 25, n=603 BMI < 25, n=524; BMI ≥ 25, n=387 Population-based Sex, region TaqMan 
Woo et al. 2006 Korea Asians Breast cancer 45/45 NA NA BMI < 25, n=27, BMI ≥ 25, n=18 BMI < 25, n=37; BMI ≥ 25, n=8 Hospital-based Age, sex DNA sequencing 
Skibola et al. 2004 America Caucasians Non-Hodgkin’s lymphoma 376/805 21–74 NA BMI < 25, n=213; BMI ≥ 25, n=162 BMI < 25: n=480; BMI ≥ 25, n=321 Population-based Age, sex and region TaqMan 
Mahmoudi et al. 2016 Iran Caucasians Colorectal cancer 261/339 56.1 ± 12.6 44.3 ± 16.3 25.6 ± 4.9 25.2 ± 4.2 Hospital-based Sex and BMI PCR-RFLP 
Dai et al. 2010 China Asians Hepatocellular carcinoma 80/102 32–65 28–60 NA NA Hospital-based Age, sex, ethnicity PCR-RFLP 
Teras et al. 2009 America Caucasians Breast cancer 648/659 mean: 69 mean: 69 NA NA Population-based Age, sex SNPstream 
Kuptsova et al. 2008 Russia Caucasians Breast cancer 110/105 56–65 50–70 NA NA Hospital-based Age, sex PCR 

Abbreviations: MS-TOF, mass spectrometry time of flight; NA, not available; PCR, polymerase chain reaction; PCR-RFLP, PCR-restriction fragment length polymorphism.

Table 2
Distribution of LEPR rs1137101 G>A polymorphism genotype and allele
Study Publication year Case Control Case Control HWE 
  AA AG GG AA AG GG A [n (%)] G A [n (%)] A A [n (%)] G A [n (%)]  
Zhang et al. 2018 119 453 10 193 717 125 (10.87) 1025 (89.13) 213 (11.58) 1627 (88.42) Yes 
Liu et al. 2018 AA/AG = 101 NA 346 AA/AG = 99 NA 363 NA NA NA NA Yes 
Liu et al. 2018 AA/AG = 80 NA 264 AA/AG = 79 NA 260 NA NA NA NA Yes 
Qiu et al. 2017 108 390 21 322 1146 120 (11.90) 888 (88.10) 364 (12.22) 2,614 (87.78) Yes 
Yuan et al. 2017 AA/AG = 18 NA 59 AA/AG = 178 NA 623 NA NA NA NA Yes 
Yuan et al. 2017 AA/AG = 17 NA 62 AA/AG = 178 NA 623 NA NA NA NA Yes 
Yuan et al. 2017 AA/AG = 96 NA 314 AA/AG = 178 NA 623 NA NA NA NA Yes 
Yuan et al. 2017 AA/AG = 30 NA 105 AA/AG = 178 NA 623 NA NA NA NA Yes 
El-Hussiny et al. 2017 15 24 24 22 33 (34.38) 63, 65.63) 28 (29.17) 68 (70.83) Yes 
Ali et al. 2017 51 96 53 67 97 36 198 (49.50) 202 (50.50) 231 (57.75) 169 (42.25) Yes 
Zhang et al. 2016 21 61 52 102 25 (14.88) 143 (85.12) 66 (20.50) 256 (79.50) Yes 
Rodrigues et al. 2015 60 61 68 92 26 181 (70.16) 77 (29.84) 228 (61.29) 144 (38.71) Yes 
Slattery et al. 2015 77 NA AG/GG:162 69 NA AG/GG:183 NA NA NA NA Yes 
Slattery et al. 2015 50 NA AG/GG:126 50 NA AG/GG:100 NA NA NA NA Yes 
Slattery et al. 2015 22 NA AG/GG:89 38 NA AG/GG:88 NA NA NA NA Yes 
Slattery et al. 2015 63 NA AG/GG:190 80 NA AG/GG:159 NA NA NA NA Yes 
Slattery et al. 2015 46 NA AG/GG:159 83 NA AG/GG:221 NA NA NA NA Yes 
Slattery et al. 2015 43 NA AG/GG:105 52 NA AG/GG:172 NA NA NA NA Yes 
Mahmoudi et al. 2015 19 25 17 18 63 (70.00) 27 (30.00) 52 (63.41) 30 (36.59) Yes 
Hussain et al. 2015 48 110 148 12 72 144 206 (33.66) 406 (66.34) 96 (21.05) 360 (78.95) Yes 
Mohammadzadeh et al. 2014 25 56 19 54 40 106 (53.000) 94 (47.00) 148 (74.00) 52 (26.00) Yes 
Unsal et al. 2014 75 62 25 56 55 19 212 (65.43) 112 (34.57) 167 (64.23) 93 (35.77) Yes 
Mu et al. 2014 20 55 41 116 24 (15.58) 130 (84.42) 49 (15.22) 273 (84.78) Yes 
Domingos et al. 2014 12 40 38 11 31 (62.00) 19 (38.00) 118 (66.29) 60 (33.71) Yes 
Li et al. 2012 87 208 122 189 256 106 382 (45.80) 452 (54.20) 634 (57.53) 468 (42.47) Yes 
Kim et al. 2012 88 294 91 350 104 (13.33) 676 (86.67) 103 (11.52) 791 (88.48) Yes 
Karimi et al. 2011 77 75 21 67 80 26 229 (66.18) 117 (33.82) 214 (61.85) 132 (38.15) Yes 
Nyante et al. 2011 494 952 526 416 874 485 1940 (49.19) 2004 (50.86) 1706 (48.06) 1844 (51.94) Yes 
Cleveland et al. 2010 173 521 355 187 551 360 867 (41.33) 1231 (58.67) 925 (42.12) 1271 (57.88) Yes 
Pechlivanis et al. 2009 179 320 140 202 361 143 678 (53.05) 600 (46.95) 765 (54.18) 647 (45.82) Yes 
Okobia et al. 2008 46 107 56 56 107 46 199 (47.61) 219 (52.39) 219 (52.39) 199 (47.61) Yes 
Vasků et al. 2009 23 56 21 34 45 21 102 (51.00) 98 (49.00) 113 (56.50) 87 (43.50) Yes 
Doecke et al. 2008 73 140 47 419 663 270 286 (55.00) 234 (45.00) 1501 (55.51) 1203 (44.49) Yes 
Doecke et al. 2008 84 164 62 419 663 270 332 (53.55) 288 (46.45) 1501 (55.51) 1203 (44.49) Yes 
Doecke et al. 2008 64 106 43 419 663 270 234 (54.93) 192 (45.07) 1501 (55.51) 1203 (44.49) Yes 
Gallicchio et al. 2007 14 24 15 278 443 151 52 (49.07) 54 (50.94) 999 (57.28) 745 (42.72) Yes 
Snoussi et al. 2006 98 145 65 102 90 30 341 (55.36) 275 (44.64) 294 (66.22) 150 (33.78) Yes 
Willett et al. 2005 336 554 183 234 387 133 1226 (57.13) 920 (42.87) 855 (56.70) 653 (43.30) Yes 
Woo et al. 2006 12 33 37 12 (13.33) 78 (86.67) 8 (8.89) 82 (91.11) Yes 
Skibola et al. 2004 115 173 87 226 379 198 403 (53.73) 347 (46.27) 831 (51.74) 775 (48.26) Yes 
Mahmoudi et al. 2016 127 101 33 146 147 46 355 (68.01) 167 (31.99) 439 (64.75) 239 (35.25) Yes 
Dai et al. 2010 10 14 58 19 81 34 (20.73) 130 (79.27) 23 (11.27) 181 (88.73) Yes 
Teras et al. 2009 AA/AG = 460 NA 181 AA/AG = 439 NA 211 NA NA NA NA Yes 
Kuptsova et al. 2008 17 69 24 18 51 36 103 (46.82) 117 (53.18) 87 (41.43) 123 (58.57) Yes 
Study Publication year Case Control Case Control HWE 
  AA AG GG AA AG GG A [n (%)] G A [n (%)] A A [n (%)] G A [n (%)]  
Zhang et al. 2018 119 453 10 193 717 125 (10.87) 1025 (89.13) 213 (11.58) 1627 (88.42) Yes 
Liu et al. 2018 AA/AG = 101 NA 346 AA/AG = 99 NA 363 NA NA NA NA Yes 
Liu et al. 2018 AA/AG = 80 NA 264 AA/AG = 79 NA 260 NA NA NA NA Yes 
Qiu et al. 2017 108 390 21 322 1146 120 (11.90) 888 (88.10) 364 (12.22) 2,614 (87.78) Yes 
Yuan et al. 2017 AA/AG = 18 NA 59 AA/AG = 178 NA 623 NA NA NA NA Yes 
Yuan et al. 2017 AA/AG = 17 NA 62 AA/AG = 178 NA 623 NA NA NA NA Yes 
Yuan et al. 2017 AA/AG = 96 NA 314 AA/AG = 178 NA 623 NA NA NA NA Yes 
Yuan et al. 2017 AA/AG = 30 NA 105 AA/AG = 178 NA 623 NA NA NA NA Yes 
El-Hussiny et al. 2017 15 24 24 22 33 (34.38) 63, 65.63) 28 (29.17) 68 (70.83) Yes 
Ali et al. 2017 51 96 53 67 97 36 198 (49.50) 202 (50.50) 231 (57.75) 169 (42.25) Yes 
Zhang et al. 2016 21 61 52 102 25 (14.88) 143 (85.12) 66 (20.50) 256 (79.50) Yes 
Rodrigues et al. 2015 60 61 68 92 26 181 (70.16) 77 (29.84) 228 (61.29) 144 (38.71) Yes 
Slattery et al. 2015 77 NA AG/GG:162 69 NA AG/GG:183 NA NA NA NA Yes 
Slattery et al. 2015 50 NA AG/GG:126 50 NA AG/GG:100 NA NA NA NA Yes 
Slattery et al. 2015 22 NA AG/GG:89 38 NA AG/GG:88 NA NA NA NA Yes 
Slattery et al. 2015 63 NA AG/GG:190 80 NA AG/GG:159 NA NA NA NA Yes 
Slattery et al. 2015 46 NA AG/GG:159 83 NA AG/GG:221 NA NA NA NA Yes 
Slattery et al. 2015 43 NA AG/GG:105 52 NA AG/GG:172 NA NA NA NA Yes 
Mahmoudi et al. 2015 19 25 17 18 63 (70.00) 27 (30.00) 52 (63.41) 30 (36.59) Yes 
Hussain et al. 2015 48 110 148 12 72 144 206 (33.66) 406 (66.34) 96 (21.05) 360 (78.95) Yes 
Mohammadzadeh et al. 2014 25 56 19 54 40 106 (53.000) 94 (47.00) 148 (74.00) 52 (26.00) Yes 
Unsal et al. 2014 75 62 25 56 55 19 212 (65.43) 112 (34.57) 167 (64.23) 93 (35.77) Yes 
Mu et al. 2014 20 55 41 116 24 (15.58) 130 (84.42) 49 (15.22) 273 (84.78) Yes 
Domingos et al. 2014 12 40 38 11 31 (62.00) 19 (38.00) 118 (66.29) 60 (33.71) Yes 
Li et al. 2012 87 208 122 189 256 106 382 (45.80) 452 (54.20) 634 (57.53) 468 (42.47) Yes 
Kim et al. 2012 88 294 91 350 104 (13.33) 676 (86.67) 103 (11.52) 791 (88.48) Yes 
Karimi et al. 2011 77 75 21 67 80 26 229 (66.18) 117 (33.82) 214 (61.85) 132 (38.15) Yes 
Nyante et al. 2011 494 952 526 416 874 485 1940 (49.19) 2004 (50.86) 1706 (48.06) 1844 (51.94) Yes 
Cleveland et al. 2010 173 521 355 187 551 360 867 (41.33) 1231 (58.67) 925 (42.12) 1271 (57.88) Yes 
Pechlivanis et al. 2009 179 320 140 202 361 143 678 (53.05) 600 (46.95) 765 (54.18) 647 (45.82) Yes 
Okobia et al. 2008 46 107 56 56 107 46 199 (47.61) 219 (52.39) 219 (52.39) 199 (47.61) Yes 
Vasků et al. 2009 23 56 21 34 45 21 102 (51.00) 98 (49.00) 113 (56.50) 87 (43.50) Yes 
Doecke et al. 2008 73 140 47 419 663 270 286 (55.00) 234 (45.00) 1501 (55.51) 1203 (44.49) Yes 
Doecke et al. 2008 84 164 62 419 663 270 332 (53.55) 288 (46.45) 1501 (55.51) 1203 (44.49) Yes 
Doecke et al. 2008 64 106 43 419 663 270 234 (54.93) 192 (45.07) 1501 (55.51) 1203 (44.49) Yes 
Gallicchio et al. 2007 14 24 15 278 443 151 52 (49.07) 54 (50.94) 999 (57.28) 745 (42.72) Yes 
Snoussi et al. 2006 98 145 65 102 90 30 341 (55.36) 275 (44.64) 294 (66.22) 150 (33.78) Yes 
Willett et al. 2005 336 554 183 234 387 133 1226 (57.13) 920 (42.87) 855 (56.70) 653 (43.30) Yes 
Woo et al. 2006 12 33 37 12 (13.33) 78 (86.67) 8 (8.89) 82 (91.11) Yes 
Skibola et al. 2004 115 173 87 226 379 198 403 (53.73) 347 (46.27) 831 (51.74) 775 (48.26) Yes 
Mahmoudi et al. 2016 127 101 33 146 147 46 355 (68.01) 167 (31.99) 439 (64.75) 239 (35.25) Yes 
Dai et al. 2010 10 14 58 19 81 34 (20.73) 130 (79.27) 23 (11.27) 181 (88.73) Yes 
Teras et al. 2009 AA/AG = 460 NA 181 AA/AG = 439 NA 211 NA NA NA NA Yes 
Kuptsova et al. 2008 17 69 24 18 51 36 103 (46.82) 117 (53.18) 87 (41.43) 123 (58.57) Yes 

Abbreviation: NA, not available.

Results of meta-analysis

Table 3 lists the overall and subgroup analysis results of the present study. After combining all eligible case–control studies, we identified null relationship between rs1137101 polymorphism in LEPR gene and overall cancer risk under four genetic models (A vs. G: OR  =  0.97, 95% CI  =  0.89–1.06, P=0.547; AA vs. GG: OR  =  0.93, 95% CI  =  0.78–1.13, P =0.476; AA/GA vs. GG: OR  =  0.99, 95% CI  =  0.91–1.09, P=0.890 and AA vs. GA/GG: OR  = 0.92, 95% CI  =  0.82–1.04, P=0.198, Figures 25).

Meta-analysis of the association between LEPR rs1137101 G>A polymorphism and cancer risk (AA/GA vs. GG, random–effects model)

Figure 2
Meta-analysis of the association between LEPR rs1137101 G>A polymorphism and cancer risk (AA/GA vs. GG, random–effects model)
Figure 2
Meta-analysis of the association between LEPR rs1137101 G>A polymorphism and cancer risk (AA/GA vs. GG, random–effects model)

Meta-analysis of the association between LEPR rs1137101 G>A polymorphism and cancer risk (AA vs. GG, random–effects model)

Figure 3
Meta-analysis of the association between LEPR rs1137101 G>A polymorphism and cancer risk (AA vs. GG, random–effects model)
Figure 3
Meta-analysis of the association between LEPR rs1137101 G>A polymorphism and cancer risk (AA vs. GG, random–effects model)

Meta-analysis of the association between LEPR rs1137101 G>A polymorphism and cancer risk (AA vs. GG/GA, random–effects model)

Figure 4
Meta-analysis of the association between LEPR rs1137101 G>A polymorphism and cancer risk (AA vs. GG/GA, random–effects model)
Figure 4
Meta-analysis of the association between LEPR rs1137101 G>A polymorphism and cancer risk (AA vs. GG/GA, random–effects model)

Meta-analysis of the association between LEPR rs1137101 G>A polymorphism and cancer risk (A vs. G, random–effects model)

Figure 5
Meta-analysis of the association between LEPR rs1137101 G>A polymorphism and cancer risk (A vs. G, random–effects model)
Figure 5
Meta-analysis of the association between LEPR rs1137101 G>A polymorphism and cancer risk (A vs. G, random–effects model)
Table 3
Results of the meta-analysis from different genetic models
 Number of studies A vs. G AA vs. GG AA+GA vs. GG AA vs.GA+GG 
  OR (95% CI) P I2 P (Q-test) OR (95% CI) P I2 P (Q-test) OR (95% CI) P I2 P (Q-test) OR (95% CI) P I2 P (Q-test) 
Total 44 0.97 (0.89–1.06) 0.547 73.4% <0.001 0.93 (0.78–1.13) 0.476 70.7% <0.001 0.99 (0.91–1.09) 0.890 48.7% <0.001 0.92 (0.82–1.04) 0.198 66.2% <0.001 
Ethnicity                  
Caucasians 20 0.97 (0.87–1.08) 0.565 73.6% <0.001 0.95 (0.77–1.17) 0.621 68.6% <0.001 1.00 (0.87–1.15) 0.976 56.9% 0.001 0.93 (0.80–1.09) 0.381 68.4% <0.001 
Asians 14 0.98 (0.77–1.25) 0.864 76.6 <0.001 0.88 (0.44–1.79) 0.733 67.7% 0.005 0.96 (0.87–1.06) 0.456 32.2% 0.118 0.91 (0.49–1.69) 0.765 60.1% 0.020 
Mixed 1.13 (0.87–1.47) 0.369 54.6% 0.110 1.25 (0.61–2.58) 0.539 60.6% 0.050 1.11 (0.55–2.24) 0.775 68.6% 0.041 0.98 (0.80–1.20) 0.837 53.9% 0.027 
Africans 0.83 (0.63–1.08) 0.167 0.67 (0.39–1.17) 0.162 0.77 (0.49–1.21) 0.255 0.77 (0.49–1.21) 0.255 
Cancer type                  
Breast cancer 24 0.91 (0.77–1.07) 0.248 75.5% <0.001 0.82 (0.57–1.17) 0.269 74.6% <0.001 1.00 (0.88–1.13) 0.946 44.1% 0.024 0.84 (0.69–1.02) 0.076 67.3% <0.001 
Colorectal cancer 1.01 (0.90–1.14) 0.814 30.4% 0.230 1.00 (0.79–1.26) 0.967 0.0% 0.398 0.98 (0.80–1.21) 0.860 0.0% 0.742 1.04 (0.88–1.23) 0.623 48.2% 0.122 
Esophageal cancer 0.96 (0.87–1.06) 0.407 0.0% 0.967 0.93 (0.75–1.16) 0.525 0.0% 0.957 1.01 (0.87–1.18) 0.873 0.0% 0.916 0.88 (0.74–1.03) 0.118 0.0% 0.925 
Hepatocellular carcinoma 0.98 (0.60–1.62) 0.948 89.4% <0.001 0.96 (0.21–4.42) 0.956 84.1% 0.002 0.89 (0.55–1.44) 0.633 80.8% 0.005 1.03 (0.25–4.14) 0.971 81.5% 0.005 
Oral and oropharyngeal cancer 1.46 (1.00–2.13) 0.050 64.1% 0.062 2.02 (0.72–5.65) 0.179 75.1% 0.018 1.43 (0.67–3.07) 0.360 69.1% 0.039 1.83 (1.01–3.33) 0.048 60.6% 0.079 
Renal cell carcinoma 0.82 (0.57–1.18) 0.291 19.7% 0.264 0.67 (0.21–2.14) 0.500 0.0% 0.509 0.81 (0.53–1.23) 0.318 13.9% 0.281 0.72 (0.22–2.28) 0.570 0.0% 0.576 
Non-Hodgkin’s lymphoma 1.04 (0.94–1.16) 0.451 0.0% 0.577 1.09 (0.88–1.35) 0.438 0.0% 0.641 1.06 (0.88–1.28) 0.547 0.0% 0.839 1.05 (0.90–1.24) 0.528 0.0% 0.526 
Others 0.86 (0.59–1.25) 0.426 65.9% 0.087 0.68 (0.44–1.04) 0.076 55.3% 0.135 0.71 (0.48–1.04) 0.079 9.8% 0.292 0.86 (0.63–1.18) 0.600 60.7% 0.111 
Sample size                  
<1000 32 0.98 (0.82–1.17) 0.816 81.1% <0.001 0.98 (0.67–1.43) 0.913 78.6% <0.001 0.97 (0.81–1.15) 0.694 62.4% <0.001 0.93 (0.75–1.14) 0.466 72.9% <0.001 
≥1000 12 1.00 (0.95–1.05) 0.950 0.0% 0.926 1.01 (0.91–1.11) 0.906 0.0% 0.882 1.02 (0.95–1.09) 0.548 0.0% 0.945 1.00 (0.92–1.08) 0.907 0.0% 0.695 
Source of control                  
Hospital-based 22 1.03 (0.86–1.23) 0.744 82.7% <0.001 1.04 (0.68–1.57) 0.869 80.2% <0.001 1.03 (0.90–1.19) 0.651 57.4% <0.001 0.99 (0.72–1.36) 0.965 78.6% <0.001 
Population-based 17 0.99 (0.94–1.04) 0.715 20.9% 0.251 0.99 (0.87–1.03) 0.787 25.0% 0.214 1.01 (0.94–1.10) 0.754 29.3% 0.167 0.97 (0.90–1.04) 0.368 29.1% 0.132 
NA 0.92 (0.68–1.23) 0.560 61.7% 0.034 1.02 (0.48–2.13) 0.968 68.6% 0.013 0.82 (0.61–1.10) 0.190 38.7% 0.163 0.89 (0.53–1.48) 0.649 69.4% 0.011 
 Number of studies A vs. G AA vs. GG AA+GA vs. GG AA vs.GA+GG 
  OR (95% CI) P I2 P (Q-test) OR (95% CI) P I2 P (Q-test) OR (95% CI) P I2 P (Q-test) OR (95% CI) P I2 P (Q-test) 
Total 44 0.97 (0.89–1.06) 0.547 73.4% <0.001 0.93 (0.78–1.13) 0.476 70.7% <0.001 0.99 (0.91–1.09) 0.890 48.7% <0.001 0.92 (0.82–1.04) 0.198 66.2% <0.001 
Ethnicity                  
Caucasians 20 0.97 (0.87–1.08) 0.565 73.6% <0.001 0.95 (0.77–1.17) 0.621 68.6% <0.001 1.00 (0.87–1.15) 0.976 56.9% 0.001 0.93 (0.80–1.09) 0.381 68.4% <0.001 
Asians 14 0.98 (0.77–1.25) 0.864 76.6 <0.001 0.88 (0.44–1.79) 0.733 67.7% 0.005 0.96 (0.87–1.06) 0.456 32.2% 0.118 0.91 (0.49–1.69) 0.765 60.1% 0.020 
Mixed 1.13 (0.87–1.47) 0.369 54.6% 0.110 1.25 (0.61–2.58) 0.539 60.6% 0.050 1.11 (0.55–2.24) 0.775 68.6% 0.041 0.98 (0.80–1.20) 0.837 53.9% 0.027 
Africans 0.83 (0.63–1.08) 0.167 0.67 (0.39–1.17) 0.162 0.77 (0.49–1.21) 0.255 0.77 (0.49–1.21) 0.255 
Cancer type                  
Breast cancer 24 0.91 (0.77–1.07) 0.248 75.5% <0.001 0.82 (0.57–1.17) 0.269 74.6% <0.001 1.00 (0.88–1.13) 0.946 44.1% 0.024 0.84 (0.69–1.02) 0.076 67.3% <0.001 
Colorectal cancer 1.01 (0.90–1.14) 0.814 30.4% 0.230 1.00 (0.79–1.26) 0.967 0.0% 0.398 0.98 (0.80–1.21) 0.860 0.0% 0.742 1.04 (0.88–1.23) 0.623 48.2% 0.122 
Esophageal cancer 0.96 (0.87–1.06) 0.407 0.0% 0.967 0.93 (0.75–1.16) 0.525 0.0% 0.957 1.01 (0.87–1.18) 0.873 0.0% 0.916 0.88 (0.74–1.03) 0.118 0.0% 0.925 
Hepatocellular carcinoma 0.98 (0.60–1.62) 0.948 89.4% <0.001 0.96 (0.21–4.42) 0.956 84.1% 0.002 0.89 (0.55–1.44) 0.633 80.8% 0.005 1.03 (0.25–4.14) 0.971 81.5% 0.005 
Oral and oropharyngeal cancer 1.46 (1.00–2.13) 0.050 64.1% 0.062 2.02 (0.72–5.65) 0.179 75.1% 0.018 1.43 (0.67–3.07) 0.360 69.1% 0.039 1.83 (1.01–3.33) 0.048 60.6% 0.079 
Renal cell carcinoma 0.82 (0.57–1.18) 0.291 19.7% 0.264 0.67 (0.21–2.14) 0.500 0.0% 0.509 0.81 (0.53–1.23) 0.318 13.9% 0.281 0.72 (0.22–2.28) 0.570 0.0% 0.576 
Non-Hodgkin’s lymphoma 1.04 (0.94–1.16) 0.451 0.0% 0.577 1.09 (0.88–1.35) 0.438 0.0% 0.641 1.06 (0.88–1.28) 0.547 0.0% 0.839 1.05 (0.90–1.24) 0.528 0.0% 0.526 
Others 0.86 (0.59–1.25) 0.426 65.9% 0.087 0.68 (0.44–1.04) 0.076 55.3% 0.135 0.71 (0.48–1.04) 0.079 9.8% 0.292 0.86 (0.63–1.18) 0.600 60.7% 0.111 
Sample size                  
<1000 32 0.98 (0.82–1.17) 0.816 81.1% <0.001 0.98 (0.67–1.43) 0.913 78.6% <0.001 0.97 (0.81–1.15) 0.694 62.4% <0.001 0.93 (0.75–1.14) 0.466 72.9% <0.001 
≥1000 12 1.00 (0.95–1.05) 0.950 0.0% 0.926 1.01 (0.91–1.11) 0.906 0.0% 0.882 1.02 (0.95–1.09) 0.548 0.0% 0.945 1.00 (0.92–1.08) 0.907 0.0% 0.695 
Source of control                  
Hospital-based 22 1.03 (0.86–1.23) 0.744 82.7% <0.001 1.04 (0.68–1.57) 0.869 80.2% <0.001 1.03 (0.90–1.19) 0.651 57.4% <0.001 0.99 (0.72–1.36) 0.965 78.6% <0.001 
Population-based 17 0.99 (0.94–1.04) 0.715 20.9% 0.251 0.99 (0.87–1.03) 0.787 25.0% 0.214 1.01 (0.94–1.10) 0.754 29.3% 0.167 0.97 (0.90–1.04) 0.368 29.1% 0.132 
NA 0.92 (0.68–1.23) 0.560 61.7% 0.034 1.02 (0.48–2.13) 0.968 68.6% 0.013 0.82 (0.61–1.10) 0.190 38.7% 0.163 0.89 (0.53–1.48) 0.649 69.4% 0.011 

I2: ≥50% indicate significant heterogeneity. P (Q-test): <0.10 considered as the criterion of significant heterogeneity.

Abbreviation: NA, not available. Bold values are statistically significant (P<0.05).

When we conducted a stratified analysis by ethnicity, null association was found in mixed populations, Africans, Asians, and Caucasians. However, when we performed a subgroup analysis by cancer type, there was an increased susceptibility of oral and oropharyngeal cancer in AA vs. GA/GG genetic model (OR, 1.83; 95% CI, 1.01–3.33; P=0.048).

Heterogeneity analysis

Significant heterogeneity among the eligible studies was found in this pooled analysis (Table 3). In the present study, we carried out subgroup analysis to explore the sources of heterogeneity. We found that Caucasians, breast cancer, hepatocellular carcinoma, other cancers, small sample sizes (<1000) and hospital-based case–control studies might contribute the major source to heterogeneity.

Sensitivity analysis

Sensitivity analysis of one-way method identified that any individual study deleted did not materially influence the pooled ORs and CIs under all genetic comparisons. These findings indicated that our observations were stable and reliable (Supplementary Figure S1).

Publication bias

Begg’s funnel plot and Egger’s test were used to evaluate the potential bias. The results of the bias detecting were shown as following: A vs. G: PBegg’s= 0.852, PEgger’s = 0.973; AA vs. GG: PBegg’s= 0.775, PEgger’s = 0.897; AA/GA vs. GG: PBegg’s = 0.950, PEgger’s = 0.869 and AA vs. GA/GG: PBegg’s= 0.703, PEgger’s = 0.897 (Supplementary Figure S2).

Discussion

Recently, variants in LEPR gene and their potential associations with cancer risk have been explored. Rs1137101 G>A polymorphism is one of the important variants in LEPR gene. LEPR rs1137101 G>A polymorphism is located on the exon region of LEPR gene, and it has been thought to be involved in the development of cancer by a number of studies. Several case–control studies reported that LEPR rs1137101 G>A polymorphism might be associated with the decreased risk of cancer [16,22,26,41,42]. However, several primary studies also suggested that LEPR rs1137101 locus could promote the progression of cancers [15,18]. Meanwhile, two meta-analyses were carried out to clarify the correlation of this SNP with susceptibility of overall cancer [9,10]. The results of these meta-analyses indicated that LEPR rs1137101 G>A polymorphism might not be associated with the risk of cancer. Moreover, more epidemiologic data were reported [11–26]. Therefore, an updated meta-analysis is necessary to calrify this issue precisely. In this meta-analysis, data of 44 case–control studies involving 13,711 cases and 22,225 controls, which is higher compared with these previous pooled-analyses mentioned above, were included and analyzed. Therefore, the obtained results may be more convincing.

LEP/LEPR signaling may promote cell proliferation and inhibit epithelial cell apoptosis [6]. In addition, Ben et al. [49] reported that LEPR rs1137101 G>A SNP may affect plasma LEP levels and BMI. A previous study suggested that leptin level was associated with the development of breast cancer [50]. However, null associations of LEPR rs1137101 locus with cancer susceptibility was identified, which was analogous to the results reported in previous meta-analyses [9,10,51,52], but unlike the other four meta-analyses [48,53–55]. Due to lack of sufficient data, the findings of previous systematic reviews and meta-analyses might be conflicting. Ethnicity also may be a vital factor for the difference. The minor allele frequency of LEPR rs1137101 G>A polymorphism was difference among different populations, but in stratified analysis by race, null relationship was found. Additionally, results of stratified analyses by sample size and source of control both found no relationship between LEPR rs1137101 G>A polymorphism with overall cancer susceptibility, highlighting that these variables could not influence the negative findings either.

According to the findings of stratified analysis, we found that LEPR rs1137101 locus might be associated with the susceptibility of oral and oropharyngeal cancer. However, the increased risk of cancer was dubious and hard to explain. Sample size was an important factor to determine the relationship between LEPR rs1137101 G>A polymorphism and cancer risk. In this subgroup, only 460 oral and oropharyngeal cancer cases and 503 controls were included for analysis, the findings might be underpowered.

Significant heterogeneity was found among the eligible studies in multiple genetic models. Thus, we carried out stratified analysis by ethnicity, cancer type, sample size, and source of control. It was obvious that Caucasians, hepatocellular carcinoma, breast cancer, other cancers, small sample sizes (<1000 subjects) and hospital-based case–control studies might contribute to heterogeneity.

In this meta-analysis, as mentioned in results, no publication bias was detected. In addition, findings of sensitivity analysis also indicated that our observation was convincing. Overall, results of this meta-analysis were stable and credible for the studied populations.

However, there were several limitations in the present pooled-analysis. First, some small sample size studies were recruited in this meta-analysis, which could promote the power of study. On the other hand, they led to potential publication bias and significant heterogeneity as well. Second, our findings were based on the crude pooled-results of the eligible case–control studies, the adjustment on characteristics and risk factors (e.g. BMI, physical exercise, age, gender, smoking, alcohol consumption, vegetable and fruit intake, and so on) was not performed. In the future, a more detailed assessment is needed, in which the characteristics and potential risk factors should be considered to adjust the findings. Third, although the cases and controls in eligible studies were fully matched, there was heterogeneity in different cancer types which may influence our results. Fourth, only PubMed and Embase databases were searched to retrieve the eligible. Finally, significant heterogeneity was found in all genetic models, which might influence the findings of our study. Thus, these results should be interpreted with caution.

In summary, this meta-analysis may be the largest sample size so far to assess the potential association of cancer risk with rs1137101 G>A polymorphism in LEPR gene. There is no significant association of cancer risk was identified to be correlated with rs1137101 G>A variants in the overall comparison, and the similar findings was also found in stratified analysis by ethnicity, cancer type, sample size, and source of control. In the future, more large-scale studies are needed to confirm or refute our findings.

Funding

This work was supported by the Young and Middle-aged Talent Training Project of Health Development Planning Commission in Fujian Province [grant number 2016-ZQN-25]; the Program for New Century Excellent Talents in Fujian Province University [grant number NCETFJ-2017B015]; the Joint Funds for the Innovation of Science and Technology, Fujian province [grant number 2017Y9099]; and the Natural Science Foundation of Fujian Province [grant number 2017J01291].

Competing Interests

The authors declare that there are no competing interests associated with the manuscript.

Author Contribution

All authors contributed significantly to the present study. Conceived and designed the experiments: G.R. and Y.W. Performed the experiments: G.R., Y.W. and W.T. Analyzed the data: G.R., Y.W., W.T. and H.Q. Contributed reagents/materials/analysis tools: S.C. Wrote the manuscript: G.R., Y.W. and W.T.

Abbreviations

     
  • BMI

    body mass index

  •  
  • CI

    confidence interval

  •  
  • HWE

    Hardy–Weinberg equilibrium

  •  
  • LEP

    leptin

  •  
  • LEPR

    leptin receptor

  •  
  • OR

    odds ratio

  •  
  • SNP

    single nucleotide polymorphism

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Author notes

*

These authors contributed equally to this work.

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